Modeling Metabolism and Subsequent Reactivity of Drugs
Adverse drug reactions (ADRs) are dangerous and expensive. Idiosyncratic ADRs, especially rare and severe hypersensitivity-driven ADRs, are the leading cause of medicine withdrawal and termination of clinical development. At the same time, a large proportion of drugs are not associated with hypersensitivity driven ADRs, offering hope that new medicines could avoid them entirely with reliable predictors of risk. Hypersensitivity driven ADRs are caused by the formation of chemically reactive metabolites by metabolic enzymes. These reactive metabolites covalently attach to proteins to become immunogenic and provoke an ADR. Unfortunately, current computational and experimental approaches do not reliably identify drug candidates that form reactive metabolites. These approaches are limited because they inadequately model metabolism, which can both render toxic molecules safe and safe molecules toxic. To overcome this limitation, we have been building mathematical models of both metabolism and reactivity. The models are constructed using machine-learning algorithms that quantitatively summarize the knowledge from thousands of published studies. Taken together, this approach is more accurately modeling whether metabolism renders drugs toxic or safe.