Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA.
J Clin Pharmacol. 2021 Jun;61(6):782-788. doi: 10.1002/jcph.1819. Epub 2021 Feb 9.
The key parameters necessary to predict drug-drug interactions (DDIs) are intrinsic clearance (CL ) and fractional contribution of the metabolizing enzyme toward total metabolism (f ). Herein, we summarize the accumulated knowledge from 53 approved new drug applications submitted to the Office of Clinical Pharmacology, US Food and Drug Administration, from 2016 to 2018 that contained physiologically based pharmacokinetic (PBPK) models to understand how in vitro data are used in PBPK models to assess drug metabolism and predict DDIs. For evaluation of CL and f , 29 and 20 new drug applications were included for evaluation, respectively. For CL , 86.2% of the PBPK models used modified values based on in vivo data with modifications ranging from -82.5% to 2752.5%. For f , 45.0% of the models used modified values with modifications ranging from -28% to 178.6%. When values for CL were used from in vitro testing without modification, the model resulted in up to a 14.3-fold overprediction of the area under the concentration-time curve of the substrate. When values for f from in vitro testing were used directly, the model resulted in up to a 2.9-fold underprediction of its DDI magnitude with an inducer, and up to a 1.7-fold overprediction of its DDI magnitude with an inhibitor. Our analyses suggested that the in vitro system usually provides a reasonable estimation of f when the drug metabolism by a given CYP pathway is more than 70% of the total clearance. In vitro experiments provide important information about basic PK properties of new drugs and can serve as a starting point for building a PBPK model. However, key PBPK parameters such as CL and f still need to be optimized based on in vivo data.
预测药物-药物相互作用（DDI）所需的关键参数是内在清除率（CL）和代谢酶对总代谢的分数贡献（f）。本文总结了 2016 年至 2018 年期间，美国食品和药物管理局临床药理学办公室收到的 53 份已批准新药申请中的累积知识，这些申请包含了基于生理学的药代动力学（PBPK）模型，以了解如何在 PBPK 模型中使用体外数据来评估药物代谢并预测 DDI。对于 CL 和 f 的评估，分别有 29 和 20 个新药申请被纳入评估。对于 CL，86.2%的 PBPK 模型使用了基于体内数据的修正值，修正范围从-82.5%到 2752.5%。对于 f，45.0%的模型使用了修正值，修正范围从-28%到 178.6%。当使用未经修正的体外测试值时，模型导致底物的浓度-时间曲线下面积的预测值最高高出 14.3 倍。当直接使用体外测试值时，模型导致诱导剂的 DDI 幅度预测值最高低至 2.9 倍，抑制剂的 DDI 幅度预测值最高高至 1.7 倍。我们的分析表明，当特定 CYP 途径的药物代谢超过总清除率的 70%时，体外系统通常可以对 f 提供合理的估计。体外实验提供了关于新药基本 PK 特性的重要信息，并且可以作为构建 PBPK 模型的起点。然而，关键的 PBPK 参数，如 CL 和 f，仍然需要基于体内数据进行优化。
