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受体变构的定量分析及其在药物发现中的意义。

Quantitative analysis of receptor allosterism and its implication for drug discovery.

作者信息

Zhang Rumin, Kavana Michael

机构信息

Merck Research Laboratories, Department of In Vitro Pharmacology , 2015 Galloping Hill Road, Kenilworth, NJ 07033 , USA

出版信息

Expert Opin Drug Discov. 2015 Jul;10(7):763-80. doi: 10.1517/17460441.2015.1041498. Epub 2015 Apr 30.

DOI:10.1517/17460441.2015.1041498
PMID:25927503
Abstract

INTRODUCTION

G protein-coupled receptors represent the largest class of druggable targets and are known to be modulated by both orthosteric agonists and positive/negative allosteric modulators (PAMs/NAMs). Proper experimental design and data analysis for the dose matrix between an agonist and PAM or NAM are critical to elucidate the key parameters for understanding molecular mechanism and structure-activity relationship (SAR) in drug discovery.

AREAS COVERED

The authors provide an overview and best practice recommendations on the quantitative analysis of receptor allosterism. The authors propose a simple classification system for receptor modulators on the basis of their efficacy and affinity modifiers. The authors also outline the optimal assay designs for both fixed dose screening and dose matrix study of receptor modulators.

EXPERT OPINION

The authors recommend the global curve fitting approach to reliably yield system- and modulator-specific parameters for SAR ranking. Furthermore, the authors suggest that the uncertainty in maximal system response has insignificant impact on SAR ranking. The authors anticipate that systems pharmacology models integrating both binding kinetics and functional allosterism will be needed to address the inherent limitations of current allosterism models.

摘要

引言

G蛋白偶联受体是最大的可成药靶点类别,已知可被正位激动剂和正/负变构调节剂(PAM/NAM)调节。对于激动剂与PAM或NAM之间的剂量矩阵进行恰当的实验设计和数据分析,对于阐明药物发现中理解分子机制和构效关系(SAR)的关键参数至关重要。

涵盖领域

作者提供了关于受体变构定量分析的概述和最佳实践建议。作者基于其效能和亲和力修饰剂提出了一种受体调节剂的简单分类系统。作者还概述了受体调节剂固定剂量筛选和剂量矩阵研究的最佳测定设计。

专家观点

作者推荐全局曲线拟合方法,以可靠地产生用于SAR排序的系统和调节剂特异性参数。此外,作者表明最大系统反应中的不确定性对SAR排序影响不大。作者预计需要整合结合动力学和功能变构的系统药理学模型来解决当前变构模型的固有局限性。

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