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

Besma Benredjem, Jonathan Gallion, Dennis Pelletier, Paul Dallaire, Johanie Charbonneau, Darren Cawkill, Karim Nagi, Mark Gosink, Viktoryia Lukasheva, Stephen Jenkinson, Yong Ren, Christopher Somps, Brigitte Murat, Emma Westhuizen, Christian Gouill, Olivier Lichtarge, Anne Schmidt, Michel Bouvier () and Graciela Pineyro ()
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Besma Benredjem: Université de Montréal
Jonathan Gallion: Baylor College of Medicine
Dennis Pelletier: Pfizer Inc
Paul Dallaire: Université de Montréal
Johanie Charbonneau: CHU Sainte-Justine research center
Darren Cawkill: Pfizer Inc
Karim Nagi: Qatar University
Mark Gosink: Pfizer Inc
Viktoryia Lukasheva: Université de Montréal
Stephen Jenkinson: Pfizer Inc
Yong Ren: Pfizer Inc
Christopher Somps: Pfizer Inc
Brigitte Murat: Université de Montréal
Emma Westhuizen: Université de Montréal
Christian Gouill: Université de Montréal
Olivier Lichtarge: Baylor College of Medicine
Anne Schmidt: Pfizer Inc
Michel Bouvier: Université de Montréal
Graciela Pineyro: Université de Montréal

Nature Communications, 2019, vol. 10, issue 1, 1-15

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-11875-6

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