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Updated: Mar 30, 2026

Quantifying Agonist Activity at G Protein-coupled Receptors
Published on: December 26, 2011
Quantification of adenosine A(1) receptor biased agonism: Implications for drug discovery
Jo-Anne Baltos1, Karen J Gregory1, Paul J White1
1Drug Discovery Biology and Department of Pharmacology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC 3052, Australia.
Abstract:
Adenosine A1 receptor (A1AR) stimulation is a powerful protective mechanism in cerebral and cardiac ischemia-reperfusion injury. Despite this, therapeutic targeting of the A1AR for the treatment of ischemia-reperfusion injury has been largely unsuccessful, as high concentrations of prototypical A1AR agonists impart significant hemodynamic effects, particularly pronounced bradycardia, atrioventricular block and hypotension. Exploiting the phenomenon of biased agonism to develop ligands that promote A1AR cytoprotection in the absence of adverse hemodynamic effects remains a relatively unexplored, but exciting, approach to overcome current limitations. In native systems, the atypical A1AR agonists VCP746 and capadenoson retain cytoprotective signaling in the absence of bradycardia, a phenomenon suggestive of biased agonism. The current study used pharmacological inhibitors to investigate A1AR mediated cytoprotective signal transduction in a CHO FlpIn cell background, thus identifying candidate pathways for quantitative bias profiling, including cAMP, extracellular signal-regulated kinases 1 and 2 and Akt1/2/3. Subsequently, effects on cell survival and the bias profile of VCP746 and capadenoson were determined and compared to that of the prototypical A1AR agonists, NECA, R-PIA, MeCCPA and CPA. We found that prototypical agonists do not display significant bias for any of the pathways assessed. In contrast, VCP746 and capadenoson show significant bias away from calcium mobilization relative to all pathways tested. These studies demonstrate that quantitative "fingerprinting" of biased agonism within a model system can enable ligands to be clustered by their bias profile, which in turn may be predictive of preferential physiologically relevant in vivo pharmacology.
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