A Model to predict severity of drug-induced liver injury in humans

Drug-induced liver injury (DILI) is a major public health concern, and improving its prediction remains an unmet challenge. Recently, we reported the Rule-of-2 (RO2) and found lipophilicity (logP ≥3) and daily dose ≥100 mg of oral medications to be associated with significant risk for DILI; however, the RO2 failed to estimate grades of DILI severity. In an effort to develop a quantitative metrics, we analyzed the association of daily dose, logP, and formation of reactive metabolites (RM) in a large set of Food and Drug Administration-approved oral medications and found factoring RM into the RO2 to highly improve DILI prediction. Based on these parameters and by considering n = 354 drugs, an algorithm to assign a DILI score was developed. In univariate and multivariate logistic regression analyses the algorithm (i.e., DILI score model) defined the relative contribution of daily dose, logP, and RM and permitted a quantitative assessment of risk of clinical DILI. Furthermore, a clear relationship between calculated DILI scores and DILI risk was obtained when applied to three independent studies. The DILI score model was also functional with drug pairs defined by similar chemical structure and mode of action but divergent toxicities. Specifically, for drug pairs where the RO2 failed, the DILI score correctly identified toxic drugs. Finally, the model was applied to n = 159 clinical cases collected from the National Institutes of Health's LiverTox database to demonstrate that the DILI score correlated with the severity of clinical outcome.

Based on daily dose, lipophilicity, and RM, a DILI score algorithm was developed that provides a scale of assessing the severity of DILI risk in humans associated with oral medications. (Hepatology 2016;64:931-940).