Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning
Joseph Cave, Anne Christiono, Carmine Schiavone, Henry J. Pownall, Vittorio Cristini, Daniela I. Staquicini, Renata Pasqualini, Wadih Arap, C. Jeffrey Brinker, Matthew J. Campen, Zhihui Wang, Hien Van Nguyen, Achraf Noureddine, Prashant Dogra
Abstract
High Resolution Image Download MS PowerPoint Slide The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both in vitro and in vivo, leveraging physicochemical properties and experimental conditions. A curated in vitro cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse inorganic NPs validated the predictive accuracy of the framework for in vitro settings. To enable organ-specific toxicity predictions in vivo, we integrated a physiologically based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential alternative approach to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation.