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Nat Commun. 2019 Sep 9;10(1):4075. doi: 10.1038/s41467-019-11875-6.

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

Author information

1
Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montréal, QC, H3T 1J4, Canada.
2
CHU Sainte-Justine research center, Montréal, QC, H3T 1C5, Canada.
3
Baylor College of Medicine, Houston, TX, 77030, USA.
4
Pfizer Inc, Groton, CT, 06340, USA.
5
Apollo Therapeutics LLP, Stevenage Bioscience Catalyst, Gunnels Wood Road, Stevenage, SG1, 2FX, UK.
6
College of Medicine, Member of QU Health, Qatar University, Doha, Qatar.
7
Institute for Research in Immunology and Cancer, Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, QC, H3T 1J4, Canada.
8
Pfizer Inc, La Jolla, CA, 92121, USA.
9
Decibel Therapeutics, 1325 Boylston Street, Boston, MA, 02215, USA.
10
Monash Institute of Pharmaceutical Sciences, Parkville, VIC, 3052, Australia.
11
Institute for Research in Immunology and Cancer, Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, QC, H3T 1J4, Canada. michel.bouvier@umontreal.ca.
12
Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montréal, QC, H3T 1J4, Canada. graciela.pineyro.filpo@umontreal.ca.
13
CHU Sainte-Justine research center, Montréal, QC, H3T 1C5, Canada. graciela.pineyro.filpo@umontreal.ca.

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.

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