基于形态修正的描述符构建可电离化合物对大型溞急性毒性的QSAR模型
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摘要
在环境水体中,可电离有机化合物(IOCs)可解离为分子和离子形态。研究表明,IOCs离子形态的环境行为、毒性效应等都与其分子形态存在较大差异,因而在研究IOCs环境行为、毒性效应时不应忽略离子化的影响。在构建IOCs相关预测模型时如何表征离子化的影响是当前研究的重要内容之一。探讨了采用基于形态修正的描述符构建IOCs水生毒性预测模型的可行性。具体而言,采用逐步多元线性回归(MLR)方法,构建了可预测63种取代酚、取代苯甲酸和取代苯胺等IOCs对大型溞急性毒性的定量结构-活性关系(QSAR)模型。与仅采用分子形态描述符的模型相比,使用基于形态修正描述符的模型决定系数(R2)、去一法交叉验证系数(Q2LOO)、外部验证系数(Q2EXT)等参数从0.622～0.705提高到了0.840～0.875,表明基于形态修正描述符的模型具有更好的拟合优度、稳健性和预测能力。因此,在将来的研究中,可采用基于形态修正的描述符构建IOCs水生毒性效应预测模型。
Ionogenic organic chemicals(IOCs) may ionize to form anion and/or cation in aquatic environment. It had been elucidated that the environmental behavior,toxic effects of ionic form differ greatly from that of neutral form. Thus,ionization is nonnegligible in performing the research of the environmental behavior,toxic effects of IOCs. To date,how to characterize the ionization is one of the critical issues in developing the predictive models for IOCs. Here,the feasibility of using chemical form adjusted descriptors as predictive variable to derive model for the endpoint of aquatic toxicity was studied. In this regard,the acute toxicity data of 63 substituted phenols,anilines and benzoic acids to Daphnia magna were collected firstly. Then,the quantitative structure-activity relationship(QSAR) model was developed by stepwise multiple linear regressions(MLR) analysis. The modeling results indicated that the values of coefficient determination(R2),cross validated Q2 leave-one-out(Q2 LOO),external validation coefficient(Q2 EXT) for the QSAR model based on chemical form adjusted descriptors was significantly improved from 0.622-0.705 to 0.840-0.875,compared with that of the model constructed from neutral form descriptors only.The results indicated that the QSAR model based on chemical form adjusted descriptors had high goodness-of-fit,robustness,and predictive ability. Thus,the predictive models of IOCs for aquatic toxicity could be developed by employing chemical form adjusted descriptors in the future QSAR modeling.
Ionogenic organic chemicals(IOCs) may ionize to form anion and/or cation in aquatic environment. It had been elucidated that the environmental behavior,toxic effects of ionic form differ greatly from that of neutral form. Thus,ionization is nonnegligible in performing the research of the environmental behavior,toxic effects of IOCs. To date,how to characterize the ionization is one of the critical issues in developing the predictive models for IOCs. Here,the feasibility of using chemical form adjusted descriptors as predictive variable to derive model for the endpoint of aquatic toxicity was studied. In this regard,the acute toxicity data of 63 substituted phenols,anilines and benzoic acids to Daphnia magna were collected firstly. Then,the quantitative structure-activity relationship(QSAR) model was developed by stepwise multiple linear regressions(MLR) analysis. The modeling results indicated that the values of coefficient determination(R2),cross validated Q2 leave-one-out(Q2 LOO),external validation coefficient(Q2 EXT) for the QSAR model based on chemical form adjusted descriptors was significantly improved from 0.622-0.705 to 0.840-0.875,compared with that of the model constructed from neutral form descriptors only.The results indicated that the QSAR model based on chemical form adjusted descriptors had high goodness-of-fit,robustness,and predictive ability. Thus,the predictive models of IOCs for aquatic toxicity could be developed by employing chemical form adjusted descriptors in the future QSAR modeling.
引文
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[31] Liu H H,Wei M B,Yang X H,et al. Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water[J]. Science of the Total Environment,2017,574:1371-1378
[32] Karelson M,Lobanov V S,Katritzky A R. Quantumchemical descriptors in QSAR/QSPR studies[J].Chemical Reviews,1996,96(3):1027-1044
[33] Murray J S,Brinck T,Lane P,et al. Statistically-based interaction indices derived from molecular surface electrostatic potentials:A general interaction properties function(GIPF)[J]. Journal of Molecular Structure:THEOCHEM,1994,307:55-64
[2] Bittermann K,Spycher S,Goss K U. Comparison of different models predicting the phospholipid-membrane water partition coefficients of charged compounds[J].Chemosphere,2016,144:382-391
[3] Manallack D T. The pK(a)distribution of drugs:Application to drug discovery[J]. Perspectives in Medicinal Chemistry,2007,1:25-38
[4] Boxall A B,Rudd M A,Brooks B W,et al. Pharmaceuticals and personal care products in the environment:What are the big questions?[J]. Environmental Health Perspectives,2012,120(9):1221-1229
[5] Schwarzenbach R P,Escher B I,Fenner K,et al. The challenge of micropollutants in aquatic systems[J]. Science,2006,313(5790):1072-1077
[6] Rendal C,Kusk K O,Trapp S. Optimal choice of p H for toxicity and bioaccumulation studies of ionizing organic chemicals[J]. Environmental Toxicology and Chemistry,2011,30(11):2395-2406
[7] Yang X H,Xie H B,Chen J W,et al. Anionic phenolic compounds bind stronger with transthyretin than their neutral forms:Nonnegligible mechanisms in virtual screening of endocrine disrupting chemicals[J]. Chemical Research in Toxicology,2013,26(9):1340-1347
[8] Yang X H,Lyakurwa F,Xie H B,et al. Different binding mechanisms of neutral and anionic poly-/perfluorinated chemicals to human transthyretin revealed by in silico models[J]. Chemosphere,2017,182:574-583
[9] Card M L,Gomez-Alvarez V,Lee W H,et al. History of EPI SuiteTMand future perspectives on chemical property estimation in US Toxic Substances Control Act new chemical risk assessments[J]. Environmental Science:Processes&Impacts,2017,19(3):203-212
[10] Kavlock R,Dix D. Computational toxicology as implemented by the U.S. EPA:Providing high throughput decision support tools for screening and assessing chemical exposure,hazard and risk[J]. Journal of Toxicology and Environmental Health,Part B,2010,13(2-4):197-217
[11]王中钰，陈景文，乔显亮，等．面向化学品风险评价的计算(预测)毒理学[J]．中国科学:化学，2016,46(2):222-240Wang Z Y,Chen J W,Qiao X L,et al. Computational toxicology:Oriented for chemicals risk assessment[J].Scientia Sinica Chimica,2016,46(2):222-240(in Chinese)
[12] Tang W,Chen J W,Wang Z Y,et al. Deep learning for predicting toxicity of chemicals:A mini review[J].Journal of Environmental Science and Health. Part C,2018,36(4):252-271
[13] Kah M,Brown C D. Log D:Lipophilicity for ionisable compounds[J]. Chemosphere,2008,72(10):1401-1408
[14] Zhao Y H,Yuan X,Su L M,et al. Classification of toxicity of phenols to tetrahymena pyriformis and subsequent derivation of QSARs from hydrophobic,ionization and electronic parameters[J]. Chemosphere,2009,75(7):866-871
[15] Henneberger L,Goss K U,Endo S. Partitioning of organic ions to muscle protein:Experimental data,modeling,and implications for in vivo distribution of organic ions[J]. Environmental Science&Technology,2016,50(13):7029-7036
[16] Yang X H,Ou W,Xi Y,et al. Emerging polar phenolic disinfection byproducts are high-affinity human transthyretin disruptors:An in vitro and in silico study[J]. Environmental Science&Technology,2019,53(12):7019-7028
[17] Ding F,Yang X H,Chen G S,et al. Development of bovine serum albumin-water partition coefficients predictive models for ionogenic organic chemicals based on chemical form adjusted descriptors[J]. Ecotoxicology and Environmental Safety,2017,144:131-137
[18] Ou W,Liu H H,He J Y,et al. Development of chicken and fish muscle protein—Water partition coefficients predictive models for ionogenic and neutral organic chemicals[J]. Ecotoxicology and Environmental Safety,2018,157:128-133
[19] Lin S Y,Yang X H,Liu H H. Development of liposome/water partition coefficients predictive models for neutral and ionogenic organic chemicals[J]. Ecotoxicology and Environmental Safety,2019,179:40-49
[20]包信，张栩嘉，赵元慧．可离子化有机污染物对大型溞的毒性及QSAR研究[J]．生态毒理学报，2016,11(2):720-731Bao X,Zhang X J,Zhao Y H. The toxicities of ionized organic compounds to Daphnia magna and QSAR study[J]. Asian Journal of Ecotoxicology,2016,11(2):720-731(in Chinese)
[21] Frisch M J,Trucks G W,Schlegel H B,et al. Gaussian16,Revision A.03[CP]. Wallingford,CT:Gaussian,Inc.,2016
[22] OECD. Guidance Document on the Validation of(Quantitative)Structure Activity Relationships[(Q)SAR].Models Environment Health and Safety Publications Series on Testing and Assessment No. 69[R]. Paris:OECD,2007
[23] Schüürmann G,Ebert R U,Chen J W,et al. External validation and prediction employing the predictive squared correlation coefficient test set activity mean vs training set activity mean[J]. Journal of Chemical Information and Modeling,2008,48(11):2140-2145
[24] Chirico N,Gramatica P. Real external predictivity of QSAR models. Part 2. New intercomparable thresholds for different validation criteria and the need for scatter plot inspection[J]. Journal of Chemical Information and Modeling,2012,52(8):2044-2058
[25] Liu H H,Yang X H,Lu R. Development of classification model and QSAR model for predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding globulin[J]. Chemosphere,2016,156:1-7
[26] Eriksson L,Jaworska J,Worth A P,et al. Methods for reliability and uncertainty assessment and for applicability evaluations of classification-and regression-based QSARs[J]. Environmental Health Perspectives,2003,111(10):1361-1375
[27]杨先海，刘会会，杨倩，等．双酚A类似物雌激素干扰效应的定量结构-活性关系模型[J]．生态毒理学报，2016,11(4):69-78Yang X H,Liu H H,Yang Q,et al. Predicting estrogenic activity of bisphenols using quantitative structure-activity relationships[J]. Asian Journal of Ecotoxicology,2016,11(4):69-78(in Chinese)
[28] Cronin M T.(Q)SARs to predict environmental toxicities:Current status and future needs[J]. Environmental Science:Processes&Impacts,2017,19(3):213-220
[29] Wang Y,Chen J W,Yang X H,et al. In silico model for predicting soil organic carbon normalized sorption coefficient(K(OC))of organic chemicals[J]. Chemosphere,2015,119:438-444
[30]刘羽晨，乔显亮．水生生物急性毒性QSAR模型研究进展[J]．生态毒理学报，2015,10(2):26-35Liu Y C,Qiao X L. Progress in quantitative structure-activity relationship models for acute aquatic toxicity[J].Asian Journal of Ecotoxicology,2015,10(2):26-35(in Chinese)
[31] Liu H H,Wei M B,Yang X H,et al. Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water[J]. Science of the Total Environment,2017,574:1371-1378
[32] Karelson M,Lobanov V S,Katritzky A R. Quantumchemical descriptors in QSAR/QSPR studies[J].Chemical Reviews,1996,96(3):1027-1044
[33] Murray J S,Brinck T,Lane P,et al. Statistically-based interaction indices derived from molecular surface electrostatic potentials:A general interaction properties function(GIPF)[J]. Journal of Molecular Structure:THEOCHEM,1994,307:55-64