Static Versus Dynamic Model Predictions of Competitive Inhibitory Metabolic Drug-Drug Interactions via Cytochromes P450: One Step Forward and Two Steps Backwards.

IF 4.6 2区 医学 Q1 PHARMACOLOGY & PHARMACY
Clinical Pharmacokinetics Pub Date : 2025-01-01 Epub Date: 2024-12-10 DOI:10.1007/s40262-024-01457-1
Ivan Tiryannik, Aki T Heikkinen, Iain Gardner, Anthonia Onasanwo, Masoud Jamei, Thomas M Polasek, Amin Rostami-Hodjegan
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引用次数: 0

Abstract

Background: Predicting metabolic drug-drug interactions (DDIs) via cytochrome P450 enzymes (CYP) is essential in drug development, but controversy has reemerged recently about whether in vitro-in vivo extrapolation (IVIVE) using static models can replace dynamic models for some regulatory filings and label recommendations.

Objective: The aim of this study was to determine if static and dynamic models are equivalent for the quantitative prediction of metabolic DDIs arising from competitive CYP inhibition.

Methods: Drug parameter spaces were varied to simulate 30,000 DDIs between hypothetical substrates and inhibitors of CYP3A4. Predicted area under the plasma concentration-time profile ratios for substrates (AUCr = AUC(presence of precipitant)/AUC(absence of precipitant)) were compared between dynamic simulations (Simcyp® V21) and corresponding static calculations, giving an inter-model discrepancy ratio (IMDR = AUCrdynamic/AUCrstatic). Dynamic simulations were conducted using a 'population' representative and a 'vulnerable patient' representative with maximal concentration (Cmax) or average steady-state concentration (Cavg,ss) as the inhibitor driver concentrations. IMDRs outside the interval 0.8-1.25 were defined as discrepancy between models.

Results: The highest rate of IMDR <0.8 and IMDR >1.25 discrepancies in the 'population' representative was 85.9% and 3.1%, respectively, when using Cavg,ss as the inhibitor driver concentration. Using the 'vulnerable patient' representative showed the highest rate of IMDR >1.25 discrepancies at 37.8%.

Conclusion: Static models are not equivalent to dynamic models for predicting metabolic DDIs via competitive CYP inhibition across diverse drug parameter spaces, particularly for vulnerable patients. Caution is warranted in drug development if static IVIVE approaches are used alone to evaluate metabolic DDI risks.

通过细胞色素P450的竞争性抑制代谢药物相互作用的静态与动态模型预测:前进一步,后退两步。
背景:通过细胞色素P450酶(CYP)预测代谢药物-药物相互作用(ddi)在药物开发中是必不可少的,但最近关于使用静态模型的体外-体内外推(IVIVE)是否可以取代一些监管文件和标签推荐的动态模型的争议再次出现。目的:本研究的目的是确定静态和动态模型是否等效于定量预测竞争性CYP抑制引起的代谢性ddi。方法:改变药物参数空间,模拟假设底物和CYP3A4抑制剂之间的30,000 ddi。在动态模拟(Simcyp®V21)和相应的静态计算之间比较底物的血浆浓度-时间剖面比下的预测面积(AUCr = AUC(存在沉淀剂)/AUC(不存在沉淀剂)),给出模型间差异比(IMDR = AUCrdynamic/AUCrstatic)。以最大浓度(Cmax)或平均稳态浓度(Cavg,ss)作为抑制剂驱动浓度,使用“群体”代表和“脆弱患者”代表进行动态模拟。在0.8-1.25区间之外的imdr定义为模型之间的差异。结果:当使用Cavg,ss作为抑制剂驱动浓度时,“人群”代表的IMDR 1.25差异率最高,分别为85.9%和3.1%。使用“脆弱患者”代表显示IMDR bbbb1.25差异率最高,为37.8%。结论:通过不同药物参数空间的竞争性CYP抑制来预测代谢性ddi的静态模型与动态模型并不等效,特别是对于易感患者。如果单独使用静态IVIVE方法来评估代谢性DDI风险,在药物开发中需要谨慎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.80
自引率
4.40%
发文量
86
审稿时长
6-12 weeks
期刊介绍: Clinical Pharmacokinetics promotes the continuing development of clinical pharmacokinetics and pharmacodynamics for the improvement of drug therapy, and for furthering postgraduate education in clinical pharmacology and therapeutics. Pharmacokinetics, the study of drug disposition in the body, is an integral part of drug development and rational use. Knowledge and application of pharmacokinetic principles leads to accelerated drug development, cost effective drug use and a reduced frequency of adverse effects and drug interactions.
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