Systems Pharmacology of Metal–Benzimidazole Complexes in Triple-Negative Breast Cancer
Keywords:
triple-negative breast cancer, metal complexes, benzimidazole, CDK4, network pharmacology, molecular simulationAbstract
Background: Triple-negative breast cancer (TNBC) remains difficult to treat because of molecular heterogeneity and rapid emergence of resistance. Metal–benzimidazole complexes may affect multiple cancer-relevant pathways but require systematic prioritisation.
Objective: To map the predicted target landscape of a small panel of metal–benzimidazole complexes and determine whether any compound simultaneously engages cell-cycle and oxidative-stress vulnerabilities in TNBC.
Methods: Twelve previously characterised scaffold variants were subjected to target prediction, disease-network mapping, gene-set enrichment and docking against a panel of eight proteins. The two highest-ranking complexes underwent 120-ns molecular-dynamics simulation.
Results: Network analysis identified 96 compound–target links converging on cell-cycle regulation, DNA-damage signalling, apoptosis and redox homeostasis. MBZ-Cu4 had the highest composite network score and docked favourably to CDK4 (-9.0 kcal/mol) and NUDT1 (-9.4 kcal/mol). Simulations demonstrated stable coordination geometry and sustained occupancy of the ligand-binding cavities, with no major unfolding events. The CDK4 complex exhibited a mean backbone RMSD of 2.08±0.31 Å, whereas the NUDT1 complex remained below 2.2 Å for 91% of frames. Predicted oral bioavailability was limited by molecular weight, but the complex showed a low probability of blood–brain barrier penetration, which may be favourable for systemic oncology development.
Conclusion: MBZ-Cu4 showed a multi-target computational profile consistent with simultaneous interference in proliferative and oxidative-stress pathways. The study provides a rationale for prioritising metal–benzimidazole complexes for biochemical and TNBC cell-line evaluation while highlighting formulation and exposure as critical translational challenges.
Publication format: research abstract. No full-text manuscript accompanies this record.
References
Miah R, Issahaku AR, Uddin MN, Hamali MA, Miah MM, Kassim K, Wilhelm A, Mohd Aluwi MFF. Benzimidazole Derivative as a Promising Targeted Therapeutic for Lung Cancer: In-Silico Approaches. J Pharm Innov. 2026;21:409. doi:10.1007/s12247-026-10615-w.
Tasnim J, Hasan MM, Hossain A, Miah MM, Hossain KR, Molla MTH, Ahmed Z, Reza AFGM, Alam MT. In silico identification of a potential NUDT1 inhibitor for cancer treatment. Sci Rep. 2026. doi:10.1038/s41598-026-66781-x.
Nguyen VT, Huynh TKC, Ho GTT, Nguyen THA, Nguyen TLA, Dao DQ, et al. Metal complexes of benzimidazole-derived ligands as potential anticancer agents: synthesis, characterization, combined experimental and computational studies. R Soc Open Sci. 2022;9(9):220659.
Hussain A, AlAjmi MF, Rehman MT, Khan AA, Shaikh PA, Khan RA. Evaluation of transition metal complexes of benzimidazole-derived scaffold as promising anticancer chemotherapeutics. Molecules. 2018;23(5):1232.
Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11):682-690.
Meng XY, Zhang HX, Mezei M, Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput Aided Drug Des. 2011;7(2):146-157.
Ferreira LG, dos Santos RN, Oliva G, Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015;20(7):13384-13421.
Hollingsworth SA, Dror RO. Molecular dynamics simulation for all. Neuron. 2018;99(6):1129-1143.
Hospital A, Goñi JR, Orozco M, Gelpí JL. Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem. 2015;8:37-47.
Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov. 2015;10(5):449-461.
Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, Hou T. End-point binding free energy calculation with MM/PBSA and MM/GBSA: strategies and applications in drug design. Chem Rev. 2019;119(16):9478-9508.
Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V. SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res. 2014;42(W1):W32-W38.
Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37(1):1-13.
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607-D613.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-2504.
Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11.
Day PJ, Cleasby A, Tickle IJ, O'Reilly M, Coyle JE, Holding FP, et al. Crystal structure of human CDK4 in complex with a D-type cyclin. Proc Natl Acad Sci U S A. 2009;106(11):4166-4170.
Maier JA, Martinez C, Kasavajhala K, Wickstrom L, Hauser KE, Simmerling C. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput. 2015;11(8):3696-3713.
Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA. Development and testing of a general AMBER force field. J Comput Chem. 2004;25(9):1157-1174.
Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7:42717.
Banerjee P, Kemmler E, Dunkel M, Preissner R. ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2024;52(W1):W513-W520.
Rácz A, Mihalovits LM, Bajusz D, Héberger K, Miranda-Quintana RA. Molecular dynamics simulations and diversity selection by extended continuous similarity indices. J Chem Inf Model. 2022;62(14):3415-3425.
Moukengue B, Brown HK, Charrier C, Battaglia S, Baud'Huin M, Quillard T, et al. TH1579, MTH1 inhibitor, delays tumour growth and inhibits metastases development in osteosarcoma model. EBioMedicine. 2020;53:102704.
Zhou W, Ma L, Yang J, Qiao H, Li L, Guo Q, et al. Potent and specific MTH1 inhibitors targeting gastric cancer. Cell Death Dis. 2019;10(6):434.
Zhan D, Zhang X, Li J, Ding X, Cui Y, Jia J. MTH1 inhibitor TH287 suppresses gastric cancer development through the regulation of PI3K/AKT signaling. Cancer Biother Radiopharm. 2020;35(3):223-232.
Kang N, Ma J, Hu Y, Di R, Wang L, Zhang X, et al. Targeting MutT Homolog 1 (MTH1) for breast cancer suppression by using a novel MTH1 inhibitor MA-24 with tumor-selective toxicity. Pharmaceuticals. 2024;17(3):291.
Azuaje F. Computational models for predicting drug responses in cancer research. Brief Bioinform. 2017;18(5):820-829.
Beckers M, Sturm N, Sirockin F, Fechner N, Stiefl N. Prediction of small-molecule developability using large-scale in silico ADMET models. J Med Chem. 2023;66(20):14047-14060.
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, et al. The Protein Data Bank. Nucleic Acids Res. 2000;28(1):235-242.
Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455-461.
Published
Issue
Section
License
Copyright (c) 2026 Maya Thompson, Chinedu Okafor
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright. This work is licensed under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Sharing and adaptation are permitted with appropriate attribution, a link to the licence and an indication of changes. Third-party material may be subject to separate terms stated in its credit line.
