Computational drug discovery and nanoformulation of Ajmaline from Rauwolfia serpentina: Machine learning-assisted development of pH-responsive solid lipid nanoparticles for enhanced anticancer therapy

This work examines an exploratory screening and formulation workflow involving Rauwolfia serpentina alkaloids and Ajmaline-loaded solid lipid nanoparticles (SLNs). A Random Forest model was reported to reach 82.26% accuracy for ACE-associated activity classes and to classify 23 of 135 plant constituents as active; the available validation tables contain inconsistencies that require reconciliation. Docking scores favored serpentinine (-9.7 kcal/mol), rescinnamine (-9.2 kcal/mol) and raupine (-8.9 kcal/mol), rather than establishing Ajmaline as the best-scoring ligand. A 100 ns ACE-ligand simulation and MM/GBSA estimate (-67.00 kcal/mol) provide computational, not biochemical, evidence. Ajmaline-loaded SLNs showed 90.11% encapsulation efficiency and 8.30% drug-loading capacity. Cumulative release at 75 h was 64.56% at pH 4.0 and 95.24% at pH 7.2; the first-order fit at pH 7.2 had R² = 0.980. A reported MTT dose response showed reduced viability, but the supplied records do not identify the tested cell line or establish selectivity for breast cancer. These findings support further validation of the formulation and target hypothesis; they do not yet demonstrate anticancer efficacy or clinical safety.

Abstract from PubMed (PMID 42987478). This entry was added automatically by our daily literature monitor because it matches the topics we follow; read the full paper at the original source.