Computational models predict nanoparticle toxicity
|Mar 07, 2011|
|Contact: JRC-IHCP, Computational Toxicology|
Researchers are developing computational models to predict the behaviour of nanomaterials in biological systems. Such predictions will allow researchers to streamline and prioritise the toxicological testing of nanomaterials.
Enrico Burello and Andrew Worth, both scientists at the JRC's Institute for Health and Consumer Protection, describe this approach in reviewing a recent paper by Jerzy Leszczynski and coworkers on the development of a Quantitative Structure–Activity Relationship (QSAR) model to predict the cytotoxicity of metal oxide nanoparticles.
Although QSAR methodologies are well-known and have been extensively used in the areas of drug discovery and chemical toxicology, their application to nanomaterials is still in its infancy. Burello & Worth's recent contribution in this domain is the JRC study published in July 2010, proposing a theoretical framework for predicting the oxidative stress potential of oxide nanoparticles.
E. Burello & A. Worth, Computational nanotoxicology: Predicting toxicity of nanoparticles, Nature Nanotechnology 6, 138–139 (2011); published online 04 March 2011 - doi:10.1038/nnano.2011.27
T. Puzyn, B. Rasulev, A. Gajewicz, X. Hu, T. P. Dasari, A. Michalkova, H. Hwang, A. Toropov, D. Leszczynska & J. Leszczynski, Using nano-QSAR to predict the cytotoxicity of metal oxide nanoparticles, Nature Nanotechnology 6, 175–178 (2011); published online13 February 2011 - doi:10.1038/nnano.2011.10
E. Burello & A. Worth, A theoretical framework for predicting the oxidative stress potential of oxide nanoparticles, Nanotoxicology, 2010; Early Online, 1–8 - doi:10.3109/17435390.2010.502980