Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/28193
標題: DFT-based Quantitative Structure-Activity Relationships for Predicting Mixture Toxicity of Organic Pollutants
以密度泛函理論為基礎建立預測有機污染物混合毒性之定量結構活性關係
作者: 張瓊文
Chang, Chiung-Wen
關鍵字: 密度泛函理論
DFT
定量結構活性關係
有機磷農藥
苯類化合物
有機污染物
density functional theory
QSAR
quantitative structure-activity relationships
organophosphorus pesticides
benzene
organic pollutants
出版社: 土壤環境科學系所
引用: [1] 王斌、趙勁松、郁亞娟、王曉棟、王連生。2004。取代聯苯的定量結構活性相關及聯合毒性研究。環境科學。25(3): 89-93。 [2] 王斌、余剛、張祖麟、胡洪营、王連生。2006。烷基醇化合物的定量結構活性相關及聯合毒性預測。科學通報。51(13): 1513-1518。 [3] 林浩、鄒柏祥、姜家良。1980。農藥混劑的選擇性研究。植物保護學報。7(2): 123-131。 [4] 林斯叢柏、鮑加登。1986。有機化學。初版,111-126。台北:曉園。 [5] 孫金秀、陳波、姚佩佩。2000。有機磷與擬除蟲菊酯農藥聯合作用的毒性評價。衛生毒理學雜誌。14(3): 141-144。 [6] 孫金秀、陳波、姚佩佩。2000。農藥混劑聯合毒性評價。衛生研究。29(2): 65-68。 [7] 孫斐、翁愫慎、李國欽。2002。常用有機磷劑農藥對水生生物風險評估。植物保護學會會刊。44: 171-183。 [8] Altenburger, R., M. Nendza, and G. Schüürmann. 2003. Mixture toxicity and its modeling by quantitative structure-activity relationships. Environ. Toxicol. Chem. 22: 1900-1915. [9] Burra, Ö. 1927. Berechnung des energiewertes des wasserstoffmolekel-ions (Calculation of the energy value of the ionized hydrogen molecular in its normal state). Kgl. Danske Vid. Selskab. 7(14): 1-18. [10] Bearden, A. P. and T. W. Schultz. 1997. Structure-activity relationships for Pimephales and Tetrahymena: a mechanism of action approach. Environ. Toxicol. Chem. 16: 1311-1317. [11] Bingham, R. C., M. J. S. Dewar, and H. L. Donald. 1975. Ground states of molecules. XXV. MINDO/3. Improved version of the MINDO semiempirical SCF-MO method. J. Am. Chem. Soc. 97(6): 1285-1293. [12] Bradbury, S. P. 1995. Quantitative structure-activity relationships and ecological risk assessment: an overview of predictive aquatic toxicology research. Toxicol. Lett. 79: 229-237. [13] Dewar, M. J. S., E. G. Zoebisch, E. F. Healy, and J. J. P. Stewart. 1985. Development and use of quantum mechanical molecular models. 76. AM1: a new general purpose quantum mechanical molecular model. J. Am. Chem. Soc. 107(13): 3902-3909. [14] Dewar, M. J. S. and W. Thiel. 1977. MINDO/3 study of the addition of singlet oxygen (1.DELTA.gO2) to 1,3-butadiene. J. Am. Chem. Soc. 99(7): 2338-2339. [15] Edwards, J. O. and R. G. Pearson. 1962. The factors determining nucleophilic reactivities. J. Am. Chem. Soc. 84(1): 16-24. [16] Eldred, D. V. and P. C. Jurs. 1999. Prediction of acute mammalian toxicity of organophosphorus pesticide compounds from molecular structure. SAR QSAR Environ. Res. 10: 75-99. [17] Fermi, E. 1927. Un metodo statistico per la determinazione di alcune priorieta dell''atome". Rend. Accad. Naz. Lincei 6: 602-607. [18] García-Domenech, R., P. Alarcón-Elbal, G. Bolas, R. Bueno-Marí, F. A. Chordá-Olmos, S. A. Delacour, M.C. Mouriño, A. Vidal, and J. Gálvez. 2007. Prediction of acute toxicity of organophosphorus pesticides using topological indices. SAR QSAR Environ. Res. 18: 745-755. [19] Hansch, C., P. P. Maloney, and T. Fujita. 1962. Correlation of biological activity of phenoxyacetic acids with Hammett substituted constants and partition coefficients. Nature 14: 178-180. [20] Hansch, C. 1978. Correlation analysis in chemistry. In: Chapman, N.B., Shorter, J.(Eds.), Plenum Press, New York, pp. 397-438. [21] Hayes, W. J. and E. R. Laws. 1991. Handbook of pesticide toxicology. New York: San Francisco Academic Press. 2: 588. [22] Heitler, W. and F. London. 1927. Wechselwirkung neutraler atome und homöopolare bindung nach der quantenmechanik. Z. Phys. 44: 455-472. [23] Hermens, J. and P. Leeuwanch. 1982. Joint toxicity of mixtures of 8 and 24 chemicals to the guppy(Poecilia reticulata). Ecotoxicol. Environ. Saf. 6: 302-310. [24] Hohenberg, P. and W. Kohn. 1964. Inhomogeneous electron gas. Phys. Rev. 136: B864-B871. [25] Iczkowsksi, R. P. and J. L. Margrave. 1961. Electronegativity. J. Am. Chem. Soc. 83: 3547-3551. [26] Keplinger, M. L. and W. B. Deichmann. 1967. Acute toxicity of combinations of pesticides. Toxicol. Appl. Pharmacol. 10(3): 586-595. [27] Kohn, W. and L. J. Sham. 1965. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140: A1133-A1138. [28] Kohn, W. 1999. Nobel Lecture: Electronic structure of matter—wave functions and density functionals. Rev. Mod. Phys. 71: 1253-1266. [29] Koopmans, T. 1934. Über die zuordnung von wellenfunktionen und eigenwerten zu den einzelnen elektronen eines atoms. Physica 1(1-6): 104-113. [30] Lange, P. and W. D. Wiezorek. 1975. The effects of diethyldithiocarbamate on acute toxicity and acetylcholinesterase inhibition by methyl-parathion in mice. Acta Biol. Med. Ger. 34(2): 427-433. [31] Lin, Z., H. Yu, D. Wen, G. Wang, J. Feng, and L. Wang. 2002. Prediction of mixture toxicity with its total hydrophobicity. Chemosphere 46: 305-310. [32] Lin, Z., P. Shi, S. Gao, L. Wang, and H. Yu. 2003a. Use of partition coefficient to predict mixture toxicity. Water Res. 37: 2223-2227. [33] Lin, Z., P. Zhong, K. Yin, L. Wang, and H. Yu. 2003b. Quantification of joint effect for hydrogen bond and development of QSARs for predicting mixture toxicity. Chemosphere 52: 1199-1208. [34] Mansour, N. A., M. E. Eldefrawi, and A. Toppodaza. 1966. Toxicological studies on the Egyptian cotton leafworm, prodenia lityra. VI. potentiation and antagonism of organophosphorus and carbamate insecticides. Ecol. Entomol. 59(2): 307-311. [35] Mumtaz, M. M., P. Ruiz, and C. T. D. Rosa. 2007. Toxicity assessment of unintentional exposure to multiple chemicals. Toxicol. Appl. Pharmacol. 223: 104-113. [36] Nirmalakhandan, N., V. Arulgnanendran, and M. Mohsin. 1994. Toxicity of mixtures of organic chemicals to microorganisms. Water Res. 28: 543-551. [37] Pariser, R. and R. G. Parr. 1953. A semi-empirical theory of the electronic spectra and electronic structure of complex unsaturated molecules. I. J. Chem. Phys. 21(3): 466-471. [38] Pariser, R. and R. G. Parr. 1953. A semi-empirical theory of the electronic spectra and electronic structure of complex unsaturated molecules. II. J. Chem. Phys. 21(5): 767-776. [39] Parr, R. G., R. A. Donnelly, M. Levy, and W. E. Palke. 1978. Electronegativity: The density functional viewpoint. J. Chem. Phys. 68(8): 3801-3807. [40] Parr, R. G. and R. G. Pearson. 1983. Density functional theory for fractional particle number: Derivative discontinuities of the energy. J. Am. Chem. Soc. 105: 7512-7516. [41] Pearson, R. G. 1963. Hard and soft acids and bases. J. Am. Chem. Soc. 85(22): 3533-3539. [42] Pearson, R. G. 2005. Chemical hardness and density functional theory. J. Chem. Sci. 117(5): 369-377. [43] Pople, J. A. 1953. Electron interaction in unsaturated hydrocarbons. Trans. Faraday Soc. 49: 1375-1385. [44] Prakash, J.,N. Nirmalakhandan, B. Sun, and J. Peace. 1996. Toxicity of binary mixtures of organic chemicals to microorganisms. Water Res. 30(6): 1459-1463. [45] Preston, S., N. Coad, J. Townend, K. Killham, and G. I. Paton. 2000. Biosensing the acute toxicity or metal interactions: Are they additive, synergistic, or antagonistic? Environ. Toxicol. Chem. 19: 775-780. [46] Safety Bulletin. 2004. The effect of unusual work schedules and concurrent exposures on occupational exposure limits (OELs). Available: http://www.hre.gov.ab.ca/documents/WHS/WHS-PUB_ch055.pdf. [47] Singh, N. N. and A. K. Srivastava. 1984. Toxicity of a mixture of aldrin and formothion and other organophosphorus, organochlorine and carbamate pesticides to the Indian catfish, Heteropneustes fossilis. Comp. Physiol. Ecol. 9(1): 63-66. [48] Stewart, J. J. P. 1988. Optimization of parameters for semiempirical methods I. Method. J. Comput. Chem. 10(2): 209-220. [49] Stewart, J. J. P. 1988. Optimization of parameters for semiempirical methods II. Applications. J. Comput. Chem. 10(2): 221-264. [50] Suk, W. A., K. Olden, and R. S. H. Yang. 2002. Chemical mixtures research: significance and future perspectives. Environ. Health Perspect. 110: 891-892. [51] Sultatos, L. G. and S. D. Murphy. 1983. Kinetic analusis of the microsomal biotransmation of the phosphorothioate insecticides chlorpyrifos and parathion. Fundam. Appl. Toxicol. 3(1): 16-21. [52] The Health Canada. 2007. Reference manual for the requirements of the consumer chemicals and containers regulations, 2001 of the hazardous products act (HPA). [53] Thomas, L. H. 1927. The calculation of atomic fields. Proc. Cambridge Phil. Soc. 23(5): 542-548. [54] Verhaar, H. J. M., F. J. M. Busser, and J. L. M. Hermens. 1995. A surrogate parameter for the baseline toxicity content of contaminated water. Environ. Sci. Technol. 29(3): 726-734. [55] Walker, J. D. 2003. Applications of QSARs in toxicology: a US government perspective. J. Mol. Struct. (Theochem) 622: 167-184. [56] Wei, D. B., L. H. Zhai, and H. Y. Hu. 2004. QSAR-based toxicity classification and prediction for single and mixed aromatic compounds. SAR QSAR Environ. Res. 15(3): 207-216. [57] Xu, S. and N. Nirmalakhandan. 1998. Use of QSAR models in predicting joint effects in multi-component mixtures of organic chemicals. Water Res. 32: 2391-2399. [58] Zhang, L., P. J. Zhou, F. Yang, and Z. D. Wang. 2007. Computer-based QSARs for predicting mixture toxicity of benzene and its derivatives. Chemosphere 67: 396-401. [59] Zhang, Y. H., S. S. Liu, X. Q. Song, and H. L. Ge. 2008. Predicition for the mixture toxicity of six organophosphorus pesticides to the luminescent bacterium Q67. Ecotox. Environ. Safe. 71: 880-888. 網站 [60] 行政院農業委員會 農業藥物毒物試驗所 [61] http://pcddsv.tactri.gov.tw/moa/ [62] 行政院農業委員會 動植物防疫檢疫局 農藥MSDS [63] http://pesticide.baphiq.gov.tw/ghs/msds.aspx?page=1 [64] Compendium of Pesticide Common Names [65] http://www.alanwood.net/pesticides/index.html [66] NIST Chemistry WebBook [67] http://webbook.nist.gov/chemistry/ [68] The Ecological Assessment of Storm Impacts on Marine Resources [69] http://www.chbr.noaa.gov/easi/data/default.aspx [70] The Pesticide Action Network (PAN) Pesticide Database [71] http://pesticideinfo.org/ [72] The Pesticide Properties Database [73] http://sitem.herts.ac.uk/aeru/footprint/en/index.htm [74] The Pollution Information Site [75] http://www.scorecard.org/chemical-profiles/index.tcl
摘要: The environment is often exposed to chemical mixtures from multiple sources. The toxicity of various chemical mixtures is higher than single chemicals. However, the vast majority of toxicity studies deal with single chemicals, and therefore the prediction of mixture toxicity becomes a necessary and vital issue. In recent years, the development of quantum mechanical theory was combined with the progress of computational technology, which means quantitative calculation can be conducted from the atomic or molecular structure of a substance with little or even without empirical results. Besides, the parameter calculated was directly connected to organic activity, toxicity, chemical reaction, to construct the projection patterns, among which the QSAR is generally used to project mixture toxicity now. In this study, the objective is the binary mixtures toxicity of 12 benzene and its derivatives in the environment and 9 organophosphorus pesticides used with high frequency domestically, from which the DFT of quantum mechanical theory developed in recent years is used as a basis to build up the QSAR of toxicity prediction. The differences between Semi-empirical (AM1) and DFT (B3LYP) are discussed as well, and the prediction pattern will further be applied to each field to access mixtures and reach the goal of fast prediction. The results suggest that the results of prediction pattern are similar from either B3LYP or AM1 that are used to calculate benzene and its derivatives and mixtures. When using one parameter to predict toxicity, total surface area (TSA), apolar surface area (APSA), electron affinity (EA), and chemical potential (μ) are major factors. As multi-parameters are concerned, the increase of reaction energy (ΔEAB) and global soft (S) are required as parameters. Thus, AM1 has priority for choices in the future due to its fast calculation. When constructing prediction patterns of toxicity, surface area is an important parameter no matter benzene and its derivatives or organophosphorus pesticides are concerned with single parameter. And surface area could be influenced by chemical polarity depending on different subjects of prediction. Moreover, groups of fat and fragrance are factors to influence toxicity as well, so the number of ring and atom are used as parameters while the variety of toxic substances are complicated. When predicting mixture toxicity with multi-parameters, TSA, Etot, η, S, and μ are necessary parameters. And avoid aromatic compounds induce difference, so use sum of ring (R) and sum of atoms (NO, NN, NS, NP, NCl). Besides, ΔEAB is an important parameter in mixture.
環境中常同時存在多種來源的化學混合物,而許多混合化學物質的毒性高於單一化學物質。然而大部分的毒性研究對象都是單一化學物質,因此預測混合物毒性為必要且重要的議題。近年來,量子力學的興起結合了電腦計算技術的提升,可由較少甚至不需實驗結果即可從物質的原子、分子結構進行定量計算,並藉由計算所得的參數直接與生物活性、毒性、化學反應性等建構預測模式,其中,定量結構活性關係(QSAR)是目前被廣泛使用於預測混合毒性的方法。 本研究分別以環境中常出現的12種苯類化合物與國內使用量較高的9種有機磷劑農藥之混合毒性為研究標的,以近年在量子力學興起的密度泛函理論(DFT)為基礎建立預測毒性之定量結構活性關係,並探討以半經驗(AM1)及密度泛函法(B3LYP)計算的差異,欲由此預測模式進一步套用在各個領域中評估混合物,並達到快速預測的目的。 研究結果顯示,無論是以B3LYP或AM1法計算苯類化合物和混合物都可得到相似的預測模式結果-以單參數為考量時,總表面積、非極性表面積、電子親和力和化學勢為主要的影響因子;而以多參數為考量時,需增加能量差(ΔEAB)與整體軟度為參數。因此未來在選擇使用方法上可以計算較快速的AM1法為優先。而在有機磷劑農藥之毒性預測方面,以單參數為考量時,單一型態的結果顯示環數與體積為主要參數,其次是總表面積、極性表面積與分子量;而混合型態則是以環數為主要參數,次之為分子量、總表面積、極性表面積與體積。當以多參數為考量時,須再添加總能量與化學勢,而在混合型態需增加各原子數目作為參數。另外,在建立毒性預測模式時,不論是苯類化合物或有機磷劑在考量單參數時,表面積皆是一重要的參數,而且依預測的對象不同,會受極性與非極性的影響。另外,脂肪族與芳香族也是影響毒性的因子之一,故當致毒物的種類較複雜時,須以環數和各原子數目作為參數。而以多參數預測毒性時,總能量、整體軟硬度與化學勢為必考慮的參數。另外,在混合型態預測上,混合物作用能量是必考慮因子,因其可表示混合物間的交互作用。
URI: http://hdl.handle.net/11455/28193
其他識別: U0005-1507200914004800
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1507200914004800
Appears in Collections:土壤環境科學系

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.