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A QICAR model for metal ion toxicity established via PLS method

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Abstract

The partial least squares(PLS) method was employed to establish a quantitative ion characteristics-activity relationship(QICAR) model for metal ion toxicity(EC50 of 15 metal ions). The ion characteristics included AN(the atomic number), ΔIP(the change in ionization potential, eV), X m(the electronegativity, eV), AW(the atomic weight), X m 2 r(the covalent index), ΔE 0(the absolute difference between electrochemical potential of the ion and that of its first stable reduced state, eV), |lgK OH|(the absolute value of the lg of the first hydrolysis constant), AR(the atomic radius, nm), AR/AW(the ratio between atomic radius and atomic weight) and σ p(the softness index) selected based on relative correlation analysis. The simulated and tested(with the other four metals) efficiency coefficients of the model are 0.88 and 0.96, respectively. The information revealed from the QICAR model indicates that the value of the metal ion toxicity was positively correlated with variables AN, ΔIP, X m, AW and X m 2 r; negatively correlated with variables ΔE 0, |lgK OH|, AR/AW, AR and σ p, and ion characteristics ΔE 0, X m, σ p and X m 2 r were found to contribute more to the toxicity of metal ions via the accurate analysis method provided by PLS. The model could be used to predict the toxicity of the target metals and preliminary to assess combined pollution and environmental risk for heavy metals in the environments.

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References

  1. Wei B. G., Jiang F. Q., Li X. M., Mu S. Y., Micro Chemical Journal, 2011, 93(2), 147

    Article  Google Scholar 

  2. Fisher N. S., Limnology and Oceanography, 1986, 31(2), 443

    Article  CAS  Google Scholar 

  3. Magwood S., George S., Marine Environmental Research, 1996, 42(14), 37

    Article  CAS  Google Scholar 

  4. Tatara C. P., Newman M. C., McCloskey J. T., Williams P. L., Aquat Toxicol., 1997, 39(3/4), 279

    Article  CAS  Google Scholar 

  5. Newman M. C., McCloskey J. T., Tatara C. P., Environ Health Perspect, 1998, 106(Suppl. 6), 1419

    Article  CAS  Google Scholar 

  6. Mccloskey J. T., Newman M. C., Environ. Toxicol Chem., 1996, 15(10), 1730

    Article  CAS  Google Scholar 

  7. Wolterbeek H. T., Verburg T. G., Sci. Total Environ., 2001, 279(1–3), 87

    Article  CAS  Google Scholar 

  8. Kaiser K. L. E., Molecular Informatics, 2003, 22(2), 185

    CAS  Google Scholar 

  9. Capitani J. F., Di Toro D. M., Center for the Study of Metals in the Environment—Annual Report Submitted to US Environmental Protection Agency, Washington, 2004, 84

    Google Scholar 

  10. Zhou D. M., Li L. Z., Peijnenburg W. J., Ownby D. R., Hendriks A. J., Wang P., Li D. D., Bioresour Technology, 2011, 74(4), 1036

    CAS  Google Scholar 

  11. Ownby D. R., Newman M. C., QSAR Comb. Sci., 2003, 22(2), 241

    Article  CAS  Google Scholar 

  12. Tatara C. P., Newman M. C., McCloskey J. T., Williams P. L., Auqat Toxicol., 1998, 42(4), 255

    Article  CAS  Google Scholar 

  13. Kaiser K. L. E., Canadian Journal of Fisheries and Aquatic Sciences, 1980, 37(2), 211

    Article  CAS  Google Scholar 

  14. Kaiser K. L. E., Science of the Total Environment, 1985, 46(1–4), 113

    Article  CAS  Google Scholar 

  15. Wold H.; Ed.: Krishnaiah P. R., Multivariate Analysis, Academic Press, New York, 1966, 391

  16. Wold H., Journal of Marketing Research, 1982, 19(4), 440

    Article  Google Scholar 

  17. Rapp A., Trainor K. J., Agnihotri R., Journal of Business Research, 2010, 63(11), 1229

    Article  Google Scholar 

  18. Ringle C. M., Saratedt M., Straub D. W., MIS Quarterly, 2012, 36(1), 3

    Google Scholar 

  19. Okazaki S., Taylor C. R., Journal of Business Research, 2008, 61(1), 4

    Article  Google Scholar 

  20. Fang Y. Y., Wang X., Li H. Y., Ma Y., Yuan M. X., Huang R. Q., Lai C. M., Chem. J. Chinese Universities, 2001, 22(4), 587

    CAS  Google Scholar 

  21. Zhu C., Huang M., Liang Q. L., Wang Y. M., Hu P., Li P., Zhang H. J., Lu X. G., Luo G. A., Chem. J. Chinese Universities, 2011, 32(7), 1512

    CAS  Google Scholar 

  22. Dong J. Y., Deng L. L., Cheng K. K., Griffin J. L., Chen Z., Chem. J. Chinese Universities, 2011, 32(12), 2769

    CAS  Google Scholar 

  23. Hair J. F., Ringle C. M., Sarstedt M., Journal of Marketing Theory and Practice, 2011, 19(2), 139

    Article  Google Scholar 

  24. Martens H., Chemometrics and Intelligent Laboratory Systems, 2001, 58(2), 85

    Article  CAS  Google Scholar 

  25. Anjali K., Lynne J. W., Anthony R. M., Hervé A., Neurolmage, 2011, 56(2), 455

    Article  Google Scholar 

  26. Wold S., Sjöström M., Eriksson L., Chemometrics and Intelligent Laboratory Systems, 2001, 58(2), 109

    Article  CAS  Google Scholar 

  27. Eldén L., Computational Statistics & Data Analysis, 2004, 46(1), 11

    Article  Google Scholar 

  28. Du X. Y., Liu J. L., Li Y., Fresen Environ. Bull., 2011, 20(1), 121

    CAS  Google Scholar 

  29. Danielli J. F., Davies J. F., Advances in Enzymology and Related Areas of Molecular Biology, 1951, 11, 35

    CAS  Google Scholar 

  30. Dingstad G. I., Westad F., Næs T., Chemometrics and Intelligent Laboratory Systems, 2004, 71, 33

    Article  CAS  Google Scholar 

  31. Abdul-Wahab S. A., Bakheit C. S., Al-Alawi S. M., Environmental Modelling & Software, 2005, 20(10), 1263

    Article  Google Scholar 

  32. Cash G. G., Breen J. J., Chemosphere, 1992, 24(11), 1607

    Article  CAS  Google Scholar 

  33. Wang H. W., Wu Z. B., Meng J., Partial Least-Squares Regression-Linear and Nonlinear Methods, National Defence Industry Press, Beijing, 2006, 307

    Google Scholar 

  34. Zamil S. S., Ahmad S., Choi M. H., Park J. Y., Yoon S. C., Bioresource Technology, 2009, 100(6), 1895

    Article  CAS  Google Scholar 

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Correspondence to Yu Li.

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Supported by the Key Projects in the National Science & Technology Pillar Program in the Eleventh Five-Year Plan Period, China(No.2008BAC43B01).

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Li, Y., Jiang, L., Li, Xl. et al. A QICAR model for metal ion toxicity established via PLS method. Chem. Res. Chin. Univ. 29, 568–573 (2013). https://doi.org/10.1007/s40242-013-2244-2

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