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Mohamed R. Khidre

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Sep 2026

Design, synthesis, molecular docking, ADMET and anticancer evaluation of novel pyridine/thiazole hybrids as dual VEGFR-2 and EGFR kinase inhibitors.

Here, we highlight the synthesis of a number of novel pyridine/thiazole hybrids 1 to 6a,b and their screening as possible anticancer drugs through dual targeting of VEGFR-2 and EGFR. The novel compounds were created in accordance with the structural specifications of the target receptors. The MTT assay was used to assess the compounds' cytotoxicity against the cancer cell lines HepG2, MCF-7, HCT116, and A549. With IC50 values of 6.50, 7.00, 5.55, and 6.85 μM against HepG2, MCF-7, A549, and HCT116 cell lines, respectively, compound 5b demonstrated the strongest anticancer activity. Although it was less effective than sorafenib against the studied cell lines, it was more effective than erlotinib against HepG2, MCF-7, and HCT116, while it was less effective against A549. The six potent compounds 4a, 4b, 4c, 5a, 5b, 6a, and 6b were assessed for their toxic effects on VERO normal cell lines to determine cytotoxicity levels. Results across all studies indicated that every synthesized compound demonstrated minimal toxicity toward VERO normal cells, with IC50 values ranging between 47.60 and 52.22 μM. Additionally, molecular modeling studies were used to investigate how well the novel derivatives could interact with EGFR and VEGFR-2 receptors. Furthermore, the compounds underwent additional testing to measure their capacity to inhibit both EGFRT790M and VEGFR-2. Compounds 5b, 4b, 4c, and 6b demonstrated excellent inhibitory potency against VEGFR-2, exhibiting IC50 values of 0.95, 1.00, 1.15, and 1.35 μM, respectively. Also, compounds 5b, 4c, 4b, and 6b inhibited EGFRWT activity with IC50 = 0.24, 0.30, 0.37 and 0.40 μM respectively. Similarly, these same compounds displayed superior inhibitory effects on EGFRT790M, with corresponding IC50 values of 0.26, 0.36, 0.44, and 0.50 μM, respectively. Notably, derivatives 5b, 4b, and 4c exhibited good in silico ADMET predictions.

N. Ahmed, Rizk E. Khidre, Mohamed R. Khidre et al. · 0 citations

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