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Modeling in vitro cellular responses to silver nanoparticles.

Dwaipayan Mukherjee1, Steven G Royce2, Srijata Sarkar3

  • 1Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ, USA ; Department of Environmental and Occupational Medicine, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA ; Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ, USA.

Journal of Toxicology
|December 27, 2014
PubMed
Summary

This study developed a computational model to predict nanoparticle toxicity in macrophages. The model uses in vitro data to understand cellular responses and guide future in vivo research.

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Area of Science:

  • Toxicology
  • Computational Biology
  • Nanotechnology

Background:

  • Engineered nanoparticles (NPs) can cause toxic effects.
  • In vitro cell exposure systems offer high-throughput screening for NP toxicity.
  • Predicting in vivo NP effects requires robust computational models.

Purpose of the Study:

  • To develop a biologically based computational model of cellular interactions with NPs.
  • To parameterize the model using in vitro measurements from human cell cultures.
  • To support the analysis and prediction of in vivo NP effects.

Main Methods:

  • A mechanistic mathematical model was created for cellular interactions with NPs.
  • The model incorporates cellular mechanisms like proliferation, apoptosis, and cytokine production.
  • In vitro measurements of cellular viability and cytokine mRNA levels were used for parameterization.

Main Results:

  • The model was implemented for macrophages, incorporating in vitro dosimetry.
  • Essential cellular parameters were optimized based on experimental data.
  • The model successfully simulates cellular responses to NP exposure.

Conclusions:

  • The developed computational model provides a foundation for predicting NP toxicity.
  • This model can serve as a stepping stone for more advanced in vivo models.
  • It aids in understanding NP interactions and their effects on cellular pathways.