Network Pharmacology and Molecular Docking Studies of Flavonoids from Indigofera Tinctoria against Prostate Cancer
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DOI:
https://doi.org/10.33974/2nn08x11
Keywords:
Prostate cancer, Indigofera tinctoria, in silico studies, Network Pharmacology, Molecular dockingAbstract
The increasing prevalence of prostate cancer, a leading cause of cancer-related morbidity and mortality among men, necessitates the exploration of novel therapeutic strategies. Indigofera tinctoria, an Indian medicinal plant known for its rich phytochemical profile, has emerged as a potential source of bioactive compounds for cancer treatment. This study employs network pharmacology and in silico techniques to evaluate the flavonoids derived from I. tinctoria against prostate cancer. Phytochemicals were extracted from the literature and the Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) database. The SMILES and SDF formats of the compounds were retrieved from the PubChem database. The pharmacokinetic and toxicological profiles were assessed through Swiss ADME and Protox 3.0 tools, ensuring a comprehensive analysis of the drug-like properties. A network pharmacology approach was employed to identify the multi-target interactions of flavonoids from I tinctoria against prostate cancer, revealing that the compounds will be active against EGFR, CTNNB1, AKT1, PIK3CA, ERBB2, MTOR and BCL2. The drug scores were estimated utilising OSIRIS Property Explorer, revealing a favourable pharmacological potential. Molecular docking studies were conducted using MZ-Dock and visualised by Pymol and Discovery Studio Visualizer. The results revealed that quercetin, dehydrodeguelin, luteolin, and apigenin exhibited significant binding affinities and favourable interaction profiles with the selected targets. These findings illuminate the druggable nature of these compounds and highlight their therapeutic promise in combating prostate cancer. In conclusion, the results underscore the value of leveraging traditional medicinal plants and modern computational approaches in the search for effective cancer treatments, paving the way for future experimental validation and clinical application.


