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Article

Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform

1
NovaMechanics Ltd., Nicosia 1065, Cyprus
2
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
3
Institute of Chemistry, University of Tartu, 50411 Tartu, Estonia
4
Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
5
Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
6
Division of Physical Sciences and Applications, Hellenic Military Academy, 16672 Vari, Greece
*
Authors to whom correspondence should be addressed.
Nanomaterials 2020, 10(10), 2017; https://doi.org/10.3390/nano10102017
Received: 13 September 2020 / Revised: 3 October 2020 / Accepted: 7 October 2020 / Published: 13 October 2020
(This article belongs to the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance)
A literature curated dataset containing 24 distinct metal oxide (MexOy) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robust and validated in silico model for prediction of NP cytotoxicity. The model can be used to predict the cytotoxicity (cell viability) of MexOy NPs based on the colorimetric lactate dehydrogenase (LDH) assay and the luminometric adenosine triphosphate (ATP) assay, both of which quantify irreversible cell membrane damage. Out of the 77 total descriptors used, 7 were identified as being significant for induction of cytotoxicity by MexOy NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the MexOy conduction band (EC), the coordination number of the metal atoms on the NP surface (Avg. C.N. Me atoms surface) and the average force vector surface normal component of all metal atoms (v⊥ Me atoms surface). The significance and effect of these descriptors is discussed to demonstrate their direct correlation with cytotoxicity. The produced model has been made publicly available by the Horizon 2020 (H2020) NanoSolveIT project and will be added to the project’s Integrated Approach to Testing and Assessment (IATA). View Full-Text
Keywords: cytotoxicity; metal oxide nanoparticles; Isalos analytics platform; computational descriptors; in silico modelling; machine learning; atomistic descriptors cytotoxicity; metal oxide nanoparticles; Isalos analytics platform; computational descriptors; in silico modelling; machine learning; atomistic descriptors
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MDPI and ACS Style

Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, A. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. Nanomaterials 2020, 10, 2017. https://doi.org/10.3390/nano10102017

AMA Style

Papadiamantis AG, Jänes J, Voyiatzis E, Sikk L, Burk J, Burk P, Tsoumanis A, Ha MK, Yoon TH, Valsami-Jones E, Lynch I, Melagraki G, Tämm K, Afantitis A. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. Nanomaterials. 2020; 10(10):2017. https://doi.org/10.3390/nano10102017

Chicago/Turabian Style

Papadiamantis, Anastasios G., Jaak Jänes, Evangelos Voyiatzis, Lauri Sikk, Jaanus Burk, Peeter Burk, Andreas Tsoumanis, My Kieu Ha, Tae Hyun Yoon, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki, Kaido Tämm, and Antreas Afantitis. 2020. "Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform" Nanomaterials 10, no. 10: 2017. https://doi.org/10.3390/nano10102017

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