Identification of novel PPARα/γ dual agonists by virtual screening, ADMET prediction and molecular dynamics simulations

Abstract

PPARα and PPARγ have been the most widely studied Peroxisome proliferator-activated receptor (PPAR) subtypes due to their important roles in regulating glucose, lipids, and cholesterol metabolism. By combining the lowering serum triglyceride levels benefit of PPARα agonists (such as fibrates) with the glycemic advantages of the PPARγ agonists (such as TZD), the dual PPAR agonists approach can both improve the metabolic effects and minimize the side effects caused by either agent alone, and hence, has become a promising strategy for designing effective drugs against type-2 diabetes. In this study, by means of virtual screening, ADMET prediction and molecular dynamics (MD) simulations techniques, one compound-ASN15761007 with high binding score, low toxicity were gained. It was observed by MD simulations that ASN15761007 not only possessed the same function as AZ242 did in activating PPARα and BRL did in activating PPARγ, but also had more favorable conformation for binding to the two receptors. Our results provided an approach to rapidly produce novel PPARα/γ dual agonists which might be a potential lead compound to develop against insulin resistance and hyperlipidemia.

Keywords:

• PPARα
• PPARγ
• virtual screening
• ADMET
• molecular dynamics