Department of Pharmaceutics, University of Washington, Seattle, Washington, USA.
Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
Clin Pharmacol Ther. 2021 Aug;110(2):452-463. doi: 10.1002/cpt.2259. Epub 2021 May 8.
Evaluating the potential of new drugs and their metabolites to cause drug-drug interactions (DDIs) is critical for understanding drug safety and efficacy. Although multiple analyses of proprietary metabolite testing data have been published, no systematic analyses of metabolite data collected according to current testing criteria have been conducted. To address this knowledge gap, 120 new molecular entities approved between 2013 and 2018 were reviewed. Comprehensive data on metabolite-to-parent area under the curve ratios (AUC /AUC ), inhibitory potency of parent and metabolites, and clinical DDIs were collected. Sixty-four percent of the metabolites quantified in vivo had AUC /AUC ≥ 0.25 and 75% of these metabolites were tested for cytochrome P450 (CYP) inhibition in vitro, resulting in 15 metabolites with potential DDI risk identification. Although 50% of the metabolites with AUC /AUC < 0.25 were also tested in vitro, none of them showed meaningful CYP inhibition potential. The metabolite percentage of plasma total radioactivity cutoff of ≥ 10% did not appear to add value to metabolite testing strategies. No relationship between metabolite versus parent drug polarity and inhibition potency was observed. Comparison of metabolite and parent maximum concentration (C ) divided by inhibition constant (K ) values suggested that metabolites can contribute to in vivo DDIs and, hence, quantitative prediction of clinical DDI magnitude may require both parent and metabolite data. This systematic analysis of metabolite data for newly approved drugs supports an AUC /AUC cutoff of ≥ 0.25 to warrant metabolite in vitro CYP screening to adequately characterize metabolite inhibitory DDI potential and support quantitative DDI predictions.
评估新药及其代谢物引起药物-药物相互作用(DDI)的潜力对于了解药物安全性和疗效至关重要。虽然已经发表了多项关于专有代谢物测试数据的分析,但尚未对根据当前测试标准收集的代谢物数据进行系统分析。为了解决这一知识空白,对 2013 年至 2018 年期间批准的 120 种新的分子实体进行了审查。收集了关于代谢物与母体曲线下面积比(AUC/AUC)、母体和代谢物抑制效力以及临床 DDI 的综合数据。在体内定量的代谢物中,有 64%的代谢物 AUC/AUC≥0.25,其中 75%的代谢物在体外进行了细胞色素 P450(CYP)抑制测试,导致 15 种代谢物具有潜在的 DDI 风险识别。虽然 AUC/AUC<0.25 的代谢物中有 50%也在体外进行了测试,但它们都没有表现出有意义的 CYP 抑制潜力。代谢物的血浆总放射性百分比(≥10%)似乎并没有为代谢物测试策略增加价值。代谢物与母体药物极性和抑制效力之间没有关系。代谢物与母体最大浓度(C)除以抑制常数(K)值的比较表明,代谢物可能会导致体内 DDI,因此,临床 DDI 程度的定量预测可能需要母体和代谢物数据。对新批准药物的代谢物数据的系统分析支持 AUC/AUC 截止值≥0.25,以保证代谢物的体外 CYP 筛选,充分描述代谢物抑制 DDI 的潜力,并支持定量 DDI 预测。