Evaluation of Curcumin as NF-κB Inhibitor for Breast Cancer Therapy Using in Silico Approach
Abstract
Purpose of the study: This study aims to evaluate the potential of curcumin as a breast cancer therapeutic agent by analyzing its ability to inhibit NF-κB activation using an in silico approach, specifically through molecular docking to assess absorption and interaction with the target protein.
Methodology: This study used a quantitative experimental method with molecular docking as an in silico approach. Tools included a Lenovo laptop (Intel® Inside™, Windows 10 Pro). Software used: PyRx 0.8 (with AutoDock Vina), YASARA, PyMOL, and Discovery Studio 2019. Ligand data were obtained from PubChem; protein from Protein Data Bank. Lipinski’s Rule was applied for drug-likeness screening.
Main Findings: Curcumin showed a binding affinity of -6.2 kcal/mol to NF-κB with RMSD < 2.00 Å. Visualization confirmed hydrogen bonds at ASN 32 and ASN 47, and hydrophobic pi-alkyl interactions at ALA 34 and ARG 50. Lipinski’s Rule of Five was fulfilled, indicating good oral drug-likeness and potential as an NF-κB inhibitor in breast cancer therapy.
Novelty/Originality of this study: This study offers new insights into the potential of curcumin as a natural NF-κB inhibitor for breast cancer therapy through a comprehensive in silico approach. By combining molecular docking, visualization, and drug-likeness analysis, it advances current knowledge by highlighting curcumin’s binding efficiency and pharmacological feasibility, supporting its development as an alternative anticancer candidate.
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