Chan Rosa, Benet Leslie Z
Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California , San Francisco, California 94143-0912, United States.
Chem Res Toxicol. 2017 Apr 17;30(4):1017-1029. doi: 10.1021/acs.chemrestox.7b00025. Epub 2017 Mar 15.
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.
药物性肝损伤（DILI）是临床试验中药物失败的主要原因，也是药物撤市的主要原因。已证明DILI既取决于每日剂量，也取决于肝脏代谢程度。然而，在药物研发早期，每日剂量是未知的。在此，我们对已发表的试图预测DILI的假说进行了全面分析，包括对生物药剂学药物处置分类系统（BDDCS）的新分析，以评估FDA批准的药物标签中DILI警告的严重程度以及因药物不良反应（ADR）导致的撤市状态。我们的分析证实，每日剂量≥50mg/天会导致DILI可能性增加，但仅这一特性不足以预测DILI可能性。我们评估了先前对DILI进行分类的尝试，如2法则、BSEP抑制以及与BDDCS分类相比的关键毒性机制测量方法。我们的结果表明，BDDCS 2类药物表现出最高的DILI严重程度，并且除了每日剂量已知的情况外，这里评估的所有已发表方法都没有比BDDCS产生明显更好的预测。广泛代谢的化合物发生DILI风险更高这一论断得到了证实，但通过区分BDDCS 2类和1类药物可以得到加强。我们并不认为不需要临床剂量知识的BDDCS分类对于新分子实体的DILI可能性具有足够的预测性/准确性，但建议将提议的DILI预测方法与BDDCS分类进行比较是评估新提议算法潜在可靠性的有用工具。
预测DILI可能性最成功的方法都包括剂量测量，但在药物研发早期，预测剂量存在可量化的不确定性。在此，我们比较了使用BDDCS分类与先前发表的方法预测DILI可能性的可能性，并指出许多假设的预测DILI指标并不比仅仅避免使用BDDCS 2类药物更好。
