Structure-based discovery of positive allosteric modulators of the A 1 adenosine receptor - Anh T. N. Nguyen; Nicolas Panel; Duc Duy Vo; Bui San Thai; Ling Yeong Chia; Cam Sinh Lu; Shane D. Hellyer; Monica Langiu; Manuela Jörg; Karen J. Gregory; Jan Kihlberg; Paul J. White; et al. (Proceedings of the National Academy of Sciences, 2025)
Allosteric modulation of G protein–coupled receptors (GPCRs) is an exciting strategy for developing new therapeutic agents, and it has several advantages over more commonly used orthosteric drugs. Recently determined GPCR structures have revealed allosteric pockets facing the lipid bilayer, enabling rational drug design. Here, we develop a virtual screening strategy to discover ligands of extrahelical binding pockets and apply this approach to the adenosine A 1 receptor (A 1 R). The A 1 R is a high-value therapeutic target for ischemia–reperfusion injury and chronic neuropathic pain. Developing effective A 1 R therapeutics remains challenging due to high structural conservation across orthosteric binding sites and on-target unwanted effects stimulated by prototypical A 1 R agonists, such as bradycardia and atrioventricular block. However, A 1 R positive allosteric modulators (PAMs) acting through spatially distinct allosteric sites can fine-tune A 1 R activity with high subtype selectivity and spatiotemporal specificity, thereby overcoming current limitations. A chemical library of 160 million compounds was computationally docked to the allosteric pocket identified in a cryo-EM structure of the A 1 R, and a set of 26 top-ranked compounds were selected for experimental evaluation. Pharmacological evaluation of these, and structure-guided hit optimization, led to the discovery of subtype-selective A 1 R PAMs. These compounds demonstrated minimal allosteric agonism and negligible impact on A 1 R-mediated beat rate of an orthosteric agonist. The discovered PAMs pave the way for potential treatments for neuropathic pain and ischemia–reperfusion injury without accompanying side effects. Our results demonstrate the utility of a synergistic computational and experimental approach in GPCR drug discovery.