Development of binary classification models for assessment of drug-induced liver injury in humans using a large set of FDA-approved drugs

Hui Zhang1,2, Hong-Rui Zhang1, Mei-Ling Hu1, Hua-Zhao Qi1
1College of Life Science, Northwest Normal University, Lanzhou, Gansu 730070, PR China
2State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan 610041, PR China

Tài liệu tham khảo

Ai, 2018, Predicting drug-induced liver injury using ensemble learning methods and molecular fingerprints, Toxicological Sciences, 165, 100, 10.1093/toxsci/kfy121 Assis, 2009, Human drug hepatotoxicity: A contemporary clinical perspective, Expert Opinion on Drug Metabolism & Toxicology, 5, 463, 10.1517/17425250902927386 Bajorath, 2014, Exploring activity cliffs from a chemoinformatics perspective, Molecular Informatics, 33, 438, 10.1002/minf.201400026 Béquignon, 2021, Computational approaches for drug-induced liver injury (DILI) prediction: State of the art and challenges, Systems Medicine Integrative, Qualitative and Computational Approaches, 2, 308 Berger, 2013 Berggren, 2012, Outlook for the next 5 years in drug innovation, Nature Reviews. Drug Discovery, 11, 435, 10.1038/nrd3744 Box, 2011 Chen, 2013, High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury, Hepatology, 58, 388, 10.1002/hep.26208 Cheng, 2003, In silico models for the prediction of dose-dependent human hepatotoxicity, Journal of Computer-Aided Molecular Design, 17, 811, 10.1023/B:JCAM.0000021834.50768.c6 Corsini, 2012, Current challenges and controversies in drug-induced liver injury, Drug Safety, 35, 1099, 10.1007/BF03261997 Davis, 1991, Handbook of genetic algorithms Dimova, 2016, Advances in activity cliff research, Molecular Informatics, 35, 181, 10.1002/minf.201600023 Egan, 2004, In silico prediction of drug safety: Despite progress there is abundant room for improvement, Drug Discovery Today, 1, 381, 10.1016/j.ddtec.2004.11.002 Greene, 2010, Developing structure-activity relationships for the prediction of hepatotoxicity, Chemical Research in Toxicology, 23, 1215, 10.1021/tx1000865 He, 2019, An in silico model for predicting drug-induced hepatotoxicity, International Journal of Molecular Sciences, 20, 1897, 10.3390/ijms20081897 Hewitt, 2013, Hepatotoxicity: A scheme for generating chemical categories for read-across, structural alerts and insights into mechanism(s) of action, Critical Reviews in Toxicology, 43, 537, 10.3109/10408444.2013.811215 Holt, 2010, Drug-induced liver injury, Annals of Internal Medicine, 137, 3 Hoofnagle, 2013, Livertox: A website on drug-induced liver injury, Hepatology, 57, 873, 10.1002/hep.26175 Kaplowitz, 2005, Idiosyncratic drug hepatotoxicity, Nature Reviews. Drug Discovery, 4, 489, 10.1038/nrd1750 Kola, 2004, Can the pharmaceutical industry reduce attrition rates?, Nature Reviews. Drug Discovery, 3, 711, 10.1038/nrd1470 Kullak-Ublick, 2017, Drug-induced liver injury: Recent advances in diagnosis and risk assessment, Gut, 66, 1154, 10.1136/gutjnl-2016-313369 Kuna, 2018, Models of drug induced liver injury (DILI) - current issues and future perspectives, Current Drug Metabolism, 19, 830, 10.2174/1389200219666180523095355 Liu, 2019, Three-level hepatotoxicity prediction system based on adverse hepatic effects, Molecular Pharmaceutics, 16, 393, 10.1021/acs.molpharmaceut.8b01048 Liu, 2015, Data-driven identification of structural alerts for mitigating the risk of drug-induced human liver injuries, The Journal of Information Literacy, 7, 4 OECD, 2014, Guidance document on the validation of (quantitative) structure-activity relationship [(Q)SAR] models, 1 Parasrampuria, 2018, Why drugs fail in late stages of development: Case study analyses from the last decade and recommendations, The AAPS Journal, 20, 46, 10.1208/s12248-018-0204-y Regev, 2014, Drug-induced liver injury and drug development: Industry perspective, Seminars in Liver Disease, 34, 227, 10.1055/s-0034-1375962 Rogers, 2005, Using extended connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up, Journal of Biomolecular Screening, 10, 682, 10.1177/1087057105281365 Rogers, 2010, Extended-connectivity fingerprints, Journal of Chemical Information and Modeling, 50, 742, 10.1021/ci100050t Siramshetty, 2016, Withdrawn—A resource for withdrawn and discontinued drugs, Nucleic Acids Research, 44, 1080, 10.1093/nar/gkv1192 Strobl, 2009, An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests, Psychological Methods, 14, 323, 10.1037/a0016973 Thakkar, 2020, Drug-induced liver injury severity and toxicity (DILIst): Binary classification of 1279 drugs by human hepatotoxicity, Drug Discovery Today, 225, 201, 10.1016/j.drudis.2019.09.022 van Tonder, 2013, Pre-clinical assessment of the potential intrinsic hepatotoxicity of candidate drugs Wang, 2019, In silico prediction of drug-induced liver injury based on ensemble classifier method, International Journal of Molecular Sciences, 20, 4106, 10.3390/ijms20174106 Watkins, 2005, Insight into hepatotoxicity: The troglitazone experience, Hepatology, 41, 229, 10.1002/hep.20598 Wu, 2019, In silico identification and mechanism exploration of hepatotoxic ingredients in traditional Chinese medicine, Frontiers in Pharmacology, 10, 1, 10.3389/fphar.2019.00458 Zhang, 2016, Predicting drug-induced liver injury in human with Naïve Bayes classifier approach, Journal of Computer-Aided Molecular Design, 30, 889, 10.1007/s10822-016-9972-6 Zhang, 2020, Development of novel in silico prediction model for drug-induced ototoxicity by using naïve Bayes classifier approach, Toxicology In Vitro, 65, 10.1016/j.tiv.2020.104812 Zhang, 2018, Development and evaluation of in silico prediction model for drug-induced respiratory toxicity by using naïve Bayes classifier method, Food and Chemical Toxicology, 121, 593, 10.1016/j.fct.2018.09.051 Zhang, 2020, Developing novel computational prediction models for assessing chemical induced neurotoxicity using naive Bayes classifier technique, Food and Chemical Toxicology, 143, 10.1016/j.fct.2020.111513