Skip to main content
Log in

Development and assessment of quantitative structure-activity relationship models for bioconcentration factors of organic pollutants

  • Articles/Environmental Chemistry
  • Published:
Chinese Science Bulletin

Abstract

Bioconcentration factors (BCFs) are of great importance for ecological risk assessment of organic chemicals. In this study, a quantitative structure-activity relationship (QSAR) model for fish BCFs of 8 groups of compounds was developed employing partial least squares (PLS) regression, based on linear solvation energy relationship (LSER) theory and theoretical molecular structural descriptors. The guidelines for development and validation of QSAR models proposed by the Organization for Economic Co-operation and Development (OECD) were followed. The model results show that the main factors governing logBCF are Connolly molecular area (CMA), average molecular polarizability (α) and molecular weight (M W). Thus molecular size plays a critical role in affecting the bioconcentration of organic pollutants in fish. For the established model, the multiple correlation coefficient square (R Y 2) = 0.868, the root mean square error (RMSE) = 0.553 log units, and the leave-many-out cross-validated Q CUM 2 = 0.860, indicating its good goodness-of-fit and robustness. The model predictivity was evaluated by external validation, with the external explained variance (Q EXT 2) = 0.755 and RMSE = 0.647 log units. Moreover, the applicability domain of the developed model was assessed and visualized by the Williams plot. The developed QSAR model can be used to predict fish logBCF for organic chemicals within the application domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Wang L S. Chemistry of organic pollutants (in Chinese). Beijing: Higher Education Press, 2005. 253–254

    Google Scholar 

  2. UNEP (United Nations Environment Program). Final act of the conference of plenipotentiaries on the Stockholm convention on persistent organic pollutants. UNEP/POPS/CONF/4, 2001

  3. Environmental Canada. Toxic substances management policy, persistence and bioaccumulation criteria. En 40-499/2-1995E, 1996

  4. Arnot J A, Mackay D, Bonnell M. Estimating metabolic biotransformation rates in fish from laboratory data. Environ Toxicol Chem, 2008, 27(2): 341–351

    Article  Google Scholar 

  5. U.S. EPA (Environmental Protection Agency). Ecological effects test guidelines, OPPTS 850 1730, Fish BCF. EPA 712-C-96-129, 1996

  6. OECD (Organisation for Economic Co-Operation and Development). OECD guidelines for testing of chemicals, proposal for updating guidelines 305, bioconcentration: Flow-through fish test. OECD 305, 1996

  7. Connell D W, Schrmann G. Evaluation of various molecular parameters as predictors of bioconcentration in fish. Ecotoxicol Environ Saf, 1988, 15(3): 324–335

    Article  Google Scholar 

  8. Bintein S, Devillers J, Karcher W. Nonlinear dependence of fish bioconcentration on n-octanol/water partition coefficient. SAR QSAR Environ Res, 1993, 1(1): 29–39

    Article  Google Scholar 

  9. Lu X X, Tao S, Hu H, et al. Estimation of bioconcentration factors of nonionic organic compounds in fish by molecular connectivity indices and polarity correction factors. Chemosphere, 2000, 41(10): 1675–1688

    Article  Google Scholar 

  10. Dimitrov S D, Dimitrova N C, Walker J D, et al. Predicting bioconcentration factors of highly hydrophobic chemicals. Effects of molecular size. Pure Appl Chem, 2002, 74(10): 1823–1830

    Article  Google Scholar 

  11. Gramatica P, Papa E. QSAR Modeling of bioconcentration factor by theoretical molecular descriptors. QSAR Comb Sci, 2003, 22(3): 374–385

    Article  Google Scholar 

  12. Dearden J C, Shinnawei N M. Improved prediction of fish bioconcentration factor of hydrophobic chemicals. SAR QSAR Environ Res, 2004, 15(5–6): 449–455

    Article  Google Scholar 

  13. Sacan M T, Erdem S S, Ozpinar G A, et al. QSPR study on the bioconcentration factors of nonionic organic compounds in fish by characteristic root index and semiempirical molecular descriptors. J Chem Inf Comput Sci, 2004, 44(3): 985–992

    Google Scholar 

  14. Papa E, Dearden J C, Gramatica P. Linear QSAR regression models for the prediction of bioconcentration factors by physicochemical properties and structural theoretical molecular descriptors. Chemosphere, 2007, 67(2): 351–358

    Article  Google Scholar 

  15. Sakuratani Y, Noguchi Y, Kobayashi K, et al. Molecular size as a limiting characteristic for bioconcentration in fish. J Environ Biol, 2008, 29(1): 89–92

    Google Scholar 

  16. Meylan W M, Howard P H, Boethling R S, et al. Improved method for estimating bioconcentration/bioaccumulation factor from octanol/water partition coefficient. Environ Toxico Chem, 1999, 18(4): 664–672

    Article  Google Scholar 

  17. Chen J W, Li X H, Yu H Y, et al. Progress and perspectives of quantitative structure-activity relationships used for ecological risk assessment of toxic organic compounds. Sci China Ser B-Chem, 2008, 51(7): 593–606

    Article  Google Scholar 

  18. OECD (Organisation for Economic Co-Operation and Development). Guideline document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. ENV/JM/MONO(2007)2, 2007

  19. Jaworska J S, Comber M, Auer C, et al. Summary of a workshop on regulatory acceptance of (Q)SARs for human health and environmental endpoints. Environ Health Perspect, 2003, 111(10): 1358–1360

    Google Scholar 

  20. Cronin M T, Jaworska J S, Walker J D, et al. Use of QSARs in international decision-making frameworks to predict health effects of chemical substances. Environ Health Perspect, 2003, 111(10): 1391–1401

    Google Scholar 

  21. Gramatica P, Papa E. An update of the BCF QSAR model based on theoretical molecular descriptors. QSAR Comb Sci, 2005, 24(8): 953–960

    Article  Google Scholar 

  22. Devillers J, Bintein S, Domine D. Comparison of BCF models based on log P. Chemosphere, 1996, 33(6): 1047–1065

    Article  Google Scholar 

  23. Sabljic A, Protic M. Molecular connectivity: A novel method for prediction of bioconcentration factor of hazardous chemicals. Chem-Bio Interact, 1982, 42(3): 301–310

    Article  Google Scholar 

  24. Lu X X, Tao S, Cao J, et al. Prediction of fish bioconcentration factors of nonpolar organic pollutants based on molecular connectivity indices. Chemosphere, 1999, 39(6): 987–999

    Article  Google Scholar 

  25. Tao S, Hu H Y, Lu X X, et al. Fragment constant method for prediction of fish bioconcentration factors of non-polar chemicals. Chemosphere, 2000, 41(10): 1563–1568

    Article  Google Scholar 

  26. Wang Y, Li Y, Ding J, et al. Estimation of bioconcentration factors using molecular electro-topological state and flexibility. SAR QSAR Environ Res, 2008, 19(3–4): 375–395

    Article  Google Scholar 

  27. Cui S H, Yang J, Liu S S, et al. Predicting bioconcentration factor values of organic pollutants based on medv descriptors derived QSARs. Sci China Ser B-Chem, 2007, 50(5): 587–592

    Article  Google Scholar 

  28. Wei D B, Zhang A Q, Wu C D, et al. Progressive study and robustness test of QSAR model based on quantum chemical parameters for predicting BCF of selected polychlorinated organic compounds (PCOCs). Chemosphere, 2001, 44(6): 1421–1428

    Article  Google Scholar 

  29. Liu H X, Yao X J, Zhang R S, et al. The accurate QSPR models to predict the bioconcentration factors of nonionic organic compounds based on the heuristic method and support vector machine. Chemosphere, 2006, 63(5): 722–733

    Article  Google Scholar 

  30. Abraham M H. Scales of solute hydrogen-bonding: Their construction and application to physicochemical and biochemical processes. Chem Soc Rev, 1993, 22(2): 73–83

    Article  Google Scholar 

  31. Chen J W, Harner T, Ding G H, et al. Universal predictive models on octanol-air partition coefficients at different temperatures for persistent organic pollutants. Environ Toxico Chem, 1993, 23(10): 2309–2317

    Article  Google Scholar 

  32. Gramatica P. Principles of QSAR models validation: Internal and external. QSAR Comb Sci, 2007, 26(5): 694–701

    Article  Google Scholar 

  33. Wilson L Y, Famini G R. Using theoretical descriptors in quantitative structure-activity relationships: Some toxicological indices. J Med Chem, 1991, 34(5): 1668–1674

    Article  Google Scholar 

  34. de Wolf W, de Bruijn J H M, Seinen W, et al. Influence of biotransformation on the relationship between bioconcentration factors and octanol-water partition coefficients. Environ Sci Technol, 1992, 26(7): 1197–1201

    Article  Google Scholar 

  35. Kelly B C. Food web-specific biomagnification of persistent organic pollutants. Science, 2007, 317(13): 236–239

    Article  Google Scholar 

  36. Han X, Nabb D L, Mingoia R T, et al. Determination of xenobiotic intrinsic clearance in freshly isolated hepatocytes from rainbow trout (Oncorhynchus mykiss) and rat and its application in bioaccumulation assessment. Environ Sci Technol, 2007, 41(9): 3269–3276

    Article  Google Scholar 

  37. Kanazawa J. Uptake and excretion of organophosphorus and carbamate insecticides by fresh water fish, motsugo, Pseudorasbora parva. Bull Environ Contam Toxicol, 1975, 14(3): 346–352

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to JingWen Chen.

Additional information

Supported by the National Basic Research Program of China (Grant No. 2006CB403302)

About this article

Cite this article

Qin, H., Chen, J., Wang, Y. et al. Development and assessment of quantitative structure-activity relationship models for bioconcentration factors of organic pollutants. Chin. Sci. Bull. 54, 628–634 (2009). https://doi.org/10.1007/s11434-009-0053-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11434-009-0053-2

Keywords

Navigation