Machine Learning in Nanoparticles Toxicology: Mechanisms, Predictive Modelling, and Risk Assessment Strategies
Year: 2025
Type: article
Abstract: The increasing application of engineered nanoparticles (ENPs) in industry and medicine will require economic and ethically acceptable procedures for toxicological assessment. This paper employed supervised machine learning (ML) to model a new set of physicochemical data, correlating the 312 ENP samples contained in the eNano-Mapper database with ENP cytotoxicity. The major characteristics were particle size (mean = 68.5 nm), zeta potential (=18.6 mV), hydrodynamic diameter (102.3 nm), surface area, as well as solubility. Four approaches of ML: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), have been tested, and XGBoost demonstrated 91% accuracy, 0.90 precision, 0.89 recall, 0.89 F1-score, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) = 0.94. The Logistic Regression and SVM models reported lower scores (accuracy: 78% and 80%, AUC: 0.81 and 0.83, respectively), indicating a diminished capacity to identify non-linear toxicological trends. The SHAP analysis provided zeta potential (0.146), hydrodynamic diameter (0.131), and particle size (0.112) as the best predictive factors of toxicity. Particle size showed a significant correlation with hydrodynamic diameter (r = 0.78), suggesting that future testing procedures could be redundant. There were only five entries (misclassifications), with the majority between the high and moderate toxicity categories. The research presents a compelling, fact-based alternative to animal testing, thereby driving the development of safer-by-design nanoparticles and enhancing risk assessment.
Authors: Ubong Bernard Essien, Akaninyene U. Akpan, Felix Isidore Ibanga, Prince-charles Omin Itu, Prince Uche Micheal, Bright Akachi Daniel, Linda I. Ozohili
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413420776