Measuring partial and biased agonism at GPCRs on the FDSS/µCell


A Quinn2; S Hoare1; P H Tewson2; T E Hughes2
1 Pharmechanics, United States;  2 Montana Molecular, United States


Opioid receptors have been the focus of extensive research, with a push to develop analgesic drugs to this receptor family with fewer side effects and reduced abuse liability. Improved tools for measuring partial and biased agonism are needed to reliably characterize efficacy and to identify agonists that can preferentially activate one signaling pathway over the other (e.g. G-protein vs. β-arrestin), with the potential for reduced side effects. Montana Molecular’s genetically-encoded, fluorescent biosensors detect multiple receptor-mediated signaling pathways in real-time, live-cell assays. The kinetics of the responses for each signaling pathway can be used to reproducibly measure agonist bias (Hoare et al. 2020), but collecting this kinetic data while simultaneously adding ligands can be experimentally challenging. Here we combine Montana Molecular’s sensors for cAMP, DAG and β-arrestin, Pharmechanics kinetic analysis, and the FDDS/µCell screening system to create a platform for GPCR drug discovery. To test this platform, cells expressing the Mu opioid receptor were seeded in a 384-well plate, and dose response measurements were made with the following compounds: DAMGO, Met-enkephalin, Endomorphin-2, Fentanyl, Morphine, Oxymorphone, Hydropmorphone, and Buprenorphine. Responses were collected every 2 seconds for 90 minutes, producing data with outstanding signal to noise and kinetic resolution. This data made it possible to accurately measure the initial rate of signaling (kTau) for each response (Hoare 2020). The initial rate was extracted from each response curve to produce highly reproducible efficacy and bias measurements indicating clear differences in the pharmacology of these agonists.

Hoare, Sam R. J., Paul H. Tewson, Anne Marie Quinn, and Thomas E. Hughes. 2020. “A Kinetic Method for Measuring Agonist Efficacy and Ligand Bias Using High Resolution Biosensors and a Kinetic Data Analysis Framework.” Scientific Reports 10 (1): 1766.

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