Abstract
In a recent report in Science Signaling (DOI: 10.1126/scisignal.aaz3140), it was suggested that low intrinsic agonism, and not biased agonism, leads to an improvement in the separation of potency in opioid-induced respiratory suppression versus antinociception. Although many of the compounds that were tested have been shown to display G protein signaling bias in prior publications, the authors conclude that since they cannot detect biased agonism in their cellular signaling studies the compounds are therefore not biased agonists. Rather, they conclude that it is low intrinsic efficacy that leads to the therapeutic window improvement. Intrinsic efficacy is the extent to which an agonist can stimulate a G protein-coupled receptor (GPCR) response in a system, while biased agonism takes into consideration not only intrinsic efficacy, but also potency of an agonist in an assay. Herein, we have re-analyzed the data presented in the published work (DOI: 10.1126/scisignal.aaz3140) (including the recent Erratum: DOI: 10.1126/scisignal.abf9803) to derive intrinsic efficacy and bias factors as ΔΔlog(τ/KA) and ΔΔlog(Emax/EC50). Based on this reanalysis, the data support the conclusion that biased agonism, favoring G protein signaling, was observed. Moreover, a conservation of rank order intrinsic efficacy was not observed upon comparing responses in each assay, further suggesting that multiple active receptor states were present. These observations agree with prior studies wherein oliceridine, PZM21 and SR-17018 were first described as biased agonists with improvement in antinociception over respiratory suppression in mice. Therefore, the data in the Science Signaling manuscript does provide strong corroborating evidence that G protein signaling bias may be a means to improve opioid analgesia while avoiding certain undesirable side effects.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
lbohn{at}scripps.edu
This version has been modified to account for an erratum published by the authors of the Gillis et al., 2020 Science Signaling paper. This required reanalysis of most parameters presented in the first edition (to account for the changes that were made). We also included an additional 2 figures and provide an additional means of analysis.