Integrated network pharmacology, molecular docking and admet screening of flavonoids from selected tephrosia species against breast cancer molecular targets.
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Date
2026
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Busitema University
Abstract
Breast cancer remains the leading cause of cancer-related mortality among women worldwide, accounting for approximately 2.3 million new cases annually. Despite advances in conventional therapies including chemotherapy, radiotherapy, surgery, and hormonal therapy, treatment challenges such as drug resistance, toxicity, recurrence, and high treatment costs continue to limit effective disease management. Medicinal plants contain bioactive compounds with significant pharmacological properties that may provide alternative therapeutic agents for cancer treatment.
This study aimed to evaluate the anticancer potential of flavonoids from selected Tephrosia species against breast cancer molecular targets using integrated network pharmacology, molecular docking, and ADMET screening approaches.
A total of 224 bioactive compounds were compiled from eight Tephrosia species (T. purpurea, T. linearis, T. candida, T. villosa, T. vogelii, T. elegans, T. pumila, and T. bracteolata) based on reported phytochemical studies and availability of chemical structures in public databases. Three-dimensional structures of ligands were obtained from PubChem, while breast cancer target proteins were retrieved from the Protein Data Bank (PDB). Swiss Target Prediction identified 354 compound-related targets, which after duplicate removal yielded 136 unique targets. Disease-related targets were identified from OMIM, GeneCards, and DisgNet databases, with 35 intersecting genes identified through Venn analysis. Protein-protein interaction (PPI) network was constructed using STRING database (version 12.0) and visualized in Cytoscape (version 3.10.4). Gene Ontology (GO) and KEGG pathway enrichment analyses were performed to elucidate biological functions and signalling pathways. Hub genes were identified using CytoHubba plugin based on degree method. Molecular docking was performed using Auto Dock Vina to evaluate binding affinity and interaction patterns between flavonoids and target proteins. Drug-likeness and pharmacokinetic properties were assessed using SwissADME and ADMET prediction tools.
PPI network analysis identified TP53 (degree=19), PIK3CA (degree=16), STAT3 (degree=14), NRAS (degree=12), and SRC (degree=12) as the top five hub genes. GO enrichment analysis revealed significant involvement in protein binding, enzyme binding, and protein phosphatase binding, with cellular components primarily in the nucleus, cytoplasm, and cytosol. Molecular docking results showed that all tested compounds exhibited negative binding energies, indicating spontaneous binding. The best docking scores were observed with 7R9V_cpd_17 (binding affinity = -10.2 kcal/mol), 7R9V_cpd_16 (binding affinity = -10.1 kcal/mol), and 7R9V_cpd_37 (binding affinity = -10.2 kcal/mol). Compounds demonstrated favourable drug-likeness properties complying with Lipinski's Rule of Five.
This integrated computational study demonstrates that flavonoids from Tephrosia species possess promising anticancer potential through multi-target mechanisms. The identified compounds warrant further experimental validation through in vitro and in vivo studies. The study also highlights the value of computational approaches in accelerating natural product-based anticancer drug discovery.
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Undergraduate research report
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Gibuni, J. (2026). Integrated network pharmacology, molecular docking and admet screening of flavonoids from selected tephrosia species against breast cancer molecular targets. [Unpublished undergraduate research report]. Busitema University.