Identification of novel PPARα/γ dual agonists by pharmacophore screening, docking analysis, ADMET prediction and molecular dynamics simulations. (February 2019)

• Record Type: Journal Article
• Authors: Feng, Xiao-Yan; Jia, Wen-Qing; Liu, Xin; Jing, Zhi; Liu, Ya-Ya; Xu, Wei-Ren; Cheng, Xian-Chao
• Is Part Of: Computational biology and chemistry. Volume 78(2019)
• Page Start: 178
• Page End: 189
• Publication Date: 2019-02
• Subjects: PPARα/γ dual agonists -- Pharmacophore screening -- Docking -- ADMET prediction -- Molecular dynamics simulations; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.11.023
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: The pharmacophore model screening, molecular docking, ADMET prediction and molecular dynamics simulations were performed to find the novel compounds against PPARγ and PPARα, which were demonstrated as the following examples: Highlights: We find out novel PPARα/γ dual agonists by means of Pharmacophore virtual screening. A compound was obtained which possessed better superimposition with pharmacophore model, higher dock score and lower toxicity. This study provided a valuable approach in developing a dual novel and powerful PPARα/γ agonist. This study might be a reference for the development of DM medicines and a foundation for further research. Abstract: PPARα and PPARγ play an important role in regulating glucose and lipid metabolism. The single and selective PPARα or PPARγ agonists have caused several side effects such as edema, weight gain and cardiac failure. In the recent years, the dual PPARs agonist development has become a hot topic in the antidiabetic medicinal chemistry field. In this paper, the compound CHEMBL230490 were gained from CHEMBL database, by means of complex-based pharmacophore (CBP) virtual screening, molecular docking, ADMET prediction and molecular dynamics (MD) simulations. The compound CHEMBL230490 not only displayed higher binding scores and better binding modes with the active site of PPARα a/γ, but also had more favorable the pharmacokinetic properties and toxicity evaluated by ADMET prediction. The representative compound CHEMBL230490 was performed to MDs for studying a stable binding conformation. The results indicated that the CHEMBL230490 might be a potential antidiabetic lead compound. The research provided a valuable approach in developing novel PPARα/γ dual agonists for the treatment of type 2 diabetes mellitus (T2DM).