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

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 undesir

SUBMITTER: Benredjem B 

PROVIDER: S-EPMC6733853 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature