Integrated molecular modelling and eriodictyol SLNs from Ocimum tenuiflorum: A promising anti-breast cancer therapeutic with enhanced drug delivery

In this work, machine-learning prediction, molecular modelling and nanocarrier development are used to assess Eriodictyol of Ocimum tenuiflorum as a possible anti-breast-cancer treatment against the progesterone receptor (PR). A BindingDB curated dataset was modelled with the aid of a variety of algorithms, and the best results were obtained with Rand Forest (accuracy 78.12, kappa 0.5361). Molecular docking predicted the presence of affinity of binding at -14.4 kcal/mol and rich interaction profile comprising six hydrogen bonds, three hydrophobic contacts, and a p-p stacking interaction with the key residues GLN725, MET756, LEU715, LEU718 and also PHE778. The stability of the complex was verified through molecular dynamics (100 ns, OPLS-2005 force field, 300K) where the protein RMSD error stayed within 1.0-1.6 A, and high hydrogen-bond occupancy (LEU715: 0.90; LEU718: 0.85). MM-GBSA analysis had produced a good binding free energy of -51.00 kcal/mol. The solid lipid nanoparticles (SLNs) loaded with erythrolictol showed good physicochemical characteristics such as; 90% encapsulation efficiency, loading capacity of 9.7, particle size of 120-130 nm and zeta potential of -30 mv. The pH-responsive release was noted (95 and 63 percent at pH 7.4 and pH 5.1, respectively) which followed the first-order reaction (r 2 = 0.981). Cytotoxicity tests revealed that it had a dose dependent inhibition with IC 50 of 37.2 μg/mL. In general, the combined results can justify Eriodictyol-SLNs as a potential therapeutic agent in breast cancer.