Opioids are powerful and effective drugs used for pain management, but their therapeutic usage is limited due to their side effects. Therefore, obtaining an extensive understanding of the pharmacological properties that underlie the actions of these drugs is much needed. Efficacy is the extent to which an agonist can stimulate the activity of the receptor it interacts with, and many studies have claimed to determine the efficacy of a wide range of opioid agonists. However, these opioids reportedly appear to be full agonists in some studies but seem to be partial agonists in others. Discrepancies from previous findings hamper the determination of accurate measurements of the efficacy of these drugs. As such, several assays focus on different aspects of opioid receptor signaling to deduce how efficacious these drugs may be. In this study, we focus on the μ-opioid receptor (MOR) as agonists that act on it represent the majority of clinically used opioids. We take advantage of a unique cellular model that captures the differential activation of each Gαi/o/z protein on top of measuring the relative efficacies of each tested opioid agonist. Using various cell-based assays, we demonstrate that these can be tools used to directly look at the interaction between the receptor and its effectors through the coupling of inhibitory heterotrimeric G proteins. The distinctions between each functional readout reveal insights about the nature of each established system, highlighting their advantages as well as their limitations. Key details about the mechanistic basis of inhibitory G protein activation are also uncovered. Precise determination of the efficacy of opioids could ultimately impact the understanding of opioid-mediated neuromodulation, as further links can be made between this important pharmacological parameter and the extent in which it induces analgesia and limits the side effects typically associated with opioid intake.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/44774 |
| Date | 30 March 2023 |
| Creators | Venes, Angelica |
| Contributors | Giguère, Patrick |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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