DOI QR코드

DOI QR Code

Is it Possible to Predict the ADI of Pesticides using the QSAR Approach?

  • Kim, Jae Hyoun (Department of Health Science, School of Natural Science, Dongduk Women's University)
  • Received : 2012.09.19
  • Accepted : 2012.12.03
  • Published : 2012.12.31

Abstract

Objectives: QSAR methodology was applied to explain two different sets of acceptable daily intake (ADI) data of 74 pesticides proposed by both the USEPA and WHO in terms of setting guidelines for food and drinking water. Methods: A subset of calculated descriptors was selected from Dragon$^{(R)}$ software. QSARs were then developed utilizing a statistical technique, genetic algorithm-multiple linear regression (GA-MLR). The differences in each specific model in the prediction of the ADI of the pesticides were discussed. Results: The stepwise multiple linear regression analysis resulted in a statistically significant QSAR model with five descriptors. Resultant QSAR models were robust, showing good utility across multiple classes of pesticide compounds. The applicability domain was also defined. The proposed models were robust and satisfactory. Conclusions: The QSAR model could be a feasible and effective tool for predicting ADI and for the comparison of logADIEPA to logADIWHO. The statistical results agree with the fact that USEPA focuses on more subtle endpoints than does WHO.

Keywords

References

  1. Winter CK, Katz JM. Dietary exposure to pesticide residues from commodities alleged to contain the highest contamination levels. J Toxicol. 2011; 2011: 589674.
  2. Neff RA, Hartle JC, Laestadius LI, Dolan K, Rosenthal AC, Nachman KE. A comparative study of allowable pesticide residue levels on produce in the United States. Global Health. 2012; 8(1): 2. https://doi.org/10.1186/1744-8603-8-2
  3. Hamilton DD, Ambrus A, Dieterle R, Felsot A, Harris C, Petersen B, Racke K, Wong SS, Gonzalez R, Tanaka K, et al. Pesticide residues in foodacute dietary exposure. Pest Manag. Sci. 2004; 60: 311-339. https://doi.org/10.1002/ps.865
  4. Damalas CA, Eleftherohorinos LG. Pesticide Exposure, Safety Issues, and Risk Assessment Indicators. Int J Environ Res Public Health. 2011; 8(5): 1402-1419. https://doi.org/10.3390/ijerph8051402
  5. Tonnelier A, Coecke S, Zaldivar JM. Screening of chemicals for human bioaccumulative potential with a physiologically based toxicokinetic model. Archives of Toxicology. 2012; 86(3): 393-403. https://doi.org/10.1007/s00204-011-0768-0
  6. Knaak JB, Dary CC, Power F, Thompson CB, Blancato JN. Physicochemical and biological data for the development of predictive organophosphorus pesticide QSARs and PBPK/PD models for human risk assessment. Crit Rev Toxicol. 2004 Mar-Apr; 34(2): 143-207. https://doi.org/10.1080/10408440490432250
  7. Chaudhry, Q., Chretien, J., Craciun, M., Guo, G., Lemke, F., Muller, J-A, Neagu, N. Piclin, N., Pintore, M., Trundle, P. (2007). Algorithms for (Q)SAR model building; in Quantitative Structure- Activity Relationship (QSAR) for Pesticide Regulatory Purposes, Benfenati, E. (Ed), pp 111, Elsevier.
  8. Bermudez-Saldana JM, Cronin MT. Quantitative structure-activity relationships for the toxicity of organophosphorus and carbamate pesticides to the Rainbow trout Onchorhyncus mykiss. Pest Manag Sci. 2006; 62(9): 819-831. https://doi.org/10.1002/ps.1233
  9. Fisher, S.W.; Lydy, M.J.; Barger, J.; Landrum, P.F. Quantitative structure-activity relationships for predicting the toxicity of pesticides in aquatic systems with sediment. Environ toxicol chem. 1993; 12(7): 1307-1318. https://doi.org/10.1002/etc.5620120721
  10. Mazzatorta P, Smiesko M, Lo Piparo E, Benfenati E. QSAR model for predicting pesticide aquatic toxicity. J Chem Inf Model. 2005; 45(6): 1767- 1774. https://doi.org/10.1021/ci050247l
  11. Devillers J. A general QSAR model for predicting the acute toxicity of pesticides to Lepomis macrochirus. SAR QSAR Environ Res. 2001; 11(5-6): 397-417. https://doi.org/10.1080/10629360108035361
  12. Zimmerman, DW. Invalidation of parametric and nonparametric statistical tests by concurrent violation of two assumptions. J Exp Edu. 1998; 67(1): 55-68. https://doi.org/10.1080/00220979809598344
  13. Blair A, Thomas K, Coble J, Sandler DP, Hines CJ, Lynch CF, Knott C, Purdue MP, Zahm SH, Alavanja MC, Dosemeci M, Kamel F, Hoppin JA, Freeman LB, Lubin JH. Impact of pesticide exposure misclassification on estimates of relative risks in the Agricultural Health Study. Occup Environ Med. 2011; 68(7): 537-541. https://doi.org/10.1136/oem.2010.059469
  14. Gray GM. Harvard Center for Risk Analysis. Risk in Perspective: The precautionary principle in practice: comparing US EPA and WHO pesticide Risk Assessment. 2004.
  15. Todeschini R, Consonni V. 2000. Handbook of Molecular Descriptors. Weinheim: Wiley-VCH.
  16. Todeschini R, Consonni V, Pavan M. 2001. DRAGON--Software for the Calculation of Molecular Descriptors. Release 1.12 for Windows.
  17. Todeschini, R. Consonni, V. Maiocchi, A. The K correlation index: theory development and its application in chemometrics. Chemom. Intell. Lab. Syst. 1999, 46, 13-29. https://doi.org/10.1016/S0169-7439(98)00124-5
  18. Geary, RC. The contiguity ratio and statistical mapping. Incorp.Statist 1954; 5: 115-145. https://doi.org/10.2307/2986645
  19. Netzeva, TI, Worth, AP, Aldenberg, T, Benigni, R, Cronin, MTD, Gramatica, P, Jaworska, JS, Kahn, S, Klopman, G, Marchant, CA, Myatt, G, Nikolova-Jeliazkova, N, Patlewicz, GY, Perkins, R, Roberts, DW, Schultz, TW, Stanton, DT, van de Sandt, JJM, Tong, W, Veith, G., Yang, C. Current status of methods for defining the applicability domain of (Quantitative) structure-activity relationships. ATLA 2005; 33: 155-173.
  20. Jaworska, JS, Aldenberg, T, Nikolova, N. Reviews of method for assign the applicability domains of SARs and QSARs. Final report to Joint Research Centre (Contract No. ECVA-CCR. 495675-Z). Part 1: Review of statistical methods for QSAR AD estimation by the training set, 2005.
  21. Parsons JR, Govers HA. Quantitative structureactivity relationships for biodegradation. Ecotoxicol Environ Saf 1990; 19(2): 212-227. https://doi.org/10.1016/0147-6513(90)90069-H
  22. Price NR, Watkins RW. Quantitative structureactivity relationships (QSAR) in predicting the environmental safety of pesticides. Pestic Outlook 2003; 14: 127-129. https://doi.org/10.1039/b305506j
  23. Liu, P., Long W. Current mathematical methods used in QSAR/QSPR Studies. Int J Mol Sci 2009; 10(5): 1978-1998. https://doi.org/10.3390/ijms10051978
  24. Bhhatarai, B. Gramatica, P. Modelling physicochemical properties of (benzo)triazoles, and screening for environmental partitioning, Environ Sci Technol 2011; 45(19): 8120-8128. https://doi.org/10.1021/es101181g
  25. Kovarich, S. Papa, E. Gramatica, P. QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants. J Haz Materials 2011; 190(1-3): 106-112. https://doi.org/10.1016/j.jhazmat.2011.03.008
  26. Hayashi, Y. Scientific basis for risk analysis of food-related substances with particular reference to health effects on children. J Toxicol Sci 2009; 34: Special issue II, SP201-SP207. https://doi.org/10.2131/jts.34.SP201
  27. Council of Canadian Academies. Integrating emerging technologies into chemical safety assessment. The expert panel on the Integrated testing of pesticides. 2012 (Jan. 17th).
  28. Pohl, HR. Chou, CH. Ruiz, P. Holler, JS. Chemical risk assessment and uncertainty associated with extrapolation across exposure duration. Regul Toxicol Pharmacol 2010; 57(1): 18-23. https://doi.org/10.1016/j.yrtph.2009.11.007
  29. MacNeil JD. The joint food and agriculture organization of the united nations/world health organization expert committee on food additives and its role in the evaluation of the safety of veterinary drug residues in foods. AAPS J 2005; 7(2): E274-E280. https://doi.org/10.1208/aapsj070228
  30. Spiegelman D. Approaches to Uncertainty in Exposure Assessment in Environmental Epidemiology. Annu Rev Public Health 2010; 31: 149-163. https://doi.org/10.1146/annurev.publhealth.012809.103720
  31. Dourson ML. Hertzberg RC. Hartung R. Blackburn K. Novel methods for the estimation of acceptable daily intake. Toxicol Ind Health. 1985; 1(4): 23-33. https://doi.org/10.1177/074823378500100404
  32. Renwick AG. Establishing the upper end of the range of adequate and safe intakes for amino acids: a toxicologist's viewpoint. J Nutr. 2004 Jun; 134(6 Suppl): 1617S-1624S; discussion 1630S- 1632S, 1667S-1672S. https://doi.org/10.1093/jn/134.6.1617S

Cited by

  1. Prediction of Human Health and Ecotoxicity of Chemical Substances Using the OECD QSAR Application Toolbox vol.39, pp.2, 2013, https://doi.org/10.5668/JEHS.2013.39.2.130