In Silico Approach Using Free Software to Optimize the Antiproliferative Activity and Predict the Potential Mechanism of Action of Pyrrolizine-Based Schiff Bases

Abstract

In the current study, a simple in silico approach using free software was used with the experimental studies to optimize the antiproliferative activity and predict the potential mechanism of action of pyrrolizine-based Schiff bases. A compound library of 288 Schiff bases was designed based on compound 10, and a pharmacophore search was performed. Structural analysis of the top scoring hits and a docking study were used to select the best derivatives for the synthesis. Chemical synthesis and structural elucidation of compounds 16a–h were discussed. The antiproliferative activity of 16a–h was evaluated against three cancer (MCF7, A2780 and HT29, IC50 = 0.01–40.50 μM) and one normal MRC5 (IC50 = 1.27–24.06 μM) cell lines using the MTT assay. The results revealed the highest antiproliferative activity against MCF7 cells for 16g (IC50 = 0.01 μM) with an exceptionally high selectivity index of (SI = 578). Cell cycle analysis of MCF7 cells treated with compound 16g revealed a cell cycle arrest at the G2/M phase. In addition, compound 16g induced a dose-dependent increase in apoptotic events in MCF7 cells compared to the control. In silico target prediction of compound 16g showed six potential targets that could mediate these activities. Molecular docking analysis of compound 16g revealed high binding affinities toward COX-2, MAP P38α, EGFR, and CDK2. The results of the MD simulation revealed low RMSD values and high negative binding free energies for the two complexes formed between compound 16g with EGFR, and CDK2, while COX-2 was in the third order. These results highlighted a great potentiality for 16g to inhibit both CDK2 and EGFR. Taken together, the results mentioned above highlighted compound 16g as a potential anticancer agent.