Author/Authors :
Toropova، A. P. نويسنده IRCCS-Istituto di Ricerche Farmacologiche Mario Negri,Via LaMasa 19,Milano, Italy , , Toropov، A. A. نويسنده IRCCS-Istituto di Ricerche Farmacologiche Mario Negri,Via LaMasa 19,Milano, Italy , , Rallo، R. نويسنده Departament d’Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Av. Països Catalans, 26, 43007 Tarragona, Catalunya, Spain , , Leszczynska، D. نويسنده Interdisciplinary Nanotoxicity Center,Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch St, Jackson,MS 39217-0510, USA , , Leszczynski، J. نويسنده InterdisciplinaryNanotoxicityCenter, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, P.O. Box 17910, Jackson,MS 39217,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 (?g/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.
