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Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data

Overview
Specialty Toxicology
Date 2023 Aug 9
PMID 37556769
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Abstract

Drug-induced liver injury (DILI) is an important safety concern and a major reason to remove a drug from the market. Advancements in recent machine learning methods have led to a wide range of in silico models for DILI predictive methods based on molecule chemical structures (fingerprints). Existing publicly available DILI data sets used for model building are based on the interpretation of drug labels or patient case reports, resulting in a typical binary clinical DILI annotation. We developed a novel phenotype-based annotation to process hepatotoxicity information extracted from repeated dose in vivo preclinical toxicology studies using INHAND annotation to provide a more informative and reliable data set for machine learning algorithms. This work resulted in a data set of 430 unique compounds covering diverse liver pathology findings which were utilized to develop multiple DILI prediction models trained on the publicly available data (TG-GATEs) using the compound's fingerprint. We demonstrate that the TG-GATEs compounds DILI labels can be predicted well and how the differences between TG-GATEs and the external test compounds (Johnson & Johnson) impact the model generalization performance.

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References
1.
Siramshetty V, Nickel J, Omieczynski C, Gohlke B, Drwal M, Preissner R . WITHDRAWN--a resource for withdrawn and discontinued drugs. Nucleic Acids Res. 2015; 44(D1):D1080-6. PMC: 4702851. DOI: 10.1093/nar/gkv1192. View

2.
Fernandez-Checa J, Bagnaninchi P, Ye H, Sancho-Bru P, Falcon-Perez J, Royo F . Advanced preclinical models for evaluation of drug-induced liver injury - consensus statement by the European Drug-Induced Liver Injury Network [PRO-EURO-DILI-NET]. J Hepatol. 2021; 75(4):935-959. DOI: 10.1016/j.jhep.2021.06.021. View

3.
Chan R, Benet L . Evaluation of DILI Predictive Hypotheses in Early Drug Development. Chem Res Toxicol. 2017; 30(4):1017-1029. PMC: 5674994. DOI: 10.1021/acs.chemrestox.7b00025. View

4.
Jaeschke H, Xie Y, McGill M . Acetaminophen-induced Liver Injury: from Animal Models to Humans. J Clin Transl Hepatol. 2015; 2(3):153-61. PMC: 4521247. DOI: 10.14218/JCTH.2014.00014. View

5.
Chen M, Borlak J, Tong W . High lipophilicity and high daily dose of oral medications are associated with significant risk for drug-induced liver injury. Hepatology. 2012; 58(1):388-96. DOI: 10.1002/hep.26208. View