4.8 Article

Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles

Journal

NATURE NANOTECHNOLOGY
Volume 6, Issue 3, Pages 175-178

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/NNANO.2011.10

Keywords

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Funding

  1. National Science Foundation (NSF) Interdisciplinary Nanotoxicity Center [HRD-0833178]
  2. NSF [362492-190200-01\NSFEPS-0903787]
  3. Department of Defense through the US Army Engineer Research and Development Center [W912HZ-06-C-0057, W912HZ-07-C-0073, W912HZ-06-C-0061]
  4. Foundation for Polish Science
  5. Norwegian Financial Mechanism and the European Economic Area Financial Mechanism in Poland
  6. Polish Ministry of Science and Higher Education [DS/8430-4-0171-0]
  7. Marie Curie fellowship [39036]
  8. Directorate For Engineering
  9. Div Of Industrial Innovation & Partnersh [0823040] Funding Source: National Science Foundation
  10. Division Of Human Resource Development
  11. Direct For Education and Human Resources [833178] Funding Source: National Science Foundation
  12. Office Of The Director
  13. EPSCoR [903787] Funding Source: National Science Foundation

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It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years(1), and there is a need for new methods to quickly test the potential toxicity of these materials(2). Because experimental evaluation of the safety of chemicals is expensive and time-consuming, computational methods have been found to be efficient alternatives for predicting the potential toxicity and environmental impact of new nanomaterials before mass production. Here, we show that the quantitative structure-activity relationship (QSAR) method commonly used to predict the physicochemical properties of chemical compounds can be applied to predict the toxicity of various metal oxides. Based on experimental testing, we have developed a model to describe the cytotoxicity of 17 different types of metal oxide nanoparticles to bacteria Escherichia coli. The model reliably predicts the toxicity of all considered compounds, and the methodology is expected to provide guidance for the future design of safe nanomaterials.

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