From Synthesis to Mechanism: Biological Evaluation of a p-Toluidine-Based Thiazolidinone-Quinoline VEGFR-2 Candidate...

In response to recent advances in computer-aided drug discovery (CADD) enabled by high-performance computing, computational approaches were employed to support and rationalize the investigation of a VEGFR-2-targeted anticancer candidate, combining molecular-level modeling with experimental validation. Initial in silico ADMET profiling and molecular docking were conducted to support the evaluation of drug-like properties and target engagement within a series of para-toluidine-based derivatives (1-14). The most biologically active compound was further evaluated through 100 ns molecular dynamics simulations and comprehensive DFT calculations to investigate binding stability and electronic characteristics. Based on a rational design strategy and supported by computational analyses, the compounds were synthesized and fully characterized using IR, MS, ¹H/¹³C NMR, and elemental analysis. Biological evaluation was performed against HepG-2, MCF-7, HCT-116, and normal WI-38 cells. Mechanistic studies included VEGFR-2 inhibition, wound-healing migration assays, cell-cycle distribution analysis, apoptosis assessment, and caspase-3 activation. Several derivatives exhibited micromolar cytotoxic activity, with compound 14 emerging as the most active against HepG-2 cells (IC₅₀ = 7.84 ± 0.5 µM), showing cytotoxic activity comparable to that of sorafenib (IC₅₀ = 9.18 ± 0.6 µM) and demonstrating favorable selectivity toward normal WI-38 cells (IC₅₀ = 67.75 ± 3.6 µM). Compound 14 showed moderate VEGFR-2 inhibitory activity (IC₅₀ = 0.55 µM), significant suppression of cell migration, pronounced G₀/G₁ cell-cycle arrest, and robust apoptosis induction supported by caspase-3 activation. Molecular docking and MD simulations supported a stable binding mode within the VEGFR-2 active site. This integrated framework highlights compound 14 as a selectively active VEGFR-2-oriented anticancer candidate scaffold with a favorable selectivity profile, supported by experimental and computational analyses, warranting further lead optimization.