Computational Tool for Risk Assessment of Nanomaterials: Novel QSTR-Perturbation Model for Simultaneous Prediction of Ecotoxicity and Cytotoxicity of Uncoated and Coated Nanoparticles under Multiple Experimental Conditions (2014)
- Authors: Valeria V. Kleandrova, Feng Luan, Humberto González-Díaz, Juan M. Ruso, Alejandro Speck-Planche, M. Natália D. S. Cordeiro
- Journal: Environmental Science & Technology
- Article Type: journal-article
- Dataset Type: Perturbation Datasets
- Subject: Environmental Chemistry,General Chemistry
- Date: 2014-11-21
- License:
- URL: http://dx.doi.org/10.1021/es503861x
- DOI: 10.1021/es503861x
Original Study Abstract
Nanomaterials have revolutionized modern science and technology due to their multiple applications in engineering, physics, chemistry, and biomedicine. Nevertheless, the use and manipulation of nanoparticles (NPs) can bring serious damages to living organisms and their ecosystems. For this reason, ecotoxicity and cytotoxicity assays are of special interest in order to determine the potential harmful effects of NPs. Processes based on ecotoxicity and cytotoxicity tests can significantly consume time and financial resources. In this sense, alternative approaches such as quantitative structure–activity/toxicity relationships (QSAR/QSTR) modeling have provided important insights for the better understanding of the biological behavior of NPs that may be responsible for causing toxicity. Until now, QSAR/QSTR models have predicted ecotoxicity or cytotoxicity separately against only one organism (bioindicator species or cell line) and have not reported information regarding the quantitative influence of characteristics other than composition or size. In this work, we developed a unified QSTR-perturbation model to simultaneously probe ecotoxicity and cytotoxicity of NPs under different experimental conditions, including diverse measures of toxicities, multiple biological targets, compositions, sizes and conditions to measure those sizes, shapes, times during which the biological targets were exposed to NPs, and coating agents. The model was created from 36488 cases (NP–NP pairs) and exhibited accuracies higher than 98% in both training and prediction sets. The model was used to predict toxicities of several NPs that were not included in the original data set. The results of the predictions suggest that the present QSTR-perturbation model can be employed as a highly promising tool for the fast and efficient assessment of ecotoxicity and cytotoxicity of NPs.
Data Sample
| CASE |
Chemical |
me |
VALUE |
TEi(cj) |
bt |
ns |
dm |
ta |
sc |
SMILES |
NMU |
V |
E |
P |
L |
References |
| 1 |
SiO2 |
CC50 (uM) |
16644,47 |
1 |
A549 (H) |
spherical |
Dry |
72 |
UC |
N/A |
0 |
13,367 |
2,927 |
2,329 |
50 |
Toxicol. In Vitro 2014; 28 (3) 354-364 |
| 2 |
SiO2 |
CC50 (uM) |
16644,47 |
1 |
A549 (H) |
spherical |
Dry |
72 |
PEG-Si(OMe)3 |
COCCOCCOCCOCCOCCOCCOCCOCCOCCOCCCSi(OC)OC |
1 |
13,367 |
2,927 |
2,329 |
50 |
Toxicol. In Vitro 2014; 28 (3) 354-364 |
| 3 |
SiO2 |
CC50 (uM) |
16644,47 |
1 |
BMSC (H) |
spherical |
Dry |
72 |
UC |
N/A |
0 |
13,367 |
2,927 |
2,329 |
50 |
Toxicol. In Vitro 2014; 28 (3) 354-364 |
| 4 |
SiO2 |
CC50 (uM) |
16644,47 |
1 |
BMSC (H) |
spherical |
Dry |
72 |
PEG-Si(OMe)3 |
COCCOCCOCCOCCOCCOCCOCCOCCOCCOCCCSi(OC)OC |
1 |
13,367 |
2,927 |
2,329 |
50 |
Toxicol. In Vitro 2014; 28 (3) 354-364 |
| 5 |
SiO2 |
CC50 (uM) |
16644,47 |
1 |
BMSC (M) |
spherical |
Dry |
72 |
UC |
N/A |
0 |
13,367 |
2,927 |
2,329 |
50 |
Toxicol. In Vitro 2014; 28 (3) 354-364 |
| 6 |
SiO2 |
CC50 (uM) |
16644,47 |
1 |
BMSC (M) |
spherical |
Dry |
72 |
PEG-Si(OMe)3 |
COCCOCCOCCOCCOCCOCCOCCOCCOCCOCCCSi(OC)OC |
1 |
13,367 |
2,927 |
2,329 |
50 |
Toxicol. In Vitro 2014; 28 (3) 354-364 |
Data Summary
| Variable |
Count (unique values) |
| Chemical |
32 |
| me |
5 |
| VALUE |
165 |
| TEi(cj) |
2 |
| bt |
50 |
| ns |
11 |
| dm |
8 |
| ta |
10 |
| sc |
13 |
| SMILES |
13 |
| NMU |
5 |
| V |
30 |
| E |
29 |
| P |
30 |
| L |
69 |
| References |
55 |