Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform
A literature curated dataset containing 24 distinct metal oxide (Me<sub>x</sub>O<sub>y</sub>) nanoparticles (NPs), including 15 physicochemical, structural and assay-related descriptors, was enriched with 62 atomistic computational descriptors and exploited to produce a robus...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/10/10/2017 |
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| author | Anastasios G. Papadiamantis 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 Antreas Afantitis |
| author_facet | Anastasios G. Papadiamantis 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 Antreas Afantitis |
| author_sort | Anastasios G. Papadiamantis |
| collection | DOAJ |
| description | A literature curated dataset containing 24 distinct metal oxide (Me<sub>x</sub>O<sub>y</sub>) 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 Me<sub>x</sub>O<sub>y</sub> 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 Me<sub>x</sub>O<sub>y</sub> NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the Me<sub>x</sub>O<sub>y</sub> conduction band (<i>E<sub>C</sub></i>), 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). |
| first_indexed | 2024-03-10T15:40:35Z |
| format | Article |
| id | doaj.art-876adad0d108441f91a92f6a0ccf0390 |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-10T15:40:35Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-876adad0d108441f91a92f6a0ccf03902023-11-20T16:51:44ZengMDPI AGNanomaterials2079-49912020-10-011010201710.3390/nano10102017Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics PlatformAnastasios G. Papadiamantis0Jaak Jänes1Evangelos Voyiatzis2Lauri Sikk3Jaanus Burk4Peeter Burk5Andreas Tsoumanis6My Kieu Ha7Tae Hyun Yoon8Eugenia Valsami-Jones9Iseult Lynch10Georgia Melagraki11Kaido Tämm12Antreas Afantitis13NovaMechanics Ltd., Nicosia 1065, CyprusInstitute of Chemistry, University of Tartu, 50411 Tartu, EstoniaNovaMechanics Ltd., Nicosia 1065, CyprusInstitute of Chemistry, University of Tartu, 50411 Tartu, EstoniaInstitute of Chemistry, University of Tartu, 50411 Tartu, EstoniaInstitute of Chemistry, University of Tartu, 50411 Tartu, EstoniaNovaMechanics Ltd., Nicosia 1065, CyprusDepartment of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, KoreaDepartment of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, KoreaSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UKSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UKDivision of Physical Sciences and Applications, Hellenic Military Academy, 16672 Vari, GreeceInstitute of Chemistry, University of Tartu, 50411 Tartu, EstoniaNovaMechanics Ltd., Nicosia 1065, CyprusA literature curated dataset containing 24 distinct metal oxide (Me<sub>x</sub>O<sub>y</sub>) 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 Me<sub>x</sub>O<sub>y</sub> 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 Me<sub>x</sub>O<sub>y</sub> NPs. These were NP core size, hydrodynamic size, assay type, exposure dose, the energy of the Me<sub>x</sub>O<sub>y</sub> conduction band (<i>E<sub>C</sub></i>), 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).https://www.mdpi.com/2079-4991/10/10/2017cytotoxicitymetal oxide nanoparticlesIsalos analytics platformcomputational descriptorsin silico modellingmachine learning |
| spellingShingle | Anastasios G. Papadiamantis 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 Antreas Afantitis Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform Nanomaterials cytotoxicity metal oxide nanoparticles Isalos analytics platform computational descriptors in silico modelling machine learning |
| title | Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform |
| title_full | Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform |
| title_fullStr | Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform |
| title_full_unstemmed | Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform |
| title_short | Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform |
| title_sort | predicting cytotoxicity of metal oxide nanoparticles using isalos analytics platform |
| topic | cytotoxicity metal oxide nanoparticles Isalos analytics platform computational descriptors in silico modelling machine learning |
| url | https://www.mdpi.com/2079-4991/10/10/2017 |
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