Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response.

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.This research was supported by a research contract from Pfizer Inc. and grants from the Natural Sciences and Engineering Research Council of Canada (Grant 311997 to G.P.) and the Canadian Institutes of Health Research MOP 324876 (to G.P.), MOP 102630 (to M.B. and O.L.) and Foundation grant (FDN-148431) to MB. MB holds a Canada Research Chair in Signal Transduction and Molecular Pharmacology. Dr Lichtarge’s research was supported by National Institutes of Health (NIH 2R01 GM066099; NIH 5R01 GM079656). B.B. was supported by a studentship from Fonds de Recherche en Santé du Québec. P.D. was supported by a MITACS fellowship

DS_DISC764678 – Supplemental material for Plate-Based Phenotypic Screening for Pain Using Human iPSC-Derived Sensory Neurons

<p>Supplemental material, DS_DISC764678 for Plate-Based Phenotypic Screening for Pain Using Human iPSC-Derived Sensory Neurons by Peter Stacey, Anne Mai Wassermann, Laura Kammonen, Emma Impey, Anna Wilbrey and Darren Cawkill in SLAS Discovery</p