Physiologically-Based Pharmacokinetic Modeling of Tenofovir Disoproxil Fumaratein Pregnant Women

基于生理学的药代动力学模型 最大值 药代动力学 医学 替诺福韦 人口 怀孕 药理学 药品 人类免疫缺陷病毒(HIV) 生物 免疫学 环境卫生 遗传学
作者
Xiqian Zhang,Tao Luo,Huan Yang,Ying Wan,Qin He,Min Xu,Yujie Yang
出处
期刊:Current Drug Metabolism [Bentham Science Publishers]
卷期号:23 (14): 1115-1123 被引量:7
标识
DOI:10.2174/1389200224666230130093314
摘要

Purpose: Physiological changes during pregnancy can affect antiretroviral drug processes and further influence drug efficacy and safety. Physiologically-based pharmacokinetic (PBPK) modeling offers a unique modality to predict PK in pregnant women. The objective of this study was to establish a PBPK modeling of tenofovir disoproxil fumarate (TDF) in pregnant women, to provide a reference for the clinical use of TDF. Methods: A full PBPK modeling of tenofovir (TFV) and TDF following i.v. and p.o. administration was developed using the simulation software PK-Sim®. The modeling was then extrapolated to pregnant women based on pregnancyrelated physiological parameters in Mobi® Simulator. The mean fold error (MFE) and geometric mean fold error (GMFE) methods were used to compare the differences between predicted and observed values of PK parameters (Cmax, tmax, AUC0-∞) to evaluate the accuracy of PBPK modeling. Results: The developed PBPK modeling successfully predicted the TDF disposition in the non-pregnant population, wherein the MFE average and GMFE of all predicted PK parameters were within a 1.5-fold error range, and more than 96.30% of the predicted drug concentration values were within a 2-fold error range of the measured values. After the extrapolation of these models to the third trimester of pregnancy, the scaling anatomy/physiology and hepatic intrinsic clearance made the pregnant population PBPK modeling meet the standard requirement of 0.5 < MFE and GMFE value < 2. It was more appropriate to simulate the in vivo process of low-dose TDF in pregnant women. Conclusion: The non-pregnant population PBPK modeling of TDF established in our study can be extrapolated to pregnant women. Our study provides a reference for realizing clinical personalized medication for pregnant women.
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