Evaluation of the profiles of CB1 cannabinoid receptor signalling bias using joint kinetic modelling.

2020 
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, Delta(9) -tetrahydrocannabinol, 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 Delta(9) -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.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    33
    References
    5
    Citations
    NaN
    KQI
    []
    Baidu
    map