CORAL and Nano-QFAR: Quantitative feature – Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2)

Ecotoxicology and Environmental Safety

Volumn 139, Issue , 2017, Pages 404-407

  Toropova, Alla P ^a   Toropov, Andrey A ^a   Leszczynska, Danuta ^b   Leszczynski, Jerzy ^c  

^a MARIO NEGRI INSTITUTE FOR PHARMACOLOGICAL RESEARCH   (Italy)

^b Jackson State University   (United States)

^c JACKSON STATE UNIVERSITY   (United States)

Author keywords

CORAL free software; Nano QSAR; QFAR; QSAR; Quasi SMILES

Indexed keywords

COPPER OXIDE NANOPARTICLE; TITANIUM DIOXIDE NANOPARTICLE; ZINC OXIDE NANOPARTICLE; COBALT; COBALT OXIDE; COPPER; CUPRIC OXIDE; METAL NANOPARTICLE; OXIDE; TITANIUM; TITANIUM DIOXIDE; ZINC OXIDE;

BIOAVAILABILITY; CARBON NANOTUBE; NANOPARTICLE; OXIDE; PREDICTION; QUANTITATIVE ANALYSIS; SOFTWARE;

ARTICLE; CHEMICAL STRUCTURE; CONTROLLED STUDY; ESCHERICHIA COLI; MATHEMATICAL MODEL; NONHUMAN; PREDICTION; QUANTITATIVE FEATURE ACTIVITY RELATIONSHIP; SOFTWARE; BIOAVAILABILITY; CHEMICAL MODEL; COMPUTER SIMULATION; MONTE CARLO METHOD; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

BIOLOGICAL AVAILABILITY; COBALT; COMPUTER SIMULATION; COPPER; METAL NANOPARTICLES; MODELS, CHEMICAL; MONTE CARLO METHOD; OXIDES; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SOFTWARE; TITANIUM; ZINC OXIDE;

EID: 85012031515     PISSN: 01476513     EISSN: 10902414     Source Type: Journal    
DOI: 10.1016/j.ecoenv.2017.01.054     Document Type: Article

References (31)

1. 2) to E. coli bacteria. J. Environ. Sci. 25:5 (2013), 882–888.
2. Gottschalk, F., Sun, T.Y., Nowack, B., Environmental concentrations of engineered nanomaterials: review of modeling and analytical studies. Environ. Pollut. 181 (2013), 287–300.
3. Hwang, H.M., Ray, P.C., Yu, H., He, X., Toxicology of designer/engineered metallic nanoparticles. (eds) Luque, R., Varma, R., (eds.) In Book: Sustainable Preparation of Metal Nanoparticles: Methods and Applications, 2012, Royal Society of Chemistry, Cambridge, United Kingdom, 190–212.
4. Kleandrova, V.V., Luan, F., González-Díaz, H., Ruso, J.M., Speck-Planche, A., Cordeiro, M.N.D.S., 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. Environ. Sci. Technol. 48:24 (2014), 14686–14694.
5. Kleandrova, V.V., Luan, F., Gonzalez-Diaz, H., Ruso, J.M., Melo, A., Speck-Planche, A., Cordeiro, M.N.D.S., Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. Environ. Int. 73C (2014), 288–294.
6. Li, Y., Zhang, W., Niu, J.F., Chen, Y.S., Mechanism of photogenerated reactive oxygen species and correlation with the antibacterial properties of engineered metal-oxide nanoparticles. ACS Nano 6:6 (2012), 5164–5173.
7. Liu, X., Tang, K., Harper, S., Harper, B., Steevens, J.A., Xu, R., Predictive modeling of nanomaterial exposure effects in biological systems. Int. J. Nanomed. 8:Suppl 1 (2013), S31–S43.
8. Luan, F., Kleandrova, V.V., Gonzalez-Diaz, H., Ruso, J.M., Melo, A., Speck-Planche, A., Cordeiro, M.N.D.S., Computer-aided nanotoxicology: assessing cytotoxicity of nanoparticles under diverse experimental conditions by using a novel QSTR-perturbation approach. Nanoscale 6 (2014), 10623–10630.
9. Melagraki, G., Afantitis, A., Enalos InSilicoNano platform: an online decision support tool for the design and virtual screening of nanoparticles. RSC Adv. 4 (2014), 50713–50725.
10. OECD document, 2007. Guidance document on the guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models.
11. Oksel, C., Ma, C.Y., Liu, J.J., Wilkins, T., Wang, X.Z., (Q)SAR modelling of nanomaterial toxicity: a critical review. Particuology 21 (2015), 1–19.
12. Posner, J.D., Engineered nanomaterials: where they go,nobody knows. Nano Today 4 (2009), 114–115.
13. Puzyn, T., Leszczynska, D., Leszczynski, J., Toward the development of “nano-QSARs”: advances and challenges. Small 5 (2009), 2494–2509.
14. Sayes, C., Ivanov, I., Comparative study of predictive computational models for nanoparticle induced cytotoxicity. Risk Anal. 30 (2010), 1723–1734.
15. Singh, K.P., Gupta, S., Nano-QSAR modeling for predicting biological activity of diverse nanomaterials. RSC Adv. 4 (2014), 13215–13230.
16. Speck-Planche, A., Kleandrova, V.V., Luan, F., Cordeiro, M.N.D.S., Computational modeling in nanomedicine: prediction of multiple antibacterial profiles of nanoparticles using a quantitative structure-activity relationship perturbation model. Nanomedicine 10 (2015), 193–204.
17. Toropov, A.A., Leszczynski, J., A new approach to the characterization of nanomaterials: predicting Young's modulus by correlation weighting of nanomaterials codes. Chem. Phys. Lett. 433 (2006), 125–129.
18. Toropov, A.A., Toropova, A.P., Optimal descriptor as a translator of eclectic data into endpoint prediction: Mutagenicity of fullerene as a mathematical function of conditions. Chemosphere 104 (2014), 262–264.
19. Toropov, A.A., Toropova, A.P., Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere 124 (2015), 40–46.
20. Toropov, A.A., Toropova, A.P., Quasi-SMILES and nano-QFAR: united model for mutagenicity of fullerene and MWCNT under different conditions. Chemosphere 139 (2015), 18–22.
21. Toropov, A.A., Rallo, R., Toropova, A.P., Use of quasi-SMILES and Monte Carlo optimization to develop quantitative feature property/activity relationships (QFPR/QFAR) for nanomaterials. Curr. Top. Med. Chem. 15 (2015), 1837–1844.
22. Toropov, A.A., Achary, P.G.R., Toropova, A.P., Quasi-SMILES and nano-QFPR: the predictive model for zeta potentials of metal oxide nanoparticles. Chem. Phys. Lett. 660 (2016), 107–110.
23. Toropova, A.P., Toropov, A.A., Rallo, R., Leszczynska, D., Leszczynski, J., Optimal descriptor as a translator of eclectic data into prediction of cytotoxicity for metal oxide nanoparticles under different conditions. Ecotoxicol. Environ. Saf. 112 (2015), 39–45.
24. 2 nanoparticles. Ecotoxicol. Environ. Saf. 108 (2014), 203–209.
25. Toropova, A.P., Toropov, A.A., Veselinović, A.M., Veselinović, J.B., Benfenati, E., Leszczynska, D., Leszczynski, J., Nano-QSAR: model of mutagenicity of fullerene as a mathematical function of different conditions. Ecotoxicol. Environ. Saf. 124 (2016), 32–36.
26. Vanli, O.A., Chen, L., Tsai, C., Zhang, C., Wang, B., An uncertainty quantification method for nanomaterial prediction models. Int. J. Adv. Manuf. Technol., 70, 2014, 33.
27. Weininger, D., SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28 (1988), 31–36.
28. Weininger, D., Smiles. 3. Depict. Graphical depiction of chemical structures. J. Chem. Inf. Comput. Sci. 30 (1990), 237–243.
29. Weininger, D., Weininger, A., Weininger, J.L., SMILES. 2. Algorithm for generation of unique SMILES notation. J. Chem. Inf. Comput. Sci. 29 (1989), 97–101.
30. Winkler, D.A., Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials. Toxicol. Appl. Pharmacol. 299 (2016), 96–100.
31. Ying, J., Zhang, T., Tang, M., Metal oxide nanomaterial QNAR models: available structural descriptors and understanding of toxicity mechanisms. Nanomaterials 5 (2015), 1620–1637.