Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials

Chemosphere

Volumn 217, Issue , 2019, Pages 243-249

  Choi, Jang Sik ^a,b   Trinh, Tung X ^b   Yoon, Tae Hyun ^b   Kim, Jongwoon ^c   Byun, Hyung Gi ^a  

^a Kangwon National University   (South Korea)

^b Hanyang University   (South Korea)

^c Korea Institute of Science and Technology Europe (KIST Europe)   (Germany)

Author keywords

BEAS 2B; Cell viability; HaCaT; metal oxide nanomaterial; Quasi QSAR; Quasi SMILES

Indexed keywords

BIOLOGICAL ORGANS; CELLS; CYTOLOGY; HIERARCHICAL CLUSTERING; HIERARCHICAL SYSTEMS; METALLIC COMPOUNDS; METALS; MOLECULAR GRAPHICS; NANOSTRUCTURED MATERIALS; NUMERICAL METHODS; PHYSICOCHEMICAL PROPERTIES; RESPIRATORY SYSTEM;

CELL VIABILITY; HACAT; METAL-OXIDE NANOMATERIAL; QUASI-QSAR; QUASI-SMILES;

COMPUTATIONAL CHEMISTRY;

METAL NANOPARTICLE; METAL OXIDE; METAL; NANOMATERIAL; OXIDE;

CELL; CLUSTER ANALYSIS; NANOPARTICLE; NUMERICAL MODEL; OXIDE; PREDICTION; SKIN; VIABILITY;

ARTICLE; BEAS-2B CELL LINE; CALIBRATION; CELL VIABILITY; HACAT CELL LINE; HYDRODYNAMICS; LUNG ALVEOLUS CELL; MODEL; NANOTOXICOLOGY; PARTICLE SIZE; PHYSICAL CHEMISTRY; PREDICTIVE VALUE; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SKIN CELL; SOFTWARE; SURFACE CHARGE; CELL LINE; CELL SURVIVAL; CHEMISTRY; CYTOLOGY; DRUG EFFECT; HUMAN; LUNG; SKIN; SUPERVISED MACHINE LEARNING;

ANTHOZOA;

CELL LINE; CELL SURVIVAL; HUMANS; LUNG; METALS; NANOSTRUCTURES; OXIDES; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; SKIN; SOFTWARE; SUPERVISED MACHINE LEARNING;

EID: 85056157124     PISSN: 00456535     EISSN: 18791298     Source Type: Journal    
DOI: 10.1016/j.chemosphere.2018.11.014     Document Type: Article

References (47)

1. A Toropov, A., P Toropova, A., M Veselinovic, A., B Veselinovic, J., Nesmerak, K., Raska, J., R Duchowicz, P., A Castro, E., O Kudyshkin, V., Leszczynska, D., The Monte Carlo method based on eclectic data as an efficient tool for predictions of endpoints for nanomaterials-two examples of application. Comb. Chem. High Throughput Screen. 18 (2015), 376–386.
2. Akhtar, M.J., Ahamed, M., Kumar, S., Khan, M.M., Ahmad, J., Alrokayan, S.A., Zinc oxide nanoparticles selectively induce apoptosis in human cancer cells through reactive oxygen species. Int. J. Nanomed. 7 (2012), 845–857.
3. Bragazzi, N., Toropov, A., Toropova, A., Pechkova, E., Nicolini, C., Quasi-qspr to predict proteins behavior under various concentrations of drug using nanoconductometric assay. NanoWorld J. 2 (2016), 2016–2034.
4. Comfort, K.K., Maurer, E.I., Hussain, S.M., Slow release of ions from internalized silver nanoparticles modifies the epidermal growth factor signaling response. Colloids Surfaces B Biointerfaces 123 (2014), 136–142.
5. Frohlich, E., Salar-Behzadi, S., Toxicological assessment of inhaled nanoparticles: role of in vivo, ex vivo, in vitro, and in silico studies. Int. J. Mol. Sci. 15 (2014), 4795–4822.
6. Gajewicz, A., Schaeublin, N., Rasulev, B., Hussain, S., Leszczynska, D., Puzyn, T., Leszczynski, J., Towards understanding mechanisms governing cytotoxicity of metal oxides nanoparticles: hints from nano-QSAR studies. Nanotoxicology 9 (2015), 313–325.
7. Gatoo, M.A., Naseem, S., Arfat, M.Y., Dar, A.M., Qasim, K., Zubair, S., Physicochemical properties of nanomaterials: implication in associated toxic manifestations. BioMed Res. Int., 2014, 2014, 498420.
8. Gibbs, S., In vitro irritation models and immune reactions. Skin Pharmacol. Physiol. 22 (2009), 103–113.
9. Ha, M.K., Trinh, T.X., Choi, J.S., Maulina, D., Byun, H.G., Yoon, T.H., Toxicity classification of oxide nanomaterials: effects of data gap filling and PChem score-based screening approaches. Sci. Rep., 8, 2018, 3141.
10. Heller, S., McNaught, A., Stein, S., Tchekhovskoi, D., Pletnev, I., InChI-the worldwide chemical structure identifier standard. J. Cheminf., 5, 2013, 7.
11. M Veselinovic, A., B Veselinovic, J., V Zivkovic, J., M Nikolic, G., Application of SMILES notation based optimal descriptors in drug discovery and design. Curr. Top. Med. Chem. 15 (2015), 1768–1779.
12. Manganelli, S., Leone, C., Toropov, A.A., Toropova, A.P., Benfenati, E., QSAR model for cytotoxicity of silica nanoparticles on human embryonic kidney Cells1. Mater. Today: Proceed. 3 (2016), 847–854.
13. Manganelli, S., Leone, C., Toropov, A.A., Toropova, A.P., Benfenati, E., QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO2 nanoparticles. Chemosphere 144 (2016), 995–1001.
14. McNaught, A.D., Wilkinson, A., Compendium of chemical terminology: IUPAC. Anonymous the Royal Society of Chemistry, 1997, Blackwell Science Cambridge, UK.
15. Ojha, P.K., Roy, K., Comparative QSARs for antimalarial endochins: importance of descriptor-thinning and noise reduction prior to feature selection. Chemometr. Intell. Lab. Syst. 109 (2011), 146–161.
16. 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.
17. Organisation for Economic Co-operation and Development, Guidance Document on the Validation of (Quantitative) Structure-activity Relationship [(Q) SAR] Models. 2014, OECD Publishing.
18. P Toropova, A., A Toropov, A., Mutagenicity: QSAR-quasi-QSAR-nano-QSAR. Mini Rev. Med. Chem. 15 (2015), 608–621.
19. Podila, R., Brown, J.M., Toxicity of engineered nanomaterials: a physicochemical perspective. J. Biochem. Mol. Toxicol. 27 (2013), 50–55.
20. Raies, A.B., Bajic, V.B., In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdisciplin. Rev. Comput. Mol. Sci. 6 (2016), 147–172.
21. Randić, M., Novel graph theoretical approach to heteroatoms in quantitative structure—activity relationships. Chemometr. Intell. Lab. Syst. 10 (1991), 213–227.
22. Toropova, A.P., Toropov, A.A., Lombardo, A., Roncaglioni, A., Benfenati, E., Gini, G., CORAL: QSAR models for acute toxicity in fathead minnow (Pimephales promelas). J. Comput. Chem. 33 (2012), 1218–1223.
23. Toropov, A.A., Toropova, A.P., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J., CORAL: QSPR model of water solubility based on local and global SMILES attributes. Chemosphere 90 (2013), 877–880.
24. Toropov, A.A., Toropova, A.P., Puzyn, T., Benfenati, E., Gini, G., Leszczynska, D., Leszczynski, J., QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells. Chemosphere 92 (2013), 31–37.
25. Toropova, A.P., Toropov, A.A., Benfenati, E., Puzyn, T., Leszczynska, D., Leszczynski, J., Optimal descriptor as a translator of eclectic information into the prediction of membrane damage: the case of a group of ZnO and TiO 2 nanoparticles. Ecotoxicol. Environ. Saf. 108 (2014), 203–209.
26. 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.
27. Toropova, A.P., Toropov, A.A., Benfenati, E., Korenstein, R., Leszczynska, D., Leszczynski, J., Optimal nano-descriptors as translators of eclectic data into prediction of the cell membrane damage by means of nano metal-oxides. Environ. Sci. Pollut. Control Ser. 22 (2015), 745–757.
28. 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.
29. Toropova, A., Toropov, A., Benfenati, E., A quasi-QSPR modelling for the photocatalytic decolourization rate constants and cellular viability (CV%) of nanoparticles by CORAL. SAR QSAR Environ. Res. 26 (2015), 29–40.
30. 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.
31. Toropova, A.P., Toropov, A.A., Manganelli, S., Leone, C., Baderna, D., Benfenati, E., Fanelli, R., Quasi-SMILES as a tool to utilize eclectic data for predicting the behavior of nanomaterials. NanoImpact 1 (2016), 60–64.
32. 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.
33. Toropova, A.P., Toropov, A.A., Leszczynska, D., Leszczynski, J., CORAL and Nano-QFAR: quantitative feature–Activity relationships (QFAR) for bioavailability of nanoparticles (ZnO, CuO, Co3O4, and TiO2). Ecotoxicol. Environ. Saf. 139 (2017), 404–407.
34. Toropova, A.P., Toropov, A.A., Optimal descriptor as a translator of eclectic information into the prediction of membrane damage by means of various TiO2 nanoparticles. Chemosphere 93 (2013), 2650–2655.
35. 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.
36. Toropova, A.P., Toropov, A.A., CORAL software: prediction of carcinogenicity of drugs by means of the Monte Carlo method. Eur. J. Pharmaceut. Sci. 52 (2014), 21–25.
37. Toropov, A.A., Toropova, A.P., Quasi-QSAR for mutagenic potential of multi-walled carbon-nanotubes. Chemosphere 124 (2015), 40–46.
38. 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.
39. Toropova, A.P., Toropov, A.A., Nano-QSAR in cell biology: model of cell viability as a mathematical function of available eclectic data. J. Theor. Biol. 416 (2017), 113–118.
40. Trinh, T.X., Choi, J., Jeon, H., Byun, H., Yoon, T., Kim, J., Quasi-smiles-based nano-quantitative structure–activity relationship model to predict the cytotoxicity of multiwalled carbon nanotubes to human lung cells. Chem. Res. Toxicol. 31 (2018), 183–190.
41. Trinh, T.X., Ha, M.K., Choi, J., Byun, H., Yoon, T.H., Dataset curation, assessment of their quality and completeness and nanoSAR classification model development for metallic nanoparticles. Environ. Sci.: Nano 5 (2018), 1902–1910.
42. 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.
43. Xiong, S., George, S., Yu, H., Damoiseaux, R., France, B., Ng, K.W., Loo, J.S., Size influences the cytotoxicity of poly (lactic-co-glycolic acid)(PLGA) and titanium dioxide (TiO 2) nanoparticles. Arch. Toxicol. 87 (2013), 1075–1086.
44. Yang, X., Liu, J., He, H., Zhou, L., Gong, C., Wang, X., Yang, L., Yuan, J., Huang, H., He, L., SiO 2 nanoparticles induce cytotoxicity and protein expression alteration in HaCaT cells. Part. Fibre Toxicol., 7, 2010, 1.
45. Zhang, H., Ji, Z., Xia, T., Meng, H., Low-Kam, C., Liu, R., Pokhrel, S., Lin, S., Wang, X., Liao, Y., Use of metal oxide nanoparticle band gap to develop a predictive paradigm for oxidative stress and acute pulmonary inflammation. ACS Nano 6 (2012), 4349–4368.
46. Zhang, Z., Murtagh, F., Van Poucke, S., Lin, S., Lan, P., Hierarchical cluster analysis in clinical research with heterogeneous study population: highlighting its visualization with R. Ann. Transl. Med., 5, 2017, 75.
47. Zhao, Y., Wang, B., Feng, W., Bai, C., Nanotoxicology: toxicological and biological activities of nanomaterials. Nanosci. Nanotechnol., 2012, 1–53.