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 of ensemble modeling and provide an uncertainty estimation based on the standard deviation of the predictions to define an applicability domain for the models. Available assays at Bayer for two of the endpoints (Bile salt export pump (BSEP) inhibition and phospholipidosis) were run on a set of public compounds and used for further evaluation (data provided in the Supporting Information). Additionally, we conducted an in-depth data analysis of the relationships among the different endpoints, as well as with DILI. The presented models can be used to derive a "Virtual Liver Safety Profile" showcasing the predicted activity of a compound on the selected endpoints to support the prioritization of assays and the elucidation of modes of action.