Clinical Pharmacology, Genentech, Inc., South San Francisco, CA, USA.
Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, CA, USA.
Cancer Chemother Pharmacol. 2022 May;89(5):707-720. doi: 10.1007/s00280-022-04434-2. Epub 2022 Apr 15.
Ipatasertib, a potent and highly selective small-molecule inhibitor of AKT, is currently under investigation for treatment of cancer. Ipatasertib is a substrate and a time-dependent inhibitor of CYP3A4. It exhibits non-linear pharmacokinetics at subclinical doses in the clinical dose escalation study. To assess the DDI risk of ipatasertib at the intended clinical dose of 400 mg with CYP3A4 inhibitors, inducers, and substrates, a fit-for-purpose physiologically based pharmacokinetic (PBPK) model of ipatasertib was developed.
The PBPK model was constructed in Simcyp using in silico, in vitro, and clinical data and was optimized and verified using clinical data.
The PBPK model described non-linear pharmacokinetics of ipatasertib and captured the magnitude of the observed clinical DDIs. Following repeated doses of 400 mg ipatasertib once daily (QD), the PBPK model predicted a 3.3-fold increase of ipatasertib exposure with itraconazole; a 2-2.5-fold increase with moderate CYP3A4 inhibitors, erythromycin and diltiazem; and no change with a weak CYP3A4 inhibitor, fluvoxamine. Additionally, in the presence of strong or moderate CYP3A4 inducers, rifampicin and efavirenz, ipatasertib exposures were predicted to decrease by 86% and 74%, respectively. As a perpetrator, the model predicted that ipatasertib (400 mg) caused a 1.7-fold increase in midazolam exposure.
This study demonstrates the value of using a fit-for-purpose PBPK model to assess the clinical DDIs for ipatasertib and to provide dosing strategies for the concurrent use of other CYP3A4 perpetrators or victims.
伊帕替膦，一种有效的、高度选择性的 AKT 小分子抑制剂，目前正在研究用于癌症治疗。伊帕替膦是 CYP3A4 的底物和时间依赖性抑制剂。在临床剂量递增研究中，在亚临床剂量下，它表现出非线性药代动力学。为了评估伊帕替膦在临床剂量 400mg 时与 CYP3A4 抑制剂、诱导剂和底物的潜在药物相互作用风险，开发了一种适合目的的基于生理的药代动力学（PBPK）模型。
使用体内、体外和临床数据在 Simcyp 中构建 PBPK 模型，并使用临床数据进行优化和验证。
PBPK 模型描述了伊帕替膦的非线性药代动力学，并捕捉到了观察到的临床药物相互作用的幅度。重复给予伊帕替膦 400mg 每日一次（QD）后，PBPK 模型预测酮康唑使伊帕替膦暴露增加 3.3 倍；中等 CYP3A4 抑制剂红霉素和地尔硫卓使伊帕替膦暴露增加 2-2.5 倍；弱 CYP3A4 抑制剂氟伏沙明则没有变化。此外，在强或中等 CYP3A4 诱导剂利福平或依非韦伦存在的情况下，预测伊帕替膦的暴露量分别减少 86%和 74%。作为加害人，该模型预测伊帕替膦（400mg）使咪达唑仑暴露增加 1.7 倍。
这项研究表明，使用适合目的的 PBPK 模型评估伊帕替膦的临床药物相互作用，并为同时使用其他 CYP3A4 加害人或受害人提供剂量策略具有价值。
