Physiologically-based pharmacokinetic modelling for the prediction of adverse drug reactions

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Adverse drug reactions (ADRs) pose significant risks to patient health and complicate drug development. Predicting ADRs remains challenging due to their unpredictable nature and the limitations of existing models. To enhance understanding and prediction of ADRs, it is crucial to contextualize findings with individual patient factors, such as diseases and genotypes. However, realistic experimental approaches are often costly and impractical. In response, mechanistic in silico models have emerged as effective alternatives for predicting ADRs. This work utilized computational modeling to identify drugs associated with a high risk of hepatic ADRs and to pinpoint susceptible patients. Factors contributing to drug toxicity were integrated to address the idiosyncratic nature of ADRs. A bile acid circulation model was developed to study drug-induced cholestasis, linked to a drug-specific whole-body pharmacokinetic model. This approach simulated bile acid levels and confirmed cholestasis susceptibility in genetically predisposed patients during cyclosporine A treatment. Additionally, time-resolved in vitro expression data facilitated the categorization of cholestasis risk for ten known hepatotoxicants. The framework established could aid in assessing drug-induced cholestasis risk during development. Furthermore, computational modeling guided a clinical testing strategy for personalized treatment by analyzing patient metabolic phenotype