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

September 9, 2019

cin (100 µg mL−1) and puromycin (10 mg mL−1). BRET assays Ligand preparation: Agonists were dissolved in DMSO and spotted on 96-well white bottom microplates (Greiner bio-one) using the HP D300 Digital Dispenser (Tecan Life Sciences). DMSO concentration was normalized for each point at 0.334%. Gαi and Gαo-activation assay: HEK 293 were co-transfected with DOR or MOR (human or rat)

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.

Benredjem, B; Gallion, J; Pelletier, D; Dallaire, P; Charbonneau, J; Cawkill, D; Nagi, K; Gosink, M; Lukasheva, V; Jenkinson, S; Ren, Y; Somps, C; Murat, B; Van Der Westhuizen, E; Le Gouill, C; Lichtarge, O; Schmidt, A; Bouvier, M; Pineyro, G;

Journal: Nat Commun Pages: 4075

Original article (31501422)