Structural Chemistry

, Volume 27, Issue 1, pp 191–198 | Cite as

Computational assessment of environmental hazards of nitroaromatic compounds: influence of the type and position of aromatic ring substituents on toxicity

  • Oleg V. Tinkov
  • Luidmila N. Ognichenko
  • Victor E. Kuz’min
  • Leonid G. Gorb
  • Anna P. Kosinskaya
  • Nail N. Muratov
  • Eugene N. Muratov
  • Frances C. Hill
  • Jerzy Leszczynski
Original Research

Abstract

This study summarizes the results of our recent QSAR and QSPR investigations on prediction of numerous aspects of environmental behavior of nitro compounds. In this study, we applied the QSAR/QSPR models previously developed by our group for virtual screening of energetic compounds, their precursors and other compounds containing nitro groups. To make predictions on the environmental impact of nitro compounds, we analyzed the trends in the change of the experimentally obtained and QSAR/QSPR-predicted values of aqueous solubility, lipophilicity, Ames mutagenicity, bioavailability, blood–brain barrier penetration, aquatic toxicity on T. pyriformis and acute oral toxicity on rats as a function of chemical structure of nitro compounds. All the models were developed using simplex descriptors in combination with random forest (RF) modeling techniques. We interpreted the possible environmental impact (different toxicological properties) in terms of dividing considered nitro compounds based on hydrophobic and hydrophilic characteristics and in terms of the influence of their molecular fragments that promote and interfere with toxicity. In particular, we found that, in general, the presence of amide or tertiary amine groups leads to an increase in toxicity. Also, it was predicted that compounds containing a NO2 group in the para-position of a benzene ring are more toxic than meta-isomers, which, in turn, are more toxic than ortho-isomers. In general, we concluded that hydrophobic nitroaromatic compounds, especially the ones with electron-accepting substituents, halogens and amino groups, are the most environmentally hazardous.

Keywords

Nitroaromatic xenobiotics Acute toxicity SiRMS Virtual screening 

Supplementary material

11224_2015_715_MOESM1_ESM.xls (842 kb)
Supplementary material 1 (XLS 842 kb)

References

  1. 1.
    Zhu H, Tropsha A, Fourches D, Varnek A, Papa E, Gramatica P, Oberg T, Dao P, Cherkasov A, Tetko IV (2008) Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. J Chem Inf Model 48:766–784CrossRefGoogle Scholar
  2. 2.
    Donlon BA, Razo-Flores E, Field JA, Lettinga G (1995) Toxicity of N-substituted aromatics to acetoclastic methanogenic activity in granular sludge. Appl Environ Microbiol 61:3889–3893Google Scholar
  3. 3.
    Nemeikaitė-Čėnienė A, Miliukienė V, Šarlauskas V, Maldutis E, Čėnas N (2006) Chemical aspects of cytotoxicity of nitroaromatic explosives: a review. Chemija 17:34–41Google Scholar
  4. 4.
    Kuz’min VE, Artemenko AG, Kovdienko NA, Tetko IV, Livingstone DJ (2000) Lattice model for QSAR studies. J Mol Model 6:517–526CrossRefGoogle Scholar
  5. 5.
    Donlon BA, Razo-Flores E, Field JA, Lettinga G (1995) Toxicity of N-substituted aromatics to acetoclastic methanogenic activity in granular sludge. Appl Environ Microbiol 61:3889–3893Google Scholar
  6. 6.
    Agrawal WK, Khadikar PV (2001) QSAR prediction of toxicity of nitrobenzenes. Bioorg Med Chem 9:3035–3040CrossRefGoogle Scholar
  7. 7.
    Cronin MTD, Schultz TW (2001) Development of quantitative structure-activity relationships for the toxicity of aromatic compounds to Tetrahymena pyriformis: comparative assessment of the methodologies. Chem Res Toxicol 14:1284–1295CrossRefGoogle Scholar
  8. 8.
    Pouretedal HR, Keshavarz MH (2011) Prediction of toxicity of nitroaromatic compounds through their molecular structures. J Iran Chem Soc 8(1):78–89CrossRefGoogle Scholar
  9. 9.
    Ayyapan G (2013) Chemo Informatics QSAR analysis of nitroaromatic compounds toxicity. Int J Innov Res Sci En Technol 2(2):372–375Google Scholar
  10. 10.
    Isayev O, Rasulev B, Gorb L, Leszczynski J (2006) Structure-toxicity relationships of nitroaromatic compounds. Mol Divers 10:233–245CrossRefGoogle Scholar
  11. 11.
    Elidrissi B, Ousaa A, Ghamalia M, Chtitaa S, Ajanaa MA, Bouachrineb M, Lakhlifia T (2014) Toxicity in vivo of nitro-aromatic compounds: DFT and QSAR results. J Comput Aided Mol Des 4(3):28–37Google Scholar
  12. 12.
    Kuz’min VE, Artemenko AG, Muratov EN (2008) Hierarchical QSAR technology based on the simplex representation of molecular structure. J Comput Aided Mol Des 22:403–421CrossRefGoogle Scholar
  13. 13.
    Muratov EN, Artemenko AG, Kuz’min AG, Lozitsky VE, Fedchuk VP, Lozytska AS, Boschenko RN, Gridina YA (2005) Investigation of anti-influenza activity using hierarchic QSAR technology on the base of simplex representation of molecular structure. Antiv Res 65:A62–A63Google Scholar
  14. 14.
    Polishchuk PG, Muratov EN, Artemenko AG, Kolumbin OG, Muratov NN, Kuz’min VE (2009) Application of random forest approach to QSAR prediction of aquatic toxicity. J Chem Inf Model 49:2481–2488CrossRefGoogle Scholar
  15. 15.
    Kuz’min VE, Muratov EN, Artemenko AG, Gorb L, Qasim M, Leszczynski J (2008) The effect of nitroaromatics’ composition on their toxicity in vivo: novel, efficient non-additive 1D QSAR analysis. Chemosphere 72:1373–1380CrossRefGoogle Scholar
  16. 16.
    Kuz’min VE, Muratov EN, Artemenko AG, Gorb L, Qasim M, Leszczynski J (2008) The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study. J Comput Aided Mol Des 22:747–759CrossRefGoogle Scholar
  17. 17.
    Muratov EN, Kuz’min VE VE, Artemenko AG, Kovdienko NA, Gorb L, Hill F, Leszczynski J (2010) New QSPR equations for prediction of aqueous solubility for military compounds. Chemosphere 79:887–890CrossRefGoogle Scholar
  18. 18.
    Kovdienko NA, Polishchuk PG, Muratov EN, Artemenko AG, Kuz’min VE, Gorb L, Hill F, Leszczynski J (2010) Application of random forest and multiple linear regression techniques to QSPR prediction of an aqueous solubility for military compounds. Mol Inform 29:394–406CrossRefGoogle Scholar
  19. 19.
    Fourches D, Muratov EN, Tropsha A (2010) Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf Model 50:1189–1204CrossRefGoogle Scholar
  20. 20.
    Ognichenko LN, Kuz’min VE, Gorb L, Hill F, Artemenko AG, Polischuk PG, Leszczynski J (2012) QSPR prediction of lipophilicity for organic compounds using random forest technique on the basis of simplex representation of molecular structure. Mol Inform 31:273–280CrossRefGoogle Scholar
  21. 21.
    Sushko Iu, Novotarskyi S, Korner R, Pandey AK et al (2010) Applicability domains for classification problems: benchmarking of distance to models for ames mutagenicity set. J Chem Inf Model 50:2094–2111CrossRefGoogle Scholar
  22. 22.
    Golovenko NYA, Kuz’min VE, Artemenko AG et al (2011) Prediction of bioavailability of drugs by the method of classification models. J Klin Inform Telemed 7:88–92Google Scholar
  23. 23.
    Kosinskaya AP, Ognichenko LN, Polishchuk PG, Kuz’min VE (2014) Structural and functional interpretation of 2D QSAR models of structure-blood-brain barrier permeability relationship of organic compounds. In: Abstracts of the 20th international conference on EuroQSAR (understanding chemical–biological interactions). St-Petersburg, Russia, 31 Aug–Sep 4, 2014, p 147Google Scholar
  24. 24.
    Tinkov OV, Polishchuk PG, Artemenko AG, Kuz’min VE (2012) The investigation of acute toxicity and physical-chemical properties of organic compounds. J Sib Fed Univ 5:95–104Google Scholar
  25. 25.
    Li X, Chen L, Cheng F et al (2014) In silico prediction of chemical acute oral toxicity using multi-classification methods. J Chem Inf Model 54:1061–1069CrossRefGoogle Scholar
  26. 26.
    Heytler PG (1979) Uncouplers of oxidative phosphorylation. Methods Enzymol 55:442–462Google Scholar
  27. 27.
    Nelson DL, Cox MM (2000) Lehniger principles of biochemistry. Worth Publishers, New YorkGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Oleg V. Tinkov
    • 1
  • Luidmila N. Ognichenko
    • 2
  • Victor E. Kuz’min
    • 2
  • Leonid G. Gorb
    • 3
  • Anna P. Kosinskaya
    • 2
    • 4
  • Nail N. Muratov
    • 5
  • Eugene N. Muratov
    • 6
  • Frances C. Hill
    • 7
  • Jerzy Leszczynski
    • 8
  1. 1.Department of ChemistryT.G. Shevchenko State UniversityTiraspolMoldova
  2. 2.Department of Molecular Structures and ChemoinformaticsA.V. Bogatsky Physical–Chemical Institute NAS of UkraineOdessaUkraine
  3. 3.HX5, LLCVicksburgUSA
  4. 4.Odessa National Medical UniversityOdessaUkraine
  5. 5.Chemical-Technological DepartmentOdessa National Polytechnic UniversityOdessaUkraine
  6. 6.Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of PharmacyUniversity of North CarolinaChapel HillUSA
  7. 7.US Army Research and Development CenterVicksburgUSA
  8. 8.Department of Civil and Environmental Engineering, Interdisciplinary Nanotoxicity CenterJackson State UniversityJacksonUSA

Personalised recommendations