建立基于生理的利托那韦药代动力学模型,以表征急性和稳态条件下的暴露和药物相互作用潜力。

IF 3.1 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Lien Thi Ngo, Woojin Jung, Tham Thi Bui, Hwi-Yeol Yun, Jung-Woo Chae, Jeremiah D Momper
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引用次数: 0

摘要

利托那韦(RTV)是一种有效的CYP3A抑制剂,被广泛用作药代动力学(PK)增强剂,以增加对某些蛋白酶抑制剂的暴露。然而,作为CYP3A相互作用的一个强大而复杂的肇事者,RTV也可以增强其他共同给药的CYP3A底物的暴露,潜在地引起毒性。因此,预测药物-药物相互作用(ddi)和估计同时给药的剂量需求是必要的。在这项研究中,我们旨在利用PK-sim®软件平台建立一个基于生理的RTV PK (PBPK)模型。共13项RTV的临床PK研究,涵盖了广泛的剂量范围(100 ~ 600 mg,包括单次和多次给药),以及8项RTV对CYP3A和P-gp底物的临床DDI研究,包括阿普唑仑、咪达唑仑、利伐沙班、克拉霉素、氟康唑、西地那非和地高辛,用于模型的开发和评估。PBPK模型纳入了时间药代动力学差异(早晨和晚上剂量之间)以及体外研究中RTV生化过程参数估计的局限性。最终开发的PBPK模型在二维误差范围内预测了100%的RTV AUClast和Cmax。所有PK数据集的几何平均折叠误差(GMFE)分别为1.275和1.194。此外,97%的DDI曲线预测与DDI比率在二维误差内。所有DDI数据集的GMFE值分别为1.297和1.212。因此,该模型可用于预测RTV和CYP3A底物的DDI谱,并用于估计伴随给药的剂量需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of a physiologically-based pharmacokinetic model for Ritonavir characterizing exposure and drug interaction potential at both acute and steady-state conditions.

Ritonavir (RTV) is a potent CYP3A inhibitor that is widely used as a pharmacokinetic (PK) enhancer to increase exposure to select protease inhibitors. However, as a strong and complex perpetrator of CYP3A interactions, RTV can also enhance the exposure of other co-administered CYP3A substrates, potentially causing toxicity. Therefore, the prediction of drug-drug interactions (DDIs) and estimation of dosing requirements for concomitantly administered drugs is imperative. In this study, we aimed to develop a physiologically-based PK (PBPK) model for RTV using the PK-sim® software platform. A total of 13 clinical PK studies of RTV covering a wide dose range (100 to 600 mg including both single and multiple dosing), and eight clinical DDI studies with RTV on CYP3A and P-gp substrates, including alprazolam, midazolam, rivaroxaban, clarithromycin, fluconazole, sildenafil, and digoxin were used for the model development and evaluation. Chronopharmacokinetic differences (between morning vs. evening doses) and limitations in parameter estimation for biochemical processes of RTV from in vitro studies were incorporated in the PBPK model. The final developed PBPK model predicted 100% of RTV AUClast and Cmax within a twofold dimension error. The geometric mean fold error (GMFE) from all PK datasets was 1.275 and 1.194, respectively. In addition, 97% of the DDI profiles were predicted with the DDI ratios within a twofold dimension error. The GMFE values from all DDI datasets were 1.297 and 1.212, respectively. Accordingly, this model could be applied to the prediction of DDI profiles of RTV and CYP3A substrates and used to estimate dosing requirements for concomitantly administered drugs.

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来源期刊
CiteScore
5.00
自引率
11.40%
发文量
146
审稿时长
8 weeks
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