Certara UK Limited, Simcyp Division, Level 2 Acero, 1 Concourse Way, Sheffield, S1 2BJ, UK.
Janssen Pharmaceutical, Companies of Johnson & Johnson, Turnhoutseweg 30, 2340, Beerse, Belgium.
Eur J Drug Metab Pharmacokinet. 2022 Jul;47(4):483-495. doi: 10.1007/s13318-022-00765-w. Epub 2022 Apr 29.
Due to health authority warnings and the recommended limited use of ketoconazole as a model inhibitor of cytochrome P450 (CYP) 3A4 in clinical drug-drug interaction (DDI) studies, there is a need to search for alternatives. Ritonavir is a strong inhibitor for CYP3A4/5-mediated DDIs and has been proposed as a suitable alternative to ketoconazole. It can also be used as a weak inhibitor for CYP2D6-mediated DDIs. Most of the currently available physiologically based pharmacokinetic (PBPK) inhibitor models developed for predicting DDIs use first-order absorption models, which do not mechanistically capture the effect of formulations on the systemic exposure of the inhibitor. Thus, the main purpose of the current study was to verify the predictive performance of a mechanistic absorption and disposition model of ritonavir when it was applied to the inhibition of CYP2D6 and CYP3A4/5 by ritonavir.
A PBPK model that incorporates formulation characteristics and enzyme kinetic parameters for post-absorptive pharmacokinetic processes of ritonavir was constructed. Key absorption-related parameters in the model were determined using mechanistic modelling of in vitro biopharmaceutics experiments. The model was verified for systemic exposure and DDI risk assessment using clinical observations from 13 and 18 studies, respectively.
Maximal inhibition of hepatic (3.53% of the activity remaining) and gut (5.16% of the activity remaining) CYP3A4 activity was observed when ritonavir was orally administered in doses of 100 mg or higher. The PBPK model accurately described the concentrations of ritonavir in the different simulated studies. The prediction accuracy for maximum concentration (C) and area under the plasma concentration versus time curve (AUC) were assessed. The bias (average fold error, AFE) for the prediction of C and AUC was 0.92 and 1.06, respectively, and the precision (absolute average fold error, AAFE) was 1.29 and 1.23, respectively. The PBPK model predictions for all C and AUC ratios when ritonavir was used as an inhibitor of CYP metabolism fell within twofold of the clinical observations. The prediction accuracy for C and AUC ratios had a bias (AFE) of 0.85 and 0.99, respectively, and a precision (AAFE) of 1.21 and 1.33, respectively.
The current model, which incorporates formulation characteristics and mechanistic disposition parameters, can be used to assess the DDI potential of CYP3A4/5 and CYP2D6 substrates administered with a twice-daily dose of 100 mg of ritonavir for 14 days.
由于卫生当局的警告以及建议将酮康唑作为临床药物相互作用（DDI）研究中细胞色素 P450（CYP）3A4 的模型抑制剂的使用受限，因此需要寻找替代品。利托那韦是一种针对 CYP3A4/5 介导的 DDI 的强抑制剂，已被提议作为酮康唑的合适替代品。它也可作为 CYP2D6 介导的 DDI 的弱抑制剂。目前大多数用于预测 DDI 的基于生理学的药代动力学（PBPK）抑制剂模型都使用一级吸收模型，该模型不能从机制上捕获制剂对抑制剂全身暴露的影响。因此，当前研究的主要目的是验证利托那韦的机制吸收和处置模型在预测利托那韦抑制 CYP2D6 和 CYP3A4/5 时的预测性能。
构建了一种包含利托那韦吸收后药代动力学过程制剂特征和酶动力学参数的 PBPK 模型。使用体外生物药剂学实验的机制建模来确定模型中关键的吸收相关参数。使用来自 13 项和 18 项研究的临床观察，分别对该模型进行了系统暴露和 DDI 风险评估的验证。
当以 100mg 或更高剂量口服给予利托那韦时，观察到肝（3.53%的活性残留）和肠道（5.16%的活性残留）CYP3A4 活性的最大抑制。PBPK 模型准确描述了不同模拟研究中利托那韦的浓度。评估了最大浓度（C）和血浆浓度-时间曲线下面积（AUC）的预测准确性。C 和 AUC 的预测偏差（平均倍误差，AFE）分别为 0.92 和 1.06，精度（绝对平均倍误差，AAFE）分别为 1.29 和 1.23。当利托那韦用作 CYP 代谢抑制剂时，所有 C 和 AUC 比值的 PBPK 模型预测均在临床观察的两倍以内。C 和 AUC 比值的预测精度（AFE）分别为 0.85 和 0.99，精度（AAFE）分别为 1.21 和 1.33。
该模型纳入了制剂特征和机制处置参数，可用于评估 CYP3A4/5 和 CYP2D6 底物在每天两次 100mg 剂量下连续 14 天给药时的 DDI 潜力。
