Computational prediction and SLN formulation of Narcissin for reverse transcriptase inhibition and controlled drug delivery applications.

The aim of this paper was to construct a stable drug delivery mechanism of Narcissin, which is phytoconstituent of Aerva lanata that has Reverse Transcriptase potential of anti-breast cancer. The Rand Forest Classifier was the most successful machine learning algorithm with an accuracy of 86.43 and independent test set validation of 80.85. High binding affinity to Narcissin (-13.3 kcal/mol) with five hydrogen bonds and positive hydrophobic interactions were observed in molecular docking. Simulations of Narcissin-Reverse Transcriptase complex using molecular dynamics revealed that it did not exhibit significant changes in RMSD, which meant that the complex was stable. MMGBSA analysis has displayed a good binding free energy of -51.12 kcal/mol with the van der Waals forces playing a major role (-62.2 kcal/mol). The Narcissin loaded solid lipid nanoparticles (SLN) had the highest encapsulation efficiency (90.12%), mean particle size of 80 nm, and zeta potential of -20 mV. In vitro release experiments revealed a zero-order, diffusion-controlled release, which was controlled and the cumulative release at pH 7.4 was 93.24%. The MTT assay exhibited dose- and time-dependent cytotoxicity particularly at 100 μg/mL indicating that Narcissin has a potential to be used as a bioactive agent in the treatment of breast cancer in SLN-based formulations.