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基于生理学的 CYP1A2 药物相互作用预测的药代动力学模型:氟伏沙明、茶碱、咖啡因、利福平、咪达唑仑的建模网络。

Physiologically-Based Pharmacokinetic Models for CYP1A2 Drug-Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam.

机构信息

Clinical Pharmacy, Saarland University, Saarbrücken, Germany.

Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2019 May;8(5):296-307. doi: 10.1002/psp4.12397. Epub 2019 Mar 13.

Abstract

This study provides whole-body physiologically-based pharmacokinetic models of the strong index cytochrome P450 (CYP)1A2 inhibitor and moderate CYP3A4 inhibitor fluvoxamine and of the sensitive CYP1A2 substrate theophylline. Both models were built and thoroughly evaluated for their application in drug-drug interaction (DDI) prediction in a network of perpetrator and victim drugs, combining them with previously developed models of caffeine (sensitive index CYP1A2 substrate), rifampicin (moderate CYP1A2 inducer), and midazolam (sensitive index CYP3A4 substrate). Simulation of all reported clinical DDI studies for combinations of these five drugs shows that the presented models reliably predict the observed drug concentrations, resulting in seven of eight of the predicted DDI area under the plasma curve (AUC) ratios (AUC during DDI/AUC control) and seven of seven of the predicted DDI peak plasma concentration (C ) ratios (C during DDI/C control) within twofold of the observed values. Therefore, the models are considered qualified for DDI prediction. All models are comprehensively documented and publicly available, as tools to support the drug development and clinical research community.

摘要

本研究提供了强指数细胞色素 P450(CYP)1A2 抑制剂氟伏沙明和中度 CYP3A4 抑制剂氟伏沙明以及敏感 CYP1A2 底物茶碱的全身生理基于药代动力学模型。这两个模型都进行了构建和彻底评估,以便在药物相互作用(DDI)预测网络中应用,结合了先前开发的咖啡因(敏感指数 CYP1A2 底物)、利福平(中度 CYP1A2 诱导剂)和咪达唑仑(敏感指数 CYP3A4 底物)模型。对这五种药物组合的所有报告的临床 DDI 研究进行模拟表明,所提出的模型可靠地预测了观察到的药物浓度,从而导致七个中的八个预测的 DDI 药时曲线下面积(AUC)比值(DDI 期间的 AUC/AUC 对照)和七个中的七个预测的 DDI 峰血浆浓度(C)比值(DDI 期间的 C/C 对照)在观察值的两倍内。因此,这些模型被认为有资格进行 DDI 预测。所有模型都经过全面记录并公开发布,作为支持药物开发和临床研究社区的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e398/6539736/5d571e8afca7/PSP4-8-296-g001.jpg

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