Identification and biological assessment of amino benzoxazole derivatives as KDR inhibitors and potential anti-cancer agents.

Authors

Ali Khudhir, Mahmoud A Al-Sha'er, Mahmoud A Alelaimat, Raed Khashan

Published in

Journal of computer-aided molecular design. Volume 39. Issue 2. Pages 93. Oct 24, 2025. Epub Oct 24, 2025.

Abstract

A library of 39 amino-benzoxazole derivatives, selected from 57 benzoxazole compounds in the NCI database, was evaluated for their potential as KDR inhibitors using computational docking methods, including CDocker, LibDock, and AutoDock Vina. At a screening concentration of 100 µM, 11 compounds demonstrated over 40% KDR inhibition, with six showing notable activity. The IC50 values of the top six compounds ranged from 6.855 to 50.118 µM, with compound 1 showing the highest inhibitory activity (IC₅₀ = 6.855 µM). Docking studies revealed that compound 1 achieved an AutoDock Vina score of - 7.5 kcal/mol, CDocker energy of - 41.4, and a LibDock score of 140.9 against KDR, indicating strong binding affinity compared with the positive control, sorafenib (AutoDock Vina - 10.7 kcal/mol, CDocker - 43.76, LibDock 96.7). Anti-proliferative assays against A549 and MCF-7 cancer cell lines showed that compounds 16 and 17 were the most effective against A549 cells, achieving inhibition rates of 79.42% and 85.81%, respectively. Compounds 16 and 17 also exhibited the highest activity against MCF-7 cells (IC50 = 6.98, 11.18 µM), respectively. The docking scores for compounds 16 (KDR: Vina - 8.9, CDocker - 32.15, LibDock 105.7) and 17 (KDR: Vina - 11.1, CDocker - 19.15, LibDock 121.9) support their potent interactions with the KDR target. These results suggest that selection of aminobenzoxazole derivatives may serve as promising anticancer agents, potentially through inhibition of KDR, EGFR, and FGFR1 pathways. Future work will focus on optimizing compound 1 to enhance therapeutic efficacy and exploring the roles of EGFR and FGFR1 pathways in the activities of compounds 16 and 17. Additionally, the relatively limited dataset constrained the statistical power for quantitative modeling; we plan to expand the aminobenzoxazole library and develop a validated 3D-QSAR model to visualize pharmacophoric hotspots and guide structure-based lead optimization.

PMID:
41134395
Bibliographic data and abstract were imported from PubMed on 24 Oct 2025.

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