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Updated: Dec 24, 2025

Quantifying Agonist Activity at G Protein-coupled Receptors
Published on: December 26, 2011
Evaluation of the profiles of CB1 cannabinoid receptor signalling bias using joint kinetic modelling
Xiao Zhu1, David B Finlay2,3, Michelle Glass2,3
1Otago Pharmacometrics Group, School of Pharmacy, University of Otago, Dunedin, New Zealand.
Background And Purpose:
Biased agonism describes the ability of ligands to differentially regulate multiple signalling pathways when coupled to a single receptor. Signalling is affected by rapid agonist-induced receptor internalisation. Hence, the conventional use of equilibrium models may not be optimal, because (i) receptor numbers vary with time and, in addition, (ii) some pathways may show non-monotonic profiles over time.
Experimental Approach:
Data were available from internalisation, cAMP inhibition and phosphorylation of ERK (pERK) of the cannabinoid-1 (CB1 ) receptor using a concentration series of six CB1 ligands (CP55,940, WIN55,212-2, anandamide, 2-arachidonylglycerol, Δ9 -tetrahydrocannabinol and BAY59,3074). The joint kinetic model of CB1 signalling was developed to simultaneously describe the time-dependent activities in three signalling pathways. Based on the insights from the kinetic model, fingerprint profiles of CB1 ligand bias were constructed and visualised.
Key Results:
A joint kinetic model was able to capture the signalling profiles across all pathways for the CB1 receptor simultaneously for a system that was not at equilibrium. WIN55,212-2 had a similar pattern as 2-arachidonylglycerol (reference). The other agonists displayed bias towards internalisation compared to cAMP inhibition. However, only Δ9 -tetrahydrocannabinol and BAY59,3074 demonstrated bias in the pERK-cAMP pathway comparison. Furthermore, all the agonists exhibited little preference between internalisation and pERK.
Conclusion And Implications:
This is the first joint kinetic assessment of biased agonism at a GPCR (e.g. CB1 receptor) under non-equilibrium conditions. Kinetic modelling is a natural method to handle time-varying data when traditional equilibria are not present and enables quantification of ligand bias.
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