Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform (2018)
- Authors: Vasyl Kovalishyn, Natalia Abramenko, Iryna Kopernyk, Larysa Charochkina, Larysa Metelytsia, Igor V. Tetko, Willie Peijnenburg, Leonid Kustov
- Journal: Food and Chemical Toxicology
- Article Type: journal-article
- Dataset Type: Environmetal Datasets
- Subject: Toxicology,General Medicine,Food Science
- Date: 2018-2
- License: https://www.elsevier.com/tdm/userlicense/1.0/
- URL: http://dx.doi.org/10.1016/j.fct.2017.08.008
- DOI: 10.1016/j.fct.2017.08.008
Original Study Abstract
Inorganic nanomaterials have become one of the new areas of modern knowledge and technology and have already found an increasing number of applications. However, some nanoparticles show toxicity to living organisms, and can potentially have a negative influence on environmental ecosystems. While toxicity can be determined experimentally, such studies are time consuming and costly. Computational toxicology can provide an alternative approach and there is a need to develop methods to reliably assess Quantitative Structure–Property Relationships for nanomaterials (nano-QSPRs). Importantly, development of such models requires careful collection and curation of data. This article overviews freely available nano-QSPR models, which were developed using the Online Chemical Modeling Environment (OCHEM). Multiple data on toxicity of nanoparticles to different living organisms were collected from the literature and uploaded in the OCHEM database. The main characteristics of nanoparticles such as chemical composition of nanoparticles, average particle size, shape, surface charge and information about the biological test species were used as descriptors for developing QSPR models. QSPR methodologies used Random Forests (WEKA-RF), k-Nearest Neighbors and Associative Neural Networks. The predictive ability of the models was tested through cross-validation, giving cross-validated coefficients q2 = 0.58–0.80 for regression models and balanced accuracies of 65–88% for classification models. These results matched the predictions for the test sets used to develop the models. The proposed nano-QSPR models and uploaded data are freely available online at http://ochem.eu/article/103451 and can be used for estimation of toxicity of new and emerging nanoparticles at the early stages of nanomaterial development.
Data Sample
| MOLECULEID |
ARTICLEID |
PUBMEDID |
PAGE |
NanoToxicity LC50 aquatic (qualitative) {measured} |
Species |
Test duration |
Material Nanoparticles of Elements |
APS |
Surface coating |
Specific surface area |
Explosure concentration |
Crystal structure of nanoparticles |
Zeta potential |
Hydrodynamic diameter |
Shape of nano particles |
composition |
| M2645220 |
A9507 |
21082828 |
4 |
high |
1.0 |
1.0 |
0.0 |
31.0 |
11.0 |
0.0 |
1.0 |
7.0 |
0.0 |
185.0 |
1.0 |
2.0 |
| M2645220 |
A9507 |
21082828 |
4 |
high |
1.0 |
1.0 |
0.0 |
10.0 |
9.0 |
0.0 |
1.0 |
7.0 |
-21.0 |
310.0 |
1.0 |
2.0 |
| M2645220 |
A9507 |
21082828 |
4 |
high |
1.0 |
1.0 |
0.0 |
49.0 |
9.0 |
0.0 |
1.0 |
7.0 |
-34.0 |
1.0 |
1.0 |
2.0 |
| M2645220 |
A9507 |
21082828 |
4 |
high |
1.0 |
1.0 |
0.0 |
61.0 |
3.0 |
0.0 |
1.0 |
7.0 |
-46.0 |
77.0 |
4.0 |
2.0 |
| M2645220 |
A9507 |
21082828 |
4 |
high |
1.0 |
1.0 |
0.0 |
36.0 |
10.0 |
0.0 |
1.0 |
7.0 |
-33.0 |
204.0 |
1.0 |
2.0 |
Data Summary
| Variable |
Count (unique values) |
| MOLECULEID |
34 |
| ARTICLEID |
58 |
| NanoToxicity LC50 aquatic (qualitative) {measured} |
2 |
| Species |
42 |
| Test duration |
23 |
| Material Nanoparticles of Elements |
35 |
| APS |
96 |
| Surface coating |
23 |
| Specific surface area |
22 |
| Explosure concentration |
6 |
| Crystal structure of nanoparticles |
9 |
| Zeta potential |
40 |
| Hydrodynamic diameter |
19 |
| Shape of nano particles |
14 |
| composition |
3 |