A physics-informed machine learning framework for predicting and mitigating doxorubicin nanocarrier toxicity in normal cells

The clinical utility of doxorubicin (DOX) has been widely hampered by a dose-dependent systemic toxicity, in particular cardiotoxicity. While nanocarrier systems represent encouraging solutions, their optimization is not an easy task due to complex, nonlinear relationships between physicochemical properties and biological outcomes. This study presents a hybrid computational framework that incorporates both classical machine learning and physics-informed machine learning to predict and optimize DOX-loaded nanocarrier cytotoxicity toward normal cells. For this purpose, we compiled an extensive dataset of 77 unique nanocomposite systems with their detailed physicochemical characterizations and biological evaluations. Several ML models were trained and compared, whereas a Physics-Informed Neural Network implemented domain knowledge such as drug release kinetics, colloidal stability constraints, and diffusion limitations. The proposed PINN model showed better predictive capability (R2 = 0.89, RMSE = 0.14) compared to conventional ML methods. SHAP analysis revealed that zeta potential and size are the most governing features on cytotoxicity. Bayesian optimization revealed an optimal design space: sizes of 120–150 nm, zeta potentials between − 25 and − 35 mV, loading efficiency of 5–10%, and encapsulation efficiency > 85%. Experimental validation on independent studies confirmed the model’s accuracy with prediction errors < 3%. The proposed PIML framework offers a robust yet interpretable method for rational nanocarrier design that significantly improves the development of safer chemotherapeutic delivery systems by reducing the reliance on empirical optimizations.