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药物研发中的驻留时间:当前见解与未来展望。

Residence time in drug discovery: current insights and future perspectives.

作者信息

Kordylewski Szymon K, Bugno Ryszard, Podlewska Sabina

机构信息

Maj Institute of Pharmacology Polish Academy of Sciences, Smętna 12, Kraków, 31-343, Poland.

出版信息

Pharmacol Rep. 2025 Jun 9. doi: 10.1007/s43440-025-00748-z.

DOI:10.1007/s43440-025-00748-z
PMID:40489055
Abstract

The temporal stability of ligand-receptor complexes is increasingly acknowledged as a critical factor in drug discovery, influencing both efficacy and pharmacodynamics. Although the relationship between the duration of compound action and complex stability can be traced back to Paul Ehrlich's 19th-century doctrine Corpora non agunt nisi fixata, its significance has gained renewed attention in recent years. This review comprehensively examines the concept of residence time (RT). We first summarize key ligand binding models (lock-and-key, induced-fit, and conformational selection) and delve into various perspectives on how RT impacts functional outcomes. Furthermore, we discuss experimental methods for measuring RT, highlighting both radioligand and non-radioligand approaches. The growing interest in RT has spurred advancements in computational techniques, particularly molecular dynamics simulations, which utilize diverse strategies to observe dissociation events. We outline these molecular dynamics-based methods, their theoretical foundations, and provide examples of their application in assessing RT. Finally, we highlight molecular determinants of prolonged RT, focusing primarily on G protein-coupled receptors (GPCRs) while also incorporating relevant data from other receptor classes.

摘要

配体-受体复合物的时间稳定性日益被认为是药物发现中的一个关键因素,它影响着药物疗效和药效学。尽管化合物作用持续时间与复合物稳定性之间的关系可以追溯到19世纪保罗·埃尔利希的“药物只有固定后才起作用”学说,但其重要性近年来再次受到关注。本综述全面审视了驻留时间(RT)的概念。我们首先总结关键的配体结合模型(锁钥模型、诱导契合模型和构象选择模型),并深入探讨RT如何影响功能结果的各种观点。此外,我们讨论了测量RT的实验方法,重点介绍了放射性配体和非放射性配体方法。对RT的兴趣日益浓厚推动了计算技术的进步,特别是分子动力学模拟,它利用多种策略来观察解离事件。我们概述了这些基于分子动力学的方法、它们的理论基础,并提供了它们在评估RT中的应用实例。最后,我们重点介绍了延长RT的分子决定因素,主要关注G蛋白偶联受体(GPCRs),同时也纳入了来自其他受体类别的相关数据。

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