Evaluation of Murrayanine's Anticancer Potential in Breast Cancer: Integrative Computational, In Vitro, and Nanoparticle Delivery Approaches.

Breast cancer continues to be a major threat to women's health on a global scale. Innovative treatments that are effective and well-tolerated are greatly needed. Nature-derived compounds, such as phytochemicals, have gained widespread interest owing to their diverse anti-tumor actions on cancer cells. Murrayanine, found in Murraya koenigii, has shown efficacy as an anticancer agent by promoting apoptosis, inducing reactive oxygen species, and regulating pivotal pathways like PI3K/Akt and NF-κB. This investigation evaluated the anti-breast cancer efficacy of Murrayanine by combining cytotoxicity assays, molecular docking and the production of solid lipid nanoparticles for improved delivery. Machine learning models, such as Random Forest, identified Murrayanine as a possible PARP10 inhibitor, suggesting molecules known as PARP inhibitors can pass through the cell barrier. Both network pharmacology and protein interaction analyses identified the proteins PARP, PTEN and TP53 as potential targets for breast cancer therapy. Molecular simulations revealed stable protein-drug interactions, suggesting its potential to produce a favorable effect on the molecular level. Murrayanine SLNs exhibited excellent biochemical features, controlled drug release and stability in in vivo settings. Dose-dependent cytotoxic effects were observed against MCF7 breast cancer cells, with an IC₅₀ of 37.35µg/mL determined. The experimental data suggest that Murrayanine delivered as SLNs is a promising preclinical candidate for breast cancer treatment. The PARP10-inhibitory mechanism and the PARP/PTEN/TP53 network targets proposed here are derived from machine learning classification and molecular modelling and therefore represent computational predictions rather than biochemically or in vivo confirmed outcomes. Conclusively, it is necessary to carry out biochemical validation, pharmacokinetic, and clinical evaluations to ascertain the anticancer efficacy of Murrayanine delivered by SLNs. An integrated use of computational and experimental approaches highlights the ability of natural product drug discovery to address breast cancer. Quantitatively, the Random Forest classifier achieved 91.23% accuracy (Kappa 0.8684, MCC 0.869, weighted F1 0.912) on the class-balanced training partition but only 80.0% accuracy (Kappa 0.2793, MCC 0.287) on an independent, natively imbalanced test partition, indicating overfitting; Murrayanine docked into the PARP10 catalytic domain (PDB 5LX6) with a binding affinity of -8.2kcal/mol in a protocol validated by redocking veliparib (RMSD 0.86Å), and the complex remained stable over 100ns (Rg 16.84 ± 0.21Å, ALA921 hydrogen-bond occupancy 78.4%, MM-GBSA ΔG -52.00kcal/mol). The SLNs showed an encapsulation efficiency of 82.64 ± 1.37%, a particle size of 179.0 ± 5.3nm, a zeta potential of -27.4 ± 1.6mV and 88.17% cumulative release at 72h, and were stable for 90 days at 4 °C.