A Computational Strategy for Identifying Self-Assembling Food-Derived Molecules for Antiparasitic Nanotherapy.

Carrier-free nanodrugs remain difficult to design, and the molecular basis of their self-assembly is still poorly understood. Here, an integrated workflow combining 2D/3D molecular screening with SHAP-assisted analysis was used to identify self-assembling pairs from food-derived compounds. Ursolic acid and 18β-glycyrrhetinic acid were thereby identified and found to self-assemble into stable nanoparticles (UA-18βGA NPs). Compared with the individual components, nanoparticles showed reduced cytotoxicity and enhanced antiparasitic activity under the tested conditions. Spectroscopic characterization together with density functional theory calculations and molecular dynamics simulations supported the intermolecular interactions and structural evolution associated with nanoparticle formation. UA-18βGA NPs exhibited synergistic antiparasitic activity against Ichthyophthirius multifiliis, while also showing reduced combined toxicity relative to the free components. Mechanistically, the nanoparticles were associated with parasite apoptosis involving Erk1/Akt-related signaling. In infected zebrafish, UA-18βGA NPs reduced oxidative stress and inflammatory responses, accompanied by altered macrophage marker expression and reduced inflammasome-related gene expression. In a murine model of experimental cerebral malaria, the nanoparticles improved therapeutic outcomes, reduced blood-brain barrier leakage, and attenuated inflammatory injury more effectively than the monomer treatments. These findings identify UA-18βGA NPs as a promising natural product-based antiparasitic nanoformulation and support integrated screening as a practical strategy for discovering self-assembling bioactive molecular combinations.