Novel derivatives of eugenol as potent anti-inflammatory agents via PPARγ agonism: rational design, synthesis, analysis, PPARγ protein binding assay and computational studies.

Eugenol is a natural product abundantly found in clove buds known for its pharmacological activities such as anti-inflammatory, antidiabetic, antioxidant, and anticancer activities. It is well known from the literature that peroxisome proliferator-activated receptors (PPARγ) have been reported to regulate inflammatory responses. In this backdrop, we rationally designed semi-synthetic derivatives of eugenol with the aid of computational studies, and synthesized, purified, and analyzed four eugenol derivatives as PPARγ agonists. Compounds were screened for PPARγ protein binding by time-resolved fluorescence (TR-FRET) assay. The biochemical assay results were favorable for 1C which exhibited significant binding affinity with an IC50 value of 10.65 µM as compared to the standard pioglitazone with an IC50 value of 1.052 µM. In addition to the protein binding studies, as a functional assay, the synthesized eugenol derivatives were screened for in vitro anti-inflammatory activity at concentrations ranging from 6.25 µM to 400 µM. Among the four compounds tested 1C shows reasonably good anti-inflammatory activity with an IC50 value of 133.8 µM compared to a standard diclofenac sodium IC50 value of 54.32 µM. Structure-activity relationships are derived based on computational studies. Additionally, molecular dynamics simulations were performed to examine the stability of the protein-ligand complex, the dynamic behavior, and the binding affinity of newly synthesized molecules. Altogether, we identified novel eugenol derivatives as potential anti-inflammatory agents via PPARγ agonism.

A promising in silico protocol to develop novel PPARγ antagonists as potential anticancer agents: Design, synthesis and experimental validation via PPARγ protein activity and competitive binding assay.

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.