AI-Driven Integration of Transcriptomics, Quantum Mechanics, and Physiology for Predicting Drug-Induced Liver Injury in Data-Limited Scenarios

Drug-induced liver injury (DILI) is a significant concern
with
prescription medications and supplements. Accordingly, it is crucial
to develop tools and approaches that can predict DILI likelihood of
existing medications and supplements, as well as potential drug candidates
under development. The complexity of liver injury mechanisms and the
limited availability of DILI data hamper the development of robust
predictive models. In order to overcome these challenges, this study
investigated enriching machine learning/artificial intelligence (ML/AI)
models that predict the risk of DILI using drug structural parameters
along with rat liver transcriptomics data, quantum mechanics-derived
features of the drug molecules, and metrics for interspecies variability
of drug exposure. The enrichment of ML/AI models with such features
dramatically improved ML/AI models’ DILI predictive ability,
even in a severely data-limited scenario. The approach used in the
study, especially the incorporation of knowledge-based features to
enrich AI models, holds tremendous promise for not only assessing
safety and toxicity assessments of drug candidates but also in other
aspects such as target engagement and efficacy of these candidates,
early in the development phase.