Exploring Dual Agonists for PPARα/γ Receptors using Pharmacophore Modeling, Docking Analysis and Molecule Dynamics Simulation

Background: The Peroxisome Proliferator-Activated Receptors (PPARs) are ligand-activated transcription factors belonging to the nuclear receptor family. The roles of PPAR alpha in fatty acid oxidation and PPAR gamma in adipocyte differentiation and lipid storage have been widely characterized. Compounds with dual PPAR alpha/gamma activity have been proposed, combining the benefits of insulin sensitization and lipid lowering into one drug, allowing a single drug to reduce hyperglycemia and hyperlipidemia while preventing the development of cardiovascular complications. Methods: The new PPAR alpha/gamma agonists were screened through virtual screening of pharmacophores and molecular dynamics simulations. First, in the article, the constructed pharmacophore was used to screen the Ligand Expo Components-pub database to obtain the common structural characteristics of representative PPAR alpha/gamma agonist ligands. Then, the accepted ligand structure was modified and replaced to obtain 12 new compounds. Using molecular docking, ADMET and molecular dynamics simulation methods to screen the designed 12 ligands, analyze their docking scores when they bind to the PPAR alpha/gamma dual targets, their stability and pharmacological properties when they bind to the PPAR alpha/gamma dual targets. Results: We performed pharmacophore-based virtual screening for 22949 molecules in Ligand Expo Components-pub database. The compounds that were superior to the original ligand were performed structural analysis and modification, and a series of compounds with novel structures were designed. Using precise docking, ADMET prediction and molecular dynamics methods to screen and verify newly designed compounds, and the above compounds show higher docking scores and lower side effects. Conclusion: 9 new PPAR alpha/gamma agonists were obtained by pharmacophore modeling, docking analysis and molecular dynamics simulation.

Automated summary

This study utilized pharmacophore modeling and molecular dynamics simulation to screen and obtain 9 new PPAR alpha/gamma agonists.