QSPR Modeling of Bioconcentration Factors of Nonionic Organic Compounds

Environmental Health Insights

Volumn 4, Issue , 2010, Pages

  Deeb, Omar ^a   Khadikar, Padmakar V ^b   Goodarzi, Mohammad ^c  

^a AL QUDS UNIVERSITY   (Palestine)

^b Laxmi Pest and Fumigation Pvt Ltd   (India)

^c ISLAMIC AZAD UNIVERSITY   (Iran)

Author keywords

BCF; non ionic organic compounds; partial least square (PLS); principal components artificial neural networks (PC ANN); structure property relationships (QSPR)

Indexed keywords

EID: 79954993114     PISSN: None     EISSN: 11786302     Source Type: Journal    
DOI: 10.4137/EHI.S5168     Document Type: Article

References (40)

1. Mackay D.A., Fraser, Bioaccumulation of persistent Organic Chemicals: mechanisms and Models. Environmental Pollution. 2000; 110: 375–91.
2. Hodgeson E., Levi P.E., Introduction to Biochemical Toxicology, (Appleton and Lange, Norwalk, CT, USA, 1994).
3. Neely B.W., Branson D.R., Blau G.E., Partition Coefficient to Measure Bio-concentration Potential of Organic Chemicals in Fish. Environ Sci Technol. 1974; 8: 1113–5.
4. Kanazawa J., Measurement of the BCFs of Pesticides by Freshwater Fish and their Correlation with Physicochemical Properties or Acute Toxicities. Pestic Sci. 1981; 12: 417–24.
5. Mackay D., Correlation of BCFs. Environ Sci Technol. 1982; 16: 274–8.
6. Veith G.D., Kosian P., Estimating Bioconcentration Potential from Octanol/Water Partition Coefficients, in: Mackay D.. editor, Physical Behaviour of PCBs in the Great Lakes, pp. 269 (Ann Arbor Science Pub., USA, 1983).
7. Isnard P., Lambert S., Estimating BCFs from Octanol-Water Partition Coefficient and Aqueous Solubility. Chemosphere. 1988; 17: 21–34.
8. Schuurmann G., Klein W., Advances in Bioconcentration Prediction. Chemosphere. 1988; 17: 1551–74.
9. Dimitrov S.V., Mekenyan O.G., Walker J.D., Non-Linear Modeling of Bioconcentration Using Partition Coefficients for Narcotic Chemicals. SAR QSAR Environ Res. 2002; 13: 177–84.
10. Bintein S., Devillers J., Karchern W., Nonlinear Dependence of Fish Bioconcentration on n-Octanol/Water Partition Coefficient. SAR QSAR Environ Res. 1993; 1: 29–39.
11. Kubinyi H., Nonlinear Dependence of Biological activity on Hydrophobic Character. J Med Chem. 1977; 20: 625–9.
12. Nendza M., QSAR of Bioconcentration: Validity Assessment of log Pow/ logBCF Correlations, in: Nagel R., Loskill R., editor, Bioaccumulation in Aquatic Systems, (VCH, Weinheim, 1991) p. 34–66.
13. Connell D.W., Hawker D.W., Use of Polynomial Expression to describe the Bioconcentration of Hydrophobic Chemicals by Fish. Environ Safety. 1988; 16: 242–57.
14. Zhao C., Boriani E., Chana A., Roncagliono A., Benfenati E., A new hybrid system of QSAR models for predicting BCFs (BCF). Chemosphere. 2008; 73: 1701–7.
15. Lu X., Tao S., Hu H., Dawson R.W., Estimation of BCFs of nonionic organic compounds in fish by molecular connectivity indices and polarity correction factors. Chemosphere. 2000; 41: 1675–88.
16. Gramatica P., Papa E., QSAR modelling of BCF by the oretical molecular descriptors. QSAR Comb Sci. 2003; 22: 374–85.
17. Gramatica P., Papa E., Dearden J.C., Linear QSAR regression models for the prediction of BCFs by physicochemical properties and structural theoretical molecular descriptors. Chemosphere. 2007; 67: 351–8.
18. Fatemi M.H., Jalai-Heravi M., Konuze E., Prediction of BCF using genetic algorithm and artificial neural network. Anal Chim Acta. 2003; 486: 101–8.
19. Qin H., Chen J.W., Wang Y.. Development and assessment of quantitative structure activity relationship models for BCFs of organic pollutants. Chin Sci Bull. 2009; 54: 628–34.
20. Chen J.W., Li X.H., Yu H.Y.. Progress and perspectives of quantitative structure-activity relationships used for ecological risk assessment of toxic organic compounds. Sci China Ser B-Chem. 2008; 51: 593–606.
21. Chen J.W., Harner T., Ding G.H.. Universal predictive models on octanol-air partition coefficients at different temperatures for persistent organic pollutants. Environ Toxico Chem. 2004; 23: 2309–17.
22. Khadikar P.V., Singh S., Mandaloi D., Joshi S., Bajaj A.V., QSAR study on BCF (BCF) of polyhaloginated biphenyls using PI index. Bioorg Med Chem. 2003; 11: 5045–50.
23. Todeschini R., Milano Chemometrics and QSAR Group, http://www.disat.unimib.it/chm.
24. Chaterjee S., Hadi A.S., Price B., Regression Analysis by Examples (3rd ed), (Wiley, New York, 2000).
25. Gemperline P.J., Long J.R.V., Gregoriou G., Gregoriou, Nonlinear multivariate calibration using principal components regression and artificial neural networks. Anal Chem. 1991; 63: 2313–23.
26. Hemmateenejad B., Shamsipur M., Quantitative Structure—Electrochemistry Relationship Study of Some Organic Compounds Using PC-ANN and PCR. Internet Electron J Mol Des. 2004; 4: 316–34.
27. Hemmateenejad B., Safarpour M., Miri R., Nesari N., Toward an Optimal Procedure for PC-ANN Model Building: Prediction of the Carcinogenic Activity of a Large Set of Drugs. J Chem Inf Model. 2005; 45: 190–9.
28. Deeb O., Hemmateenejad B., ANN-QSAR Model of Drug-binding to Human Serum Albumin. Chem Biol Drug Des. 2007; 70: 19–29.
29. Deeb O., Goodarzi M., Predicting the solubility of pesticide compounds in water using QSPR methods. Mol Phys. 2010; 108: 181–92.
30. Deeb O., Hemmateenejad B., Jaber A., Garduno-Juarez R., Miri R., Effects of electronic and physicochemical parameters on the carcinogenic activity of some sulfa drugs using QSAR analysis based on genetic-MLR and genetic-PLS. Chemosphere. 2007; 67L: 2122–30.
31. Goodarzi M., Freitas M.P., Predicting Boiling Points of Aliphatic Alcohols through Multivariate Image Analysis Applied to Quantitative Structure—Property Relationships. J Phys Chem A. 2008; 112: 11263–5.
32. Deeb O., Youssef K., Hemmateenejad B., QSAR of Novel Hydroxyphenylu-reas as Antioxidant Agents. QSAR Comb Sci. 2007; 27: 417–24.
33. Tute M.S., History and Objectives of Quantitative Drug Design in Advances in Research Drug, Harter N.J., Simmord A.B., editors. Vol. 6, (Academic Press, London, 1971) p. 1.
34. Rumelhart D.E., Hinton G.E., Williams R.J., Learning representations by back-propagating errors. Nature. 1986; 323: 533–6.
35. Derks EPPA, Buydens L.M.C., Aspects of network training and validation on noisy data Part 1. Training aspects. Chemom Intell Lab Syst. 1998; 41: 171–84.
36. Martens H., Naes T., Multivariate Calibration. John Wiley, Chichester. 1989.
37. Golbraikh A., Tropsha A., Beware of q2!. J Mol Graph Model. 2002; 20: 269–76.
38. Roy K., Roy P., Comparative chemometric modeling of cytochrome 3 A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques. Eur J Med Chem. 2009; 44: 2913–22.
39. Wang Y., Lib Y., Dinga J., Jianga Z., Changa Y., Estimation of BCFs using molecular electro-topological state and flexibility. SAR QSAR Environ Res. 2008; 19: 375–95.
40. Schüürmann G., Ebert R., Chen J.W., Wang B., Kühne R., External Validation and Prediction Employing the Predictive Squared Correlation Coefficient Test Set Activity Mean vs Training Set Activity Mean. J Chem Inf Model. 2008; 48: 2140–5.