title: Evaluation of DILI Predictive Hypotheses in Early Drug Development.
abstract: Drug-induced liver injury (DILI) is a leading cause of drug failure in clinical trials and a major reason for drug withdrawals. DILI has been shown to be dependent on both daily dose and extent of hepatic metabolism. Yet, early in drug development daily dose is unknown. Here, we perform a comprehensive analysis of the published hypotheses that attempt to predict DILI, including a new analysis of the Biopharmaceutics Drug Disposition Classification System (BDDCS) in evaluating the severity of DILI warnings in drug labels approved by the FDA and the withdrawal status due to adverse drug reactions (ADRs). Our analysis confirms that higher doses ≥50 mg/day lead to increased DILI potential, but this property alone is not sufficient to predict the DILI potential. We evaluate prior attempts to categorize DILI such as Rule of 2, BSEP inhibition, and measures of key mechanisms of toxicity compared to BDDCS classification. Our results show that BDDCS Class 2 drugs exhibit the highest DILI severity and that all of the published methodologies evaluated here, except when daily dose is known, do not yield markedly better predictions than BDDCS. The assertion that extensive metabolized compounds are at higher risk of developing DILI is confirmed but can be enhanced by differentiating BDDCS Class 2 from Class 1 drugs. We do not propose that the BDDCS classification, which does not require knowledge of the clinical dose, is sufficiently predictive/accurate of DILI potential for new molecular entities but suggest that comparison of proposed DILI prediction methodologies with BDDCS classification is a useful tool to evaluate the potential reliability of newly proposed algorithms.
  
  
  
  The most successful approaches to predict DILI potential all include a measure of dose, yet there is a quantifiable uncertainty associated with the predicted dose early in drug development. Here, we compare the possibility of predicting DILI potential using the BDDCS classification versus previously published methods and note that many hypothesized predictive DILI metrics do no better than just avoiding BDDCS Class 2 drugs.
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California , San Francisco, California 94143-0912, United States.
authors.forename: Rosa
authors.initials: R
authors.lastname: Chan
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California , San Francisco, California 94143-0912, United States.
authors.forename: Leslie Z
authors.identifier: 0000-0002-9678-2371
authors.initials: LZ
authors.lastname: Benet
mesh_terms: D001702:Biopharmaceutics / Q000145:classification; D056486:Chemical and Drug Induced Liver Injury / Q000473:pathology*; D004353:Drug Evaluation, Preclinical; D004348:Drug Labeling; D006801:Humans; D008962:Models, Theoretical; D012720:Severity of Illness Index