Novel dihydropyrimidines as promising EGFR & HER2 inhibitors: Insights from experimental and computational studies.

Dihydropyrimidines are widely recognized for their diverse biological properties and are often synthesized by the Biginelli reactions. In this backdrop, a novel series of Biginelli dihydropyrimidines were designed, synthesized, purified, and analyzed by FT-IR, 1H NMR, 13C NMR, and mass spectrometry. Anticancer activity against MCF-7 breast cancer cells was evaluated as part of their cytotoxicity in comparison with the normal Vero cells. The cytotoxicity of dihydropyrimidines ranges from moderate to significant. Among the 38 dihydropyrimidines screened, compounds 16, 21, and 39 exhibited significant cytotoxicity. These 3 compounds were subjected to flow cytometry studies and EGFRwt Kinase inhibition assay using lapatinib as a standard. The study included evaluation for the inhibition of EGFR and HER2 expression at five different concentrations. At a concentration of 1000 nM compound 21 showed 98.51 % and 96.79 % inhibition of EGFR and HER2 expression. Moreover, compounds 16, 21 and 39 significantly inhibited EGFRwt activity with IC50 = 69.83, 37.21 and 76.79 nM, respectively. In addition, 3D-QSAR experiments were conducted to elucidate Structure activity relationships in a 3D grid space by comparing the experimental and predicted cytotoxic activities. Molecular docking studies were performed to validate the results by in silico method. All together, we developed a new series of Biginelli dihydropyrimidines as dual EGFR/HER2 inhibitors.

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.