literature review of drug resistance

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

Introduction: Drug Resistance

The evolutionary pressure of survival drives the emergence of drug resistance, and thereby poses a major challenge to modern medicine. Resistance threatens the longevity of drugs and restricts treatment options for patients, with high prevalence in all areas of oncology and infectious diseases. Any biological entity capable of evolving and creating diversity can develop resistance under selective pressure. This diversity can pre-exist or occur after exposure to the inhibitors. Pathogens evolve to resist antimicrobials, which include antibiotics, antivirals, antifungals and antiprotozoals. In agriculture, resistance arises with overuse of herbicides and pesticides. In cancer, resistance emerges eventually with most treatment regimens and in infectious diseases with spread of the pathogen to large populations, which is further exacerbated with the overuse of antibiotics. The emergence and spread of drug resistance in these wide range of disease areas severely impact public health, threaten millions of people’s lives, and cause a crippling financial burden, which urges the development of new strategies to unravel and avoid drug resistance.

Despite seemingly disparate, certain mechanisms have been uncovered repeatedly to be conferring drug resistance across quickly evolving diseases. On the molecular level, resistance is often associated with genetic changes such as site mutations, deletions, gene amplifications. Alternative resistance mechanisms involve decreasing the effective drug concentration or eliminating dependence of survival on the activity of the target. “Persister” cells that change phenotype to tolerate drugs might be promoting resistance in both antibiotics and cancer. Another common problem across diverse disease areas is inherent or acquired heterogeneity, especially for viruses and cancer cells, which can cause a certain fraction of the targeted variants to survive and drive disease progression and resistance. To thwart resistance it is necessary to elucidate the molecular mechanisms by which resistance occurs, identify vulnerabilities in current drugs, and use innovative integrated methods to design and discover novel therapeutics that are ideally less susceptible to resistance. In this effort applying lessons learned for one disease in the design of therapeutics to other diseases is essential. This issue of Chemical Reviews will hopefully foster connections between various fields around the theme of drug resistance, and inspire novel applications of ideas and discoveries. Common mechanisms and challenges in drug resistance necessitate a united front with a collective effort to leverage chemistry, while considering the constraints of evolution, to discover robust drugs or drug combinations that last and avoid resistance.

Historically, the challenge of drug resistance was realized initially in the treatment of HIV-1 infections where combination therapy, which involves inhibiting multiple targets with specifically designed direct acting antivirals, significantly reduced the viral burden of patients in the late 1990s. A decade later, combination therapy and other principles learned from HIV-1 were applied to successful treatment of hepatitis C infection. In the last 20 years many of these strategies were also translated to oncology, where academic research and the resources of pharmaceutical industry have made great strides, although treatment failure is still a common outcome due to resistance. Unfortunately, due in part to being less financially lucrative, progress in the development of potent novel drugs that avoid resistance for infectious diseases has lagged. Perhaps the current CoVID-19 pandemic caused by the SARS-CoV-2 virus and the subsequent response by the scientific community will translate into advancements in our drug design strategies against evolving infectious pathogens.

In this issue of Chemical Reviews on Drug Resistance a variety of techniques across scientific fields, including structural biology, medicinal chemistry, enzymology, computational chemistry, nanotechnology, systems biology and ethnobotany are applied to identify mechanisms of drug resistance and novel drug candidates. Structure-based drug design has been key in the discovery and optimization of direct acting antivirals. With insights from structure, viral evolution, and conformational dynamics of viral enzymes, the reviews by Schiffer and co-workers and Sarafianos and co-workers detail the molecular mechanisms of antiviral resistance and strategies to apply evolutionary constraints in antiviral design. These strategies include constraints such as restricting the inhibitor within the substrate envelope, and establishing interactions with the target protein’s evolutionary conserved features —the active site, backbone atoms, metal coordinating residues or allosteric sites— that can be engaged by inhibitors to avoid resistance. Such structure-based strategies are in principle generalizable to all disease targets, antiviral, antimicrobial or oncological, where target mutations confer resistance to current inhibitors.

Similar themes discovered regarding the molecular mechanisms of resistance for antivirals are also observed in oncology when the drug target mutates, along with a broad range of other cellular mechanisms. Smith and coworkers highlight the challenges of drug resistance in small molecule cancer therapeutics, reviewing 19 therapeutic targets with over 70 drugs often optimized with structure based strategies. They describe the approaches that have been taken to combat resistance, including to overcoming the blood brain barrier, combining small molecule drugs with biologics and designing covalent linkages to the target. The review by Goldman and co-workers takes on the challenge of overcoming drug resistance in oncology through novel biomedical engineering techniques. These range from mathematical models to simulate the underlying evolutionary principles by which resistance occurs; bioengineered tumor models of resistance that utilize microfluidics to test a variety of therapeutic strategies including combination therapies and these models become effective diagnostics; and therapeutics that involve nanotechnology to enhance selective drug delivery to avoid resistance. Even with targeted therapy and recent advances in immunotherapy, resistance persists as a major problem in cancer treatment, and such innovative and alternative approaches are much needed to better understand the underlying mechanisms of resistance and devise strategies for improved patient outcomes.

In addition to site mutations which is common in antiviral resistance, additional resistance mechanisms emerge in infections caused by fungus, bacteria or parasites. These include mechanisms involving cellular pathways as in cancer, which present additional challenges to the effectiveness of drugs.

In the treatment of fungal infections, which are eukaryotes, many analogous molecular machineries that exist in the host provide particular challenges. For the pathogenic candida species, described by Cowen and coworkers, only three classes of drugs exist, polyenes, azoles and echinocandins, all three of which are compromised by resistance. While mutation of the drug target is still common for all three classes, efflux pumps and target overexpression also subvert the azoles. Strategies to avoid resistance include combination therapies, targeting virulence factors and developing immunotherapies and vaccines. Antibiotic resistance has been increasing at alarming rates with hardly any novel antibiotics in clinical development. Once a mainstay of antibiotics due to their ability to disrupt the bacterial cell wall, β-lactams have been severely compromised by resistance. As a case study against the prokaryotic gram-positive bacteria Staphylococcus aureus, Fisher and Mobashery describe the molecular mechanisms of β -lactam resistance. This class of antibiotics target a set of penicillin binding proteins, which are key enzymes in cell wall biosynthesis. Through elucidation of these enzymes and resistance, opportunities for novel antibiotics are likely. Wright and co-workers view antibiotic resistance through what they describe as the antibiotic resistome, or the collection of all genetic elements that confer resistance. They focus on two classes of antibiotics, aminoglycosides and tetracyclines, and describe the machinery by which resistance occurs to each class. This systems approach enables understanding how efflux pumps, modifying enzymes and target modifications alter the effectiveness of antibiotics. These mechanisms not only permit bacteria from subverting therapeutics but are also mechanisms by which bacteria compete with and protect themselves from each other. They propose a model of resistance-guided antibiotic discovery leveraging the bacterial synthetic machinery. Another rich source of possible antibiotics that should be less susceptible to resistance is described by Quave and co-workers as plant-derived natural products. Co-evolution of plants in bacterially rich environments has resulted in many metabolites with natural antibacterial activity often through interfering with virulence factors. In this comprehensive review, 459 such recently described plant natural products are assessed for their antibacterial activity (183 in detail). These plant-derived compounds fit within four main chemical classes (phenolic derivatives, terpenoids, alkaloids, and other metabolites) with many unique chemical scaffolds and many with quite potent antibacterial activities. Leveraging how plants evolved resistance to bacterial infections and potentially other infectious diseases is a promising pipeline for discovering new chemical entities for therapeutic development.

Overall in combating drug resistance we need to understand what happens at the molecular level, i.e. how evolution enables survival under drug pressure. By learning the constraints of evolution, we can leverage these requirements for enzymes to function in robust inhibitor design, the strategies fungi, bacteria and plants use to avoid each other’s virulence, and the involvement of our own host factors in infections or the growth of cancer. Agriculture avoids resistance, in part, by rotating crops and keeping down the stressors. In medicine sub-optimal drugs might be the stressors that drive resistance. However even with the best possible drugs, we may not be able to completely avoid resistance. Evolution is relentless. Instead we need a combination of approaches to curb evolution the best we can. Through an integrated understanding of chemical and biological entities and their impact on our own microbiome, virome and metabalome we can help strengthen innate and adaptive immune system to work in complementing drugs to decrease probability of resistance. To counter resistance, strategies must become less reactionary and rather more integrated, preemptive and robust.

ACKNOWLEDGEMENTS

CAS is supported by NIGMS R01 GM135919. We thank all the authors who contributed to this special issue.

Biographies

Nese Kurt Yilmaz is an Associate Professor of Biochemistry and Molecular Pharmacology at UMass Medical School, and has been working closely with Dr. Celia Schiffer since she joined the faculty in 2011. She has completed her undergraduate and graduate studies in Chemical Engineering at Bogazici University in Istanbul and was a visiting fellow at UMass Medical School with Dr. Schiffer during her PhD studies. After obtaining her PhD degree, she worked as a postdoctoral scholar and then a research scientist at University of Wisconsin-Madison with Dr. Silvia Cavagnero investigating protein folding using biophysical spectroscopy. Her current research focuses on protein conformational dynamics, biomolecular structure, and molecular basis of drug resistance. Dr. Kurt Yilmaz received the UMass GSBS faculty award for research mentoring in 2018.

Celia A. Schiffer has been on the faculty at UMass Medical School since 1997, and is a Professor of Biochemistry and Molecular Pharmacology and Director of the Institute for Drug Resistance which she founded in 2009. Dr. Schiffer is a structural biologist and biophysicist. She has a BA in Physics from University of Chicago (1986) and received her PhD in Biophysics from University of California San Francisco (1992). Her postdoctoral training was at the ETH Zurich (1992–94) and Genentech, Inc. (1994–1997) before joining the faculty at UMMS as an Assistant Professor. In 2009 she founded the Institute for Drug Resistance and in 2019 she became the Gladys Smith Martin Chair in Oncology . Dr. Schiffer’s scientific contributions are in defining the field of drug resistance and developing framework to avoid drug resistance from the very initial inhibitor design phase. She provides thought leadership bridging interdisciplinary fields and discovering the parallels between how resistance occurs and potentially could be averted for all evolving diseases.

Views expressed in this editorial are those of the authors and not necessarily the views of the ACS.

REVIEW article

Molecular diagnosis of drug-resistant tuberculosis; a literature review.

• 1 UMR MIVEGEC, Institute of Research for Development, Centre National de la Recherche Scientifique, University of Montpellier, Montpellier, France
• 2 Laboratory of Tuberculosis, Department of Bacteriology, National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
• 3 LMI Drug Resistance in South East Asia, National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
• 4 LISBP, CNRS, INRA, INSA, Université de Toulouse, Toulouse, France

Drug-resistant tuberculosis is a global health problem that hinders the progress of tuberculosis eradication programs. Accurate and early detection of drug-resistant tuberculosis is essential for effective patient care, for preventing tuberculosis spread, and for limiting the development of drug-resistant strains. Culture-based drug susceptibility tests are the gold standard method for the detection of drug-resistant tuberculosis, but they are time-consuming and technically challenging, especially in low- and middle-income countries. Nowadays, different nucleic acid-based assays that detect gene mutations associated with resistance to drugs used to treat tuberculosis are available. These tests vary in type and number of targets and in sensitivity and specificity. In this review, we will describe the available molecular tests for drug-resistant tuberculosis detection and discuss their advantages and limitations.

Introduction

Drug resistance is a major challenge for tuberculosis (TB) treatment and eradication. The number of drug-resistant TB (DR-TB) cases is increasing worldwide: the estimated number of new cases of multidrug-resistant TB (MDR-TB, defined as TB resistant at least to isoniazid and rifampicin) or rifampicin-resistant TB (RR-TB) was 600,000 in 2016 compared with 480,000 in 2014 ( WHO, 2017a , b ). The treatment of MDR-TB is complex, much more expensive than the treatment of non-MDR-TB, and is associated with important side effects for the patients. The treatment success rate decreases from 83% for patients with newly diagnosed or relapse non-DR-TB who start treatment with a first-line regimen (2015 cohort) to 54% for MDR/RR-TB (2014 cohort) and to 30% for extensively drug-resistant TB (XDR-TB, defined as MDR-TB showing resistance also to at least one fluoroquinolone and one second-line injectable agent; 2014 cohort) ( WHO, 2017b ). In 2015, only one–third of new patients with bacteriologically confirmed TB and previously treated TB patients underwent drug susceptibility testing (DST) for rifampicin (RIF). The others were treated based only on smear-positive results without DST and differentiation of Mycobacterium tuberculosis (MTB) from non-tuberculous mycobacteria (NTM). In low and middle income countries, DST accessibility is limited by its high cost and complexity and by technical constraints (maintenance and environmental conditions). Such difficulties could result in the transmission and emergence of highly DR-MTB strains due to inappropriate treatments.

Drug susceptibility testing, the reference method for DR-TB detection, is based on the proportional agar method on Lowenstein-Jensen medium ( Canetti et al., 1969 ). As this culture method is time-consuming (up to 3–8 weeks to get results), other solid culture methods, such as Middlebrook agar, have been developed to reduce the turn-around time to 10–12 days ( Brady et al., 2008 ; Nguyen et al., 2015 ). Liquid culture methods have been used for faster detection with high sensitivity ( Lawson et al., 2013 ). For instance, the BACTEC Mycobacteria Growth Indicator Tube (MGIT TM ) are barcoded for the automated processing of large numbers of samples (up to 960 tubes) 1 with a turn-around time between 10 and 30 days ( Lawson et al., 2013 ). However, liquid culture methods come with several disadvantages: invisible contamination, overgrowth of NTM, and need of expensive complex systems ( Singhal et al., 2012 ; Lawson et al., 2013 ; Nguyen et al., 2015 ). Moreover, culture-based methods generally require well-trained personnel and high biosafety-level laboratories for the protection of the technicians and the environment. Thus, the use of culture methods for DR-TB detection is limited, especially in low- or middle-income countries.

Previous studies demonstrated that in MTB strains, drug resistance is mainly acquired through spontaneous mutations, especially single nucleotide polymorphisms (SNPs), in the circular chromosome ( McGrath et al., 2014 ). For each anti-TB drug, mutations in one or several genes have been described, and each mutation relates to different levels of drug resistance. Mutation frequency also can be variable. For instance, 97% of the cases of resistance to RIF are linked to mutations in the rpo B gene, mostly in an 81 bp hotspot region (codon 507 to codon 533; Escherichia coli numbering system, used throughout the text for RIF) ( Laurenzo and Mousa, 2011 ). Mutations at codons 526–531 of rpoB show the highest frequency and confer high-level RIF resistance. Isoniazid (INH) resistance is acquired through mutations in the kat G, inh A and its promoter, ahp C, ndh , and fur A genes but mainly in kat G, inh A and its promoter ( Laurenzo and Mousa, 2011 ). The most frequent kat G mutations (50–90%) are found at codon 315 and confer high-level resistance to INH. In ethambutol (EMB)-resistant isolates, mutations at codon 306 of the emb B gene are the most frequent (47–62%). Mutations associated with resistance to second-line drugs also have been described. In streptomycin (STR)-resistant MTB isolates, the most frequent mutations are found at codons 43 and 88 of the rps L gene, and codon 514 of the rrs gene ( Laurenzo and Mousa, 2011 ; Nguyen H.Q. et al., 2017 ). Resistance to fluoroquinolones (FQs) arises through mutations in the gyr A and gyr B genes. About 60–70% of quinolone-resistant MTB isolates harbor mutations in the quinolone resistance-determining region of gyr A, with the highest frequency at codon 94, followed by codon 90, 91, and 88 ( Laurenzo and Mousa, 2011 ). Mutations in the gyr B gene are rare. Resistance to second-line injectable drugs, including amikacin (AMK), kanamycin (KAN), and capreomycin (CAP), is mainly associated with rrs gene mutations. Approximately 70–80% of CAP resistance and about 60% of KAN resistance are caused by the rrs A1401G mutation. Although the mechanisms of drug resistance is not clear in about 10–40% of DR-MTB isolates without mutations, the detection of known mutations enables to identify a high proportion of DR-MTB ( Jeanes and O’Grady, 2016 ).

Therefore, many nucleic acid-based assays that detect mutations associated with anti-TB drug resistance have been developed recently, in order to provide affordable, accurate, simple, and rapid diagnostic tests for DR-TB detection. In this review, we will describe the commercially available molecular tests for DR-TB detection and discuss their advantages and limitations (see Supplementary Table S1 for a summary of the available molecular tests).

Molecular Assays for the Detection of Drug Resistance in MTB

Dna line probe assays.

Line probe assays (LPAs) are basically DNA–DNA hybridization assays that allow the simultaneous detection of different mutations by using multiple probes 2 ( Makinen et al., 2006 ). After DNA extraction and target amplification, amplicons are hybridized to specific oligonucleotide probes that are complementary to the target sequences and are immobilized on the surface of a strip. After several post-hybridization washes to remove non-specific binding, the amplicon-probe hybrids are visualized by eye as colored bands on the strip. The turnaround time of the whole assay is 5–7 h ( Makinen et al., 2006 ; Mitarai et al., 2012 ).

Although several LPAs have been developed, most of them focus only on the hotspot regions of drug-resistance and different assays target different genes. For instance, the INNO-LiPA Rif TB LPA (Innogenetics, Zwijndrecht, Belgium) analyzes only the rpo B hotspot region (codon 509 to codon 534; Asp516Val, His526Tyr, His526Asp, and Ser531Leu mutations) for MTB identification and RIF resistance screening ( WHO, 2008 ). The AID TB Resistance LPA ( Aid Diagnostika GmbH ) includes three modules to detect first-line and second-line anti-TB drug resistance in culture and clinical specimens 3 ( Ritter et al., 2014 ). Module 1 targets rpo B, kat G, and the inhA promoter for RIF and INH resistance screening; module 2 covers rps L and rrs to detect aminoglycoside resistance (STR, AMK, CAP); and module 3 analyzes gyr A and emb B for FQ and EMB resistance detection. The three modules include wild type and mutant probes to cover the most common mutations. The AID TB Resistance LPA showed high sensitivity and specificity for the detection of RIF, INH, STR, FQs, and second-line injectable agents resistance (between 90 and 100%), but lower sensitivity for EMB resistance (72.9%) ( Molina-Moya et al., 2014 ). However, with the AID TB Resistance LPA, uninterpretable results have been reported in up to 8.3% of smear-positive and 65% of smear-negative samples ( Deggim-Messmer et al., 2016 ).

Currently, the LPAs recommended by WHO for the initial drug resistance screening of sputum smear-positive samples include GenoType MTBDR plus , GenoType MTBDR sl ( Hain LifeScience GmbH , Germany), and Nipro NTM+MDR-TB (Nipro Co., Osaka, Japan) ( WHO, 2017b ). GenoType MTBDR plus VER2.0 has the advantage of detecting both RIF and INH resistance by screening mutations in rpo B, kat G, and the inhA promoter 4 . GenoType MTBDR sl VER1.0 and VER2.0 detect the MTB complex and its resistance to FQs, EMB and aminoglycosides/cyclic peptides by analyzing the gyr A, gyr B, rrs , emb B, and eis genes 5 . It may be used as initial test for patients with confirmed RR-TB or MDR-TB ( WHO, 2017b ). Nipro NTM+MDR-TB detects MDR-TB cases by targeting rpo B, kat G, and inh A and also differentiates four important Mycobacterium species (MTB, M. avium , M. intracellulare , and M. kansasii ) that cause the human disease ( Ruesch-Gerdes and Ismail, 2015 ; Nathavitharana et al., 2016 ).

GenoType MTBDR plus VER2.0 shows good accuracy for the detection of MDR isolates in smear-positive specimens (sensitivity between 83.3 and 96.4%, and specificity between 98.6 and 100%) ( Bai et al., 2016 ; Nathavitharana et al., 2016 ; Dantas et al., 2017 ; Meaza et al., 2017 ). The Nipro NTM+MDRTB strips also show high specificity (between 97 and 100%) for INH and RIF resistance screening in cultured isolates and clinical (sputum) samples, but sensitivity varies between studies (from 50 to 95%) ( Mitarai et al., 2012 ; Ruesch-Gerdes and Ismail, 2015 ; Nathavitharana et al., 2016 ). Regarding second-line drugs, GenoType MTBDR sl VER2.0 displays high sensitivity and specificity (between 91 and 100%) for detecting FQ resistance, but variable sensitivity and specificity for the screening of resistance to second-line injectable drugs (SLIDs) ( Bang et al., 2016 ; Brossier et al., 2016 ; Gardee et al., 2017 ). Therefore, the overall GenoType MTBDR sl VER2.0 specificity (between 59 and 100%) and sensitivity (between 83 and 87%) for detecting XDR isolates differ among studies. Moreover, uninterpretable results were reported for drug resistance screening in sputum specimens, especially smear-negative samples ( Tomasicchio et al., 2016 ; Meaza et al., 2017 ). In addition, when GenoType MTBDR sl (both VER1.0 and VER2.0) is used for FQ resistance screening, some synonymous and non-synonymous mutations (i.e., that do not and that do change the encoded amino acid, respectively) in the gyr A gene prevent the hybridization of either the wild type or mutant probe, leading to false-resistance results. Although these mutations (T80A + A90G, gcG/gcA A90A, and atC/atT I92I) are not frequent, they account for around 7% of all MDR-TB strains in some studied regions ( Ajileye et al., 2017 ).

In conclusion, LPAs are rapid, simple and easy to perform. Result analysis (manually or automatically) is simple. However, LPAs require complex laboratory infrastructure and expensive equipment that is normally only available in reference laboratories ( WHO, 2017c ). The number of uninterpretable results is high, and LPA target coverage is limited to the main mutations. Thus, their sensitivity and specificity vary according to the mutation prevalence in the area under study.

Real-Time PCR Assays

Real-time PCR is now broadly applied for the development of rapid diagnostic tests. Two main approaches are commonly used in real-time PCR: (i) the use of non-specific fluorescent dyes to detect any double-stranded DNA generated by PCR amplification, and (ii) the use of sequence-specific probes tagged with a fluorescent reporter for the specific detection of the hybridization between probes and amplicons 6 . Each probe has a specific melting temperature (Tm), and a Tm change reflects the presence of mutations in the target. This feature has been used to develop real-time PCR tests for drug resistance screening.

Some types of probes can detect mutations conferring drug resistance in MTB, such as dual labeled linear probes ( Edwards et al., 2001 ; Espasa et al., 2005 ; Liu et al., 2013 ) and sloppy molecular beacon (SMB) probes ( Chakravorty et al., 2011 , 2012 , 2015 ; Roh et al., 2015 ). The dual labeled probes (DLPs) consist of a fluorescence-quencher pair that generates different melting profiles between wild-type and mutant sequences when they hybridize to amplified targets ( Roh et al., 2015 ). Besides, SMB probes consist of stem-loop structures that are thermodynamically more stable than DLPs. SMB probes have the highest sensitivity and specificity (up to 100%) for the detection of mutations in the 81 bp RIF resistance determining region. The presence of 10-fold excess of NTM DNA or 10 5 -fold excess of human DNA does not affect the results with SMB probes ( Chakravorty et al., 2012 ). SMB probes can detect correctly 100% of mutations in 2 pg DNA templates. This suggests that SMB probes can detect rpo B gene mutations in both smear-positive and smear-negative samples. SMB probes identify heteroresistance in a mixture of 10% of mutant DNA and 90% of wild type DNA, while dual labeled probes require at least 40% of mutant DNA ( Roh et al., 2015 ). Although the limit of detection depends on the mutation, overall, SMB probes show higher sensitivity than dual labeled probes.

The reduced risk of contamination (all steps after DNA extraction are performed in one tube) and the shorter turn-around time (compared with LPAs because of the absence of the hybridization step) are among the advantages of real-time PCR. However, real-time PCR has some remarkable disadvantages. First, it requires expensive, specific equipment and skilled technicians. Second, the number of probes that can be used in one reaction is limited, resulting in limited target numbers. This is a major drawback for MTB, a pathogen that can harbor many mutations. Third, the size range of the amplified targets should be limited to 75–200 bp for efficiently detecting multiple mutations (see text foot note 6).

The following sections present some examples of real-time PCR-based commercial kits and new strategies for DR-TB detection.

Xpert MTB/RIF and Xpert MTB/RIF Ultra

Xpert MTB/RIF ( Cepheid , United States), a fast molecular-based test, is endorsed by WHO for the detection of the MTB complex and RIF resistance screening in suspected cases ( WHO, 2017b ). This test was first recommended in 2010 for the diagnosis of pulmonary TB in adults from sputum specimens. Since 2013, it has been recommended also for the diagnosis of TB in children and of some specific extra-pulmonary forms.

The Xpert MTB/RIF assay uses semi-quantitative nested real-time PCR to amplify a fragment containing the 81 bp hotspot region of the rpo B gene (codons 507–533) that is then hybridized to five molecular beacon probes ( Bunsow et al., 2014 ; Steingart et al., 2014 ; Ochang et al., 2016 ). Each probe covers a separate sequence and is labeled with a fluorescent dye. The whole experiment is performed in a self-contained cartridge, like a mini-laboratory, to minimize cross-contamination between samples. Sensitivity and specificity for smear-positive samples can reach 100 and 99%, respectively, and for smear-negative samples are 67 and 99%, respectively, compared to the standard culture-based DST. It significantly decreases the detection time of RIF resistance from 4 to 8 weeks (culture and DST) to 2 h. It has immediately a good impact on patients because it allows starting rapidly the MDR-TB treatment. Moreover, Xpert MTB/RIF increases of 23% the MTB detection rate among culture-confirmed cases compared with smear microscopy, with high accuracy of TB detection and limiting the misdiagnosis between MTB and NTM ( Steingart et al., 2014 ; Sharma et al., 2015 ).

However, some studies reported false-positive results with Xpert MTB/RIF due to silent mutations [e.g., at codon 514 of rpo B ( Bunsow et al., 2014 )], and false-negative results because of the impossibility to detect RIF-resistance mutations outside the hotspot region [e.g., mutations at codon 572 ( Sanchez-Padilla et al., 2015 )]. Consequently, Xpert MTB/RIF scope might be limited, for instance, in Swaziland where more than 30% of patients with RR-TB carry the I572F mutation (I491F in the original paper, according to the MTB numbering system). In addition, this test does not detect mutations in genes associated with INH resistance, but uses RIF resistance as a proxy for MDR-TB detection ( Manson et al., 2017 ). Consequently, many INH mono-resistant TB cases are misdiagnosed. Recently, whole genome sequencing (WGS) studies discovered that INH resistance arises before RIF resistance in all lineages, geographical regions and time periods. As Xpert MTB/RIF can detect only RIF resistance, it is unable to identify MDR-TB in its earliest form (i.e., INH mono-resistance). In addition, it must always be used together with other tests, such as DST, to confirm and identify the whole resistance phenotype of each MTB isolate. Another remarkable limitation is its high cost due to the use of complex GeneXpert system and disposable cartridges (the GeneXpert apparatus costs between US$12 000 and $71 000, depending on the number of test modules, and the price of each single-use test cartridge is $9⋅98) ( WHO, 2017b ; Walzl et al., 2018 ). Therefore, its use as initial diagnostic test for all suspected TB cases is unaffordable in many high TB-burden countries. According to a WHO report (2016), only 15 of the 48 high TB-burden countries have used the Xpert tests for all suspected TB cases (i.e., 10% of all estimated TB cases globally in 2015) ( WHO, 2017b ). Finally, the GeneXpert machine requires a constant electricity source and is sensitive to heat and dust. A number of machine failures has been reported due to these problems ( Walzl et al., 2018 ).

Recently, the WHO evaluated the next-generation Xpert MTB/RIF Ultra system ( Cepheid ) that has a larger amplification chamber to increase the amount of sputum and two additional targets (IS1081 and IS6110) to identify MTB ( Chakravorty et al., 2017 ; Perez-Risco et al., 2018 ). The new Ultra cartridge can be used in the old GeneXpert machine and has the same price as the old one ( WHO, 2017d ). However, the analytical sensitivity is significantly increased, more than 10 times. Its higher MTB detection sensitivity (16 bacilli/ml compared with 131 bacilli/ml for the current Xpert MTB/RIF cartridge) facilitates MTB screening in specimens with low numbers of bacilli, such as sputum samples from children and from patients co-infected by HIV, and in difficult-to-diagnose cases, such as smear-negative pulmonary and extra-pulmonary TB. As a result of its higher sensitivity, Xpert Ultra specificity for MTB detection is lower than that of Xpert MTB/RIF 7 ( WHO, 2017b ). However, RR-TB detection accuracy is similar with both cartridge types. Xpert MTB/RIF Ultra overcomes some limitations of the current cartridge by excluding the silent rpo B mutations Q513Q and F514F ( Chakravorty et al., 2017 ). Therefore, in the latest report, WHO recommended to use the Ultra cartridge as initial diagnostic test for all adults and children with signs and symptoms of TB, and also for screening some extra-pulmonary specimens, such as cerebrospinal fluid, lymph node and tissue samples ( WHO, 2017c ). Interestingly, Xpert XDR cartridge is in development for the detection of XDR-TB.

Finally, a new device named GeneXpert Omni ( Cepheid , United States) is under development. It uses the same cartridges as the Xpert system, but will be smaller, lighter and cheaper than the current Xpert system. It will have a 4-h battery, and thus will be better adapted to overcome the requirement of a stable electric supply 8 .

Genedrive MTB/RIF ID Kit

The Genedrive MTB/RIF ID Kit ( Epistem , United Kingdm) is an innovative system developed after the Xpert test for the detection of MTB and RR-TB from raw sputum samples. It is a portable low-power thermal cycling apparatus (560 g in weight) that can be operated using a 12V DC power supply and used at TB point-of-care sites ( Niemz and Boyle, 2012 ). The system uses a simple paper-based DNA extraction method combined with asymmetric real-time PCR and a proprietary hybridization probe technology (Highlighter Probes) ( Castan et al., 2014 ). The composite paper is chemically treated to decontaminate and extract DNA from bacteria without any additional equipment, such as vortex or centrifuge. The system employs multiplex real-time PCR to target two regions: a short repetitive region (the REP13E12 family), and the 81 bp hotspot region of rpo B. The Highlighter Probes detect the most important mutations associated with RIF resistance at codons 516, 526, and 531, with an overall sensitivity for rpo B mutation detection of 72.3%. For MTB identification, Genedrive MTB/RIF ID is comparable to the Xpert assay (100% vs. 93.5% of sputum samples). The platform can detect as low as five genome copies. It is user-friendly and fast (results in 75 min). Although the Genedrive system can analyze only eight samples per working day 9 , the low price ($4000 for the system and $10–$17 for the disposable cartridge) makes the assay affordable for low-income regions ( Niemz and Boyle, 2012 ). Due to its light weight, stable power supply and capacity to function without air conditioning (up to 40°C), screening with the Genedrive system could be implemented at many acid fast bacilli (AFB) smear microscopy centers ( WHO, 2017c ).

Anyplex II MTB/MDR and MTB/XDR

The Anyplex kits ( Seegene , South Korea) have been designed for the detection of MTB, MDR-TB, and XDR-TB from sputum and bronchial wash samples, culture isolates, and fresh tissues based on a semi-automated multiplex real-time PCR method 10 . The two proprietary technologies use dual-priming oligonucleotides (DPO TM ) and tagging-oligonucleotide cleavage and extension (TOCE TM ) and the real-time PCR CFX96 TM apparatus for the highly specific detection of specific SNPs. The Anyplex kits can identify multiple targets in one reaction because the PCR CFX96 TM system allows the simultaneous detection of five different fluorescent dyes. Specifically, Anyplex II MTB/MDR detects the 34 most frequent mutations in rpo B, kat G, and inh A for MDR-TB screening, and the MTB/XDR kit detects the 13 main mutations in the gyr A and rrs genes and eis promoter associated with XDR-TB (see text foot note 10) ( Igarashi et al., 2017 ). The entire protocol requires 3.5 h from DNA extraction to result interpretation using the provided software ( Molina-Moya et al., 2015 ).

The Anyplex MTB/MDR kit accurately detects more than 83% of MTB complex bacilli in pulmonary and extra-pulmonary samples, and therefore, is much more sensitive than the AFB smear microscopy method ( Sali et al., 2016 ), and can improve the diagnosis of extra-pulmonary TB with high specificity. In evaluation studies of both kits ( Molina-Moya et al., 2015 ; Sali et al., 2016 ; Igarashi et al., 2017 ), specificity ranged between 94 and 100% for the detection of MTB resistant to all targeted drugs in clinical specimens, and sensitivity was between 50 and 100%. The lowest sensitivity was obtained for FQ resistance screening, but was similar to that of pyrosequencing and GenoType MTBDR sl ( Molina-Moya et al., 2015 ). Anyplex MTB/MDR sensitivity for detecting resistance to RIF was always higher than 90% for all sample types ( Molina-Moya et al., 2015 ; Sali et al., 2016 ; Igarashi et al., 2017 ). This real-time PCR method seems to be better than the Xpert technology because of the very low rate of false-positive results. However, the limited number of targets remains a limitation. Overall, the Anyplex II MTB/MDR and MTB/XDR kits are potential tools for the efficient and rapid detection of MTB and DR-TB in clinical specimens, especially for the diagnosis of extra-pulmonary TB.

Digital PCR

Heteroresistance is found in 9–20% of clinical isolates, and up to 25.8% of clinical samples in some high TB incidence regions ( Zhang X. et al., 2012 ). Heteroresistance is the result of the super-infection by at least two isolates, or of the evolution of a single isolate leading to various subpopulations of drug-resistant and -susceptible bacteria in the presence of antibiotics ( Pholwat et al., 2013 ). The detection of heteroresistance is still a challenge for rapid DR-TB diagnosis, even when using sequencing methods ( Folkvardsen et al., 2013 ). Pholwat et al., developed an assay based on digital PCR ( Pholwat et al., 2013 ) that can identify and quantify different resistant subpopulations in mixtures containing as little as one XDR-MTB among thousand susceptible MTB bacilli. Digital PCR is basically a quantitative PCR that relies on the partitioning of samples into thousands of individual small-volume reactions before amplification ( Morley, 2014 ). In these compartments, the reaction components and amplification process are similar to quantitative PCR, but some will contain the DNA target, whereas others will not. After amplification, the number of positive and negative reactions is determined and analyzed using the Poisson distribution. The recent development of micro- and nano-fluidic technologies makes digital PCR simpler and more practical ( Morley, 2014 ). This technique is reproducible and can detect bacilli at concentrations as low as 1000 CFU/ml ( Pholwat et al., 2013 ). With these advantages, digital PCR enables the earlier detection of new mutations emerging during treatment that could require a therapy change.

LATE-PCR With Lights-On/Lights-Off Probes

PCR-based detection of variant sequences is often carried out using probes labeled with different fluorescent dyes. However, the number of fluorescent dyes and the analysis capacity of a PCR machine are often limited to 4–6 colors. To overcome this problem, linear-after-the-exponential PCR (LATE-PCR), an asymmetric PCR method with sets of Lights-On/Lights-Off probes, has been developed to analyze nucleotide substitutions ( Rice et al., 2012 ). In each probe pair, the Lights-Off probe is labeled with a quencher moiety and the Lights-On probe with a fluorescent molecule. The Tm of the probes are at least 5°C lower than that of the primers. After amplification, the number of single-stranded DNA molecules is much higher than that of double-stranded DNA molecules, due to the asymmetric PCR. The use of low temperature at the end of the process enables the hybridization of single-stranded DNA to the probes with lower Tm. After both probes are bound to their target sequence, the quencher of the Lights-Off probe extinguishes the fluorescence signal. Although each probe pair hybridizes only to a short part of the target, each fluorescent signal can be integrated to analyze sequences of several hundred nucleotides in length. The signal of the whole set of probes during the melting curve analysis can be used to create an annealing curve that shows the time-dependent fluorescence contour. In the case of mutations in the target sequence, the Tm of the hybridization changes, leading to a shift in the annealing curve. This new analytical technology is very sensitive for the detection of each single nucleotide change in a sequence of hundreds base pairs even with the use of a single fluorescent dye. The use of different color probes allows detecting various target genes in one single tube. However, the analysis of many different targets with multiple fluorescent dyes requires a powerful system. Besides, the use of one pair of modified probes for each mutation is expensive.

Hain Lifescience has applied this technology to develop FluoroType MTBDR VER1.0 for the simultaneously detection of the MTB complex and MDR-TB by targeting rpo B, kat G, and inh A in one single tube 11 . The kit includes probe pairs to target a set of known mutations in these genes (T508A, S509T, E510H, L511P, S512K, Q513L, Q513P, Q513R, D516A, D516F, D516V, D516Y, N518I, S522L, S522Q, H526C, H526D, H526G, H526L, H526N, H526P, H526Q, H526R, H526S, H526Y, R529K, S531F, S531L, S531L, S531Q, S531W, L533E, L533P in rpo B; S315T1, S315T2, S315N, S315R in kat G; and G-17T, A-16G, C-15T, G-9A, T-8A, T-8C, and T-8G in inh A), and can also detect unknown mutations, but not identify the exact type of mutation ( Hillemann et al., 2018 ). Compared with GenoType MTBDRplus, FluoroType MTBDR is characterized by shorter hands-on time, faster results (within 3 h), no DNA contamination, and automatic result interpretation. The sensitivity and specificity are comparable to those of Genotype MTBDR plus and Xpert MTB/RIF for the detection of RIF resistance ( Hillemann et al., 2018 ). However, they are lower than those of GenoType MTBDR plus for the detection of INH resistance-associated mutations in kat G and/or inh A. Hain Lifescience stated that the test can be used with decontaminated sputum and cultured samples 12 . More evaluation studies in different TB regions are needed before its implementation at TB point-of-care sites. WHO will start to evaluate the use of FluoroType MTBDR in the period 2018–2019 ( WHO, 2017c ).

Sequencing is the best technology to rapidly analyze the genotype of an organism. Beside targeted gene sequencing (TGS), the development of Next Generation Sequencing (NGS) has been a major breakthrough in molecular biology because it can rapidly provide whole genome data in a single run ( Phelan et al., 2016 ; Dheda et al., 2017 ; Manson et al., 2017 ). This allows species identification, screening of all (known and new) mutations (synonymous and non-synonymous mutations, insertions and deletions) in a sample, detecting drug resistance, and predicting the organism evolution. NGS-based kits for targeted sequencing have appeared on the market for DR-TB screening. For instance, Life technology has developed a novel protocol for rapid (2 days) full-length Mycobacterium tuberculosis gene analysis to detect first- and second-line drug resistance using the Ion Torrent Personal Genome Machine (PGM) ( Daum et al., 2012 ). Eight genes ( rpo B, kat G, and inh A, pnc A, gyr A, eis , emb B, and rps L) are amplified (PCR amplification of full-length genes, not of full genomes like for WGS) and sequenced. The AmpliSeq for Illumina TB Research Panel (Illumina) targets the same genome regions (see text foot note 12). Besides, WGS is now only valuable for cultured strains due to its requirement in terms of quantity and quality of DNA ( WHO, 2018 ). Although some studies have performed WGS for direct sputum samples but the results were variable with a high level of human genome contamination. Currently, numbers of commercially novel NGS platforms such as Oxford Nanopore MinION (Oxford Nanopore Sequencing Technology, Oxford, United Kingdom) and PacBio RSII (Pacific Biosciences) are now available with advantage of short run time and long read. However, the massive output and high error rate are still main issues for their application ( WHO, 2018 ). The Oxford Nanopore MinION, which is a small benchtop device that can plug directly into a laptop via a USB port cable, could generate 10–20 GB of data output per sample that requires a high speed computer and large storage space adequate for data analysis. In addition, the error rate is still high, up to 20–35%, but is expected to be improved as the MinION and its associated base-calling software continue to be developed ( Senol Cali et al., 2018 ). Though, outstanding advantages of WGS including higher genome coverage, providing epidemiological information and understanding of new resistance mechanisms for both current and new drugs bring precious information to research and treatment of disease. Therefore, the use of WGS as a DR-TB detection tool could be potentially used in clinical settings in the future.

The large-scale application of sequencing, especially in middle- and low-income countries is still difficult for the following reasons: (i) robust software and database tools need to be developed for the full exploitation of this technology in this specialized area of medicine; (ii) specialized personnel and bioinformatics facilities are required for the experiments, data acquisition and data analysis; (iii) the high cost of NGS platforms; (iv) need to determine whether new mutations confer anti-TB drug resistance; and (v) high amounts of high quality DNA are required for sequencing ( Phelan et al., 2016 ; Dheda et al., 2017 ; Zignol et al., 2018 ). Moreover, the sample preparation procedure and DNA extraction method should be standardized for consistency ( Zignol et al., 2018 ).

Nevertheless, the cost of sequencing is progressively decreasing and is already lower than that of phenotypic testing for first- and second-line drug resistance in most settings ( Dheda et al., 2017 ; Zignol et al., 2018 ). The Foundation for Innovative Diagnostics (FIND) and their partners are developing a system designed to perform targeted amplicon sequencing directly from primary sputum samples ( Dolinger et al., 2016 ). Several studies have reported that WGS can be successfully performed directly using sputum samples thanks to new strategies, such as targeted DNA enrichment ( McNerney et al., 2017 ; Doyle et al., 2018 ). The procedure is progressively becoming simpler, affordable and rapid. NGS and TGS are promising tools for the surveillance of drug resistance and the WHO has published a technical guideline for use of NGS technology for the detection of mutations associated with drug resistance in MTB ( WHO, 2018 ). Two consortiums of NGS experts, the ReSeqTB Consortium and CRyPTIC, have established huge repositories of genomic and phenotypic data accumulated from thousands of MTB strains worldwide. These consortiums provide platforms that allow automatic processing raw WGS data to comprehensive mutation lists and drug resistance panels for users that are convenient for non-expert bioinformatics 13 . Currently, these platforms are only available for surveillance and research use. Ultimately, they will be interoperable and transferred to the WHO to support global DR-TB surveillance in the short term and clinical diagnosis of DR-TB in the long term ( WHO, 2018 ).

DNA Microarray

Apart from sequencing, the DNA microarray technology displays the highest capacity of multiple target detection with thousands of sequences in one reaction. In many studies, this technology has been used for detecting mutations associated with drug resistance in MTB. After DNA extraction, the targets are amplified and labeled with fluorescent dyes during the PCR step ( Noyer et al., 2015 ). Then, the labeled amplicons are complementarily hybridized to the probes immobilized on the array to form double-stranded DNA. Non-specific targets and non-specific binding are eliminated in the post-hybridization washing steps. The hybridization signal intensity is detected by a scanner at the appropriate wavelength. Different kinds of fluorescent dyes are used, such as Cy3 ( Naiser et al., 2008 ) and Cy5 ( Noyer et al., 2015 ). Often, very short targets are amplified (from 60 to 300 bp) to optimize the hybridization step. The probe length varies between 10 and 40 bp ( Naiser et al., 2008 ; Yao et al., 2010 ; Noyer et al., 2015 ). Two-round PCR amplification ( Yao et al., 2010 ) or multiplex PCR (classical or asymmetric) ( Shimizu et al., 2008 ; Zimenkov et al., 2013 ; Linger et al., 2014 ) are generally used to obtain sufficient amplicons for hybridization. However, the hybridization procedure varies between studies [55°C for 2 h ( Yao et al., 2010 ), 42°C for 60 min ( Shimizu et al., 2008 ), or 37°C for 10–16 h ( Zimenkov et al., 2013 )]. A threshold signal-to-noise ratio (often higher than 3) is defined to avoid false results ( Shimizu et al., 2008 ; Yao et al., 2010 ; Zimenkov et al., 2013 ; Linger et al., 2014 ).

To simplify the numerous steps of the microarray-based workflow, Akonni developed the TruArray MDR-TB Assay that uses a microfluidic chamber to integrate almost all steps (amplification, hybridization and target detection) in one platform 14 ( Linger et al., 2014 ; Maltseva et al., n.d. ), and to limit the risk of cross-contamination. The assay covers up to 30 rpo B mutations, 6 kat G and inh A mutations, 1 emb B mutation, and 2 rps L mutations. In addition, it targets the IS6110 and IS1245 markers for the detection of the MTB complex and M. avium complex, respectively. The results are available in few hours. VereMTB TM (Veredus Laboratories, Singapore) is another kit based on a multiplexed PCR-microarray-based method and microfluidic chamber to detect MDR-TB, MTB and nine other Mycobacterium species 15 . Interestingly, Zimenkov et al. (2016) recently developed a low-density hydrogel microarray (TB-TEST) that enables to detect simultaneously first- and second-line drug resistances in one run. This microarray was developed from two previous biochips (TB-Biochip, one for MDR and one for XDR) with an additional of targeting emb B gene and 12 probes for identification of the main lineages circulating in Russia. By using two separate asymmetrical PCRs and universal adapters, this array covers nine genes including rpo B, kat G, inh A, ahp C, gyr A, gyr B, rrs , eis , and emb B genes and six SNPs for lineage identification. The turn-around time is about 19 h (3 h for PCR and 16 h for hybridization), that is quite long compared to other molecular-based methods but detecting both MDR-TB and XDR-TB is the greatest advantage of this microarray. The sensitivity and the specificity of TB-TEST are identical between clinical samples and clinical isolates. This microarray has high potential to apply directly in clinical samples.

Currently, no microarray-based assay has been endorsed by WHO for the detection of DR-TB ( WHO, 2017c ). Most tests are still in development. The GeneChip MDR Kit (CapitalBio, China) is the only microarray-based assay on the market that has been evaluated in the framework of the NHFPC-Bill & Melinda Gates Foundation tuberculosis project, but not by WHO ( CapitalBio, n.d. ; WHO, 2017c ). It is considered to be an accurate and feasible tool with high sensitivity and specificity for the direct detection of RIF and INH resistance in clinical samples in China ( Zhang Z. et al., 2012 ). In a study using spinal tuberculosis samples, this test showed a sensitivity and specificity of 88.9 and 90.7%, respectively, for RIF resistance detection, and of 80 and 91%, respectively, for INH resistance screening ( Zhang Z. et al., 2012 ). The GeneChip MDR Kit has been widely implemented in different provinces of China 16 .

Overall, DNA microarray-based tests show high specificity and sensitivity for the detection of mutations associated with anti-TB drug resistance in clinical isolates: 100% sensitivity and specificity for INH and RIF detection ( Yao et al., 2010 ); and 90 and 95.7% sensitivity for the detection of resistance to FQ, and SLIDs, respectively, with a specificity of 90.9% for FQ resistance and 90.2% for SLIDs ( Zimenkov et al., 2016 ). The lowest sensitivity (89.9%) and specificity (57%) is for EMB resistance. For the detection of resistance to RIF, INH, STR, EMB and KAN in sputum samples from patients with TB, specificity varied from 60 to 95%, and sensitivity was higher than 90% ( Shimizu et al., 2008 ). The microarray detection limit varies among platforms and studies. The GeneChip MDR Kit ( CapitalBio ) could detect at as low as 25 genome copies ( Guo et al., 2009 ; Zhang Z. et al., 2012 ). The microarray developed by Linger et al. has an analytical sensitivity of 110 genome copies per assay ( Linger et al., 2014 ). Zimenkov et al. (2013) tested the biochip using various clinical specimens, such as sputum and bronchoalveolar lavage samples, and found that its diagnostic sensitivity varies according to the smear grade, from 67% for smear “1+” to 100% for smear “3+.”

DNA microarray could become an effective method for the rapid screening of MTB resistance. However, additional improvements, such as dedicated software to analyze and interpret the fluorescent signal intensity, are necessary to make it accessible to all TB point-of-care sites and promote its application in the clinical routine.

Challenges for the Development of Molecular Markers for the Detection of DR-TB

Many molecular tools for DR-TB diagnosis have been developed and are employed worldwide. However, there are still many challenges and issues that need to be solved to provide efficient and affordable point-of-care diagnostic tests.

Technically, most of the tests only focus on one or few genes, hotspot genomic regions, or frequent mutations ( Supplementary Table S1 ). Therefore, one sample should use several tests to get the complete drug-resistance genotype and to choose the most appropriate treatment for each patient. In Vietnam, a middle-income country, presumptive MDR-TB cases such as TB patients with treatment failure or HIV /TB co-infected patients are tested with Xpert MTB/RIF ( Vietnam’s Ministry of Health, 2018 ) (see Figure 1 ). Many steps are necessary from diagnosis to treatment, especially for the diagnosis of XDR-TB cases. The total diagnostic time might be extended up to 4 months. The application of molecular tests such as NGS, TGS, or DNA microarray would reduce the diagnostic time to only 2 days. Moreover, the mutation frequency and distribution vary among populations, and many mutations linked to DR-TB are outside the regions targeted by commercial kits. Consequently, the sensitivity and specificity of a test can vary in different populations. An ideal test should cover all mutations worldwide to provide high sensitivity and specificity. The number of fluorophores, target size, and the complexity of investigating multiple targets are the main limitations that affect the test design and efficiency. DNA microarray could overcome the multiple target limitation because it allows querying thousands of sequences with only one fluorophore in one assay. Several studies demonstrated that DNA microarray can detect low numbers of DR-TB bacilli with high sensitivity and specificity in clinical specimens ( Shimizu et al., 2008 ; Guo et al., 2009 ; Zimenkov et al., 2013 ). Several microarray-based devices are currently in development or under evaluation to be used in clinical settings ( WHO, 2017c ). However, a simpler platform and an easier assay/analysis are necessary to implement DNA microarray as a routine test.

Figure 1. Diagnostic flowchart for presumptive multi-drug resistant tuberculosis in Vietnam (Vietnam’s Ministry of Health) and perspectives. MTB, Mycobacterium tuberculosis ; RIF, rifampicin; Hain 2, GenoType MTBDR sl (Hain Lifescience, Germany); MDR, multi-drug resistance; XDR, extensively drug resistance; NGS, next generation sequencing; TGS, targeted gene sequencing; DST, drug susceptibility testing; ∗ If the result of consultation is non-TB, non-tuberculosis mycobacteria identification by culture is recommended; ∗∗ Culture and culture DST are performed when the patient is still suspected of having second-line drug resistant TB.

Drug resistance emerges quickly after the introduction of new anti-TB drugs ( Bañuls et al., 2018 ). To develop accurate genetic-based diagnostic tests for DR-TB, the first crucial step is to understand the molecular mechanisms of resistance. For example, a recent review alerted about resistance of MTB to bedaquiline, a new potential drug for the treatment of DR-TB, and its cross-resistance with clofazimine ( Nguyen T.V.A. et al., 2017 ). The mechanism of resistance was linked to several genes: atp E, Rv0678 , and pep Q. However, it is only a non-exhaustive list because a standardized DST for bedaquiline does not exist yet, and not all resistance genotypes are known. Due to MTB high capacity of drug resistance acquisition, the development of a standardized DST should be started simultaneously with the evaluation of a new drug in clinical trials. Thereby, DST would be available when the new drug will be introduced in treatment regimens. However, it is a long and difficult path. For instance, although pyrazinamide has been used for many years, the standardized DST for this drug is still “work in progress” due to its current not satisfactory accuracy and reproducibility ( Chedore et al., 2010 ; Huy et al., 2017 ; Zignol et al., 2018 ). Thus, the development of quick molecular tests for drug resistance is still limited by the lack of highly accurate standard phenotypic tests, as reference.

Harsh climatic and environmental conditions are another big issue for the development of a molecular test. For instance, the Xpert machine is sensitive to dust and temperature ( Walzl et al., 2018 ). In high TB-burden countries, such as India and Vietnam, the harsh climate conditions (intense heat, high humidity, and pollution) require setting up air-conditioned laboratories and regular maintenance check-ups to ensure the shelf life of the machine and the test accuracy. This kind of working environment might cost more than the machine itself. Although the Xpert test is rapid and easy to use, such difficulties hamper its implementation in low- or middle-income countries.

Besides these issues, commercial pressure might limit the screening of optimal biomarkers ( Walzl et al., 2018 ). The numerous steps from the design/development to the scaled-up manufacture and marketing might take more than 10 years and cost more than $100 million. Thus, although many academic and private-sector research groups have been working on the development of novel diagnostic tools, only few tools were endorsed by WHO because most of them do not meet the required performance standards.

The progressive increase of DR-TB cases emphasizes the vital need for accurate and rapid diagnostic tools for their detection. The main current molecular techniques are LPA, real-time PCR, DNA microarray and sequencing. Cost, shelf life, sample throughput and accuracy are key factors for the development and application of such tests/systems. Other factors should also be considered, such as target capacity (i.e., how many drug resistance mutations can be detected) and the personnel skills required for running the test. Furthermore, to have a real impact, molecular diagnostic tests should be affordable for resource-poor countries, where TB and DR-TB are major problems. The turnaround time should be as short as possible to quickly prescribe the adapted treatment to the patients. As DR-TB is associated with mutations in different genes, an ideal test should simultaneously detect many mutations in one reaction. A simple testing procedure will increase the test accessibility at different laboratory levels.

Author Contributions

All authors contributed to the review, manuscript writing, critical review of the manuscript, and approved the final manuscript.

This work was supported by a PHC Lotus project. TNN was supported by the “Programme de Bourses d’Excellence de l’Ambassade de France au Vietnam.”

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank the National Institute of Hygiene and Epidemiology (Vietnam), the Institute of Research for Development (France), the LMI DRISA, and the “Institut National des Sciences Appliquées,” Toulouse (France) for their support. We are grateful to Elisabetta Andermarcher for editing the English.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2019.00794/full#supplementary-material

• ^ https://www.bd.com/en-uk/products/diagnostics-systems/identification-and-susceptibility-systems/bactec-mgit-
• ^ http://www.hain-lifescience.de/en/technologies/dnastrip.html
• ^ https://www.aid-diagnostika.com/en/kits/molecular-genetic-assay/infections-diseases/ antibiotic-resistences/tb-isoniazid-rifampicin/
• ^ https://www.hain-lifescience.de/en/products/microbiology/mycobacteria/tuberculosis/genotype-mtbdrplus.html
• ^ http://www.hain-lifescience.de/en/products/microbiology/mycobacteria/tuberculosis/genotype-mtbdrsl.html
• ^ http://www.bio-rad.com/fr-fr/applications-technologies/what-real-time-pcr-qpcr
• ^ http://www.who.int/tb/features_archive/Xpert-Ultra/en/
• ^ https://www.tbfacts.org/genexpert/
• ^ https://www.genedrive.com/documents/Clinical-performance-evaluation-Genedrive-MTB_RIF-assay-India-July-2016.pdf
• ^ http://www.seegene.com/neo/en/products/tuberculosis/tuberculosis_list.php
• ^ http://www.hain-lifescience.de/en/products/microbiology/mycobacteria/tuberculosis/fluorotype-mtbdr.html
• ^ https://www.illumina.com/products/by-brand/ampliseq/community-panels/tb.html
• ^ https://platform.reseqtb.org/
• ^ https://akonni.com/products/trudiagnosis/truarray-mdr-tb-assay/
• ^ https://vereduslabs.com/products/clinical/veremtb/
• ^ http://en.thholding.com.cn/2016-08/22/c_56177.htm

Aid Diagnostika GmbH (n.d.). Autoimmun Diagnostika GmbH - Kits: TB Isoniazid, Rifampicin . Available at: https://www.aid-diagnostika.com/en/kits/molecular-genetic-assay/infections-diseases/antibiotic-resistences/tb-isoniazid-rifampicin/ (accessed July 13, 2017).

Google Scholar

Ajileye, A., Alvarez, N., Merker, M., Walker, T. M., Akter, S., Brown, K., et al. (2017). Some synonymous and nonsynonymous gyrA mutations in Mycobacterium tuberculosis lead to systematic false-positive fluoroquinolone resistance results with the Hain GenoType MTBDRsl assays. Antimicrob. Agents Chemother. 61:e02169-16. doi: 10.1128/AAC.02169-16

PubMed Abstract | CrossRef Full Text | Google Scholar

Akonni Biosystems (n.d.). TruArray ® MDR-TB Assay. Available at: https://akonni.com/products/trudiagnosis/truarray-mdr-tb-assay/ (accessed July 27, 2017).

Bai, Y., Wang, Y., Shao, C., Hao, Y., Jin, Y., Espinal, M., et al. (2016). GenoType MTBDRplus assay for rapid detection of multidrug resistance in Mycobacterium tuberculosis : a meta-analysis. PLoS One 11:e0150321. doi: 10.1371/journal.pone.0150321

Bang, D., Andersen, S. R., Vasiliauskienë, E., and Rasmussen, E. M. (2016). Performance of the GenoType MTBDRplus assay (v2.0) and a new extended GenoType MTBDRsl assay (v2.0) for the molecular detection of multi- and extensively drug-resistant Mycobacterium tuberculosis on isolates primarily from Lithuania. Diagn. Microbiol. Infect. Dis. 86, 377–381. doi: 10.1016/j.diagmicrobio.2016.08.026

Bañuls, A.-L., Nguyen, T. V. A., Nguyen, Q. H., Nguyen, T. N. A., Tran, H. H., and Godreuil, S. (2018). “Antimicrobial resistance: the 70-year arms race between humans and bacteria,” in Ecology and Evolution of Infectious Diseases , eds B. Roche, H. Broutin, and F. Simard (Oxford: Oxford University Press),77–90.

Brady, M. F., Coronel, J., Gilman, R. H., and Moore, D. A. (2008). The MODS method for diagnosis of tuberculosis and multidrug resistant tuberculosis. J. Vis. Exp. 18:845. doi: 10.3791/845

Brossier, F., Guindo, D., Pham, A., Reibel, F., Sougakoff, W., Veziris, N., et al. (2016). Performance of the new version (v2.0) of the GenoType MTBDR sl test for detection of resistance to second-line drugs in multidrug-resistant Mycobacterium tuberculosis complex strains. J. Clin. Microbiol. 54, 1573–1580. doi: 10.1128/JCM.00051-16

Bunsow, E., Ruiz-Serrano, M. J., López Roa, P., Kestler, M., Viedma, D. G., and Bouza, E. (2014). Evaluation of GeneXpert MTB/RIF for the detection of Mycobacterium tuberculosis and resistance to rifampin in clinical specimens. J. Infect. 68, 338–343. doi: 10.1016/j.jinf.2013.11.012

Canetti, G., Fox, W., Khomenko, A., Mahler, H. T., Menon, N. K., Mitchison, D. A., et al. (1969). Advances in techniques of testing mycobacterial drug sensitivity, and the use of sensitivity tests in tuberculosis control programmes. Bull. World Health Organ. 41, 21–43.

PubMed Abstract | Google Scholar

CapitalBio (n.d.). CapitalBio Tuberculosis Rapid Molecular Diagnostics Series. Available at: http://en.thholding.com.cn/2016-08/22/c_56177.htm (accessed July 28, 2017).

Castan, P., de Pablo, A., Fernández-Romero, N., Rubio, J. M., Cobb, B. D., Mingorance, J., et al. (2014). Point-of-care system for detection of Mycobacterium tuberculosis and rifampin resistance in sputum samples. J. Clin. Microbiol. 52, 502–507. doi: 10.1128/JCM.02209-13

Cepheid (n.d.). 2017 Launch of New TB Test Ultra Backed by WHO Recommendation. Available at: http://www.cepheid.com/us/about-us/news-events/press-releases/216-2017-launch-of-new-tb-test-ultra-backed-by-who-recommendation (accessed June 9, 2017).

Cepheid (n.d.). Xpert MTB/RIF. Available at: http://www.cepheid.com/us/cepheid-solutions/clinical-ivd-tests/critical-infectious-diseases/xpert-mtb-rif (accessed June 9, 2017).

Chakravorty, S., Aladegbami, B., Thoms, K., Lee, J. S., Lee, E. G., Rajan, V., et al. (2011). Rapid detection of fluoroquinolone-resistant and heteroresistant Mycobacterium tuberculosis by use of sloppy molecular beacons and dual melting-temperature codes in a real-time PCR assay. J. Clin. Microbiol. 49, 932–940. doi: 10.1128/JCM.02271-10

Chakravorty, S., Kothari, H., Aladegbami, B., Cho, E. J., Lee, J. S., Roh, S. S., et al. (2012). Rapid, high-throughput detection of rifampin resistance and heteroresistance in Mycobacterium tuberculosis by use of sloppy molecular beacon melting temperature coding. J. Clin. Microbiol. 50, 2194–2202. doi: 10.1128/JCM.00143-12

Chakravorty, S., Lee, J. S., Cho, E. J., Roh, S. S., Smith, L. E., Lee, J., et al. (2015). Genotypic susceptibility testing of Mycobacterium tuberculosis isolates for amikacin and kanamycin resistance by use of a rapid sloppy molecular beacon-based assay identifies more cases of low-level drug resistance than phenotypic Lowenstein-Jensen testin. J. Clin. Microbiol. 53, 43–51. doi: 10.1128/JCM.02059-14

Chakravorty, S., Simmons, M., Rowneki, M., Parmar, H., Schumacher, S. G., Nabeta, P., et al. (2017). The new xpert MTB/RIF ultra: improving detection of Mycobacterium tuberculosis and resistance to rifampin in an assay suitable for point-of-care testing. mBio 8:e00812-17. doi: 10.1128/mBio.00812-17

Chedore, P., Bertucci, L., Wolfe, J., Sharma, M., and Jamieson, F. (2010). Potential for erroneous results indicating resistance when using the bactec MGIT 960 system for testing susceptibility of Mycobacterium tuberculosis to pyrazinamide. J. Clin. Microbiol. 48, 300–301. doi: 10.1128/JCM.01775-09

Dantas, N. G. T., Suffys, P. N., Carvalho, W. D. S., Gomes, H. M., Almeida, I. N., Figueiredo, L. J. A., et al. (2017). Correlation between the BACTEC MGIT 960 culture system with genotype MTBDRplus and TB-SPRINT in multidrug resistant Mycobacterium tuberculosis clinical isolates from Brazil. Mem. Inst. Oswaldo Cruz 112, 769–774. doi: 10.1590/0074-02760170062

Daum, L. T., Rodriguez, J. D., Worthy, S. A., Ismail, N. A., Omar, S. V., Dreyer, A. W., et al. (2012). Next-generation ion torrent sequencing of drug resistance mutations in Mycobacterium tuberculosis strains. J. Clin. Microbiol. 50, 3831–3837. doi: 10.1128/JCM.01893-12

Deggim-Messmer, V., Bloemberg, G. V., Ritter, C., Voit, A., Hömke, R., Keller, P. M., et al. (2016). Diagnostic molecular mycobacteriology in regions with low tuberculosis endemicity: combining real-time PCR Assays for detection of multiple mycobacterial pathogens with line probe assays for identification of resistance mutations. EBioMedicine 9, 228–237. doi: 10.1016/j.ebiom.2016.06.016

Dheda, K., McNerney, R., Esmail, A., Gumbo, T., Pasipanodya, J. G., Maartens, G., et al. (2017). The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respir. Med. Comm. 5, 291–360. doi: 10.1016/S2213-2600(17)30079-6

CrossRef Full Text | Google Scholar

Dolinger, D. L., Colman, R. E., Engelthaler, D. M., and Rodwell, T. C. (2016). Next-generation sequencing-based user-friendly platforms for drug-resistant tuberculosis diagnosis: a promise for the near future. Int. J. Mycobacteriol. 5, S27–S28. doi: 10.1016/j.ijmyco.2016.09.021

Doyle, R. M., Burgess, C., Williams, R., Gorton, R., Booth, H., Brown, J., et al. (2018). Direct whole-genome sequencing of sputum accurately identifies drug-resistant Mycobacterium tuberculosis faster than MGIT culture sequencing. J. Clin. Microbiol. 56:e00666-18. doi: 10.1128/JCM.00666-18

Edwards, K. J., Metherell, L. A., Yates, M., and Saunders, N. A. (2001). Detection of rpoB mutations in Mycobacterium tuberculosis by biprobe analysis. Microbiology 39, 3350–3352. doi: 10.1128/JCM.39.9.3350

Epistem (n.d.). Performance Evaluation of the Genedrive MTB/RIF Assay on Indian Patients. Available at: https://www.genedrive.com/documents/Clinical-performance-evaluation-Genedrive-MTB_RIF-assay-India-July-2016.pdf

Espasa, M., González-Martín, J., Alcaide, F., Aragón, L. M., Lonca, J., Manterola, J. M., et al. (2005). Direct detection in clinical samples of multiple gene mutations causing resistance of Mycobacterium tuberculosis to isoniazid and rifampicin using fluorogenic probes. J. Antimicrob. Chemother. 55, 860–865. doi: 10.1093/jac/dki132

Folkvardsen, D. B., Thomsen, V. O., Rigouts, L., Rasmussen, E. M., Bang, D., Bernaerts, G., et al. (2013). Rifampin heteroresistance in Mycobacterium tuberculosis Cultures as Detected by phenotypic and genotypic drug susceptibility test methods. J. Clin. Microbiol. 51, 4220–4222. doi: 10.1128/JCM.01602-13

Gardee, Y., Dreyer, A. W., Koornhof, H. J., Omar, S. V., Silva, P., and Bhyat, Z. (2017). Evaluation of the GenoType MTBDRsl version 2.0 assay for second-line drug resistance detection of Mycobacterium tuberculosis isolates in South Africa. J. Clin. Microbiol. 55, 791–800. doi: 10.1128/JCM.01865-16

Guo, Y., Zhou, Y., Wang, C., Zhu, L., Wang, S., Li, Q., et al. (2009). Rapid, accurate determination of multidrug resistance in M. tuberculosis isolates and sputum using a biochip system. Int. J. Tuberc. Lung Dis. 13, 914–920.

Hain LifeScience GmbH (n.d.). DNASTRIP ® Technology. Available at: http://www.hain-lifescience.de/en/technologies/dnastrip.html (accessed July 12, 2017).

Hillemann, D., Haasis, C., Andres, S., and Behn, T. (2018). Validation of the FluoroType MTBDR assay for detection of rifampin and isoniazid resistance in Mycobacterium tuberculosis complex isolates. J. Clin. Microbiol. 56:e00072-18. doi: 10.1128/JCM.00072-18

Huy, N. Q., Lucie, C., Hoa, T. T. T., Van Hung, N., Lan, N. T. N., Son, N. T., et al. (2017). Molecular analysis of pyrazinamide resistance in Mycobacterium tuberculosis in Vietnam highlights the high rate of pyrazinamide resistance-associated mutations in clinical isolates. Emerg. Microbes Infect. 6:e86. doi: 10.1038/emi.2017.73

Igarashi, Y., Chikamatsu, K., Aono, A., Yi, L., Yamada, H., Takaki, A., et al. (2017). Laboratory evaluation of the AnyplexTM II MTB/MDR and MTB/XDR tests based on multiplex real-time PCR and melting-temperature analysis to identify Mycobacterium tuberculosis and drug resistance. Diagn. Microbiol. Infect. Dis. 89, 276–281. doi: 10.1016/j.diagmicrobio.2017.08.016

Jeanes, C., and O’Grady, J. (2016). Diagnosing tuberculosis in the 21st century – Dawn of a genomics revolution? Int. J. Mycobacteriol. 5, 384–391. doi: 10.1016/j.ijmyco.2016.11.028

Laurenzo, D., and Mousa, S. A. (2011). Mechanisms of drug resistance in Mycobacterium tuberculosis and current status of rapid molecular diagnostic testing. Acta Trop. 119, 5–10. doi: 10.1016/j.actatropica.2011.04.008

Lawson, L., Emenyonu, N., Abdurrahman, S. T., Lawson, J. O., Uzoewulu, G. N., Sogaolu, O. M., et al. (2013). Comparison of Mycobacterium tuberculosis drug susceptibility using solid and liquid culture in Nigeria. BMC Res. Notes 6:215. doi: 10.1186/1756-0500-6-215

Linger, Y., Kukhtin, A., Golova, J., Perov, A., Lambarqui, A., Bryant, L., et al. (2014). Simplified microarray system for simultaneously detecting rifampin, isoniazid, ethambutol, and streptomycin resistance markers in Mycobacterium tuberculosis . J. Clin. Microbiol. 52, 2100–2107. doi: 10.1128/JCM.00238-14

Liu, Q., Luo, T., Li, J., Mei, J., and Gao, Q. (2013). Triplex real-time pcr melting curve analysis for detecting Mycobacterium tuberculosis mutations associated with resistance to second-line drugs in a single reaction. J. Antimicrob. Chemother. 68, 1097–1103. doi: 10.1093/jac/dks509

Makinen, J., Marttila, H. J., Marjama, M., Viljanen, M. K., and Soini, H. (2006). Comparison of two commercially available DNA line probe assays for detection of multidrug-resistant Mycobacterium tuberculosis . J. Clin. Microbiol. 44, 350–352. doi: 10.1128/JCM.44.2.350

Maltseva, E., Musralina, L., Ismagulova, G., Patel, K., Zimmerman, C., Jacobs, J. S., et al. (n.d.). Comparison of the Akonni Biosystems MDR-TB Microarray Test to the Hain Lifescience GenoType MTBDRplus for Resistance to Rifampin and Isoniazid in Kazakh M. tuberculosis Isolates. Available at: http://www.akonni.com/wp-content/uploads/2016/12/AkonniASTMH-Skiba-Maltseva-et-al.pdf

Manson, A. L., Cohen, K. A., Abeel, T., Desjardins, C. A., Armstrong, D. T., Barry, C. E., et al. (2017). Genomic analysis of globally diverse Mycobacterium tuberculosis strains provides insights into the emergence and spread of multidrug resistance. Nat. Genet. 49, 395–402. doi: 10.1038/ng.3767

McGrath, M., Gey van Pittius, N. C., van Helden, P. D., Warren, R. M., and Warner, D. F. (2014). Mutation rate and the emergence of drug resistance in Mycobacterium tuberculosis . J. Antimicrob. Chemother. 69, 292–302. doi: 10.1093/jac/dkt364

McNerney, R., Clark, T. G., Campino, S., Rodrigues, C., Dolinger, D., Smith, L., et al. (2017). Removing the bottleneck in whole genome sequencing of Mycobacterium tuberculosis for rapid drug resistance analysis: a call to action. Int. J. Infect. Dis. 56, 130–135. doi: 10.1016/j.ijid.2016.11.422

Meaza, A., Kebede, A., Yaregal, Z., Dagne, Z., Moga, S., Yenew, B., et al. (2017). Evaluation of genotype MTBDRplus VER 2.0 line probe assay for the detection of MDR-TB in smear positive and negative sputum samples. BMC Infect. Dis. 17:280. doi: 10.1186/s12879-017-2389-6

Mitarai, S., Kato, S., Ogata, H., Aono, A., Chikamatsu, K., Mizuno, K., et al. (2012). Comprehensive multicenter evaluation of a new line probe assay kit for identification of Mycobacterium species and detection of drug-resistant Mycobacterium tuberculosis . J. Clin. Microbiol. 50, 884–890. doi: 10.1128/JCM.05638-11

Molina-Moya, B., Lacoma, A., Prat, C., Diaz, J., Dudnyk, A., Haba, L., et al. (2014). AID TB resistance line probe assay for rapid detection of resistant Mycobacterium tuberculosis in clinical samples. J. Infect. 70, 400–408. doi: 10.1016/j.jinf.2014.09.010

Molina-Moya, B., Lacoma, A., Prat, C., Pimkina, E., Diaz, J., García-Sierra, N., et al. (2015). Diagnostic accuracy study of multiplex PCR for detecting tuberculosis drug resistance. J. Infect. 71, 220–230. doi: 10.1016/j.jinf.2015.03.011

Morley, A. A. (2014). Digital PCR: a brief history. Biomol. Detect. Quantif. 1, 1–2. doi: 10.1016/j.bdq.2014.06.001

Naiser, T., Ehler, O., Kayser, J., Mai, T., Michel, W., and Ott, A. (2008). Impact of point-mutations on the hybridization affinity of surface-bound DNA/DNA and RNA/DNA oligonucleotide-duplexes: comparison of single base mismatches and base bulges. BMC Biotechnol. 8:48. doi: 10.1186/1472-6750-8-48

Nathavitharana, R. R., Hillemann, D., Schumacher, S. G., Schlueter, B., Ismail, N., Omar, S. V., et al. (2016). Multicenter noninferiority evaluation of hain genotype MTBDRplus version 2 and Nipro NTM+MDRTB line probe assays for detection of rifampin and isoniazid resistance. J. Clin. Microbiol. 54, 1624–1630. doi: 10.1128/JCM.00251-16

Nguyen, H. Q., Nguyen, N. V., Contamin, L., Tran, T. H. T., Vu, T. T., Nguyen, H. V., et al. (2017). Quadruple-first line drug resistance in Mycobacterium tuberculosis in Vietnam: what can we learn from genes? Infect. Genet. Evol. 50, 55–61. doi: 10.1016/j.meegid.2017.02.012

Nguyen, T. V. A., Anthony, R. M., Bañuls, A.-L., Vu, D. H., and Alffenaar, J.-W. C. (2017). Bedaquiline resistance: its emergence, mechanism, and prevention. Clin. Infect. Dis. 66, 1625–1630. doi: 10.1093/cid/cix992

Nguyen, V. A. T., Nguyen, H. Q., Vu, T. T., Nguyen, N. A. T., Duong, C. M., Tran, T. H. T., et al. (2015). Reduced turn-around time for Mycobacterium tuberculosis drug susceptibility testing with a proportional agar microplate assay. Clin. Microbiol. Infect. 21, 1084–1092. doi: 10.1016/j.cmi.2015.08.024

Niemz, A., and Boyle, D. S. (2012). Nucleic acid testing for tuberculosis at the point-of-care in high-burden countries. Expert Rev. Mol. Diagn. 12, 687–701. doi: 10.1586/erm.12.71

Noyer, C., Abot, A., Trouilh, L., Leberre, V. A., and Dreanno, C. (2015). Phytochip: development of a DNA-microarray for rapid and accurate identification of Pseudo-nitzschia spp and other harmful algal species. J. Microbiol. Methods 112, 55–66. doi: 10.1016/j.mimet.2015.03.002

Ochang, E. A., Udoh, U. A., Emanghe, U. E., Tiku, G. O., Offor, J. B., Odo, M., et al. (2016). Evaluation of rifampicin resistance and 81-bp rifampicin resistant determinant region of rpoB gene mutations of Mycobacterium tuberculosis detected with Xpert MTB/RIF in Cross River State, Nigeria. Int. J. Mycobacteriol. 5, S145–S146. doi: 10.1016/J.IJMYCO.2016.09.007

Perez-Risco, D., Rodriguez-Temporal, D., Valledor-Sanchez, I., and Alcaide, F. (2018). Evaluation of the Xpert MTB/RIF ultra assay for direct detection of Mycobacterium tuberculosis complex in smear-negative extrapulmonary samples. J. Clin. Microbiol. 56:e00659-18. doi: 10.1128/JCM.00659-18

Phelan, J., O’Sullivan, D. M., Machado, D., Ramos, J., Whale, A. S., O’Grady, J., et al. (2016). The variability and reproducibility of whole genome sequencing technology for detecting resistance to anti-tuberculous drugs. Genome Med. 8:132. doi: 10.1186/s13073-016-0385-x

Pholwat, S., Stroup, S., Foongladda, S., and Houpt, E. (2013). Digital PCR to detect and quantify heteroresistance in drug resistant Mycobacterium tuberculosis . PLoS One 8:e57238. doi: 10.1371/journal.pone.0057238

Rice, J. E., Reis, A. H., Rice, L. M., Carver-Brown, R. K., Wangh, L. J., and Wangh, L. J. (2012). Fluorescent signatures for variable DNA sequences. Nucleic Acids Res. 40:e164. doi: 10.1093/nar/gks731

Ritter, C., Lucke, K., Sirgel, F. A., Warren, R. W., Van Helden, P. D., Böttger, E. C., et al. (2014). Evaluation of the AID TB resistance line probe assay for rapid detection of genetic alterations associated with drug resistance in Mycobacterium tuberculosis strains. J. Clin. Microbiol. 52, 940–946. doi: 10.1128/JCM.02597-13

Roh, S. S., Smith, L. E., Lee, J. S., Via, L. E., Barry, C. E., Alland, D., et al. (2015). Comparative evaluation of sloppy molecular beacon and dual-labeled probe melting temperature assays to identify mutations in Mycobacterium tuberculosis resulting in rifampin, fluoroquinolone and aminoglycoside resistance. PLoS One 10:e0126257. doi: 10.1371/journal.pone.0126257

Ruesch-Gerdes, S., and Ismail, N. (2015). REPORT FOR WHO Non-inferiority Evaluation of Nipro NTM+MDRTB and Hain GenoType MTBDRplus V2 Line Probe Assays.

Sali, M., De Maio, F., Caccuri, F., Campilongo, F., Sanguinetti, M., Fiorentini, S., et al. (2016). Multicenter evaluation of anyplex plus MTB / NTM MDR-TB assay for rapid detection of Mycobacterium tuberculosis complex and multidrug-resistant isolates in pulmonary and extrapulmonary. J. Clin. Microbiol. 54, 59–63. doi: 10.1128/JCM.01904-15.Editor

Sanchez-Padilla, E., Merker, M., Beckert, P., Jochims, F., Dlamini, T., Kahn, P., et al. (2015). Detection of drug-resistant tuberculosis by Xpert MTB/RIF in Swaziland. N. Engl. J. Med. 372, 1181–1182. doi: 10.1056/NEJMc1413930

Seegene (n.d.). Tuberculosis MTB Detection Product. Available at: http://www.seegene.com/neo/en/products/tuberculosis/tuberculosis_list.php (accessed August 16, 2018).

Senol Cali, D., Kim, J. S., Ghose, S., Alkan, C., and Mutlu, O. (2018). Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions. Brief. Bioinform. doi: 10.1093/bib/bby017 [Epub ahead of print].

Sharma, S. K., Kohli, M., Yadav, R. N., Chaubey, J., Bhasin, D., Sreenivas, V., et al. (2015). Evaluating the diagnostic accuracy of xpert MTB/RIF assay in pulmonary tuberculosis. PLoS One 10:e0141011. doi: 10.1371/journal.pone.0141011

Shimizu, Y., Dobashi, K., Yoshikawa, Y., Yabe, S., Higuchi, S., Koike, Y., et al. (2008). Five-antituberculosis drug-resistance genes detection using array system. J. Clin. Biochem. Nutr. 42, 228–234. doi: 10.3164/jcbn.2008033

Singhal, R., Arora, J., Lal, P., Bhalla, M., Myneeedu, V. P., and Behera, D. (2012). Comparison of line probe assay with liquid culture for rapid detection of multi-drug resistance in Mycobacterium tuberculosis . Indian J. Med. Res. 136, 1044–1047.

Steingart, K. R., Schiller, I., Horne, D. J., Pai, M., Boehme, C. C., and Dendukuri, N. (2014). Xpert ® MTB/RIF assay for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst. Rev. 1:CD009593. doi: 10.1002/14651858.CD009593.pub3

Tomasicchio, M., Theron, G., Pietersen, E., Streicher, E., Stanley-Josephs, D., van Helden, P., et al. (2016). The diagnostic accuracy of the MTBDRplus and MTBDRsl assays for drug-resistant TB detection when performed on sputum and culture isolates. Sci. Rep. 6:17850. doi: 10.1038/srep17850

Vietnam’s Ministry of Health (2018). Decision No. 3126 / QD-BYT of 2018 on Guidelines for Diagnosis, Treatment and Prevention of TB Disease Issued by the Ministry of Health. Available at: https://vanbanphapluat.co/quyet-dinh-3126-qd-byt-2018-huong-dan-chan-doan-dieu-tri-du-phong-benh-lao .

Walzl, G., McNerney, R., du Plessis, N., Bates, M., McHugh, T. D., Chegou, N. N., et al. (2018). Tuberculosis: advances and challenges in development of new diagnostics and biomarkers. Lancet Infect. Dis. 18, E199–E210. doi: 10.1016/S1473-3099(18)30111-7

WHO (2008). Molecular Line Probe Assays for Rapid Screening of Patients at Risk of Multi-Drug Resistant Tuberculosis. Available at: http://www.who.int/tb/dots/laboratory/lpa_policy.pdf (accessed 18 November 2009).

WHO (2017a). Frequently Asked Questions about the WHO Technical Expert Consultation Findings on Xpert ® MTB / RIF Ultra. Available at: http://www.who.int/tb/areas-of-work/laboratory/diagnostics/XpertUltraFAQs.pdf

WHO (2017b). Global Tuberculosis Report 2016. Geneva: WHO.

WHO (2017c). Global Tuberculosis Report 2017. Geneva: WHO.

WHO (2017d). Next-Generation Xpert ® MTB/RIF Ultra Assay Recommended by WHO. Geneva: WHO.

WHO (2018). The Use of Next-Generation Sequencing Technologies for the Detection of Mutations Associated with Drug Resistance in Mycobacterium tuberculosis Complex: Technical Guide. Geneva: World Health Organization.

Yao, C., Zhu, T., Li, Y., Zhang, L., Zhang, B., Huang, J., et al. (2010). Detection of rpoB, katG and inhA gene mutations in Mycobacterium tuberculosis clinical isolates from Chongqing as determined by microarray. Clin. Microbiol. Infect. 16, 1639–1643. doi: 10.1111/j.1469-0691.2010.03267.x

Zhang, X., Zhao, B., Liu, L., Zhu, Y., Zhao, Y., and Jin, Q. (2012). Subpopulation analysis of heteroresistance to fluoroquinolone in Mycobacterium tuberculosis isolates from Beijing, China. J. Clin. Microbiol. 50, 1471–1474. doi: 10.1128/JCM.05793-11

Zhang, Z., Li, L., Luo, F., Cheng, P., Wu, F., Wu, Z., et al. (2012). Rapid and accurate detection of RMP- and INH- resistant Mycobacterium tuberculosis in spinal tuberculosis specimens by CapitalBioTM DNA microarray: a prospective validation study. BMC Infect. Dis. 12:303. doi: 10.1186/1471-2334-12-303

Zignol, M., Cabibbe, A. M., Dean, A. S., Glaziou, P., Alikhanova, N., Ama, C., et al. (2018). Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study. Lancet Infect. Dis. 18, 675–683. doi: 10.1016/S1473-3099(18)30073-2

Zimenkov, D. V., Antonova, O. V., Kuz’min, A. V., Isaeva, Y. D., Krylova, L. Y., Popov, S. A., et al. (2013). Detection of second-line drug resistance in Mycobacterium tuberculosis using oligonucleotide microarrays. BMC Infect. Dis. 13:240. doi: 10.1186/1471-2334-13-240

Zimenkov, D. V., Kulagina, E. V., Antonova, O. V., Zhuravlev, V. Y., and Gryadunov, D. A. (2016). Simultaneous drug resistance detection and genotyping of Mycobacterium tuberculosis using a low-density hydrogel microarray. J. Antimicrob. Chemother. 71, 1520–1531. doi: 10.1093/jac/dkw015

Keywords : tuberculosis, drug resistance, diagnosis, mutations, molecular markers, challenges

Citation: Nguyen TNA, Anton-Le Berre V, Bañuls A-L and Nguyen TVA (2019) Molecular Diagnosis of Drug-Resistant Tuberculosis; A Literature Review. Front. Microbiol. 10:794. doi: 10.3389/fmicb.2019.00794

Received: 04 October 2018; Accepted: 28 March 2019; Published: 16 April 2019.

Reviewed by:

Copyright © 2019 Nguyen, Anton-Le Berre, Bañuls and Nguyen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Thi Ngoc Anh Nguyen, [email protected]

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 24 March 2022

Malaria chemoprevention and drug resistance: a review of the literature and policy implications

• Christopher V. Plowe   ORCID: orcid.org/0000-0002-0045-9888 1  

Malaria Journal volume  21 , Article number:  104 ( 2022 ) Cite this article

8517 Accesses

39 Citations

33 Altmetric

Metrics details

Chemoprevention strategies reduce malaria disease and death, but the efficacy of anti-malarial drugs used for chemoprevention is perennially threatened by drug resistance. This review examines the current impact of chemoprevention on the emergence and spread of drug resistant malaria, and the impact of drug resistance on the efficacy of each of the chemoprevention strategies currently recommended by the World Health Organization, namely, intermittent preventive treatment in pregnancy (IPTp); intermittent preventive treatment in infants (IPTi); seasonal malaria chemoprevention (SMC); and mass drug administration (MDA) for the reduction of disease burden in emergency situations. While the use of drugs to prevent malaria often results in increased prevalence of genetic mutations associated with resistance, malaria chemoprevention interventions do not inevitably lead to meaningful increases in resistance, and even high rates of resistance do not necessarily impair chemoprevention efficacy. At the same time, it can reasonably be anticipated that, over time, as drugs are widely used, resistance will generally increase and efficacy will eventually be lost. Decisions about whether, where and when chemoprevention strategies should be deployed or changed will continue to need to be made on the basis of imperfect evidence, but practical considerations such as prevalence patterns of resistance markers can help guide policy recommendations.

After decades of dramatic reductions in malaria cases and deaths worldwide, progress toward malaria control and elimination had plateaued before the COVID-19 pandemic [ 1 ], and malaria cases and deaths rose in 2020 [ 2 ]. Further erosion of the recent gains in malaria control will lead to resurgences, at great cost to the health, lives, and economies of the world’s poorest countries [ 3 ]. Chemoprevention strategies, i.e., the use of anti-malarial medicines for prophylaxis and for preventive treatment, can be effective tools for malaria control and elimination, but the risks of resistance to anti-malarial drugs used for prevention and treatment must be mitigated and managed for momentum to be regained and sustained.

The chemoprevention strategies currently recommended by the World Health Organization (WHO) include intermittent preventive treatment in pregnancy (IPTp), intermittent preventive treatment in infants (IPTi), seasonal malaria chemoprevention (SMC), and mass drug administration (MDA) for the reduction of disease burden in emergency situations (Table 1 ). From the time that these chemoprevention strategies were first conceived, concerns have been raised both about their potential impact on the development and spread of drug resistance that might compromise the treatment efficacy of the drug classes used, and about the impact of drug resistance on the efficacy of different chemoprevention strategies.

Measuring and monitoring resistance and efficacy

Clinical trials remain the gold standard for measuring and monitoring efficacy.

As they have been developed and implemented, each chemoprevention strategy has been evaluated in complex prospective, controlled trials that typically randomly assign either individuals or clusters (e.g., villages or districts) to receive either the drug prevention regimen being tested, or an alternative regimen, or no preventive regimen. Because their primary outcome measures are affected by a number of host and parasite factors in addition to parasite resistance, efficacy trials do not directly measure drug resistance per se, but these prospective studies remain the gold standard for measuring the chemoprevention efficacy.

The efficacy of drugs used to treat malaria is monitored worldwide in single-arm clinical trials known as WHO Therapeutic Efficacy Studies (TES), which follow standardized protocols to provide direct evidence of drug efficacy to guide policy decisions [ 5 ]. Unlike these TES for routine monitoring of treatment efficacy, simplified protocols for routine monitoring of chemoprevention efficacy are not yet in use, although the WHO is presently developing such streamlined protocols.

Because anti-malarial drug resistance is considered paramount among the many host, parasite, pharmacological and other factors that affect the efficacy of anti-malarial drugs, methods for detecting the presence of resistant parasites have long been employed as a surrogate for efficacy trials.

In vitro susceptibility testing is of limited use for monitoring chemoprevention efficacy

In vitro assays for measuring drug resistance provide a direct measure of parasite response to drugs [ 6 ], but have proven to be even more limited in scope and suitability for surveillance than clinical trials. In vitro testing is particularly unreliable for antifolate drugs, especially the sulfas, because the tests are exquisitely sensitive to host folate blood levels, which are affected by diet and vary widely among different individuals [ 7 ]. For all these reasons, in vitro tests have not played a significant role in assessing resistance in relation to the currently recommended chemoprevention strategies. Nevertheless, in vitro methods are indispensable for confirming and characterizing newly emerging forms of resistance and for establishing and confirming the molecular mechanisms of resistance [ 8 , 9 ].

Molecular resistance markers can be useful surrogates for efficacy

Elucidation of the molecular basis of in vitro Plasmodium falciparum resistance to the antifolates made it possible to define the determinants of in vivo resistance to these drugs, and to develop simple assays for molecular markers of antifolate resistance that can potentially serve as surrogate indicators of drug efficacy. Pyrimethamine and other antifolates such as proguanil (via its metabolite cycloguanil) and trimethoprim target P. falciparum dihydrofolate reductase (DHFR), while sulfadoxine and other sulfas target dihydropteroate synthase (DHPS). Resistance to DHFR inhibitors and sulfa drugs in vitro is conferred by single nucleotide polymorphisms (SNPs) in P. falciparum DHFR and DHPS, respectively [ 10 , 11 , 12 , 13 , 14 , 15 ]. Mutations in both genes tend to occur in a progressive, step-wise fashion, with higher levels of in vitro resistance occurring in the presence of multiple mutations.

Potential molecular markers have been identified for P. falciparum resistance to many but not all anti-malarial drugs [ 16 , 17 ]), including currently used chemoprevention agents such as amodiaquine (SNPs in pfcrt and pfmdr1 ), lumefantrine, mefloquine (copy number variation in pfmdr1 ), piperaquine (copy number variation in plasmepsin2 and SNPs in pfcrt) , and the artemisinins (SNPs in kelch13 ). Several of these putative markers are less well validated than those for SP and chloroquine resistance, and in some cases (e.g., lumefantrine), “resistance” markers are associated only with modest differences in susceptibility in vitro but not with clinical measures of resistance or treatment failure.

These resistance markers generally correlate very well with in vitro measures of resistance, but relationships between resistance mutations and chemoprevention outcomes are less straightforward, primarily because intrinsic drug resistance is only one of many factors that affect these outcomes, along with drug quality, intake, absorption, metabolism and clearance; nutritional and other health status indicators; and, especially, naturally acquired immunity to malaria, which can aid in clearing parasites, including drug-resistant parasites [ 18 , 19 , 20 , 21 , 22 ]. Because all these factors vary widely across individuals and populations, validating molecular markers as useful tools for measuring and monitoring drug treatment efficacy and chemoprevention outcomes has been challenging, and no marker or set of markers (haplotype) can reliably predict the outcome of a given drug regimen in an individual person.

Nevertheless, many clinical trials and epidemiological studies have demonstrated strong and consistent associations between the presence of specific mutations and outcomes of interest for both treatment and chemoprevention regimens, particularly those that rely on the antifolate drug sulfadoxine-pyrimethamine (SP). In most settings the presence of dhps K540E, which is highly prevalent in East African but scarce in West Africa, reliably signals the presence of four other key mutations, making it possible to use this single marker as a surrogate for the dhps “quintuple mutant” that is strongly associated with SP treatment failure [ 23 ], a strategy that has been recommended by the WHO for monitoring the efficacy of IPTi with SP [ 24 ].

Correlating parasite genetic markers with clinical outcomes can be even more challenging for chemoprevention than it is for treatment efficacy. This is because the relationships between parasite genotypes and efficacy outcomes are comparatively more straightforward in the case of drug treatment of clinical malaria. Drugs are administered and parasites are either cleared or not over the ensuing days. While factors other than resistance affect outcomes for both drug treatment and chemoprevention strategies, in the latter instance, the outcome is less immediate (e.g., birth outcomes following two or more doses of SP administered weeks apart), and the other factors influencing outcome are likely to play a more prominent role in chemoprevention outcomes.

In summary, drug resistance is but one of many factors that determine the efficacy of IPTp, IPTi, SMC and MDA. Clinical trials that measure health outcomes are the gold standard for measuring the efficacy of these chemoprevention strategies. Clinical trials of treatment efficacy cannot be used as a surrogate for chemoprevention efficacy. For antifolates and some other drugs, molecular markers accurately indicate the presence of drug resistant parasites, and are a useful but imperfect means of predicting the efficacy of chemoprevention strategies.

Impact of chemoprevention on resistance

Chemoprevention selects for drug resistant parasites.

More than 60 years ago David Clyde showed that the prevalence of antifolate-resistant parasites increased rapidly and dramatically in Tanzanian villages whose residents received weekly pyrimethamine for malaria prophylaxis [ 25 ]. Parasitological evidence of resistance was also detected in nearby villages whose residents did not receive chemoprevention, with the highest rates of resistance found in villages closest to those whose residents received pyrimethamine. The molecular basis for this rapid emergence and spread of resistant parasites was demonstrated 45 years later when this field experiment was repeated in a village in Mali, where resistance-conferring mutations in P. falciparum dhfr rapidly and dramatically increased in prevalence in the village within just a few weeks of starting all consenting villagers on weekly pyrimethamine [ 26 ]. Please note that in this review, “prevalence” of a given mutation or haplotype is defined as the proportion of infected individuals in whom that marker or haplotype is detected, irrespective of whether other alleles or haplotypes (e.g., wild-type) are also present in the infection.

Many subsequent studies have confirmed that community use of SP, whether for treatment or chemoprevention, is often followed by increases in community prevalence of resistance mutations in both dhfr and dhps . In many of these studies, only very general temporal or ecological trends are reported that are consistent with, but not proof that, various chemoprevention strategies directly select for forms of resistance that affect clinical outcomes. For example, one report described trends of increasing prevalence of molecular markers for antifolate resistant P. falciparum in Kenya over a 20-year period when SP was in use, initially for treatment, and subsequently for IPTp [ 27 ]. While one novel dhps mutation, S436H, more than doubled in prevalence between 2010 and 2017/2018, most of “the usual suspects” of dhfr and dhps mutations that have been associated with reduced efficacy of SP treatment and chemoprevention were already at near-fixation in 2000, and their prevalence rose only marginally: dhfr N51I was prevalent at 90% in 2005, 99% in 2010, and 100% in 2017/2018, and dhps A437G was prevalent at 98% in 2000 and 2010 and 100% in 2017/2018. Even if the use of SP for treatment and IPTp was chiefly responsible for these increases, such small changes in prevalence would have minimal impact on SP efficacy. Another molecular survey pooled data from nearly 40,000 samples collected in 38 African countries between 1998 and 2018 [ 28 ], and found generally higher prevalences of dhps A581G in East compared to West Africa, with extensive heterogeneity including within countries.

While Clyde’s Tanzania study [ 25 ] and subsequent observations of the rapid selection of resistance mutations suggest that new forms of resistance can easily emerge locally under drug pressure, genomic epidemiology surveys have found that the most highly resistant forms of resistance to nearly all anti-malarial drugs do not tend to arise de novo wherever drugs are used; rather, parasites with levels of resistance sufficient to cause treatment failure have arisen just a few times, usually in Asia, before spreading to Africa [ 29 , 30 , 31 , 32 ] (reviewed in [ 22 ]). This means that before highly resistant parasites have arrived in an area, even heavy drug selection pressure may not lead to loss of efficacy, as may be the case for antifolate resistance in much of West Africa. However, once highly resistant parasites have been imported and are present even at low prevalence, they can increase in response to drug pressure, as appears to be the case with antifolate resistance in much of East Africa. As more genomic epidemiology studies are undertaken, they are uncovering exceptions to the general rule that clinically relevant forms of resistance tend to emerge in Asia and spread to Africa. It is also possible for new, highly-resistant variants to arise locally on existing genetic backgrounds, as has been reported for dhps A581G in East Africa [ 33 ].

One important consideration in evaluating the impact of chemoprevention strategies on resistance is that, if the strategy is effective at reducing malaria infections, the number of resistant infections in a population or setting may decrease even while the proportion of infections that are resistant increases as a result of selection pressure exerted by the chemoprevention drugs. Thus when a chemoprevention strategy is highly effective, such as MDA using an ACT, selection favouring resistant parasites may have minimal public health impact if there are so few post-MDA infections that the resistant parasites in a given individual are rarely if ever transmitted. This is why MDA has tended to work best when it is implemented in parallel with rigorous vector control strategies [ 34 ].

In contrast, chemoprevention strategies that are less effective at reducing infections, such as IPTp-SP, may be more likely to result in increasing not only the proportion but the number of resistant infections, since they exert their effect less by preventing infections than by reducing parasite densities. Thus, the impact of chemoprevention on resistance depends both on the probability that resistant parasites emerge in an individual infection, and the probability that such resistant infections occur and are successfully transmitted. Mathematical models that incorporate these factors can be helpful in assessing the impact of specific chemoprevention strategies on drug resistance and efficacy [ 35 ].

Published data on the prevalence of resistance markers including A581G have been compiled and made available online. For example, Fig.  1 shows global prevalence data for dhps A581G between 2000 and 2020. The geotemporal trends for this mutation are consistent with the generally observed pattern of clinically significant resistance mutations being found earlier and at higher prevalence in East as compared to West Africa [ 22 ]. The reasons for this pattern are unclear, but plausible potential explanations include: 1) earlier introduction of resistance as a result of more-frequent human migration between Asia, the most common site of origin of highly resistant parasites, and East Africa; 2) more-rapid spread of mutations as a result of higher and more perennial malaria transmission in East Africa; and/or 3) earlier introduction of next-line anti-malarial drugs (first SP, then ACT) in East Africa owing to the earlier emergence of chloroquine resistance there has resulted in earlier and more intense selection pressure favouring parasites resistant to the new drugs in East Africa before these drugs were widely introduced in West Africa.

Global map of the prevalence of sulfadoxine-pyrimethamine resistance marker dihydrofolate reductase A581G. Data are from published sources and available at http://wwwarn.org/dhfr-dhps-surveyor/#0 (accessed 12 April 2021)

These global and regional patterns of emergence and spread of drug resistance illustrate a key point about the impact of chemoprevention on resistance: while there are ample examples of malaria chemoprevention strategies being followed by increased drug resistance, it is clear that not every chemoprevention scheme in every setting and population leads to measurable increases in resistance that in turn lead to meaningful loss of drug efficacy in that setting and population. Moreover, it can be difficult to assess the impact of drug use on resistance, and vice-versa, in the many studies that report only prevalences of individual mutations, which by themselves are less reliable predictors of efficacy than full haplotypes. Other issues that commonly cloud interpretation of chemoprevention’s impact on resistance include neglecting to genotype mutations previously believed to be absent from an area, and failure to account for differences in exposure risk among comparator groups in non-randomized observational studies.

Impact of resistance on chemoprevention efficacy

Antifolate-resistant P. falciparum was already well established in Africa by the time IPTp, IPTi, and SMC were implemented there. Based on declining SP treatment efficacy in countries that were early adopters of SP following the rise of chloroquine resistance [ 23 , 36 ], it was reasonable to expect SP-based chemoprevention strategies to follow a similar pattern. However, IPTp performed well even in settings where antifolate resistance led to SP treatment failure rates of 25% or higher in children [ 37 ] (discussed in more detail below).

About eight years after IPTp-SP was recommended by WHO in 1998, reports of increasing SP resistance led to renewed concern that, as one publication asserted, “In northern Tanzania, SP is a failed drug for treatment and its utility for prophylaxis is doubtful ” (italics added) [ 38 ]. This assertion was based on the results of an open label single arm trial of SP efficacy for treating P. falciparum in symptomatic children and asymptomatic infants in Korogwe District, about 30 km north of Muheza, Tanzania. The trial had been stopped early owing to an early treatment failure rate of 39% and day 28 failure rate of 82% in the symptomatic children. The authors implicated dhps A581G as the culprit in these alarming failure rates, despite multivariate analyses showing that factors associated with treatment failure included young age, high parasite density, and presence of three dhfr mutations, but not the presence of dhps A581G, which was prevalent at 55%. Notably, the dhfr triple mutant had a prevalence of 96% in this study. The findings that this dhfr haplotype was at near-fixation in this setting and was nevertheless significantly associated with treatment failure, while dhps A581G was not, despite being present in roughly half of the infections, suggests that the lack of association of A581G with treatment failure was real, and not a result of low prevalence or insufficient study power.

This and other studies of SP efficacy for treating clinical malaria in Africa thus raised alarms about the potential impact of antifolate resistance on IPTp and other chemoprevention strategies, but their inconclusive results called for directly examining this question in chemoprevention efficacy trials.

Intermittent preventive treatment during pregnancy and resistance

Impact of iptp on resistance.

As IPTp-SP was being evaluated and implemented in the early 2000s, studies began to examine selection of resistant parasites by ITPp-SP. When dhfr and dhps mutations were compared in Malawian women from 2003–2006 before they started SP-IPTp and after delivery, the prevalence of the dhfr/dhps quintuple mutation increased significantly, from 81% before the intervention to 100% after delivery [ 39 ]. Around the same time, studies in other African countries compared marker prevalences in women receiving SP-IPTp and in those not receiving it. The prevalence of dhfr mutations was compared in pregnant Ghanaian women at early gestation who had not received IPTp, and in women at delivery, nearly all of whom had received at least one dose of ITPp-SP [ 40 ]. Prevalence of the dhfr triple mutant was similar between the two groups and did not increase with an increasing number of IPTp-SP doses. Thus, even though the overall prevalence of dhfr mutations in the study population doubled between 1998 and 2006 in parallel with the implementation of SP-IPTp, the authors suggested that SP-IPTp might not be responsible for this increase. Similarly, in a study of peripheral and placental samples obtained from pregnant women over a 13-year period in western Kenya, the prevalence of the dhfr/dhps quintuple mutant rose contemporaneously with the implementation of IPTp-SP [ 41 ]. However, presence of the quintuple mutant was not associated with IPTp-SP use in multivariate analyses, suggesting that other factors were chiefly responsible for its rising prevalence.

In Mozambique, the prevalence of the quintuple mutant was higher in placentas of women receiving IPTp-SP than those receiving a placebo [ 42 ]. This association was only significant in women who had received a dose of SP within the 2.5 months before delivery, reflecting the “selection window” [ 43 , 44 ] during which blood concentrations of sulfadoxine and pyrimethamine remain sufficient to select resistant parasites. In an ITPp-SP study done in Burkina Faso in 2014–2015, dhfr and dhps triple mutants were more common at delivery than at first antenatal care visit, but the same mutations were even more common in the general population than in pregnant women at either encounter, and recent use of ITPp-SP was not associated with increased prevalence of mutations [ 45 ]. In this study, dhps K540E was very rare, and dhfr I164 and dhps A581G and A613S/T were not assessed. Another study in Burkina Faso reported a similar increase in lower-level dhfr mutations, but no increase in dhps mutations [ 46 ].

As dhps A581G began to rise in prevalence in Africa, more studies focused on this mutation, which typically occurred together with the other dhfr and dhps mutations comprising the quintuple mutant to form the so-called sextuple mutant. In a Tanzanian study discussed at length in the next section, dhps A581G prevalence was significantly higher in IPTp-SP recipients compared to pregnant women who had not received IPTp [ 47 ]. Surprisingly, a survey done ten years later found that A581G was rare or absent in all but one of seven sites in Tanzania [ 48 ].

The prevalence of resistance markers before and during IPTp with SP or dihydroartemisinin-piperaquine was compared in clinical efficacy trials conducted in two Ugandan districts, Tororo in 2014–2015, and Busia in 2016–2017 [ 49 ]. The dhfr/dhps quintuple mutant was already near fixation at both sites, while dhps A581G was absent in Tororo and prevalent at only 3% in Busia. Mutations associated with 4-aminoquinoline resistance, pfmdr N86Y and Y184F and pfcrt K76T, all appeared to be selected in the dihydroartemisinin-piperaquine arms of both trials. The dhfr/dhps quintuple mutations were all already prevalent at > 90% and did not increase significantly in the SP arms at either site. The prevalence of dhfr I164L remained less than 2% both before and during IPTp-SP in Tororo, but I164L rose in prevalence from 4% to 13.7% in Busia. This is consistent with selection by IPTp-SP at this site, but prevalence of this mutation also rose to 9% in women in the dihydroartemisinin-piperaquine arm who were unexposed to SP, so it is not possible to distinguish between selection by ITPp and community-wide trends in prevalence in Busia over the course of the study. The dhps A581G mutation remained absent in Tororo and did not increase in prevalence in either arm in Busia, decreasing from 3% at baseline to 0% in the dihydroartemisinin-piperaquine arm and to 1.9% in the SP arm. The authors speculated that the apparent lack of selection of A581G in Busia was due to its low baseline prevalence. This explanation is unconvincing, in that sharp increases in the prevalence of resistance, and resistance markers, is commonly seen under antifolate drug pressure for other antifolate mutations found at low baseline prevalence [ 25 , 26 ]. Another study reported apparent selection favouring A581G in Uganda after a single dose of IPTp-SP, but this conclusion was based on very small numbers: A581G was found in two of 52 infected women at the first antenatal visit, compared with two of 12 at the second visit [ 50 ].

The dhps A581G and A613S/T mutations were reported to be selected by IPTp-SP in another study of antifolate resistance marker prevalence conducted in Ghana in 2015–2017. This cross-sectional study compared marker prevalence in pregnant women at their first antenatal visit and at delivery [ 51 ]. At delivery more than 70% of women had received at least two doses of IPTp-SP, so parasites were presumed to have been under selection pressure from SP. Unlike in the contemporaneous Ugandan study that found no increase in prevalence in dhps A581G, this West African study saw A581G increase from 9 to 16%. Statistical analyses were not presented, but this increase is not statistically significant (uncorrected X 2  = 2.95, P = 0.09). While A613S/T had similar prevalence before and after delivery (15.2% to 17.5%, uncorrected X 2  = 0.82, P = 0.37), the authors reported in the abstract that a septuple mutant with both A581G and A613S/T increased significantly from 6.1% at enrolment to 18.2% at delivery (P = 0.03). These results are difficult to compare with those of studies in East Africa, most of which have found that A581G is usually accompanied by K540E. In contrast, in this study A581G was always accompanied by the dhps triple mutant and dhps S436G, A437G and A613S/T, but never by K540E. Another recent West African survey, of asymptomatically infected pregnant women in Nigeria, similarly found that K540E was absent despite high prevalences of A581G (71%), S436A (55%) and A613S/T (36%) [ 52 ], and a survey in Ghana reported similar results [ 53 ]. This tendency for K540E to be uncoupled from A581G at some West African sites likely reflects the global patterns of spread of dhps haplotypes, with more highly resistant forms commonly found in East Africa having Asian origins, while less resistant homegrown dhps haplotypes predominate in West Africa [ 32 ].

The single study that provides the most convincing evidence that ITPp-SP does not strongly select dhps A581G comes from a well-designed randomized clinical trial done in Malawi in 2011–2014 [ 54 ]. Pregnant women were randomized to one of two intervention arms: standard IPTp-SP, or intermittent screening by rapid diagnostic test (RDT), and treatment of RDT-positive infections with dihydroartemisinin-piperaquine. No differences were found in the prevalence of dhps A581G in either the peripheral or placental blood among women in the IPTp group who had been exposed to SP, compared to women randomized to the screen-and-treat group who were not exposed to SP.

In summary, IPTp-SP appears to select for antifolate resistance mutations associated with low to moderate increases in drug resistance, but there is no convincing evidence of selection favouring the key mutations—especially dhps A581G—associated with higher level antifolate resistance and loss of ITPp-SP efficacy.

Impact of resistance on IPTp efficacy

The most recent WHO Guidelines for Malaria continue to recommend IPTp-SP for women living in areas of moderate-to-high transmission in Africa, including in areas with > 90% prevalence of the dhfr/dhps quintuple mutant [ 55 ]. The guidelines note that where infections with the quintuple mutant plus either dhfr I164L or dhps A581G are prevalent, “…the efficacy of IPTp-SP may be compromised. It is unclear by how much.” The following discussion considers whether currently available evidence can add clarity on this topic.

Many studies of widely varying quality have assessed the impact of SP resistance on IPTp efficacy. An influential systematic review and meta-analysis published in 2007 pooled data from seven clinical trials of IPTp-SP in relation to SP efficacy for treating symptomatic malaria in young children at or near the same times and locations of the IPTp trials [ 37 ]. The authors concluded that even in areas where SP had lost treatment efficacy in children (day 14 treatment failure rates of 19–26%), IPTp-SP continued to provide important health benefits to HIV-negative semi-immune pregnant women and their infants. Moreover, they found no evidence of a substantial loss of IPTp efficacy as SP treatment failure rose from 3 to 39% across sites. In women living with HIV, a group in which IPTp benefit is reduced, IPTp efficacy did decline with rising treatment failure.

The discordance between IPTp-SP benefit in HIV-uninfected pregnant women and SP treatment efficacy in children was attributed mainly to greater levels of acquired immunity in pregnant women. This systematic review did not directly address drug resistance as distinct from treatment failure, nor did it examine relationships between dhfr and dhps mutations and IPTp outcomes. Nevertheless, policymakers were reassured by the persistent benefit of IPTp-SP in the face of high rates of SP treatment failure in children, and the WHO recommended adopting IPTp-SP in Africa even where the prevalence of parasitological failure at Day 14 after SP treatment among children was as high as 50%, or even higher in areas where IPTp was already implemented [ 56 ].

A study done in the same region of Tanzania where earlier studies had found that rising prevalence of dhps A581G curtailed the efficacy of both SP treatment of children and IPTp-SP, appeared to support the concern that this mutation boded ill for ITPp-SP [ 47 ]. This study, which assessed clinical, parasitological, and histopathological outcomes of IPTp, was even more alarming than the report of very high SP treatment failure rates in Tanzanian children [ 38 ]. Based on a study in mice showing that resistant parasites grew to unexpectedly high densities when drug treatment eliminated sensitive parasites [ 57 ], the authors hypothesized that, with its compromised efficacy, SP might “select resistant parasites and exacerbate infections in the placenta”. SP resistance mutations, placental parasite densities, and placental inflammation were assessed in women enrolled at delivery between 2002 and 2005 who reported having received, or not having received, SP-IPTp. Those who reported receiving IPTp were classified as “recent IPTp” if they had measurable sulfa levels in their blood, and “early IPTp” if sulfa levels were undetectable.

The authors reported that IPTp was associated not only with higher prevalence of dhps A581G but with dramatically higher placental parasite density, and, most concerning, with increased placental inflammation. They reasoned that inflammation indicates chronic placental malaria infection; that inflammation should thus be absent in acute placental malaria; and that placental parasite density normally decreases as placental inflammation increases. Based on these expectations and the observation that inflammation was more common in women who received IPTp, they deduced that the high parasite densities could not be attributed to new acute infections and, therefore, must have resulted from the greater presence of resistant parasites carrying dhps A581G in the women who received IPTp. In a subsequent publication of data from the same observational study, the authors reported that IPTp did not reduce the odds of placental malaria, increase mean maternal haemoglobin, or increase birthweight, and IPTp was associated with lower cord haemoglobin and increased risk of foetal anaemia [ 58 ]. The implications for ITPp-SP seemed ominous.

This dire interpretation, however, depended on the inference that differences in outcomes were the result of IPTp and not other confounding factors in what was essentially a retrospective case–control study nested within a birth cohort. And while most baseline characteristics showed no significant differences between women who did or did not report receiving IPTp, there was one important, highly significant difference: only 29% of women who received no IPTp lived in rural areas, while 68% of women who received IPTp lived in rural villages. The paper did not discuss differences in malaria epidemiology or risk between the rural and urban sites. An earlier paper describing the parent birth cohort study [ 59 ] cited an annual entomological inoculation rate (EIR) of around 400 infected bites/person/year for the study area of Muheza District, but that paper in turn cited another paper from a decade earlier that reported heterogeneous transmission in Muheza, with EIRs ranging from 34 to 405 [ 60 ].

With the only available data on transmission intensity in the study area being a ten-year-old study that reported a more than tenfold range of EIRs, it is difficult to dismiss the baseline observation that significantly more rural women had received IPTp. A plausible alternative explanation for the higher parasite densities and placental inflammation in women who received IPTp would be that rural women may have been exposed to up to tenfold higher malaria transmission intensity than their peri-urban counterparts, increasing their risk of acute-on-chronic placental infections. Bed net use was also different at baseline: women who reported no IPTp also reported marginally and insignificantly higher use of bed nets (76.5% vs. 64.4%). However, while none of the (more urban) non-IPTp women reported using insecticide-treated nets (ITN), 16% of (more rural) women who had received IPTp reported using ITNs. The complete absence of ITN use among the non-IPTp women is consistent with alternative explanations, including the possibility that the parasitological and histopathological findings attributed to IPTp selection of A581G-carrying resistant parasites were actually a result of baseline differences in malaria risk between women who received IPTp and those who did not.

Subsequent studies failed to replicate the disquieting finding that IPTp-SP led to increased parasite growth in a setting with prevalent dhfr/dhps sextuple mutants. In another cohort of pregnant women in Korogwe District, less than 30 km from Muheza, dhfr and dhps were genotyped in samples from women who had P. falciparum -positive RDTs, and pregnancy outcomes were assessed [ 61 ]. During the study period of 2008–2010, the prevalence of the sextuple mutant with dhps A581G in Korogwe was 44%, slightly lower than in nearby Muheza several years earlier. The presence of the sextuple mutant was associated with substantially lower birthweights. However, in contrast to the Muheza cohort, the presence of the sextuple mutant was not associated with whether or not women had received IPTp-SP or with how many doses they received; peripheral parasite density tended to be lower, not higher, in women with the sextuple mutant; and there was no relationship between early or recent IPTp and the effect of dhfr/dhps haplotypes on birth weight. Studies in Mozambique [ 42 ] and Malawi [ 62 ] similarly failed to support the notion that IPTp was harmful in settings with high levels of antifolate resistance, although dhps A581G was rare (but present) in both studies.

Another systematic review was published in 2013 by the same group that conducted the 2007 review of IPTp efficacy. A meta-analysis of data from seven trials, one each in Kenya, Tanzania, Zambia, Burkina Faso, and Mali, and two in Malawi, found higher average birthweights and lower risk of low birthweight in women who had received three or more doses of IPTp-SP, compared with those who received only two doses [ 63 ]. This association was consistent across sites where the prevalence of the dhfr/dhps quintuple mutant—as indicated by the presence of dhps K540E—ranged from 0–96%. The dhps A581G mutant was not prevalent at any of the study sites. Based on these relatively encouraging findings, WHO recommended at least three doses of IPTp-SP irrespective of the presence of dhfr/dhps quintuple mutants [ 64 ].

To recap, as of 2013, two high quality systematic reviews [ 37 , 63 ] and more recent clinical trials [ 42 ] and surveys [ 62 ] supported the WHO position that IPTp-SP was beneficial and should be used across a wide range of antifolate resistance and SP treatment efficacy. On the other hand, an open label SP efficacy study in children [ 38 ] and two observational studies in pregnant women [ 47 , 58 , 61 ] suggested that the sextuple mutant represented a dangerous threat to IPTp-SP efficacy, and might be causing IPTp to be not only ineffective but harmful in pregnancy. Each of these three studies portending bad news for IPTp had significant limitations in design and interpretation, and all three were conducted in two adjacent districts in Tanzania, limiting their generalizability to other sites in Africa, where the sextuple mutant remained mostly rare or absent.

Aiming to resolve these discrepant results, an observational study followed by a clinical trial in Malawi and a multi-country efficacy trial of IPTp-SP efficacy directly examined the relationship between SP resistance and IPTp outcomes. The effectiveness of IPTp-SP was assessed in 2009–2011 in Malawi, where the prevalence of the sextuple mutant was 8.4% [ 65 ]. The presence of A581G was associated with an approximately threefold increase in the occurrence of “patent” infections (both PCR and microscopy positive) in both peripheral and placental blood, and with higher parasite densities. However, A581G was not associated with any of the following: (1) histological evidence of active placental infection; (2) mean haemoglobin; (3) anaemia; (4) severe anaemia; (5) pre-term delivery; or (6) infants born small for gestational age. Furthermore, women infected with parasites carrying dhps A581G gave birth to infants with slightly higher birthweights and had a nearly twofold lower incidence of low birthweight, although these trends did not achieve statistical significance. And, the finding of higher parasite densities in A581G-carrying infections disappeared when the analysis was limited to women with “patent” infections, i.e., when infections that were PCR-positive but microscopy-negative were excluded from the analysis.

Some methodological issues cloud the interpretation of these results. For example, even though more than 90% of both patent (PCR and microscopy-positive) and “subpatent” (PCR-positive, microscopy-negative) infections were successfully genotyped, the prevalence of A581G was reported to be tenfold lower in subpatent infections, a surprising finding that is not explained by the authors. Further muddying the picture, most of the data are presented as pooled results from two study sites, one rural and one urban, even though the prevalence of A581G was more than twice as high at the rural site. Different microscopy staining and reading protocols were used at the two sites, with more rigorous standards at the urban site in Blantyre, and no quality control procedures were described, raising the possibility that rural–urban differences in both malaria epidemiology and the quality of microscopic diagnosis could account for some of the study findings. As with the Tanzanian study described above [ 47 ], it is possible that higher parasite densities attributed to resistant parasites were actually a reflection of higher transmission intensity or other epidemiological differences at the rural site where more A581G-carrying infections were found.

A subsequent trial, also done in Malawi by the same group, randomized pregnant women at three sites in 2011–2014 either to receive standard IPTp-SP or intermittent screening with RDTs and treatment of RDT-positive infections with dihydroartemisinin-piperaquine [ 54 ]. By the time of this study, the dhfr/dhps quintuple mutant was at near-fixation, and the dhfr I164L mutation was absent, while dhps A581G had a prevalence of 4% in infections found at enrolment in the IPTp-SP group, and 6% in placental infections at delivery. The presence of A581G in placental infections was associated with a significant decrease in gestational age and lower birthweights, but not with parasite placental density, placental inflammation, maternal haemoglobin level, or weight-for-age Z score. Overall, the timing of SP exposure had no impact on birth outcomes, but in the small group of women who had placental infections with A581G, more recent SP exposure was associated with significantly longer pregnancies and higher birthweights. However, a sensitivity analysis showed that this result was driven by a single premature birth of a very small infant to a woman who had received only a single dose of SP; when this outlier was accounted for, the association between recent SP and birth outcomes among women with A581G was not significant.

Taken together, the results of these two studies in Malawi confirmed a partial diminution of IPTp efficacy against A581G-containing placental malaria, but they did not support findings of the studies that had raised the alarm about ITPp-SP causing harm where A581G is prevalent.

None of the studies described so far that promoted the notion that antifolate resistance in the form of dhps A581G spelled doom for IPTp-SP in Africa were prospective, controlled trials designed specifically to address this question. In contrast, the relationship between antifolate resistance mutations and efficacy of IPTp-SP was prospectively assessed in a multi-country trial among asymptomatic, microscopy-confirmed P. falciparum -infected, HIV-uninfected, pregnant women. Prospective efficacy studies were undertaken between 2009 and 2013 at eight sites in six African countries spanning the continent and a range of prevalences of mutations in dhfr and dhps [ 66 ]. With weekly follow-up, treatment failure was defined as smear-positive P. falciparum on or after day 4, with both uncorrected and PCR-corrected efficacy estimates. Resistance genotyping was done using pooled sequencing, so any novel mutations should have been detected.

Study sites were characterized as having low, moderate, or high SP resistance, based on prevalence of dhps K540E of < 10%, 10–90% or > 90%, respectively. Defining SP resistance on the basis of this one mutation was reasonable, in that K540E serves as a good surrogate for the dhfr/dhps quintuple mutant in settings where dhps A581G is absent or rare, as it was in the study sites. A581G, the surrogate for the dhfr/dhps sextuple mutant, was absent in the moderate (Zambia) and low (Burkina Faso and two sites in Mali) resistance sites. A581G was found in each of the high resistance sites, with prevalence of less than 0.25% in Uganda, less than 2% in both Malawian sites, and just over 5% in Kenya. At these low levels, it was not possible to assess the relationship between the sextuple mutant and IPTp-SP outcomes in this study.

While SP resistance mutations in this multicentre study did appear to compromise parasite clearance and result in more reinfections as well as a shorter time to reinfection, resistance did not appear to affect birth outcomes. As the authors note, prevalence of resistance mutations was not the only difference across the sites. Although transmission intensity was not measured in this study, malaria transmission has historically been reported to be lower and more sharply seasonal in the West African low-resistance sites, and higher and more year-round in the moderate- and high-transmission sites in East and Southern Africa. This pattern makes it difficult to attribute outcomes solely to resistance. Moreover, lower resistance and transmission might lead us to expect better IPTp outcomes in West Africa, but this same lower transmission may also mean lower natural immunity contributing to poorer outcomes, making it challenging to sort out the relative contributions of these countervailing effects of resistance, exposure risk, and immune protection on IPTp birth outcomes. Finally, women who had been enrolled in the in vivo SP efficacy study were excluded from the birth outcome study. If women who had patent infections early in pregnancy were at higher risk of malaria, their exclusion from the birth outcome study might have reduced the study power to detect SP’s anti-malarial efficacy, lending more weight to non-malaria factors in determining outcomes. Nevertheless, this well-designed study further added to the growing body of evidence that the benefits of IPTp persist in the face of high rates of SP resistance as conferred by dhfr/dhps quintuple mutants. The impact of the sextuple mutant (the addition of dhps A581G) on IPTp outcomes remained unaddressed by this study, which finished data collection in 2013.

One potential factor contributing to the apparent lack of impact of antifolate resistance mutations on IPTp-SP efficacy is suggested by studies showing that IPTp with either dihydroartemisinin-piperaquine or SP results in comparable pregnancy outcomes despite dihydroartemisinin-piperaquine’s superior efficacy at clearing and preventing malaria infection [ 67 , 68 , 69 ]. Studies are underway to assess whether some of SP’s impact on pregnancy outcomes is mediated by non-malaria benefits, e.g., antibacterial activity.

Two additional systematic reviews have attempted to identify a threshold prevalence of dhps A581G above which IPTp-SP efficacy is lost. One pooled data from nine IPTp studies (five clinical trials and four observational studies completed at a total of 12 sites) to assess the impact of malaria transmission intensity on IPTp outcomes, as modulated by A581G prevalence [ 70 ]. Transmission intensity did not appear to influence the efficacy of IPTp on low birth weight. Data on A581G prevalence collected within two years and 250 miles of the IPTp studies were pooled. Two sites had > 50% prevalence of A581G, and the others had 10% prevalence or less, with four sites having no A581G. Among women who had received two or more doses, IPTp-SP efficacy was preserved at sites with 10% or lower prevalence of A581G. At the two sites with > 50% prevalence, efficacy was diminished but still significant among primigravid and secundigravid women, and absent in multigravid women. The authors concluded that the A581G prevalence threshold above which IPTp-SP should not be used was somewhere between 10–52%.

Another, larger, meta-analysis and systematic review was recently published by the same group that performed the two earlier meta-analyses discussed above, each of which pooled data from just seven studies [ 37 , 63 ]. This meta-analysis pooled data from 57 studies done in 17 African countries between 1994 and 2014, and confirmed the relationship between prevalence of the quintuple mutant (signalled by dhps K540E) and diminished IPTp-SP efficacy [ 71 ]. The dhps A581G mutation, used as a surrogate for the dhfr/dhps sextuple mutant, was present in 16 of the studies, at prevalences ranging from 2.5% to 47%. The pooled analysis thus overcame the sample size limitations of the individual studies that had made it difficult to draw definitive conclusions about the impact of the sextuple mutant in IPTp-SP outcomes. However, the authors’ conclusion that IPTp-SP effectiveness is lost where dhps A531G prevalence exceeds 10% is based on just five of the 57 studies included in the meta-analysis.

Three of these five studies had small sample sizes in the reference group and a pooled prevalence of A581G of 21%, and together yielded a relative risk reduction (RRR) of 35%. This means that IPTp-SP retained good effectiveness comparable to that seen in low resistance sites, despite A581G prevalence greater than 20%. Of these three studies, two were from Tanzania and had limitations and discordant results that are discussed at length above [ 47 , 58 , 61 ]. Interpreting results from the third study, from Uganda, is confounded by the unusual finding that both dhps A581G and dhfr I164L had 36% prevalence, occurring together almost as often as not [ 72 ]. The dhfr I164L mutation, which has been largely absent or unreported in other studies of IPTp and resistance, is found in parasites with the highest measured pyrimethamine IC50s, approximately tenfold higher than IC50s of parasites carrying the dhfr triple mutant. The frequent occurrence of parasites carrying both of these mutations prevents clear attribution of IPTp outcomes in this study to A581G.

Of the five studies that provided the basis for concluding that IPTp-SP efficacy is lost above a 10% prevalence threshold for A581G, the remaining two were from the Democratic Republic of Congo [ 73 ] and Uganda [ 74 ]. Both of these studies had larger sample sizes and were conducted in areas with a pooled prevalence of A581G of 46%, much higher than that seen in the three smaller studies. Together, these two studies had an RRR of -2% for low birthweight (compared with 35% for the other three studies), signifying a complete loss of IPTp-SP efficacy. This means that among the 57 studies include in the meta-analysis, just two reported that IPTp-SP efficacy was lost where A581G prevalence exceeds 10%. Both studies were also done in areas where the prevalence of K540E was above 90%, making it difficult to attribute the loss of IPTp efficacy to the presence of A581G. Neither of these studies included molecular analyses of dhfr or dhps mutations—the meta-analysis relied on molecular data collected as close as possible in space and time to the field studies [ 75 , 76 , 77 ]. While some of these molecular studies did report prevalence of dhfr I164L, the meta-analysis only used data on dhps mutations.

In summary, despite some convincing evidence that the presence of dhps A581G at least partially compromises the efficacy of IPTp-SP, the worst-case scenario [ 47 ] was not borne out by subsequent trials [ 54 , 65 , 66 ]. The evidence supporting a recommendation to withhold ITPp-SP where the prevalence of dhps A581G exceeds a threshold of 10% is not strong.

Intermittent preventive treatment during infancy and resistance

As with IPTp, resistance has been of concern since IPTi was first conceived and evaluated. Most early studies incorporated molecular surveillance to assess the relationships between resistance markers and IPTi-SP efficacy, and to measure the impact of IPTi on the prevalence of resistance markers.

Impact of IPTi on resistance

In a 2003–2005 trial of IPTi-SP in aparasitaemic Ghanaian infants, the incidence of dhfr/dhps quintuple mutants during two months after the third dose of IPTi was twice as high in the treatment group compared a placebo group [ 78 ]. In contrast, the prevalence of dhfr triple and dhfr/dhps quadruple mutants ( dhfr triple mutant plus dhps A437G in the same infection) remained stable over a one-year period as IPTi-SP was implemented in Mali in 2006–2007, with no differences in marker prevalences between 11 IPTi implementation zones and 11 control zones [ 79 ].

Ecological surveys accompanied IPTi-SP evaluations in Tanzania (2004–2007) [ 80 ] and Senegal (2006–2008) [ 81 ]. Two years after implementation of IPTi-SP, prevalence of the dhfr triple mutant in both countries was significantly higher in areas subjected to IPTi compared to nearby control areas. Prevalence of dhps A437G was also significantly higher in IPTi areas in Senegal, where dhps K540E was absent. While the prevalence of the dhps A437G/K540E double mutant was also higher in IPTi areas than in control areas in Tanzania, this difference was not significant.

Follow-up surveys in Senegal in 2009–2010 confirmed that dhps K540E remained absent both in zones where IPTi-SP (or IPTc-SP, as seasonal malaria chemoprevention was then called) had been implemented, as well as in control zones. The dhps A437G mutation decreased in prevalence in both IPT and control zones between 2009 and 2010. In the IPT zone both A581G and A613S declined from 3 to 0% and from 5 to 0%, respectively, while in the control zone A581G and A613S both rose from 0 to 3% in prevalence during the same two-year period.

In summary, while IPTi-SP has been accompanied by overall increases in the prevalence of some antifolate resistance markers, neither clinical trials of IPTi nor ecological surveys comparing IPTi implementation zones to control areas over time have shown evidence of significant selection of the dhfr/dhps haplotypes associated with reduced SP efficacy for treatment or chemoprevention. This conclusion is in agreement with that of an Institute of Medicine expert committee discussed in the next section [ 82 ], and with results of two independent modelling studies [ 83 , 84 ].

Impact of resistance on IPTi efficacy

In 2008, the Institute of Medicine (now the National Academy of Medicine) in the USA convened an expert committee to evaluate the evidence concerning IPTi-SP efficacy, including an assessment of the impact of antifolate resistance [ 82 ]. The committee undertook a detailed review of published and unpublished data, including new meta-analyses of pooled data from pilot studies of IPTi-SP done at six sites in Tanzania, Ghana, Mozambique, and Gabon. They concluded that: (1) SP treatment efficacy for clinical malaria is not a reliable indicator of IPTi effectiveness; and (2) IPTi-SP retained 20–30% efficacy in the face of 40–80% prevalence of the dhfr triple mutant. Among the six sites where IPTi showed measurable efficacy were Ashanti, Ghana, where more than 60% of baseline infections had four or more dhfr / dhps mutations [ 85 ]; Tamale, Ghana, where the dhfr triple mutant plus dhps A437G (i.e., the quadruple mutant) was found in 44% of infected children aged less than five years [ 86 ]; and Manhica, Mozambique, where prevalence of the dhfr/dhps quintuple mutant in the placebo arm was 44% [ 87 ]. The committee was unable to evaluate the impact of dhfr I164L because it was absent or rare at African sites where studies had looked for this mutation. The committee did not consider the role of dhps mutations in IPTi efficacy in more detail, owing to the paucity of available data at a time when few studies had examined the role of dhps K540E in IPTi efficacy, and the A581G and A613S/T mutations were still rare in Africa.

Subsequent studies found that IPTi-SP efficacy diminished in parallel with rising prevalence of the dhfr/dhps quintuple mutant. While the sample sizes were small, all nine genotyped baseline infections had the quintuple mutant in an IPTi trial Korogwe, Tanzania, where four infections (44%) also carried the dhps A581G mutation [ 38 ]. Although marker prevalence was not reported for infants participating in a trial in Same, Tanzania, a 2001 survey of two sites nearby had found a 60–75% prevalence of the dhfr triple mutant and a 43–55% prevalence of the dhps double mutant [ 88 ]. Neither of these two trials demonstrated significant protective efficacy of IPTi-SP [ 89 ].

A pooled analysis of results from seven IPTi trials conducted between 1999 and 2008 found that the duration of protective efficacy was shortened by 50% in the presence of quintuple mutant parasites, from 42 days in Navrongo, Ghana (no dhfr/dhps quintuple mutant), to 21 days in Korogwe, Tanzania (89% prevalence of the quintuple mutant) [ 90 ]. This meta-analysis also found that protective efficacy in the 35-day period after the 9-month dose of IPTi-SP decreased with an increasing number of resistance markers, although there were not enough data points to determine the effects of specific markers. These data are consistent with a meta-analysis that found that the duration of post-treatment chemoprophylaxis for different artemisinin-based combinations was shorter when the prevalence of markers of resistance to the ACT partner drug was higher [ 91 ].

Based on the data available at the time, a 2009 WHO technical consultation recommended that IPTi-SP be implemented only “when parasite resistance to SP in the area is not high”, adding that “Precise cut-offs cannot be defined on the basis of available data.” [ 24 ] Just a few months later another technical consultation that included expertise in drug resistance reviewed essentially the same body of evidence and recommended that IPTi-SP not be implemented where prevalence of dhfr K540E exceeded 50% [ 24 ]. This recommendation was based on just two IPTi-SP trials, one showing 21% protective efficacy in Mozambique where baseline prevalence of dhfr K540E was 55%, and one in Tanzania showing no significant efficacy where K540E prevalence was 94%.

A subsequent analysis of molecular marker data collected across Africa from 2005–2011 found that, based on the 50% threshold for K540E prevalence, eight East African countries were classified as unsuitable for SP-IPTi; 14 Central and West African countries were classified as suitable; and seven countries could not be classified owning to a lack of available contemporary data [ 92 ]. A cost-effectiveness analysis concluded that IPTi-SP remained cost-effective across a range of SP resistance levels, but the analysis did not consider the high-level resistance conferred by dhps K540E, A581G and A613S/T, limiting relevance of the study for areas where these mutations are prevalent [ 93 ].

In the last decade, few new studies that inform the impact of resistance on IPTi efficacy have been published. When a cluster randomized trial of IPTi-SP in Tanzania found no survival benefit, the authors speculated that drug resistance was one of many possible factors that could account for this finding, along with operational deficits, decreasing malaria transmission, improving vector control, and better case management [ 94 ]. A recent Cochrane review of IPTi noted overall trends of declining IPTi efficacy in parallel with increasing antifolate resistance in Africa, but no new data on SP resistance markers underly this observation, so this meta-analysis does not help in more precisely defining a resistance threshold to guide IPTi implementation decisions [ 95 ].

As countries consider implementing IPTi or introducing new drug combinations where IPTi-SP has lost efficacy in the face of resistance, studies directly assessing not only efficacy but duration of protection against both asymptomatic infection and clinical malaria episodes in relation to the prevalence of resistance markers would be of value. As was shown for different ACT treatment regimens [ 91 ], the benefits of different chemoprevention regimens may be different in areas with different resistance patterns.

Given the continued paucity of data on the relationships between SP resistance markers and IPTi efficacy to justify a threshold of resistance above which IPTi should not be implemented or continued, more creative approaches may be needed. For example, Fig.  2 shows the frequency distribution of prevalence measures of dhps K540E and A581G for studies completed in sub-Saharan African countries between 2015 and 2021, arranged in increasing order of prevalence. The prevalence of the K540E mutation ranged from 0–100%, with a clear “break point” (sharp change in slope) at around 40% prevalence, providing a natural point for grouping sites with prevalences above and below that point. In contrast, the prevalence of A581G ranged from 0–53%, with a less obvious break point around 15%. When there is a wide range between prevalence levels at which IPTi efficacy persists or is lost, it might be reasonable to choose thresholds based on these break points in the data, to reflect naturally occurring clustering of prevalence levels. This approach might help policy makers avoid difficult decisions when measured prevalences lie very close to the thresholds.

Frequency distributions of prevalence estimates of dhps K540E (L) and A581G (R) mutations measured in studies completed in sub-Saharan Africa from 2015–2021. Data were downloaded from http://www.wwarn.org/dhfr-dhps-surveyor and studies completed before 2015 and outside of Africa were excluded. Recent measures of K540E prevalence tend to cluster below 20% and above 50%, while A581G prevalence estimates lack an obvious break point

In the case of IPTi efficacy and dhps K540E prevalence, based on the observation that IPTi retained 21% efficacy where K540E was prevalent at 55% but not where it was 94%, any threshold between 55 and 94% could have been selected to segregate sites where IPTi-SP might be expected to retain and lose efficacy. The WHO technical consultation recommended a 50% threshold based on the assumption that where prevalence was less than 50%, efficacy should be at least 21%. Based on this new analysis of more recent marker prevalence data as shown in Fig.  2 , a case could be made for implementing IPTi where K540E has a prevalence of 40% or lower.

The prevalence of A581G is generally lower, with many studies having 0% prevalence and only two of 152 studies having more than 50% prevalence. Choosing a 10% threshold of A581G for implementing IPTi-SP would be problematic in that eight studies had measured prevalences between 9.9 and 10.1%. Choosing a prevalence threshold of 15% would make it easier for policy makers to segregate sites where IPTi should or should not be used, based on available recent data.

With additional analysis it might be possible to select thresholds based not only on clustering of prevalence estimates, but also geographical clustering. The intent would be to avoid having geographically adjacent areas with prevalence estimates just above and just below a given threshold. Having different IPTi policies in areas that are both geographically close and with similar malaria epidemiology could be confusing to policy makers. Selecting thresholds that would group countries or regions in a logical, understandable fashion could make recommendations easier to understand and follow. For example, choosing a threshold that results in IPTi being recommended in most of francophone West Africa. but not in anglophone East Africa, would be more palatable than one that results in different policies being recommended in coastal and western Kenya, or in northern and southern Tanzania.

These potential new approaches to setting guidelines for chemoprevention when data on resistance and efficacy are limited could be assessed in both field and modelling studies to gauge their utility and feasibility.

In summary, the evidence supporting a recommendation that IPTi-SP not be deployed where prevalence of dhps K540E exceeds 50% was thin when an expert group identified this threshold based essentially on just two trials ten years ago, and little new evidence is available to validate this threshold, or to set new criteria to guide IPTi policy (e.g., a prevalence threshold for dhfr I164L, dhps A581G, and/or dhps A613S/T). Efficacy studies of potential new IPTi drug regimens should include assessments of efficacy and duration of protection in relation to resistance markers. Until more evidence is available on the relationship between SP resistance and IPTi-SP efficacy, an alternative approach would be to select thresholds for implementing IPTi based in part on natural clustering of prevalence data in recent studies.

Seasonal malaria chemoprevention and resistance

In 2012 WHO recommended another chemoprevention strategy, seasonal malaria chemoprevention (SMC, formerly called Intermittent Preventive Treatment in Children or IPTc). SMC with SP and amodiaquine (SP-AQ) is recommended for children aged less than five years in regions of the West African Sahel with intense seasonal malaria transmission. As recommended by the WHO, SMC consists of a complete treatment course of SP-AQ administered to children aged 3–59 months at monthly intervals, beginning at the start of the transmission season, up to a maximum of four doses during the malaria transmission season. The relatively lower levels of antifolate resistance in West Africa, and the addition of amodiaquine to the regimen, gave rise to optimism that SMC might be less threatened by resistance than IPTp was in East Africa.

Impact of SMC on resistance.

In a 2008 trial in Burkina Faso, after three monthly rounds of SMC with SP-AQ the prevalence of infections with dhfr / dhps quadruple mutants (triple dhfr and dhps A437G mutants) was comparable in the treatment and placebo arms, with an overall increase over baseline prevalence in both groups [ 96 ]. In contrast, a contemporaneous trial in Mali appeared to show SMC selection of low- and mid-level antifolate resistance markers. While the dhfr/dhps quintuple mutant (quadruple plus dhps K540E) was absent, the prevalence of quadruple mutants was significantly higher in the SP-AQ group than in the placebo group, and prevalence increased from baseline in the SMC group but not in the placebo group [ 97 ]. In a trial of SMC with SP plus artesunate in Senegal, the post-intervention prevalence of quadruple mutants was also significantly higher in the intervention arm than the placebo arm, again with an increase in both groups from baseline [ 98 ]. Prevalence of resistance markers continued to rise in both groups, and no difference between the intervention and placebo arms was detected after the second year of follow up, possibly as a result of increased SP use in the general population following a change in national first-line treatment policy to SP-AQ [ 98 ]. A subsequent comparison of SMC with SP-AQ and dihydroartemisinin-piperaquine in Burkina Faso similarly found evidence of modest selection dhfr S108N and C59R and pfcrt K76T in the SP-AQ arm of the trial [ 99 ].

As noted above, the impact of SMC on resistance is related not only to the proportion of infections that carry resistant parasites, but on the proportion of people who become infected. Modelling studies may be useful in assessing whether SMC’s efficacy at reducing the prevalence of infection mitigates the risks posed by its effect of increasing the prevalence of resistance (defined here as the proportion of infections carrying resistant parasites).

Based on these early studies, it appeared that at least short-term selection of resistance markers may follow SMC implementation. Surveys of health districts that had or had not implemented SMC or IPTi in Senegal found significant selection of the dhfr triple mutant, but not for dhps mutations [ 100 ]. An ecological survey in Ghana that included areas where SMC had and had not been implemented reported similar increases in dhfr/dhps quintuple mutants, but this study did not test for the higher-level resistance mutations dhfr I164L and dhps A581G and A613S/T [ 101 ]. Another prospective SMC trial done in Mali in 2014 found that prevalence of the quintuple mutant remained similar and below 5% before and after IPTp-SP was implemented in two districts (this trial also did not assess higher-level SP resistance mutations) [ 102 ]. The Mali trial also reported no increases in the prevalence of pfcrt or pfmdr1 polymorphisms associated with diminished AQ susceptibility.

A large observational study of the scale-up of SMC with SP-AQ in seven Central and West African countries measured the prevalence of resistance markers in 2016 and 2018 among 10–30 year-olds to assess the overall trends in resistance markers in communities where under-fives were given SMC [ 103 ]. The dhfr triple mutant was already prevalent at more than 90% across the sites, and increased yet more; and dhps mutations were initially lower and increased proportionally more, with up to fourfold increases in prevalence over time. However, AQ resistance markers in pfmdr1 and pfcrt decreased modestly during the scale-up period. These results are consistent with SMC with SP-AQ selecting for antifolate resistance but not 4-aminoquinoline resistance. However, other plausible reasons for these changes in marker prevalence include reduced CQ use in the region resulting in reduced selection pressure for resistance to 4-aminoquinolines, and other sources of selection pressure favouring antifolate resistance by the use of SP or other antifolates such as trimethoprim-sulfamethoxazole (co-trimoxazole) for antibacterial treatment or chemoprevention. Notably, the fold-increases in the prevalence of dhps markers as well as various dhfr-dhps haplotypes associated with intermediate to high antifolate resistance were all lower (in many cases, 2–threefold lower) in the under-fives than in 10–30 year-olds, despite the younger group being subjected to direct selection for antifolates under SMC. This marked age difference further clouds the interpretation that SMC was solely responsible for the rise in antifolate markers over the study period.

In summary, while some prospective trials and ecological studies of SMC with SP-AQ in West Africa have reported increased prevalence of the dhfr/dhps quadruple and quintuple mutants, other studies found no such evidence of selection. No evidence has been reported of SMC being followed by increased prevalence of the higher-level resistance mutations that most severely impair SP efficacy, nor does SMC appear to select for parasites carrying mutations associated with diminished AQ susceptibility.

Impact of resistance on SMC efficacy.

While the dhfr/dhps quadruple mutant was already prevalent in West Africa as SMC was being tested and implemented, dhps K540E was still rare in the region [ 104 ]. SMC efficacy using SP combined with either amodiaquine or artesunate ranged from 70–87% at sites in Senegal, Mali, and Burkina Faso with baseline prevalences of 32–58% of the dhfr triple mutant and 22–29% for dhps A437G [ 96 , 97 , 98 ], suggesting that SMC benefit persists in the face of moderate levels of the quadruple mutant. A meta-analysis of SMC trials was conducted [ 105 ], but because baseline prevalence of resistance markers prior to implementation was generally not reported, marker prevalence could not be associated with efficacy, nor could selection be measured. Putative molecular markers for amodiaquine resistance, including mutations in pfcrt and pfmdr1 , have generally not proven reliable predictors of SMC efficacy. For example, a clinical trial of SP, AQ and SP-AQ for treatment of clinical malaria in Cameroon found that prevalence of pfcrt and pfmdr1 mutations thought to be associated with reduced susceptibility to AQ was higher at sites where AQ and SP-AQ treatment failures were lower [ 106 ].

In summary, unless and until high-level resistance mutations become more prevalent in areas where SMC is used, it will not be possible to draw conclusions about the impact of resistance on SMC efficacy.

Mass drug administration and resistance

Mass drug administration (MDA) refers to mass drug treatment of an entire population, irrespective of the presence of symptoms and without individual testing for malaria [ 34 , 107 ]. During the last century MDA schemes often led to declines in malaria rates, but gains were usually temporary [ 108 ]. Exceptions to this pattern include instances of MDA being deployed in combination with aggressive vector control and rigorous surveillance in low-transmission areas, and in geographically conscribed areas, such as islands [ 34 , 109 ]. MDA was blamed for driving drug resistance, most notably after introduction of anti-malarial drugs in table salt in the 1950s [ 110 ], and the WHO stopped recommending it. However, in response to the renewed call for malaria eradication and the emergence of artemisinin resistance, MDA has been re-examined [ 107 , 109 , 111 ]. Trials and implementation projects have been undertaken both in low burden settings slated for elimination such as the Greater Mekong Subregion [ 112 ] as well as in Africa [ 113 ]. These more recent experiences with MDA have provided the opportunity to gain a better understanding of the impact of MDA on the emergence and spread of resistance, and the impact of drug resistance on MDA efficacy.

Impact of MDA on resistance

In MDA, every consenting member of a malaria-exposed population is administered curative doses of anti-malarial drugs, irrespective of infection status. This is often repeated at intervals, e.g., two monthly cycles repeated annually for two years. It would seem obvious that such a massive drug exposure would exert powerful selection pressure favouring resistant parasites—and, indeed, MDA has been indicted for hastening resistance throughout the history of malaria control. Malaria icon Walther Wernsdorfer (who literally wrote the book on malaria) asserted 40 years ago that “Mass drug administration in its various forms, and insufficient treatment are obviously the most important motors of selection.” [ 114 ] While this is an oft-repeated notion, the evidence is less clear cut.

Theoretical arguments have been made that MDA prevents rather than fosters resistance, based on calculating probabilities of emergence and spread of resistance in relation to parasite density [ 115 ]. This prediction appears to be supported by recent well-executed MDA schemes in low-transmission elimination zones with highly efficacious drugs that found no evidence of selection for drug resistant parasites. For example, mutations in P. falciparum kelch13 associated with artemisinin resistance were already prevalent when MDA with dihydroartemisinin-piperaquine and low-dose primaquine was evaluated in eastern Myanmar, where a piperaquine resistance marker (multiple copies of the P. falciparum genes plasmepsin2/3 , or pfpm2/3 ) was absent at baseline. There was no evidence of selection of resistance by MDA: after MDA, the piperaquine resistance marker was still absent, and kelch13 mutations had decreased in prevalence from 86 to 57% [ 116 ].

A cluster-randomized trial of MDA with dihydroartemisinin-piperaquine included Southeast Asian sites with varying levels of resistance. MDA was randomly either initiated or delayed in 16 villages with about 500 residents each [ 117 ]. A highly resistant parasite lineage with both the kelch13 artemisinin-resistance mutation C580Y and the piperaquine resistance marker, multiple copies of pfpm2/3 , was absent at baseline in Myanmar and Lao PDR, but present in Vietnam and Cambodia at prevalences of 4% and 63% of genotyped infections, respectively. Only 14 of the 258 individuals who were infected with P. falciparum at baseline and completed three rounds of MDA were persistently infected a month later, 13 in Vietnam and one in Cambodia. Only the single persistent infection in Cambodia carried the highly resistant haplotype.

In Mozambique, where malaria transmission and parasite densities are much higher than in Southeast Asia, the prevalence of resistance markers was compared before and after two annual cycles of two monthly rounds of MDA with dihydroartemisinin-piperaquine [ 118 ]. No evidence of selection was found for markers of resistance to artemisinins ( k13 ) or piperaquine ( pfpm2 and pfcrt ).

Modelling studies have both supported and undermined the notion that MDA is a potent force driving resistance. One study concluded that the “windows of selection” for drugs used in chemoprevention were longer than estimated based on clinical data, leading the authors to assert that MDA and other chemoprevention strategies using full treatment regimens “will be far more potent drivers of resistance than previously thought” [ 119 ]. However, another modelling study that also incorporated pharmacodynamic properties as well as resistance mechanisms of MDA drugs came to different conclusions. This study found that while MDA using drugs to which parasites can become highly resistant with a single mutation, such as atovaquone, would result in high levels of resistance even after a single round, MDA with artemisinin-based combinations would retain efficacy because of the lower grade of resistance generated by more complex and therefore less frequently occurring genetic mechanisms [ 120 ]. The latter model appears to align better with the results of recent MDA experiences with ACT in both low and high malaria transmission settings.

In summary, there is no evidence that MDA in the modern era using highly effective artemisinin-based combination results in increased drug resistance, although studies addressing this topic are limited.

Impact of resistance on MDA efficacy.

In early experiences with MDA using sub-curative drug regimens, MDA quickly selected for resistance, which in turn compromised efficacy [ 25 , 34 ]. However, in more recent MDA schemes in Southeast Asia, the high efficacy of ACT has been preserved, even in areas with more than 60% prevalence of artemisinin resistance, and efficacy has been stable across sites with low and high rates of resistance to both artemisinins and ACT partner drugs [ 112 , 117 ]. MDA with ACT has been less efficacious in Africa, not because of drug resistance but because of epidemiological and parasitological factors that differ from low-transmission areas slated for elimination. For example, MDA has either failed or been followed by rebounding malaria incidence when it has been attempted in limited areas adjacent to non-MDA areas that serve as a source for rapid re-introduction of malaria to the populations subjected to MDA [ 34 , 109 ]. Even in lower transmission settings, MDA’s effects are short-lived if it is applied with less-than-ideal rigor in the absence of effective vector control methods [ 121 ]. The near-complete absence of clinically relevant levels of resistance to ACT drugs in Africa precludes any assessment of the impact of resistance on MDA efficacy there.

In summary, in the past drug resistance diminished the efficacy of MDA when drugs were used in sub-curative formulations and dosing regimens (e.g., single drugs used at doses that fail to clear infection). However, in the twenty-first century, MDA with highly effective combination drugs has proven efficacious even in the face of high levels of resistance. Nevertheless, policy makers continue to express worries about MDA promoting resistance [ 122 ].

Other potential uses of chemoprevention and resistance

While other potential uses of chemoprevention for malaria control and elimination are not presently recommended by the WHO, evidence from evaluations of new chemoprevention strategies can shed light on the relationships between drug resistance and the WHO-recommended strategies reviewed here. For example, several studies have explored the benefits of preventive drug treatment for malaria among school-age children in East and Southern Africa, where malaria transmission tends to be more perennial than in the West African countries where SMC has been tested and implemented.

A recent systematic review and meta-analysis of preventive treatment among school-age children in Africa that pooled data from 13 studies [ 123 ] noted that “…the only study to measure directly the effect of school-based treatment on drug resistance showed that recent treatment with dihydroartemisinin–piperaquine was associated with higher prevalence of molecular markers of drug resistance.” The study in question, from Uganda [ 124 ], measured the proportion of P. falciparum infections carrying only the “pure mutant” forms of known resistance markers in relation to the time of the most recent dose of dihydroartemisinin–piperaquine given as monthly chemoprevention to school-age children. This analysis thus combined mixed infections (containing both resistant mutant parasites and sensitive wild-type parasites) with pure wild-type infections in the reference (ostensibly non-resistant) group. This analytical approach limited the “resistant” outcome to those infections in which only “pure mutant” forms were detected. For the purpose of assessing selection of resistance and risk of treatment failure, arguably the more appropriate analysis would have been to compare the proportion of infections containing any resistant parasites, whether or not wild-type parasites were also present in the infection. This is because it is the presence of resistant parasites (irrespective of the presence or absence of sensitive parasites) that signals the risk of treatment failure—the additional presence of wild-type sensitive parasites should have no effect on whether or not the resistant parasites are cleared by drug treatment.

As shown in Fig.  3 , when the data in the Uganda paper [ 124 ] are re-analysed using this approach of assessing the presence or absence of resistant parasite genotypes (irrespective of presence of wild-type genotypes), there is no suggestion of increased prevalence of any resistance markers in infections occurring further in time from dihydroartemisinin-piperaquine administration. In fact, one of the resistance markers, pfmdr1 N86Y, appears to be significantly less prevalent in infections that occurred sooner after drug treatment, consistent with selection favouring wild-type parasites. The other marker that had appeared in the original analysis to be selected by chemoprevention in this setting, pfcrt K76T, was prevalent at near-fixation levels in all infections, irrespective of temporal proximity to drug treatment, as shown in Fig.  3 .

Re-analysis of data purportedly showing selection of resistance markers by monthly seasonal malaria chemoprevention in school-age Ugandan children. For each resistance marker, the three bars represent proportion of infections containing mutant genotypes at increasingly distant times from last drug treatment with Dihydroartemisinin-piperaquine. Panel A shows the original analysis, depicted here in graph form, and showing apparent selection of “pure mutant” genotypes of pfmdr1 N86Y and pfcrt K76T based on their increasing in prevalence after drug treatment. Panel B depicts a re-analysis of the same data showing no evidence of positive selection for mutant genotypes when all infections containing the mutation in question are considered to have resistant parasites. Data from Nankabirwa et al . Antimicrob Agents Chemother 2016, 60:5649–54

In summary, the evidence that malaria chemoprevention in school-age children increases drug resistance does not stand up to careful scrutiny. This example illustrates the importance of rigorous study design and analysis in assessing the relationships between drug resistance and malaria chemoprevention strategies and lends further support to the idea that selection of clinically relevant forms of resistance by chemoprevention is not inevitable.

Potential approaches to manage and mitigate the risk of resistance

The history of antimicrobial use is rife with examples of drugs being used in inappropriate ways that hasten the emergence and spread of resistance, such as overprescribing antibacterial drugs for viral illnesses, or adding antibiotics to livestock feed to enhance animal growth. In the case of malaria, it is hard to dispute the inadvisability of practices like adding anti-malarial drugs to table salt [ 110 ] and the unfettered sale and use of drugs of questionable quality in the private sector [ 125 ]. Concerns about resistance can trigger policymakers to resist new or expanded uses of valuable drugs. While this protective urge is understandable, and can lead to useful initiatives such as expanding diagnostic capacity to reduce empiric malaria treatment for all fever cases, it comes with a risk of restricting access to beneficial drugs that could be deployed in ways that do not appreciably shorten their useful lifespans. Understanding of resistance mechanisms may offer potential approaches for finding the optimal balance between treating and preventing malaria and preserving drug efficacy.

Can countervailing resistance mechanisms be exploited to preserve efficacy?

The WHO and others have recommended that the risk of chemoprevention hastening the demise of treatment drugs should be mitigated by using different drugs for chemoprevention and first-line treatment. IPTp, IPTi and SMC programmes generally follow this recommendation, as SP and SP-AQ are not recommended first-line treatments in countries where these strategies are deployed. Recent MDA programs have been less compliant with this advice, in that MDA with ACT has been used in areas where ACT is also the first-line malaria treatment. This means that the same class of drug—the artemisinins—are subjected to potential selection pressure for resistance in both treatment and chemoprevention regimens, in the same areas if not in the same populations. ACT is likely to remain the first choice for MDA until other equally highly efficacious and well-tolerated regimens are available.

In the meantime, one approach for reducing the potential for MDA to select forms of resistance that impair ACT efficacy is to use different regimens for MDA and first-line treatment, with ACT partner drugs that have antagonistic resistance mechanisms. For example, resistance to mefloquine has been associated with increased copy number of the pfmdr1 gene [ 126 , 127 , 128 ] and piperaquine resistance is associated with increased copy number of the pfpm2 and pfpm3 genes [ 129 , 130 ]. Parasites with increased copy numbers of pfpm2/3 signalling piperaquine resistance usually occur together with the wild-type single-copy pfmdr1 associated with mefloquine sensitivity. These antagonistic resistance mechanisms could potentially be exploited to preserve efficacy by deploying artemisinin-based combinations with countervailing resistance selection pressure, e.g., using dihydroartemisinin-piperaquine for MDA and artesunate-mefloquine or artemether-lumefantrine for treatment. A recent trial of ITPp with mefloquine reported apparent selection against the pfmdr1 N86Y mutation that is associated with chloroquine resistance, raising the possibility that IPTp-mefloquine could drive selection of mefloquine-resistant but chloroquine-sensitive parasites [ 131 ].

The two anti-malarial drugs for which counter-resistance is best documented, chloroquine and mefloquine, have recovered efficacy after being withdrawn in some areas and are being evaluated for reintroduction into use. When chloroquine was withdrawn and replaced with SP as the first-line drug in Malawi, chloroquine resistance disappeared over a period of about eight years [ 132 ]. Chloroquine was shown to be highly efficacious once again for malaria treatment [ 133 ], and weekly and intermittent chloroquine chemoprevention had similar efficacy to IPTp-SP in pregnant women [ 134 ]. Chloroquine resistance also declined dramatically after chloroquine was no longer recommended in Tanzania [ 135 ] and Zambia [ 136 ]. Similarly, after six years as first-line treatment in Thailand, mefloquine efficacy declined from 98% to up to 50% [ 137 ]. When dihydroartemisinin-piperaquine was used in the region, mefloquine efficacy recovered, and it is now being studied in the region as a component of a triple ACT [ 138 ].

Whether recovery of efficacy results from counter-resistance favouring drugs lost to resistance, or simply resurgence of sensitive parasites in the absence of drug pressure [ 139 ], rotating or alternating anti-malarial drugs could be a useful approach for managing resistance. Alternatively, drugs with countervailing resistance profiles could be deployed in parallel: a strategy of “multiple first line therapies” has been proposed to preserve efficacy of treatment drugs [ 140 ], and the rationale for “triple therapy” ACT includes the possibility of using drugs with antagonistic resistance profiles [ 138 , 141 ].

Triple therapy in the form of dihydroartemisinin combined with piperaquine and mefloquine has been proposed as a way to protect ACT partner drug efficacy [ 142 ] and is being evaluated for malaria treatment in western Cambodia [ 138 ]. Where ACT efficacy is severely compromised triple drug therapy offers a valuable option for malaria treatment, but the added expense and safety considerations make triple therapy less viable for chemoprevention strategies. A recent systematic review of mefloquine for preventing malaria in pregnancy found that while it had superior efficacy to ITPp-SP, high rates of mefloquine-related adverse events limit its potential effectiveness [ 143 ]. Other proposed approaches for mitigating or overcoming the impact of resistance on chemoprevention include using antibacterial drugs that have modest anti-malarial efficacy and are thought to be refractory to resistance, such as azithromycin [ 144 , 145 ], doxycycline [ 146 ], or trimethoprim-sulfamethoxazole [ 147 ]; increasing the dosage or changing the dosing interval to protect against resistant parasites [ 148 ]; and adopting screen-and-treat instead of intermittent treatment [ 54 , 67 , 149 , 150 , 151 ]. None of these approaches has gained acceptance as a viable alternative to IPTp-SP.

Another potential approach for deterring resistance is matching pharmacokinetic properties of drugs used in combination, so that longer-acting partner drugs are not left “unprotected” by persisting at levels that select for resistance after the shorter-acting partner drug has been eliminated [ 152 , 153 , 154 ]. Matching half-lives and elimination curves is an attractive approach that should ideally be incorporated into the design of future anti-malarial drug combinations. In the meantime, with the limited number of effective drugs currently available, most drug combinations in use now, and all artemisinin-based combinations, include partner drugs with grossly mis-matched pharmacokinetic profiles. Compared to artemisinin-based combinations, which all pair longer-acting partners with extremely rapidly cleared artemisinins, SP and SP-AQ are reasonably well-matched combinations.

Each of these approaches to mitigating and deterring resistance comes with significant challenges. In discussions about multiple first-line therapies, National Malaria Control Programme managers have explained to researchers and modellers that implementing changes in first-line malaria treatment drugs is not simply a matter of issuing recommendations—doing so effectively requires major investment of resources, time, and effort in training health providers, educating the public, and establishing new procurement and distribution systems. With mathematical models yielding divergent predictions about the benefits of multiple first-line therapies [ 155 ], policy makers remained understandably sceptical about this approach.

Proposed chemoprevention strategies that rely on drugs with adverse effects that are tolerable when treating ill patients (e.g., doxycycline, mefloquine) may not be acceptable to the healthy people who are the target population for chemoprevention strategies. Increasing drug dosages to overcome resistance likewise increases safety concerns, especially for use in infants, children, and pregnant women. Screen-and-treat strategies are appealingly efficient, in that they avoid treating uninfected individuals, but they also miss the large reservoir of sub-patent infections and miss out on the post-treatment prophylaxis benefit for people infected shortly after treatment that accounts for much of the benefit of IPT and SMC.

In summary, standardized protocols for measuring and monitoring chemoprevention efficacy are needed. With imperfect evidence, practical considerations such as known prevalence patterns can help guide recommendations on when and where to deploy chemoprevention strategies. Using different drugs for chemoprevention and treatment and combining drugs with countervailing resistance mechanisms may help to preserve efficacy. The best approach for mitigating and managing drug resistance to protect the efficacy of chemoprevention strategies is to ensure that there is a pipeline of safe and effective new malaria drugs, ideally with diverse mechanisms of action and resistance, to replace those lost to resistance.

Summary and final perspectives

The evidence reviewed here about the relationships between drug resistance and malaria chemoprevention strategies comes from a patchwork of studies of diverse designs and varying quality that sometimes yield conflicting results. Studying the relationships between resistance and efficacy is only possible where there is both a high enough prevalence of resistance and high enough level of efficacy to measure associations with adequate statistical power. The heterogeneous settings, populations, and malaria epidemiologies where chemoprevention strategies are tested and used limit the generalizability of individual studies.

Meta-analyses of pooled data have been helpful in guiding policy recommendations, but even large meta-analyses are limited by the small numbers of well-designed studies in which data on resistance were directly collected. This can mean that seemingly robust analyses that draw conclusions based on pooled data from dozens of studies may in fact base those conclusions as few as one or two studies. Most of the meta-analyses reviewed here also pooled molecular marker data from separate surveys that were done as close as possible in time and space to chemoprevention trials. Conclusions thus rely on the suspect assumption that the prevalence of molecular markers is stable across time, space, and populations.

These limitations in the quality and comparability of the available data mean that it is much easier to draw conclusions about what is not known than to develop clear evidence-based guidance based on what we do know. For this reason, many of the key findings summarized in Table 2 are conclusions that the evidence justifying various recommendations is insufficient or weak. Ultimately, health policy makers must make decisions in the face of substantial uncertainty. For example, the WHO has recommended specific molecular marker prevalence thresholds above which certain chemoprevention strategies should not be implemented. The available evidence may support only wide ranges—if the data tell us that a given strategy is likely to retain efficacy if the prevalence of a given marker is somewhere between 10–50%, do we recommend a threshold of 10%, or 50%, or something in between?

Recommendations may need to be tempered to offer broader guidelines than precise prevalence thresholds for resistance markers. For example, guidelines may include statements along the lines of: “IPTx has been shown to be efficacious in settings with a prevalence of [resistance marker] up to XX% but not in a setting with a [resistance marker] of YY%. The relationship between efficacy and mutation frequencies between XX% and YY% remains unknown. Resistance may not have been the only factor influencing efficacy in these settings.”

When selecting thresholds for recommending where and when chemoprevention strategies should be used, practical factors unrelated to evidence about resistance and efficacy should also be considered, such as whether or not current or future treatment drugs share resistance mechanisms with chemoprevention drugs, or whether a given threshold might result in confusing situations such as different recommendations in adjacent areas with similar malaria epidemiologies that happen to have resistance prevalences just above and below the threshold.

These limitations can be mitigated to some extent by standardizing study designs and coordinating multi-centre trials and pooled analyses, as has been done by consortia that have formed to test and implement some chemoprevention strategies. The WHO recommendations on research priorities can also guide researchers to conduct studies that will yield data useful to policy makers, to the extent that researchers are made aware of and follow such recommendations. For example, a standardized protocol for “Preventive Efficacy Studies (PES)” akin to TES studies is currently being developed by the WHO Global Malaria Programme.

It is somewhat encouraging that malaria chemoprevention does not inevitably lead to meaningful increases in resistance, and even high rates of resistance do not necessarily impair chemoprevention efficacy. At the same time, it can reasonably be anticipated that, over time, as drugs are widely used, resistance will generally increase, and sooner or later efficacy will be lost. Decisions about whether, where and when chemoprevention strategies should be deployed will continue to need to be made on the basis of imperfect evidence. It is hoped that this assessment of what is known about the relationships between resistance and chemoprevention will be useful as the WHO evaluates and updates its chemoprevention recommendations.

Availability of data and materials

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

Artemisinin-based combination therapy

Amodiaquine

Dihydroartemisinin

Plasmodium falciparum Dihydrofolate reductase enzyme

P. falciparum Gene that encodes DHFR

P. falciparum Dihydropteroate synthase enzyme

P. falciparum Gene that encodes DHPS

Entomological inoculation rate

50% Inhibitory concentration (measure of in vitro drug resistance)

Intermittent preventive treatment in infants

Intermittent preventive treatment in pregnancy

Insecticide-treated net

P. falciparum kelch 13 Gene

Mass drug administration

P. falciparum Chloroquine resistance transporter gene

P. falciparum Multi-drug resistance gene

P. falciparum Plasmepsin 2/3 gene(s)

Rapid diagnostic test

Relative risk reduction

Seasonal malaria chemoprevention

Single nucleotide polymorphism (also known as point mutation)

Sulfadoxine-pyrimethamine

World Health Organization

The use of anti-malarial medicines for prophylaxis and for preventive treatment. This review focuses on preventive treatment, and not on prophylaxis for visitors to endemic areas

Prevalence of a given mutation or haplotype is defined here as the proportion of infected individuals in whom that marker or haplotype is detected, irrespective of whether other alleles or haplotypes (e.g., wild-type) are also present in the infection

Dhfr triple mutant plus dhps A437G in the same infection

Dhfr N51I/C59R/S108N and dhps A437G/K540E2 in the same P. falciparum infection. Some publications use “quintuple mutant” (or sextuple or septuple mutant) to refer to any dhfr/dhps haplotype containing any combination of five (or six or seven) mutations, but most use the definition applied here

Dhfr/dhps quintuple mutant plus dhps A581G in the same infection

Dhfr mutations N51I/C59R/S108N in the same infection

WHO. World malaria report 2020. Geneva: World Health Organization; 2020.

Google Scholar  

WHO. World malaria report 2021. Geneva: World Health Organization; 2021.

Cohen JM, Smith DL, Cotter C, Ward A, Yamey G, Sabot OJ, Moonen B. Malaria resurgence: a systematic review and assessment of its causes. Malar J. 2012;11:122.

Article   PubMed   PubMed Central   Google Scholar  

WHO. WHO malaria terminology. Geneva: World Health Organization; 2019.

WHO. Methods for surveillance of antimalarial drug efficacy. Geneva: World Health Organization; 2009.

Nguyen-Dinh P, Payne D. Pyrimethamine sensitivity in Plasmodium falciparum : determination in vitro by a modified 48-hour test. Bull World Health Organ. 1980;58:909–12.

CAS   PubMed   PubMed Central   Google Scholar  

Wang P, Brobey RK, Horii T, Sims PF, Hyde JE. Utilization of exogenous folate in the human malaria parasite Plasmodium falciparum and its critical role in antifolate drug synergy. Mol Microbiol. 1999;32:1254–62.

Article   CAS   PubMed   Google Scholar  

Plowe CV. Monitoring antimalarial drug resistance: making the most of the tools at hand. J Exp Biol. 2003;206:3745–52.

Article   PubMed   Google Scholar  

Laufer MK, Djimde AA, Plowe CV. Monitoring and deterring drug resistant malaria in the era of combination therapy. Am J Trop Med Hyg. 2007;77(Suppl 6):160–9.

Peterson DS, Walliker D, Wellems TE. Evidence that a point mutation in dihydrofolate reductase-thymidylate synthase confers resistance to pyrimethamine in falciparum malaria. Proc Natl Acad Sci USA. 1988;85:9114–8.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Cowman AF, Morry MJ, Biggs BA, Cross GA, Foote SJ. Amino acid changes linked to pyrimethamine resistance in the dihydrofolate reductase-thymidylate synthase gene of Plasmodium falciparum . Proc Natl Acad Sci USA. 1988;85:9109–13.

Snewin VA, England SM, Sims PF, Hyde JE. Characterisation of the dihydrofolate reductase-thymidylate synthetase gene from human malaria parasites highly resistant to pyrimethamine. Gene. 1989;76:41–52.

Zolg JW, Plitt JR, Chen G-X, Palmer S. Point mutations in the dihydrofolate reductase-thymidylate synthase gene as the molecular basis for pyrimethamine resistance in Plasmodium falciparum . Mol Biochem Parasitol. 1989;36:253–62.

Peterson DS, Milhous WK, Wellems TE. Molecular basis of differential resistance to cycloguanil and pyrimethamine in Plasmodium falciparum malaria. Proc Natl Acad Sci USA. 1990;87:3018–22.

Foote SJ, Galatis D, Cowman AF. Amino acids in the dihydrofolate reductase-thymidylate synthase gene of Plasmodium falciparum involved in cycloguanil resistance differ from those involved in pyrimethamine resistance. Proc Natl Acad Sci USA. 1990;87:3014–7.

Ippolito MM, Moser KA, Kabuya JB, Cunningham C, Juliano JJ. Antimalarial drug resistance and implications for the WHO Global Technical Strategy. Curr Epidemiol Rep. 2021;8:46–62.

Conrad MD, Rosenthal PJ. Antimalarial drug resistance in Africa: the calm before the storm? Lancet Infect Dis. 2019;19:e338–51.

Plowe CV, Kublin JG, Doumbo OKP. falciparum dihydrofolate reductase and dihydropteroate synthase mutations: epidemiology and role in clinical resistance to antifolates. Drug Resist Updat. 1998;1:389–96.

Djimde AA, Doumbo OK, Traore O, Guindo AB, Kayentao K, Diourte Y, et al. Clearance of drug-resistant parasites as a model for protective immunity in Plasmodium falciparum malaria. Am J Trop Med Hyg. 2003;69:558–63.

Gregson A, Plowe CV. Mechanisms of resistance of malaria parasites to antifolates. Pharmacol Rev. 2005;57:117–45.

Laufer MK, Djimde AA, Plowe CV. Monitoring and deterring drug-resistant malaria in the era of combination therapy. Am J Trop Med Hyg. 2007;77:160–9.

Plowe CV. The evolution of drug-resistant malaria. Trans R Soc Trop Med Hyg. 2009;103(Suppl 1):S11–4.

Kublin JG, Dzinjalamala FK, Kamwendo DD, Malkin EM, Cortese JF, Martino LM, et al. Molecular markers for failure of sulfadoxine-pyrimethamine and chlorproguanil-dapsone treatment of Plasmodium falciparum malaria. J Infect Dis. 2002;185:380–8.

WHO. Report of the Technical Consultation on Intermittent Preventive Treatment in Infants (IPTi). Geneva: World Health Organization; 2009.

Clyde DF, Shute GT. Resistance of Plasmodium falciparum in Tanganyika to pyrimethamine administered at weekly intervals. Trans R Soc Trop Med Hyg. 1957;51:505–13.

Doumbo OK, Kayentao K, Djimde A, Cortese JF, Diourte Y, Konare A, et al. Rapid selection of Plasmodium falciparum dihydrofolate reductase mutants by pyrimethamine prophylaxis. J Infect Dis. 2000;182:993–6.

Pacheco MA, Schneider KA, Cheng Q, Munde EO, Ndege C, Onyango C, et al. Changes in the frequencies of Plasmodium falciparum dhps and dhfr drug-resistant mutations in children from Western Kenya from 2005 to 2018: the rise of Pfdhps S436H. Malar J. 2020;19:378.

Amimo F, Lambert B, Magit A, Sacarlal J, Hashizume M, Shibuya K. Plasmodium falciparum resistance to sulfadoxine-pyrimethamine in Africa: a systematic analysis of national trends. BMJ Glob Health. 2020;5:e003217.

Wootton JC, Feng X, Ferdig MT, Cooper RA, Mu J, Baruch DI, et al. Genetic diversity and chloroquine selective sweeps in Plasmodium falciparum . Nature. 2002;418:320–3.

Cortese JF, Caraballo A, Contreras CE, Plowe CV. Origin and dissemination of Plasmodium falciparum drug-resistance mutations in South America. J Infect Dis. 2002;186:999–1006.

Roper C, Pearce R, Nair S, Sharp B, Nosten F, Anderson T. Intercontinental spread of pyrimethamine-resistant malaria. Science. 2004;305:1124.

Mita T, Venkatesan M, Ohashi J, Culleton R, Takahashi N, Tsukahara T, et al. Limited geographical origin and global spread of sulfadoxine-resistant dhps alleles in Plasmodium falciparum populations. J Infect Dis. 2011;204:1980–8.

Alifrangis M, Nag S, Schousboe ML, Ishengoma D, Lusingu J, Pota H, et al. Independent origin of Plasmodium falciparum antifolate super-resistance, Uganda, Tanzania, and Ethiopia. Emerg Infect Dis. 2014;20:1280–6.

von Seidlein L, Greenwood BM. Mass administrations of antimalarial drugs. Trends Parasitol. 2003;19:452–60.

Article   Google Scholar  

Pongtavornpinyo W, Yeung S, Hastings IM, Dondorp AM, Day NP, White NJ. Spread of anti-malarial drug resistance: mathematical model with implications for ACT drug policies. Malar J. 2008;7:229.

Plowe CV, Cortese JF, Djimde A, Nwanyanwu OC, Watkins WM, Winstanley PA, et al. Mutations in Plasmodium falciparum dihydrofolate reductase and dihydropteroate synthase and epidemiologic patterns of pyrimethamine-sulfadoxine use and resistance. J Infect Dis. 1997;176:1590–6.

ter Kuile FO, van Eijk AM, Filler SJ. Effect of sulfadoxine-pyrimethamine resistance on the efficacy of intermittent preventive therapy for malaria control during pregnancy: a systematic review. JAMA. 2007;297:2603–16.

Gesase S, Gosling RD, Hashim R, Ord R, Naidoo I, Madebe R, et al. High resistance of Plasmodium falciparum to sulphadoxine/pyrimethamine in northern Tanzania and the emergence of dhps resistance mutation at Codon 581. PLoS ONE. 2009;4:e4569.

Lin JT, Mbewe B, Taylor SM, Luntamo M, Meshnick SR, Ashorn P. Increased prevalence of dhfr and dhps mutants at delivery in Malawian pregnant women receiving intermittent preventive treatment for malaria. Trop Med Int Health. 2013;18:175–8.

Mockenhaupt FP, Bedu-Addo G, Eggelte TA, Hommerich L, Holmberg V, Von OC, et al. Rapid increase in the prevalence of sulfadoxine-pyrimethamine resistance among Plasmodium falciparum isolated from pregnant women in Ghana. J Infect Dis. 2008;198:1545–9.

Iriemenam NC, Shah M, Gatei W, van Eijk AM, Ayisi J, Kariuki S, et al. Temporal trends of sulphadoxine-pyrimethamine (SP) drug-resistance molecular markers in Plasmodium falciparum parasites from pregnant women in western Kenya. Malar J. 2012;11:134.

Menendez C, Serra-Casas E, Scahill MD, Sanz S, Nhabomba A, Bardaji A, et al. HIV and placental infection modulate the appearance of drug-resistant Plasmodium falciparum in pregnant women who receive intermittent preventive treatment. Clin Infect Dis. 2011;52:41–8.

Hastings IM, Watkins WM. Tolerance is the key to understanding antimalarial drug resistance. Trends Parasitol. 2006;22:71–7.

Stepniewska K, White NJ. Pharmacokinetic determinants of the window of selection for antimalarial drug resistance. Antimicrob Agents Chemother. 2008;52:1589–96.

Ruizendaal E, Tahita MC, Geskus RB, Versteeg I, Scott S, d’Alessandro U, et al. Increase in the prevalence of mutations associated with sulfadoxine-pyrimethamine resistance in Plasmodium falciparum isolates collected from early to late pregnancy in Nanoro, Burkina Faso. Malar J. 2017;16:179.

Cisse M, Awandare GA, Soulama A, Tinto H, Hayette MP, Guiguemde RT. Recent uptake of intermittent preventive treatment during pregnancy with sulfadoxine-pyrimethamine is associated with increased prevalence of Pfdhfr mutations in Bobo-Dioulasso, Burkina Faso. Malar J. 2017;16:38.

Harrington WE, Mutabingwa TK, Muehlenbachs A, Sorensen B, Bolla MC, Fried M, et al. Competitive facilitation of drug-resistant Plasmodium falciparum malaria parasites in pregnant women who receive preventive treatment. Proc Natl Acad Sci USA. 2009;106:9027–32.

Ngondi JM, Ishengoma DS, Doctor SM, Thwai KL, Keeler C, Mkude S, et al. Surveillance for sulfadoxine-pyrimethamine resistant malaria parasites in the Lake and Southern Zones, Tanzania, using pooling and next-generation sequencing. Malar J. 2017;16:236.

Nayebare P, Asua V, Conrad MD, Kajubi R, Kakuru A, Nankabirwa JI, et al. Associations between malaria-preventive regimens and Plasmodium falciparum drug resistance-mediating polymorphisms in Ugandan pregnant women. Antimicrob Agents Chemother. 2020;64:e01047-e1120.

Mbonye AK, Birungi J, Yanow SK, Shokoples S, Malamba S, Alifrangis M, et al. Prevalence of Plasmodium falciparum resistance markers to sulfadoxine-pyrimethamine among pregnant women receiving intermittent preventive treatment for malaria in Uganda. Antimicrob Agents Chemother. 2015;59:5475–82.

Tornyigah B, Coppee R, Houze P, Kusi KA, Adu B, Quakyi I, et al. Effect of drug pressure on promoting the emergence of antimalarial-resistant parasites among pregnant women in Ghana. Antimicrob Agents Chemother. 2020;64:e02029-e2119.

Fagbemi KA, Adebusuyi SA, Nderu D, Adedokun SA, Pallerla SR, Amoo AOJ, et al. Analysis of sulphadoxine-pyrimethamine resistance-associated mutations in Plasmodium falciparum isolates obtained from asymptomatic pregnant women in Ogun State, Southwest Nigeria. Infect Genet Evol. 2020;85:104503.

Osarfo J, Tagbor H, Magnussen P, Alifrangis M. Molecular markers of Plasmodium falciparum drug resistance in parasitemic pregnant women in the middle forest belt of Ghana. Am J Trop Med Hyg. 2018;98:1714–7.

Taylor SM, Levitt B, Freedman B, Madanitsa M, Thwai KL, Kalilani-Phiri L, et al. Interactions between antenatal sulfadoxine-pyrimethamine, drug-resistant Plasmodium falciparum parasites, and delivery outcomes in Malawi. J Infect Dis. 2020;222:661–9.

WHO. Guidelines for malaria 2021. Geneva, World Health Organization, 2021. https://www.who.int/publications/i/item/guidelines-for-malaria

WHO. Recommendations on the use of sulfadoxine-pyrimethamine for intermittent preventive treatment during pregnancy in areas of moderate to high resistance to sulfadoxinepyrimethamine in the African region. Geneva, World Health Organization, 2005.

Wargo AR, Huijben S, de Roode JC, Shepherd J, Read AF. Competitive release and facilitation of drug-resistant parasites after therapeutic chemotherapy in a rodent malaria model. Proc Natl Acad Sci USA. 2007;104:19914–9.

Harrington WE, Mutabingwa TK, Kabyemela E, Fried M, Duffy PE. Intermittent treatment to prevent pregnancy malaria does not confer benefit in an area of widespread drug resistance. Clin Infect Dis. 2011;53:224–30.

Mutabingwa TK, Bolla MC, Li JL, Domingo GJ, Li X, Fried M, et al. Maternal malaria and gravidity interact to modify infant susceptibility to malaria. PLoS Med. 2005;2:e407.

Ellman R, Maxwell C, Finch R, Shayo D. Malaria and anaemia at different altitudes in the Muheza district of Tanzania: childhood morbidity in relation to level of exposure to infection. Ann Trop Med Parasitol. 1998;92:741–53.

Minja DT, Schmiegelow C, Mmbando B, Bostrom S, Oesterholt M, Magistrado P, et al. Plasmodium falciparum mutant haplotype infection during pregnancy associated with reduced birthweight. Tanzania Emerg Infect Dis. 2013;19:1446–54.

Taylor SM, Antonia AL, Chaluluka E, Mwapasa V, Feng G, Molyneux ME, et al. Antenatal receipt of sulfadoxine-pyrimethamine does not exacerbate pregnancy-associated malaria despite the expansion of drug-resistant Plasmodium falciparum : clinical outcomes from the QuEERPAM study. Clin Infect Dis. 2012;55:42–50.

Kayentao K, Garner P, van Eijk AM, Naidoo I, Roper C, Mulokozi A, et al. Intermittent preventive therapy for malaria during pregnancy using 2 vs 3 or more doses of sulfadoxine-pyrimethamine and risk of low birth weight in Africa: systematic review and meta-analysis. JAMA. 2013;309:594–604.

WHO. Policy brief for the implementation of intermittent preventive treatment of malaria in pregnancy using sulfadoxine-pyrimethamine (IPTp-SP). Geneva, World Health Organization, 2013.

Gutman J, Kalilani L, Taylor S, Zhou Z, Wiegand RE, Thwai KL, et al. The A581G mutation in the gene encoding Plasmodium falciparum dihydropteroate synthetase reduces the effectiveness of sulfadoxine-pyrimethamine preventive therapy in Malawian pregnant women. J Infect Dis. 2015;211:1997–2005.

Desai M, Gutman J, Taylor SM, Wiegand RE, Khairallah C, Kayentao K, et al. Impact of sulfadoxine-pyrimethamine resistance on effectiveness of intermittent preventive therapy for malaria in pregnancy at clearing infections and preventing low birth weight. Clin Infect Dis. 2016;2:323–33.

Desai M, Gutman J, L’Lanziva A, Otieno K, Juma E, Kariuki S, et al. Intermittent screening and treatment or intermittent preventive treatment with dihydroartemisinin-piperaquine versus intermittent preventive treatment with sulfadoxine-pyrimethamine for the control of malaria during pregnancy in western Kenya: an open-label, three-group, randomised controlled superiority trial. Lancet. 2015;386:2507–19.

Kajubi R, Ochieng T, Kakuru A, Jagannathan P, Nakalembe M, Ruel T, et al. Monthly sulfadoxine-pyrimethamine versus dihydroartemisinin-piperaquine for intermittent preventive treatment of malaria in pregnancy: a double-blind, randomised, controlled, superiority trial. Lancet. 2019;393:1428–39.

Kakuru A, Jagannathan P, Kajubi R, Ochieng T, Ochokoru H, Nakalembe M, et al. Impact of intermittent preventive treatment of malaria in pregnancy with dihydroartemisinin-piperaquine versus sulfadoxine-pyrimethamine on the incidence of malaria in infancy: a randomized controlled trial. BMC Med. 2020;18:207.

Chico RM, Cano J, Ariti C, Collier TJ, Chandramohan D, Roper C, et al. Influence of malaria transmission intensity and the 581G mutation on the efficacy of intermittent preventive treatment in pregnancy: systematic review and meta-analysis. Trop Med Int Health. 2015;20:1621–33.

van Eijk AM, Larsen DA, Kayentao K, Koshy G, Slaughter DEC, Roper C, et al. Effect of Plasmodium falciparum sulfadoxine-pyrimethamine resistance on the effectiveness of intermittent preventive therapy for malaria in pregnancy in Africa: a systematic review and meta-analysis. Lancet Infect Dis. 2019;19:546–56.

Braun V, Rempis E, Schnack A, Decker S, Rubaihayo J, Tumwesigye NM, et al. Lack of effect of intermittent preventive treatment for malaria in pregnancy and intense drug resistance in western Uganda. Malar J. 2015;14:372.

Likwela JL, D’Alessandro U, Lokwa BL, Meuris S, Dramaix MW. Sulfadoxine-pyrimethamine resistance and intermittent preventive treatment during pregnancy: a retrospective analysis of birth weight data in the Democratic Republic of Congo (DRC). Trop Med Int Health. 2012;17:322–9.

CAS   PubMed   Google Scholar  

Ndyomugyenyi R, Clarke SE, Hutchison CL, Hansen KS, Magnussen P. Efficacy of malaria prevention during pregnancy in an area of low and unstable transmission: an individually-randomised placebo-controlled trial using intermittent preventive treatment and insecticide-treated nets in the Kabale Highlands, southwestern Uganda. Trans R Soc Trop Med Hyg. 2011;105:607–16.

Okell LC, Griffin JT, Roper C. Mapping sulphadoxine-pyrimethamine-resistant Plasmodium falciparum malaria in infected humans and in parasite populations in Africa. Sci Rep. 2017;7:7389.

Taylor SM, Antonia AL, Parobek CM, Juliano JJ, Janko M, Emch M, et al. Plasmodium falciparum sulfadoxine resistance is geographically and genetically clustered within the DR Congo. Sci Rep. 2013;3:1165.

Lynch C, Pearce R, Pota H, Cox J, Abeku TA, Rwakimari J, et al. Emergence of a dhfr mutation conferring high-level drug resistance in Plasmodium falciparum populations from southwest Uganda. J Infect Dis. 2008;197:1598–604.

Marks F, von Kalckreuth V, Kobbe R, Adjei S, Adjei O, Horstmann RD, et al. Parasitological rebound effect and emergence of pyrimethamine resistance in Plasmodium falciparum after single-dose sulfadoxine-pyrimethamine. J Infect Dis. 2005;192:1962–5.

Dicko A, Sagara I, Djimde AA, Toure SO, Traore M, Dama S, et al. Molecular markers of resistance to sulphadoxine-pyrimethamine one year after implementation of intermittent preventive treatment of malaria in infants in Mali. Malar J. 2010;9:9.

Pearce RJ, Ord R, Kaur H, Lupala C, Schellenberg J, Shirima K, et al. A community-randomized evaluation of the effect of intermittent preventive treatment in infants on antimalarial drug resistance in southern Tanzania. J Infect Dis. 2013;207:848–59.

Faye B, Ndiaye M, Ndiaye JL, Annie A, Tine RC, Lo AC, et al. Prevalence of molecular markers of Plasmodium falciparum resistance to sulfadoxine-pyrimethamine during the intermittent preventive treatment in infants coupled with the expanded program immunization in Senegal. Parasitol Res. 2011;109:133–8.

Institute of Medicine. Assessment of the role of intermittent preventive treatment for malaria in infants: letter report. Washington: DC. National Academy of Sciences; 2008.

O’Meara WP, Smith DL, McKenzie FE. Potential impact of intermittent preventive treatment (IPT) on spread of drug-resistant malaria. PLoS Med. 2006;3:e141.

Alexander N, Sutherland C, Roper C, Cissé B, Schellenberg D. Modelling the impact of intermittent preventive treatment for malaria on selection pressure for drug resistance. Malar J. 2007;6:9.

Kobbe R, Kreuzberg C, Adjei S, Thompson B, Langefeld I, Thompson PA, et al. A randomized controlled trial of extended intermittent preventive antimalarial treatment in infants. Clin Infect Dis. 2007;45:16–25.

Mockenhaupt FP, Teun BJ, Eggelte TA, Schreiber J, Ehrhardt S, Wassilew N, et al. Plasmodium falciparum dhfr but not dhps mutations associated with sulphadoxine-pyrimethamine treatment failure and gametocyte carriage in northern Ghana. Trop Med Int Health. 2005;10:901–8.

Mayor A, Serra-Casas E, Sanz S, Aponte JJ, Macete E, Mandomando I, et al. Molecular markers of resistance to sulfadoxine-pyrimethamine during intermittent preventive treatment for malaria in Mozambican infants. J Infect Dis. 2008;197:1737–42.

Pearce RJ, Drakeley C, Chandramohan D, Mosha F, Roper C. Molecular determination of point mutation haplotypes in the dihydrofolate reductase and dihydropteroate synthase of Plasmodium falciparum in three districts of northern Tanzania. Antimicrob Agents Chemother. 2003;47:1347–54.

Gosling RD, Gesase S, Mosha JF, Carneiro I, Hashim R, Lemnge M, et al. Protective efficacy and safety of three antimalarial regimens for intermittent preventive treatment for malaria in infants: a randomised, double-blind, placebo-controlled trial. Lancet. 2009;374:1521–32.

Griffin JT, Cairns M, Ghani AC, Roper C, Schellenberg D, Carneiro I, et al. Protective efficacy of intermittent preventive treatment of malaria in infants (IPTi) using sulfadoxine-pyrimethamine and parasite resistance. PLoS ONE. 2010;5:e12618.

Bretscher MT, Dahal P, Griffin J, Stepniewska K, Bassat Q, Baudin E, et al. The duration of chemoprophylaxis against malaria after treatment with artesunate-amodiaquine and artemether-lumefantrine and the effects of pfmdr1 86Y and pfcrt 76T: a meta-analysis of individual patient data. BMC Med. 2020;18:47.

Naidoo I, Roper C. Drug resistance maps to guide intermittent preventive treatment of malaria in African infants. Parasitology. 2011;138:1469–79.

Ross A, Maire N, Sicuri E, Smith T, Conteh L. Determinants of the cost-effectiveness of intermittent preventive treatment for malaria in infants and children. PLoS ONE. 2011;6:e18391.

Schellenberg JR, Maokola W, Shirima K, Manzi F, Mrisho M, Mushi A, et al. Cluster-randomized study of intermittent preventive treatment for malaria in infants (IPTi) in southern Tanzania: evaluation of impact on survival. Malar J. 2011;10:387.

Esu EB, Oringanje C, Meremikwu MM. Intermittent preventive treatment for malaria in infants. Cochrane Database Syst Rev. 2019;12:CD011525.

PubMed   Google Scholar  

Konate AT, Yaro JB, Ouedraogo AZ, Diarra A, Gansane A, Soulama I, et al. Intermittent preventive treatment of malaria provides substantial protection against malaria in children already protected by an insecticide-treated bednet in Burkina Faso: a randomised, double-blind, placebo-controlled trial. PLoS Med. 2011;8:e1000408.

Dicko A, Diallo AI, Tembine I, Dicko Y, Dara N, Sidibe Y, et al. Intermittent preventive treatment of malaria provides substantial protection against malaria in children already protected by an insecticide-treated bednet in Mali: a randomised, double-blind, placebo-controlled trial. PLoS Med. 2011;8:e1000407.

Cissé B, Sokhna C, Boulanger D, Milet J, el Bâ H, Richardson K, et al. Seasonal intermittent preventive treatment with artesunate and sulfadoxine-pyrimethamine for prevention of malaria in Senegalese children: a randomised, placebo-controlled, double-blind trial. Lancet. 2006;367:659–67.

Somé AF, Zongo I, Compaoré YD, Sakandé S, Nosten F, Ouédraogo JB, et al. Selection of drug resistance-mediating Plasmodium falciparum genetic polymorphisms by seasonal malaria chemoprevention in Burkina Faso. Antimicrob Agents Chemother. 2014;58:3660–5.

Ndiaye M, Tine R, Faye B, Ndiaye JL, Diouf I, Lo AC, et al. Selection of antimalarial drug resistance after intermittent preventive treatment of infants and children (IPTi/c) in Senegal. C R Biol. 2013;336:295–300.

Dieng CC, Gonzalez L, Pestana K, Dhikrullahi SB, Amoah LE, Afrane YA, et al. Contrasting asymptomatic and drug resistance gene prevalence of Plasmodium falciparum in Ghana. Implications on seasonal malaria chemoprevention. Genes (Basel). 2019;10:538.

Article   CAS   PubMed Central   Google Scholar  

Diawara F, Steinhardt LC, Mahamar A, Traore T, Kone DT, Diawara H, et al. Measuring the impact of seasonal malaria chemoprevention as part of routine malaria control in Kita, Mali. Malar J. 2017;16:325.

Partnership A-S. Effectiveness of seasonal malaria chemoprevention at scale in west and central Africa: an observational study. Lancet. 2020;396:1829–40.

Naidoo I, Roper C. Spatial distribution of dhfr and dhps mutations in Africa. Trends Parasitol. 2013;12:249.

Wilson AL. A systematic review and meta-analysis of the efficacy and safety of intermittent preventive treatment of malaria in children (IPTc). PLoS ONE. 2011;6:e16976.

Mbacham WF, Evehe MS, Netongo PM, Ateh IA, Mimche PN, Ajua A, et al. Efficacy of amodiaquine, sulphadoxine-pyrimethamine and their combination for the treatment of uncomplicated Plasmodium falciparum malaria in children in Cameroon at the time of policy change to artemisinin-based combination therapy. Malar J. 2010;9:34.

Newby G, Hwang J, Koita K, Chen I, Greenwood B, von Seidlein L, et al. Review of mass drug administration for malaria and its operational challenges. Am J Trop Med Hyg. 2015;93:125–34.

Molineaux L, Gramiccia G. The Garki Project. Research on the epidemiology and control of malaria in the Sudan Savanna of West Africa. World Health Organization, Geneva; 1980.

malERA Consultative Group on Drugs. A research agenda for malaria eradication: drugs. PLoS Med. 2011;8:e1000402.

Pinotti M. A new method of malaria prophylaxis: the addition of an antimalarial drug to cooking salt used in daily preparation of food. Rev Brasil Malariol. 1953;10:241–6 ( in Portuguese ).

CAS   Google Scholar  

Eisele TP. Mass drug administration can be a valuable addition to the malaria elimination toolbox. Malar J. 2019;18:281.

Lwin KM, Imwong M, Suangkanarat P, Jeeyapant A, Vihokhern B, Wongsaen K, et al. Elimination of Plasmodium falciparum in an area of multi-drug resistance. Malar J. 2015;14:319.

Eisele TP, Bennett A, Silumbe K, Finn TP, Chalwe V, Kamuliwo M, et al. Short-term impact of mass drug administration with dihydroartemisinin plus piperaquine on malaria in southern province Zambia: a cluster-randomized controlled trial. J Infect Dis. 2016;214:1831–9.

Wernsdorfer WH, Payne D. The dynamics of drug resistance in Plasmodium falciparum . Pharmacol Ther. 1991;50:95–121.

White NJ. Does antimalarial mass drug administration increase or decrease the risk of resistance? Lancet Infect Dis. 2017;17:e15–20.

Landier J, Kajeechiwa L, Thwin MM, Parker DM, Chaumeau V, Wiladphaingern J, et al. Safety and effectiveness of mass drug administration to accelerate elimination of artemisinin-resistant falciparum malaria: a pilot trial in four villages of Eastern Myanmar. Wellcome Open Res. 2017;2:81.

von Seidlein L, Peto TJ, Landier J, Nguyen TN, Tripura R, Phommasone KP, et al. The impact of targeted malaria elimination with mass drug administrations on falciparum malaria in Southeast Asia: a cluster randomised trial. PLoS Med. 2019;16:e1002745.

Gupta H, Galatas B, Chidimatembue A, Huijben S, Cistero P, Matambisso G, et al. Effect of mass dihydroartemisinin-piperaquine administration in southern Mozambique on the carriage of molecular markers of antimalarial resistance. PLoS ONE. 2020;15:e0240174.

Kay K, Hastings IM. Measuring windows of selection for anti-malarial drug treatments. Malar J. 2015;14:292.

Maude RJ, Nguon C, Dondorp AM, White LJ, White NJ. The diminishing returns of atovaquone-proguanil for elimination of Plasmodium falciparum malaria: modelling mass drug administration and treatment. Malar J. 2014;13:380.

Garfield RM, Vermund SH. Changes in malaria incidence after mass drug administration in Nicaragua. Lancet. 1983;2:500–3.

Kaehler N, Adhikari B, Cheah PY, Day NPJ, Paris DH, Tanner M, et al. The promise, problems and pitfalls of mass drug administration for malaria elimination: a qualitative study with scientists and policymakers. Int Health. 2019;11:166–76.

Cohee LM, Opondo C, Clarke SE, Halliday KE, Cano J, Shipper AG, et al. Preventive malaria treatment among school-aged children in sub-Saharan Africa: a systematic review and meta-analyses. Lancet Glob Health. 2020;8:e1499–511.

Nankabirwa JI, Conrad MD, Legac J, Tukwasibwe S, Tumwebaze P, Wandera B, et al. Intermittent preventive treatment with dihydroartemisinin-piperaquine in Ugandan schoolchildren selects for Plasmodium falciparum transporter polymorphisms that modify drug sensitivity. Antimicrob Agents Chemother. 2016;60:5649–54.

Djimde A, Plowe CV, Diop S, Dicko A, Wellems TE, Doumbo O. Use of antimalarial drugs in Mali: policy versus reality. Am J Trop Med Hyg. 1998;59:376–9.

Peel SA, Bright P, Yount B, Handy J, Baric RS. A strong association between mefloquine and halofantrine resistance and amplification, overexpression, and mutation in the P-glycoprotein gene homolog (pfmdr) of Plasmodium falciparum in vitro. Am J Trop Med Hyg. 1994;51:648–58.

Price RN, Uhlemann AC, van Vugt M, Brockman A, Hutagalung R, Nair S, et al. Molecular and pharmacological determinants of the therapeutic response to artemether-lumefantrine in multidrug-resistant Plasmodium falciparum malaria. Clin Infect Dis. 2006;42:1570–7.

Sidhu ABS, Uhlemann AC, Valderramos SG, Valderramos JC, Krishna S, Fidock DA. Decreasing pfmdr1 copy number in Plasmodium falciparum malaria heightens susceptibility to mefloquine, lumefantrine, halofantrine, quinine, and artemisinin. J Infect Dis. 2006;194:528–35.

Amato R, Lim P, Miotto O, Amaratunga C, Dek D, Pearson RD, et al. Genetic markers associated with dihydroartemisinin-piperaquine failure in Plasmodium falciparum malaria in Cambodia: a genotype-phenotype association study. Lancet Infect Dis. 2017;17:164–73.

Witkowski B, Duru V, Khim N, Ross LS, Saintpierre B, Beghain J, et al. A surrogate marker of piperaquine-resistant Plasmodium falciparum malaria: a phenotype-genotype association study. Lancet Infect Dis. 2017;17:174–83.

Huijben S, Macete E, Mombo-Ngoma G, Ramharter M, Kariuki S, Desai M, et al. Counter-selection of antimalarial resistance polymorphisms by intermittent preventive treatment in pregnancy. J Infect Dis. 2020;221:293–303.

Kublin JG, Cortese JF, Njunju EM, RA GM, Wirima JJ, Kazembe PN, et al. Reemergence of chloroquine-sensitive Plasmodium falciparum malaria after cessation of chloroquine use in Malawi. J Infect Dis. 2003;187:1870–5.

Laufer MK, Thesing PC, Eddington ND, Masonga R, Dzinjalamala FK, Takala SL, et al. Return of chloroquine antimalarial efficacy in Malawi. N Engl J Med. 2006;355:1959–66.

Divala TH, Mungwira RG, Mawindo PM, Nyirenda OM, Kanjala M, Ndaferankhande M, et al. Chloroquine as weekly chemoprophylaxis or intermittent treatment to prevent malaria in pregnancy in Malawi: a randomised controlled trial. Lancet Infect Dis. 2018;18:1097–107.

Mohammed A, Ndaro A, Kalinga A, Manjurano A, Mosha JF, Mosha DF, et al. Trends in chloroquine resistance marker, Pfcrt-K76T mutation ten years after chloroquine withdrawal in Tanzania. Malar J. 2013;2:415.

Mwanza S, Joshi S, Nambozi M, Chileshe J, Malunga P, Kabuya JB, et al. The return of chloroquine-susceptible Plasmodium falciparum malaria in Zambia. Malar J. 2016;15:584.

Nosten F, ter Kuile F, Chongsuphajaisiddhi T, Luxemburger C, Webster HK, Edstein M, et al. Mefloquine-resistant falciparum malaria on the Thai-Burmese border. Lancet. 1991;337:1140–3.

van der Pluijm RW, Tripura R, Hoglund RM, Pyae Phyo A, Lek D, Islam AU, et al. Triple artemisinin-based combination therapies versus artemisinin-based combination therapies for uncomplicated Plasmodium falciparum malaria: a multicentre, open-label, randomised clinical trial. Lancet. 2020;395:1345–60.

Laufer MK, Takala-Harrison S, Dzinjalamala FK, Stine OC, Taylor TE, Plowe CV. Return of chloroquine-susceptible falciparum malaria in Malawi was a reexpansion of diverse susceptible parasites. J Infect Dis. 2010;202:801–8.

Boni MF, Smith DL, Laxminarayan R. Benefits of using multiple first-line therapies against malaria. Proc Natl Acad Sci USA. 2008;105:14216–21.

Dini S, Zaloumis S, Cao P, Price RN, Fowkes FJI, van der Pluijm RW, et al. Investigating the efficacy of triple artemisinin-based combination therapies for treating Plasmodium falciparum malaria patients using mathematical modeling. Antimicrob Agents Chemother. 2018;62:e01068-e1118.

van der Pluijm RW, Amaratunga C, Dhorda M, Dondorp AM. Triple Artemisinin-based combination therapies for malaria - a new paradigm? Trends Parasitol. 2021;37:15–24.

Gonzalez R, Pons-Duran C, Piqueras M, Aponte JJ, ter Kuile FO, Menendez C. Mefloquine for preventing malaria in pregnant women. Cochrane Database Syst Rev. 2018;3:CD011444.

Chico RM, Pittrof R, Greenwood B, Chandramohan D. Azithromycin-chloroquine and the intermittent preventive treatment of malaria in pregnancy. Malar J. 2008;7:255.

Kimani J, Phiri K, Kamiza S, Duparc S, Ayoub A, Rojo R, et al. Efficacy and safety of azithromycin-chloroquine versus sulfadoxine-pyrimethamine for intermittent preventive treatment of Plasmodium falciparum malaria infection in pregnant women in Africa: an open-label, randomized trial. PLoS ONE. 2016;11:e0157045.

Gaillard T, Boxberger M, Madamet M, Pradines B. Has doxycycline, in combination with anti-malarial drugs, a role to play in intermittent preventive treatment of Plasmodium falciparum malaria infection in pregnant women in Africa? Malar J. 2018;17:469.

Denoeud-Ndam L, Zannou DM, Fourcade C, Taron-Brocard C, Porcher R, Atadokpede F, et al. Cotrimoxazole prophylaxis versus mefloquine intermittent preventive treatment to prevent malaria in HIV-infected pregnant women: two randomized controlled trials. J Acquir Immune Defic Syndr. 2014;65:198–206.

Wallender E, Zhang N, Conrad M, Kakuru A, Muhindo M, Tumwebaze P, et al. Modeling prevention of malaria and selection of drug resistance with different dosing schedules of dihydroartemisinin-piperaquine preventive therapy during pregnancy in Uganda. Antimicrob Agents Chemother. 2019;63:e01393-e1418.

Tagbor H, Bruce J, Agbo M, Greenwood B, Chandramohan D. Intermittent screening and treatment versus intermittent preventive treatment of malaria in pregnancy: a randomised controlled non-inferiority trial. PLoS ONE. 2010;5:e14425.

Tagbor H, Cairns M, Bojang K, Coulibaly SO, Kayentao K, Williams J, et al. A non-inferiority, individually randomized trial of intermittent screening and treatment versus intermittent preventive treatment in the control of malaria in pregnancy. PLoS ONE. 2015;10:e0132247.

Esu E, Berens-Riha N, Pritsch M, Nwachuku N, Loescher T, Meremikwu M. Intermittent screening and treatment with artemether-lumefantrine versus intermittent preventive treatment with sulfadoxine-pyrimethamine for malaria in pregnancy: a facility-based, open-label, non-inferiority trial in Nigeria. Malar J. 2018;17:251.

Peters W. The problem of drug resistance in malaria. Parasitology. 1985;90:705–15.

Nyunt MM, Plowe CV. Pharmacologic advances in the global control and treatment of malaria. Combination therapy and resistance. Clin Pharmacol Ther. 2007;82:601–5.

Hastings I. How artemisinin-containing combination therapies slow the spread of antimalarial drug resistance. Trends Parasitol. 2011;27:67–72.

WHO. Minutes of the Drug Resistance and Containment Technical Expert Group (TEG). Geneva: World Health Organization; 2013.

Download references

Acknowledgements

The author thanks Philip Rosenthal and colleagues at the WHO Global Malaria Programme for reading drafts of the review and providing helpful feedback.

This work was supported by the WHO Global Malaria Programme.

Author information

Authors and affiliations.

University of Maryland School of Medicine, Baltimore, MD, USA

Christopher V. Plowe

You can also search for this author in PubMed   Google Scholar

Contributions

The author reviewed the literature and wrote the review.

Corresponding author

Correspondence to Christopher V. Plowe .

Ethics declarations

Ethics approval and consent to participate.

Not applicable.

Consent to publish

Competing interests.

The author has no conflicts of interest to declare.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Plowe, C.V. Malaria chemoprevention and drug resistance: a review of the literature and policy implications. Malar J 21 , 104 (2022). https://doi.org/10.1186/s12936-022-04115-8

Download citation

Received : 31 January 2022

Accepted : 03 March 2022

Published : 24 March 2022

DOI : https://doi.org/10.1186/s12936-022-04115-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Malaria Journal

ISSN: 1475-2875

• Submission enquiries: [email protected]

• Open access
• Published: 28 March 2024

Drug resistance in ovarian cancer: from mechanism to clinical trial

• Ling Wang 1 , 2 , 3   na1 ,
• Xin Wang 1 , 2 , 3   na1 ,
• Xueping Zhu 1 , 2 , 3 ,
• Lin Zhong 1 , 2 , 3 ,
• Qingxiu Jiang 1 , 2 , 3 ,
• Ya Wang 1 , 2 , 3 ,
• Qin Tang 1 , 2 , 3 ,
• Qiaoling Li 1 , 2 , 3 ,
• Cong Zhang 2 , 3 , 4 ,
• Haixia Wang 1 , 2 , 3 &
• Dongling Zou 1 , 2 , 3  

Molecular Cancer volume  23 , Article number:  66 ( 2024 ) Cite this article

270 Accesses

Metrics details

Ovarian cancer is the leading cause of gynecological cancer-related death. Drug resistance is the bottleneck in ovarian cancer treatment. The increasing use of novel drugs in clinical practice poses challenges for the treatment of drug-resistant ovarian cancer. Continuing to classify drug resistance according to drug type without understanding the underlying mechanisms is unsuitable for current clinical practice. We reviewed the literature regarding various drug resistance mechanisms in ovarian cancer and found that the main resistance mechanisms are as follows: abnormalities in transmembrane transport, alterations in DNA damage repair, dysregulation of cancer-associated signaling pathways, and epigenetic modifications. DNA methylation, histone modifications and noncoding RNA activity, three key classes of epigenetic modifications, constitute pivotal mechanisms of drug resistance. One drug can have multiple resistance mechanisms. Moreover, common chemotherapies and targeted drugs may have cross (overlapping) resistance mechanisms. MicroRNAs (miRNAs) can interfere with and thus regulate the abovementioned pathways. A subclass of miRNAs, “epi-miRNAs”, can modulate epigenetic regulators to impact therapeutic responses. Thus, we also reviewed the regulatory influence of miRNAs on resistance mechanisms. Moreover, we summarized recent phase I/II clinical trials of novel drugs for ovarian cancer based on the abovementioned resistance mechanisms. A multitude of new therapies are under evaluation, and the preliminary results are encouraging. This review provides new insight into the classification of drug resistance mechanisms in ovarian cancer and may facilitate in the successful treatment of resistant ovarian cancer.

Introduction

Ovarian cancer (OC) is the third most common and the most lethal malignancy of the female reproductive system. Seventy percent of patients are diagnosed at an advanced stage (FIGO stage III and IV) with distant metastasis [ 1 ]. Despite receiving standard-of-care therapy (optimal cytoreductive surgery followed by adjuvant chemotherapy), most patients develop recurrent disease, which is resistant to chemotherapy, resulting in a 5-year survival rate of approximately 30–40% worldwide [ 2 ]. Although maintenance therapy with poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitors (PARPis) has prolonged progression-free survival (PFS) and 5-year overall survival (OS) [ 3 , 4 , 5 ], unfortunately, many patients do not respond to PARPi treatment due to intrinsic or acquired resistance. Drug resistance is a formidable challenge in the treatment of ovarian cancer and is the primary contributor to poor prognosis.

According to the National Comprehensive Cancer Network (NCCN) guidelines (version 1.2023), there are many therapeutic regimens for resistant ovarian cancer, including some novel agents. However, the objective remission rate is still low, and the median survival time is less than 12 months due to complicated resistance mechanisms. In resistant ovarian cancer, the classical mechanisms of action of common drugs can be disrupted or altered, possibly resulting in impaired therapeutic effects. Thus, treatment regimens should not rely only on empirical options. In addition to traditional drugs, novel compounds are being investigated and tested in early clinical trials [ 6 ]. As the number of categories of agents increases, after the development of multidrug resistance (MDR), the decision of appropriate later-line therapeutic regimens is very challenging. This issue prompted us to consider the interactions of drug resistance mechanisms among different agents.

Even if resistance can develop to different drugs, the underlying mechanisms may be similar. Thus, instead of simply distinguishing resistance by agent, we attempted to classify drug resistance by mechanism. We summarized four major mechanisms (Fig.  1 ) from the published literature: 1) abnormalities in transmembrane transport, 2) alterations in DNA damage repair (DDR), 3) dysregulation of cancer-associated signaling pathways, and 4) epigenetic modifications. MicroRNAs (miRNAs) post-transcriptionally regulate the expression of target genes and affect a variety of biological processes, including cancer cell proliferation, metastasis, and therapeutic resistance [ 7 ]. miRNAs significantly regulate drug resistance by acting on molecules or/and pathways related to the four abovementioned mechanisms. Abnormal miRNA expression can lead to dysregulation of drug transporters, which control drug influx and efflux [ 8 , 9 ]. The expression of some components of DDR mechanisms, such as homologous recombination repair (HRR) and nonhomologous end joining (NHEJ), is modulated by miRNAs [ 10 ]. In addition, miRNAs can interfere with multiple cancer-associated signaling pathways by targeting their components, thereby promoting tumor resistance to therapy [ 11 ].

The summery of miRNA-mediated resistance mechanisms ( a ) Abnormal transmembrane transport; ( b ) Alterations of DNA damage repair; ( c ) Dysregulation of cancer-associated signal pathway; ( d ) Epigenetic modification

Based on the abovementioned findings, we retrieved phase I/II clinical trials (Table  1 , Figure S 1 and S 2 ) of novel drugs for resistant ovarian cancer. Understanding the underlying resistance mechanisms is expected to contribute to the identification of new clinical options for reversing resistance and improving the prognosis of ovarian cancer patients.

Mechanisms of drug resistance in ovarian cancer

Abnormal transmembrane transport.

Decreased influx and increased efflux are two forms of abnormal transmembrane transport that reduce the intracellular drug concentration and result in resistance (Fig.  2 ). Moreover, in platinum-resistant ovarian cancer (PROC), the expression of the related genes and transporters is decreased. Thus, the intracellular concentration of platinum is insufficient, and platinum resistance subsequently develops [ 12 , 13 , 14 , 15 , 16 ]. miRNAs can directly target transmembrane transporters, thereby regulating cellular resistance to drugs [ 17 ]. They directly bind to the 3'-untranslated region (3'-UTR) of a targeted transporter gene to regulate its transcription, leading to abnormalities in drug influx and efflux [ 18 ].

Abnormal transmembrane transport. The SLC31A1, SLC22A1/2/3, as members of SLC superfamily, are significant transporters in charge of drug inflow. Downregulation of SLC transporters reduce platinum uptake, leading to chemoresistance in ovarian cancer. The role of miRNA in SLC expression lacks sufficient evidence. The ABC transporter family include ABCB1, ABCG2, ABCC1, which are responsible for drug efflux and then reduce intracellular concentration of platinum. miR130a/b, miR-186, miR-495 can directly bind with the 3'-UTR of ABCB1 mRNA or regulate PTEN, XIPA, and PI3K, leading to decreased ABCB1 transcription or translation level. miR-21-5p and miR-212-3p also have a regulatory factor of ABCB1 and ABCG2, respectively. miR-185-5p, miR-326, miR-508-3p and miR-134 can regulate the expression of ABCC1. ATP7A/7B are another contributor of drug efflux. miR-139 can directly bind to the 3'-UTR of ATP7A/7B, leading to apoptosis induction and increasing the chemosensitivity of ovarian cancer. MT can bind to cisplatin and deactivates it, which decreases drug efficacy and induces drug resistance. GST catalyzes glutathione to bind platinum and causes drug inactivation, which is associated with platinum resistance in ovarian cancer. (SLC, solute carrier superfamily; GST, Glutathione transferase; MT, Metallothionein)

Reduced drug influx

Sodium pumps, copper ion transporters, and organic cation transporters on the cell membrane or plasma membrane, such as the drug-transporting solute carrier (SLC) superfamily transporters (e.g., SLC31A1, SLC22A1/2/3), are key transporters controlling drug influx. SLC31A1 has been convincingly demonstrated to transport cisplatin and its analogs carboplatin and oxaliplatin, leading to intracellular accumulation of platinum [ 19 ]. The low expression of SLC22A2 in ovarian cancer may correlate with platinum drug resistance via a reduction in platinum uptake [ 20 ]. miRNAs play pivotal roles in the expression of drug-transporting SLC transporters and may influence treatment responses in prostate cancer, hepatocellular carcinoma and colorectal cancer [ 9 ]. However, the association and interaction mechanisms of miRNAs and SLC transporters in drug resistance in ovarian cancer have not been investigated, and further research is warranted.

Increased drug efflux

The ABC transporter family is mainly responsible for drug efflux. Abnormal expression of miRNAs (e.g., the miR-200 family, let-7 family and miR-130a/b) plays a role in ABC transporter regulation, thereby inducing resistance in ovarian cancer [ 21 ]. The characterized efflux transporters in the ABC family include ABCB1, ABCG2 and ABCCs [ 8 , 22 ]. The abovementioned miRNAs can bind to the 3'-UTRs of ABC transporter-encoding mRNAs, or participate in imperfect base pairing with genes encoding nuclear receptors, transcription factors (TFs), and signaling molecules associated with ABC transporters. Through this action, the mRNAs of ABC transporters are degraded or the translation of the corresponding proteins is inhibited [ 8 ]. In addition, the vault protein lung drug resistance-related protein (LRP) can transport cytostatic drugs from intracellular targets, conferring drug resistance [ 23 ].

Whole-genome microarray analysis revealed that ABCB1 was the only drug transporter with increased expression in resistant ovarian cancer cells, while the expression of several other ABC transporters was significantly decreased [ 24 ]. The membrane transporter P-glycoprotein (P-gp) is encoded by ABCB1 and is an ATP-dependent drug efflux pump. Its overexpression in resistant cell lines is considered the crucial mechanism of resistance to paclitaxel, doxorubicin, sorafenib [ 25 ], and PARPis [ 26 ]. Notably, dysregulated miRNAs can mediate the overexpression of ABCB1, resulting in MDR. For instance, miR130a/b, miR-186, and miR-495 can directly bind to the 3'-UTR of ABCB1 mRNA or regulate the expression of other targets (e.g., PTEN, XIAP, and PI3K) [ 11 , 27 ], leading to ABCB1 mRNA degradation or translational inhibition. A strong increase in ABCB1 expression was found to correlate with decreased expression of miR-21-5p, but the regulatory mechanism involved remains unknown [ 21 ]. In addition, upregulation of ABCB1 is associated with the transcriptional fusion of ABCB1 and SLC25A40, which was identified through whole-genome analysis in patients with high-grade serous ovarian cancer (HGSOC) who underwent prior chemotherapy and targeted therapy [ 28 ]. These findings indicate that ABCB1 upregulation frequently induces cross-resistance to chemotherapeutics and targeted drugs. Therefore, PARPis that are not dependent on the P-gp transporter might show greater therapeutic efficacy in patients who have received chemotherapy [ 24 ]. ABCC1 is associated with poor survival and chemoresistance in HGSOC. miR-185-5p and miR-326 both target the ABCC1 3'-UTR to regulate the expression of ABCC1 [ 2 ]. miR-508-3p [ 29 ] and miR-134 [ 30 ], which are sponged by CircETDB1 and LINC01118, respectively, can posttranscriptionally regulate the expression of ABCC1. ABCG2 is involved in topotecan resistance in ovarian cancer, which is associated with miR-212-3p downregulation [ 31 ].

In addition, upregulation of the copper efflux transporters ATP7A and ATP7B contributes to chemoresistance in ovarian cancer [ 32 ]. miR-139 can directly bind to the 3'-UTR of ATP7A/B, contributing to apoptosis induction and increasing the chemosensitivity of ovarian cancer cells [ 33 ].

Drug inactivation

Metallothionein (MT) and glutathione (GSH) are two major thiol-containing proteins that bind to platinum-based drugs. Detoxification of cisplatin by intracellular thiol-containing proteins is considered a major hurdle to overcome. MT binding to cisplatin can induce drug resistance, which can be reversed by short hairpin MT (shMT) [ 34 ]. GSH reacts with cisplatin to form a GS-platinum complex, reducing the available intracellular platinum content [ 35 ]. Glutathione S-transferase (GST) catalyzes the binding of GSH to platinum and causes drug inactivation, which is associated with platinum resistance in ovarian cancer [ 36 , 37 ].

Alterations in DDR

If DNA damage is not repaired promptly, cellular senescence or apoptotic signals are activated, while abnormal activation of DDR maintains the viability of cancer cells, significantly inducing resistance to chemotherapeutic drugs and PARPis and affecting therapeutic efficacy [ 38 ]. DDR generally consists of seven pathways (Fig.  3 ): the HRR, NHEJ, base excision repair (BER), nucleotide excision repair (NER), mismatch repair (MMR), translesion DNA synthesis (TLS), and Fanconi anemia (FA) pathways. Interactions among the DNA damage response, DNA repair components and miRNAs have been reported [ 39 ]. The ectopic expression of miRNAs, as regulatory factors, can interfere with the activity of DNA repair mechanisms, which have been implicated in multiple types of resistance [ 40 ]. Some miRNAs can reverse drug resistance by targeting genes encoding DDR-related enzymes [ 41 ].

Alterations of DNA damage repair. DDR generally consists of HRR, NHEJ, Replication fork, BER, NER, MMR, TLS, and FA. The repair of DSBs occurs predominately through NHEJ repair pathway in conjunction with HRR pathway. NHEJ are initiated by binding of Ku70–Ku80 heterodimer to DNA ends. The subsequent recruitment and autophosphorylation of DNA-PKcs bring the DNA ends together and allow their ligation by XRCC4–LIG4. MRN complex (MRE11-RAD50-NBS1), an important repair factor of HRR, detects the DNA damage firstly and activates downstream signaling. Besides, it exerts nuclease activity to resect DNA end, guiding to HRR. Further, DYNLL1 binds directly to MRE11 to limit its end-resection activity. Decreased DYNLL1 restores HR-mediated double-strand DNA breaks repair. Replication fork protection is a modality independent of DSBs, which contributes to gene stabilization, leading to chemoresistance and PARPi resistance. Additionally, down-expression of 53BP1 protein is another mechanism to restore DNA end resection. Shieldin (SHLD1, SHLD2, SHLD3 and REV7), as an effector complex of 53BP1, can mediate 53BP1 dependent DNA repair in a BRCA-independent manner. The kinases ATR and ATM have crucial roles in DDR pathway, such as maintaining replication fork stability and regulating CHK1 and CHK2.CHK1 can activate the G2/M inhibiting kinase WEE1 to maintain genomic integrity. Some miRNAs were shown to regulate the expression of components involved in HRR, NHEJ, Replication fork protection, TLS, and FA, but the interaction between miRNA and BER/ NER/ MMR lack sufficient evidence. (SLC, solute carrier superfamily; GST, Glutathione transferase; MT, Metallothionein)

HR deficiency is characteristic of many HGSOC cases (approximately 50%) and is considered a predictive biomarker of sensitivity to platinum agents and PARPis [ 42 ]. Restoration of HR pathway activity likely results in acquired resistance to platinum agents and PARPis in ovarian cancer patients with HR deficiency [ 43 ]. Notably, miRNAs have been revealed to impede DDR by directly targeting components of the DDR response, leading to reduced drug resistance [ 44 ]. miR-146 targets BRCA1 and is associated with the response to double-strand breaks (DSBs) [ 45 ]. Overexpressed miR-182 and miR-9 mediate the downregulation of BRCA1 and increase sensitivity to cisplatin and PARPis in ovarian cancer [ 46 , 47 ]. miR-96 directly targets the coding region of RAD51 and the 3'-UTR of REV1 and decreases the efficiency of HRR [ 43 ]. miR-1255b, miR-193b*, and miR-148b* (“*” indicates minor products at low concentrations) can target the transcripts of the HR-mediated DSB repair factors BRCA1, BRCA2, and RAD51, respectively, thereby regulating PARPi sensitivity [ 48 ]. miR-506, miR-103 and miR-107 are robust clinical markers for the chemotherapy response and survival in patients with ovarian cancer and can sensitize cancer cells to DNA damage by directly targeting RAD51 and inhibiting the formation of RAD51 foci [ 49 , 50 ]. Importantly, reversion mutations in BRCA1 /2, RAD51C , and PALB have been identified during prolonged exposure to platinum agents and PARPis and in post-progression biopsies. The restoration of the open reading frame by these mutations leads to the functional restoration of HRR [ 51 , 52 ]. Furthermore, HSP90 was found to mediate the stabilization of BRCA1, which interacts with the PALB2-BRCA2-RAD51 complex. This interaction was found to be essential for RAD51 focus formation and for conferring PARPi and cisplatin resistance [ 53 ]. Combination therapy with an HSP90 inhibitor and platinum is an innovative antitumor strategy that has the potential to reverse platinum resistance in ovarian cancer [ 54 , 55 ].

The MRE11-RAD50-NBS1 (MRN) complex, an important factor of HRR, first detects DNA damage and then activates signaling molecules [ 56 ]. In addition, it exerts nuclease activity to resect DNA ends, guiding HRR. Furthermore, recombinant human cytoplasmic dynein light chain 1 ( DYNLL1) was found to bind directly to MRE11 to limit its end resection activity. Thus, downregulation of DYNLL1 restores HR-mediated DNA DSB repair, thereby inducing chemoresistance and PARPi resistance in ovarian cancer [ 57 ]. Additionally, loss-of-function mutations in the TP53BP1 gene result in decreased 53BP1 protein expression and facilitate BRCA1-independent DNA end resection, which accounts for platinum and PARPi resistance [ 58 ].

Given the expanding role of immune checkpoint inhibitors as therapeutic agents, the interaction of tumor DNA damage and repair with the immune response has recently come into focus. HGSOC patients with BRCA mutation and homologous recombination deficiency (HRD) were found to exhibit increases in CD3 + /CD8 + tumor-infiltrating lymphocytes (TILs), immunohistochemical staining for PD-1/PD-L1, and neoantigen load. Moreover, wild-type BRCA1/2 ovarian tumors with mutations in RAD51, ATM, and ATR had higher predicted neoantigen levels than HR-proficient tumors [ 59 , 60 ]. Mu Chen et al. showed that DNA damage resulted in the production of many DNA fragments in the cytoplasm, leading to increased antigen presentation on the cell surface and activation of the immune response [ 61 ]. However, a clinical trial of avelumab did not show an improved response in patients with BRCA1/2 -mutated ovarian cancer (NCT01772004). Thus, additional clinical trials are warranted to determine the complexities of the interactions between DNA damage and immunomodulatory agents.

NHEJ repairs DNA DSBs by competing with HRR during the repair process, and its machinery includes TP53BP1, DNA-PK, etc. [ 62 ] miRNAs play important roles in regulating the expression of these NHEJ-related genes [ 39 ]. miR-136 overexpression downregulates DNA-PK, cell cycle-related genes, and antiapoptotic genes, resensitizing ovarian cancer cells to paclitaxel [ 63 ]. miR-622 suppresses NHEJ and facilitates HR-mediated DSB repair by targeting the Ku complex. Therefore, high expression of miR-622 in BRCA1-deficient HGSOC cells induces platinum and PARPi resistance [ 64 ]. DNA-PK, composed of DNA-PKcs and the DNA end-binding Ku70/80 heterodimer, has emerged as an intriguing therapeutic target within the NHEJ pathway [ 65 , 66 ]. This heterodimer can recognize DSBs and form the Ku-DNA complex, which can recruit DNA-PKs to DSB sites [ 67 ]. DNA-PKcs plays a major role in promoting NHEJ through autophosphorylation and recruitment of downstream effectors, such as endonucleases (Artemis) [ 68 ] and polymerases (DNA POLM (Pol µ) and POLL (Pol λ)) [ 69 , 70 ]. DNA-PK inhibition was found to induce restoration of HR function and resulted in resistance to PARPis in patient-derived ovarian cancer xenografts [ 71 ]. Ectopic expression of XRCC5/Ku80 [ 66 ] and XRCC6/Ku70 [ 65 ] induces platinum and PARPi resistance. Crucially, TP53BP1 can promote NHEJ and reduce BRCA1-mediated HRR by restricting DSB resection and antagonizing BRCA2/RAD51 loading in BRCA1-deficient cells [ 72 ]. The shieldin complex (comprising SHLD1, SHLD2, and SHLD3), an effector complex of 53BP1, regulates 53BP1-dependent NHEJ in various settings and impacts resistance to PARPis in HRD-defective cells [ 73 , 74 ]. Finally, XRCC4, DNA ligase IV (LIG4) and XLF are central components of end ligation.

Replication fork protection

Replication fork protection contributes to genome stability in a manner independent of DSB-induced HR, leading to chemoresistance and PARPi resistance [ 75 ]. PARP1, BRCA1 and BRCA2 play key roles in protecting the replication fork under replication stress (RS) conditions [ 76 , 77 ]. PTIP, PARP1 and CHD4 deficiency in BRCA-deficient cells prevent the recruitment of the MRE11 nuclease to stall replication forks and subsequently protect nascent DNA from degradation, thus conferring chemoresistance and PARPi resistance [ 78 ]. In both cells and patients with BRCA2 mutation, EZH2 downregulation leads to inhibition of the MUS81 nuclease, which restores DNA replication fork protection, leading to PARPi resistance [ 79 ]. miRNA-493-5p significantly preserves replication fork stability in BRCA2-mutant ovarian cancer cells through downregulation of MRE11 and CHD4 , conferring platinum and PARPi resistance [ 10 ]. However, restoration of miR223-3p expression, which delays the repair of the replication fork, leads to genomic instability and enhances drug sensitivity in BRCA1-deficient OC [ 80 ].

NER and BER

NER is responsible for repairing single-stranded DNA damage, and 8% of HGSOC patients exhibit alterations in some NER genes, according to The Cancer Genome Atlas (TCGA) database [ 81 ]. The NER signaling pathway can repair platinum-induced adducts, therefore, upregulation of NER genes, including ERCC1, ERCC2-XPD, ERCC3-XPB, ERCC4-XPF, ERCC5-XPG, ERCC6, ERCC8 and XPA, might mediate chemoresistance [ 63 ]. Indeed, overexpression of ERCC1 or XPF not only increased platinum resistance but also decreased the toxicity of olaparib [ 82 ]. Although certain NER gene mutations (ERCC6-Q524* and ERCC4-A583T) were found to be functionally associated with platinum sensitivity in vitro, these NER alterations did not affect HR or confer sensitivity to PARPis.

BER is accelerated by PARPs and the scaffold protein XRCC1. Currently, it has been reported that the BER pathway has both positive and negative associations with platinum resistance. Although BER pathway intermediates underlie the efficacy of PARPis, they mediate the activity of PARP family proteins (especially PARP1) to initiate repair, resulting in PARPi resistance.

MMR deficiency

MMR defects in OC are relatively underinvestigated, although they are the most common cause of hereditary ovarian cancer after BRCA1/2 mutations. The MMR pathway contains seven proteins (MSH2, MSH3, MSH6, MLH1, MLH3, PMS1, and PMS2) [ 66 ]. The frequency of MMR deficiency (loss of any protein) reportedly ranges from 2 to 29% in patients with ovarian cancer [ 67 ]. A small number of studies have suggested that MMR deficiency is associated with drug resistance, but the results were inconclusive [ 83 , 84 , 85 , 86 ]. The possible role of MMR defects in drug resistance in ovarian cancer deserves further investigation. Currently, MMR deficiency is proposed to occur due to loss of ineffective MMR activity, replication fork stalling, the inability to recognize DNA damage, an increase in the net replicative bypass of cisplatin adducts and modulation of the level of recombination-dependent bypass [ 87 , 88 ].

Other DDR pathways

The FA core complex consists of at least 10 FA-associated proteins (FANCA, FANCB, FANCC, FANCE, FANCF, FANCG, FANCL, FAAP100, FAAP20 and FAAP24) [ 89 ]. Inhibition of components of the FA repair pathway such as FA complementation group D2 (FANCD2) and FANCI, can increase sensitivity to chemotherapeutic agents [ 90 ]. miR-15a-5p, miR-494-3p and miR-544a potentially inhibit the entire FA/HR pathway [ 91 ].

TLS is mediated by DNA polymerases (e.g., Pol η and REV1). It increases the tolerance of tumor cells to platinum-induced DNA adducts and results in platinum resistance [ 92 ]. Pol η and REV1 are translesion DNA polymerases [ 93 ]. Upregulation of miR-93 might reverse resistance through targeting of DNA Pol η [ 92 ]. It has been reported that miR-96 can prevent the emergence of chemoresistance by inhibiting REV1-mediated TLS.

Dysregulated cancer-associated signaling pathways

A series of signaling pathways (Fig.  4 ) collectively regulate biological processes in human malignancies and are associated with the proliferation, invasion and therapeutic resistance of cancer cells [ 94 ]. The expression of signaling pathway components can be modulated by miRNAs through miRNA–mRNA binding, typically to miRNA target sites in the mRNA 3’-UTR [ 40 , 95 ]. Although cancer-associated signaling pathways are complex, the identification of potential therapeutic targets is promising.

Dysregulation of cancer-associated signal pathway. A series of signal pathways collectively regulates the biological process in human malignancies, which is associated with the proliferation, invasion and therapeutic resistance. The signaling pathways mainly include NFκB, PI3K/Akt, JAK/STAT, Notch, GAS6/AXL, TGF-β, MAPK, Hippo/YAP patwhay. Some miRNAs have ability to regulate the key members of these mentioned pathway, including JAK/STAT, GAS/AXL, MAPK, PI3K/Akt, NFκB,, TGF-β, Hippo/YAP, but there are no investigations about the interaction between miRNAs and Notch in ovarian cancer. The dysregulated cancer-associated signal pathway interfere with apoptosis, cell cycle, and immune status, resulting in multidrug resistance. Molecule targets in these pathway may provide a new approach for drug resistance in OC. The γ-secretase inhibitor DAPT, c-Myc targeting small molecule JQ1, an inhibitor of NFκB DHMEQ suppress the proliferation and induce apoptosis to reversing drug resistance in OC. (JQ1, novel cell-permeable small molecule; BAD, Bcl-2 antagonist of death; IKKα, inhibitor of nuclear factor-κB subunit-α; mTOR, mammalian target of rapamycin; NF-κB, nuclear factor-κB; DHMEQ, Dehydroxymethylepoxyquinomicin; MDSCs, Myeloid-derived suppressor cells; CSCs, cancer stem cells;BEZ235,a dual PI3K/mTOR inhibitor; DAPT, γ-secretase inhibitor N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester)

NFκB signaling pathway

NFκB can perform a biphasic function in ovarian cancer. It plays an anticarcinoma role in ovarian cancer cells and renders them sensitive to apoptosis induced by carboplatin and paclitaxel, but it also has carcinogenic effects on promoting aggressiveness and chemoresistance in ovarian cancer cells and confers resistance to these therapeutic agents [ 96 ]. Common chemotherapeutic drugs, including taxanes, platinum agents, vinca alkaloids and erlotinib, activate NFκB and its prosurvival downstream targets, which contribute to chemoresistance [ 97 ]. Activation of the NFκB pathway is correlated with platinum resistance and leads to poor prognosis in patients with ovarian cancer [ 98 ]. Mechanistically, increased nuclear translocation of the p65 subunit and phosphorylation of inhibitor of IκB kinase subunits alpha and beta are markers of NFκB activation, which promotes chemoresistance [ 99 ]. Moreover, NF-κB p65 increases miR-200b/c expression by binding to its promoter, subsequently sensitizing ovarian cancer cells to cisplatin [ 100 ]. It also regulates the downstream miRNAs miR-452-5p and miR-335-5p through the NF-κB TFs RelA and RelB, preventing the recurrence of OC [ 101 ]. Moreover, the NF-κB signaling pathway has been implicated in immunosuppression and immune evasion in ovarian cancer cells partly via NFκB-dependent production of IL-6, which impairs DCs but generates and recruits immunosuppressive MDSCs, and IL-8, which increases the expression of the immunosuppressive enzyme arginase [ 102 ]. Dehydroxymethylepoxyquinomicin (DHMEQ), an inhibitor of NFκB, induces apoptosis, increases the response to platinum-based drugs and reverses immunosuppression in ovarian cancer cells [ 102 , 103 ].

PI3K/Akt pathway

The PI3K/Akt pathway is frequently upregulated in ovarian cancer, and activated PI3K/Akt signaling contributes to increased cancer cell chemoresistance [ 104 , 105 ]. Many miRNAs have been found to modulate the PI3K/Akt pathway, influencing ovarian cancer chemosensitivity [ 106 ]. miR-337-3p directly targets PIK3CA and PIK3CB, suppresses the proliferation of epithelial ovarian cancer cells and reverses resistance [ 107 ]. The let-7 miRNA family deregulates this pathway by governing PI3K and Akt1 phosphorylation and activity [ 108 ]. However, miR-20a and miR-200c activate and upregulate this pathway, contributing to paclitaxel resistance [ 109 ]. The aberrant PI3K-Akt signaling in tumor cells is attributed to the platinum-resistant phenotype, and the combination of cisplatin and LY-294002 (a PI3K-Akt dual kinase inhibitor) was found to prevent 3D spheroid formation and sensitize cells to cisplatin [ 110 ]. Furthermore, mTOR is a key downstream signaling kinase in the PI3K/Akt pathway [ 111 ]. Activated mTOR signaling can trigger epithelial–mesenchymal transition (EMT) and promote the maintenance of cancer stem cells (CSCs), resulting in chemoresistance in ovarian cancer patients, and treatment with BEZ235 (a dual PI3K/mTOR inhibitor) might be a promising approach for reversing chemoresistance [ 112 ]. In addition, miR-497 and miR-199a were found to quantitatively control mTOR expression to induce apoptosis in ovarian cancer cells [ 106 ].

JAK/STAT pathway

Following the phosphorylation of JAK, STAT is phosphorylated and activated, after which its nuclear translocation induces the transcription of its target genes involved in growth and apoptosis. M Koti et al. reported that STAT1 was the most significantly differentially expressed gene between chemoresistant and chemosensitive HGSOC. Upregulation of STAT1 is associated with platinum resistance [ 113 ]. c-Myc is a downstream target of the JAK/STAT signaling pathway and is linked with the malignancy and chemotherapeutic response of OC [ 114 ]. The novel cell-permeable small molecule JQ1 can target c-Myc to suppress the proliferation and induce the apoptosis of OC cells. Along with chemotherapeutic agents and PARPis, JQ1 warrants further investigation regarding its ability to reverse drug resistance in OC patients through interaction with the JAK-STAT signaling pathway [ 115 ]. This pathway is also regulated by miRNAs, and miRNA interactions are linked to drug resistance. Restoration of miR-503-5p expression can block the downstream JAK2/STAT3 pathway through the binding of this miRNA to the 3’-UTR of the mediator CD97 [ 116 ]. miR-340 can also directly target LGR5, FHL2, CTNNB1, and BAG3 to inhibit the JAK/STAT3, Wnt/β-catenin, Notch and PI3K/Akt pathways, respectively [ 117 ]. miR-637 is regulated by competing endogenous RNAs (ceRNAs) and is involved in five signaling pathways, including the JAK/STAT3, Wnt/β-catenin, and PI3K/Akt signaling pathways, in OC [ 118 ]. Additionally, the JAK/STAT pathway can exert effects on ovarian cancer by shaping immune cell infiltration. Interferon-mediated activation of STAT1 leads to the expression of the downstream target CXCL10, which is key to the trafficking and differentiation of effector Th1 CD4 + cells, natural killer (NK) cells and CD8 + cells [ 113 ]. Moreover, attenuation of the JAK/STAT3 signaling pathway mediated by overexpression of miR-217 can suppress M2 macrophage polarization and regulate the immune status [ 119 ].

Notch signaling pathway

The Notch signaling pathway is activated by the binding of ligands to Notch receptors. Following proteolytic cleavage of Notch by γ-secretase (an instrumental proteolytic enzyme in the Notch pathway), the active NICD fragment is translocated to the nucleus, where it induces the transcription of Notch target genes through interaction with CSL transcriptional regulators [ 120 ]. Aberrant Notch pathway can cause drug resistance in ovarian cancer cells, whereas Notch knockdown can increase platinum sensitivity through downregulation of ABCC1 and ABCB1 [ 121 , 122 ]. In addition, inhibition of the Notch signaling pathway can induce apoptosis and reverse drug resistance. The γ-secretase inhibitor N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT) can induce apoptosis by downregulating Notch signaling, in turn reversing platinum resistance in ovarian cancer cells [ 123 , 124 ]. In addition, suppression of Notch signaling can increase apoptosis in ovarian cancer cells in animal models and reverse resistance to cisplatin and paclitaxel [ 121 , 125 ].

GAS6/AXL pathway

GAS6 binding to AXL leads to AXL dimerization and autophosphorylation at tyrosine residues, which results in intracellular signal transduction [ 126 ]. The GAS6/AXL pathway influences drug resistance through interactions with other signals and regulation of the tumor microenvironment (TME). For instance, AXL-related EMT mediates resistance to chemotherapy and targeted therapy [ 127 , 128 ]. The GAS6/AXL pathway also confers resistance through interactions with other signaling pathways, such as the PI3K, JAK/STAT and MAPK pathways, in ovarian cancer [ 129 ]. Moreover, the role of the GAS/AXL pathway in DDR has gradually been revealed in ovarian cancer. Inhibition of AXL (via bemcentinib or MYD1-72) resensitizes ovarian cancer cells to platinum, ATR inhibitors (ATRis) and PARPis by increasing DNA damage and inducing RS [ 130 , 131 , 132 ]. Furthermore, GAS6/AXL signaling promotes the generation of an immunosuppressive TME by modulating the expression of MHC and PD-L1 in neoplastic cells, increasing the secretion of immunosuppressive chemokines, and interfering with the infiltration of immune cells [ 133 ]. Although miR-515-3p regulates oxaliplatin sensitivity in mucinous ovarian cancer, in part by targeting AXL [ 134 ], there is still a lack of sufficient evidence demonstrating the roles of miRNAs in regulating the GAS6/AXL pathway.

Transforming growth factor-beta (TGF-β) pathway

Activation of the TGF-β signaling pathway occurs via the interaction of the dimeric TGF-β ligand with its specific transmembrane receptors [ 135 ]. TGF-β signaling is transduced via downstream SMAD effectors and non-SMAD proteins, such as AKT and MAPK [ 136 ]. miRNAs can target the components of the TGF-β signaling pathway to mediate drug resistance in ovarian cancer. For instance, miR-33a-5p influences the expression of SMAD2/4 by targeting carnitine O-octanoyl transferase (CROT), which induces paclitaxel resistance in ovarian cancer [ 137 ]. Decreased miR-30a expression can result in upregulation of TGF-β and SMAD4 to ultimately activate autophagy, mediating cisplatin resistance in ovarian cancer [ 138 ]. However, miR-181a plays an unappreciated role in mediating resistance in HGSOC via the activation of TGF-β signaling by directly targeting SMAD7 [ 139 ].

The TGF-β pathway has biphasic effects and acts as a tumor suppressor at early stages but later stimulates cancer progression by impacting tumor cells and their microenvironment [ 135 ]. Aberrant activation of this pathway blocks apoptosis and confers chemoresistance on ovarian cancer cells [ 140 ]. In addition, the TGF-β pathway plays a vital role in platinum resistance via canonical downstream EMT-related molecules [ 141 ]. The TGF-β pathway also suppresses immunity within the TME and contributes to chemoresistance. Daniel Newsted et al. developed an inhibitory antibody (anti-TGFBR2) to block TGF-β signaling and showed that this antibody improved the efficacy of chemotherapy and the limited antitumor immune response [ 142 ]. Moreover, the immunosuppressive effects of the TGF-β signaling pathway can be induced via CRISPR/Cas9-mediated knockout of TGF-β receptor 2 (TGFBR2) in TILs [ 143 ].

MAPK pathway

RAS/RAF/MEK/ERK are the classical and key signaling mediators in the MAPK pathway, and low-grade serous carcinoma (LGSC) of the ovary and peritoneum are characterized by MAPK pathway alterations and chemoresistance [ 144 ]. Excessive activation of Ras and Erk1/2 is positively and significantly correlated with chemoresistance in ovarian cancer [ 145 ]. Both the PI3K/Akt and Ras/MAPK signaling pathways can mediate the phosphorylation of the proapoptotic protein BAD, which leads to increased platinum resistance by inhibiting apoptosis [ 146 ]. miRNAs also play regulatory roles in the MAPK pathway by interfering with its components. For example, miR-634 can directly repress GRB2, ERK2 and RSK2, hence, inhibition of the Ras-MAPK pathway restores chemosensitivity in ovarian cancer cells [ 147 ]. Low levels of miR-508/miR-18a and increased expression of MAPK1 and ERK were identified in ovarian cancer, while miR-508 mimics were found to repress MAPK1 and ERK, resulting in suppression of EMT and the malignant progression of cancer cells [ 148 , 149 ].

Hippo/yes-associated protein (YAP) pathway

The Hippo pathway confers resistance to therapeutic agents that are commonly used to treat ovarian cancer [ 150 , 151 ]. YAP and its paralog TAZ are the main downstream effectors of the Hippo–YAP pathway and act as transcriptional coactivators, and their signaling has emerged as key mechanism of drug resistance [ 152 , 153 ]. YAP and TAZ mediate gene transcription by binding to TFs, such as the TEA domain family (TEAD) proteins, to promote tumor progression and resistance [ 153 , 154 ]. miRNAs can regulate the expression of YAP1 and modulate the Hippo pathway, but the regulatory mechanism involved remains vague. miR-509-3p, miR-509–3-5p [ 155 ] and miR-141 [ 156 ] are associated with cisplatin resistance via YAP1 and the Hippo signaling pathway. It is hypothesized that miR-509–3-5p can directly regulate YAP1 expression by targeting its coding region [ 155 ].

Epigenetic modifications

Epigenetic regulation refers to the effects of heritable changes in gene expression without DNA sequence changes. DNA methylation, histone modification and noncoding RNA (ncRNA) activity (Fig.  5 ) are common epigenetic regulatory mechanisms [ 157 ]. Increasing evidence shows that abnormal epigenetic regulation leads to tumor drug resistance.

Epigenetic modification. Epigenetic processes regulate gene expression through DNA methylation, histone modification, and non-coding RNA (ncRNAs) without altered DNA sequences. Hypermethylation of ABCB1 and demethylation of ABCG2 promoter lead to chemoresistance in ovarian cancer. The loss of RAD51C promoter methylation and the downregulation of BRCA1 methylation have been verified to cause drug resistance. The specific H3K27 methyltransferase EZH2 confers chemoresistance on ovarian cancer cells through H3K27 methylation. A subclass of miRNAs, “epi-miRNAs”, can modulate epigenetic regulators to impact therapeutic responses. miR-152 and miR-185 co-contribute to the cisplatin resistance by directly targeting DNMT1, miR-15a and miR-16 directly target the Bmi-1 (a member of Polycomb complexes). They may serve as potential epigenetic therapeutic targets. Epigenetic therapy including DNMTi and HDACi can increase the number of CD45 + immune cells, active CD8 + T and NK cells in TME, reducing immunosuppression. Thus, the epigenetic therapy combined with immunotherapy may be a promising therapeutic strategy for resistant OC. (HDACs, histone deacetylases; H3K27, histone H3 lysine 27; EZH2, enhancer of zeste homolog 2; DNMTis, DNA methyltransferase inhibitors; HDACis, histone deacetylase inhibitors; Bmi-1: a member of Polycomb complexes)

DNA methylation can affect therapeutic responses through various mechanisms, including affecting membrane transport, DNA repair, signaling pathway activity and apoptosis [ 158 ]. For instance, hypermethylation of ABCB1 and demethylation of the ABCG2 promoter may affect therapeutic efficacy and lead to chemoresistance in ovarian carcinoma, effects attributed to upregulation of P-gp [ 159 , 160 ]. Abnormal methylation of genes involved in the PI3K-AKT, MAPK, and Wnt pathways and in EMT confers resistance on HGSOC cells [ 161 , 162 , 163 ]. In addition, loss of RAD51C promoter methylation and a low level of BRCA1 methylation have been verified to cause drug resistance. Homozygous RAD51C methylation and hypermethylation of BRCA1 could be predictive biomarkers for the treatment response in HGSOC [ 164 ]. Epigenetic alterations in the docking protein 2 (DOK2) gene can induce carboplatin resistance in ovarian cancer via suppression of apoptosis [ 165 ].

Histone modifications mainly include histone methylation and acetylation [ 166 ]. Min-Gyun Kim et al. confirmed the correlation between overexpression of histone deacetylases (HDACs) and cisplatin resistance in the ovarian cancer cell lines SKOV3 and OVCAR3 [ 167 ]. Recent data have provided novel insight into the role of histone H3 lysine 27 (H3K27) methylation in resistance mechanisms [ 168 ]. The specific H3K27 methyltransferase enhancer of zeste homolog 2 (EZH2) confers chemoresistance on ovarian cancer cells through H3K27 methylation [ 169 ]. In addition, Yujie Fang et al. revealed the roles of histone acetylation in a weak immune response and chemoresistance in ovarian cancer based on analysis of the TCGA and Gene Expression Omnibus (GEO) databases [ 170 ]. In terms of treatments, epigenetic therapy, including treatment with DNA methyltransferase and histone deacetylase inhibitors (DNMTis and HDACis, respectively), can increase the numbers of CD45 + immune cells, active CD8 + T cells and NK cells in the TME, reducing immunosuppression and the tumor burden through activation of type I interferon signaling in murine ovarian cancer [ 171 , 172 ].

NcRNAs, comprising long ncRNAs (lncRNAs), small ncRNAs (sncRNAs) and circular RNAs (circRNAs), can regulate gene expression via epigenetic modification [ 173 ]. Most commonly, lncRNAs and circRNAs play roles in drug resistance by acting as miRNA sponges to regulate downstream gene expression [ 174 ]. “Epi-miRNAs” exert their effects by directly targeting epigenetic regulators, such as DNMTs and HDACs, or components of polycomb repressor complexes [ 175 ]. miRNAs affect mRNA transcription by binding to mRNA 3'-UTRs, leading to restoration of the expression of hypermethylated tumor suppressor genes [ 176 ]. Downregulated miR-152 and miR-185 contribute cooperatively to cisplatin resistance by directly targeting DNMT1 and may thus serve as epigenetic therapeutic targets [ 177 ]. miR-15a and miR-16 directly target the 3'-UTR of Bmi-1 (a component of Polycomb complexes), and their expression levels are significantly correlated with the Bmi-1 protein level in ovarian cancer [ 178 ].

Other mechanisms

Indeed, determining the complex mechanisms of resistance in ovarian cancer remains highly challenging. The resistance mechanisms cross-talk with each other and may interfere by generating an immunosuppressive environment, thus resulting in drug resistance, including immunotherapy resistance. An imbalance of Treg/Th17 cells [ 179 ], M2 polarization of macrophages [ 180 ], NK-cell exhaustion [ 181 ], and aberrant expression of IFNγ [ 182 ] and PD-L1 [ 183 , 184 ] mediate immunosuppression, promoting tumor progression and resistance. miRNAs, such as miR-29a-3p, miR-21-5p, miR-1246, miR-29c, and miR-424, can modulate the expression of immune-related molecules to influence the immune status. Conversely, the TME or immunotherapy can regulate the expression of many miRNAs to promote drug resistance [ 185 , 186 ]. The Hedgehog (Hh) and Wnt/β-catenin pathways can also promote T-cell exclusion and checkpoint inhibitor resistance [ 187 , 188 ]. However, monotherapy with the Hh pathway inhibitor vismodegib did not show any significant antitumor activity in patients with ovarian cancer in a phase II clinical trial (NCT00739661) [ 189 ]. Interestingly, although Wnt signaling is a driver of resistance in ovarian cancer, the genetic driver of Wnt signaling is largely unknown [ 190 ].

In addition to the above mechanisms, aberrations in apoptosis, ferroptosis, autophagy, and endoplasmic reticulum stress (ER stress) act simultaneously or sequentially to enable cancer cells to survive treatment with antitumor agents. miR-130a [ 191 ] and miR-142-5p [ 192 ] have been reported to modulate apoptosis by targeting XIAP. An in-depth study of ferroptosis revealed that ferroptosis played a pivotal role in acquired resistance to sorafenib [ 193 ], EGFR tyrosine kinase inhibitors [ 194 ], and immunotherapy tolerance [ 195 ]. Intriguingly, autophagic flux can be driven by paclitaxel to promote paclitaxel resistance in ovarian cancer [ 196 ] and can be regulated by miR-30a [ 138 ], miR-200c [ 197 ], and miR-133a [ 198 ]. Furthermore, as a popular research topic, ER stress has a considerable impact on drug resistance in ovarian cancer [ 199 ]. The IRE1α/XBP1s pathway activates the unfolded protein response (UPR) during ER stress, resulting in microenvironment remodeling or resistance to treatment [ 199 , 200 ].

Strategies for overcoming drug resistance

Clinical trials targeting transmembrane transport.

Overexpression of ABCB1 (also known as p-gp/MDR1) mediates increased drug efflux. Increased drug efflux makes attaining a sufficient intracellular concentration of drugs challenging, thus resulting in drug resistance [ 201 , 202 ]. The ABCB1 inhibitors (verapamil and elacridar) can reverse MDR through reducing the efflux of many drugs, including paclitaxel, olaparib, doxorubicin and rucaparib [ 24 ]. Moreover, PARPi resistance was evaluated in a mouse model and was found to be reversed by coadministration of tariquidar (a P-gp inhibitor) [ 26 ]. Although preclinical studies of the response to P-gp inhibitors have been performed, clinical trials of P-gp inhibitors are limited and outdated due to the severe toxic effects of these drugs [ 203 ]. For instance, P-gp inhibition increases the intracellular accumulation of paclitaxel, leading to paclitaxel-induced peripheral neuropathy [ 204 ]. NcRNAs play key roles in the regulation of ABC transporters and their clinical implications for MDR [ 8 ]. Thus, novel strategies for post-resistance therapy include delivering ncRNA mimics or antisense oligonucleotides of ncRNAs to interfere with ncRNA-ABC transporter axes. Moreover, codelivery of miR-129-5p and doxorubicin via polypeptide nanoparticles was found to effectively overcome MDR by directly inhibiting P-gp, thereby increasing intracellular doxorubicin accumulation and enhancing chemosensitivity [ 205 ].

Recently, antibody‒drug conjugates (ADCs), which can directly deliver potent cytotoxic drugs to cancer cells with appropriate target antigens while avoiding toxic effects on healthy cells, have gained increasing attention. Currently, the only FDA-approved ADC, namely, mirvetuximab soravtansine, has attracted widespread attention in the context of ovarian cancer drug resistance. A phase III clinical trial, MIRASOL (NCT04209855), is underway to compare the efficacy of chemotherapy and mirvetuximab soravtansine in FRα-positive, platinum-resistant HGSOC. The novel ADC BA3011 can target the Axl receptor on cancer cells through conditionally active biologics technology. A phase II clinical trial is underway to evaluate the combination of BA3011 and durvalumab in patients with platinum-resistant HGSOC (NCT04918186). MUC16 is another common target for platinum-resistant ovarian cancer treatment evaluated in two completed phase I trials (NCT01335958 [ 206 ] and NCT02146313 [ 207 ]). The results showed that the anti-MUC16 ADC had a tolerable safety profile and encouraging antitumor activity in patients with platinum-resistant ovarian cancer with high MUC16 expression. Additionally, down-regulation of some miRNAs could lead to abnormal MUC16 levels in OC. Thus, their up-regulation or mimics could be potential options along with anti-MUC16 for OC patients [ 208 ]. Mesothelin is an glycoprotein overexpressed on the surface of cancer cells. Two phase I clinical trials (NCT01469793/NCT02751918) evaluated a novel anti-mesothelin ADC in platinum-resistant ovarian cancer. Conclusions drawn from these trials indicated the tolerability and promising clinical activity of anetumab ravtansine combined with PEGylated liposomal doxorubicin [ 209 ], although the results of previous trials were inconsistent. Another ADC drug Zilovertamab Vedotin, targeting ROR1, was applicated in the II-phase clinical trials (NCT04504916). ROR1 also can be targeted by miR‑382, which might serve as another option for OC [ 210 ]. Additionally, HER2, TROP2, DLL3, and Nectin-4 are major targets of ADCs. Combination strategies with ADCs have shown considerable promise as emerging therapies in further investigations and clinical trials [ 211 ].

Clinical trials targeting DDR

The HRR pathway contributes to a key mechanism of acquired platinum and PARPi resistance in ovarian cancer. DNA repair-targeted therapy is a promising precision medicine strategy for ovarian cancer. Many clinical trials, including trials evaluating drugs targeting ATR, ATM, WEE1, checkpoint kinase 1/2 (CHK1/2), BRCA1/2 and RAD51, have been designed to evaluate interference with DDR pathways to overcome platinum and PARPi resistance in ovarian cancer.

ATR/ATM kinase inhibitors

ATR/ATM kinases, key molecules in DDR, are potential therapeutic targets for overcoming drug resistance in ovarian cancer. miR-203a-3p mimics and ATMis were reported to synergistically hinder OC progression, which could serve as a potential therapeutic option for OC [ 212 ]. It has been reported that ATRis can reverse PARPi resistance by blocking RAD51 loading onto DSBs and disrupting fork protection in human-derived cell lines [ 213 ]. An increasing number of clinical trials have evaluated the efficacy of ATRi or ATMi in combination with chemotherapeutic agents or PARPis. An interventional and crossover phase II randomized clinical trial (NCT02595892) was the first randomized clinical trial of an ATRi and demonstrated the benefit of adding berzosertib to gemcitabine for the treatment of platinum-resistant HGSOC [ 214 ]. M4344 enhances the activity of clinical DNA-damaging agents, including topoisomerase inhibitors, gemcitabine, cisplatin, and talazoparib, in advanced solid tumors [ 215 ]. Recently, another single-group interventional phase I trial (NCT04149145) in patients with PARPi-resistant HGSOC was just announced, in which a combination regimen of M4344 (an ATRi) plus niraparib will be evaluated.

WEE1 inhibitors

WEE1 is a vital target in the HRR pathway, and WEE1 inhibitors have been widely evaluated in combination with chemotherapeutic agents or PARPis in many ongoing clinical trials. A phase Ib nonrandomized, multicenter study (NCT04516447) in patients with platinum-resistant ovarian cancer evaluated the preclinical activity of ZN-c3 in combination with carboplatin, PLD, paclitaxel, and gemcitabine individually. The WEE1 inhibitor MK-1775 in combination with carboplatin or gemcitabine hydrochloride was tested in two phase II trials (NCT01164995 and NCT02272790). Adavosertib combined with chemotherapy showed preliminary therapeutic efficacy in platinum-resistant ovarian cancer, but the hematologic toxicity of this combination may limit its application [ 216 , 217 ]. In addition, a phase I/II clinical trial of the WEE1 inhibitor ZN-c3 combined with niraparib was conducted in patients with platinum-resistant ovarian cancer (NCT05198804), but no results have been published. In addition, a conference abstract (ASCO 2021) reported that adavosertib alone or in combination with olaparib demonstrated efficacy in patients with PARPi resistance. Although grade 3 and 4 toxicities could be managed, they led to dose interruption and reduction (NCT03579316).

CHK1/2 inhibitors

Investigations of CHK1/2 inhibitors have been limited until recently. CHK1 inhibitors play a preliminary role in the clinical treatment of PARPi-resistant HGSOC by inducing DNA damage and RS [ 218 ]. miRNA‑199b‑3p suppressed CHK1 expression and EMT transition, which may represent a promising therapeutic target for ovarian cancer [ 219 ]. A phase Ia dose-escalation trial, the combination of PHI-101 (a selective CHK2 inhibitor) with a PARPi showed good safety and tolerability, and is a potential therapeutic regimen for platinum-resistant recurrent ovarian cancer [ 220 ]. In summary, the therapeutic efficacy and underlying mechanisms of CHK1/2 inhibitors are unknown, and further studies are attractive and needed.

Downregulation of BRCA1/2 and RAD51

The reactivation of the HRD genes BRCA1/2 and RAD51 is the genetic mechanism of PARPi resistance and confers a dismal prognosis [ 221 ]. Cediranib can potentially reverse PARPi resistance by downregulating BRCA1/2 and RAD51 and ultimately resensitizing cells to PARPis [ 222 ]. However, this combination regimen showed activity in patients with ovarian cancer who progressed on PARPi therapy in another phase II trial (EVOLVE) [ 221 ]. However, in a randomized phase II trial (BAROCCO), the combination of a PARPi and cediranib did not improve PFS in platinum-resistant ovarian cancer patients compared with chemotherapy alone [ 223 ]. The underlying mechanisms of these combination strategies have not been thoroughly elucidated.

Clinical trials targeting signaling pathways

Targeting the pi3k/akt pathway.

The PI3K/AKT pathway is regarded as a common oncogenic signaling pathway. Approximately 70% of ovarian cancer patients have aberrations in the PI3K/AKT signaling pathway, and mutations in the gene encoding the catalytic subunit PIK3CA occur in 6–12% of patients [ 224 , 225 ]. CYH33, a PI3Kα inhibitor, exhibited a manageable safety profile and preliminary antitumor efficacy in patients with PI3KCA-mutant ovarian cancer (NCT03544905). In addition, a phase I clinical trial (NCT04586335) is underway to further evaluate the therapeutic efficacy of CYH33 in combination with olaparib in platinum-resistant ovarian cancer. In addition, a combination regimen of PARPi and copanlisib (a PI3K inhibitor) was tested in phase I/II trials (NCT03586661 and NCT05295589) in patients with BRCA-mutated, resistant ovarian cancer. PI3K inhibition is believed to lead to downregulation of the BRCA1/2 proteins, which enhances HRR deficiency and the efficacy of PARPis. In addition, the Akt inhibitor afuresertib is under assessment in an interventional randomized clinical trial (NCT04374630) in patients with platinum-resistant ovarian, fallopian tube, or peritoneal cancer.

Targeting the GAS6-AXL pathway

The GAS6-AXL signaling pathway is another crucial player in drug resistance in ovarian cancer. Carboplatin/olaparib plus AVB-500, a selective inhibitor of GAS6-AXL, can increase DNA damage and RAD51 focus formation and slow replication fork progression, resulting in rapid death of ovarian cancer cells in vitro and decreased tumor burden in vivo [ 131 ]. A phase 1b trial (NCT03639246) evaluated AVB-S6-500 in combination with paclitaxel or PEGylated liposomal doxorubicin. PROC patients may derive the greatest benefit from AVB-500 treatment [ 226 ]. Another phase I/II clinical trial (NCT04019288) was designed and was commenced in 2019 to evaluate the safety and clinical benefit of durvalumab plus AVB-S6-500 (an AXL inhibitor) in platinum-resistant ovarian cancer patients. It was reported that the combination of AVB-S6-500 and durvalumab was tolerable in PROC patients [ 227 ]. Moreover, a humanized anti-AXL monoclonal antibody, tilvestamab, blocks GAS6-mediated AXL receptor activation and has been tested in platinum-resistant HGSOC patients (NCT04893551), but no results have been published.

Targeting the MAPK pathway

The RAS/RAF/MEK/ERK kinase pathway, also known as the MAPK pathway, participates in cancerogenesis, metastasis and resistance. Although VS-6766 (a RAF/MEK inhibitor) exhibited antitumor activity in platinum-resistant low-grade serous ovarian cancer and endometrial adenocarcinoma with RAF–RAS–MEK pathway mutations, patients later experienced progression. Thus, the use of VS-6766 in combination regimens warrants further evaluation. The combination of defactinib (a FAK inhibitor) and VS-6766 was evaluated for its pharmacodynamic activity in PROC patients (NCT03875820). In addition, combined PI3K/mTOR and ERK inhibition can reverse therapeutic resistance in ovarian cancer cell lines, but the clinical efficacy of these agents requires further preclinical determination [ 228 ]. ONC201, a dual inhibitor of Akt and ERK, is being evaluated in combination with paclitaxel for the treatment of platinum-resistant ovarian cancer in an ongoing phase II trial (NCT04055649). The unpublished results of this trial are likely to provide strong evidence for the development of novel treatment strategies.

Targeting the Notch pathway

The Notch pathway is linked to the proliferation, migration, and drug resistance of ovarian cancer cells [ 229 ]. Pretreatment with the γ-secretase inhibitor DAPT increased the sensitivity of PROC to platinum by downregulating the Notch pathway, suggesting a promising approach for treating patients with PROC [ 123 , 230 ]. The SIERRA open-label phase Ib trial (NCT01952249) was conducted to observe the safety and efficacy of demcizumab (potent inhibitor of the Notch pathway) combined with paclitaxel for the treatment of platinum-resistant ovarian, primary peritoneal, and fallopian tube cancer. The results indicated that this combination had a manageable toxicity profile and showed a clinical benefit rate of 42% in patients with heavily pretreated platinum-resistant ovarian cancer [ 231 ].

Targeting the NF-κB pathway

Activation of the NF-κB pathway contributes to aggressive behaviors, mediating the oncogenic activity of DDR-related genes [ 232 ]. Furthermore, the scientific literature supports the interaction and colocalization of NF-κB and BRCA1 [ 233 ]. Denosumab, an inhibitor of RANKL (an NF-κB ligand) and NF-κB signaling, was evaluated in ovarian cancer patients with BRCA1 mutations. However, the pilot study (NCT03382574), which compared growth and metastatic spread between the denosumab and control groups, was terminated early due to the inability to enroll participants [ 234 ].

In addition, components of the cell cycle and apoptosis machineries, including topoisomerase I (NCT04029909), P53 (NCT03113487), and CDK2 (NCT05252416), could be promising treatment targets. An increasing number of early-phase clinical trials involving the glucocorticoid receptor (GR), FAK, and HER2 are underway. Although the results are pending, these studies could provide sufficient rationale for the involvement of these signaling pathways. The restoration of miR-206 expression represented a potential anti-FAK strategy to control ovarian cancer progression in EOC lines [ 221 ]. Some miRNAs were designed to target 3’-UTR of HER2 to inhibit HER2 protein expression [ 235 ]. However, the miRNA targeting drugs lacks application in clinical trials. Emerging peptide vaccines aimed to elicit a host immune response against tumor-specific antigens, such as p53, HER2, NY-ESO-1, and FRα, are being evaluated [ 236 ]. However, cancer vaccines have had limited clinical success, and research on most peptide vaccines for gynecological malignancies is still at an exploratory stage.

Clinical trials targeting epigenetic modifications

Increased DNA methylation and histone modifications can alter the transcription of tumor suppressors and genes related to the apoptotic response to chemotherapy [ 224 , 237 ]. An increasing number of trials have provided insight into the role of epigenetic modifications in the drug resistance of ovarian cancer. Researchers have attempted to overcome platinum resistance by coadministration of hypomethylating agents. For instance, guadecitabine plus carboplatin was tolerable and resulted in a detectable clinical response in patients with PROC in a phase I clinical trial [ 238 ]. However, in the phase II trial, the guadecitabine plus carboplatin group did not show any superior effect compared with the traditional chemotherapy group [ 239 ]. Furthermore, combination regimens of hypomethylating agents with PARPis or immune checkpoint inhibitors are increasingly being developed. Talazoparib and ZEN003694 (a BET inhibitor) are being evaluated in an ongoing phase II clinical trial (NCT05327010) for recurrent PARPi-resistant cancer. This series of novel therapeutic regimens has spurred the development of triplet regimens. In an ongoing phase I trial (NCT04840589), ZEN003694 and nivolumab alone or combined with ipilimumab were assessed in PROC patients. In addition, another combination therapy comprising CDX-1401 (a vaccine), atezolizumab, and guadecitabine was evaluated in a clinical trial (NCT03206047) to improve clinical efficacy. These innovative clinical trials are anticipated to provide therapeutic opportunities for drug-resistant patients.

With the increasing use of novel therapeutic drugs for ovarian cancer, the development of later-line treatments has been under enormous pressure. Recently, resistance to a variety of therapeutic drugs, such as PARPis, angiogenesis inhibitors, and immune checkpoint inhibitors, has been found to occur. In the past, therapeutic agents for ovarian cancer have been limited, and researchers have usually described the underlying mechanism and explored therapeutic strategies for overcoming resistance based on the drug classification. However, as increasing numbers of new agents are applied in clinical practice, the resistance mechanisms of these various new drugs must be identified, and these mechanisms may be similar or even identical to those of other drugs. Thus, the classification of drug resistance should not be confined to the drug category, and we should attempt to obtain insight into classification of resistance based on molecular mechanisms. The concept of drug resistance classification provides a sound basis for further research to develop more precise reversal strategies.

Although the resistance mechanisms of different agents are complicated, we classified miRNA-mediated mechanisms into four categories: abnormalities in transmembrane transport, dysregulation of DDR, dysregulation of signaling pathways and epigenetic modification. On the basis of the above four mechanisms, clinical trials of new agents are underway to overcome drug resistance. Notably, ADCs, a current research hotspot, hold promise for overcoming resistance in patients with ovarian cancer. The FDA's approval of mirvetuximab soravtansine-gynx for FRα-positive, platinum-resistant HGSOC was based on Study 0417 (SORAYA, NCT04296890) [ 240 ]. Thus, many additional ADCs against various targets, including NaPi2b, HER2/3, mesothelin, and MUC16, which are expressed in ovarian cancer, are under investigation [ 241 ]. Future innovative studies and targeted therapies with ADCs will provide opportunities for reversing drug resistance in ovarian cancer. In addition, another potential approach for reversing resistance is based on miRNAs [ 242 ]. Codelivery of miRNAs with chemotherapeutic agents is a promising option for overcoming resistance, but further investigations of the underlying mechanism and the clinical application of this strategy are needed [ 243 ]. Polypeptide nanoparticles carrying doxorubicin and miR-129-5p could be a promising and synergistic strategy to overcome drug resistance in ovarian cancer [ 205 ].

In the context of the increasing number of novel agents, our summary of the four resistance mechanisms of ovarian cancer provides a new concept for resistance classification by molecular mechanism, not by drug category. Given the intersections between drug resistance mechanisms, this concept is likely to result in the realization of “two birds with one stone” effects on the reversal of drug resistance in ovarian cancer. Furthermore, these findings are anticipated to have broad implications for the development of precise therapeutic approaches for reversing drug resistance in ovarian cancer. On this basis, umbrella trials can be carried out to explore the diagnostic and therapeutic targets of the four resistance mechanisms, and this may be a direction of future researches on drug resistance in ovarian cancer.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

Antibody–drug conjugate

Ataxia telangiectasia and Rad3-related protein inhibitor

Ataxia telangiectasia mutated protein inhibitor

Breast cancer drug resistance protein

Base excision repair

Competitive endogenous RNA

Checkpoint kinase 1/2

Cancer stem cell

N-[N-(3,5-Difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester

DNA damage repair

Dehydroxymethylepoxyquinomicin

DNA methyltransferase inhibitor

Docking protein 2

Dynein light chain 1

Zeste homologue 2

Epithelial mesenchymal transformation

Endoplasmic Reticulum

Fanconi anemia complementation group

Glutathione transferase

Histone deacetylase

Homologous recombination deficiency

Homologous recombination repair

High-grade serous ovarian cancer

Heat shock protein

Low grade serous carcinoma

Lung drug resistance-related protein

Mismatch repair

Multidrug resistance-related protein

National Comprehensive Cancer Network

Non-coding RNA

Non-homologous end junction

Nucleotide excision repair

Overall survival

Poly ADP-ribose polymerase inhibitor

Progression-free survival

P-glycoproteins

Transforming growth factor-beta

Tumor-infiltrating lymphocytes

Translesion DNA synthesis

TEA domain family

Urothelial carcinoma-associated 1

3’-Untranslated region

X-linked inhibitor of apoptosis

Yes-associated protein

Cortez AJ, Tudrej P, Kujawa KA, Lisowska KM. Advances in ovarian cancer therapy. Cancer Chemother Pharmacol. 2018;81:17–38.

Article   CAS   PubMed   Google Scholar  

Ovarian Cancer — Cancer Stat Facts. [cited 2023 Aug 10]. Available from: https://seer.cancer.gov/statfacts/html/ovary.html .

Ray-Coquard I, Leary A, Pignata S, Cropet C, González-Martin A, Marth C, et al. Olaparib plus bevacizumab first-line maintenance in ovarian cancer: final overall survival results from the PAOLA-1/ENGOT-ov25 trial. Ann Oncol Off J Eur Soc Med Oncol. 2023;S0923–7534(23):00686–95.

Google Scholar  

DiSilvestro P, Banerjee S, Colombo N, Scambia G, Kim B-G, Oaknin A, et al. Overall Survival With Maintenance Olaparib at a 7-Year Follow-Up in Patients With Newly Diagnosed Advanced Ovarian Cancer and a BRCA Mutation: The SOLO1/GOG 3004 Trial. J Clin Oncol Off J Am Soc Clin Oncol. 2023;41:609–17.

Article   CAS   Google Scholar  

González-Martín A, Pothuri B, Vergote I, Graybill W, Lorusso D, McCormick CC, et al. Progression-free survival and safety at 3.5years of follow-up: results from the randomised phase 3 PRIMA/ENGOT-OV26/GOG-3012 trial of niraparib maintenance treatment in patients with newly diagnosed ovarian cancer. Eur J Cancer Oxf Engl. 1990;2023(189):112908.

Richardson DL, Eskander RN, O’Malley DM. Advances in Ovarian Cancer Care and Unmet Treatment Needs for Patients With Platinum Resistance: A Narrative Review. JAMA Oncol. 2023;9:851.

Article   PubMed   Google Scholar  

Kandettu A, Adiga D, Devi V, Suresh PS, Chakrabarty S, Radhakrishnan R, et al. Deregulated miRNA clusters in ovarian cancer: Imperative implications in personalized medicine. Genes Dis. 2022;9:1443–65.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Wang Y, Wang Y, Qin Z, Cai S, Yu L, Hu H, et al. The role of non-coding RNAs in ABC transporters regulation and their clinical implications of multidrug resistance in cancer. Expert Opin Drug Metab Toxicol. 2021;17:291–306.

Yi C, Yu A-M. MicroRNAs in the Regulation of Solute Carrier Proteins Behind Xenobiotic and Nutrient Transport in Cells. Front Mol Biosci. 2022;9: 893846.

Meghani K, Fuchs W, Detappe A, Drané P, Gogola E, Rottenberg S, et al. Multifaceted Impact of MicroRNA 493–5p on Genome-Stabilizing Pathways Induces Platinum and PARP Inhibitor Resistance in BRCA2-Mutated Carcinomas. Cell Rep. 2018;23:100–11.

Mihanfar A, Fattahi A, Nejabati HR. MicroRNA-mediated drug resistance in ovarian cancer. J Cell Physiol. 2019;234:3180–91.

Yonezawa A, Masuda S, Yokoo S, Katsura T, Inui K. Cisplatin and oxaliplatin, but not carboplatin and nedaplatin, are substrates for human organic cation transporters (SLC22A1-3 and multidrug and toxin extrusion family). J Pharmacol Exp Ther. 2006;319:879–86.

Fu S, Naing A, Fu C, Kuo MT, Kurzrock R. Overcoming platinum resistance through the use of a copper-lowering agent. Mol Cancer Ther. 2012;11:1221–5.

Hsu KF, Shen MR, Huang YF, Cheng YM, Lin SH, Chow NH, et al. Overexpression of the RNA-binding proteins Lin28B and IGF2BP3 (IMP3) is associated with chemoresistance and poor disease outcome in ovarian cancer. Br J Cancer. 2015;113:414–24.

Sun S, Zhao S, Yang Q, Wang W, Cai E, Wen Y, et al. Enhancer of zeste homolog 2 promotes cisplatin resistance by reducing cellular platinum accumulation. Cancer Sci. 2018;109:1853–64.

Kalayda GV, Wagner CH, Jaehde U. Relevance of copper transporter 1 for cisplatin resistance in human ovarian carcinoma cells. J Inorg Biochem. 2012;116:1–10.

Rashid K, Ahmad A, Liang L, Liu M, Cui Y, Liu T. Solute carriers as potential oncodrivers or suppressors: their key functions in malignant tumor formation. Drug Discov Today. 2021;26:1689–701.

Barbier RH, McCrea EM, Lee KY, Strope JD, Risdon EN, Price DK, et al. Abiraterone induces SLCO1B3 expression in prostate cancer via microRNA-579-3p. Sci Rep. 2021;11:10765.

Kuo MT, Chen HHW, Song I-S, Savaraj N, Ishikawa T. The roles of copper transporters in cisplatin resistance. Cancer Metastasis Rev. 2007;26:71–83.

Burger H, Zoumaro-Djayoon A, Boersma AWM, Helleman J, Berns EMJJ, Mathijssen RHJ, et al. Differential transport of platinum compounds by the human organic cation transporter hOCT2 (hSLC22A2). Br J Pharmacol. 2010;159:898–908.

Kazmierczak D, Jopek K, Sterzynska K, Nowicki M, Rucinski M, Januchowski R. The Profile of MicroRNA Expression and Potential Role in the Regulation of Drug-Resistant Genes in Cisplatin- and Paclitaxel-Resistant Ovarian Cancer Cell Lines. Int J Mol Sci. 2022;23:526.

Beretta GL, Benedetti V, Cossa G, Assaraf YG, Bram E, Gatti L, et al. Increased levels and defective glycosylation of MRPs in ovarian carcinoma cells resistant to oxaliplatin. Biochem Pharmacol. 2010;79:1108–17.

Izquierdo MA, van der Zee AG, Vermorken JB, van der Valk P, Beliën JA, Giaccone G, et al. Drug resistance-associated marker Lrp for prediction of response to chemotherapy and prognoses in advanced ovarian carcinoma. J Natl Cancer Inst. 1995;87:1230–7.

Vaidyanathan A, Sawers L, Gannon A-L, Chakravarty P, Scott AL, Bray SE, et al. ABCB1 (MDR1) induction defines a common resistance mechanism in paclitaxel- and olaparib-resistant ovarian cancer cells. Br J Cancer. 2016;115:431–41.

Zhang B, Kang Z, Zhang J, Kang Y, Liang L, Liu Y, et al. Simultaneous binding mechanism of multiple substrates for multidrug resistance transporter P-glycoprotein. Phys Chem Chem Phys PCCP. 2021;23:4530–43.

Rottenberg S, Jaspers JE, Kersbergen A, van der Burg E, Nygren AOH, Zander SAL, et al. High sensitivity of BRCA1-deficient mammary tumors to the PARP inhibitor AZD2281 alone and in combination with platinum drugs. Proc Natl Acad Sci U S A. 2008;105:17079–84.

Zou Z, Zou R, Zong D, Shi Y, Chen J, Huang J, et al. miR-495 sensitizes MDR cancer cells to the combination of doxorubicin and taxol by inhibiting MDR1 expression. J Cell Mol Med. 2017;21:1929–43.

Dias MP, Moser SC, Ganesan S, Jonkers J. Understanding and overcoming resistance to PARP inhibitors in cancer therapy. Nat Rev Clin Oncol. 2021;18:773–91.

Huang C, Qin L, Chen S, Huang Q. CircSETDB1 contributes to paclitaxel resistance of ovarian cancer cells by sponging miR-508-3p and regulating ABCC1 expression. Anticancer Drugs. 2023;34:395–404.

Shi C, Wang M. LINC01118 Modulates Paclitaxel Resistance of Epithelial Ovarian Cancer by Regulating miR-134/ABCC1. Med Sci Monit Int Med J Exp Clin Res. 2018;24:8831–9.

CAS   Google Scholar  

Stasiak P, Kaźmierczak D, Jopek K, Nowicki M, Rucinski M, Januchowski R. The Profile of MicroRNA Expression and Potential Role in the Regulation of Drug-Resistant Genes in Doxorubicin and Topotecan Resistant Ovarian Cancer Cell Lines. Int J Mol Sci. 2022;23:5846.

Lukanović D, Herzog M, Kobal B, Černe K. The contribution of copper efflux transporters ATP7A and ATP7B to chemoresistance and personalized medicine in ovarian cancer. Biomed Pharmacother Biomedecine Pharmacother. 2020;129: 110401.

Article   Google Scholar  

Xiao F, Li Y, Wan Y, Xue M. MircroRNA-139 sensitizes ovarian cancer cell to cisplatin-based chemotherapy through regulation of ATP7A/B. Cancer Chemother Pharmacol. 2018;81:935–47.

Lee J-H, Chae J-W, Kim JK, Kim HJ, Chung JY, Kim Y-H. Inhibition of cisplatin-resistance by RNA interference targeting metallothionein using reducible oligo-peptoplex. J Control Release Off J Control Release Soc. 2015;215:82–90.

Ishikawa T, Ali-Osman F. Glutathione-associated cis-diamminedichloroplatinum(II) metabolism and ATP-dependent efflux from leukemia cells. Molecular characterization of glutathione-platinum complex and its biological significance. J Biol Chem. 1993;268:20116–25.

Zhang J, Xie S, Zhou L, Tang X, Guan X, Deng M, et al. Up-regulation of GSTT1 in serous ovarian cancer associated with resistance to TAXOL / carboplatin. J Ovarian Res. 2021;14:122.

Article   PubMed   PubMed Central   Google Scholar  

Boušová I, Skálová L. Inhibition and induction of glutathione S-transferases by flavonoids: possible pharmacological and toxicological consequences. Drug Metab Rev. 2012;44:267–86.

Pilié PG, Tang C, Mills GB, Yap TA. State-of-the-art strategies for targeting the DNA damage response in cancer. Nat Rev Clin Oncol. 2019;16:81–104.

Peraza-Vega RI, Valverde M, Rojas E. Interactions between miRNAs and Double-Strand Breaks DNA Repair Genes, Pursuing a Fine-Tuning of Repair. Int J Mol Sci. 2022;23:3231.

Hu X-Y, Song Z, Yang Z-W, Li J-J, Liu J, Wang H-S. Cancer drug resistance related microRNAs: recent advances in detection methods. Analyst. 2022;147:2615–32.

Si W, Shen J, Zheng H, Fan W. The role and mechanisms of action of microRNAs in cancer drug resistance. Clin Epigenetics. 2019;11:25.

González-Martín A, Desauw C, Heitz F, Cropet C, Gargiulo P, Berger R, et al. Maintenance olaparib plus bevacizumab in patients with newly diagnosed advanced high-grade ovarian cancer: Main analysis of second progression-free survival in the phase III PAOLA-1/ENGOT-ov25 trial. Eur J Cancer Oxf Engl. 1990;2022(174):221–31.

Burdett NL, Willis MO, Alsop K, Hunt AL, Pandey A, Hamilton PT, et al. Multiomic analysis of homologous recombination-deficient end-stage high-grade serous ovarian cancer. Nat Genet. 2023;55:437–50.

He M, Zhou W, Li C, Guo M. MicroRNAs, DNA Damage Response, and Cancer Treatment. Int J Mol Sci. 2016;17:2087.

Zhang X, Wan G, Berger FG, He X, Lu X. The ATM kinase induces microRNA biogenesis in the DNA damage response. Mol Cell. 2011;41:371–83.

Moskwa P, Buffa FM, Pan Y, Panchakshari R, Gottipati P, Muschel RJ, et al. miR-182-mediated downregulation of BRCA1 impacts DNA repair and sensitivity to PARP inhibitors. Mol Cell. 2011;41:210–20.

Sun C, Li N, Yang Z, Zhou B, He Y, Weng D, et al. miR-9 regulation of BRCA1 and ovarian cancer sensitivity to cisplatin and PARP inhibition. J Natl Cancer Inst. 2013;105:1750–8.

Choi YE, Pan Y, Park E, Konstantinopoulos P, De S, D’Andrea A, et al. MicroRNAs down-regulate homologous recombination in the G1 phase of cycling cells to maintain genomic stability. eLife. 2014;3:e02445.

Liu G, Yang D, Rupaimoole R, Pecot CV, Sun Y, Mangala LS, et al. Augmentation of response to chemotherapy by microRNA-506 through regulation of RAD51 in serous ovarian cancers. J Natl Cancer Inst. 2015;107:djv108.

Huang J-W, Wang Y, Dhillon KK, Calses P, Villegas E, Mitchell PS, et al. Systematic Screen Identifies miRNAs that Target RAD51 and RAD51D to Enhance Chemosensitivity. Mol Cancer Res MCR. 2013;11(12):1564–73.

Kondrashova O, Nguyen M, Shield-Artin K, Tinker AV, Teng NNH, Harrell MI, et al. Secondary Somatic Mutations Restoring RAD51C and RAD51D Associated with Acquired Resistance to the PARP Inhibitor Rucaparib in High-Grade Ovarian Carcinoma. Cancer Discov. 2017;7:984–98.

Sakai W, Swisher EM, Karlan BY, Agarwal MK, Higgins J, Friedman C, et al. Secondary mutations as a mechanism of cisplatin resistance in BRCA2-mutated cancers. Nature. 2008;451:1116–20.

Johnson N, Johnson SF, Yao W, Li Y-C, Choi Y-E, Bernhardy AJ, et al. Stabilization of mutant BRCA1 protein confers PARP inhibitor and platinum resistance. Proc Natl Acad Sci U S A. 2013;110:17041–6.

Lombardi R, Sonego M, Pucci B, Addi L, Iannelli F, Capone F, et al. HSP90 identified by a proteomic approach as druggable target to reverse platinum resistance in ovarian cancer. Mol Oncol. 2021;15:1005–23.

Wang Y, Chen Q, Wu D, Chen Q, Gong G, He L, et al. Lamin-A interacting protein Hsp90 is required for DNA damage repair and chemoresistance of ovarian cancer cells. Cell Death Dis. 2021;12:786.

Altan B, Yokobori T, Ide M, Bai T, Yanoma T, Kimura A, et al. High Expression of MRE11-RAD50-NBS1 Is Associated with Poor Prognosis and Chemoresistance in Gastric Cancer. Anticancer Res. 2016;36:5237–47.

He YJ, Meghani K, Caron M-C, Yang C, Ronato DA, Bian J, et al. DYNLL1 binds to MRE11 to limit DNA end resection in BRCA1-deficient cells. Nature. 2018;563:522–6.

Jaspers JE, Kersbergen A, Boon U, Sol W, van Deemter L, Zander SA, et al. Loss of 53BP1 causes PARP inhibitor resistance in Brca1-mutated mouse mammary tumors. Cancer Discov. 2013;3:68–81.

McAlpine JN, Porter H, Köbel M, Nelson BH, Prentice LM, Kalloger SE, et al. BRCA1 and BRCA2 mutations correlate with TP53 abnormalities and presence of immune cell infiltrates in ovarian high-grade serous carcinoma. Mod Pathol Off J U S Can Acad Pathol Inc. 2012;25:740–50.

Clarke B, Tinker AV, Lee C-H, Subramanian S, van de Rijn M, Turbin D, et al. Intraepithelial T cells and prognosis in ovarian carcinoma: novel associations with stage, tumor type, and BRCA1 loss. Mod Pathol Off J U S Can Acad Pathol Inc. 2009;22:393–402.

Chen M, Huang B, Zhu L, Wang Q, Pang Y, Cheng M, et al. DNA Damage Response Evaluation Provides Novel Insights for Personalized Immunotherapy in Glioma. Front Immunol. 2022;13: 875648.

Dev H, Chiang TW, Lescale C, de Krijger I, Martin AG, Pilger D, et al. Shieldin complex promotes DNA end-joining and counters homologous recombination in BRCA1-null cells. Nat Cell Biol. 2018;20:954–65.

Zhao M, Li S, Zhou L, Shen Q, Zhu H, Zhu X. Prognostic values of excision repair cross-complementing genes mRNA expression in ovarian cancer patients. Life Sci. 2018;194:34–9.

Choi YE, Meghani K, Brault M-E, Leclerc L, He YJ, Day TA, et al. Platinum and PARP Inhibitor Resistance Due to Overexpression of MicroRNA-622 in BRCA1-Mutant Ovarian Cancer. Cell Rep. 2016;14:429–39.

Sakamoto M, Kondo A, Kawasaki K, Goto T, Sakamoto H, Miyake K, et al. Analysis of gene expression profiles associated with cisplatin resistance in human ovarian cancer cell lines and tissues using cDNA microarray. Hum Cell. 2001;14:305–15.

CAS   PubMed   Google Scholar  

Li G-M. Mechanisms and functions of DNA mismatch repair. Cell Res. 2008;18:85–98.

Xiao X, Melton DW, Gourley C. Mismatch repair deficiency in ovarian cancer – molecular characteristics and clinical implications. Gynecol Oncol. 2014;132:506–12.

Riballo E, Kühne M, Rief N, Doherty A, Smith GCM, Recio M-J, et al. A pathway of double-strand break rejoining dependent upon ATM, Artemis, and proteins locating to gamma-H2AX foci. Mol Cell. 2004;16:715–24.

Bebenek K, Pedersen LC, Kunkel TA. Structure-function studies of DNA polymerase λ. Biochemistry. 2014;53:2781–92.

Moon AF, Pryor JM, Ramsden DA, Kunkel TA, Bebenek K, Pedersen LC. Sustained active site rigidity during synthesis by human DNA polymerase μ. Nat Struct Mol Biol. 2014;21:253–60.

McCormick A, Donoghue P, Dixon M, O’Sullivan R, O’Donnell RL, Murray J, et al. Ovarian Cancers Harbor Defects in Nonhomologous End Joining Resulting in Resistance to Rucaparib. Clin Cancer Res Off J Am Assoc Cancer Res. 2017;23:2050–60.

Dev H, Chiang T-WW, Lescale C, de Krijger I, Martin AG, Pilger D, et al. Shieldin complex promotes DNA end-joining and counters homologous recombination in BRCA1-null cells. Nat Cell Biol. 2018;20:954–65.

Noordermeer SM, Adam S, Setiaputra D, Barazas M, Pettitt SJ, Ling AK, et al. The shieldin complex mediates 53BP1-dependent DNA repair. Nature. 2018;560:117–21.

Gupta R, Somyajit K, Narita T, Maskey E, Stanlie A, Kremer M, et al. DNA Repair Network Analysis Reveals Shieldin as a Key Regulator of NHEJ and PARP Inhibitor Sensitivity. Cell. 2018;173:972–988.e23.

Liptay M, Barbosa JS, Rottenberg S. Replication Fork Remodeling and Therapy Escape in DNA Damage Response-Deficient Cancers. Front Oncol. 2020;10:670.

Classen S, Rahlf E, Jungwirth J, Albers N, Hebestreit LP, Zielinski A, et al. Partial Reduction in BRCA1 Gene Dose Modulates DNA Replication Stress Level and Thereby Contributes to Sensitivity or Resistance. Int J Mol Sci. 2022;23:13363.

Kanev P-B, Atemin A, Stoynov S, Aleksandrov R. PARP1 roles in DNA repair and DNA replication: The basi(c)s of PARP inhibitor efficacy and resistance. Semin Oncol. 2023;S0093–7754(23):00061–71.

Ray Chaudhuri A, Callen E, Ding X, Gogola E, Duarte AA, Lee J-E, et al. Replication fork stability confers chemoresistance in BRCA-deficient cells. Nature. 2016;535:382–7.

Rondinelli B, Gogola E, Yücel H, Duarte AA, van de Ven M, van der Sluijs R, et al. EZH2 promotes degradation of stalled replication forks by recruiting MUS81 through histone H3 trimethylation. Nat Cell Biol. 2017;19:1371–8.

Srinivasan G, Williamson EA, Kong K, Jaiswal AS, Huang G, Kim H-S, et al. MiR223-3p promotes synthetic lethality in BRCA1-deficient cancers. Proc Natl Acad Sci U S A. 2019;116:17438–43.

Ovejero-Sánchez M, González-Sarmiento R, Herrero AB. DNA Damage Response Alterations in Ovarian Cancer: From Molecular Mechanisms to Therapeutic Opportunities. Cancers. 2023;15:448.

Mesquita KA, Alabdullah M, Griffin M, Toss MS, Fatah TMAA, Alblihy A, et al. ERCC1-XPF deficiency is a predictor of olaparib induced synthetic lethality and platinum sensitivity in epithelial ovarian cancers. Gynecol Oncol. 2019;153:416–24.

Ercoli A, Ferrandina G, Raspaglio G, Marone M, Maggiano N, Del Mastro P, et al. hMSH2 and GTBP expression in advanced stage epithelial ovarian cancer. Br J Cancer. 1999;80:1665–71.

Marcelis CL, van der Putten HW, Tops C, Lutgens LC, Moog U. Chemotherapy resistant ovarian cancer in carriers of an hMSH2 mutation? Fam Cancer. 2001;1:107–9.

Samimi G, Fink D, Varki NM, Husain A, Hoskins WJ, Alberts DS, et al. Analysis of MLH1 and MSH2 expression in ovarian cancer before and after platinum drug-based chemotherapy. Clin Cancer Res Off J Am Assoc Cancer Res. 2000;6:1415–21.

Helleman J, van Staveren IL, Dinjens WNM, van Kuijk PF, Ritstier K, Ewing PC, et al. Mismatch repair and treatment resistance in ovarian cancer. BMC Cancer. 2006;6:201.

Durant ST, Morris MM, Illand M, McKay HJ, McCormick C, Hirst GL, et al. Dependence on RAD52 and RAD1 for anticancer drug resistance mediated by inactivation of mismatch repair genes. Curr Biol CB. 1999;9:51–4.

Vaisman A, Varchenko M, Umar A, Kunkel TA, Risinger JI, Barrett JC, et al. The role of hMLH1, hMSH3, and hMSH6 defects in cisplatin and oxaliplatin resistance: correlation with replicative bypass of platinum-DNA adducts. Cancer Res. 1998;58:3579–85.

Burkitt K, Ljungman M. Phenylbutyrate interferes with the Fanconi anemia and BRCA pathway and sensitizes head and neck cancer cells to cisplatin. Mol Cancer. 2008;7:24.

Yuan M, Wu Q, Zhang M, Lai M, Chen W, Yang J, et al. Disulfiram enhances the antitumor activity of cisplatin by inhibiting the Fanconi anemia repair pathway. J Zhejiang Univ Sci B. 2023;24:207–20.

Hater N, Iwaniuk KM, Leifeld C, Grüten P, Wiek C, Raba K, et al. Identification of new RAD51D-regulating microRNAs that also emerge as potent inhibitors of the Fanconi anemia/homologous recombination pathways. Hum Mol Genet. 2022;31:4241–54.

Srivastava AK, Han C, Zhao R, Cui T, Dai Y, Mao C, et al. Enhanced expression of DNA polymerase eta contributes to cisplatin resistance of ovarian cancer stem cells. Proc Natl Acad Sci U S A. 2015;112:4411–6.

Wang Y, Huang J-W, Calses P, Kemp CJ, Taniguchi T. MiR-96 downregulates REV1 and RAD51 to promote cellular sensitivity to cisplatin and PARP inhibition. Cancer Res. 2012;72:4037–46.

You M, Xie Z, Zhang N, Zhang Y, Xiao D, Liu S, et al. Signaling pathways in cancer metabolism: mechanisms and therapeutic targets. Signal Transduct Target Ther. 2023;8:196.

Pasquinelli AE. MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship. Nat Rev Genet. 2012;13:271–82.

Yang G, Xiao X, Rosen DG, Cheng X, Wu X, Chang B, et al. The Biphasic Role of NF-κB in Progression and Chemoresistance of Ovarian Cancer. Clin Cancer Res Off J Am Assoc Cancer Res. 2011;17:2181–94.

Devanaboyina M, Kaur J, Whiteley E, Lin L, Einloth K, Morand S, et al. NF-κB Signaling in Tumor Pathways Focusing on Breast and Ovarian Cancer. Oncol Rev. 2022;16:10568.

Harrington BS, Annunziata CM. NF-κB Signaling in Ovarian Cancer Cancers. 2019;11:1182.

Shen H, Liao B, Wan Z, Zhao Y, You Z, Liu J, et al. PTOV1 promotes cisplatin-induced chemotherapy resistance by activating the nuclear factor kappa B pathway in ovarian cancer. Mol Ther Oncolytics. 2021;20:499–507.

Huang X, Yan Y, Gui A, Zhu S, Qiu S, Chen F, et al. A Regulatory Loop Involving miR-200c and NF-κB Modulates Mortalin Expression and Increases Cisplatin Sensitivity in an Ovarian Cancer Cell Line Model. Int J Mol Sci. 2022;23:15300.

Kamdar RD, Harrington BS, Attar E, Korrapati S, Shetty J, Zhao Y, et al. NF-κB Signaling Modulates miR-452-5p and miR-335-5p Expression to Functionally Decrease Epithelial Ovarian Cancer Progression in Tumor-Initiating Cells. Int J Mol Sci. 2023;24:7826.

Nishio H, Yaguchi T, Sugiyama J, Sumimoto H, Umezawa K, Iwata T, et al. Immunosuppression through constitutively activated NF-κB signalling in human ovarian cancer and its reversal by an NF-κB inhibitor. Br J Cancer. 2014;110:2965–74.

Michalak M, Lach MS, Borska S, Nowakowski B, Umezawa K, Suchorska WM. DHMEQ enhances the cytotoxic effect of cisplatin and carboplatin in ovarian cancer cell lines. Am J Cancer Res. 2021;11:6024–41.

CAS   PubMed   PubMed Central   Google Scholar  

Huang T-T, Lampert EJ, Coots C, Lee J-M. Targeting the PI3K pathway and DNA damage response as a therapeutic strategy in ovarian cancer. Cancer Treat Rev. 2020;86: 102021.

Hoarau-Véchot J, Blot-Dupin M, Pauly L, Touboul C, Rafii S, Rafii A, et al. Akt-Activated Endothelium Increases Cancer Cell Proliferation and Resistance to Treatment in Ovarian Cancer Cell Organoids. Int J Mol Sci. 2022;23:14173.

Gasparri ML, Besharat ZM, Farooqi AA, Khalid S, Taghavi K, Besharat RA, et al. MiRNAs and their interplay with PI3K/AKT/mTOR pathway in ovarian cancer cells: a potential role in platinum resistance. J Cancer Res Clin Oncol. 2018;144:2313–8.

Zhang Z, Zhang L, Wang B, Wei R, Wang Y, Wan J, et al. MiR-337-3p suppresses proliferation of epithelial ovarian cancer by targeting PIK3CA and PIK3CB. Cancer Lett. 2020;469:54–67.

Frederick MI, Siddika T, Zhang P, Balasuriya N, Turk MA, O’Donoghue P, et al. miRNA-Dependent Regulation of AKT1 Phosphorylation. Cells. 2022;11:821.

Luo X, Dong Z, Chen Y, Yang L, Lai D. Enrichment of ovarian cancer stem-like cells is associated with epithelial to mesenchymal transition through an miRNA-activated AKT pathway. Cell Prolif. 2013;46:436–46.

Parashar D, Geethadevi A, Mittal S, McAlarnen LA, George J, Kadamberi IP, et al. Patient-Derived Ovarian Cancer Spheroids Rely on PI3K-AKT Signaling Addiction for Cancer Stemness and Chemoresistance. Cancers. 2022;14:958.

Musa F, Alard A, David-West G, Curtin JP, Blank SV, Schneider RJ. Dual mTORC1/2 Inhibition as a Novel Strategy for the Resensitization and Treatment of Platinum-Resistant Ovarian Cancer. Mol Cancer Ther. 2016;15:1557–67.

Santiskulvong C, Konecny GE, Fekete M, Chen K-YM, Karam A, Mulholland D, et al. Dual targeting of phosphoinositide 3-kinase and mammalian target of rapamycin using NVP-BEZ235 as a novel therapeutic approach in human ovarian carcinoma. Clin Cancer Res Off J Am Assoc Cancer Res. 2011;17:2373–84.

Koti M, Siu A, Clément I, Bidarimath M, Turashvili G, Edwards A, et al. A distinct pre-existing inflammatory tumour microenvironment is associated with chemotherapy resistance in high-grade serous epithelial ovarian cancer. Br J Cancer. 2015;112:1215–22.

Iba T, Kigawa J, Kanamori Y, Itamochi H, Oishi T, Simada M, et al. Expression of the c-myc gene as a predictor of chemotherapy response and a prognostic factor in patients with ovarian cancer. Cancer Sci. 2004;95:418–23.

Bagratuni T, Mavrianou N, Gavalas NG, Tzannis K, Arapinis C, Liontos M, et al. JQ1 inhibits tumour growth in combination with cisplatin and suppresses JAK/STAT signalling pathway in ovarian cancer. Eur J Cancer Oxf Engl. 1990;2020(126):125–35.

Park GB, Kim D. MicroRNA-503-5p Inhibits the CD97-Mediated JAK2/STAT3 Pathway in Metastatic or Paclitaxel-Resistant Ovarian Cancer Cells. Neoplasia N Y N. 2019;21:206–15.

Huang Z, Xu Y, Wan M, Zeng X, Wu J. miR-340: A multifunctional role in human malignant diseases. Int J Biol Sci. 2021;17:236–46.

Shen J, Liang C, Su X, Wang Q, Ke Y, Fang J, et al. Dysfunction and ceRNA network of the tumor suppressor miR-637 in cancer development and prognosis. Biomark Res. 2022;10:72.

Jiang B, Zhu S-J, Xiao S-S, Xue M. MiR-217 Inhibits M2-Like Macrophage Polarization by Suppressing Secretion of Interleukin-6 in Ovarian Cancer. Inflammation. 2019;42:1517–29.

Bray SJ. Notch signalling in context. Nat Rev Mol Cell Biol. 2016;17:722–35.

Zhou Y, Chen Q, Qin R, Zhang K, Li H. MicroRNA-449a reduces cell survival and enhances cisplatin-induced cytotoxicity via downregulation of NOTCH1 in ovarian cancer cells. Tumour Biol J Int Soc Oncodevelopmental Biol Med. 2014;35:12369–78.

Rahman MT, Nakayama K, Rahman M, Katagiri H, Katagiri A, Ishibashi T, et al. Notch3 overexpression as potential therapeutic target in advanced stage chemoresistant ovarian cancer. Am J Clin Pathol. 2012;138:535–44.

Wang M, Ma X, Wang J, Wang L, Wang Y. Pretreatment with the γ-secretase inhibitor DAPT sensitizes drug-resistant ovarian cancer cells to cisplatin by downregulation of Notch signaling. Int J Oncol. 2014;44:1401–9.

Akbarzadeh M, Akbarzadeh S, Majidinia M. Targeting Notch signaling pathway as an effective strategy in overcoming drug resistance in ovarian cancer. Pathol Res Pract. 2020;216: 153158.

Zou W, Ma X, Hua W, Chen B, Cai G. Caveolin-1 mediates chemoresistance in cisplatin-resistant ovarian cancer cells by targeting apoptosis through the Notch-1/Akt/NF-κB pathway. Oncol Rep. 2015;34:3256–63.

Nagata K, Ohashi K, Nakano T, Arita H, Zong C, Hanafusa H, et al. Identification of the product of growth arrest-specific gene 6 as a common ligand for Axl, Sky, and Mer receptor tyrosine kinases. J Biol Chem. 1996;271:30022–7.

Wilson C, Ye X, Pham T, Lin E, Chan S, McNamara E, et al. AXL inhibition sensitizes mesenchymal cancer cells to antimitotic drugs. Cancer Res. 2014;74:5878–90.

Byers LA, Diao L, Wang J, Saintigny P, Girard L, Peyton M, et al. An epithelial-mesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance. Clin Cancer Res Off J Am Assoc Cancer Res. 2013;19:279–90.

Antony J, Tan TZ, Kelly Z, Low J, Choolani M, Recchi C, et al. The GAS6-AXL signaling network is a mesenchymal (Mes) molecular subtype-specific therapeutic target for ovarian cancer. Sci Signal. 2016;9:ra97.

Yeo XH, Sundararajan V, Wu Z, Phua ZJC, Ho YY, Peh KLE, et al. The effect of inhibition of receptor tyrosine kinase AXL on DNA damage response in ovarian cancer. Commun Biol. 2023;6:660.

Mullen MM, Lomonosova E, Toboni MD, Oplt A, Cybulla E, Blachut B, et al. GAS6/AXL Inhibition Enhances Ovarian Cancer Sensitivity to Chemotherapy and PARP Inhibition through Increased DNA Damage and Enhanced Replication Stress. Mol Cancer Res MCR. 2022;20:265–79.

Kariolis MS, Miao YR, Diep A, Nash SE, Olcina MM, Jiang D, et al. Inhibition of the GAS6/AXL pathway augments the efficacy of chemotherapies. J Clin Invest. 2017;127:183–98.

Tanaka M, Siemann DW. Gas6/Axl Signaling Pathway in the Tumor Immune Microenvironment. Cancers. 2020;12:1850.

Hisamatsu T, McGuire M, Wu SY, Rupaimoole R, Pradeep S, Bayraktar E, et al. PRKRA/PACT Expression Promotes Chemoresistance of Mucinous Ovarian Cancer. Mol Cancer Ther. 2019;18:162–72.

Colak S, ten Dijke P. Targeting TGF-β Signaling in Cancer. Trends Cancer. 2017;3:56–71.

Ali S, Rehman MU, Yatoo AM, Arafah A, Khan A, Rashid S, et al. TGF-β signaling pathway: Therapeutic targeting and potential for anti-cancer immunity. Eur J Pharmacol. 2023;947: 175678.

Li X, Gao X, Yuan J, Wang F, Xu X, Wang C, et al. The miR-33a-5p/CROT axis mediates ovarian cancer cell behaviors and chemoresistance via the regulation of the TGF-β signal pathway. Front Endocrinol. 2022;13: 950345.

Cai Y, An B, Yao D, Zhou H, Zhu J. MicroRNA miR-30a inhibits cisplatin resistance in ovarian cancer cells through autophagy. Bioengineered. 2021;12:10713–22.

Parikh A, Lee C, Joseph P, Marchini S, Baccarini A, Kolev V, et al. microRNA-181a has a critical role in ovarian cancer progression through the regulation of the epithelial–mesenchymal transition. Nat Commun. 2014;5:2977.

Liang S, Liu Y, He J, Gao T, Li L, He S. Family with sequence similarity 46 member a confers chemo-resistance to ovarian carcinoma via TGF-β/Smad2 signaling. Bioengineered. 2022;13:10629–39.

Marchini S, Fruscio R, Clivio L, Beltrame L, Porcu L, Fuso Nerini I, et al. Resistance to platinum-based chemotherapy is associated with epithelial to mesenchymal transition in epithelial ovarian cancer. Eur J Cancer Oxf Engl. 1990;2013(49):520–30.

Newsted D, Banerjee S, Watt K, Nersesian S, Truesdell P, Blazer LL, et al. Blockade of TGF-β signaling with novel synthetic antibodies limits immune exclusion and improves chemotherapy response in metastatic ovarian cancer models. Oncoimmunology. 2018;8: e1539613.

Fix SM, Forget M-A, Sakellariou-Thompson D, Wang Y, Griffiths TM, Lee M, et al. CRISPR-mediated TGFBR2 knockout renders human ovarian cancer tumor-infiltrating lymphocytes resistant to TGF-β signaling. J Immunother Cancer. 2022;10: e003750.

Musacchio L, Valsecchi AA, Salutari V, Valabrega G, Camarda F, Tuninetti V, et al. MEK inhibitor as single agent in low grade serous ovarian and peritoneal cancer: a systematic review and meta-analysis. Cancer Treat Rev. 2022;110: 102458.

Lee S, Yoon S, Kim D-H. A high nuclear basal level of ERK2 phosphorylation contributes to the resistance of cisplatin-resistant human ovarian cancer cells. Gynecol Oncol. 2007;104:338–44.

Hayakawa J, Ohmichi M, Kurachi H, Kanda Y, Hisamoto K, Nishio Y, et al. Inhibition of BAD phosphorylation either at serine 112 via extracellular signal-regulated protein kinase cascade or at serine 136 via Akt cascade sensitizes human ovarian cancer cells to cisplatin. Cancer Res. 2000;60:5988–94.

van Jaarsveld MTM, van Kuijk PF, Boersma AWM, Helleman J, van IJcken WF, Mathijssen RHJ, et al. miR-634 restores drug sensitivity in resistant ovarian cancer cells by targeting the Ras-MAPK pathway. Mol Cancer. 2015;14:196.

Hong L, Wang Y, Chen W, Yang S. MicroRNA-508 suppresses epithelial-mesenchymal transition, migration, and invasion of ovarian cancer cells through the MAPK1/ERK signaling pathway. J Cell Biochem. 2018;119:7431–40.

Zhao Y, Liu X-L, Huang J-H, Yin A-J, Zhang H. MicroRNA-18a suppresses ovarian carcinoma progression by targeting CBX7 and regulating ERK/MAPK signaling pathway and epithelial-to-mesenchymal transition. Eur Rev Med Pharmacol Sci. 2020;24:5292–302.

Hall CA, Wang R, Miao J, Oliva E, Shen X, Wheeler T, et al. Hippo pathway effector Yap is an ovarian cancer oncogene. Cancer Res. 2010;70:8517–25.

Zhang X, George J, Deb S, Degoutin JL, Takano EA, Fox SB, et al. The Hippo pathway transcriptional co-activator, YAP, is an ovarian cancer oncogene. Oncogene. 2011;30:2810–22.

Omori H, Nishio M, Masuda M, Miyachi Y, Ueda F, Nakano T, et al. YAP1 is a potent driver of the onset and progression of oral squamous cell carcinoma. Sci Adv. 2020;6:eaay3324.

Zhang H, Liu C-Y, Zha Z-Y, Zhao B, Yao J, Zhao S, et al. TEAD transcription factors mediate the function of TAZ in cell growth and epithelial-mesenchymal transition. J Biol Chem. 2009;284:13355–62.

Zhao B, Ye X, Yu J, Li L, Li W, Li S, et al. TEAD mediates YAP-dependent gene induction and growth control. Genes Dev. 2008;22:1962–71.

Yoshida K, Yokoi A, Sugiyama M, Oda S, Kitami K, Tamauchi S, et al. Expression of the chrXq27.3 miRNA cluster in recurrent ovarian clear cell carcinoma and its impact on cisplatin resistance. Oncogene. 2021;40:1255–68.

Mo Y, Leung LL, Mak CSL, Wang X, Chan W-S, Hui LMN, et al. Tumor-secreted exosomal miR-141 activates tumor-stroma interactions and controls premetastatic niche formation in ovarian cancer metastasis. Mol Cancer. 2023;22:4.

Borley J, Brown R. Epigenetic mechanisms and therapeutic targets of chemotherapy resistance in epithelial ovarian cancer. Ann Med. 2015;47:359–69.

Balch C, Matei DE, Huang TH-M, Nephew KP. Role of epigenomics in ovarian and endometrial cancers. Epigenomics. 2010;2:419–47.

Vaclavikova R, Klajic J, Brynychova V, Elsnerova K, Alnaes GIG, Tost J, et al. Development of high-resolution melting analysis for ABCB1 promoter methylation: Clinical consequences in breast and ovarian carcinoma. Oncol Rep. 2019;42:763–74.

Bram EE, Stark M, Raz S, Assaraf YG. Chemotherapeutic drug-induced ABCG2 promoter demethylation as a novel mechanism of acquired multidrug resistance. Neoplasia N Y N. 2009;11:1359–70.

Su H-Y, Lai H-C, Lin Y-W, Liu C-Y, Chen C-K, Chou Y-C, et al. Epigenetic silencing of SFRP5 is related to malignant phenotype and chemoresistance of ovarian cancer through Wnt signaling pathway. Int J Cancer. 2010;127:555–67.

Alghamian Y, Soukkarieh C, Abbady AQ, Murad H. Investigation of role of CpG methylation in some epithelial mesenchymal transition gene in a chemoresistant ovarian cancer cell line. Sci Rep. 2022;12:7494.

Chan DW, Lam WY, Chen F, Yung MMH, Chan YS, Chan WS, et al. Genome-wide DNA methylome analysis identifies methylation signatures associated with survival and drug resistance of ovarian cancers. Clin Epigenetics. 2021;13:142.

Nesic K, Kondrashova O, Hurley RM, McGehee CD, Vandenberg CJ, Ho G-Y, et al. Acquired RAD51C Promoter Methylation Loss Causes PARP Inhibitor Resistance in High-Grade Serous Ovarian Carcinoma. Cancer Res. 2021;81:4709–22.

Lum E, Vigliotti M, Banerjee N, Cutter N, Wrzeszczynski KO, Khan S, et al. Loss of DOK2 induces carboplatin resistance in ovarian cancer via suppression of apoptosis. Gynecol Oncol. 2013;130:369–76.

Wolffe AP, Matzke MA. Epigenetics: regulation through repression. Science. 1999;286:481–6.

Vriezen ER, Moscovitch M. Memory for temporal order and conditional associative-learning in patients with Parkinson’s disease. Neuropsychologia. 1990;28:1283–93.

Abbosh PH, Montgomery JS, Starkey JA, Novotny M, Zuhowski EG, Egorin MJ, et al. Dominant-negative histone H3 lysine 27 mutant derepresses silenced tumor suppressor genes and reverses the drug-resistant phenotype in cancer cells. Cancer Res. 2006;66:5582–91.

Hu S, Yu L, Li Z, Shen Y, Wang J, Cai J, et al. Overexpression of EZH2 contributes to acquired cisplatin resistance in ovarian cancer cells in vitro and in vivo. Cancer Biol Ther. 2010;10:788–95.

Fang Y, Zhao J, Guo X, Dai Y, Zhang H, Yin F, et al. Establishment, immunological analysis, and drug prediction of a prognostic signature of ovarian cancer related to histone acetylation. Front Pharmacol. 2022;13: 947252.

Silva R, Glennon K, Metoudi M, Moran B, Salta S, Slattery K, et al. Unveiling the epigenomic mechanisms of acquired platinum-resistance in high-grade serous ovarian cancer. Int J Cancer. 2023;153:120–32.

Stone ML, Chiappinelli KB, Li H, Murphy LM, Travers ME, Topper MJ, et al. Epigenetic therapy activates type I interferon signaling in murine ovarian cancer to reduce immunosuppression and tumor burden. Proc Natl Acad Sci U S A. 2017;114:E10981–90.

Guttman M, Rinn JL. Modular regulatory principles of large non-coding RNAs. Nature. 2012;482:339–46.

Qin M, Zhang C, Li Y. Circular RNAs in gynecologic cancers: mechanisms and implications for chemotherapy resistance. Front Pharmacol. 2023;14:1194719.

Papadimitriou M-A, Panoutsopoulou K, Pilala K-M, Scorilas A, Avgeris M. Epi-miRNAs: Modern mediators of methylation status in human cancers. Wiley Interdiscip Rev RNA. 2023;14: e1735.

Zhang L, Volinia S, Bonome T, Calin GA, Greshock J, Yang N, et al. Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer. Proc Natl Acad Sci U S A. 2008;105:7004–9.

Xiang Y, Ma N, Wang D, Zhang Y, Zhou J, Wu G, et al. MiR-152 and miR-185 co-contribute to ovarian cancer cells cisplatin sensitivity by targeting DNMT1 directly: a novel epigenetic therapy independent of decitabine. Oncogene. 2014;33:378–86.

Bhattacharya R, Nicoloso M, Arvizo R, Wang E, Cortez A, Rossi S, et al. MiR-15a and MiR-16 control Bmi-1 expression in ovarian cancer. Cancer Res. 2009;69:9090–5.

Zhou J, Li X, Wu X, Zhang T, Zhu Q, Wang X, et al. Exosomes Released from Tumor-Associated Macrophages Transfer miRNAs That Induce a Treg/Th17 Cell Imbalance in Epithelial Ovarian Cancer. Cancer Immunol Res. 2018;6:1578–92.

Kanlikilicer P, Bayraktar R, Denizli M, Rashed MH, Ivan C, Aslan B, et al. Exosomal miRNA confers chemo resistance via targeting Cav1/p-gp/M2-type macrophage axis in ovarian cancer. EBioMedicine. 2018;38:100–12.

Deng M, Wu D, Zhang Y, Jin Z, Miao J. MiR-29c downregulates tumor-expressed B7–H3 to mediate the antitumor NK-cell functions in ovarian cancer. Gynecol Oncol. 2021;162:190–9.

Miyamoto T, Murakami R, Hamanishi J, Tanigaki K, Hosoe Y, Mise N, et al. B7–H3 Suppresses Antitumor Immunity via the CCL2-CCR2-M2 Macrophage Axis and Contributes to Ovarian Cancer Progression. Cancer Immunol Res. 2022;10:56–69.

Li S, Wu Y, Zhang J, Sun H, Wang X. Role of miRNA-424 in Cancers. OncoTargets Ther. 2020;13:9611–22.

Nguyen HT, Phung CD, Tran TH, Pham TT, Pham LM, Nguyen TT, et al. Manipulating immune system using nanoparticles for an effective cancer treatment: Combination of targeted therapy and checkpoint blockage miRNA. J Control Release Off J Control Release Soc. 2021;329:524–37.

Guyon N, Garnier D, Briand J, Nadaradjane A, Bougras-Cartron G, Raimbourg J, et al. Anti-PD1 therapy induces lymphocyte-derived exosomal miRNA-4315 release inhibiting Bim-mediated apoptosis of tumor cells. Cell Death Dis. 2020;11:1048.

Zhu X, Shen H, Yin X, Yang M, Wei H, Chen Q, et al. Macrophages derived exosomes deliver miR-223 to epithelial ovarian cancer cells to elicit a chemoresistant phenotype. J Exp Clin Cancer Res CR. 2019;38:81.

Doo DW, Meza-Perez S, Londoño AI, Goldsberry WN, Katre AA, Boone JD, et al. Inhibition of the Wnt/β-catenin pathway enhances antitumor immunity in ovarian cancer. Ther Adv Med Oncol. 2020;12:1758835920913798.

Cascio S, Chandler C, Zhang L, Sinno S, Gao B, Onkar S, et al. Cancer-associated MSC drive tumor immune exclusion and resistance to immunotherapy, which can be overcome by Hedgehog inhibition. Sci Adv. 2021;7:eabi5790.

Kaye SB, Fehrenbacher L, Holloway R, Amit A, Karlan B, Slomovitz B, et al. A Phase II, Randomized, Placebo-Controlled Study of Vismodegib as Maintenance Therapy in Patients with Ovarian Cancer in Second or Third Complete Remission. Clin Cancer Res. 2012;18:6509–18.

Nagaraj AB, Knarr M, Sekhar S, Connor RS, Joseph P, Kovalenko O, et al. The miR-181a-SFRP4 axis regulates Wnt activation to drive stemness and platinum resistance in ovarian cancer. Cancer Res. 2021;81:2044–55.

Article   CAS   PubMed Central   Google Scholar  

Zhang X, Huang L, Zhao Y, Tan W. Downregulation of miR-130a contributes to cisplatin resistance in ovarian cancer cells by targeting X-linked inhibitor of apoptosis (XIAP) directly. Acta Biochim Biophys Sin. 2013;45:995–1001.

Li X, Chen W, Jin Y, Xue R, Su J, Mu Z, et al. miR-142-5p enhances cisplatin-induced apoptosis in ovarian cancer cells by targeting multiple anti-apoptotic genes. Biochem Pharmacol. 2019;161:98–112.

Sun X, Niu X, Chen R, He W, Chen D, Kang R, et al. Metallothionein-1G facilitates sorafenib resistance through inhibition of ferroptosis. Hepatol Baltim Md. 2016;64:488–500.

Zhang T, Sun B, Zhong C, Xu K, Wang Z, Hofman P, et al. Targeting histone deacetylase enhances the therapeutic effect of Erastin-induced ferroptosis in EGFR-activating mutant lung adenocarcinoma. Transl Lung Cancer Res. 2021;10:1857–72.

Jiang Z, Lim S-O, Yan M, Hsu JL, Yao J, Wei Y, et al. TYRO3 induces anti-PD-1/PD-L1 therapy resistance by limiting innate immunity and tumoral ferroptosis. J Clin Invest. 2021;131(e139434): 139434.

Zhang S-F, Wang X-Y, Fu Z-Q, Peng Q-H, Zhang J-Y, Ye F, et al. TXNDC17 promotes paclitaxel resistance via inducing autophagy in ovarian cancer. Autophagy. 2015;11:225–38.

Vescarelli E, Gerini G, Megiorni F, Anastasiadou E, Pontecorvi P, Solito L, et al. MiR-200c sensitizes Olaparib-resistant ovarian cancer cells by targeting Neuropilin 1. J Exp Clin Cancer Res CR. 2020;39:3.

Zhou Y, Wang C, Ding J, Chen Y, Sun Y, Cheng Z. miR-133a targets YES1 to reduce cisplatin resistance in ovarian cancer by regulating cell autophagy. Cancer Cell Int. 2022;22:15.

Urra H, Dufey E, Avril T, Chevet E, Hetz C. Endoplasmic Reticulum Stress and the Hallmarks of Cancer. Trends Cancer. 2016;2:252–62.

Mo J, Ruan S, Yang B, Jin Y, Liu K, Luo X, et al. A novel defined risk signature of endoplasmic reticulum stress-related genes for predicting the prognosis and immune infiltration status of ovarian cancer. J Zhejiang Univ Sci B. 2023;24:64–77.

Morosi L, Matteo C, Ceruti T, Giordano S, Ponzo M, Frapolli R, et al. Quantitative determination of niraparib and olaparib tumor distribution by mass spectrometry imaging. Int J Biol Sci. 2020;16:1363–75.

Wang N, Yang Y, Jin D, Zhang Z, Shen K, Yang J, et al. PARP inhibitor resistance in breast and gynecological cancer: Resistance mechanisms and combination therapy strategies. Front Pharmacol. 2022;13: 967633.

Karthika C, Sureshkumar R, Zehravi M, Akter R, Ali F, Ramproshad S, et al. Multidrug Resistance of Cancer Cells and the Vital Role of P-Glycoprotein. Life. 2022;12:897.

Stage TB, Mortensen C, Khalaf S, Steffensen V, Hammer HS, Xiong C, et al. P-glycoprotein Inhibition Exacerbates Paclitaxel Neurotoxicity in Neurons and Cancer Patients. Clin Pharmacol Ther. 2020;108:671–80.

Yi H, Liu L, Sheng N, Li P, Pan H, Cai L, et al. Synergistic Therapy of Doxorubicin and miR-129-5p with Self-Cross-Linked Bioreducible Polypeptide Nanoparticles Reverses Multidrug Resistance in Cancer Cells. Biomacromol. 2016;17:1737–47.

Liu JF, Moore KN, Birrer MJ, Berlin S, Matulonis UA, Infante JR, et al. Phase I study of safety and pharmacokinetics of the anti-MUC16 antibody-drug conjugate DMUC5754A in patients with platinum-resistant ovarian cancer or unresectable pancreatic cancer. Ann Oncol Off J Eur Soc Med Oncol. 2016;27:2124–30.

Liu J, Burris H, Wang JS, Barroilhet L, Gutierrez M, Wang Y, et al. An open-label phase I dose-escalation study of the safety and pharmacokinetics of DMUC4064A in patients with platinum-resistant ovarian cancer. Gynecol Oncol. 2021;163:473–80.

Zhang S, Lu Z, Unruh AK, Ivan C, Baggerly KA, Calin GA, et al. Clinically relevant microRNAs in ovarian cancer. Mol Cancer Res MCR. 2015;13:393–401.

Santin AD, Vergote I, González-Martín A, Moore K, Oaknin A, Romero I, et al. Safety and activity of anti-mesothelin antibody-drug conjugate anetumab ravtansine in combination with pegylated-liposomal doxorubicin in platinum-resistant ovarian cancer: multicenter, phase Ib dose escalation and expansion study. Int J Gynecol Cancer Off J Int Gynecol Cancer Soc. 2023;33:562–70.

Tan H, He Q, Gong G, Wang Y, Li J, Wang J, et al. miR-382 inhibits migration and invasion by targeting ROR1 through regulating EMT in ovarian cancer. Int J Oncol. 2016;48:181–90.

Qi X, Li Y, Liu W, Wang Y, Chen Z, Lin L. Research Trend of Publications Concerning Antibody-Drug Conjugate in Solid Cancer: A Bibliometric Study. Front Pharmacol. 2022;13: 921385.

Liu H-Y, Zhang Y-Y, Zhu B-L, Feng F-Z, Zhang H-T, Yan H, et al. MiR-203a-3p regulates the biological behaviors of ovarian cancer cells through mediating the Akt/GSK-3β/Snail signaling pathway by targeting ATM. J Ovarian Res. 2019;12:60.

Yazinski SA, Comaills V, Buisson R, Genois M-M, Nguyen HD, Ho CK, et al. ATR inhibition disrupts rewired homologous recombination and fork protection pathways in PARP inhibitor-resistant BRCA-deficient cancer cells. Genes Dev. 2017;31:318–32.

Konstantinopoulos PA, Cheng S-C, Wahner Hendrickson AE, Penson RT, Schumer ST, Doyle LA, et al. Berzosertib plus gemcitabine versus gemcitabine alone in platinum-resistant high-grade serous ovarian cancer: a multicentre, open-label, randomised, phase 2 trial. Lancet Oncol. 2020;21:957–68.

Jo U, Senatorov IS, Zimmermann A, Saha LK, Murai Y, Kim SH, et al. Novel and Highly Potent ATR Inhibitor M4344 Kills Cancer Cells With Replication Stress, and Enhances the Chemotherapeutic Activity of Widely Used DNA Damaging Agents. Mol Cancer Ther. 2021;20:1431–41.

Moore KN, Chambers SK, Hamilton EP, Chen L-M, Oza AM, Ghamande SA, et al. Adavosertib with Chemotherapy in Patients with Primary Platinum-Resistant Ovarian, Fallopian Tube, or Peritoneal Cancer: An Open-Label, Four-Arm, Phase II Study. Clin Cancer Res Off J Am Assoc Cancer Res. 2022;28:36–44.

Embaby A, Kutzera J, Geenen JJ, Pluim D, Hofland I, Sanders J, et al. WEE1 inhibitor adavosertib in combination with carboplatin in advanced TP53 mutated ovarian cancer: A biomarker-enriched phase II study. Gynecol Oncol. 2023;174:239–46.

Do KT, Kochupurakkal B, Kelland S, de Jonge A, Hedglin J, Powers A, et al. Phase 1 Combination Study of the CHK1 Inhibitor Prexasertib and the PARP Inhibitor Olaparib in High-grade Serous Ovarian Cancer and Other Solid Tumors. Clin Cancer Res Off J Am Assoc Cancer Res. 2021;27:4710–6.

Wei L, He Y, Bi S, Li X, Zhang J, Zhang S. miRNA-199b-3p suppresses growth and progression of ovarian cancer via the CHK1/E-cadherin/EMT signaling pathway by targeting ZEB1. Oncol Rep. 2021;45:569–81.

Park SJ, Chang S-J, Suh DH, Kong TW, Song H, Kim TH, et al. A phase IA dose-escalation study of PHI-101, a new checkpoint kinase 2 inhibitor, for platinum-resistant recurrent ovarian cancer. BMC Cancer. 2022;22:28.

Lheureux S, Oaknin A, Garg S, Bruce JP, Madariaga A, Dhani NC, et al. EVOLVE: A Multicenter Open-Label Single-Arm Clinical and Translational Phase II Trial of Cediranib Plus Olaparib for Ovarian Cancer after PARP Inhibition Progression. Clin Cancer Res Off J Am Assoc Cancer Res. 2020;26:4206–15.

An D, Banerjee S, Lee J-M. Recent advancements of antiangiogenic combination therapies in ovarian cancer. Cancer Treat Rev. 2021;98: 102224.

Colombo N, Tomao F, Benedetti Panici P, Nicoletto MO, Tognon G, Bologna A, et al. Randomized phase II trial of weekly paclitaxel vs. cediranib-olaparib (continuous or intermittent schedule) in platinum-resistant high-grade epithelial ovarian cancer. Gynecol Oncol. 2022;164:505–13.

Barton CA, Clark SJ, Hacker NF, O’Brien PM. Epigenetic markers of ovarian cancer. Adv Exp Med Biol. 2008;622:35–51.

Levine DA, Bogomolniy F, Yee CJ, Lash A, Barakat RR, Borgen PI, et al. Frequent mutation of the PIK3CA gene in ovarian and breast cancers. Clin Cancer Res Off J Am Assoc Cancer Res. 2005;11:2875–8.

Fuh KC, Bookman MA, Liu JF, Coleman RL, Herzog TJ, Thaker PH, et al. Phase 1b study of AVB-500 in combination with paclitaxel or pegylated liposomal doxorubicin platinum-resistant recurrent ovarian cancer. Gynecol Oncol. 2021;163:254–61.

Phase Ib study of AVB-S6-500 (axl inhibition) in combination with durvalumab (MEDI4736) in patients with platinum-resistant, recurrent epithelial ovarian cancer (NCT04019288) (320) - ScienceDirect [cited 2023 Aug 18]. Available from: https://www.sciencedirect.com/science/article/pii/S0090825822015426?via%3Dihub .

Dunn E, Chitcholtan K, Sykes P, Garrill A. The Anti-Proliferative Effect of PI3K/mTOR and ERK Inhibition in Monolayer and Three-Dimensional Ovarian Cancer Cell Models. Cancers. 2022;14:395.

Diao B, Sun C, Yu P, Zhao Z, Yang P. LAMA5 promotes cell proliferation and migration in ovarian cancer by activating Notch signaling pathway. FASEB J Off Publ Fed Am Soc Exp Biol. 2023;37: e23109.

Muñoz-Galván S, Felipe-Abrio B, García-Carrasco M, Domínguez-Piñol J, Suarez-Martinez E, Verdugo-Sivianes EM, et al. New markers for human ovarian cancer that link platinum resistance to the cancer stem cell phenotype and define new therapeutic combinations and diagnostic tools. J Exp Clin Cancer Res CR. 2019;38:234.

Coleman RL, Handley KF, Burger R, Molin GZD, Stagg R, Sood AK, et al. Demcizumab combined with paclitaxel for platinum-resistant ovarian, primary peritoneal, and fallopian tube cancer: The SIERRA open-label phase Ib trial. Gynecol Oncol. 2020;157:386–91.

Li Y, Wang S, Li P, Li Y, Liu Y, Fang H, et al. Rad50 promotes ovarian cancer progression through NF-κB activation. J Cell Mol Med. 2021;25:10961–72.

Morrone M da S, Somensi N, Franz L, Ramos V de M, Gasparotto J, da Rosa HT, et al. BRCA-1 depletion impairs pro-inflammatory polarization and activation of RAW 264.7 macrophages in a NF-κB-dependent mechanism. Mol Cell Biochem. 2019;462:11–23.

Trivedi MS, Arber N, Friedman E, Garber JE, Holcomb K, Horowitz NS, et al. Lessons from the Failure to Complete a Trial of Denosumab in Women With a Pathogenic BRCA1/2 Variant Scheduling Risk-Reducing Salpingo-Oophorectomy. Cancer Prev Res Phila Pa. 2022;15:721–6.

Tsuda N, Kawano K, Efferson CL, Ioannides CG. Synthetic microRNA and double-stranded RNA targeting the 3’-untranslated region of HER-2/neu mRNA inhibit HER-2 protein expression in ovarian cancer cells. Int J Oncol. 2005;27:1299–306.

Tang M, Cai J-H, Diao H-Y, Guo W-M, Yang X, Xing S. The progress of peptide vaccine clinical trials in gynecologic oncology. Hum Vaccines Immunother. 2022;18:2062982.

Balch C, Fang F, Matei DE, Huang TH-M, Nephew KP. Minireview: epigenetic changes in ovarian cancer. Endocrinology. 2009;150:4003–11.

Matei D, Ghamande S, Roman L, Alvarez Secord A, Nemunaitis J, Markham MJ, et al. A Phase I Clinical Trial of Guadecitabine and Carboplatin in Platinum-Resistant, Recurrent Ovarian Cancer: Clinical, Pharmacokinetic, and Pharmacodynamic Analyses. Clin Cancer Res Off J Am Assoc Cancer Res. 2018;24:2285–93.

Oza AM, Matulonis UA, Alvarez Secord A, Nemunaitis J, Roman LD, Blagden SP, et al. A Randomized Phase II Trial of Epigenetic Priming with Guadecitabine and Carboplatin in Platinum-resistant, Recurrent Ovarian Cancer. Clin Cancer Res Off J Am Assoc Cancer Res. 2020;26:1009–16.

Dilawari A, Shah M, Ison G, Gittleman H, Fiero MH, Shah A, et al. FDA Approval Summary: Mirvetuximab Soravtansine-Gynx for FRα-Positive, Platinum-Resistant Ovarian Cancer. Clin Cancer Res. 2023;29(19):OF1–6.

Tolcher A, Hamilton E, Coleman RL. The evolving landscape of antibody-drug conjugates in gynecologic cancers. Cancer Treat Rev. 2023;116: 102546.

Gandhi NS, Tekade RK, Chougule MB. Nanocarrier mediated delivery of siRNA/miRNA in combination with chemotherapeutic agents for cancer therapy: current progress and advances. J Control Release Off J Control Release Soc. 2014;194:238–56.

Pereira DM, Rodrigues PM, Borralho PM, Rodrigues CMP. Delivering the promise of miRNA cancer therapeutics. Drug Discov Today. 2013;18:282–9.

Download references

Acknowledgements

We thank our colleagues for the critical reading of this manuscript, their valuable suggestions, as well as their useful comments for the preparation of this manuscript. Figures were created with Biorender.com.

This work was funded by the National Natural Science Foundation of China (No. 82073129), Chongqing Science and Technology Bureau (No. CSTB2023NSCQ-MSX1030, No. cstc2022jxjl120039), Chongqing Health Commission (No. 2023ZDXM029, No. 2023MSXM043), Special Project for Improving Scientific Research Ability of Chongqing University Cancer Hospital (2023nlts005, 2023nlts009).

Author information

Ling Wang and Xin Wang contributed equally to this work.

Authors and Affiliations

Department of Gynecologic Oncology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, China

Ling Wang, Xin Wang, Xueping Zhu, Lin Zhong, Qingxiu Jiang, Ya Wang, Qin Tang, Qiaoling Li, Haixia Wang & Dongling Zou

Chongqing Specialized Medical Research Center of Ovarian Cancer, Chongqing, China

Ling Wang, Xin Wang, Xueping Zhu, Lin Zhong, Qingxiu Jiang, Ya Wang, Qin Tang, Qiaoling Li, Cong Zhang, Haixia Wang & Dongling Zou

Organoid Transformational Research Center, Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, China

Biological and Pharmaceutical Engineering, School of Medicine, Chongqing University, Chongqing, China

You can also search for this author in PubMed   Google Scholar

Contributions

Ling Wang and Xin Wang summerized the related papers and originally drafted the manuscript. Xin Wang prepared the figures and Ling Wang made the tables. Xueping Zhu and Lin Zhong searched the clinical trials about drug resistance of ovarian cancer. Qingxiu Jiang and Ya Wang summarized the targets involved in these clinical trials. Qin Tang, Qiaoling Li, and Cong Zhang searched literatures about drug resistance in ovarian cancer. Haixia Wang and Dongling Zou initiated the study, revised and finalized the manuscript.

All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Haixia Wang or Dongling Zou .

Ethics declarations

Ethics approval and consent to participate.

This is a review article; data is not related to ethics approval.

Consent for publication

All authors agree with the content of the manuscript and consent to publication.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: figure s1/s2..

The summary of flow charts of clinical trials in I/II phase. The components in these flow chart include inclusion criteria, sample size, study duration, study arms, and study endpoints of these I-phase clinical trials about resistant ovarian cancer.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Wang, L., Wang, X., Zhu, X. et al. Drug resistance in ovarian cancer: from mechanism to clinical trial. Mol Cancer 23 , 66 (2024). https://doi.org/10.1186/s12943-024-01967-3

Download citation

Received : 15 November 2023

Accepted : 22 February 2024

Published : 28 March 2024

DOI : https://doi.org/10.1186/s12943-024-01967-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Ovarian cancer
• Resistance mechanisms
• Clinical trials

Molecular Cancer

ISSN: 1476-4598

• General enquiries: [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 25 August 2022

Understanding the genetics of viral drug resistance by integrating clinical data and mining of the scientific literature

• An Goto 1 ,
• Raul Rodriguez-Esteban 2 ,
• Sebastian H. Scharf 2 &
• Garrett M. Morris 1  

Scientific Reports volume  12 , Article number:  14476 ( 2022 ) Cite this article

2223 Accesses

1 Citations

5 Altmetric

Metrics details

• Clinical genetics
• Literature mining

Drug resistance caused by mutations is a public health threat for existing and emerging viral diseases. A wealth of evidence about these mutations and their clinically associated phenotypes is scattered across the literature, but a comprehensive perspective is usually lacking. This work aimed to produce a clinically relevant view for the case of Hepatitis B virus (HBV) mutations by combining a chronic HBV clinical study with a compendium of genetic mutations systematically gathered from the scientific literature. We enriched clinical mutation data by systematically mining 2,472,725 scientific articles from PubMed Central in order to gather information about the HBV mutational landscape. By performing this analysis, we were able to identify mutational hotspots for each HBV genotype (A-E) and gene (C, X, P, S), as well as the location of disulfide bonds associated with these mutations. Through a modelling study, we also identified a mutation position common in both the clinical data and the literature that is located at the binding pocket for a known anti-HBV drug, namely entecavir. The results of this novel approach show the potential of integrated analyses to assist in the development of new drugs for viral diseases that are more robust to resistance. Such analyses should be of particular interest due to the increasing importance of viral resistance in established and emerging viruses, such as for newly developed drugs against SARS-CoV-2.

Similar content being viewed by others

Long COVID: major findings, mechanisms and recommendations

Hannah E. Davis, Lisa McCorkell, … Eric J. Topol

Compensatory evolution in NusG improves fitness of drug-resistant M. tuberculosis

Kathryn A. Eckartt, Madeleine Delbeau, … Jeremy M. Rock

Natural products in drug discovery: advances and opportunities

Atanas G. Atanasov, Sergey B. Zotchev, … Claudiu T. Supuran

Introduction

Gene mutations that confer drug resistance to pathogens are an emerging public health and medical threat with, additionally, potential consequences for current and future pandemic response. A step towards combating this scourge is to study the way in which particular mutations lead to viral drug-resistant phenotypes. Viral species hosted by an individual patient can contain multiple mutations that interact to produce specific phenotypes. To understand mutational interactions, the scientific literature can help in interpreting existing knowledge on the biological mechanisms associated with viral mutation-phenotype relationships. Here we explore the feasibility of a novel integrated approach that combines clinical and text mining data to gather existing knowledge and produce new insights on drug resistance based on data from viral species hosted by individual patients, and in particular for the case of HBV. Such approaches have the potential to be applied to other viral diseases, singularly due to the growing acknowledgement of the critical importance of viral mutation monitoring in patients.

Hepatitis B is an infectious disease that affects approximately 292 million people worldwide. Despite being a major global health concern, it has been estimated that only 10% of individuals who are chronically infected with HBV have been diagnosed, and only 5% of those individuals who are eligible for treatment receive an antiviral therapy 1 . Chronic HBV infection is known to progress to cirrhosis of the liver in up to 40% of untreated patients 2 ; and in 2015, HBV led to an estimated 887,000 deaths, which were largely caused by cirrhosis of the liver and hepatocellular carcinoma (HCC) 3 . There are two classes of treatments that are known to be effective against suppressing HBV infections: interferons and nucleotide analogues. These treatments can reduce the patient’s viral load and the main impacts of the disease on the patient. However, although these treatments have been available for nearly two decades, they have not eliminated HBV 4 .

HBV is classified into ten HBV genotypes, A to J, each with a distinct geographic distribution 5 , 6 . The infectious HBV virion, also known as the Dane particle , is a spherical, double-shelled structure with a diameter of 42 nm 7 . This particle consists of an outer lipoprotein envelope and an inner nucleocapsid core, which encapsulates the viral genome 6 . The genome of HBV is approximately 3.2 kilobase pairs long and has a partially double-stranded circular DNA 7 . The viral genome codes for all five viral proteins required for HBV replication: the HBV surface antigen; the HBV core antigen; the HBV envelope antigen; the X protein; and the HBV reverse transcriptase/polymerase.

There are four known genes in the viral genome: C, X, P, and S. Gene C encodes the core protein, and it also produces the pre-core protein by promoting an upstream AUG start codon as its start codon 8 . Genes P and S encode for DNA polymerase and HBsAg, respectively. The S gene is an open long reading frame that is divided into three sections based on the start codons: pre-S1, pre-S2, and S 9 . The mechanism and function of the protein coded by gene X, HBV X antigen, remains largely unknown, but it is known to be associated with the development of liver cancer 10 .

Due to its high mutation rate, with commonly accepted rates of ~ 2.0 × 10 –5 nucleotide substitutions per site per year 11 , HBV can become resistant to HBV drugs relatively quickly. With the emergence of drug resistance, patients may experience a notable compromise in the efficacy of anti-viral therapy and deterioration of the disease condition 12 , 13 . Thus, investigation of resistance mutations is crucial for the successful development of robust HBV drugs. Lamivudine (phosphorylated lamivudine) and adefovir dipivoxil were the first nucleotide analogues that were developed but had limited success because of the development of resistant variants. Resistance mutations pertaining to nucleotide analogues are usually reported in the polymerase gene of HBV DNA ( i.e., gene P) 14 . The resistance phenotype is typically a consequence of nucleotide analogues failing to bind to DNA polymerase 15 .

Lamivudine and adefovir diphosphate are widely known for treating HBV 16 . After conducting randomized clinical trials of lamivudine, it was found that, compared with placebo, HBV mutant variants were associated with reduced sensitivity to lamivudine in approximately 30% of patients after only a year of treatment 17 , 18 . In addition, mutations causing resistance to adefovir dipivoxil were detected in up to 20 to 29% of patients after five years of treatment 19 , 20 . However, more-recently developed nucleotide analogues, such as entecavir and tenofovir disoproxil, have been reported to dramatically decrease the rate of development of resistance.

In the case of entecavir, only 1.2% of patients developed a resistant strain after five years of treatment if they had never been treated with nucleotide analogues previously 21 . For tenofovir disoproxil, there were no clinically significant resistant variants identified during up to seven years of follow-up 22 . Cross-resistance is also known to occur and is another concern for the development of drug resistance. For example, between lamivudine- and entecavir-resistant HBV strains, the cumulative probability of developing entecavir resistant variants is more than 50% for those individuals with lack of resistance toward lamivudine 21 .

In order to create new therapies that can circumvent or negate mechanisms of drug resistance, we need to understand resistance mutations better with the aid of analytics tools such as text mining. Text mining has been applied to extract evidence for generic drug resistance 23 , 24 , impact of mutations in disease 25 and to harvest viral mutation data 26 . It has also been leveraged to create comprehensive databases of the viral mutation literature 27 , 28 . There is no report, however, that it has been used to produce new insights that support clinical data analysis of viral mutations with integration of literature data. This study aimed to build on a clinical study that ultra-deep sequenced HBV quasispecies from a European cohort 29 by comparing the data from this clinical study against genetic mutation information automatically extracted from the scientific literature regarding all known launched anti-HBV drugs. Such data are typically scattered across many publications; therefore, we built a pipeline to mine literature sources systematically. In order to extract mutations from the literature we applied a rule-based text mining approach 30 , 31 , 32 . With our approach, we were able to produce a landscape of disease-specific viral mutations associated to drug resistance, which allowed us to derive new insights into mechanisms associated to drug resistance.

There exist manually curated databases 33 , 34 , 35 , 36 that aim to gather HBV resistance mutations from patients' data. However, it is hard to evaluate their coverage since they do not give a complete list of resistance mutations and instead provide a mean to analyze the genetic sequences that the user has obtained. In addition, most of these databases are not open source. Hence, the use of text mining approaches is able to mine through the literature efficiently are essential to tackle the challenge of identifying known resistance mutations.

Code and supplementary information

The analysis was performed using standard Python packages ( e.g., ElementTree 37 for parsing XML-formatted literature, re for identifying information about mutations, and pandas for data analysis). The resulting code and supplementary information (Supplementary Figures S1 – S15 , Tables S1 – S5 , and Note S1 ) is available at: https://github.com/angoto/HBV_Code .

Source of data

Mining of the scientific literature was conducted by using all 2,472,725 XML files from PubMed Central (commercial and non-commercial use), which consisted of information about each publication such as title, authors, DOI, abstract, full body content, and references 38 . Clinical data for genetic mutations of HBV was collected from a total of 186 plasma samples from a Western European cohort of chronic HBV patients during the period from 1985 to 2012 29 . HBV DNA was extracted from 200 μL of plasma using the QIAamp MinElute Virus Kit (Qiagen) or the Roche MagNA Pure LC instrument according to the manufacturer’s instructions. These samples are stored at the Erasmus University Medical Center in Rotterdam, the Netherlands. The guidelines followed in the study were in accordance with the Declaration of Helsinki 39 and the principles of Good Clinical Practice 40 . The study was approved by the ethical review board of the Erasmus Medical Center, Rotterdam, the Netherlands. The clinical data used in this study was anonymized and permission was not required to access the raw data of the clinical study. The dataset used to build the anti-HBV drugs’ vocabulary included 69 launched anti-HBV drugs obtained from the Cortellis Drug Discovery Intelligence database (CDDI), formerly known as Integrity 41 . We extracted each drug’s code name, generic name, brand name, and drug name from the CDDI database to enrich this vocabulary, which gave 312 unique drug expressions.

Scientific literature mining

The workflow in Fig.  1 was used to search through the sentences from journal articles in PubMed Central (PMC) that mentioned both an HBV drug and a mutation. In Step 2, we have used GNU parallel in order to select relevant literature that refer to ‘hepatitis b’ and/or ‘hbv’ (both keywords are case insensitive). In Step 4, a regular expression was used to split sentences from PMC and mutations were identified by using regular expressions for HBV mutations, as described in the next section. We avoid any problems with abbreviations that could lead to false positives, such as confusing the viral gene “C” with the element “C”, by focusing our search for specific mutations found in the clinical study, and ensuring co-occurrence of HBV drugs in the same sentence. By basing our rules on co-occurrence relationships between drug and mutation, our approach is generalizable to new data, and new diseases. The output data from this workflow was later used to conduct the data analysis ( i.e., Step 5 in Fig.  1 ). Further details of Steps 1–5 are available at: https://github.com/angoto/HBV_Code .

Workflow for mining the scientific literature.

Regular expressions for HBV mutations

Mutations relevant to HBV were found from articles in PubMed Central by combining regular expressions.

Use of clinical study data

Six different pieces of data from the Western Europe cohorts described in Mueller-Breckenridge, et al . 29 were used to conduct the analysis: sample, reference genome, gene, effect, amino acid variant, and nucleotide variant. “Sample” identifies the unique patients’ number used during the clinical study. Reference genome refers to a genotype-specific reference sequence (namely, GenBank accession numbers AF090842, AB033554, AB033556, AF121240, and AB032431, for genotypes A to E, respectively) that was used to map the outputs generated from quality-trimmed demultiplexed FASTQ reads 29 . “Gene” indicates four known genes encoded by the genome: C, P, S, and X. “Effect” refers to whether a particular mutation was either an intragenic or a missense variant. For this report, we have only considered missense variants because intragenic variants from the literature were not associated with the clinical study’s phenotypes. “Amino acid variant” ( n  = 7285) and “nucleotide variant” ( n  = 10,658) are two different ways to describe the same mutation, i.e. mutation in the amino acid ( e.g. p.Pro156Ser) and nucleotide ( e.g. c.314C > T) sequences, respectively. Although it is relatively straightforward to obtain HBV-related genetic mutations from the literature, it is difficult to map the position number of nucleotides to the position number of amino acids, and vice-versa . This is because we are unsure about the reference sequence that was used to report these mutations for each journal article in PubMed Central. In order to do this type of mapping with our current approach, we would need to manually go over each journal article that mentioned both an HBV drug and a mutation to conduct the translation of DNA sequences to amino acid sequences because an ambiguous inverse mapping would be impossible (except for Tryptophan). Hypothetical clinical study data is available on the GitHub repository for this study 42 to provide a better idea of the data structure used in the clinical study.

Comparison of scientific literature and clinical study

The workflow in Fig.  2 was used to compare mutations from the clinical study with the genetic mutations found in the scientific literature downloaded from PubMed Central after translating the mutations from the literature to the format used in the clinical study: amino acid (category vi ) and nucleotide (category ii ) variants ( n  = 4214).

Normalization of DNA and amino acid mutations found from text mining the literature, to enable the direct comparison with clinical study mutations.

Mutation hotspot for entecavir

We independently built our own models of entecavir bound with HBV RT and DNA, using the X-ray crystal structures of HIV RT also bound to entecavir and DNA, from PDB entries 5XN1 43 and 6IKA 44 . Homology modelling was performed using SWISS-MODEL 45 , and UniProt ID Q9WRJ9 46 residues 349–692 for the sequence of HBV RT genotype A. The resulting model underwent energy minimization using the open-source build of PyMOL version 2.3.0 47 , relaxing the entecavir ligand and all residues and bases within 4 Å of entecavir.

Comparison between the clinical study and the literature

The number of publications found to have a sentence co-occurrence of an approved HBV drug and a mutation in the same sentence was 30,686. There were 4,214 unique mutations mentioned in those sentences and, 7.5% of those, a total of 316 mutations ( i.e., 254 amino acid and 62 nucleotide variants) were also found in the clinical data from Western Europe cohorts ( n  = 182 patients, 31,977 mutations). After analyzing the clinical data using solely those unique mutations that were common between the clinical study and the literature, we found that 180 out of 182 patients (corresponding to 1750 amino acid and 302 nucleotide variants) had mutations that also appeared in the literature.

Prevalence of genetic mutations in the clinical study

Figure  3 shows the ten most frequent amino acid (left) and nucleotide variants (right) in the clinical study that were also found in the literature. Their prevalence ranged between 1 and 52 patients for amino acid variants and between 1 and 78 patients for nucleotide variants. Supplementary Figures S1 – S2 show the frequency of mutations in the clinical data ( i.e., number of patients reported to have each mutation type).

Top 10 most frequent amino acid mutations (left) and nucleotide mutations (right) in the clinical data that were also found in the literature.

Mutation likelihood of patients in the clinical study

The 180 patients presented between 16 and 753 HBV mutations. Out of these mutations, between 1 and 35 appeared in the literature (Supplementary Figure S3 - 1 ), with an average of 11.2 mutations and a median of 10 mutations. Supplementary Figure S3 - 2 shows the patients in the clinical study who had the most mutations matching those found in the literature.

Mutation hotspots for HBV genotypes in the clinical study

We identified amino acid mutations in the four genes X, P, C, and S, across five HBV genotypes, A, B, C, D, and E, that emerged during the clinical study. There were 182 patients in the clinical study, of which there were 56 patients with genotype A, 19 with genotype B, 43 with genotype C, 62 with genotype D, and 2 with genotype E. In order to create a mutation hotspot map for each of these genotypes (Supplementary Figures S4 – S8 ) where there was at least one report in the literature of that mutation, overlaps between the mutations from the literature and the clinical study mutations were identified. It should be noted that there are mutations in the patients that were present in the clinical study but were not found in the analysis of the literature, and hence are not shown in these heatmaps.

We defined a “mutation hotspot” as a mutation with a count above the average for that particular gene and genotypes. For example, for HBV RT genotype A, mutation V214A had a count of 1 patient, while the average of the counts was 5.18 patients, so this was excluded from the map; while mutation L217R had a count of 30 patients, which was above the average for HBV RT genotype A, and thus was included as a mutation hotspot. For nucleotide variants, similar plots were made based on the genotypes (A-E), with mutations sorted in order of position number (Supplementary Figures S9 – S13 ). Figures 4 , 5 , 6 represent heatmaps of the mutational hotspots that we identified for eight different gene products ( i.e., polymerase, reverse transcriptase, X, precore, core, PreS1, PreS2, and HBsAg) and nucleotide variants, with darker, more saturated colors indicating more mutations at that position in that genotype. The summary tables of hotspots for each HBV genotype for amino acid and nucleotide variants are shown in Supplementary Tables S1 – S5 .

Heatmap of mutation hotspots for gene P in genotypes A-E: ( a ) HBV polymerase, and ( b ) HBV reverse transcriptase (RT) and heatmap of mutation hotspots for gene C in genotypes A-E: ( c ) HBV precore, and ( d ) HBV core. The white regions represent counts of mutations that are null, while dark blue (in a and b ) or dark green (in c or d ) indicates the largest number of mutations.

Heatmap of mutation hotspots for gene S in genotypes A-E: ( a ) HBV PreS1, and ( b ) HBV PreS2; heatmap of mutation hotspots for: ( c ) HBV HBsAg (gene S), and (d) HBV gene X in genotypes A-E. The white regions represent counts of mutations that are null, while dark red indicates the largest number of mutations.

Heatmap of mutation hotspots for HBV nucleotide variants in genotypes A-E. The white regions represent counts of mutations that are null, while dark blue indicates the largest number of mutations.

Our analysis revealed there were “genotype-common” mutation hotspots, i.e., mutations with the same position number, in two or more genotypes (Figs.  4 , 5 , 6 ), above the average count of patient. These were: polymerase at position K293; reverse transcriptase at positions S213, S219, D263, and Q267; PreS1 at positions S5, F141, and N214; PreS2 at position R16; HBsAg at positions E2, L77, K122, P127, M133, Y161, W182, A184, S204, S210, and F220; X at position P33; precore at positions G29, L66, and S78; core at positions P5, T12, L60, and T147; and nucleotide variants at positions 34A, 120A, and 389A.

Thus, although we were unable to discriminate HBV drug-resistant mutations unambiguously from other mutations, we were able to identify common mutations in the clinical study and the literature, across genotypes A-E. This can further our understanding of HBV mutations by highlighting mutations that may be potentially relevant to resistance.

Mutations occurring in disulfide bonds

We investigated the number of common mutations between the clinical study and the literature that arose from mutating wild type cysteine residues. Cysteine residues can be responsible for the formation of disulfide bonds, which play an important role in folding and stability of the proteins 48 . The HBV envelope proteins, which corresponds to gene S, are known to form an intermolecular disulfide network through cysteine residues in the cysteine-rich antigenic loop that are in positions 102 to 161 49 , 50 , 51 . Hence, mutations for cysteine residues reported in Table 1 for positions C125 and C149 are in fact in regions of disulfide bonds. For gene C, the cysteine residue in position C48 is expected to form a disulfide bond with C149 52 . We were unable to find any evidence of a disulfide bond involving residue C1 in gene S.

Therefore, by comparing the literature and the clinical study, we were able to identify which gene, and in what particular position, mutations occurred at disulfide bond. We found mutations located at C124 and C149 in gene S to occur in regions of disulfide bonds, which are likely to have an effect towards destabilizing the HBV proteins. Table 1 shows the list of cysteine residues in each genotype, A-E, for mutations that were common between the clinical data and the literature.

Based on a search conducted using DrugBank 16 , one out of 69 approved drugs listed in the CDDI database had a modelling study published with a known target and binding site 53 . By using our own models of entecavir bound with HBV RT and DNA, we identified which of these binding pocket residues were common between the clinical data and the scientific literature. For entecavir, we found common mutations located at I169, M204, and N238 for genotype A (within 12 Å of entecavir in our model).

Figure  7 shows the binding pocket of entecavir, together with the binding pocket residues within 5 Å of entecavir. The original modelling study by Langley, et al . 53 used a sequence alignment between HIV and HBV reverse transcriptase to determine the most conserved domains, and they used the HIV reverse transcriptase DNA X-ray structure (PDB ID 1RTD) 54 to build the HBV RT model.

Model of HBV RT (genotype A) based on the HIV RT-DNA-entecavir complex X-ray crystal structure, PDB ID 5XN1 43 . ( a ) Entecavir-triphosphate is shown as spheres with teal-colored carbon atoms, red oxygen, blue nitrogen, and orange phosphorus atoms. Entecavir is at the 3′ end of the DNA strand (orange cylinders). The secondary structure of the HBV RT model is shown as white coils ( \(\alpha \) -helices), white arrows ( \(\beta \) -strands), and white loops. ( b ) Close-up view of the binding pocket, showing entecavir-triphosphate with teal-colored carbons, and a Mg 2+ cation as a green sphere. Note that hydrogen bonds and metal bonds are shown as light blue dashed lines. The deoxyguanine (dG)-moiety of entecavir can be seen “base-pairing” with deoxycytosine (dC) in a second strand of DNA.

We also investigated the location of the mutational hotspots we identified for HBV RT in genotype A and mapped these to our model. It can be seen from Fig.  8 that the mutations occur throughout the structure.

Locations of the mutations in HBV RT found both in patients in the clinical study, and in our analysis of the literature, shown as spheres placed at the α-carbon atom of the amino acid. The pink spheres are mutations with above-average counts of patients, and are at positions 129, 163, 213, 217, 219, 263, and 267; while the light blue spheres indicate positions of mutations below the average threshold. The secondary structure of our model of the HBV RT is shown as a white cartoon, while the DNA backbone is in orange. The location of the DNA, entecavir-triphosphate (shown with teal carbons), and Mg 2+ (green sphere) was modelled on PDB ID 5XN1 43 .

Monitoring of drug resistance driven by viral mutations is relevant for antiviral drugs beyond those used for HBV, such as human cytomegalovirus (HMCV) 55 , HIV 56 , hepatitis C virus 57 , influenza virus 58 , SARS-CoV-2 59 and more. Hence, the method proposed could be useful for a range of antiviral drugs and associated diseases.

Discussion and conclusions

This study showed the feasibility of integrating clinical and text mined viral mutation data and its potential to produce new insights on disease-relevant viral mutations. The focus of the study was on HBV, but the methods are applicable to other viruses. While mutational hotspots can be identified by considering only the clinical study’s mutations, the difficulty associated with this process is that, even when new mutational hotspots are identified through the study, they are often disregarded because there are no literature reports available or there are not enough resources to comb the literature to confirm its role in resistance mutations. Thus, it is typical for clinical studies to report only those mutational hotspots that have been reported in the past. Hence, the methodology presented in this study serves as a bridge to fill in that gap by leveraging the full text of 2.47 million articles from PubMed Central to produce a landscape of disease-specific viral mutations. Although it is not possible for this method to confirm a direct correlation between the mutational hotspots and resistance mutations, it is able to provide new hypotheses that are potentially relevant to the resistance phenotype.

Our study identified known hotspots that were found in four genes, P, C, S, and X, in genotypes A to E, as shown in Figs. 4 , 5 , 6 . There are other HBV mutational hotspots that are known such as L180, which is a compensatory mutation for resistance to entecavir, lamivudine, and telbivudine 60 ; rt202 which causes resistance to entecavir in Asian population 61 ; and rt236 which is responsible for resistance to adefovir dipivoxil in Caucasian 62 . Hence, the list of mutational hotspots that were identified in this study was not necessarily conclusive. In addition, two amino acid positions ( i.e., C124 and C149) exhibited mutations in disulfide bonds, which are likely to impact the structure of HBV proteins. This finding is confirmatory since these regions of the disulfide bonds have already been identified in the past 49 , 50 , 51 . Moreover, we identified a mutation in a position that was relevant to the binding site for the anti-HBV drug, entecavir. However, the I169 mutation, which is known to be the primary mutation responsible for entecavir’s resistance was found to be reported below the average threshold when comparing mutational hotspots between the clinical study and the literature 63 . Therefore, further refinement of the definition of mutational hotspots used in this study may be necessary in the future.

One limitation of our approach lies in terms of the amount of data we collected from the literature. This resulted in an overall coverage of the number of genetic mutations reported in the literature against the common mutations of the literature and the clinical study of 7.5%. In order to further increase the coverage of genetic mutations data, literature sources could be increased to include repositories from publishers such as ScienceDirect 64 and Springer Nature 65 . We could also obtain additional data from the PubMed Central literature by mining the information included in tables and figures. It is also important to be aware of errors that may arise during DNA sequencing and patient data collection during the clinical study.

In addition, we considered the rate of false positive identifications of mutations. An example of a false positive would be the following sentence: “Some non-resistance-associated mutations of rtD134N (ranging from 20.33 to 74.63%), rtL145M (ranging from 2.83 to 78.82%), rtF151Y (ranging from 2.92 to 75.51%) and rtS223A (ranging from 5.77 to 18.44%) increased significantly with ADV monotherapy, then declined with the addition of LdT” 66 . In this sentence, it satisfies the criterion for co-occurrence of an HBV drug and a mutation, but it is in fact not referring to resistance mutations. False positives could also be considered hypothetical mutations, such as the one described in the sentence: “The lamivudine resistance-linked G529A (rtD134N) site in HBV was found to be associated with HCC outcomes, which implied potential correlation between resistance to the anti-HBV nucleoside analog lamivudine and HCC prognosis” 67 . Furthermore, it is possible that some drug-resistant mutants involve multiple locations, so in the future we must consider such combinatorial possibilities as well. To estimate the false positive rate, we selected 50 random sentences with co-occurrence of drug and mutation and inspected them manually. It was found that two of them were false positives. Hence, we estimate the rate of false positives to be approximately 4% of the overall data.

To improve the methods used here, additional text mining strategies could be used to improve the extraction, such as using additional synonyms from UMLS and SNOMED for both the drugs and the disease, which would decrease the number of false negatives. By using machine learning algorithms, thus we could also increase our confidence that sentences describe HBV-related information. For example, the following sentence refers to two mutations, M204I and M204V, which are not captured by simple regular expressions: “LVDr is well characterized and arises through replacement of M204 within the YMDD motif of the HBV RT with isoleucine or valine, with or without the adaptive change L180M” 46 . By performing the literature mining in this manner, we could be able to develop a more powerful text mining tool that would allow us to identify a more comprehensive set of resistance mutations. However, it is important to note that, while more-recently published mutation-extraction approaches are machine-learning based 68 , 69 , these require costly gold standard corpora, which hinders their re-application to different viral species, each with its own notation particularities. Thus, the development of machine-learning algorithms may lead to improved performance but could result in more restricted application to a particular disease.

Data availability

The code and supplementary information (Supplementary Figures S1 – S15 , Tables S1 – S5 , and Note S1 ) are available at: https://github.com/angoto/HBV_Code . Access to the data in reference 29 can be requested by anyone by contacting Alan Mueller-Breckenridge at Roche, using the email address: [email protected].

Polaris Observatory Collaborators. Global prevalence, treatment, and prevention of hepatitis B virus infection in 2016: A modelling study. Lancet Gastroenterol. Hepatol. 3 , 383–403 (2018).

Article   Google Scholar  

Sarin, S. K. et al. Asian-pacific clinical practice guidelines on the management of hepatitis B: A 2015 update. Hepatol Int. 10 , 1–98 (2016).

Article   CAS   PubMed   Google Scholar  

World Health Organization. Hepatitis B. World Health Organization https://www.who.int/en/news-room/fact-sheets/detail/hepatitis-b (2019).

Lok, A. S. F. et al. Antiviral therapy for chronic hepatitis B viral infection in adults: A systematic review and meta-analysis. Hepatology 63 , 284–306 (2016).

Spradling, P., Hu, D. & McMahon, B. J. Epidemiology and prevention. In Viral Hepatitis (eds Thomas, H. et al. ) (Wiley, 2013).

Google Scholar  

Tang, L. S. Y., Covert, E., Wilson, E. & Kottilil, S. Chronic hepatitis B infect a review. JAMA 319 , 1802–1813 (2018).

Liang, T. J. Hepatitis B: The virus and disease. Hepatology 9 , S13–S21 (2009).

Article   CAS   Google Scholar  

Kay, A. & Zoulim, F. Hepatitis B virus genetic variability and evolution. Virus Res. 127 , 164–167 (2007).

Buti, M., Rodriguez-Frias, F., Jardi, R. & Esteban, R. Hepatitis B virus genome variability and disease progression: The impact of pre-core mutations and HBV genotypes. J. Clin. Virol. 34 , S79–S82 (2005).

Beck, J. & Nassal, M. Hepatitis B virus replication. World J. Gastroenterol. 13 , 48–64 (2007).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Orito, E. et al. Host-independent evolution and a genetic classification of the hepadnavirus family based on nucleotide sequences. Proc. Natl. Acad. Sci. USA 86 , 7059–7062 (1989).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Cross, J. C., Wen, P. & Rutter, W. J. Transactivation by hepatitis B virus X protein is promiscuous and dependent on mitogen-activated cellular serine/threonine kinases. Proc. Natl. Acad. Sci. USA 90 , 8078–8082 (1993).

Hu, Z., Zhang, Z., Kim, J. W., Huang, Y. & Liang, T. J. Altered proteolysis and global gene expression in hepatitis B virus X transgenic mouse liver. J. Virol. 80 , 1405–1413 (2006).

Milich, D. & Liang, T. J. Exploring the biological basis of hepatitis B e antigen in hepatitis B virus infection. Hepatology 38 , 1075–1086 (2003).

Zhang, Z., Torii, N., Hu, Z., Jacob, J. & Liang, T. J. X-deficient woodchuck hepatitis virus mutants behave like attenuated viruses and induce protective immunity in vivo. J. Clin. Invest. 108 , 1523–1531 (2001).

Wishart, D. S. et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 46 , D1074–D1082 (2018).

Dienstag, J. L. et al. Lamivudine as initial treatment for chronic hepatitis B in the United States. N. Engl. J. Med. 341 , 1256–1263 (1999).

Chan, H. L. et al. Two-year lamivudine treatment for hepatitis B e antigen-negative chronic hepatitis B. a double-blind, placebo-controlled trial. Antivir. Ther. 12 , 345–353 (2007).

Marcellin, P. et al. Long-term efficacy and safety of adefovir dipivoxil for the treatment of hepatitis B e antigen-positive chronic hepatitis B. Hepatology 48 , 750–758 (2008).

Hadziyannis, S. J. et al. Long-term therapy with adefovir dipivoxil for HBeAg-negative chronic hepatitis B for up to 5 years. Gastroenterology 131 , 1743–1751 (2006).

Tenney, D. J. et al. Long-term monitoring shows hepatitis B virus resistance to entecavir in nucleoside-naïve patients is rare through 5 years of therapy. Hepatology 49 , 1503–1514 (2009).

Buti, M. et al. Seven-year efficacy and safety of treatment with tenofovir disoproxil fumarate for chronic hepatitis B virus infection. Dig Dis Sci. 60 , 1457–1464 (2015).

Bui, Q. C., Nualláin, B. O., Boucher, C. A. & Sloot, P. M. Extracting causal relations on HIV drug resistance from literature. BMC Bioinf. 11 , 101 (2010).

Khalid, Z. & Sezerman, O. U. ZK DrugResist 20: A TextMiner to extract semantic relations of drug resistance from PubMed. J Biomed Inform. 69 , 93–98 (2017).

Article   PubMed   Google Scholar  

Naderi, N. & Witte, R. Automated extraction and semantic analysis of mutation impacts from the biomedical literature. BMC Genomics. 13 (Suppl 4), S10 (2012).

Article   PubMed   PubMed Central   Google Scholar  

Roberts, K. et al. TREC-COVID: Rationale and structure of an information retrieval shared task for COVID-19. J. Am. Med. Inform. Assoc. 27 , 1431–1436 (2020).

Davey, N. E. et al. The HIV mutation browser: A resource for human immunodeficiency virus mutagenesis and polymorphism data. PLoS Comput. Biol. 10 , e1003951 (2014).

Wang, Y. et al. ViMIC: A database of human disease-related virus mutations, integration sites and cis-effects. Nucleic Acids Res. https://doi.org/10.1093/nar/gkab779 (2021).

Mueller-Breckenridge, A. J. et al. Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts. Sci Rep. 9 , 18892 (2019).

Caporaso, J. G., Baumgartner, W. A. Jr., Randolph, D. A., Cohen, K. B. & Hunter, L. MutationFinder: A high-performance system for extracting point mutation mentions from text. Bioinformatics 23 , 1862–1865 (2007).

Doughty, E. et al. Toward an automatic method for extracting cancer- and other disease-related point mutations from the biomedical literature. Bioinformatics 27 , 408–415 (2011).

Thomas, P., Rocktäschel, T., Hakenberg, J., Lichtblau, Y. & Leser, U. SETH detects and normalizes genetic variants in text. Bioinformatics 32 , 2883–2885 (2016).

National Center for Biotechnology Information: U.S. National Library of Medicine. Hepatitis B Virus. NCBI https://www.ncbi.nlm.nih.gov/genome/?term=hbv (2019).

Hayer, J. et al. HBVdb: a knowledge database for Hepatitis B Virus. Nucleic Acids Res. 41 , D566–D570 (2013).

Yuen, L. K. et al. SeqHepB: A sequence analysis program and relational database system for chronic hepatitis B. Antiviral Res. 75 , 64–74 (2007).

EuResist Network GEIE. EuResist Network. euresist network https://www.euresist.org/ (2021).

Python. xml.etree.ElementTree - The Element Tree XML API. Python . https://docs.python.org/3/library/xml.etree.elementtree.html (2019).

National Center for Biotechnology Information: U.S. National Library of Medicine. PMC. NCBI https://www.ncbi.nlm.nih.gov/pmc/ (2019).

World Medical Association. World medical association declaration of Helsinki. JAMA 310 , 2191–2194 (2013).

Vijayananthan, A. & Nawawi, O. The importance of Good Clinical Practice guidelines and its role in clinical trials. Biomed. Imaging Interv. J. 4 , e5 (2008).

Clarivate Analytics. Integrity. Clarivate Analytics https://integrity.clarivate.com//integrity/xmlxsl/ (accessed on August 14, 2019).

GitHub. HBV_Code. GitHub https://github.com/angoto/HBV_Code (2021).

Yasutake, Y. et al. HIV-1 with HBV-associated Q151M substitution in RT becomes highly susceptible to entecavir: Structural insights into HBV-RT inhibition by entecavir. Sci Rep. 8 , 1624–1624 (2018).

Article   ADS   PubMed   PubMed Central   CAS   Google Scholar  

Yasutake, Y. et al. Active-site deformation in the structure of HIV-1 RT with HBV-associated septuple amino acid substitutions rationalizes the differential susceptibility of HIV-1 and HBV against 4′-modified nucleoside RT inhibitors. Biochem. Biophys. Res. Commun. 509 , 943–948 (2019).

Bienert, S. et al. The SWISS-MODEL Repository – new features and functionality. Nucleic Acids Res. 45 , D313–D319 (2017).

UniProt. UniProtKB - Q9WRJ9 (Q9WRJ9_HBV). UniProt https://www.uniprot.org/uniprot/Q9WRJ9 (2020).

The PyMOL Molecular Graphics System, Version 2.3.0 Schrödinger, LLC.

Sevier, C. S. & Kaiser, C. A. Formation and transfer of disulfide bonds in living cells. Nat. Rev. Mol. Cell Biol. 3 , 836–847 (2002).

Nassal, M., Rieger, A. & Steinau, O. Topological analysis of the hepatitis B virus core particle by cysteine-cysteine cross-linking. J. Mol. Biol. 225 , 1013–1025 (1992).

Mangold, C. M. & Streeck, R. E. Mutational analysis of the cysteine residues in the hepatitis B virus small envelope protein. J. Virol. 67 , 4588–4597 (1993).

Mangold, C. M., Unckell, F., Werr, M. & Streeck, R. E. Analysis of intermolecular disulfide bonds and free sulfhydryl groups in hepatitis B surface antigen particles. Arch. Virol. 142 , 2257–2267 (1997).

Mangold, C. M., Unckell, F., Werr, M. & Streeck, R. E. Secretion and antigenicity of hepatitis B virus small envelope proteins lacking cysteines in the major antigenic region. Virology 211 , 535–543 (1995).

Langley, D. R. et al. Inhibition of hepatitis B virus polymerase by entecavir. J. Virol. 81 , 3992–4001 (2007).

Huang, H., Chopra, R., Verdine, G. L. & Harrison, S. C. Structure of a covalently trapped catalytic complex of HIV-1 reverse transcriptase: implications for drug resistance. Science 282 , 1669–1675 (1998).

Article   ADS   CAS   PubMed   Google Scholar  

Guermouche, H. et al. Characterization of the dynamics of human cytomegalovirus resistance to antiviral drugs by ultra-deep sequencing. Antiviral Res. 173 , 104647 (2020).

da Silva, J. et al. Monitoring emerging human immunodeficiency virus drug resistance in Sub-Saharan Africa in the era of Dolutegravir. J. Infect. Dis. 225 (3), 364–366 (2022).

Article   PubMed   CAS   Google Scholar  

Popping, S. et al. The need for a European hepatitis C programme monitoring resistance to direct-acting antiviral agents in real life to eliminate hepatitis C. J. Virus Erad. 4 (3), 179–181 (2018).

Lina, B. et al. Five years of monitoring for the emergence of oseltamivir resistance in patients with influenza A infections in the Influenza Resistance Information Study. Influenza Other Respir Viruses. 12 (2), 267–278 (2018).

Mari A. et al. Global surveillance of potential antiviral drug resistance in SARS-CoV-2: proof of concept focussing on the RNA-dependent RNA polymerase. medRxiv 2020.12.28.20248663.

Lai, C. L. et al. A 1-year trial of telbivudine, lamivudine, and the combination in patient with hepatitis B e antigen-positive chronic hepatitis B. Gastroenterology 129 , 528–536 (2005).

Kim, H. S. et al. Management of entecavir-resistant chronic hepatitis B with adefovir-based combination therapies. World J. Gastroenterol. 21 , 10874–10882 (2015).

Angus, P. et al. Resistance to adefovir dipivoxil therapy associated with the selection of a novel mutation in the HBV polymerase. Gastroenterology 125 , 292–297 (2003).

Lok, A. S. & McMahon, B. J. Chronic hepatitis B: update of recommendations. Hepatology 39 , 857–861 (2004).

Elsevier. ScienceDirect https://www.sciencedirect.com/ (2019).

Springer Nature. Springer Nature https://www.springernature.com/gp (2019).

Zhang, X. et al. Pre-existing mutations related to tenofovir in chronic hepatitis B patients with long-term nucleos(t)ide analogue drugs treatment by ultra-deep pyrosequencing. Oncotarget 7 , 70264–70275 (2016).

Lin, C., Chien, R., Hu, C., Lai, M. & Yeh, C. Identification of hepatitis B virus rtS117F substitution as a compensatory mutation for rtM204I during lamivudine therapy. J Antimicrob Chemother. 67 , 39–48 (2012).

Cejuela, J. M. et al. nala: Text mining natural language mutation mentions. Bioinformatics 33 , 1852–1858 (2017).

Wei, C. H. et al. tmVar 2.0: Integrating genomic variant information from literature with dbSNP and ClinVar for precision medicine. Bioinformatics 34 , 80–87 (2018).

Download references

Acknowledgements

We thank the Roche Innovation Center Basel for their computational support.

A.G. and G.M.M. thank the EPSRC and MRC Centre for Doctoral Training (CDT) in Systems Approaches to Biomedical Science (EP/L016044/1). A.G. thanks the Clarendon and Oxford Kobe Scholarships. G.M.M. also thanks the EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research—SABS:R 3 (EP/S024093/1).

Author information

Authors and affiliations.

Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles’, Oxford, OX1 3LB, UK

An Goto & Garrett M. Morris

Roche Innovation Center Basel, Basel, Switzerland

Raul Rodriguez-Esteban & Sebastian H. Scharf

You can also search for this author in PubMed   Google Scholar

Contributions

A.G., R.R., G.M.M., and S.S. contributed to the conceptual idea of the study. A.G., R.R., and G.M.M. conceived and designed the methods. A.G. performed the computational work and analyzed the results. A.G., R.R., and G.M.M. wrote the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Garrett M. Morris .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Goto, A., Rodriguez-Esteban, R., Scharf, S.H. et al. Understanding the genetics of viral drug resistance by integrating clinical data and mining of the scientific literature. Sci Rep 12 , 14476 (2022). https://doi.org/10.1038/s41598-022-17746-3

Download citation

Received : 15 December 2021

Accepted : 30 July 2022

Published : 25 August 2022

DOI : https://doi.org/10.1038/s41598-022-17746-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Exploring the chemotherapeutic potential and therapeutic insight of phloretin against human malignancies: a systematic review

• Published: 30 March 2024

Cite this article

• Md. Sohel 1 , 2 ,
• Nishat Ulfat Nity 2 ,
• Md. Rifat Sarker 2 ,
• Md. Rezoan Hossain 3 ,
• K. M. Tanjida Islam 4 ,
• Ahona Rahman 2 ,
• Partha Biswas 5 ,
• Mohammad Nurul Amin 6 ,
• Zitu Barman 1 ,
• Md. Mahmudul Hasan 1 &
• Abdullah Al Mamun   ORCID: orcid.org/0000-0003-3839-9603 2  

T he search of alternative therapeutic agents for the use  of cancer patients has dramatically expanded. Natural products are especially in focus since their structures already function in nature and are more likely to be potent with fewer side effects. Phloretin is a natural product that has been studied for a wide variety of pharmacological actions against human malignancies. This systematic review aims to present up-to-date critical and comprehensive information on the anti-cancer ability of Phloretin with all associated molecular and cellular mechanisms in various forms of cancers. Data retrieved according to PRISMA guidelines from Science Direct, PubMed, and Scopus searching servers by using keywords including Phloretin, cancer name, synergistic, resistance and Pharmacokinetics property was analyzed via some in silico tools. This systematic review comprised 127 articles from different types of study, where Phloretin is hypothesized to be effective against 20 various forms of cancer. Phloretin has been found to inhibit cancer initiation and progression by modulating many imbalanced signalling pathways, including apoptosis, autophagy, necrosis, metastasis, angiogenesis, cell proliferation, glucose absorption, oxidative stress, inflammation, DNA damage, and many other pathways. This wide range of activity may be due to the structural targeting of numerous proteins including, Bcl-2, Bax, Bak, Bad, caspase, cyclins (B1, D1, E) and CDKs (4, 6,7) p18, p21, p27, p53, MMP-2, MMP- 8, MMP-9, Wnt/-catenin, PARP, TNF-α, NF-κB, IκB kinase, IL-1β, TNF-α, phospho-Akt, phosphor-p65, NF-κB, PI3K/Akt, MAPK/ERK, p-mTOR. The introduction of nano-technology-based strategies can improve the efficacy of Phloretin for cancer treatment. Existing evidence shows that Phloretin has synergistic effects with other natural compounds and conventional drugs, and this mechanism assists in reversing the resistance of anticancer drugs by regulating resistance-related proteins. However, Phloretin possesses favorable pharmacokinetic properties with  low toxicity in the human body by in silico methods. Therefore, Phloretin could be a potential anti-cancer drug against numerous cancer treatment by mitigating it's toxic effect and enhancing efficacy using nano-technology-based strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Naturally occurring anti-cancer compounds: shining from Chinese herbal medicine

Hua Luo, Chi Teng Vong, … Yitao Wang

Evodiamine as an anticancer agent: a comprehensive review on its therapeutic application, pharmacokinetic, toxicity, and metabolism in various cancers

Munmun Panda, Surya Kant Tripathi, … Bijesh K. Biswal

Myricetin: targeting signaling networks in cancer and its implication in chemotherapy

Zeeshan Javed, Khushbukhat Khan, … William C. Cho

Abbreviations

B-cell leukemia 2

Bcl-2-Associated X protein

Phosphoinositide 3-kinase

Ak strain transforming

Mammalian target of rapamycin

Matrix metalloproteinase 9

Glucose transporters

Unc-51 like autophagy activating kinase

Extracellular signal-regulated kinase 1/2

Tissue plasminogen activator

Light chain 3B

B-cell lymphoma-extra large

X-linked inhibitor of apoptosis protein

Vascular endothelial growth factor

3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide

Chloramphenicol acetyltransferase

Superoxide dismutase

Nuclear factor-B

Macrophage 1 antigen

Phosphoenolpyruvate

Cellular Myc

Reactive oxygen species

Hepatocellular carcinoma

Src homology region 2 domain-containing phosphatase-1

Signal transducer and activator of transcription 3

Protein kinase RNA-like endoplasmic reticulum kinase

Vascular endothelial growth factor receptor

Mitogen-activated protein kinase

C-X-C motif chemokine ligand 12

C-X-C motif chemokine receptor 4

Toll-like receptor 1

Toll-like receptor 2

Tumor necrosis factor-alpha

Nucleotide-binding oligomerization domain

Nucleotide-binding domain (NOD)-like receptor protein 3

Cyclin-dependent kinase 4

Cyclin-dependent kinase 6

Bcl-2 homologous antagonist/killer

Poly-ADP ribose polymerase

Hypoxia-inducible factor-1

Hexokinase 2

6-Phosphofructo-2-kinase/fructose-2,6-bisphosphatase isozyme 3

Pyruvate dehydrogenase kinase 1

Lactate dehydrogenase

PTEN-induced kinase 1

Glutathione

Non-small cell lung cancer

C-Jun N-terminal kinase

Thioredoxin

Thioredoxin interacting protein

2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) amino]-2-deoxy-D-glucose

Protein kinase C

Myelin transcription factor 1

Cyclin-dependent kinase 2

70 Kilodalton heat shock protein

Interleukin-6

Multi-drug resistance associated protein 1

Breast cancer resistance protein

Multi-drug resistance

Abkin SV, Ostroumova OS, Komarova EY, Meshalkina DA, Shevtsov MA, Margulis BA, Guzhova IV (2016) Phloretin increases the anti-tumor efficacy of intratumorally delivered heat-shock protein 70 KDa (HSP70) in a murine model of melanoma. Cancer Immunol Immunother. https://doi.org/10.1007/s00262-015-1778-1

Article   PubMed   Google Scholar  

Abu-Azzam O, Aldalaen S, Nasr M (2022) Potentiation of the cytotoxic activity of nutraceutical phloretin against cervical cancer by formulation into microemulsion. Pharm Chem J 55(11):1215–1218

Article   CAS   Google Scholar  

Akhdar H, Legendre C, Aninat C, More F (2012) Anticancer drug metabolism: chemotherapy resistance and new therapeutic approaches. Top Drug Metabol 22:138–170

Google Scholar  

Alsanani A, Pate BB, Wondisford S, Johnson N, Banerjee D (2015) Stromal carcinoma associated fibroblasts promote drug resistance of human pancreatic cancer cells by modulation of ROS via CXCR4/CXCL12 signaling. Int J Transl Sci. https://doi.org/10.13052/ijts2246-8765.20151006

Article   Google Scholar  

Anand M, Suresh K (2014) Biochemical Profiling and chemopreventive activity of phloretin on 7,12-dimethylbenz (A) anthracene induced oral carcinogenesis in male golden syrian hamsters. Toxicol Int. https://doi.org/10.4103/0971-6580.139805

Article   PubMed   PubMed Central   Google Scholar  

Mariadoss A, Vijaya A, Dhanabalan AK, Munusamy H, Gunasekaran K, David E (2018) In silico studies towards enhancing the anticancer activity of phytochemical phloretin against cancer drug targets. Current Drug Therapy. https://doi.org/10.2174/1574885513666180402134054

Anand AV, Mariadoss VR, Baojun Xu, Karthikumar V, Vijayalakshmi S, Shalini V, Rani BS, Mohamed Sadiq A, Ernest D (2019) Phloretin loaded chitosan nanoparticles enhance the antioxidants and apoptotic mechanisms in DMBA induced experimental carcinogenesis. Chem Biol Interact 308:11–19. https://doi.org/10.1016/j.cbi.2019.05.008

Badwaik, Vivek D, Sarah Tockstein, David H Thompson (2018) Development of Dihydrochalcone-Functionalized Gold Nanoparticles for Augmented Antineoplastic Activity, 1917–26

Barreca D, Bellocco E, Laganà G, Ginestra G, Bisignano C (2014) Biochemical and antimicrobial activity of phloretin and its glycosilated derivatives present in apple and kumquat. Food Chem. https://doi.org/10.1016/j.foodchem.2014.03.118

Barreca D, Currò M, Bellocco E, Ficarra S, Laganà G, Tellone E, Giunta ML et al (2017) Neuroprotective effects of phloretin and its glycosylated derivative on rotenone-induced toxicity in human SH-SY5Y neuronal-like cells. BioFactors. https://doi.org/10.1002/biof.1358

Biswas P, Dey D, Biswas PK, Rahaman TI, Saha S, Parvez A, Khan DA, Lily NJ, Saha K, Sohel Md (2022) A comprehensive analysis and anti-cancer activities of quercetin in ROS-mediated cancer and cancer stem cells. Int J Mol Sci 23(19):11746

Article   CAS   PubMed   PubMed Central   Google Scholar  

Cao X, Fang L, Gibbs S, Huang Y, Dai Z, Wen P, Zheng X, Sadee W, Sun D (2007) Glucose uptake inhibitor sensitizes cancer cells to daunorubicin and overcomes drug resistance in hypoxia. Cancer Chemother Pharmacol. https://doi.org/10.1007/s00280-006-0291-9

Casarini TP, Anunciato LA, Frank TB, Onzi G, Pohlmann AR, Guterres SS (2021) Innovative hydrogel containing polymeric nanocapsules loaded with phloretin: enhanced skin penetration and adhesion. Mater Sci Eng, C 120:111681

Casarini TPA, Frank LA, Pohlmann AR, Guterres SS (2020) Dermatological applications of the flavonoid phloretin. Eur J Pharmacol 889:173593

Chang WT, Huang WC, Liou CJ (2012) Evaluation of the anti-inflammatory effects of phloretin and phlorizin in lipopolysaccharide-stimulated mouse macrophages. Food Chem. https://doi.org/10.1016/j.foodchem.2012.03.002

Chen M, Gowd V, Wang M, Chen F, Cheng KW (2021) The apple dihydrochalcone phloretin suppresses growth and improves chemosensitivity of breast cancer cells: via inhibition of cytoprotective autophagy. Food Funct 12(1):177–190. https://doi.org/10.1039/d0fo02362k

Article   CAS   PubMed   Google Scholar  

Young CB (2019) Biochemical basis of anti-cancer-effects of phloretin—a natural dihydrochalcone. Molecules 24(2):278

Choi I, Kim J, Jeong HK, Kim B, Jou I, Park M, Chen L, Kang UJ, Zhuang X, Eun hye Joe. (2013) PINK1 deficiency attenuates astrocyte proliferation through mitochondrial dysfunction, reduced AKT and increased P38 MAPK activation, and downregulation of EGFR. Glia. https://doi.org/10.1002/glia.22475

Costea T, Vlad OC, Miclea LC, Ganea C, Szöllősi J, Mocanu MM (2020) Alleviation of multidrug resistance by flavonoid and non-flavonoid compounds in breast, lung, colorectal and prostate cancer. Int J Mol Sci. https://doi.org/10.3390/ijms21020401

Crespy V, Aprikian O, Morand C, Besson C, Manach C, Demigné C, Rémésy C (2001) Bioavailability of phloretin and phloridzin in rats. J Nutr 131(12):3227–3230. https://doi.org/10.1093/jn/131.12.3227

Cui D, Liu S, Tang M, Yongzhi Lu, Zhao M, Mao R, Wang C, Yuan Y, Li L, Chen Y (2020) Phloretin ameliorates hyperuricemia-induced chronic renal dysfunction through inhibiting NLRP3 inflammasome and uric acid reabsorption. Phytomedicine 66:153111

Davis ME (2016) Glioblastoma: overview of disease and treatment. Clin J Oncol Nurs 20(5):1–8. https://doi.org/10.1188/16.CJON.S1.2-8

Debela DT, Muzazu SGY, Heraro KD, Ndalama MT, Mesele BW, Haile DC, Kitui SK, Manyazewal T (2021) New approaches and procedures for cancer treatment: current perspectives. SAGE Open Medicine. https://doi.org/10.1177/20503121211034366

Dewanjee S, Sohel M, Hossain MS, Ansari F, Islam MT, Sultana F, Al Mamun A, Islam MM, Amin MN (2023) A Comprehensive Review on Clinically Proven Natural Products in the Management of Nerve Pain, with Mechanistic Insights. Heliyon

Duan H, Wang R, Yan X, Liu H, Zhang Y, Deguang Mu, Han J, Li X (2017) Phloretin induces apoptosis of human esophageal cancer via a mitochondria-dependent pathway. Oncol Lett 14(6):6763–6768

PubMed   PubMed Central   Google Scholar  

Fan C, Yilin Zhang Yu, Tian XZ, Teng J (2022) Phloretin enhances autophagy by impairing AKT activation and inducing JNK-beclin-1 pathway activation. Exp Mol Pathol 127(July):104814. https://doi.org/10.1016/j.yexmp.2022.104814

Frerichs H, Ball EG (1964) Studies on the metabolism of adipose tissue. XVI. Inhibition by phlorizin and phloretin of the insulin-stimulated uptake of glucose. Biochemistry 3(7):981–985

Friday E, Ledet J, Turturro F (2011) Response to dexamethasone is glucose-sensitive in multiple myeloma cell lines. J Exp Clin Cancer Res 30:1–7

Funari CS, Passalacqua TG, Rinaldo D, Napolitano A, Festa M, Capasso A, Piacente S, Pizza C, Young MCM, Durigan G (2011) Interconverting flavanone glucosides and other phenolic compounds in lippia salviaefolia Cham. Ethanol Extracts. Phytochemistry 72(16):2052–2061

FΑΟ. 2013. Food Wastage Footprint. Fao

Geohagen BC, Korsharskyy B, Vydyanatha A, Nordstroem L, LoPachin RM (2018) Phloretin cytoprotection and toxicity. Chem Biol Interact 296:117–123

Gonzalez-Menendez P, Hevia D, Rodriguez-Garcia A, Mayo JC, Sainz RM (2014) Regulation of GLUT transporters by flavonoids in androgen-sensitive and-insensitive prostate cancer cells. Endocrinology (united States). https://doi.org/10.1210/en.2014-1260

Grigoras CG, Destandau E, Fougère L, Elfakir C (2013) Evaluation of apple pomace extracts as a source of bioactive compounds. Ind Crops Prod. https://doi.org/10.1016/j.indcrop.2013.06.026

Hagl S, Deusser H, Soyalan B, Janzowski C, Will F, Dietrich H, Albert FW, Rohner S, Richling E (2011) Colonic availability of polyphenols and D-(-)-quinic acid after apple smoothie consumption. Mol Nutr Food Res. https://doi.org/10.1002/mnfr.201000252

Han L, Zhang Y, Li J, Xiao Y, Mei Lu, Li Y, Wang M (2021) Phloretin attenuation of hepatic steatosis: via an improvement of mitochondrial dysfunction by activating AMPK-dependent signaling pathways in C57BL/6J mice and HepG2 cells. Food Funct. https://doi.org/10.1039/d1fo02577e

He C, Wang J, Chakraborty T, Roy S (2020) A newfangled coordinated ruthenium phloretin complex reprogramming breast cancer microenvironment interceded by modulation of PI3K/Akt/Mtor/VEGF pathway and modifying the antioxidant status correlated with intensied apoptotic events. https://doi.org/10.21203/rs.3.rs-42460/v1

Hilt P, Schieber A, Yildirim C, Arnold G, Klaiber I, Conrad J, Beifuss U, Carle R (2003) Detection of phloridzin in strawberries (Fragaria x ananassa Duch) by HPLC− PDA− MS/MS and NMR spectroscopy. J Agric Food Chem 51(10):2896–2899. https://doi.org/10.1021/jf021115k

Hsiao Y-H, Hsieh M-J, Yang S-F, Chen S-P, Tsai W-C, Chen P-N (2019) Phloretin suppresses metastasis by targeting protease and inhibits cancer stemness and angiogenesis in human cervical cancer cells. Phytomedicine 62:152964

Huang WC, Fang LW, Liou CJ (2017) Phloretin attenuates allergic airway inflammation and oxidative stress in asthmatic mice. Front Immunol. https://doi.org/10.3389/fimmu.2017.00134

Hui J (2020) Down-regulation of aquaporin 9 by natural product phloretin mediates luminal bladder cancer migration in vitro. Johns Hopkins University

Wang H (2012) Phloretin induced apoptosis of human hepatoma cells SMMC-7721 and its correlative biological mechanisms. Afr J Pharm Pharmacol. https://doi.org/10.5897/ajpp11.869

Jacob V, Hagai T, Soliman K (2011) Structure-activity relationships of flavonoids. Curr Org Chem. https://doi.org/10.2174/138527211796367309

Jin G, Zhao Z, Chakraborty T, Mandal A, Roy A, Roy S, Guo Z (2020) Decrypting the molecular mechanistic pathways delineating the chemotherapeutic potential of ruthenium-phloretin complex in colon carcinoma correlated with the oxidative status and increased apoptotic events. Oxidative Med Cell Longev. https://doi.org/10.1155/2020/7690845

Kang D, Zuo W, Wu Q, Zhu Q, Liu P (2020) Inhibition of specificity protein 1 is involved in phloretin-induced suppression of prostate cancer. BioMed Res Int. https://doi.org/10.1155/2020/1358674

Kapoor S, Padwad YS (2023) Phloretin induces G2/M arrest and apoptosis by suppressing the β-catenin signaling pathway in colorectal carcinoma cells. Apoptosis. https://doi.org/10.1007/s10495-023-01826-4

Karpuz M, Silindir-Gunay M, Ozer AY (2018) Current and future approaches for effective cancer imaging and treatment. Cancer Biother Radiopharm. https://doi.org/10.1089/cbr.2017.2378

Kim J, Durai P, Jeon D, Jung ID, Lee SJ, Park Y-M, Kim Y (2018) Phloretin as a potent natural TLR2/1 inhibitor suppresses TLR2-induced inflammation. Nutrients 10(7):868

Kim J-L, Lee D-H, Cheol-Ho Pan Su, Park J, Sang-Cheul Oh, Lee S-Y (2022a) Role of phloretin as a sensitizer to TRAIL-induced apoptosis in colon cancer. Oncol Lett 24(3):1–13

Kim JL, Lee DH, Pan CH, Park SJ, Oh SC, Lee SY (2022b) Role of phloretin as a sensitizer to TRAIL-induced apoptosis in colon cancer. Oncol Lett. https://doi.org/10.3892/ol.2022.13441

Kim M-S, Kwon JY, Kang NJ, Lee KW, Lee HJ (2009) Phloretin induces apoptosis in H-Ras MCF10A Human breast tumor cells through the activation of P53 via JNK and P38 mitogen-activated protein kinase signaling. Ann N Y Acad Sci 1171(1):479–483

KoBoRI M, Iwashita K, Shinmoto H, Tojiro TSUSHIDA (1999) Phloretin-induced apoptosis in B16 melanoma 4A5 cells and HL60 human leukemia cells. Biosci Biotechnol Biochem 63(4):719–725

Kuruppu AI, Paranagama P, Goonasekara CL (2019) Medicinal plants commonly used against cancer in traditional medicine formulae in Sri Lanka. Saudi Pharmaceut J. https://doi.org/10.1016/j.jsps.2019.02.004

Lee SY, Park JH, Ko SH, Shim JS, Kim DD, Cho HJ (2017) Mussel-inspired hyaluronic acid derivative nanostructures for improved tumor targeting and penetration. ACS Appl Mater Interfaces 9(27):22308–22320. https://doi.org/10.1021/acsami.7b06582

Lee YJ, Kim WI, Kim SY, Cho SW, Nam HS, Lee SH, Cho MK (2019) Flavonoid morin inhibits proliferation and induces apoptosis of melanoma cells by regulating reactive oxygen species, Sp1 and Mcl-1. Arch Pharmacal Res 42(6):531–542. https://doi.org/10.1007/s12272-019-01158-5

Lin J, Sahakian D, de Morais S, Jinghai Xu, Polzer R, Winter S (2005) The role of absorption, distribution, metabolism, excretion and toxicity in drug discovery. Curr Top Med Chem. https://doi.org/10.2174/1568026033452096

Lin ST, Tu SH, Yang PS, Hsu SP, Lee WH, Ho CT, Wu CH, Lai YH, Chen MY, Chen LC (2016) Apple polyphenol phloretin inhibits colorectal cancer cell growth via inhibition of the type 2 glucose transporter and activation of p53-mediated signaling. J Agric Food Chem 64(36):6826–6837. https://doi.org/10.1021/acs.jafc.6b02861

Liu Y, Fan C, Lv Pu, Wei C, Jin H, Teng Y, Zhao M et al (2016) Phloretin induces cell cycle arrest and apoptosis of human glioblastoma cells through the generation of reactive oxygen species. J Neurooncol 128(2):217–223. https://doi.org/10.1007/s11060-016-2107-z

Lu Y, Chen J, Ren D, Yang X, Zhao Y (2017) Hepatoprotective effects of phloretin against CCl4-induced liver injury in mice. Food Hydrocolloids. https://doi.org/10.1080/09540105.2016.1258546

Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, Harris PL et al (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non–small-cell lung cancer to Gefitinib. N Engl J Med 350(21):2129–2139. https://doi.org/10.1056/nejmoa040938

Ma L, Wang R, Nan Y, Li W, Wang Q, Jin F (2016) Phloretin exhibits an anticancer effect and enhances the anticancer ability of cisplatin on non-small cell lung cancer cell lines by regulating expression of apoptotic pathways and matrix metalloproteinases. Int J Oncol 48(2):843–853

Ma P, Wu Y, Tao L, Han J (2022) Effect of Gardenia tea on apoptosis of human thyroid cancer cell line SW579 through PI3K-Akt pathway. Food Sci Technol 42:e73122

“Manchurian Apricot Facts and Health Benefits.”. Accessed April 7, 2023. https://www.healthbenefitstimes.com/manchurian-apricot/ .

Mansoori B, Mohammadi A, Davudian S, Shirjang S, Baradaran B (2017) The different mechanisms of cancer drug resistance: a brief review. Adv Pharmaceut Bull. https://doi.org/10.15171/apb.2017.041

Mariadoss AVA, Vinyagam R, Rajamanickam V, Sankaran V, Venkatesan S, David E (2019a) Pharmacological aspects and potential use of phloretin: a systemic review. Mini-Rev Med Chem. https://doi.org/10.2174/1389557519666190311154425

Mariadoss AV, Anand RV, Senthilkumar V, Paulpandi M, Murugan K, Baojun Xu, Gothandam KM, Kotakadi VS, David E (2019b) Phloretin loaded chitosan nanoparticles augments the ph-dependent mitochondrial-mediated intrinsic apoptosis in human oral cancer cells. Int J Biol Macromol 130(June):997–1008. https://doi.org/10.1016/J.IJBIOMAC.2019.03.031

Matsui C, Takatani-Nakase T, Maeda S, Nakase I, Takahashi K (2017) Potential roles of GLUT12 for glucose sensing and cellular migration in MCF-7 human breast cancer cells under high glucose conditions. Anticancer Res 37(12):6715–6722. https://doi.org/10.21873/anticanres.12130

McBrayer SK, Cheng JC, Singhal S, Krett NL, Rosen ST, Shanmugam M (2012) Multiple myeloma exhibits novel dependence on GLUT4, GLUT8, and GLUT11: implications for glucose transporter-directed therapy. Blood J Am Soc Hematol 119(20):4686–4697

CAS   Google Scholar  

Mehmood S, Haojie Yu, Li Wang Md, Uddin A, Amin BU, Haq F, Fahad S, Haroon M (2022) Cross-linked poly(cyclotriphosphazene-co-phloretin) microspheres and their application for controlled drug delivery. Macromol Res 30(9):623–630. https://doi.org/10.1007/S13233-022-0066-0/METRICS

Min J, Li Xu, Huang K, Tang H, Ding X, Qi C, Qin X, Zhifei Xu (2015) Phloretin induces apoptosis of non-small cell lung carcinoma A549 cells via JNK1/2 and P38 MAPK pathways. Oncol Rep 34(6):2871–2879

Minsat L, Peyrot C, Brunissen F, Renault JH, Allais F (2021) Synthesis of biobased phloretin analogues: an access to antioxidant and anti-tyrosinase compounds for cosmetic applications. Antioxidants. https://doi.org/10.3390/antiox10040512

Mintie CA, Singh CK, Ahmad N (2020) Whole fruit phytochemicals combating skin damage and carcinogenesis. Transl Oncol 13(2):146–156

Molnár J, Engi H, Hohmann J, Molnár P, Deli J, Wesolowska O, Michalak K, Wang Q (2010) Reversal of multidrug resistance by natural substances from plants. Curr Top Med Chem 10(17):1757–1768

Morshed AKM, Helal SP, Hossain A, Tuli Basak Md, Sanower Hossain Md, Mehedi Hasan Md, Hasibuzzaman Al, Rahaman TI, Mia MAR, Shing P (2023) Baicalein as promising anticancer agent: a comprehensive analysis on molecular mechanisms and therapeutic perspectives. Cancers 15(7):2128

Movius EG, Phyillaier MM, Robbins J (1989) Phloretin Inhibits cellular uptake and nuclear receptor binding of triiodothyronine in human hep G2 hepatocarcinoma cells. Endocrinology. https://doi.org/10.1210/endo-124-4-1988

Nahar Metu CL, Sutihar SK, Sohel M, Zohora F, Hasan A, Miah MT, Rani Kar T, Hossain MA, Rahman MH (2023) Unraveling the signaling mechanism behind astrocytoma and possible therapeutics strategies: a comprehensive review. Cancer Reports. 6(10):e1889

Nair SV, Ziaullah Rupasinghe HV (2014) Fatty acid esters of phloridzin induce apoptosis of human liver cancer cells through altered gene expression. PLoS ONE 9(9):e107149

Nakhate KT, Badwaik H, Choudhary R, Sakure K, Agrawal YO, Sharma C, Ojha S, Goyal SN (2022) Therapeutic potential and pharmaceutical development of a multitargeted flavonoid phloretin. Nutrients 14(17):3638

Nam S, Lee SY, Cho HJ (2017) Phloretin-loaded fast dissolving nanofibers for the locoregional therapy of oral squamous cell carcinoma. J Colloid Interface Sci. https://doi.org/10.1016/j.jcis.2017.08.030

Nguyen H, Zhang S, Morris ME (2003) Effect of flavonoids on MRP1-mediated transport in panc-1 cells. J Pharm Sci 92(2):250–257. https://doi.org/10.1002/jps.10283

Nobili S, Lippi D, Witort E, Donnini M, Bausi L, Mini E, Capaccioli S (2009) Natural compounds for cancer treatment and prevention. Pharmacol Res. https://doi.org/10.1016/j.phrs.2009.01.017

Ogawa T, Sasaki A, Ono K, Ohshika S, Ishibashi Y, Yamada K (2021) Uptake of fluorescent D-and L-glucose analogues, 2-NBDG and 2-NBDLG, into human osteosarcoma U2OS cells in a phloretin-inhibitable manner. Hum Cell 34:634–643

Oliveira MR (2016) Phloretin-induced cytoprotective effects on mammalian cells: a mechanistic view and future directions. BioFactors 42(1):13–40

Park SY, Kim EJ, Shin HK, Kwon DY, Kim MS, Surh YJ, Park JHY (2007) Induction of apoptosis in HT-29 colon cancer cells by phloretin. J Med Food 10(4):581–586. https://doi.org/10.1089/jmf.2007.116

Patel BB, Alsanani A, Wondisford S, Johnson N, Banerjee D (2016) Stromal carcinoma associated fibroblasts promote drug resistance of human pancreatic cancer cells by modulation of ROS via CXCR4/CXCL12 signaling. Int J Transl Sci 2016(1):107–130

Patel BB, Ackerstaff E, Serganova IS, Kerrigan JE, Blasberg RG, Koutcher JA, Banerjee D (2017) Tumor stroma interaction is mediated by monocarboxylate metabolism. Exp Cell Res 352(1):20–33. https://doi.org/10.1016/j.yexcr.2017.01.013

Paul P, Biswas P, Dey D, Saikat ASM, Islam MA, Sohel Md, Hossain R et al (2021) Exhaustive plant profile of ‘dimocarpus longan lour’ with significant phytomedicinal properties: a literature based-review. Processes. https://doi.org/10.3390/pr9101803

Payne JN, Badwaik VD, Waghwani HK, Moolani HV, Tockstein S, Thompson DH, Dakshinamurthy R (2018) Development of dihydrochalcone-functionalized gold nanoparticles for augmented antineoplastic activity. Int J Nanomed. https://doi.org/10.2147/IJN.S143506

“Phloretin | C15H14O5 - PubChem.”.

“Phloretin CF | Topical Vitamin C | Vitamin C Gel | SkinCeuticals.”.

Picinelli A, Dapena E, Mangas JJ (1995) Polyphenolic pattern in apple tree leaves in relation to scab resistance a preliminary study. J Agric Food Chem. https://doi.org/10.1021/jf00056a057

Po, Ughachukwu, and Unekwe Pc. 2012. “Efflux Pump _ Mediated Resistance in Chemotherapy” 2 (2): 191–98. https://doi.org/10.4103/2141-9248.105671 .

Pope ED, Kimbrough EO, Vemireddy LP, Surapaneni PK, Copland JA, Mody K (2019) Aberrant lipid metabolism as a therapeutic target in liver cancer. Expert Opin Ther Targets. https://doi.org/10.1080/14728222.2019.1615883

Prasanna S, Doerksen R (2008) Topological polar surface area: a useful descriptor in 2D-QSAR. Curr Med Chem 16(1):21–41. https://doi.org/10.2174/092986709787002817

Ranjanamala T, Vanmathiselvi K, Shreemaya R, Rajeswari SN, Sangeetha CC, Ghidan AY, Ghidan FY (2021) Virtual screening of anticancer efficacy of phloretin against apoptotic targets–An In Silico molecular docking study. J Phytol 13:152–160. https://doi.org/10.25081/JP.2021.V13.7268

Rawla P, Sunkara T, Gaduputi V (2019) epidemiology of pancreatic cancer: global trends, etiology and risk factors. World J Oncol 10(1):10–27. https://doi.org/10.14740/wjon1166

Rezk BM, Haenen GRMM, Van der Vijgh WJF, Bast A (2002) The antioxidant activity of phloretin: the disclosure of a new antioxidant pharmacophore in flavonoids. Biochem Biophys Res Commun. https://doi.org/10.1016/S0006-291X(02)00618-6

Rodriguez EB, Flavier ME, Rodriguez-Amaya DB, Amaya-Farfán J (2015) Phytochemicals and functional foods. Current situation and prospect for developing countries”. Segurança Alimentar e Nutricional. https://doi.org/10.20396/san.v13i1.1841

Roy S, Mondru AK, Chakraborty T, Das A, Dasgupta S (2022) Apple polyphenol phloretin complexed with ruthenium is capable of reprogramming the breast cancer microenvironment through modulation of PI3K/Akt/MTOR/VEGF pathways. Toxicol Appl Pharmacol. https://doi.org/10.1016/j.taap.2021.115822

Saraswati S, Alhaider A, Abdelgadir AM, Tanwer P, Korashy HM (2019) Phloretin attenuates STAT-3 activity and overcomes sorafenib resistance targeting SHP-1-mediated inhibition of STAT3 and Akt/VEGFR2 pathway in hepatocellular carcinoma. Cell Commun Signaling. https://doi.org/10.1186/s12964-019-0430-7

Schaefer S, Baum M, Eisenbrand G, Dietrich H, Will F, Janzowski C (2006) Polyphenolic apple juice extracts and their major constituents reduce oxidative damage in human colon cell lines. Mol Nutr Food Res. https://doi.org/10.1002/mnfr.200500136

Schiavano GF, De Santi M, Brandi G, Fanelli M, Bucchini A, Giamperi L, Giomaro G (2015) Inhibition of breast cancer cell proliferation and in vitro tumorigenesis by a new red apple cultivar. PLoS ONE. https://doi.org/10.1371/JOURNAL.PONE.0135840

Shang Ao, Liu H-Y, Min Luo Yu, Xia XY, Li H-Y, Ding-Tao Wu, Sun Q, Geng F, Gan R-Y (2022) Sweet Tea ( Lithocarpus Polystachyu s Rehd.) as a new natural source of bioactive dihydrochalcones with multiple health benefits. Crit Rev Food Sci Nutr 62(4):917–934

Shrihastini V, Muthuramalingam P, Adarshan S, Sujitha M, Chen JT, Shin H, Ramesh M (2021) Plant derived bioactive compounds, their anti-cancer effects and in silico approaches as an alternative target treatment strategy for breast cancer: an updated overview. Cancers. https://doi.org/10.3390/cancers13246222

Si W, Shen J, Zheng H, Fan W (2019) The role and mechanisms of action of MicroRNAs in cancer drug resistance. Clin Epigenetics. https://doi.org/10.1186/s13148-018-0587-8

Siegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer Statistics, 2023. CA Cancer J Clin. https://doi.org/10.3322/caac.21763

Slimestad R, Fossen T, Verheul MJ (2008) The flavonoids of tomatoes. J Agric Food Chem 56(7):2436–2441

So, Felicia V, Najla Guthrie, Ann F Chambers, Madeleine Moussa, Kenneth K Carroll, Felicia V So, Najla Guthrie, et al. 2009. “Inhibition of Human Breast Cancer Cell Proliferation and Delay of Mammary Tumorigenesis by Flavonoids and Citrus Juices Inhibition of Human Breast Cancer Cell Proliferation and Delay of Mammary Tumorigenesis by Flavonoids and Citrus Juices” 5581 (November 2017). https://doi.org/10.1080/01635589609514473 .

Sohel M, Aktar S, Biswas P, Amin MA, Hossain MA, Ahmed N, Mim MI, Islam F, Mamun AA (2023) Exploring the anti-cancer potential of dietary phytochemicals for the patients with breast cancer: a comprehensive review. Cancer Med 12(13):14556–14583

Sohel Md, Partha Biswas Md, Al Amin Md, Hossain A, Sultana H, Dey D, Aktar S et al (2022a) Genistein, a potential phytochemical against breast cancer treatment-insight into the molecular mechanisms. Processes. https://doi.org/10.3390/pr10020415

Sohel Md, Islam MN, Hossain MA, Sultana T, Amit Dutta Md, Rahman S, Aktar S, Islam K, Mamun AA (2022b) Pharmacological Properties to Pharmacological Insight of Sesamin in Breast Cancer Treatment: A Literature-Based Review Study. International Journal of Breast Cancer. https://doi.org/10.1155/2022/2599689

Sohel M, Sultana H, Sultana T, Al Amin M, Aktar S, Ali MC, Rahim ZB, Hossain MA, Al Mamun A, Amin MN, Dash R (2022c) Chemotherapeutic potential of hesperetin for cancer treatment, with mechanistic insights: a comprehensive review. Heliyon. 8(1):e08815

Sohel Md, Sultana H, Sultana T, Mamun AA, Amin MN, Md Hossain Md, Ali SA, Sultana A, Rahim ZB (2022cd) Chemotherapeutic activities of dietary phytoestrogens against prostate cancer: from observational to clinical studies. Curr Pharm Des 28(19):1561–1580

Suhail S, Gupta S, Kumar V, Gupta OP (2016) Role of curcumin in oral cancer prevention. Fract Geomet Nonlinear Anal Med Biol. https://doi.org/10.15761/fgnamb.1000119

Sun YS, Zhao Z, Yang ZN, Fang Xu, Hang Jing Lu, Zhu ZY, Shi W, Jiang J, Yao PP, Zhu HP (2017) Risk factors and preventions of breast cancer. Int J Biol Sci. https://doi.org/10.7150/ijbs.21635

Szabo K, Mitrea L, Călinoiu LF, Teleky BE, Martău GA, Plamada D, Pascuta MS, Nemeş SA, Varvara RA, Vodnar DC (2022) Natural polyphenol recovery from apple-, cereal-, and tomato-processing by-products and related health-promoting properties. Molecules. https://doi.org/10.3390/molecules27227977

Szliszka E, Czuba ZP, Mazur B, Paradysz A, Krol W (2010) Chalcones and Dihydrochalcones Augment TRAIL-Mediated Apoptosis in Prostate Cancer Cells. Molecules 15(8):5336–5353

Tang J, Gong Y (2021) Synergistic effect of phloretin combined with radiotherapy on lung cancer. Int J Radiat Oncol Biol Phys. https://doi.org/10.1016/j.ijrobp.2021.07.809

Tuli HS, Rath P, Chauhan A, Ramniwas S, Vashishth K, Varol M, Jaswal VS, Haque S, Sak K (2022a) Phloretin, as a potent anticancer compound: from chemistry to cellular interactions. Molecules 27(24):8819

Tuli HS, Rath P, Chauhan A, Ramniwas S, Vashishth K, Varol M, Jaswal VS, Haque S, Sak K (2022b) Phloretin, as a potent anticancer compound: from chemistry to cellular interactions. Molecules. https://doi.org/10.3390/molecules27248819

Ulaszewska M, Vázquez-Manjarrez N, Garcia-Aloy M, Llorach R, Mattivi F, Dragsted LO, Praticò G, Manach C (2018) Food intake biomarkers for apple, pear, and stone fruit lars dragsted. Genes Nutr. https://doi.org/10.1186/s12263-018-0620-8

Wu C-H, Ho Y-S, Tsai C-Y, Wang Y-J, Tseng H, Wei P-L, Lee C-H, Liu R-S, Lin S-Y (2009a) In vitro and in vivo study of phloretin-induced apoptosis in human liver cancer cells involving inhibition of type ii glucose transporter. Int J Cancer 124(9):2210–2219

Wu CH, Ho YS, Tsai CY, Wang YJ, Tseng H, Wei PL, Lee CH, Liu RS, Lin SY (2009b) In vitro and in vivo study of phloretin-induced apoptosis in human liver cancer cells involving inhibition of type II glucose transporter. Int J Cancer. https://doi.org/10.1002/ijc.24189

Wu F, Zhou Y, Li L, Shen X, Chen G, Wang X, Liang X, Tan M, Huang Z (2020) Computational approaches in preclinical studies on drug discovery and development. Front Chem. https://doi.org/10.3389/fchem.2020.00726

Wu KH, Ho CT, Chen ZF, Chen LC, Whang-Peng J, Lin TN, Ho YS (2018) The apple polyphenol phloretin inhibits breast cancer cell migration and proliferation via inhibition of signals by type 2 glucose transporter. J Food Drug Anal 26(1):221–231. https://doi.org/10.1016/j.jfda.2017.03.009

Xintaropoulou C, Ward C, Wise A, Marston H, Turnbull A, Langdon SP (2015) A comparative analysis of inhibitors of the glycolysis pathway in breast and ovarian cancer cell line models. Oncotarget 6(28):25677

Xue X, Liang XJ (2012) Overcoming drug efflux-based multidrug resistance in cancer with nanotechnology. Chin J Cancer. https://doi.org/10.5732/cjc.011.10326

Yang C-H, Yen-Chuan Ou, Lin C-C, Lin Y-S, Tung M-C, Chia-Cheng Yu, Lin J-T, Wen C-Y (2022) Phloretin in benign prostate hyperplasia and prostate cancer: a contemporary systematic review. Life 12(7):1029

Yang G, Yin X, Ma D, Su Z (2020) Anticancer Activity of Phloretin against the Human Oral Cancer Cells Is Due to G0/G1 Cell Cycle Arrest and ROS Mediated Cell Death. J BUON 25:344–349

PubMed   Google Scholar  

Yang KC, Tsai CY, Wang YJ, Wei PL, Lee CH, Chen JH, Chih Hsiung Wu, Ho YS (2009) Apple polyphenol phloretin potentiates the anticancer actions of paclitaxel through induction of apoptosis in human hep G2 cells. Mol Carcinog. https://doi.org/10.1002/mc.20480

You Q, Xu JP, Zhu ZX, Hu Z, Cai QP (2020) Phloretin flavonoid exhibits selective antiproliferative activity in doxorubicin-resistant gastric cancer cells by inducing autophagy, inhibiting cell migration and invasion, cell cycle arrest and targeting ERK1/2 MAP pathway. J BUON 25:308–313

Zhang S, Morris ME (2003) Effects of the flavonoids biochanin a, morin, phloretin, and silymarin on p-glycoprotein-mediated transport. J Pharmacol Exp Ther. https://doi.org/10.1124/jpet.102.044412

Zhao W, Bai B, Hong Z, Zhang X, Zhou B (2020) Berbamine (BBM), a natural STAT3 inhibitor, synergistically enhances the antigrowth and proapoptotic effects of sorafenib on hepatocellular carcinoma cells. ACS Omega. https://doi.org/10.1021/acsomega.0c03527

Zhao Y, Fan Y, Wang M, Wang J, Cheng J, Zou J, Zhang X, Shi Y, Guo D (2020) Studies on pharmacokinetic properties and absorption mechanism of phloretin: in vivo and in vitro. Biomed Pharmacother 132:110809

Zhou Mo, Zheng J, Bi J, Xinye Wu, Lyu J, Gao K (2018) Synergistic inhibition of colon cancer cell growth by a combination of atorvastatin and Phloretin. Oncol Lett 15(2):1985–1992. https://doi.org/10.3892/ol.2017.7480

Zhu S-P, Liu G, Xiao-Ting Wu, Chen F-X, Liu J-Q, Zhou Z-H, Zhang J-F, Fei S-J (2013a) The effect of Phloretin on human Γδ T cells killing colon cancer SW-1116 cells. Int Immunopharmacol 15(1):6–14

Zhu SP, Liu G, Xiao Ting Wu, Chen FX, Liu JQ, Zhou ZH, Zhang JF, Fei SJ (2013b) The effect of phloretin on human Γδ t cells killing colon cancer SW-1116 cells. Int Immunopharmacol 15(1):6–14. https://doi.org/10.1016/j.intimp.2012.11.001

Download references

Author information

Authors and affiliations.

Department of Biochemistry and Molecular Biology, Primeasia University, Banani, Dhaka, 1213, Bangladesh

Md. Sohel, Zitu Barman & Md. Mahmudul Hasan

Department of Biochemistry and Molecular Biology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh

Md. Sohel, Nishat Ulfat Nity, Md. Rifat Sarker, Ahona Rahman & Abdullah Al Mamun

Department of Biochemistry and Molecular Biology, Rajshahi University, Rajshahi, 1205, Bangladesh

Md. Rezoan Hossain

Department of Biotechnology and Genetic Engineering, Faculty of Life Science, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh

K. M. Tanjida Islam

Department of Genetic Engineering and Biotechnology, Faculty of Biological Science and Technology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh

Partha Biswas

Department of Pharmacy, Atish Dipankar University of Science and Technology, Dhaka, 1230, Bangladesh

Mohammad Nurul Amin

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Abdullah Al Mamun .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Sohel, M., Nity, N.U., Sarker, M.R. et al. Exploring the chemotherapeutic potential and therapeutic insight of phloretin against human malignancies: a systematic review. Phytochem Rev (2024). https://doi.org/10.1007/s11101-024-09938-8

Download citation

Received : 08 May 2023

Accepted : 23 February 2024

Published : 30 March 2024

DOI : https://doi.org/10.1007/s11101-024-09938-8

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Anti-cancer perspective
• Nano-formulation
• Synergistic
• Pharmacokinetics
• Find a journal
• Publish with us
• Track your research

Chemical Science

Lanthanide mof-based luminescent sensor arrays for the detection of castration-resistant prostate cancer curing drugs and biomarkers.

In recent years, castration-resistant prostate cancer (CRPC) has profoundly impacted the lives of many men, and early diagnosis of medication and illness is crucial. Therefore, a highly efficient detection method of CRPC biomarkers and curing drugs is required. However, the complex and diverse structures of CRPC drugs pose significant challenges for their detection and differentiation. Lanthanide Metal-Organic Frameworks (Ln-MOFs) show great potential for sensing applications due to their intense and characteristic luminescence. In this work, a series of new bimetallic Ln-MOFs (EuxTb1-x-MOF) based luminescent sensor arrays have been developed to identify CRPC drugs, including in mixtures, via principal component analysis (PCA) and hierarchical cluster analysis (HCA) methods. These Ln-MOFs are built with a highly conjugated H2L linker (H2L = 5-(4-(triazole-1-yl)phenyl)isophthalic acid) and exhibit robust strong luminescence emissions and high energy transfer efficiencies. More specifically, Eu0.096Tb0.904-MOF (MOF 3) has demonstrated good sensing performances for CRPC curing drugs in real human serum samples. Further, the curing drug hydroxyflutamide has been combined with MOF 3, to construct a robust composite sensing platform MOF 3@Hydroxyflutamide for highly efficient detection of CRPC biomarkers such as androgen receptor (AR) and prostate-specific antigen (PSA). Finally, luminescence lifetime, zeta potential measurements, and density functional theory (DFT) calculations were performed to gain insights into the sensing mechanism.

Supplementary files

• Supplementary information PDF (2208K)
• Crystal structure data CIF (491K)

Transparent peer review

To support increased transparency, we offer authors the option to publish the peer review history alongside their article.

View this article’s peer review history

Article information

Download Citation

Permissions.

X. Wang, G. karuppasamy, G. Clavier, G. Maurin, B. Ding, A. Tissot and C. Serre, Chem. Sci. , 2024, Accepted Manuscript , DOI: 10.1039/D3SC06899D

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence . You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content .

Social activity

Search articles by author.

This article has not yet been cited.

Advertisements