Predicting Liver-Related In Vitro Endpoints with Machine Learning to Support Early Detection of Drug-Induced Liver Injury.

ABSTRACT: Drug-induced liver injury (DILI) is a major cause of drug development failures and postmarket drug withdrawals, posing significant challenges to public health and pharmaceutical research. The biological mechanisms leading to DILI are highly complex and the adverse reaction is often difficult to foresee. Hence, mechanistic insights into DILI, as well as machine learning models to predict molecular events that trigger adverse outcomes, pharmacokinetics and pharmacodynamics in the liver, are essential tools for understanding and preventing DILI. In this study, we collected a comprehensive data set of 28 in vitro endpoints related to liver toxicity and function, as well as data specific to DILI, to explore the potential of multi-task learning for their prediction. We demonstrate the benefits o

SUBMITTER: Garcia de Lomana M 

PROVIDER: S-EPMC12015958 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature