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开发和评估生理基于药代动力学模型,以预测健康成年人中美托洛尔-帕罗西汀代谢性药物相互作用,并将其外推至虚拟慢性心力衰竭患者以优化剂量。

Development and evaluation of a physiologically based pharmacokinetic model to predict carvedilol-paroxetine metabolic drug-drug interaction in healthy adults and its extrapolation to virtual chronic heart failure patients for dose optimization.

机构信息

Department of Pharmacy Practice, Bahauddin Zakariya University, Multan, Pakistan.

Department of Clinical Pharmacy and Pharmacotherapy, Heinrich-Heine University, Düsseldorf, Germany.

出版信息

Expert Opin Drug Metab Toxicol. 2021 Jun;17(6):717-724. doi: 10.1080/17425255.2021.1921145. Epub 2021 May 4.

DOI:10.1080/17425255.2021.1921145
PMID:33910429
Abstract

: The metabolic drug-drug interactions (mDDIs) are one of the most important challenges faced by the pharmaceutical industry during the drug development stage and are frequently associated with labeling restrictions and withdrawal of drugs. The capacity of physiologically based pharmacokinetic (PBPK) models to absorb and upgrade with the newly available information on drug and population-specific parameters, makes them a preferred choice over the conventional pharmacokinetic models for predicting mDDIs.: A PBPK model capable of predicting the stereo-selective disposition of carvedilol after administering paroxetine by incorporating mechanism (time) based inhibition of CYP2D6 and CYP3A4 was developed by using the population-based absorption, distribution, metabolism and elimination (ADME) simulator, Simcyp®.: The model predictions for both carvedilol enantiomers were in close agreement with the observed PK data, as the ratios for observed/predicted PK parameters were within the 2-fold error range. The developed PBPK model was successful in capturing an increase in exposures of R and S-carvedilol, due to the time-based inhibition of CYP2D6 enzyme caused by paroxetine.: The developed model can be used for exploring complex clinical scenarios, where multiple drugs are given concurrently, particularly in diseased populations where no clinical trial data is available.

摘要

代谢性药物-药物相互作用(mDDI)是制药行业在药物开发阶段面临的最重要挑战之一,经常与标签限制和药物撤市相关。生理药代动力学(PBPK)模型能够吸收和利用新出现的药物和人群特异性参数信息,使其成为预测 mDDI 的首选模型,而不是传统的药代动力学模型。该模型通过纳入基于时间的 CYP2D6 和 CYP3A4 抑制机制,开发了一种能够预测帕罗西汀给药后卡维地洛立体选择性处置的 PBPK 模型,该模型使用基于人群的吸收、分布、代谢和消除(ADME)模拟器 Simcyp®。两种卡维地洛对映体的模型预测与观察到的 PK 数据非常吻合,因为观察到的/预测的 PK 参数比值在 2 倍误差范围内。该开发的 PBPK 模型成功地捕获了由于帕罗西汀引起的 CYP2D6 酶的基于时间的抑制,导致 R 和 S-卡维地洛暴露量增加。该模型可用于探索复杂的临床情况,特别是在没有临床试验数据的疾病人群中,同时使用多种药物。

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