Developing a Workflow to Detect Agonists for GPR119 Using Machine Learning Techniques

BACKGROUND
G-protein-coupled receptors (GPCRs) are seven transmembrane receptors that couple with Gproteins on activation by external stimuli to trigger intracellular signaling cascades [1]. GPCRs comprise the largest family of membrane receptors that are prominent drug targets (35% of all approved drugs exploit GPCR signal transduction) [2]. GPCRs are thus clinically relevant targets for the development of new as well as repurposed drugs. 

This study focuses on identifying ligands for a class A cannabinoid receptor-like GPCR called GPR119 (glucose-dependent insulino tropic receptor) [3]. Pancreatic β and intestinal enteroendocrine L cells show significant levels of GPR119 expression on activation by oleoylethanolamide (OEA) [3, 4]. GPR119 participates in feeding behavior and glucose homeostasis signaling cascades via G-protein (specifically Gs) coupling, thus triggering the cyclic AMP pathway, and causing adenylate cyclase activation [4]. GPR119 activation initiates the release of incretins from the intestine and insulin from the pancreas and is also responsible for weight decline and reduced food intake in rats [4]. Determining pharmacologically relevant agonist targets for GPR119 is essential to identify lead compounds for management of metabolic disorders like type 2 diabetes and obesity [3,4]. GPR119 ligands are also determined to be a viable novel treatment for metabolic-associated fatty liver disease