可用于表征可电离化合物离子化影响的描述符研究进展

杨先海, 刘会会, 王连军. 可用于表征可电离化合物离子化影响的描述符研究进展[J]. 生态毒理学报, 2021, 16(5): 1-13. doi: 10.7524/AJE.1673-5897.20201104001
引用本文: 杨先海, 刘会会, 王连军. 可用于表征可电离化合物离子化影响的描述符研究进展[J]. 生态毒理学报, 2021, 16(5): 1-13. doi: 10.7524/AJE.1673-5897.20201104001
Yang Xianhai, Liu Huihui, Wang Lianjun. Progress in Descriptors Used to Correct Influence of Ionization for Ionizable Organic Chemicals[J]. Asian journal of ecotoxicology, 2021, 16(5): 1-13. doi: 10.7524/AJE.1673-5897.20201104001
Citation: Yang Xianhai, Liu Huihui, Wang Lianjun. Progress in Descriptors Used to Correct Influence of Ionization for Ionizable Organic Chemicals[J]. Asian journal of ecotoxicology, 2021, 16(5): 1-13. doi: 10.7524/AJE.1673-5897.20201104001

可用于表征可电离化合物离子化影响的描述符研究进展

    作者简介: 杨先海(1985-),男,博士,研究方向为计算毒理学,E-mail:xhyang@njust.edu.cn
    通讯作者: 刘会会, E-mail: hhliu@njust.edu.cn
  • 基金项目:

    中国博士后科学基金资助项目(2020T130301,2020M671502);江苏省博士后科研资助计划项目(2020Z288)

  • 中图分类号: X171.5

Progress in Descriptors Used to Correct Influence of Ionization for Ionizable Organic Chemicals

    Corresponding author: Liu Huihui, hhliu@njust.edu.cn
  • Fund Project:
  • 摘要: 在人为有意生产的化学品或无意识产生的化学品中,可电离有机化合物(IOCs)均占有较大比重。在环境水体、生理或实验pH条件下,IOCs可解离为分子和离子形态。研究表明,IOCs的分子和离子形态均对其表观物理化学、环境归趋和行为、生态和健康毒性效应参数具有不可忽视的影响,因而在开展IOCs相关实验或理论研究时不应忽略离子化的影响。在构建IOCs相关预测模型时,核心是如何表征离子化的影响。本文从描述符入手,总结了可用于表征IOCs离子化影响的4类描述符,即酸碱解离常数(pKa)及其衍生参数(分子态和离子态的比例分数(δ分子和δ离子))、考虑离子化影响的分配系数包括正辛醇-水分布系数(logDOW(pH))和进行形态修正的脂质体-水分配系数(logDlip/w(pH))、考虑离子参数的多参数线性自由能关系(离子描述符J+和J-)、基于形态修正的量化参数,并展望了表征IOCs离子化影响的未来研究重点。
  • 加载中
  • Trapp S, Franco A, MacKay D. Activity-based concept for transport and partitioning of ionizing organics[J]. Environmental Science & Technology, 2010, 44(16):6123-6129
    Armitage J M, Arnot J A, Wania F, et al. Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish[J]. Environmental Toxicology and Chemistry, 2013, 32(1):115-128
    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
    Manallack D T, Prankerd R J, Nassta G C, et al. A chemogenomic analysis of ionization constants-implications for drug discovery[J]. ChemMedChem, 2013, 8(2):242-255
    Karlsson M V, Carter L J, Agatz A, et al. Novel approach for characterizing pH-dependent uptake of ionizable chemicals in aquatic organisms[J]. Environmental Science & Technology, 2017, 51(12):6965-6971
    Liu X Y, Chen L, Yang M T, et al. The occurrence, characteristics, transformation and control of aromatic disinfection by-products:A review[J]. Water Research, 2020, 184:116076
    European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC). Environmental exposure assessment of ionizable organic compounds. Technical Report No. 123[R]. Brussels:ECETOC, 2013
    席越, 杨先海, 张红雨, 等. 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[J]. 生态毒理学报, 2019, 14(4):183-191

    Xi Y, Yang X H, Zhang H Y, et al. Development of acute toxicity of Daphnia magna QSAR models for ionogenic organic chemicals based on chemical form adjusted descriptors[J]. Asian Journal of Ecotoxicology, 2019, 14(4):183-191(in Chinese)

    United States Environmental Protection Agency (US EPA). Estimation Programs Interface SuiteTM for Microsoft® Windows, V 4.10. Washington DC:US EPA, 2012
    Bittner L, Klüver N, Henneberger L, et al. Combined ion-trapping and mass balance models to describe the pH-dependent uptake and toxicity of acidic and basic pharmaceuticals in zebrafish embryos (Danio rerio)[J]. Environmental Science & Technology, 2019, 53(13):7877-7886
    Henneberger L, Goss K U. Environmental Sorption Behavior of Ionic and Ionizable Organic Chemicals[J]. Reviews of Environmental Contamination and Toxicology, 2019, 253:1-21
    Manallack D T, Prankerd R J, Yuriev E, et al. The significance of acid/base properties in drug discovery[J]. Chemical Society Reviews, 2013, 42(2):485-496
    Strope C L, Mansouri K, Clewell H J Ⅲ, et al. High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling[J]. Science of the Total Environment, 2018, 615:150-160
    徐世积, 何影, 李思齐, 等. 环境中可电离有机化合物生物有效性研究进展[J]. 生态与农村环境学报, 2017, 33(5):385-395

    Xu S J, He Y, Li S Q, et al. Review of researches on bioavailability of ionizable organic compounds in environment[J]. Journal of Ecology and Rural Environment, 2017, 33(5):385-395(in Chinese)

    Franco A, Fu W J, Trapp S. Influence of soil pH on the sorption of ionizable chemicals:Modeling advances[J]. Environmental Toxicology and Chemistry, 2009, 28(3):458-464
    Tülp H C, Fenner K, Schwarzenbach R P, et al. pH-dependent sorption of acidic organic chemicals to soil organic matter[J]. Environmental Science & Technology, 2009, 43(24):9189-9195
    Endo S, Goss K U. Serum albumin binding of structurally diverse neutral organic compounds:Data and models[J]. Chemical Research in Toxicology, 2011, 24(12):2293-2301
    Henneberger L, Goss K U, Endo S. Equilibrium sorption of structurally diverse organic ions to bovine serum albumin[J]. Environmental Science & Technology, 2016, 50(10):5119-5126
    Endo S, Bauerfeind J, Goss K U. Partitioning of neutral organic compounds to structural proteins[J]. Environmental Science & Technology, 2012, 46(22):12697-12703
    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
    Droge S T J, Hermens J L M, Gutsell S, et al. Predicting the phospholipophilicity of monoprotic positively charged amines[J]. Environmental Science Processes & Impacts, 2017, 19(3):307-323
    Neuwoehner J, Escher B I. The pH-dependent toxicity of basic pharmaceuticals in the green algae Scenedesmus vacuolatus can be explained with a toxicokinetic ion-trapping model[J]. Aquatic Toxicology, 2011, 101(1):266-275
    Endo S, Escher B I, Goss K U. Capacities of membrane lipids to accumulate neutral organic chemicals[J]. Environmental Science & Technology, 2011, 45(14):5912-5921
    Wei X X, Chen J W, Xie Q, et al. Distinct photolytic mechanisms and products for different dissociation species of ciprofloxacin[J]. Environmental Science & Technology, 2013, 47(9):4284-4290
    Xie Q, Chen J W, Zhao H X, et al. Different photolysis kinetics and photooxidation reactivities of neutral and anionic hydroxylated polybrominated diphenyl ethers[J]. Chemosphere, 2013, 90(2):188-194
    Luo X, Wei X X, Chen J W, et al. Rate constants of hydroxyl radicals reaction with different dissociation species of fluoroquinolones and sulfonamides:Combined experimental and QSAR studies[J]. Water Research, 2019, 166:115083
    Xie J, Meng W N, Wu D Y, et al. Removal of organic pollutants by surfactant modified zeolite:Comparison between ionizable phenolic compounds and non-ionizable organic compounds[J]. Journal of Hazardous Materials, 2012, 231-232:57-63
    Trapp S. Bioaccumulation of Polar and Ionizable Compounds in Plants[M]//Ecotoxicology Modeling. Boston, MA:Springer US, 2009:299-353
    Cronin M T D, Zhao Y H, Yu R L. pH-Dependence and QSAR analysis of the toxicity of phenols and anilines to Daphnia magna[J]. Environmental Toxicology, 2000, 15(2):140-148
    He Q, Wang X H, Sun P, et al. Acute and chronic toxicity of tetrabromobisphenol A to three aquatic species under different pH conditions[J]. Aquatic Toxicology, 2015, 164:145-154
    Kamaya Y, Fukaya Y, Suzuki K. Acute toxicity of benzoic acids to the crustacean Daphnia magna[J]. Chemosphere, 2005, 59(2):255-261
    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
    Crisan M E, Bourosh P, Maffei M E, et al. Synthesis, crystal structure and biological activity of 2-hydroxyethylammonium salt of p-aminobenzoic acid[J]. PLoS One, 2014, 9(7):e101892
    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
    Nakamura Y, Yamamoto H, Sekizawa J, et al. The effects of pH on fluoxetine in Japanese medaka (Oryzias latipes):Acute toxicity in fish larvae and bioaccumulation in juvenile fish[J]. Chemosphere, 2008, 70(5):865-873
    Rendal C, Kusk K O, Trapp S. Optimal choice of pH for toxicity and bioaccumulation studies of ionizing organic chemicals[J]. Environmental Toxicology and Chemistry, 2011, 30(11):2395-2406
    Xing L Q, Liu H L, Giesy J P, et al. pH-dependent aquatic criteria for 2,4-dichlorophenol, 2,4,6-trichlorophenol and pentachlorophenol[J]. Science of the Total Environment, 2012, 441:125-131
    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
    Goss K U, Bittermann K, Henneberger L, et al. Equilibrium biopartitioning of organic anions-A case study for humans and fish[J]. Chemosphere, 2018, 199:174-181
    Seward J R, Schultz T W. QSAR analyses of the toxicity of aliphatic carboxylic acids and salts to Tetrahymena pyriformis[J]. SAR and QSAR in Environmental Research, 1999, 10(6):557-567
    Bostr m M L, Berglund O. Influence of pH-dependent aquatic toxicity of ionizable pharmaceuticals on risk assessments over environmental pH ranges[J]. Water Research, 2015, 72:154-161
    Zhao Y H, Ji G D, Cronin M T D, et al. QSAR study of the toxicity of benzoic acids to Vibrio fischeri, Daphnia magna and carp[J]. Science of the Total Environment, 1998, 216(3):205-215
    Card M L, Gomez-Alvarez V, Lee W H, et al. History of EPI SuiteTM and 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
    Judson R, Richard A, Dix D J, et al. The toxicity data landscape for environmental chemicals[J]. Environmental Health Perspectives, 2009, 117(5):685-695
    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):222-240

    Wang 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)

    Chen C C, Kuo D T F. Bioconcentration model for non-ionic, polar, and ionizable organic compounds in amphipod[J]. Environmental Toxicology and Chemistry, 2018, 37(5):1378-1386
    Worth A P, Bassan A, De Bruijn J, et al. The role of the European Chemicals Bureau in promoting the regulatory use of (Q)SAR methods[J]. SAR and QSAR in Environmental Research, 2007, 18(1-2):111-125
    Cronin M T D. (Q)SARs to predict environmental toxicities:Current status and future needs[J]. Environmental Science Processes & Impacts, 2017, 19(3):213-220
    Cronin M T D, Richarz A N, Schultz T W. Identification and description of the uncertainty, variability, bias and influence in quantitative structure-activity relationships (QSARs) for toxicity prediction[J]. Regulatory Toxicology and Pharmacology, 2019, 106:90-104
    Wang Z Y, Chen J W. Background, Tasks, Modeling Methods, and Challenges for Computational Toxicology[M]//Challenges and Advances in Computational Chemistry and Physics. Cham:Springer International Publishing, 2019:15-36
    European Commission. Technicalguidance document on risk assessment in support of Commission Directive 93/67/EEC on risk assessment for new notified substances, Commission Regulation (EC) No. 1488/94 on risk assessment for existing substances, Directive 98/8/EC of the European Parliament and of the Council concerning the placing of biocidal products on the market Part Ⅱ.[S]. Brussel:European Communities, 2003
    Trapp S, Schwartz S. Proposals to overcome limitations in the EU chemical risk assessment scheme[J]. Chemosphere, 2000, 41(7):965-971
    Huchthausen J, Mühlenbrink M, K nig M, et al. Experimental exposure assessment of ionizable organic chemicals in in vitro cell-based bioassays[J]. Chemical Research in Toxicology, 2020, 33(7):1845-1854
    Aalizadeh R, von der Ohe P C, Thomaidis N S. Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by Ant Colony Optimization-Support Vector Machine QSTR models[J]. Environmental Science:Processes & Impacts, 2017, 19(3):438-448
    Vitale C M, Di Guardo A. A review of the predictive models estimating association of neutral and ionizable organic chemicals with dissolved organic carbon[J]. Science of the Total Environment, 2019, 666:1022-1032
    Katritzky A R, Kuanar M, Slavov S, et al. Quantitative correlation of physical and chemical properties with chemical structure:Utility for prediction[J]. Chemical Reviews, 2010, 110(10):5714-5789
    Todeschini R, Consonni V. Descriptors from Molecular Geometry[M]//Handbook of Chemoinformatics. Weinheim, Germany:Wiley-VCH Verlag GmbH, 2008:1004-1033
    Mansouri K, Cariello N F, Korotcov A, et al. Open-source QSAR models for pKa prediction using multiple machine learning approaches[J]. Journal of Cheminformatics, 2019, 11(1):1-20
    Zvinavashe E, Murk A J, Rietjens I M C M. Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity[J]. Chemical Research in Toxicology, 2008, 21(12):2229-2236
    Schaffer M, Licha T. A guideline for the identification of environmentally relevant, ionizable organic molecule species[J]. Chemosphere, 2014, 103:12-25
    Fujita T. The analysis of physiological activity of substituted phenols with substituent Constants1[J]. Journal of Medicinal Chemistry, 1966, 9(6):797-803
    Lee Y G, Hwang S H, Kim S D. Predicting the toxicity of substituted phenols to aquatic species and its changes in the stream and effluent waters[J]. Archives of Environmental Contamination and Toxicology, 2006, 50(2):213-219
    Ramos-Nino M E, Clifford M N, Adams M R. Quantitative structure activity relationship for the effect of benzoic acids, cinnamic acids and benzaldehydes on Listeria monocytogenes[J]. The Journal of Applied Bacteriology, 1996, 80(3):303-310
    Nolte T M, Ragas A M J. A review of quantitative structure-property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps[J]. Environmental Science Processes & Impacts, 2017, 19(3):221-246
    Wayne Schultz T. The use of the ionization constant (pKa) in selecting models of toxicity in phenols[J]. Ecotoxicology and Environmental Safety, 1987, 14(2):178-183
    Schultz T W, Lin D T, Wesley S K. QSARs for monosubstituted phenols and the polar narcosis mechanism of toxicity[J]. Quality Assurance, 1992, 1(2):132-143
    Schultz T W, Bearden A P, Jaworska J S. A novel QSAR approach for estimating toxicity of phenols[J]. SAR and QSAR in Environmental Research, 1996, 5(2):99-112
    Vierke L, Berger U, Cousins I T. Estimation of the acid dissociation constant of perfluoroalkyl carboxylic acids through an experimental investigation of their water-to-air transport[J]. Environmental Science & Technology, 2013, 47(19):11032-11039
    Goss K U. The pKa values of PFOA and other highly fluorinated carboxylic acids[J]. Environmental Science & Technology, 2008, 42(2):456-458
    Qin W C, Su L M, Zhang X J, et al. Toxicity of organic pollutants to seven aquatic organisms:Effect of polarity and ionization[J]. SAR and QSAR in Environmental Research, 2010, 21(5-6):389-401
    Zhao Y H, Zhang X J, Wen Y, et al. Toxicity of organic chemicals to Tetrahymena pyriformis:Effect of polarity and ionization on toxicity[J]. Chemosphere, 2010, 79(1):72-77
    Su L, Fu L, He J, et al. Comparison of Tetrahymena pyriformis toxicity based on hydrophobicity, polarity, ionization and reactivity of class-based compounds[J]. SAR and QSAR in Environmental Research, 2012, 23(5-6):537-552
    Franco A, Trapp S. Estimation of the soil-water partition coefficient normalized to organic carbon for ionizable organic chemicals[J]. Environmental Toxicology and Chemistry, 2008, 27(10):1995-2004
    Vitale C M, Di Guardo A. Predicting dissolved organic carbon partition and distribution coefficients of neutral and ionizable organic chemicals[J]. Science of the Total Environment, 2019, 658:1056-1063
    Hansch C, Maloney P P, Fujita T, et al. Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients[J]. Nature, 1962, 194(4824):178-180
    范莱文, 韦梅尔. 化学品风险评估[M]. 北京:化学工业出版社, 2010:280-350
    Scherrer R A, Howard S M. Use of distribution coefficients in quantitative structure-activity relations[J]. Journal of Medicinal Chemistry, 1977, 20(1):53-58
    Kah M, Brown C D. LogD:lipophilicity for ionisable compounds[J]. Chemosphere, 2008, 72(10):1401-1408
    Abbasitabar F, Zare-Shahabadi V. In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach[J]. Chemosphere, 2017, 172:249-259
    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
    Kah M, Brown C D. Prediction of the adsorption of ionizable pesticides in soils[J]. Journal of Agricultural and Food Chemistry, 2007, 55(6):2312-2322
    Wang Y Q, Liu H H, Yang X H, et al. Aquatic toxicity and aquatic ecological risk assessment of wastewater-derived halogenated phenolic disinfection byproducts[J]. Science of the Total Environment, 2021, Doi:10.1016/j.scitotenv.2021.151089
    Bayliss M K, Butler J, Feldman P L, et al. Quality guidelines for oral drug candidates:Dose, solubility and lipophilicity[J]. Drug Discovery Today, 2016, 21(10):1719-1727
    Li J J, Zhang X J, Wang X H, et al. Discrimination of excess toxicity from baseline level for ionizable compounds:Effect of pH[J]. Chemosphere, 2016, 147:382-388
    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
    Escher B I, Schwarzenbach R P, Westall J C. Evaluation of liposome-water partitioning of organic acids and bases. 2. Comparison of experimental determination methods[J]. Environmental Science & Technology, 2000, 34(18):3962-3968
    Baumer A, Bittermann K, Klüver N, et al. Baseline toxicity and ion-trapping models to describe the pH-dependence of bacterial toxicity of pharmaceuticals[J]. Environmental Science Processes & Impacts, 2017, 19(7):901-916
    Schweigert N, Hunziker R W, Escher B I, et al. Acute toxicity of (chloro-) catechols and (chloro-) catechol-copper combinations in Escherichia coli corresponds to their membrane toxicity in vitro[J]. Environmental Toxicology and Chemistry, 2001, 20(2):239-247
    Klüver N, Bittermann K, Escher B I. QSAR for baseline toxicity and classification of specific modes of action of ionizable organic chemicals in the zebrafish embryo toxicity test[J]. Aquatic Toxicology, 2019, 207:110-119
    Escher B I, Baumer A, Bittermann K, et al. General baseline toxicity QSAR for nonpolar, polar and ionisable chemicals and their mixtures in the bioluminescence inhibition assay with Aliivibrio fischeri[J]. Environmental Science Processes & Impacts, 2017, 19(3):414-428
    Ng C A, Hungerbühler K. Bioaccumulation of perfluorinated alkyl acids:Observations and models[J]. Environmental Science & Technology, 2014, 48(9):4637-4648
    Zhang K, Wiseman S, Giesy J P, et al. Bioconcentration of dissolved organic compounds from oil sands process-affected water by medaka (Oryzias latipes):Importance of partitioning to phospholipids[J]. Environmental Science & Technology, 2016, 50(12):6574-6582
    Timmer N, Droge S T J. Sorption of cationic surfactants to artificial cell membranes:Comparing phospholipid bilayers with monolayer coatings and molecular simulations[J]. Environmental Science & Technology, 2017, 51(5):2890-2898
    Endo S, Goss K U. Applications of polyparameter linear free energy relationships in environmental chemistry[J]. Environmental Science & Technology, 2014, 48(21):12477-12491
    Abraham M H. Scales of solute hydrogen-bonding:Their construction and application to physicochemical and biochemical processes[J]. Chemical Society Reviews, 1993, 22(2):73
    Abraham M H, Ibrahim A, Zissimos A M. Determination of sets of solute descriptors from chromatographic measurements[J]. Journal of Chromatography A, 2004, 1037(1-2):29-47
    Goss K U. Predicting the equilibrium partitioning of organic compounds using just one linear solvation energy relationship (LSER)[J]. Fluid Phase Equilibria, 2005, 233(1):19-22
    Abraham M H, Zhao Y H. Determination of solvation descriptors for ionic species:Hydrogen bond acidity and basicity[J]. The Journal of Organic Chemistry, 2004, 69(14):4677-4685
    Abraham M H, Zhao Y H. Characterisation of the water/o-nitrophenyl octyl ether system in terms of the partition of nonelectrolytes and of ions[J]. Physical Chemistry Chemical Physics, 2005, 7(12):2418-2422
    Abraham M H, Acree W E. Equations for the transfer of neutral molecules and ionic species from water to organic phases[J]. The Journal of Organic Chemistry, 2010, 75(4):1006-1015
    Abraham M H, Acree W E. The transfer of neutral molecules, ions and ionic species from water to wet octanol[J]. Physical Chemistry Chemical Physics, 2010, 12(40):13182
    Zhao Y F, Lin S, Choi J W, et al. Prediction of adsorption properties for ionic and neutral pharmaceuticals and pharmaceutical intermediates on activated charcoal from aqueous solution via LFER model[J]. Chemical Engineering Journal, 2019, 362:199-206
    Yu C L, Devlin J F, Bi E P. Bonding of monocarboxylic acids, monophenols and nonpolar compounds onto goethite[J]. Chemosphere, 2019, 214:158-167
    Enoch S J. The Use of Quantum Mechanics Derived Descriptors in Computational Toxicology[M]//Challenges and Advances in Computational Chemistry and Physics. Dordrecht:Springer Netherlands, 2009:13-28
    Zhang H B. A QSAR study of the brain/blood partition coefficients on the basis of pKa values[J]. QSAR & Combinatorial Science, 2006, 25(1):15-24
    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
    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
    Xi Y, Yang X H, Zhang H Y, et al. Binding interactions of halo-benzoic acids, halo-benzenesulfonic acids and halo-phenylboronic acids with human transthyretin[J]. Chemosphere, 2020, 242:125135
    Yang X H, Ou W, Zhao S S, et al. Rapid screening of human transthyretin disruptors through a tiered in silico approach[J]. ACS Sustainable Chemistry & Engineering, 2021, 9(16):5661-5672
    Yang X H, Ou W, Zhao S S, et al. Human transthyretin binding affinity of halogenated thiophenols and halogenated phenols:An in vitro and in silico study[J]. Chemosphere, 2021, 280:130627
    Mamy L, Patureau D, Barriuso E, et al. Prediction of the fate of organic compounds in the environment from their molecular properties:A review[J]. Critical Reviews in Environmental Science and Technology, 2015, 45(12):1277-1377
    Bittermann K, Spycher S, Endo S, et al. Prediction of phospholipid-water partition coefficients of ionic organic chemicals using the mechanistic model COSMOmic[J]. The Journal of Physical Chemistry B, 2014, 118(51):14833-14842
  • 加载中
计量
  • 文章访问数:  4336
  • HTML全文浏览数:  4336
  • PDF下载数:  116
  • 施引文献:  0
出版历程
  • 收稿日期:  2020-11-04
杨先海, 刘会会, 王连军. 可用于表征可电离化合物离子化影响的描述符研究进展[J]. 生态毒理学报, 2021, 16(5): 1-13. doi: 10.7524/AJE.1673-5897.20201104001
引用本文: 杨先海, 刘会会, 王连军. 可用于表征可电离化合物离子化影响的描述符研究进展[J]. 生态毒理学报, 2021, 16(5): 1-13. doi: 10.7524/AJE.1673-5897.20201104001
Yang Xianhai, Liu Huihui, Wang Lianjun. Progress in Descriptors Used to Correct Influence of Ionization for Ionizable Organic Chemicals[J]. Asian journal of ecotoxicology, 2021, 16(5): 1-13. doi: 10.7524/AJE.1673-5897.20201104001
Citation: Yang Xianhai, Liu Huihui, Wang Lianjun. Progress in Descriptors Used to Correct Influence of Ionization for Ionizable Organic Chemicals[J]. Asian journal of ecotoxicology, 2021, 16(5): 1-13. doi: 10.7524/AJE.1673-5897.20201104001

可用于表征可电离化合物离子化影响的描述符研究进展

    通讯作者: 刘会会, E-mail: hhliu@njust.edu.cn
    作者简介: 杨先海(1985-),男,博士,研究方向为计算毒理学,E-mail:xhyang@njust.edu.cn
  • 南京理工大学环境与生物工程学院, 江苏省化工污染控制与资源化高校重点实验室, 南京 210094
基金项目:

中国博士后科学基金资助项目(2020T130301,2020M671502);江苏省博士后科研资助计划项目(2020Z288)

摘要: 在人为有意生产的化学品或无意识产生的化学品中,可电离有机化合物(IOCs)均占有较大比重。在环境水体、生理或实验pH条件下,IOCs可解离为分子和离子形态。研究表明,IOCs的分子和离子形态均对其表观物理化学、环境归趋和行为、生态和健康毒性效应参数具有不可忽视的影响,因而在开展IOCs相关实验或理论研究时不应忽略离子化的影响。在构建IOCs相关预测模型时,核心是如何表征离子化的影响。本文从描述符入手,总结了可用于表征IOCs离子化影响的4类描述符,即酸碱解离常数(pKa)及其衍生参数(分子态和离子态的比例分数(δ分子和δ离子))、考虑离子化影响的分配系数包括正辛醇-水分布系数(logDOW(pH))和进行形态修正的脂质体-水分配系数(logDlip/w(pH))、考虑离子参数的多参数线性自由能关系(离子描述符J+和J-)、基于形态修正的量化参数,并展望了表征IOCs离子化影响的未来研究重点。

English Abstract

参考文献 (112)

返回顶部

目录

/

返回文章
返回