ABSTRACT:
Allicin (diallyl thiosulfinate), the principal bioactive compound of Allium sativum (garlic), exhibits well-documented anticancer, anti-inflammatory, antiviral, and antibacterial activities [1,2]. However, its rapid degradation in aqueous environments represents a critical pharmacological limitation. This study presents the first Digital Twin (DT) framework for modeling allicin aqueous stability, integrating three computational layers: (1) a thermodynamic ODE kinetic engine parametrized by DFT data (Ead, ΔH, ΔG) from Al₁₂N₁₂, B₁₂N₁₂, and C₂₄ nanocomplexes [1]; (2) a Gaussian Process surrogate emulating GROMACS molecular dynamics outputs — RMSD, radius of gyration (Rg), solvent-accessible surface area (SASA), and B-factors; and (3) an experimental validation layer calibrated against published stability, UV-Vis, IR, and molecular docking data. The Allicin/Al₁₂N₁₂ complex achieved a Nanocage Protection Factor (NPF) of 5,498,905× at 37 °C, pH 7.4, extending the half-life from 3.6 days (free allicin) to beyond 9,999 days. The MD surrogate predicted equilibrium RMSD of 0.485 Å and SASA of 6.96 nm² for the Al₁₂N₁₂ complex, consistent with strong encapsulation. Stability validation yielded R² = 0.9997 and MAPE = 10.3% against literature data [27,28]. IR frequencies were reproduced within 1.18% [1,7]. This work establishes Digital Twin methodology as a novel paradigm for computational drug delivery optimization of natural bioactive compounds.