基于生理学的药代动力学模型
血脑屏障
药代动力学
流出
脑脊液
药理学
渗透(战争)
膜透性
化学
运输机
磁导率
中枢神经系统
膜
医学
神经科学
生物
生物化学
运筹学
基因
工程类
作者
Christine M. Bowman,Fang Ma,Jialin Mao,Emile G. Plise,Eugene Chen,Liling Liu,Shu Zhang,Yuan Chen
摘要
Abstract Predicting the brain penetration of drugs has been notoriously difficult; however, recently, permeability‐limited brain models have been constructed. Lead optimization for central nervous system compounds often focuses on compounds that have low transporter efflux, where passive permeability could be a main driver in determining cerebrospinal fluid (CSF)/brain concentrations. The main objective of this study was to evaluate the translatability of passive permeability data generated from different in vitro systems and its impact on the prediction of human CSF/brain concentrations using physiologically‐based pharmacokinetic (PBPK) modeling. In vitro data were generated using gMDCK and parallel artificial membrane permeability assay‐blood–brain barrier for comparison and predictions using a quantitative structure‐activity relationship model were also evaluated. PBPK modeling was then performed for seven compounds with moderate‐high permeability and a range of efflux in vitro, and the CSF/brain mass concentrations and Kpuu were reasonably predicted. This work provides the first step of a promising approach using bottom‐up PBPK modeling for CSF/brain penetration prediction to support lead optimization and clinical candidate selection.
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