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Publications

Journal Article Preprint Epublish 1 citation

PubMed: 40034433 DOI: 10.21203/rs.3.rs-5960303/v1

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

Res Sq

Authors: Joseph Cave, Anne Christiono, Carmine Schiavone, Henry J Pownall, Vittorio Cristini, Daniela I Staquicini, C Jeffrey Brinker, Matthew J Campen, Zhihui Wang, Hien Van Nguyen, Achraf Noureddine, Prashant Dogra

Affiliations

  • Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, TX, USA.
  • Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, NY, USA.
  • Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Department of Chemical, Materials, and Industrial Production Engineering, University of Naples Federico II, Naples, Italy.
  • Department of Medicine, Houston Methodist, Houston, TX, USA.
  • Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
  • Neal Cancer Center, Houston Methodist Research Institute, Houston, TX, USA.
  • Department of Imaging Physics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
  • Rutgers Cancer Institute, Newark, NJ, USA.
  • Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA.
  • Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, NM, USA.
  • College of Pharmacy, University of New Mexico, Albuquerque, NM, USA.
  • Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, USA.
  • Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA.

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 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 mesoporous silica NPs validated the framework's predictive accuracy 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 Novel Alternative Method (NAM) to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation.

MeSH

Safety, Nanoparticles, Machine Learning, Learning, Artificial Intelligence