Toropova, A., Toropov, A., Rallo, R., Leszczynska, D., Leszczynski, J. (2016). Nano-QSAR: Genotoxicity of Multi-Walled Carbon Nanotubes. International Journal of Environmental Research, 10(1), 59-64. doi: 10.22059/ijer.2016.56888
A. P. Toropova; A. A. Toropov; R. Rallo; D. Leszczynska; J. Leszczynski. "Nano-QSAR: Genotoxicity of Multi-Walled Carbon Nanotubes". International Journal of Environmental Research, 10, 1, 2016, 59-64. doi: 10.22059/ijer.2016.56888
Toropova, A., Toropov, A., Rallo, R., Leszczynska, D., Leszczynski, J. (2016). 'Nano-QSAR: Genotoxicity of Multi-Walled Carbon Nanotubes', International Journal of Environmental Research, 10(1), pp. 59-64. doi: 10.22059/ijer.2016.56888
Toropova, A., Toropov, A., Rallo, R., Leszczynska, D., Leszczynski, J. Nano-QSAR: Genotoxicity of Multi-Walled Carbon Nanotubes. International Journal of Environmental Research, 2016; 10(1): 59-64. doi: 10.22059/ijer.2016.56888
Nano-QSAR: Genotoxicity of Multi-Walled Carbon Nanotubes
1Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy
2Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Catalunya, Spain
3Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, USA
4Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, USA
Abstract
The study was carried out to develop an efficient approach for prediction the genotoxicity of carbon nanotubes. The experimental data on the bacterial reverse mutation test (TA100) on multi-walled carbon nanotubes (MWCNTs) was collected from the literature and examined as an endpoint. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of the endpoint was built up. The model is represented by a function of: (i) dose (µg/plate); (ii) metabolic activation (i.e. with S9 mix or without S9 mix); and (iii) two types of MWCNTs. The above listed conditions were represented by so-called quasi-SMILES. Simplified molecular input-line entry system (SMILES) is a tool for representation of molecular structure. The quasi-SMILES is a tool to represent physicochemical and / or biochemical conditions for building up a predictive model. Thus, instead of well-known paradigm of predictive modeling “endpoint is a mathematical function of molecular structure” a fresh paradigm “endpoint is a mathematical function of available eclectic data (conditions) is suggested.
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