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        有機污染物的被動采樣材料-水分配系數(shù)的QSAR研究

        2016-03-17 08:27:11楊蕾羅翔王雅張亞南陳景文
        生態(tài)毒理學(xué)報 2016年6期
        關(guān)鍵詞:描述符硅橡膠被動

        楊蕾,羅翔,王雅,張亞南,陳景文

        工業(yè)生態(tài)與環(huán)境工程教育部重點實驗室,大連理工大學(xué)環(huán)境學(xué)院,大連 116024

        有機污染物的被動采樣材料-水分配系數(shù)的QSAR研究

        楊蕾,羅翔,王雅,張亞南,陳景文*

        工業(yè)生態(tài)與環(huán)境工程教育部重點實驗室,大連理工大學(xué)環(huán)境學(xué)院,大連 116024

        被動采樣材料-水分配系數(shù);聚乙烯;聚丙烯酸酯;硅橡膠;定量構(gòu)效關(guān)系

        Received 15 April 2016 accepted 26 May 2016

        近年來,被動采樣技術(shù)在水中痕量有機污染物的監(jiān)測領(lǐng)域得到了廣泛應(yīng)用。半透膜采樣裝置(SPMDs)[1]、聚乙烯被動采樣器(PE)[2]和梯度擴散薄膜技術(shù)(DGT)[3]等被動采樣技術(shù)被用于測定污染物的自由溶解態(tài)的濃度,對于生物有效性評價具有重要意義。在被動采樣過程中,被動采樣器通過吸附作用將水中的污染物富集到采樣材料上,從而得到污染物的時間加權(quán)平均濃度[4]。有機污染物的被動采樣材料-水分配系數(shù)(KPW),是衡量被動采樣器性能和進行優(yōu)化的一個重要指標(biāo)[5]。目前,大部分污染物的KPW值都是通過實驗測定獲得,但實驗方法費時費力[6],難以滿足數(shù)量龐大且與日俱增的有機污染物的監(jiān)測需求,需要發(fā)展預(yù)測方法來獲得有機污染物的KPW值。

        定量構(gòu)效關(guān)系(QSAR)是一種可以有效預(yù)測有機污染物理化性質(zhì)、環(huán)境行為和毒理學(xué)效應(yīng)參數(shù)的方法[7]。許多研究曾采用有機化合物的正辛醇-水分配系數(shù)(logKOW)、正十六烷-水分配系數(shù)(logKHW)或水溶解度(logSW)對其KPW進行預(yù)測[8-16]。Lohmann[8]分別用logKHW和logSW預(yù)測了100種化合物的聚乙烯-水分配系數(shù),相關(guān)系數(shù)(R2)分別為0.86和0.92;用logKOW預(yù)測79種化合物的聚乙烯-水分配系數(shù),R2達到0.91。Hale等[9]分別用logKHW和logKOW預(yù)測了34種化合物的聚乙烯-水分配系數(shù),R2分別為0.85和0.53。另外,有些研究基于線性溶解能關(guān)系(LSER)預(yù)測KPW。Endo等[5]構(gòu)建了79種化合物的聚丙烯酸酯-水分配系數(shù)的LSER模型,R2達到0.97。然而這些模型所采用的預(yù)測變量通常也是需要實驗測定的經(jīng)驗性參數(shù),導(dǎo)致模型的適用范圍有限。本研究針對3類常用的被動采樣材料,即聚乙烯(PE)、聚丙烯酸酯(PA)和硅橡膠(SR),遵循經(jīng)濟合作與發(fā)展組織(OECD)發(fā)布的QSAR構(gòu)建與驗證導(dǎo)則[17],構(gòu)建KPW的QSAR預(yù)測模型,并對模型進行表征和機理解釋。

        1 材料與方法(Materials and methods)

        1.1 數(shù)據(jù)來源

        考慮7種被動采樣材料(聚乙烯、聚丙烯和5種不同的硅橡膠),有機污染物在19 ~ 26 ℃下的KPW實測數(shù)據(jù)均來自文獻報道[2,5-6,8-9,18-27],包括:215種有機物的聚乙烯-水分配系數(shù)(此處為區(qū)分不同采樣材料記為logKPE),數(shù)值范圍為2.3 ~ 7.8;107種有機物的聚丙烯酸酯-水分配系數(shù)(logKPA),數(shù)值范圍為0.0 ~ 6.0;67種有機物的Silastic A型硅橡膠-水分配系數(shù)(logKSR1),數(shù)值范圍為3.0 ~ 7.6;67種有機物的SR batch 0型硅橡膠-水分配系數(shù)(logKSR2),數(shù)值范圍為2.8 ~ 7.4;93種有機物的AlteSil型硅橡膠-水分配系數(shù)(logKSR3),數(shù)值范圍為3.0 ~ 7.8;67種有機物的SR-RED型硅橡膠-水分配系數(shù)(logKSR4),數(shù)值范圍為3.0 ~ 7.6;67種有機物的SR-TF型硅橡膠-水分配系數(shù)(logKSR5),數(shù)值范圍為2.9 ~ 7.4。有機物涵蓋烷烴、烯烴、芳香類、醇類、酮類、酯類、醚類等多種類別。

        將各個數(shù)據(jù)集以4∶1的比例隨機拆分為訓(xùn)練集和驗證集,其中,訓(xùn)練集中的化合物用于構(gòu)建模型,驗證集中的化合物用于模型驗證。

        1.2 分子結(jié)構(gòu)描述符的計算

        采用ChemBio3D Ultra (Version 12.0)軟件中MOPAC 2012模塊的PM7算法[28],對化合物結(jié)構(gòu)進行優(yōu)化并獲得穩(wěn)定構(gòu)型。同時,基于化合物優(yōu)化后的穩(wěn)定結(jié)構(gòu),由Dragon (Version 6.0)軟件計算得到分子結(jié)構(gòu)描述符。

        1.3 模型的建立

        (1)

        (2)

        (3)

        1.4 應(yīng)用域表征

        采用基于標(biāo)準(zhǔn)殘差(δ)對leverage值(hi)的Williams圖對模型的應(yīng)用域進行表征[30]。δ和hi及其預(yù)警值(h*)的計算公式如下:

        (4)

        hi= xiT(XTX)-1xi

        (5)

        h*= 3(k + 1)/n

        (6)

        式中,k為自變量的個數(shù),xi是第i個化合物的描述符矢量,X是描述符矩陣。將|δ| > 3的化合物視為離群點。

        2 結(jié)果(Results)

        2.1 最優(yōu)QSAR模型

        得到7種被動采樣材料的最優(yōu)QSAR模型如下:

        (1) 聚乙烯-水分配系數(shù)(logKPE)

        logKPE= 0.015Vx- 0.034TPSA(NO) + 0.110nBM + 0.137nCl - 0.841

        (2) 聚丙烯酸酯-水分配系數(shù)(logKPA)

        logKPA= 0.010Vx+ 0.154nCl + 0.078nBM - 0.026TPSA(NO)+ 0.940NddsN - 0.778nROH + 0.701

        (3) Silastic A型硅橡膠-水分配系數(shù)(logKSR1)

        logKSR1= 0.024Vx- 0.117Rperim - 0.088

        (4) SR batch 0型硅橡膠-水分配系數(shù)(logKSR2)

        logKSR2= 0.024Vx- 0.139Rperim - 0.088

        (5) AlteSil型硅橡膠-水分配系數(shù)(logKSR3)

        logKSR3= 0.021Vx- 0.824

        (6) SR-RED型硅橡膠-水分配系數(shù)(logKSR4)

        logKSR4= 0.017Vx+ 0.140nCl

        (7) SR-TF型硅橡膠-水分配系數(shù)(logKSR5)

        logKSR5= 0.023Vx- 0.144Rperim + 0.327

        2.2 模型的應(yīng)用域表征

        7個模型的應(yīng)用域表征結(jié)果如圖2所示。訓(xùn)練集和驗證集所有化合物的|δ| < 3,模型無離群點,表明訓(xùn)練集化合物具有很好的代表性。logKPE模型中訓(xùn)練集的4種化合物和logKPA模型中訓(xùn)練集的1種化合物,hi> h*但|δ| < 3,說明這些化合物增加了模型的穩(wěn)定性和準(zhǔn)確性。logKPE模型中驗證集的1種化合物,hi> h*但|δ| < 3,落在描述符域外,但其預(yù)測效果較好,說明模型適用于遠離描述符中心的化合物,進一步推論出模型具有一定的延展能力和外推性。因此,建立的模型可用于預(yù)測應(yīng)用域內(nèi)其他化合物的logKPW值。

        圖1 logKPW的實測值與預(yù)測值擬合關(guān)系圖Fig. 1 Plot of the predicted versus experimental logKPW values

        圖2 logKPW模型的Williams應(yīng)用域表征圖Fig. 2 Williams plot of logKPW models

        表1 本研究構(gòu)建的預(yù)測模型中分子結(jié)構(gòu)描述符的含義Table 1 Definitions of the molecular structural descriptors involved in the developed models

        3 討論(Discussion)

        3.1 機理解釋

        7個模型中,共包含7個描述符(如表1所示),其中,Vx, nBM, nCl和NddsN與logKPW呈正相關(guān);TPSA(NO), nROH和Rperim與logKPW呈負(fù)相關(guān)。在所有模型中,Vx的貢獻最大,是影響KPW的最主要因素。Vx是分子McGowan體積,表征空穴形成作用。由于水分子排列高度有序且凝聚性強[31],在水中形成空穴所需能量遠大于其在被動采樣材料中所需能量,因此化合物分子更容易通過空穴形成作用分配到被動采樣材料相中。具有較大Vx值的化合物,其logKPW值越大。nCl是氯原子的個數(shù)。有研究表明,logKOW與鹵素原子個數(shù)有關(guān)[32],可用化合物的鹵素原子數(shù)表征其疏水作用,nCl值越大的化合物疏水作用越強,因而越容易分配到采樣材料中。TPSA(NO)是由N, O極性貢獻的拓?fù)錁O性表面積,nROH是羥基的個數(shù)。這些親水結(jié)構(gòu)中的氮和氧原子具有孤對電子,易形成氫鍵,增加了與水的氫鍵相互作用[33],使化合物更不易被采樣材料吸附,從而具有更小的logKPW值。Rperim是環(huán)的周長,化合物的環(huán)周長越大,空間位阻越大,越難進入到被動采樣材料相中,從而具有更小的logKPW值。此外,nBM和NddsN表明logKPW還與化合物多重鍵和[-N(=)=] (硝基氮)原子的個數(shù)有關(guān)。

        表2 本研究模型和前人相關(guān)模型的比較Table 2 Comparison of KPW prediction models from the current study and previous studies

        注:N表示無應(yīng)用域表征,Y表示有應(yīng)用域表征;— 表示未報道。

        Note: N, applicability domain was not characterized; Y, applicability domain was characterized; —, unreported.

        3.2 模型比較

        將本研究構(gòu)建的模型與前人的一些代表性模型進行比較,見表2。本研究logKPE, logKPA和logKSR模型與前人模型相比,化合物種類更豐富且數(shù)量更多,而且所采用的分子結(jié)構(gòu)參數(shù)均可通過計算獲得,不依賴于實驗測定。此外,本研究將數(shù)據(jù)集劃分為訓(xùn)練集和驗證集,利用MLR方法建立模型,所有模型均進行了外部驗證和應(yīng)用域的表征,并進行了機理解釋。

        綜上,本研究遵循OECD關(guān)于QSAR模型構(gòu)建和驗證的導(dǎo)則,構(gòu)建了3類共7種被動采樣材料的KPW的QSAR預(yù)測模型。模型具有良好的擬合優(yōu)度、穩(wěn)健性和預(yù)測能力,能夠用于預(yù)測含有>C=C<, -OH, -O-, >C=O, -C=O(O), -C6H5, -NO2, -NH2, -NH-, -X(F, Cl, Br, I)等多種結(jié)構(gòu)官能團的有機污染物的logKPW值,可為快速獲取有機污染物的KPW值以及為被動采樣器的應(yīng)用提供基礎(chǔ)數(shù)據(jù)。

        輔助信息:化合物logKPW實測值、預(yù)測值以及模型中包含的分子結(jié)構(gòu)描述符值,需要者請和通訊作者聯(lián)系。

        [1] Booij K, van Bommel R, van Aken H M, et al. Passive sampling of nonpolar contaminants at three deep-ocean sites [J]. Environmental Pollution, 2014, 195: 101-108

        [2] Sacks V P, Lohmann R. Development and use of polyethylene passive samplers to detect triclosans and alkylphenols in an urban estuary [J]. Environmental Science & Technology, 2011, 45(6): 2270-2277

        [3] Chen C E, Zhang H, Ying G G, et al. Evidence and recommendations to support the use of a novel passive water sampler to quantify antibiotics in wastewaters [J]. Environmental Science & Technology, 2013, 47(23): 13587-13593

        [4] Alvarez D A, Cranor W L, Perkins S D, et al. Chemical and toxicologic assessment of organic contaminants in surface water using passive samplers [J]. Journal of Environmental Quality, 2008, 37(3): 1024-1033

        [5] Endo S, Droge S T J, Goss K U. Polyparameter linear free energy models for polyacrylate fiber-water partition coefficients to evaluate the efficiency of solid-phase microextraction [J]. Analytical Chemistry, 2011, 83(4): 1394-1400

        [6] Choi Y, Cho Y M, Luthy R G. Polyethylene-water partitioning coefficients for parent-and alkylated-polycyclic aromatic hydrocarbons and polychlorinated biphenyls [J]. Environmental Science & Technology, 2013, 47(13): 6943-6950

        [7] 陳景文, 全燮. 環(huán)境化學(xué)[M]. 大連: 大連理工大學(xué)出版社, 2009: 260

        Chen J W, Quan X. Environmental Chemistry [M]. Dalian: Dalian University of Technology Press, 2009: 260 (in Chinese)

        [8] Lohmann R. Critical review of low-density polyethylene’s partitioning and diffusion coefficients for trace organic contaminants and implications for its use as a passive sampler [J]. Environmental Science & Technology, 2011, 46(2): 606-618

        [9] Hale S E, Martin T J, Goss K U, et al. Partitioning of organochlorine pesticides from water to polyethylene passive samplers [J]. Environmental Pollution, 2010, 158(7): 2511-2517

        [10] Josefsson S, Arp H P H, Kleja D B, et al. Determination of polyoxymethylene (POM)-water partition coefficients for oxy-PAHs and PAHs [J]. Chemosphere, 2015, 119: 1268-1274

        [11] Perron M M, Burgess R M, Suuberg E M, et al. Performance of passive samplers for monitoring estuarine water column concentrations: 1. Contaminants of concern [J]. Environmental Toxicology and Chemistry, 2013, 32(10): 2182-2189

        [12] Reitsma P J, Adelman D, Lohmann R. Challenges of using polyethylene passive samplers to determine dissolved concentrations of parent and alkylated PAHs under cold and saline conditions [J]. Environmental Science & Technology, 2013, 47(18): 10429-10437

        [13] Sacks V P, Lohmann R. Freely dissolved PBDEs in water and porewater of an urban estuary [J]. Environmental Pollution, 2012, 162: 287-293

        [14] Kwon J H, Wuethrich T, Mayer P, et al. Dynamic permeation method to determine partition coefficients of highly hydrophobic chemicals between poly (dimethylsiloxane) and water [J]. Analytical Chemistry, 2007, 79(17): 6816-6822

        [15] Booij K, Smedes F, van Weerlee E M, et al. Environmental monitoring of hydrophobic organic contaminants: The case of mussels versus semipermeable membrane devices [J]. Environmental Science & Technology, 2006, 40(12): 3893-3900

        [16] Hawthorne S B, Miller D J, Grabanski C B. Measuring low picogram per liter concentrations of freely dissolved polychlorinated biphenyls in sediment pore water using passive sampling with polyoxymethylene [J]. Analytical Chemistry, 2009, 81(22): 9472-9480

        [17] OECD. Guidance document on the validation of (Quantitative) Structure-Activity Relationships [(Q)SAR] models [R]. Paris: OECD, 2007

        [18] Booij K, Hofmans H E, Fischer C V, et al. Temperature-dependent uptake rates of nonpolar organic compounds by semipermeable membrane devices and low-density polyethylene membranes [J]. Environmental Science & Technology, 2003, 37(2): 361-366

        [19] Adams R G, Lohmann R, Fernandez L A, et al. Polyethylene devices: Passive samplers for measuring dissolved hydrophobic organic compounds in aquatic environments [J]. Environmental Science & Technology, 2007, 41(4): 1317-1323

        [20] Paschke A, Popp P. Solid-phase microextraction fibre-water distribution constants of more hydrophobic organic compounds and their correlations with octanol-water partition coefficients [J]. Journal of Chromatography A, 2003, 999(1): 35-42

        [21] Yates K, Davies I, Webster L, et al. Passive sampling: Partition coefficients for a silicone rubber reference phase [J]. Journal of Environmental Monitoring, 2007, 9(10): 1116-1121

        [22] Smedes F, Geertsma R W, Zande T, et al. Polymer-water partition coefficients of hydrophobic compounds for passive sampling: Application of cosolvent models for validation [J]. Environmental Science & Technology, 2009, 43(18): 7047-7054

        [23] Thompson J M, Hsieh C H, Luthy R G. Modeling uptake of hydrophobic organic contaminants into polyethylene passive samplers [J]. Environmental Science & Technology, 2015, 49(4): 2270-2277

        [24] Hale S E, Tomaszewski J E, Luthy R G, et al. Sorption of dichlorodiphenyltrichloroethane (DDT) and its metabolites by activated carbon in clean water and sediment slurries [J]. Water Research, 2009, 43(17): 4336-4346

        [25] Cornelissen G, Pettersen A, Broman D, et al. Field testing of equilibrium passive samplers to determine freely dissolved native polycyclic aromatic hydrocarbon concentrations [J]. Environmental Toxicology and Chemistry, 2008, 27(3): 499-508

        [26] Fernandez L A, MacFarlane J K, Tcaciuc A P, et al. Measurement of freely dissolved PAH concentrations in sediment beds using passive sampling with low-density polyethylene strips [J]. Environmental Science & Technology, 2009, 43(5): 1430-1436

        [27] Bao L J, You J, Zeng E Y. Sorption of PBDE in low-density polyethylene film: Implications for bioavailability of BDE-209 [J]. Environmental Toxicology and Chemistry, 2011, 30(8): 1731-1738

        [28] Jhin C, Hwang K T. Prediction of radical scavenging activities of anthocyanins applying adaptive neuro-fuzzy inference system (ANFIS) with quantum chemical descriptors [J]. International Journal of Molecular Sciences, 2014, 15(8): 14715-14727

        [29] Noru?is M J. SPSS Inc. SPSS 7.5 Guide to Data Analysis [M]. Upper Saddle River, New Jersey: Prentice Hall, 1997: 458

        [30] Gramatica P. Principles of QSAR models validation: Internal and external [J]. QSAR & Combinatorial Science, 2007, 26(5): 694-701

        [31] Vitha M, Carr P W. The chemical interpretation and practice of linear solvation energy relationships in chromatography [J]. Journal of Chromatography A, 2006, 1126(1): 143-194

        [32] Li F, Xie Q, Li X H, et al. Hormone activity of hydroxylated polybrominated diphenyl ethers on human thyroid receptor-β: In vitro and in silico investigations [J]. Environmental Health Perspectives, 2010, 118(5): 602-606

        [33] Schüürmann G, Ebert R U, Kühne R. Prediction of the sorption of organic compounds into soil organic matter from molecular structure [J]. Environmental Science & Technology, 2006, 40(22): 7005-7011

        [34] Fernández A, Rallo R, Giralt F. Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability [J]. Environmental Research, 2015, 142: 161-168

        [35] Abraham M H, McGowan J C. The use of characteristic volumes to measure cavity terms in reversed phase liquid chromatography [J]. Chromatographia, 1987, 23(4): 243-246

        [36] Wang Y, Chen J W, Yang X H, et al. In silico model for predicting soil organic carbon normalized sorption coefficient (KOC) of organic chemicals [J]. Chemosphere, 2015, 119: 438-444

        [37] Tang W Z, Wang F. Quantitative structure activity relationship (QSAR) of chlorine effects on ELUMOof disinfection by-product: Chlorinated alkanes [J]. Chemosphere, 2010, 78(7): 914-921

        [38] Edraki N, Hemmateenejad B, Miri R, et al. QSAR study of phenoxypyrimidine derivatives as potent inhibitors of p38 kinase using different chemometric tools [J]. Chemical Biology & Drug Design, 2007, 70(6): 530-539

        [39] Butina D. Performance of Kier-hall E-state descriptors in quantitative structure activity relationship (QSAR) studies of multifunctional molecules [J]. Molecules, 2004, 9(12): 1004-1009

        [40] Patel J R, Prajapati L M. Predictive QSAR modeling on tetrahydropyrimidine-2-one derivatives as HIV-1 protease enzyme inhibitors [J]. Medicinal Chemistry Research, 2013, 22(6): 2795-2801

        QSAR Models for Predicting Partition Coefficients of Organic Pollutants between Passive Sampling Materials and Water

        Yang Lei, Luo Xiang, Wang Ya, Zhang Ya’nan, Chen Jingwen*

        Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China

        passive sampling materials-water partition coefficients; polyethylene; polyacrylate; silicone rubber; quantitative structure-activity relationships

        國家自然科學(xué)基金(21325729; 21661142001)

        楊蕾(1990-),女,碩士,研究方向為污染生態(tài)化學(xué),E-mail: yanglei_dlut@mail.dlut.edu.cn;

        *通訊作者(Corresponding author), E-mail: jwchen@dlut.edu.cn

        10.7524/AJE.1673-5897.20160415001

        2016-04-15 錄用日期:2016-05-26

        1673-5897(2016)6-053-08

        X171.5

        A

        陳景文(1969-),男,博士,教授,研究方向為污染生態(tài)化學(xué)、污染控制化學(xué)和環(huán)境生態(tài)技術(shù)。

        楊蕾, 羅翔, 王雅, 等. 有機污染物的被動采樣材料-水分配系數(shù)的QSAR研究[J]. 生態(tài)毒理學(xué)報,2016, 11(6): 53-60

        Yang L, Luo X, Wang Y, et al. QSAR models for predicting partition coefficients of organic pollutants between passive sampling materials and water [J]. Asian Journal of Ecotoxicology, 2016, 11(6): 53-60 (in Chinese)

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