Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

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 <i>in vitro</i> and <i>in vivo</i>, leveraging physicochemical properties and experimental conditions. A curated <i>in vitro</i> 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 <i>in vitro</i> settings. To enable organ-specific toxicity predictions <i>in vivo</i>, 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.

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Publication Details

Journal
ACS Nano
Published
2025-06-03
DOI
https://doi.org/10.1021/acsnano.5c03590
Citations
12
Primary Topic
Nanoparticle-Based Drug Delivery
Type
article
Field-Weighted Citation Impact
7.17

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article

Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

C. Jeffrey Brinker, Renata Pasqualini, Carmine Schiavone, Vittorio Cristini et al.
12 citations
ACS Nano
Nanoparticle-Based Drug Delivery
7.17
article

Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

C. Jeffrey Brinker, Renata Pasqualini, Carmine Schiavone, Vittorio Cristini, Matthew J. Campen, Wadih Arap, Prashant Dogra, Daniela I. Staquicini, Achraf Noureddine, Henry J. Pownall, Zhihui Wang, Joseph Cave, Hien Van Nguyen, Anne Christiono
article en
12 citations

Abstract

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 <i>in vitro</i> and <i>in vivo</i>, leveraging physicochemical properties and experimental conditions. A curated <i>in vitro</i> 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 <i>in vitro</i> settings. To enable organ-specific toxicity predictions <i>in vivo</i>, 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.

ACS NanoVol. 19(23)
Rutgers, The State University of New Jersey (US), Houston Methodist (US), The University of Texas MD Anderson Cancer Center (US), University of New Mexico (US), Cornell University (US), Cancer Institute (WIA) (IN), Rutgers Sexual and Reproductive Health and Rights (NL), Methodist Hospital (US), Weill Cornell Medicine (US), Rutgers New Jersey Medical School (US), University of Houston (US), Massachusetts Institute of Technology (US), University of Naples Federico II (IT)
National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering
Openalex Percentile: Top 2%
Nanoparticle-Based Drug Delivery
7.17
DOIOpen Access PDFOpenAlex
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