Is it Possible to Predict the ADI of Pesticides using the QSAR Approach?
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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.
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.
참고문헌 (32)
- Winter CK, "Dietary exposure to pesticide residues from commodities alleged to contain the highest contamination levels", J Toxicol, 2011, pp.589674~ (2011)
- Neff RA, "A comparative study of allowable pesticide residue levels on produce in the United States", Global Health, 8(1), pp.2~ (2012)
- Hamilton DD, "Pesticide residues in foodacute dietary exposure", Pest Manag. Sci, 60, pp.311~339 (2004)
- Damalas CA, "Pesticide Exposure, Safety Issues, and Risk Assessment Indicators", Int J Environ Res Public Health, 8(5), pp.1402~1419 (2011)
- Tonnelier A, "Screening of chemicals for human bioaccumulative potential with a physiologically based toxicokinetic model", Archives of Toxicology, 86(3), pp.393~403 (2012)
- Knaak JB, "Physicochemical and biological data for the development of predictive organophosphorus pesticide QSARs and PBPK", PD models for human risk assessment, 2004, pp.34(2), 143~207
- Chaudhry, Q, "Algorithms for (Q)SAR model building; in Quantitative Structure- Activity Relationship (QSAR) for Pesticide Regulatory Purposes", pp.111~ (2007), Elsevier
- Bermudez-Saldana JM, "Quantitative structure-activity relationships for the toxicity of organophosphorus and carbamate pesticides to the Rainbow trout Onchorhyncus mykiss", Pest Manag Sci, 62(9), pp.819~831 (2006)
- Fisher, S.W, "Quantitative structure-activity relationships for predicting the toxicity of pesticides in aquatic systems with sediment", Environ toxicol chem, 12(7), pp.1307~1318 (1993)
- Mazzatorta P, "QSAR model for predicting pesticide aquatic toxicity", J Chem Inf Model, 45(6), pp.1767~1774 (2005)
- Devillers J., "A general QSAR model for predicting the acute toxicity of pesticides to Lepomis macrochirus", SAR QSAR Environ Res, 11(5-6), pp.397~417 (2001)
- Zimmerman, DW, "Invalidation of parametric and nonparametric statistical tests by concurrent violation of two assumptions", J Exp Edu, 67(1), pp.55~68 (1998)
- Blair A, "Impact of pesticide exposure misclassification on estimates of relative risks in the Agricultural Health Study", Occup Environ Med, 68(7), pp.537~541 (2011)
- Gray GM, "Harvard Center for Risk Analysis. Risk in Perspective: The precautionary principle in practice: comparing US EPA and WHO pesticide Risk Assessment", comparing US EPA and WHO pesticide Risk Assessment, ~ (2004)
- Todeschini R, "Handbook of Molecular Descriptors" (2000), Wiley-VCH
- Todeschini R, "DRAGON--Software for the Calculation of Molecular Descriptors. Release 1.12 for Windows", ~ (2001)
- Todeschini, R, "The K correlation index: theory development and its application in chemometrics", Chemom. Intell. Lab. Syst, 46, pp.13~29 (1999)
- Geary, RC, "The contiguity ratio and statistical mapping", Incorp.Statist, 5, pp.115~145 (1954)
- Netzeva, TI, "Current status of methods for defining the applicability domain of (Quantitative) structure–-activity relationships", ATLA, 33, pp.155~173 (2005)
- Jaworska, JS, "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)
- Parsons JR, "Quantitative structureactivity relationships for biodegradation", Ecotoxicol Environ Saf, 19(2), pp.212~227 (1990)
- Price NR, "Quantitative structureactivity relationships (QSAR) in predicting the environmental safety of pesticides", Pestic Outlook, 14, pp.127~129 (2003)
- Liu, P, "Current mathematical methods used in QSAR", QSPR Studies, 2009, pp.10(5), 1978~1998
- Bhhatarai, B, "Modelling physicochemical properties of (benzo)triazoles, and screening for environmental partitioning", Environ Sci Technol, 45(19), pp.8120~8128 (2011)
- Kovarich, S, "QSAR classification models for the prediction of endocrine disrupting activity of brominated flame retardants", J Haz Materials, 190(1-3), pp.106~112 (2011)
- Hayashi, Y., "Scientific basis for risk analysis of food-related substances with particular reference to health effects on children", J Toxicol Sci, 34(Special issue II), pp.SP201~SP207 (2009)
- Council of Canadian Academies, "Integrating emerging technologies into chemical safety assessment", The expert panel on the Integrated testing of pesticides, 17 (2012)
- Pohl, HR., "Chemical risk assessment and uncertainty associated with extrapolation across exposure duration", Regul Toxicol Pharmacol, 57(1), pp.18~23 (2010)
- 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, 2005, pp.7(2), E274~E280
- Spiegelman D, "Approaches to Uncertainty in Exposure Assessment in Environmental Epidemiology", Annu Rev Public Health, 31, pp.149~163 (2010)
- Dourson ML, "Novel methods for the estimation of acceptable daily intake", Toxicol Ind Health, 1(4), pp.23~33 (1985)
- Renwick AG, "Establishing the upper end of the range of adequate and safe intakes for amino acids: a toxicologist's viewpoint", J Nutr, 134(6 Suppl), pp.1617S~1672S (2004)