ABSTRACT:
The Allicin Digital Twin series (Morais et al., IJRPAS 2026) has progressively modeled allicin aqueous stability (v2.0), Mpro inhibition (v3.0), and simultaneous dual-target blockade of SARS-CoV-2 (v3.1). Predicting target affinity, however, does not address the core pharmacokinetic challenge: how does the allicin/Al₁₂N₁₂ nanocomplex release its cargo across biologically distinct environments, and what sustained inhibitory concentration does the released drug achieve over a therapeutically relevant time window? This work presents Digital Twin v4.0, a two-layer extension integrating Layer 8 — a pH- and temperature-dependent controlled release engine based on a modified Korsmeyer–Peppas equation coupled to a colloidal desorption barrier — and Layer 9 — a three-compartment ordinary differential equation (ODE) pharmacokinetic model solved by the LSODA algorithm (scipy.integrate.solve_ivp), driving the frozen dual-target pharmacodynamic model from v3.1. Two biological scenarios are simulated simultaneously over 40 days (dt = 0.05 day): Scenario A (healthy human plasma, pH 7.4, 37°C) and Scenario B (endosomal/inflammatory microenvironment, pH 5.5, 39°C). The modified Korsmeyer–Peppas model (n = 0.65, anomalous diffusion) yields K_release = 1.777×10⁻² day⁻¹ at pH 7.4 and 2.405×10⁻² day⁻¹ at pH 5.5, a 35.4% acceleration attributed to protonation of Al–O surface bonds. The reservoir effect of Al₁₂N₁₂ (NPF = 5,498,905×) maintains 98.3% of the intact nanocomplex intact at t = 40 days in Scenario A, sustaining a free allicin plateau of 0.012 µM while free allicin collapses to zero within 5 days. The cumulative dual-target inhibition score (DTAUC) for Al₁₂N₁₂ is 16.5× more sustained than free allicin in Scenario B. These results quantitatively validate the transition of allicin/Al₁₂N₁₂ from a thermodynamically stable candidate to a pharmacokinetically actionable drug delivery vehicle, while defining in vitro stability assays and real GROMACS MD simulations as the critical next experimental steps.