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

Nature Nanotechnology volume 6, pages 175–178 (2011)Cite this article

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Abstract

It is expected that the number and variety of engineered nanoparticles will increase rapidly over the next few years1, and there is a need for new methods to quickly test the potential toxicity of these materials2. 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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Figure 1: Plot of experimentally determined (observed) versus predicted log values of 1/EC50.

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Acknowledgements

The authors acknowledge support from the National Science Foundation (NSF) Interdisciplinary Nanotoxicity Center (grant no. HRD-0833178) and the NSF Experimental Program to Stimulate Competitive Research (award no. 362492-190200-01\NSFEPS-0903787), the Department of Defense through the US Army Engineer Research and Development Center for three generous contracts (High Performance Computational Design of Novel Materials (HPCDNM), contract no. W912HZ-06-C-0057; Chemical Material Computational Modeling (CMCM), contract no. W912HZ-07-C-0073; Development of Predictive Techniques for Modeling Properties of NanoMaterials Using New Quantitative Structure–Property Relationships/Quantitative Structure–Activity Relationships Approach Based on Optimal NanoDescriptors, contract no. W912HZ-06-C-0061). T.P. thanks the Foundation for Polish Science for a fellowship and research grant under the ‘FOCUS 2010’ Program supported by the Norwegian Financial Mechanism and the European Economic Area Financial Mechanism in Poland. This work was supported by the Polish Ministry of Science and Higher Education (grant no. DS/8430-4-0171-0). A.T. expresses gratitude to the Marie Curie fellowship for financial support (contract no. 39036).

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Authors and Affiliations

  1. Department of Chemistry and Biochemistry, Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 Lynch Street, Jackson, 39217-0510, Mississippi, USA

    Tomasz Puzyn, Bakhtiyor Rasulev, Agnieszka Gajewicz, Andrea Michalkova & Jerzy Leszczynski

  2. Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdańsk, Sobieskiego 18, Gdańsk, 80-952, Poland

    Tomasz Puzyn & Agnieszka Gajewicz

  3. Department of Biology, Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 Lynch Street, Jackson, 39217-0940, Mississippi, USA

    Xiaoke Hu, Thabitha P. Dasari & Huey-Min Hwang

  4. Instituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milano, Italy

    Andrey Toropov

  5. Department of Civil and Environmental Engineering, Interdisciplinary Nanotoxicity Center, Jackson State University, 1325 Lynch Street, Jackson, 39217-0510, Mississippi, USA

    Danuta Leszczynska

Authors
  1. Tomasz Puzyn
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  6. Andrea Michalkova
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  9. Danuta Leszczynska
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Contributions

X.H., T.P.D. and H-M.H. carried out empirical testing of the cytotoxicity of the metal oxides to E. coli. A.M. designed molecular clusters for calculations. T.P., B.R., A.G., A.M., A.T., D.L. and J.L. performed quantum-mechanical calculations, selected the optimal structural descriptors, developed and validated the nano-QSAR model and discussed the results.

Corresponding author

Correspondence to Jerzy Leszczynski.

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The authors declare no competing financial interests.

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Puzyn, T., Rasulev, B., Gajewicz, A. et al. Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles. Nature Nanotech 6, 175–178 (2011). https://doi.org/10.1038/nnano.2011.10

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