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A promising in silico protocol to develop novel PPARγ antagonists as potential anticancer agents: Design, synthesis
Yuvaraj Sivamani1, Dhivya Shanmugarajan1, T Durai Ananda Kumar1
1Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysuru 570 015, Karnataka, India.
Abstract:
Peroxisome proliferator-activated receptor gamma (PPARγ), a member of the nuclear receptor superfamily is an excellent example of targets that orchestrates cancer, inflammation, lipid and glucose metabolism. We report a protocol for the development of novel PPARγ antagonists by employing 3D QSAR based virtual screening for the identification of ligands with anticancer properties. The models are generated based on a large and diverse set of PPARγ antagonist ligands by the HYPOGEN algorithm using Discovery Studio 2019 drug design software. Among the 10 hypotheses generated, Hypotheses 2 showed the highest correlation coefficient values of 0.95 with less RMS deviation of 1.193. Validation of the developed pharmacophore model was performed by Fischer's randomization and screening against test and decoy set. The GH score or goodness score was found to be 0.81 indicating moderate to a good model. The selected pharmacophore model Hypo 2 was used as a query model for further screening of 11,145 compounds from the PubChem, sc-PDB structure database, and designed novel ligands. Based on fit values and ADMET filter, the final 10 compounds with the predicated activity of ≤ 3 nM were subjected for docking analysis. Docking analysis revealed the unique binding mode with hydrophobic amino acid that can cause destabilization of the H12 which is an important molecular mechanism to prove its antagonist action. Based on high CDocker scores, Cpd31 was synthesized, purified, analyzed and screened for PPARγ competitive binding by TR-FRET assay. The biochemical protein binding results matched the predicted results. Further, Cpd31 was screened against cancer cells and validated the results.
Insights
Researchers developed novel anticancer drugs targeting Peroxisome proliferator-activated receptor gamma (PPARγ) using 3D QSAR virtual screening. A synthesized compound, Cpd31, demonstrated validated PPARγ antagonist activity and anticancer properties in cell screening.
Area of Science:
- Medicinal Chemistry
- Computational Drug Design
- Molecular Pharmacology
Background:
- Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor involved in cancer, inflammation, and metabolism.
- Developing selective PPARγ antagonists is crucial for therapeutic interventions.
Purpose of the Study:
- To develop novel PPARγ antagonists with anticancer properties using 3D QSAR-based virtual screening.
- To identify potent and selective PPARγ antagonist drug candidates.
Main Methods:
- Generation of 3D Quantitative Structure-Activity Relationship (QSAR) pharmacophore models using the HYPOGEN algorithm.
- Virtual screening of large compound libraries (PubChem, sc-PDB) against the validated pharmacophore model.
- Molecular docking, ADMET filtering, compound synthesis, and biochemical/cellular assays for validation.
Main Results:
- A robust pharmacophore model (Hypo 2) with high statistical significance (R=0.95, RMSD=1.193, GH score=0.81) was developed.
- Virtual screening identified 10 lead compounds with predicted activity ≤ 3 nM.
- Synthesized compound Cpd31 exhibited validated PPARγ competitive binding and demonstrated anticancer effects in cell-based assays.
Conclusions:
- The 3D QSAR approach successfully identified novel PPARγ antagonists with potential anticancer activity.
- Cpd31 represents a promising lead compound for further development as an anticancer therapeutic targeting PPARγ.
- The study validates the molecular mechanism of H12 destabilization for PPARγ antagonism.
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