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Abstract
Titanium oxide (TiO2) and zinc oxide (ZnO) nanoparticles are among the most widely used in different applications in daily life. In this study, local regression and classification models were developed for a set of ZnO and TiO2 nanoparticles tested at different concentrations for their ability to disrupt the lipid membrane in cells. Different regression techniques were applied and compared by checking the robustness of the models and their external predictive ability. Additionally, a simple classification model was developed, which predicts the potential for disruption of the studied nanoparticles with good accuracy (overall accuracy, specificity, and sensitivity >80%) on the basis of two empirical descriptors. The present study demonstrates that empirical descriptors, such as experimentally determined size and tested concentrations, are relevant to modelling the activity of nanoparticles. This information may be useful to screen the potential for harmful effect of nanoparticles in different experimental conditions and to optimize the design of toxicological tests. Results from the present study are useful to support and refine the future application of in silico tools to nanoparticles, for research and regulatory purposes.
Acknowledgement
We would like to thank Professor Paola Gramatica from the University of Insubria, Italy, for providing fruitful comments on the manuscript.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
† Presented at the 8th International Symposium on Computational Methods in Toxicology and Pharmacology Integrating Internet Resources, CMTPI-2015, 21–25 June 2015, Chios, Greece.