Essential-oil nanocapsules are bio-based antimicrobial formulations central to agri-food sustainability, yet their human and environmental hazard as formulated products cannot be assessed by conventional molecular QSAR, which ignores controlled release kinetics, nano-specific bio-interactions, and shell-material contributions. We apply a systems toxicology approach — integrating molecular QSAR (Tier 1), Korsmeyer–Peppas release kinetics (Tier 2), and a nano-specific bioavailability correction layer (Tier 3) — to produce the first quantitative integrated hazard prediction for two clove-oil advanced-material (AdMa) nanocapsule formulations: AdMa EO@PEC-GEL (pectin–gelatin shell, CaCl₂ crosslinker) and AdMa EO@Chi (chitosan shell, formaldehyde crosslinker).Eugenol (∼90 wt% core) is the principal toxicophore. Release parameters were scaled from published eugenol–chitosan kinetic data to the 1 μm target particle size; nano-correction factors were calibrated from published surface-charge and uptake relationships. The integrated model predicts AdMa EO@PEC-GEL to be 9.1× safer than free eugenol (IC₅₀ ∼3.5 mM vs. 0.38 mM), driven by anionic surface charge and 80% encapsulation efficiency. AdMa EO@Chi retains toxicity close to the free molecule (IC₅₀ ∼0.91 mM; 2.4×) because cationic surface charge offsets the encapsulation benefit. The formaldehyde crosslinker introduces independent sensitisation, genotoxicity, and IARC Group 1 carcinogenicity flags — quantified via the concentration-addition mixture model — entirely absent from PEC-GEL.Sensitivity analysis identifies zeta potential as the dominant model uncertainty driver. Tier 2 is retrospectively validated against published release kinetics (R2 = 0.997). The Korsmeyer–Peppas tier applies directly to environmental fate scenarios — pH-dependent shell dissolution in soil and aquatic compartments — providing a unified architecture for human–environment integrated hazard assessment aligned with planetary health priorities. An interactive, browser-based digital twin is provided as Supplementary Information: it recomputes all integrated predictions in real time as the user adjusts the model inputs (zeta potential, particle size, encapsulation efficiency, eugenol fraction, release time and pH/enzyme condition), enabling transparent scenario analysis and progressive refinement as experimental data become available, in line with the iterative Safe-and-Sustainable-by-Design (SSbD) workflow.
