Advertisement

Environmental Science and Pollution Research

, Volume 25, Issue 31, pp 31771–31775 | Cite as

Use of the index of ideality of correlation to improve models of eco-toxicity

  • Alla P. Toropova
  • Andrey A. Toropov
Short Research and Discussion Article
  • 134 Downloads

Abstract

Persistent organic pollutants are compounds used for various everyday purposes, such as personal care products, food, pesticides, and pharmaceuticals. Decomposition of considerable part of the above pollutants is a long-time process. Under such circumstances, estimation of toxicity for large arrays of organic substances corresponding to the above category of pollutants is a necessary component of theoretical chemistry. The CORAL software is a tool to establish quantitative structure—activity relationships (QSARs). The index of ideality of correlation (IIC) was suggested as a criterion of predictive potential of QSAR. The statistical quality of models for eco-toxicity of organic pollutants, which are built up, with use of the IIC is better than statistical quality of models, which are built up without use of data on the IIC.

Keywords

Eco-toxicity QSAR Index of ideality of correlation Monte Carlo method CORAL software 

Notes

Author contributions

Authors have done equivalent contributions to this work.

Funding information

This research was supported by the LIFE-CONCERT project (LIFE17 GIE/IT/000461).

Supplementary material

11356_2018_3291_MOESM1_ESM.docx (34 kb)
ESM 1 (DOCX 34 kb)

References

  1. Baun A, Jensen SD, Bjerg PL, Christensen TH, Nyholm N (2000) Toxicity of organic chemical pollution in groundwater downgradient of a Landfill (Grindsted, Denmark). Environ Sci Technol 34(9):1647–1652.  https://doi.org/10.1021/es9902524 CrossRefGoogle Scholar
  2. Castillo-Garit JA, Marrero-Ponce Y, Escobar J, Torrens F, Rotondo R (2008) A novel approach to predict aquatic toxicity from molecular structure. Chemosphere 73(3):415–427.  https://doi.org/10.1016/j.chemosphere.2008.05.024 CrossRefGoogle Scholar
  3. Castillo-Garit JA, Abad C, Casañola-Martin GM, Barigye SJ, Torrens F, Torreblanca A (2016) Prediction of aquatic toxicity of benzene derivatives to tetrahymena pyriformis according to OECD principles. Curr Pharm Des 22(33):5085–5094.  https://doi.org/10.2174/1381612822666160804095107 CrossRefGoogle Scholar
  4. Concu R, Kleandrova VV, Speck-Planche A, Cordeiro MNDS (2017) Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory. Nanotoxicology 11(7):891–906.  https://doi.org/10.1080/17435390.2017.1379567 CrossRefGoogle Scholar
  5. de Morais e Silva L, Alves MF, Scotti L, Lopes WS, Scotti MT (2018) Predictive ecotoxicity of MoA 1 of organic chemicals using in silico approaches. Ecotoxicol Environ Saf 153:151–159.  https://doi.org/10.1016/j.ecoenv.2018.01.054 CrossRefGoogle Scholar
  6. I-Kuei Lin L (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45(1):255–268.  https://doi.org/10.2307/2532051 CrossRefGoogle Scholar
  7. Kleandrova VV, Luan F, González-Díaz H, Ruso JM, Melo A, Speck-Planche A, Cordeiro MNDS (2014a) Computational ecotoxicology: simultaneous prediction of ecotoxic effects of nanoparticles under different experimental conditions. Environ Int 73:288–294.  https://doi.org/10.1016/j.envint.2014.08.009 CrossRefGoogle Scholar
  8. Kleandrova VV, Luan F, González-Díaz H, Ruso JM, Speck-Planche A, Cordeiro MNDS (2014b) 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):14686–14694.  https://doi.org/10.1021/es503861x CrossRefGoogle Scholar
  9. Ma S, Lv M, Deng F, Zhang X, Zhai H, Lv W (2015) Predicting the ecotoxicity of ionic liquids towards Vibrio fischeri using genetic function approximation and least squares support vector machine. J Hazard Mater 283:591–598.  https://doi.org/10.1016/j.jhazmat.2014.10.011 CrossRefGoogle Scholar
  10. Nowack B, Mitrano DM (2018) Procedures for the production and use of synthetically aged and product released nanomaterials for further environmental and ecotoxicity testing. NanoImpact 10:70–80.  https://doi.org/10.1016/j.impact.2017.12.001 CrossRefGoogle Scholar
  11. Ojha PK, Mitra I, Das RN, Roy K (2011) Further exploring Rm 2 metrics for validation of QSPR models. Chemom Intell Lab Syst 107(1):194–205.  https://doi.org/10.1016/j.chemolab.2011.03.011 CrossRefGoogle Scholar
  12. Papa E, Battaini F, Gramatica P (2005) Ranking of aquatic toxicity of esters modelled by QSAR. Chemosphere 58(5):559–570.  https://doi.org/10.1016/j.chemosphere.2004.08.003 CrossRefGoogle Scholar
  13. Parvez S, Venkataraman C, Mukherji S (2008) Toxicity assessment of organic pollutants: reliability of bioluminescence inhibition assay and univariate QSAR models using freshly prepared Vibrio fischeri. Toxicol in Vitro 22(7):1806–1813.  https://doi.org/10.1016/j.tiv.2008.07.011 CrossRefGoogle Scholar
  14. Perales E, García JI, Pires E, Aldea L, Lomba L, Giner B (2017) Ecotoxicity and QSAR studies of glycerol ethers in Daphnia magna. Chemosphere 183:277–285.  https://doi.org/10.1016/j.chemosphere.2017.05.107 CrossRefGoogle Scholar
  15. Peric B, Sierra J, Martí E, Cruañas R, Garau MA (2015) Quantitative structure-activity relationship (QSAR) prediction of (eco)toxicity of short aliphatic protic ionic liquids. Ecotoxicol Environ Saf 115:257–262.  https://doi.org/10.1016/j.ecoenv.2015.02.027 CrossRefGoogle Scholar
  16. Raevsky OA, Modina EA, Raevskaya OE (2011) QSAR models of the inhalation toxicity of organic compounds. Pharm Chem J 45(3):165–169.  https://doi.org/10.1007/s11094-011-0585-z CrossRefGoogle Scholar
  17. Roy PP, Paul S, Mitra I, Roy K (2009) On two novel parameters for validation of predictive QSAR models. Molecules 14(5):1660–1701.  https://doi.org/10.3390/molecules14051660 CrossRefGoogle Scholar
  18. Sánchez-Bayo F (2006) Comparative acute toxicity of organic pollutants and reference values for crustaceans. I. Branchiopoda, Copepoda and Ostracoda. Environ Pollut 139(3):385–420.  https://doi.org/10.1016/j.envpol.2005.06.016 CrossRefGoogle Scholar
  19. Toropov AA, Toropova AP (2017) The index of ideality of correlation: a criterion of predictive potential of QSPR/QSAR models? Mutat Res Genet Toxicol Environ Mutagen 819:31–37.  https://doi.org/10.1016/j.mrgentox.2017.05.008 CrossRefGoogle Scholar
  20. Toropov AA, Toropova AP (2018) Application of the Monte Carlo method for building up models for octanol-water partition coefficient of platinum complexes. Chem Phys Lett 701:137–146.  https://doi.org/10.1016/j.cplett.2018.04.012 CrossRefGoogle Scholar
  21. Toropov AA, Carbó-Dorca R, Toropova AP (2018) Index of ideality of correlation: new possibilities to validate QSAR: a case study. Struct Chem 29(1):33–38.  https://doi.org/10.1007/s11224-017-0997-9 CrossRefGoogle Scholar
  22. Toropova AP, Toropov AA (2014) CORAL software: prediction of carcinogenicity of drugs by means of the Monte Carlo method. Eur J Pharm Sci 52(1):21–25.  https://doi.org/10.1016/j.ejps.2013.10.005 CrossRefGoogle Scholar
  23. Toropova AP, Toropov AA (2017) The index of ideality of correlation: a criterion of predictability of QSAR models for skin permeability? Sci Total Environ 586:466–472.  https://doi.org/10.1016/j.scitotenv.2017.01.198 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory of Environmental Chemistry and ToxicologyIstituto di Ricerche Farmacologiche Mario Negri IRCCSMilanItaly

Personalised recommendations