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The GLP-1R as a model for understanding and exploiting biased agonism in next-generation medicines

痛苦 G蛋白偶联受体 功能选择性 利拉鲁肽 赛马鲁肽 艾塞那肽 受体 内分泌学 信号转导 生物 药理学 计算生物学 神经科学 生物信息学 2型糖尿病 细胞生物学 糖尿病 遗传学 政治学 政治 法学
作者
Jonathan D. Douros,Jacek Mokrosiński,Brian Finan
出处
期刊:Journal of Endocrinology [Bioscientifica]
卷期号:261 (2) 被引量:14
标识
DOI:10.1530/joe-23-0226
摘要

The glucagon-like peptide 1 receptor (GLP-1R) is a class B G protein-coupled receptor (GPCR) that emerged as a pharmacologic target in cardiometabolic disease, including diabetes and obesity, over 30 years ago. The subsequent widespread clinical use of GLP-1R agonists, including exenatide, liraglutide, and semaglutide, has made the GLP-1R a preeminent model for understanding basic GPCR biology, including the emergent field of biased agonism. Recent data demonstrate that the dual GLP-1R/glucose dependent insulinotropic polypeptide receptor (GIPR) agonist tirzepatide exhibits a biased signaling profile characterized by preferential Gαs activation over β-arrestin recruitment, which appears to contribute to its insulinotropic and body-weight reducing effects in preclinical models. This constitutes a major finding in which nuanced, mechanistic receptor signaling dynamics in vitro mediate real-world clinical differentiation within a drug class. Because of the striking bench-top-to-bed side relevance of this biased signaling phenomenon, we have undertaken a review of the emerging data detailing biased agonism at the GLP-1R. In this review, we introduce the core concept of biased agonism followed by a detailed consideration of the key mechanisms, including ligand-mediated bias, receptor-mediated bias, and systems/cell-type bias. Current industry programs are largely, if not entirely, focused on developing biased ligands, and so we have dedicated a section of the review to a brief meta-analysis of compounds reported to drive biased signaling, with a consideration of the structural determinants of receptor-ligand interactions. In this work, we aim to assess the current knowledge regarding signaling bias at the GLP-1R and how these ideas might be leveraged in future optimization.
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