The way to cover prediction for cytotoxicity for all existing nano-sized metal oxides by using neural network method

Natalja Fjodorova , Marjana Novic , Agnieszka Gajewicz , Bakhtiyor Rasulev


The regulatory agencies should fulfil the data gap in toxicity for new chemicals including nano-sized compounds, like metal oxides nanoparticles (MeOx NPs) 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 MeOx NPs 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 MeOx NPs 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 MeOx with different oxidation states. The methodology is expected to be useful for potential hazard assessment of MeOx NPs and prioritisation for further testing and risk assessment.
Author Natalja Fjodorova
Natalja Fjodorova,,
, Marjana Novic
Marjana Novic,,
, Agnieszka Gajewicz (FCh / DEChR / LECh)
Agnieszka Gajewicz,,
- Laboratory of Environmental Chemometrics
, Bakhtiyor Rasulev
Bakhtiyor Rasulev,,
Other language title versions
Journal seriesNanotoxicology, ISSN 1743-5390, (A 45 pkt)
Issue year2017
Publication size in sheets0.5
Keywords in Englishneural network, QSAR, metal oxides nanoparticles, cytotoxicity, periodic table
ASJC Classification3005 Toxicology; 2204 Biomedical Engineering
Languageen angielski
Score (nominal)45
Score sourcejournalList
ScoreMinisterial score = 45.0, 28-01-2020, ArticleFromJournal
Publication indicators WoS Citations = 6; Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.188; WoS Impact Factor: 2017 = 5.811 (2) - 2017=6.218 (5)
Citation count*12 (2020-04-06)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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