{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T17:03:15Z","timestamp":1773162195226,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009820","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,2,7]],"date-time":"2022-02-07T00:00:00Z","timestamp":1644192000000}}],"reference-count":65,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T00:00:00Z","timestamp":1643155200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"agence nationale de la recherche","award":["ToxME"],"award-info":[{"award-number":["ToxME"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug\u2013drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values &lt;18 \u03bcM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 \u03bcM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 \u03bcM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009820","type":"journal-article","created":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T18:38:40Z","timestamp":1643222320000},"page":"e1009820","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":30,"title":["Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5622-9160","authenticated-orcid":true,"given":"Elodie","family":"Goldwaser","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0459-4796","authenticated-orcid":true,"given":"Catherine","family":"Laurent","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6048-9800","authenticated-orcid":true,"given":"Nathalie","family":"Lagarde","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4145-5488","authenticated-orcid":true,"given":"Sylvie","family":"Fabrega","sequence":"additional","affiliation":[]},{"given":"Laure","family":"Nay","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6456-7730","authenticated-orcid":true,"given":"Bruno O.","family":"Villoutreix","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2655-0313","authenticated-orcid":true,"given":"Christian","family":"Jelsch","sequence":"additional","affiliation":[]},{"given":"Arnaud B.","family":"Nicot","sequence":"additional","affiliation":[]},{"given":"Marie-Anne","family":"Loriot","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6895-1214","authenticated-orcid":true,"given":"Maria A.","family":"Miteva","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,1,26]]},"reference":[{"issue":"1","key":"pcbi.1009820.ref001","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1021\/tx700079z","article-title":"Cytochrome p450 and chemical toxicology","volume":"21","author":"FP Guengerich","year":"2008","journal-title":"Chem Res Toxicol"},{"issue":"1","key":"pcbi.1009820.ref002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/toxsci\/kfq374","article-title":"Genetic polymorphism and toxicology\u2014with emphasis on cytochrome p450","volume":"120","author":"I Johansson","year":"2011","journal-title":"Toxicol Sci"},{"issue":"17\u201318","key":"pcbi.1009820.ref003","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.drudis.2011.08.003","article-title":"Novel advances in cytochrome P450 research","volume":"16","author":"D Singh","year":"2011","journal-title":"Drug Discov Today"},{"key":"pcbi.1009820.ref004","doi-asserted-by":"crossref","DOI":"10.1021\/acs.jcim.1c00628","article-title":"iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding J Chem Inf Model","author":"TH Nguyen-Vo","year":"2021"},{"key":"pcbi.1009820.ref005","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1124\/dmd.104.000794","article-title":"Drug-drug interactions for UDP-glucuronosyltransferase substrates: a pharmacokinetic explanation for typically observed low exposure (AUCi\/AUC) ratios","volume":"32","author":"JA Williams","year":"2004","journal-title":"Drug Metab Dispos"},{"key":"pcbi.1009820.ref006","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/978-3-319-12108-6_9","volume-title":"Human Cytochrome P450 Enzymes","author":"FP Guengerich","year":"2015"},{"issue":"9\u201310","key":"pcbi.1009820.ref007","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.drudis.2010.02.013","article-title":"The nasty surprise of a complex drug-drug interaction","volume":"15","author":"C. 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Maekawa","year":"2017","journal-title":"Biochemistry"},{"issue":"34","key":"pcbi.1009820.ref035","doi-asserted-by":"crossref","first-page":"35630","DOI":"10.1074\/jbc.M405427200","article-title":"The structure of human cytochrome P450 2C9 complexed with flurbiprofen at 2.0-A resolution","volume":"279","author":"MR Wester","year":"2004","journal-title":"J Biol Chem"},{"issue":"2","key":"pcbi.1009820.ref036","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"Software News and Update AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading","volume":"31","author":"O Trott","year":"2010","journal-title":"Journal of Computational Chemistry"},{"key":"pcbi.1009820.ref037","unstructured":"(MOE) MOE. Chemical Computing Group Inc., 1010 Sherbooke St. West, Montreal, QC, Canada, H3A 2R7. 2016."},{"key":"pcbi.1009820.ref038","author":"M. 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Reliable prediction of human cytochrome P450 inhibition using artificial intelligence approaches","volume":"59","author":"Z Wu","year":"2019","journal-title":"J Chem Inf Model"},{"key":"pcbi.1009820.ref044","doi-asserted-by":"crossref","first-page":"116388","DOI":"10.1016\/j.bmc.2021.116388","article-title":"CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes","volume":"46","author":"W Plonka","year":"2021","journal-title":"Bioorg Med Chem"},{"issue":"W1","key":"pcbi.1009820.ref045","doi-asserted-by":"crossref","first-page":"W5","DOI":"10.1093\/nar\/gkab255","article-title":"ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties","volume":"49","author":"G Xiong","year":"2021","journal-title":"Nucleic Acids Res"},{"issue":"64","key":"pcbi.1009820.ref046","doi-asserted-by":"crossref","first-page":"32346","DOI":"10.18632\/oncotarget.25966","article-title":"Online structure-based screening of purchasable approved drugs and natural compounds: retrospective examples of drug repositioning on cancer targets","volume":"9","author":"N Lagarde","year":"2018","journal-title":"Oncotarget"},{"key":"pcbi.1009820.ref047","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1074\/jbc.M115.685610","article-title":"In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations","volume":"291","author":"C Rakers","year":"2016","journal-title":"J Biol Chem"},{"issue":"11","key":"pcbi.1009820.ref048","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1038\/nbt.1581","article-title":"Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries","volume":"27","author":"H Veith","year":"2009","journal-title":"Nat Biotechnol"},{"issue":"2","key":"pcbi.1009820.ref049","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1021\/ci500588j","article-title":"DataWarrior: an open-source program for chemistry aware data visualization and analysis","volume":"55","author":"T Sander","year":"2015","journal-title":"J Chem Inf Model"},{"key":"pcbi.1009820.ref050","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1038\/nature01862","article-title":"Crystal structure of human cytochrome P450 2C9 with bound warfarin","volume":"424","author":"PA Williams","year":"2003","journal-title":"Nature"},{"key":"pcbi.1009820.ref051","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/S1359644602025722","article-title":"Nonleadlikeness and leadlikeness in biochemical screening","volume":"8","author":"GM Rishton","year":"2003","journal-title":"Drug Discov Today"},{"key":"pcbi.1009820.ref052","doi-asserted-by":"crossref","first-page":"CT153","DOI":"10.1158\/1538-7445.AM2016-CT153","article-title":"Abstract CT153: Pharmacokinetic drug interactions between abemaciclib and CYP3A inducers and inhibitors","volume":"76","author":"P Kulanthaivel","year":"2016","journal-title":"Cancer 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(PTK787\/ZK-222584) in cancer patients","volume":"34","author":"LM Jost","year":"2006","journal-title":"Drug Metab Dispos"},{"key":"pcbi.1009820.ref056","doi-asserted-by":"crossref","first-page":"7849","DOI":"10.1021\/acs.jmedchem.8b00812","article-title":"Metalloporphyrin-Catalyzed Oxidation of Sunitinib and Pazopanib, Two Anticancer Tyrosine Kinase Inhibitors: Evidence for New Potentially Toxic Metabolites","volume":"61","author":"MN Paludetto","year":"2018","journal-title":"J Med Chem"},{"issue":"suppl_2","key":"pcbi.1009820.ref057","doi-asserted-by":"crossref","first-page":"W622","DOI":"10.1093\/nar\/gkq325","article-title":"Frog2: Efficient 3D conformation ensemble generator for small compounds","volume":"38","author":"MA Miteva","year":"2010","journal-title":"Nucleic Acids Research"},{"issue":"16","key":"pcbi.1009820.ref058","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1002\/jcc.21256","article-title":"AutoDock4 and AutoDockTools4: Automated docking with selective 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Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"pcbi.1009820.ref062","author":"A Liaw","year":"2001","journal-title":"Classification and Regression by RandomForest"},{"issue":"3","key":"pcbi.1009820.ref063","doi-asserted-by":"crossref","first-page":"e1301","DOI":"10.1002\/widm.1301","article-title":"Hyperparameters and tuning strategies for random forest","volume":"9","author":"P Probst","year":"2019","journal-title":"WIREs Data Mining and Knowledge Discovery"},{"issue":"3","key":"pcbi.1009820.ref064","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks.","volume":"20","author":"C Cortes","year":"1995","journal-title":"Machine Learning"},{"key":"pcbi.1009820.ref065","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R Using the caret Package","volume":"28","author":"M. 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