Physics informed machine learning for predictive toxicology and optimization of curcumin nanocarriers.

ABSTRACT: Curcumin's clinical utility is limited by poor bioavailability and dose-dependent toxicity. Although nano-encapsulation can address these shortcomings, rationally optimizing nanocarrier biosafety remains challenging due to the highly multidimensional design space. Here, we develop an interpretable Physics-Informed Machine Learning (PIML) framework that integrates experimental data from 75 curcumin nanocarriers with DLVO stability theory and drug-release kinetics to predict and optimize cytotoxicity. Among the evaluated models, XGBoost attained the highest statistical performance (R² = 0.89), although the PIML model provided physically coherent predictions with similar accuracy (R² = 0.86). SHAP research indicated a moderate negative zeta potential (-30 to -40 mV), chi

SUBMITTER: Rahdar A 

PROVIDER: S-EPMC12858851 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature