Integration of In Vitro and In Silico Approaches Enables Prediction of Drug-Induced Liver Injury

Drug-induced liver injury (DILI) is a major cause of drug attrition and poses a significant threat to patient safety. However, current preclinical prediction methods, including heuristic screening rules, in vitro assays, machine learning models and animal testing, have serious limitations. Here, we demonstrate that combining in vitro toxicity data (cytotoxicity, mitochondrial toxicity, bile salt export pump (BSEP) inhibition) with pharmacokinetic information enables high DILI predictivity. In a retrospective analysis of 241 drugs, we show that the ratio of their in vivo maximum plasma concentration (Cmax) to their lowest in vitro toxicity strongly correlates with clinical DILI risks, with ROC AUC up to 96%. Then, we show that comparable predictivity (ROC AUC up to 91%) is achievable prospectively when Cmax values are predicted in silico by high-throughput physiologically based kinetic modelling. Dynamic simulations of bile acid perturbations further identify drugs potentially causing DILI specifically through BSEP inhibition, providing additional mechanistic insights. This integrative, mechanistic approach shows enhanced DILI predictivity and interpretability, offering an animal-free alternative for early drug development.