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Article

  • Title

    Influence of components structure and composition of binary mixtures of organic compounds on toxicity towards Daphnia Magna

  • Authors

    Varlamova E. V.
    Kuz’min V. Е.
    Muratov N. N.
    Shapkin V. А.

  • Subject

    CHEMISTRY. CHEMICAL ENGINEERING

  • Year 2015
    Issue 1(45)
    UDC 544.165
    DOI 10.15276/opu.1.45.2015.28
    Pages 171-175
  • Abstract

    Simplex representation of molecular structure is employed for consensus QSAR analysis of toxicity towards Daphnia magna of sodium salts of α-alkoxycarbonylsulfuric acids, aliphatic n-alcohols, phenols and their binary mixtures. The structure of a mixture is represented both using descriptors of individual compounds included in the mixture and using novel specific mixture parameters termed unconnected simplexes. The studied dataset included 15 single compounds and 20 mixtures. The logarithm of EC50 (mmole/l) is used as a target function. Aims of the research are the determination of molecular fragments with positive or negative influence on the toxicity and developing QSAR models capable to predict properly the toxicity of new compounds and mixtures from their structure and composition. Successful consensus model based on forty best QSAR models (R2=0,86…0,97; Q2=0,74…0,96; R2test=0,86…0,99) is obtained using different training and test sets.

  • Keywords QSAR, simplex representation of compounds mixtures, mixture descriptors, toxicity, Daphnia magna
  • Viewed: 1480 Dowloaded: 7
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  • References

    Література
    1.    Existing and developing approaches for QSAR analysis of mixtures / E.N. Muratov, E.V. Varlamova, A.G. Artemenko [et al.] // Molecular Informatics. — 2012. — Vol. 31, Issue 3-4. — PP. 202—221.
    2.    Development of quantitative structure activity relationships in toxicity prediction of complex mixtures / H.-X. Yu, Z.-F. Lin, J.-F. Feng [et al.] // Acta Pharmacologica Sinica. — 2001. — Vol. 22, Issue 1. — PP. 45—49.
    3.    Wei, D.B. QSAR-based toxicity classification and prediction for single and mixed aromatic compounds / D.B. Wei, L.H. Zhai, H.-Y. Hu // SAR and QSAR in Environmental Research. — 2004. — Vol. 15, Issue 3. — PP. 207—216.
    4.    Prediction of mixture toxicity with its total hydrophobicity / Z. Lin, H. Yu, D. Wei [et al.] // Chemosphere. — 2002. — Vol. 46, Issue 2. — PP. 305—310.
    5.    Quantification of joint effect for hydrogen bond and development of QSARs for predicting mixture toxicity / Z. Lin, P. Zhong, K. Yin [et al.] // Chemosphere. — 2003. — Vol. 52, Issue 7. — PP. 1199—1208.
    6.    Quantitative structure-activity relationships for joint toxicity of substituted phenols and anilines to Scenedesmus obliquus / C. Wang, G. Lu, Z. Tang, X. Guo // Journal of Environmental Sciences. — 2008. — Vol. 20, Issue 1. — PP. 115—119.
    7.    Application of QSPR to mixtures / S. Ajmani, S.C. Rogers, M.H. Barley, D.J. Livingstone // Journal of Chemical Information and Modeling. — 2006. — Vol. 46, Issue 5. — PP. 2043—2055.
    8.    Computer-based QSARs for predicting mixture toxicity of benzene and its derivatives / L. Zhang, P.-J. Zhou, F. Yang, Z.-D. Wang // Chemosphere. — 2007. — Vol. 67 Issue 2. — PP. 396—401.
    9.    Characterization of mixtures Part 1: Prediction of infinite-dilution activity coefficients using neural network-based QSPR models / S. Ajmani, S.C. Rogers, M.H. Barley [et al.] // QSAR & Combinatorial Science. — 2008. — Vol.27, Issue 11-12. — PP. 1346—1361.
    10.    Characterization of mixtures. Part 2: QSPR models for prediction of excess molar volume and liquid density using neural networks / S. Ajmani, S.C. Rogers, M.H. Barley [et al.] // Molecular Informatics. — 2010. — Vol. 29, Issue 8-9. — PP. 645—653.
    11.    Quantitative structure–property relationship (QSPR) modeling of normal boiling point temperature and composition of binary azeotropes / V.P. Solov’ev, I. Oprisiu, G. Marcou, A. Varnek // Industrial & Engineering Chemistry Research. — 2011. — Vol. 50, Issue 24. — PP. 14162—14167.
    12.    Kuz’min, V.E. Hierarchical QSAR technology based on the Simplex representation of molecular structure / V.E. Kuz’min, A.G. Artemenko, E.N. Muratov // Journal of Computer-Aided Molecular Design. — 2008. — Vol. 22, Issue 6-7. — PP. 403—421.
    13.    A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm / S. Rännar, F. Lindgren, P. Geladi, S. Wold // Journal of Chemometrics. — 1994. — Vol. 8, Issue 2. — PP. 111—125.
    14.    Defining the toxic mode of action of ester sulphonates using the joint toxicity of mixtures / G. Hodges, D.W. Roberts, S.J. Marshall, J.C. Dearden // Chemosphere. — 2006. — Vol. 64, Issue 1. — PP. 17—25.

    References
    1.    Muratov, E.N., Varlamova, E.V., Artemenko, A.G., Polishchuk, P.G., & Kuz’min, V.E. (2012). Existing and developing approaches for QSAR analysis of mixtures. Molecular Informatics, 31(3-4), 202—221.
    2.    Yu, H.-X., Lin, Z.-F., Feng, J.-F., Xu, T.-L., & Wang, L.-S. (2001). Development of quantitative structure activity relationships in toxicity prediction of complex mixtures. Acta Pharmacologica Sinica, 22(1), 45—49.
    3.    Wei, D.B., Zhai, L.H., & Hu, H.-Y. (2004). QSAR-based toxicity classification and prediction for single and mixed aromatic compounds. SAR and QSAR in Environmental Research, 15(3), 207—216.
    4.    Lin, Z., Yu, H., Wei, D. Wang, G., Feng, J., & Wang, L. (2002). Prediction of mixture toxicity with its total hydrophobicity. Chemosphere, 2002, 46(2), 305—310.
    5.    Lin, Z., Zhong, P., Yin, K., Wang, L., & Yu, H. (2003). Quantification of joint effect for hydrogen bond and development of QSARs for predicting mixture toxicity. Chemosphere, 52(7), 1199—1208.
    6.    Wang, C., Lu, G., Tang, Z., & Guo, X. (2008). Quantitative structure-activity relationships for joint toxicity of substituted phenols and anilines to Scenedesmus obliquus. Journal of Environmental Sciences, 20(1), 115—119.
    7.    Ajmani, S., Rogers, S.C., Barley, M.H., & Livingstone, D.J. (2006). Application of QSPR to mixtures. Journal of Chemical Information and Modeling, 46(5), 2043—2055.
    8.    Zhang, L., Zhou, P.-J., Yang, F., & Wang, Z.-D. (2007). Computer-based QSARs for predicting mixture toxicity of benzene and its derivatives. Chemosphere, 67(2), 396—401.
    9.    Ajmani, S., Rogers, S.C., Barley, M.H., Burgess, A.N., & Livingstone, D.J. (2008). Characterization of mixtures Part 1: Prediction of infinite-dilution activity coefficients using neural network-based QSPR models. QSAR & Combinatorial Science, 27(11-12), 1346—1361.
    10.    Ajmani, S., Rogers, S.C., Barley, M.H., Burgess, A.N., & Livingstone, D.J. (2010). Characterization of mixtures. Part 2: QSPR models for prediction of excess molar volume and liquid density using neural networks. Molecular Informatics, 29(8-9), 645—653.
    11.    Solov’ev, V.P., Oprisiu, I., Marcou, G., & Varnek, A. (2011). Quantitative structure–property relationship (QSPR) modeling of normal boiling point temperature and composition of binary azeotropes. Industrial & Engineering Chemistry Research, 50(24), 14162—14167.
    12.    Kuz’min, V.E., Artemenko, A.G., & Muratov, E.N. (2008). Hierarchical QSAR technology based on the Simplex representation of molecular structure. Journal of Computer-Aided Molecular Design, 22(6-7), 403—421.
    13.    Rännar, S., Lindgren, F., Geladi, P., & Wold, S. (1994). A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm. Journal of Chemometrics, 8(2), 111—125.
    14.    Hodges, G., Roberts, D.W., Marshall, S.J., & Dearden, J.C. (2006). Defining the toxic mode of action of ester sulphonates using the joint toxicity of mixtures. Chemosphere, 64(1), 17—25.

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