Predicting the Risks of Drug-Induced Liver Injury in Humans Utilizing Computational Modeling
Drug-induced liver injury (DILI) is a significant challenge to clinicians, drug developers, as well as regulators. There is an unmet need to reliably predict risk for DILI. Developing a risk management plan to improve the prediction of a drug’s hepatotoxic potential is a long-term effort of the research community. Robust predictive models or biomarkers are essential for assessing the risk for DILI in humans, while an improved DILI annotation is vital and largely affects the accuracy and utility of the developed predictive models. In this chapter, we will focus on the DILI research efforts at the National Center for Toxicological Research of the US Food and Drug Administration. We will first introduce our drug label-based approach to annotate the DILI risk associated with individual drugs and then upon these annotations we developed a series of predictive models that could be used to assess the potential of DILI risk, including the “rule-of-two” model, DILI score model, and conventional and modified Quantitative structure–activity relationship (QSAR) models.
Challenges and Advances in Computational Chemistry and Physics
(2019). Predicting the Risks of Drug-Induced Liver Injury in Humans Utilizing Computational Modeling. Challenges and Advances in Computational Chemistry and Physics, 30, 259-278.
Available at: https://aquila.usm.edu/fac_pubs/16448