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 warning in drug labels approved by the FDA and the withdrawal status due to ADRs. Our analysis confirms that higher doses ≥ 50mg/day lead to increased DILI potential but this property alone is not sufficient to predict 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 prediction 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 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. Conclusion 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 BDDCS classification versus previously published methods, and suggest that comparison of predictive metrics versus the outcome by just avoiding BDDCS Class 2 drugs may serve as a useful baseline in evaluating these metrics.

The Beneficial Roles of SIRT1 in Drug-Induced Liver Injury

“…DILI gained much attention due to its importance in drug development along with its roles in failure or withdrawal of drug development [7]. The key issue related to treating DILI is to stop using the drug instantly that is a cause of liver injury in a dose-dependent fashion [8, 9]. The epidemiological studies of DILI showed that the dosage is a major determining factor of the risk that humans suffer from idiosyncratic adverse drug reaction [10].…”

Human hepatocyte systems for in vitro toxicology analysis

“…There are many studies showing that DILI patients have to face a mortality or liver transplantation rate of at least 5-10% [2,3]. Moreover, in past 50 years DILI was the leading single cause of drug failure during clinical phases and after market approval [4,5]. Therefore, regulatory authorities such as the Food and Drug Administration (FDA) recommended an industry guidance to detect DILI potentials of drug candidates as early as possible [6].…”

Evaluation of the relevance of DILI predictive hypotheses in early drug development: review of in vitro methodologies vs. BDDCS classification

“…13 We have shown that the BDDCS can be useful in predicting the potential for antiepileptic drugs to cause cutaneous adverse reactions 14 and in predicting DILI. 15 Most recently, we have shown that in vitro measures of BSEP inhibition, alone and together with other efflux transporters, provides no better prediction of DILI than just avoiding BDDCS class 2 drugs. 16 BDDCS’s strong relationship between dose, metabolic susceptibility, solubility and idiosyncratic DILI highlights the potential benefits of BDDCS as a comparison matrix for DILI prediction.…”