基于生理的药代动力学模型评估胺碘酮与地高辛、利伐沙班和苯妥英的药物相互作用和联合治疗策略。

IF 3.4 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Pharmacotherapy Pub Date : 2025-09-01 Epub Date: 2025-08-12 DOI:10.1002/phar.70050
Youjun Chen, Zhiwei Liu, Yiming Li, Wenhui Wang, Tao Chen, Saiya Li, Yating Wu, Haitang Xie
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引用次数: 0

摘要

背景:胺碘酮(AMI)是细胞色素P450 (CYP) 2C9和p -糖蛋白(P-gp)的有效抑制剂,也是CYP3A4的弱抑制剂。AMI与地高辛(DIG)、利伐沙班(RIV)或苯妥英(PHT)同时用药可显著增加受害者药物的暴露。升高的RIV暴露会增加出血的风险,而DIG和PHT的治疗窗口较窄,当与AMI合用时可能导致严重的毒性。目的:采用基于生理的药代动力学(PBPK)模型来模拟、验证和预测AMI与DIG、RIV或PHT之间的药物-药物相互作用(ddi)对受害者药物药代动力学(PK)的影响。研究结果旨在为优化联合治疗方案提供循证建议。方法:采用PK-Sim软件建立AMI、RIV和PHT的PBPK模型,采用前人文献中的DIG模型。模拟DDI情景以评估暴露水平。通过将预测的血浆浓度-时间(PCT)曲线和PK参数值与先前发表的PK研究中健康受试者的临床试验数据进行比较,来评估模型的性能。最后,根据暴露水平的变化调整联合治疗的给药方案。结果:根据模型模拟,当作恶者药物AMI与DIG、RIV或PHT按照标签推荐的剂量方案共同施用时,受害者药物的稳态暴露分别增加了79%、38%和59%。与单药治疗相比,DIG、RIV和PHT的剂量分别减少40%、25%和45%,达到了相似的稳态浓度。结论:我们成功建立了AMI、RIV和PHT的PBPK模型。这些模型有效地模拟了AMI与DIG、RIV或PHT合用时发生的ddi,从而为临床联合治疗的给药方案提供指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drug-Drug Interactions and Combination Therapy Strategies of Amiodarone With Digoxin, Rivaroxaban, and Phenytoin Assessed by Physiologically Based Pharmacokinetic Modeling.

Background: Amiodarone (AMI) is a potent inhibitor of Cytochrome P450 (CYP) 2C9 and P-glycoprotein (P-gp), as well as a weak inhibitor of CYP3A4. Concomitant administration of AMI with digoxin (DIG), rivaroxaban (RIV), or phenytoin (PHT) can significantly increase the exposure of the victim drugs. Elevated RIV exposure raises the risk of bleeding, whereas DIG and PHT have narrow therapeutic windows, potentially leading to severe toxicity when co-administered with AMI.

Purpose: Physiologically based pharmacokinetic (PBPK) modeling was employed to simulate, validate, and predict the impact of drug-drug interactions (DDIs) between AMI and DIG, RIV, or PHT on the pharmacokinetics (PK) of victim drugs. The findings aim to provide evidence-based recommendations for optimizing combination therapy regimens.

Methods: PBPK models for AMI, RIV, and PHT were developed using PK-Sim, while the DIG model was adopted from previously published literature. DDI scenarios were simulated to assess exposure levels. Model performance was evaluated by comparing predicted plasma concentration-time (PCT) profiles and PK parameter values with clinical trial data from healthy subjects in previously published PK studies. Finally, dosing regimens for combination therapy were adjusted based on changes in exposure levels.

Results: According to model simulations, when the perpetrator drug AMI was co-administered with DIG, RIV, or PHT following label-recommended dosing regimens, the steady-state exposure of the victim drugs increased by 79%, 38%, and 59%, respectively. Compared to monotherapy, reducing the doses of DIG, RIV, and PHT by 40%, 25%, and 45%, respectively, achieved similar steady-state concentrations.

Conclusions: We have successfully developed PBPK models for AMI, RIV, and PHT. These models effectively simulate the DDIs that occur when AMI is co-administered with DIG, RIV, or PHT, thereby providing guidance for dosing regimens in clinical combination therapies.

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来源期刊
Pharmacotherapy
Pharmacotherapy 医学-药学
CiteScore
7.80
自引率
2.40%
发文量
93
审稿时长
4-8 weeks
期刊介绍: Pharmacotherapy is devoted to publication of original research articles on all aspects of human pharmacology and review articles on drugs and drug therapy. The Editors and Editorial Board invite original research reports on pharmacokinetic, bioavailability, and drug interaction studies, clinical trials, investigations of specific pharmacological properties of drugs, and related topics.
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