Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions

Alla P Toropova; Andrey A Toropov; Robert Rallo; Danuta Leszczynska; Jerzy Leszczynski

doi:10.1016/j.ecoenv.2014.10.003

Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions

Ecotoxicol Environ Saf. 2015 Feb:112:39-45. doi: 10.1016/j.ecoenv.2014.10.003. Epub 2014 Nov 5.

Authors

Alla P Toropova¹, Andrey A Toropov², Robert Rallo³, Danuta Leszczynska⁴, Jerzy Leszczynski⁵

Affiliations

¹ IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy.
² IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy. Electronic address: andrey.toropov@marionegri.it.
³ Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Av. Països Catalans, 26, 43007 Tarragona, Catalunya, Spain.
⁴ Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch Street, Jackson, MS 39217-0510, USA.
⁵ Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 JR Lynch Street, P.O. Box 17910, Jackson, MS 39217, USA.

PMID: 25463851
DOI: 10.1016/j.ecoenv.2014.10.003

Abstract

The Monte Carlo technique has been used to build up quantitative structure-activity relationships (QSARs) for prediction of dark cytotoxicity and photo-induced cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (minus logarithm of lethal concentration for 50% bacteria pLC50, LC50 in mol/L). The representation of nanoparticles include (i) in the case of the dark cytotoxicity a simplified molecular input-line entry system (SMILES), and (ii) in the case of photo-induced cytotoxicity a SMILES plus symbol '^'. The predictability of the approach is checked up with six random distributions of available data into the visible training and calibration sets, and invisible validation set. The statistical characteristics of these models are correlation coefficient 0.90-0.94 (training set) and 0.73-0.98 (validation set).

Keywords: Cytotoxicity; Metal oxide nanoparticle; Monte Carlo method; QSAR; Quasi-QSAR, Nano-QSAR; Quasi-SMILES.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Calibration
Environmental Pollutants / toxicity*
Escherichia coli / drug effects*
Light
Metal Nanoparticles / toxicity*
Models, Theoretical
Monte Carlo Method
Oxides / toxicity*
Quantitative Structure-Activity Relationship

Substances

Environmental Pollutants
Oxides