In-silico identification of peroxisome proliferator-activated receptor (PPAR)α/γ agonists from Ligand Expo Components database

Abstract

PPARα and PPARγ play important roles in regulating glucose and lipid metabolism. In recent years, the development of dual PPAR agonists has become a hot topic in the field of anti-diabetic medicinal chemistry. The dual PPARα/γ agonists can both improve metabolism and reduce side effects caused by single drugs, and has become a promising strategy for designing effective drugs for the treatment of type 2 diabetes. In this study, by means of virtual screening, molecular docking and ADMET prediction technology, a representative compound with higher docking score, lower toxicity than original ligands was gained from the Ligand Expo Components database. It was observed through MD simulation that the representative compound not only has the function of activating the PPARα target and the PPARγ target, but also show a more favorable binding mode when the representative compound binds to the two receptors compared to the original ligands. Our results provided an approach to rapidly find novel PPARα/γ dual agonists for the treatment of type 2 diabetes mellitus (T2DM). This paper explores novel compounds targeting PPARα/γ dual agonists by using molecular docking, ADMET prediction, and molecular dynamics simulation methods. The specific flowchart is as follows: Highlights The results show that the skeleton of compound M80 is not only similar to Saroglitazar but also higher than that of Saroglitazar in activity. This study explained the binding modes of saroglitazar-PPARα/γ complexes and provided structure reference for the research and development of novel PPARα/γ dual agonists.