Abstract
Signaling diversity of G protein-coupled (GPCR) ligands provides novel opportunities to develop more effective, better-tolerated therapeutics. Taking advantage of these opportunities requires identifying which effectors should be specifically activated or avoided so as to promote desired clinical responses and avoid side effects. However, identifying signaling profiles that support desired clinical outcomes remains challenging. This study describes signaling diversity of mu opioid receptor (MOR) ligands in terms of logistic and operational parameters for ten different in vitro readouts. It then uses unsupervised clustering of curve parameters to: classify MOR ligands according to similarities in type and magnitude of response, associate resulting ligand categories with frequency of undesired events reported to the pharmacovigilance program of the Food and Drug Administration and associate signals to side effects. The ability of the classification method to associate specific in vitro signaling profiles to clinically relevant responses was corroborated using β2-adrenergic receptor ligands.
Highlights
Signaling diversity of G protein-coupled (GPCR) ligands provides novel opportunities to develop more effective, better-tolerated therapeutics
We show that ligands with similar G protein/βarr responses cluster together, and provide evidence that ligands within different categories display distinct frequencies of gastrointestinal and respiratory events reported to the FDA pharmacovigilance program
This study introduced a stepwise analysis in which Gprotein-coupled receptors (GPCRs) ligands were organized into pharmacodynamic categories that could be associated with clinically relevant responses
Summary
Signaling diversity of G protein-coupled (GPCR) ligands provides novel opportunities to develop more effective, better-tolerated therapeutics. This study describes signaling diversity of mu opioid receptor (MOR) ligands in terms of logistic and operational parameters for ten different in vitro readouts It uses unsupervised clustering of curve parameters to: classify MOR ligands according to similarities in type and magnitude of response, associate resulting ligand categories with frequency of undesired events reported to the pharmacovigilance program of the Food and Drug Administration and associate signals to side effects. Preclinical models have indicated that β-arrestin[2] (βarr2) knockout mitigates constipation and respiratory depression induced by morphine[21], pointing to the possibility that mu opioid receptor (MOR) agonists that preferentially activate G protein signaling over βarr[2] recruitment could induce less of these side effects[12,13,14] We use this prototypical example to establish that clustering MOR ligands according to similarities in pharmacodynamic parameters for multiple responses, captures their signaling differences and preferences. The practical value of the classification method proposed is further illustrated by the fact that ligand categories defined by similarity of G protein responses at β2-adrenergic receptor (β2AR) correlate with sympatholytic CV events and bronchoconstriction
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