Registration is required for this free webinar
Hosted by the SOT Computational Toxicology Specialty Section
Please join us in hearing from the 2025 CTSS Award Winners!
This webinar will highlight our Paper of the Year "Drug Metabolizing Enzymes and Transporters, and their Roles for the Development of Drug-induced Liver Injury" published by Ayooluwa Olubamiwa et al. and presentations by three of our trainee award winners. Speaker and presentation details are available below.
Drug Metabolizing Enzymes and Transporters, and their Roles for the Development of Drug-induced Liver Injury
Ayooluwa Olubamiwa, PhD, PharmQuest LLC
Drug-induced liver injury (DILI) frequently contributes to the attrition of new drug development candidates and is a common cause for the withdrawal of approved drugs from the market. Although some non-cytochrome P450 (non-CYP) metabolism enzymes have been implicated in DILI development, their association with DILI outcomes has not been systematically evaluated. In this study, we analyzed a dataset, extracted from historical regulatory documents, which comprised of 317 drugs and their interactions in vitro with 42 non-CYP enzymes as substrates, inducers and/or inhibitors. We examined how these in vitro drug-enzyme interactions are correlated with the drugs’ potential for DILI concern, as classified in the Liver Toxicity Knowledge Base database. Our study revealed that drugs that inhibit non-CYP enzymes are significantly associated with high DILI concern, especially drug interactions with UGT enzymes. Further analysis indicated that only pure UGT inhibitors and dual substrate-inhibitors, but not pure UGT substrates, are significantly associated with high DILI concern. Notably, drug interactions with UGT enzymes may independently predict DILI, and their combined use with the rule-of-two model further improves overall predictive performance. These findings could expand the currently available tools for assessing the potential for DILI in humans.
Application of Machine Learning and Mechanistic Modeling to Predict Intravenous Pharmacokinetic Profiles in Humans
Xuelian Jia, PhD Candidate, Pharmaceutical Chemistry, Rowan University
Pharmacophore-Based In Silico Model for Prediction of Ready Biodegradation
Diana P. Garnica Acevedo, PhD Candidate, Department of Chemistry, The George Washington University
Interpretable Machine Learning to Understand Wildfire Toxicity: Bridging Chemicals, Omics, and Health Outcomes via Symbolic Regression with Novel Feature Scoring
Jessie Chappel, PhD, Postdoctoral Fellow, University of North Carolina at Chapel Hill