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Updated: Jul 1, 2025

Monitoring GPCR-β-arrestin1/2 Interactions in Real Time Living Systems to Accelerate Drug Discovery
Published on: June 28, 2019
The GLP-1R as a model for understanding and exploiting biased agonism in next-generation medicines
Jonathan D Douros1, Jacek Mokrosinski1, Brian Finan1
1Novo Nordisk Research Center Indianapolis, Indianapolis, Indiana, USA.
Abstract:
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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