The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method.
The regulatory agencies should fulfil the data gap in toxicity for new chemicals including nano-sized compounds, like metal oxides nanoparticles (MeOxNPs) according to the registration, evaluation, authorisation and restriction of chemicals (REACH) legislation policy. This study demonstrates the perspective capability of neural network models for prediction of cytotoxicity of MeOxNPs to bacteria Escherichia coli (E. coli) for the widest range of metal oxides extracted from Periodic table. The counter propagation artificial neural network (CP ANN) models for prediction of cytotoxicity of MeOxNPs for data sets of 17, 36 and 72 metal oxides were employed in the study. The cytotoxicity of studied metal oxide NPs was correlated with (i) χ-metal electronegativity (EN) by Pauling scale and composition of metal oxides characterised by (ii) number of metal atoms in oxide, (iii) number of oxygen atoms in oxide and (iv) charge of metal cation in oxide. The paper describes the models in context of five OECD principles of validation models accepted for regulatory use. The recommendations were done for the minimal number of cytotoxicity tests needs for evaluation of the large set of MeOxwith different oxidation states. The methodology is expected to be useful for potential hazard assessment of MeOxNPs and prioritisation for further testing and risk assessment.
a Department of Chemoinformatics , National Institute of Chemistry , Ljubljana , Slovenia.
Natalja
N
Fjodorova
a Department of Chemoinformatics , National Institute of Chemistry , Ljubljana , Slovenia.
Marjana
M
Novic
b Laboratory of Environmental Chemometrics, Faculty of Chemistry , University of Gdansk , Gdańsk , Poland.
Agnieszka
A
Gajewicz
c Department of Coatings and Polymeric Materials , North Dakota State University , Fargo , ND , USA.
Bakhtiyor
B
Rasulev
D004926:Escherichia coli / Q000187:drug effects*; D053768:Metal Nanoparticles / Q000737:chemistry / Q000633:toxicity*; D050296:Microbial Viability / Q000187:drug effects*; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010087:Oxides / Q000737:chemistry / Q000633:toxicity*; D011237:Predictive Value of Tests; D018675:Toxicity Tests
D010087:Oxides
Neural network; Periodic table; QSAR; cytotoxicity; metal oxides nanoparticles