Evaluating the synergistic use of advanced liver models and AI for the prediction of drug-induced liver injury

Drug-induced liver injury (DILI) is a leading cause of acute liver failure. Hepatotoxicity typically occurs only in a subset of individuals after prolonged exposure and constitutes a major risk factor for the termination of drug development projects. We provide an overview of available human liver models for DILI research and discuss how they have been used to aid in early risk assessments and to mitigate the risk of project closures due to DILI in clinical stages. We summarize the different data that can be provided by such models and illustrate how these diverse data types can be interfaced with machine learning strategies to improve predictions of liver safety liabilities. Advanced human liver models closely mimic human liver phenotypes and functions for many weeks, allowing for the recapitulation of hepatotoxicity events in vitro. Integration of the biochemical, histological, and toxicogenomic output data from these models with physicochemical compound properties using different machine learning architectures holds promise to enhance preclinical DILI predictions. However, to realize this aim, it is important to benchmark the available liver models on test sets of DILI positive and negative compounds and to carefully annotate and share the resulting data.