Suppr超能文献

肝清除模型的基本假设:认识到饱和蛋白结合对驱动力浓度的影响,以及区分肝清除模型。

Assumptions Underlying Hepatic Clearance Models: Recognizing the Influence of Saturable Protein Binding on Driving Force Concentration and Discrimination Between Models of Hepatic Clearance.

机构信息

Quantitative, Translational, & ADME Sciences (QTAS), Abbvie Inc., North Chicago, Illinois.

Quantitative, Translational, & ADME Sciences (QTAS), Abbvie Inc., North Chicago, Illinois

出版信息

Drug Metab Dispos. 2023 Aug;51(8):1046-1052. doi: 10.1124/dmd.123.001326. Epub 2023 May 15.

Abstract

One underlying assumption of hepatic clearance models is often underappreciated. Namely, plasma protein binding is assumed to be nonsaturable within a given drug concentration range, dependent only on protein concentration and equilibrium dissociation constant. However, in vitro hepatic clearance experiments often use low albumin concentrations that may be prone to saturation effects, especially for high-clearance compounds, where the drug concentration changes rapidly. Diazepam isolated perfused rat liver literature datasets collected at varying concentrations of albumin were used to evaluate the predictive utility of four hepatic clearance models (the well-stirred, parallel tube, dispersion, and modified well-stirred model) while both ignoring and accounting for potential impact of saturable protein binding on hepatic clearance model discrimination. In agreement with previous literature findings, analyses without accounting for saturable binding showed poor clearance prediction using all four hepatic clearance models. Here we show that accounting for saturable albumin binding improves clearance predictions across the four hepatic clearance models. Additionally, the well-stirred model best reconciles the difference between the predicted and observed clearance data, suggesting that the well-stirred model is an appropriate model to describe diazepam hepatic clearance when considering appropriate binding models. SIGNIFICANCE STATEMENT: Hepatic clearance models are vital for understanding clearance. Caveats in model discrimination and plasma protein binding have sparked an ongoing scientific discussion. This study expands the understanding of the underappreciated potential for saturable plasma protein binding. Fraction unbound must correspond to relevant driving force concentration. These considerations can improve clearance predictions and address hepatic clearance model disconnects. Importantly, even though hepatic clearance models are simple approximations of complex physiological processes, they are valuable tools for clinical clearance predictions.

摘要

肝清除模型的一个基本假设常常被低估。即在给定的药物浓度范围内,血浆蛋白结合被假定为非饱和的,仅依赖于蛋白浓度和平衡解离常数。然而,体外肝清除实验通常使用低白蛋白浓度,这些浓度可能容易受到饱和效应的影响,尤其是对于高清除率化合物,药物浓度变化迅速。本文使用不同白蛋白浓度下收集的地西泮离体灌流大鼠肝文献数据集,评估了四种肝清除模型(全混、平行管、弥散和改良全混模型)的预测能力,同时忽略和考虑了饱和蛋白结合对肝清除模型区分度的潜在影响。与之前的文献研究结果一致,不考虑饱和结合的分析表明,所有四种肝清除模型对地西泮清除的预测都很差。本文表明,考虑到饱和白蛋白结合,四种肝清除模型的清除预测都得到了改善。此外,全混模型最好地协调了预测清除率与观察清除率之间的差异,这表明在考虑适当的结合模型时,全混模型是描述地西泮肝清除的合适模型。

意义陈述

肝清除模型对于理解清除至关重要。模型区分度和血浆蛋白结合的局限性引发了持续的科学讨论。本研究扩展了对潜在的可饱和血浆蛋白结合的认识。未结合分数必须与相关驱动力浓度相对应。这些考虑可以改善清除预测,并解决肝清除模型之间的差异。重要的是,尽管肝清除模型是对复杂生理过程的简单近似,但它们是临床清除预测的有价值的工具。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验