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ADME 科学的下一个前沿:预测基于转运体的药物处置、组织浓度和药物-药物相互作用在人体内的情况。

The next frontier in ADME science: Predicting transporter-based drug disposition, tissue concentrations and drug-drug interactions in humans.

机构信息

Department of Pharmaceutics, University of Washington, Seattle, WA, USA.

Preclinical Sciences and Translational Safety, Janssen Research & Development, LLC, Spring House, PA, USA.

出版信息

Pharmacol Ther. 2022 Oct;238:108271. doi: 10.1016/j.pharmthera.2022.108271. Epub 2022 Aug 21.

Abstract

Predicting transporter-based drug clearance (CL) and tissue concentrations (TC) in humans is important to reduce the risk of failure during drug development. In addition, when transporters are present at the tissue:blood interface (e.g., in the liver, blood-brain barrier), predicting TC is important to predict the drug's efficacy and safety. With the advent of quantitative targeted proteomics, in vitro to in vivo extrapolation (IVIVE) of transporter-based drug CL and TC is now possible using transporter-expressing models (cells lines, membrane vesicles) and the in vivo to in vitro relative expression of transporters (REF) as a scaling factor. Unlike other approaches based on physiological scaling, the REF approach is not dependent on the availability of primary cells. Here, we review the REF approach and compare it with other IVIVE approaches such as the relative activity factor approach and physiological scaling. For each of these scaling approaches, we review their underlying principles, assumptions, methodology, predictive performance, as well as advantages and limitations. Finally, we discuss current gaps in IVIVE of transporter-based CL and TC and propose possible reasons for these gaps as well as areas to investigate to bridge these gaps.

摘要

预测人体中基于转运体的药物清除率(CL)和组织浓度(TC)对于降低药物开发过程中的失败风险非常重要。此外,当转运体存在于组织与血液的界面(如肝脏、血脑屏障)时,预测 TC 对于预测药物的疗效和安全性也很重要。随着定量靶向蛋白质组学的出现,现在可以使用表达转运体的模型(细胞系、膜囊泡)和体内到体外相对转运体表达(REF)作为缩放因子来进行基于转运体的药物 CL 和 TC 的体外到体内外推(IVIVE)。与其他基于生理缩放的方法不同,REF 方法不依赖于原代细胞的可用性。本文综述了 REF 方法,并将其与其他 IVIVE 方法(如相对活性因子方法和生理缩放)进行了比较。对于这些缩放方法中的每一种,我们都回顾了它们的基本原则、假设、方法、预测性能,以及优点和局限性。最后,我们讨论了基于转运体的 CL 和 TC 的 IVIVE 中当前存在的差距,并提出了这些差距的可能原因以及需要研究的领域以弥合这些差距。

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