ArticleScientific reports2026
A physics-informed machine learning framework for predicting and mitigating doxorubicin nanocarrier toxicity in normal cells.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 (R
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