Exploring use of unsupervised clustering to associate signaling profiles of GPCR ligands to clinical response.
Nat Commun
; 10(1): 4075, 2019 09 09.
Article
em En
| MEDLINE
| ID: mdl-31501422
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 ß2-adrenergic receptor ligands.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Transdução de Sinais
/
Receptores Acoplados a Proteínas G
Tipo de estudo:
Risk_factors_studies
Limite:
Animals
/
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article