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Molecular Modeling Approach to Study the PPARγ-Ligand Interactions.

Merilin Al Sharif1, Ivanka Tsakovska1, Petko Alov1

  • 1Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria.

Methods in Molecular Biology (Clifton, N.J.)
|May 2, 2019
PubMed
Summary

This study integrates computational methods to predict liver toxicity from peroxisome proliferator-activated receptor gamma (PPARγ) agonists and identifies natural compounds for potential antidiabetic therapies via PPARγ partial agonism.

Keywords:
3D QSARAOPDockingIn silico modelingPPARγ full agonistsPPARγ partial agonistsPharmacophore modeling

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Area of Science:

  • Computational toxicology
  • Drug design
  • Pharmacology

Background:

  • Peroxisome proliferator-activated receptor gamma (PPARγ) is a key target for metabolic diseases and toxicity.
  • Understanding ligand interactions with PPARγ is crucial for drug development and safety assessment.

Purpose of the Study:

  • To develop an integrated in silico approach for predictive toxicology and computer-aided drug design.
  • To predict PPARγ-mediated hepatotoxicity of full agonists.
  • To identify natural antidiabetic compounds acting through PPARγ partial agonism.

Main Methods:

  • Adverse outcome pathway development
  • Pharmacophore modeling
  • Molecular docking
  • 3D Quantitative Structure-Activity Relationship (QSAR) analysis

Main Results:

  • An integrated in silico approach was established to study PPARγ-ligand interactions.
  • The approach successfully predicted potential PPARγ-mediated hepatotoxicity.
  • Naturally derived antidiabetic triterpenoids were identified as potential PPARγ partial agonists.

Conclusions:

  • The integrated in silico approach supports hazard characterization for PPARγ agonists.
  • This method aids in discovering novel therapeutic agents targeting PPARγ.