有机磷类化合物大鼠急性毒性QSAR模型构建与毒性机制研究

郑子廷, 闫赛红, 查金苗. 有机磷类化合物大鼠急性毒性QSAR模型构建与毒性机制研究[J]. 生态毒理学报, 2022, 17(1): 150-159. doi: 10.7524/AJE.1673-5897.20211213002
引用本文: 郑子廷, 闫赛红, 查金苗. 有机磷类化合物大鼠急性毒性QSAR模型构建与毒性机制研究[J]. 生态毒理学报, 2022, 17(1): 150-159. doi: 10.7524/AJE.1673-5897.20211213002
Zheng Ziting, Yan Saihong, Zha Jinmiao. Development of QSAR Models for Acute Toxicity of Organophosphorus Compounds towards Rats and Study of Toxicity Mechanism[J]. Asian Journal of Ecotoxicology, 2022, 17(1): 150-159. doi: 10.7524/AJE.1673-5897.20211213002
Citation: Zheng Ziting, Yan Saihong, Zha Jinmiao. Development of QSAR Models for Acute Toxicity of Organophosphorus Compounds towards Rats and Study of Toxicity Mechanism[J]. Asian Journal of Ecotoxicology, 2022, 17(1): 150-159. doi: 10.7524/AJE.1673-5897.20211213002

有机磷类化合物大鼠急性毒性QSAR模型构建与毒性机制研究

    作者简介: 郑子廷(1997-),男,硕士研究生,研究方向为环境毒理学,E-mail:zztsaigo@126.com
    通讯作者: 闫赛红, E-mail: shyan@rcees.ac.cn 查金苗, E-mail: jmzha@rcees.ac.cn
  • 基金项目:

    国家重点研发计划项目(2019YFC1803403)

  • 中图分类号: X171.5

Development of QSAR Models for Acute Toxicity of Organophosphorus Compounds towards Rats and Study of Toxicity Mechanism

    Corresponding authors: Yan Saihong, shyan@rcees.ac.cn ;  Zha Jinmiao, jmzha@rcees.ac.cn
  • Fund Project:
  • 摘要: 有机磷化合物(OPs)广泛分布在各种环境介质中,并对各类生物的健康有潜在的危害。本研究采用基于逐步算法(SW)和遗传算法(GA)的多元线性回归(MLR)方法,收集并筛选出53种OPs的数据集并建立其关于大鼠急性口服毒性(LD50)的定量结构活性关系(QSAR)模型。构建的SW-MLR模型的参数决定系数(R2)、留一法交叉验证系数(Q2LOO)、外部检验系数(Q2F1Q2F2)分别为0.897、0.817、0.515和0.505,GA-MLR模型的参数分别为0.827、0.752、0.831和0.828。2个模型的统计参数表征了良好的预测能力。使用外部测试集对模型进行评估时,发现GA-MLR模型比SW-MLR模型具有更好的预测和泛化能力。此外,基于建立的模型预测了其他9种OPs的急性毒性,并辅以分子对接技术探究了其潜在的神经毒性。分子对接结果显示,其中8种OPs可以与人类丁酰胆碱酯酶结合。模型机理解释和分子对接结果显示,OPs取代基的高烷基化程度和支链长度的增加能够降低毒性。因此,建立的模型可以从OPs的分子结构上探索与毒性相关的信息,并为监管和筛选新的OPs提供基础数据。
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  • Pantelaki I, Voutsa D. Organophosphate flame retardants (OPFRs):A review on analytical methods and occurrence in wastewater and aquatic environment[J]. The Science of the Total Environment, 2019, 649:247-263
    Pundir C S, Malik A, Preety. Bio-sensing of organophosphorus pesticides:A review[J]. Biosensors and Bioelectronics, 2019, 140:111348
    Clercq E D. Clinical potential of the acyclic nucleoside phosphonates cidofovir, adefovir, and tenofovir in treatment of DNA virus and retrovirus infections[J]. Clinical Microbiology Reviews, 2003, 16(4):569-596
    Tan X X, Luo X J, Zheng X B, et al. Distribution of organophosphorus flame retardants in sediments from the Pearl River Delta in South China[J]. The Science of the Total Environment, 2016, 544:77-84
    Hou L, Jiang J Y, Gan Z W, et al. Spatial distribution of organophosphorus and brominated flame retardants in surface water, sediment, groundwater, and wild fish in Chengdu, China[J]. Archives of Environmental Contamination and Toxicology, 2019, 77(2):279-290
    Zainuddin A H, Wee S Y, Aris A Z. Occurrence and potential risk of organophosphorus pesticides in urbanised Linggi River, Negeri Sembilan, Malaysia[J]. Environmental Geochemistry and Health, 2020, 42(11):3703-3715
    Kim J W, Isobe T, Muto M, et al. Organophosphorus flame retardants (PFRs) in human breast milk from several Asian countries[J]. Chemosphere, 2014, 116:91-97
    Mangas I, Vilanova E, Estévez J, et al. Neurotoxic effects associated with current uses of organophosphorus compounds[J]. Journal of the Brazilian Chemical Society, 2016, 27(5):809-825
    Jokanovic M. Neurotoxic effects of organophosphorus pesticides and possible association with neurodegenerative diseases in man:A review[J]. Toxicology, 2018, 410:125-131
    Yang F W, Zhao G P, Ren F Z, et al. Assessment of the endocrine-disrupting effects of diethyl phosphate, a nonspecific metabolite of organophosphorus pesticides, by in vivo and in silico approaches[J]. Environment International, 2020, 135:105383
    Al-Salem A M, Saquib Q, Siddiqui M A, et al. Organophosphorus flame retardant (tricresyl phosphate) trigger apoptosis in HepG2 cells:Transcriptomic evidence on activation of human cancer pathways[J]. Chemosphere, 2019, 237:124519
    Burke R D, Todd S W, Lumsden E, et al. Developmental neurotoxicity of the organophosphorus insecticide chlorpyrifos:From clinical findings to preclinical models and potential mechanisms[J]. Journal of Neurochemistry, 2017, 142(Suppl.2):162-177
    Zhang Q, Lu M Y, Dong X W, et al. Potential estrogenic effects of phosphorus-containing flame retardants[J]. Environmental Science&Technology, 2014, 48(12):6995-7001
    Zhang Q, Wang J H, Zhu J Q, et al. Potential glucocorticoid and mineralocorticoid effects of nine organophosphate flame retardants[J]. Environmental Science&Technology, 2017, 51(10):5803-5810
    Busquet F, Strecker R, Rawlings J M, et al. OECD validation study to assess intra-and inter-laboratory reproducibility of the zebrafish embryo toxicity test for acute aquatic toxicity testing[J]. Regulatory Toxicology and Pharmacology, 2014, 69(3):496-511
    于洋,郑玉婷,张丽丽,等.面向我国化学物质环境管理的计算毒理学模型评估思路及建设路径[J].生态毒理学报, 2021, 16(5):24-32

    Yu Y, Zheng Y T, Zhang L L, et al. Evaluation thoughts of computational toxicological models and constructional path for environmental management of chemicals in China[J]. Asian Journal of Ecotoxicology, 2021, 16(5):24-32(in Chinese)

    Brad R, Mayeno A N. What is computational toxicology?[J]. Methods in Molecular Biology, 2012, 929:3-7
    Schultz T W, Cronin M T D, Walker J D, et al. Quantitative structure-activity relationships (QSARs) in toxicology:A historical perspective[J]. Journal of Molecular Structure:THEOCHEM, 2003, 622(1-2):1-22
    Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE):Application of QSAR and molecular docking to drug discovery[J]. Current Topics in Medicinal Chemistry, 2008, 8(18):1555-1572
    Alves V M, Bobrowski T, Melo-Filho C C, et al. QSAR modeling of SARS-CoV Mpro inhibitors identifies sufugolix, cenicriviroc, proglumetacin, and other drugs as candidates for repurposing against SARS-CoV-2[J]. Molecular Informatics, 2021, 40(1):e2000113
    Jeon H K, Sarma S N, Kim Y J, et al. Toxicokinetics and metabolisms of benzophenone-type UV filters in rats[J]. Toxicology, 2008, 248(2-3):89-95
    Zou X M, Lin Z F, Deng Z Q, et al. The joint effects of sulfonamides and their potentiator on Photobacterium phosphoreum :Differences between the acute and chronic mixture toxicity mechanisms[J]. Chemosphere, 2012, 86(1):30-35
    Li F, Xie Q, Li X H, et al. Hormone activity of hydroxylated polybrominated diphenyl ethers on human thyroid receptor-beta: in vitro and in silico investigations[J]. Environmental Health Perspectives, 2010, 118(5):602-606
    Hanane F, Taoufiq F, Mohamed M. Evaluation of the model prediction toxicity (LD50) for series of 42 organophosphorus pesticides[J]. Journal of Engineering Studies and Research, 2019, 25(1):30-35
    Topliss J G, Edwards R P. Chance factors in studies of quantitative structure-activity relationships[J]. Journal of Medicinal Chemistry, 1979, 22(10):1238-1244
    Knaak J B, Dary C C, Power F, et al. Physicochemical and biological data for the development of predictive organophosphorus pesticide QSARs and PBPK/PD models for human risk assessment[J]. Critical Reviews in Toxicology, 2004, 34(2):143-207
    Liagkouridis I, Cousins A P, Cousins I T. Physical-chemical properties and evaluative fate modelling of'emerging'and'novel'brominated and organophosphorus flame retardants in the indoor and outdoor environment[J]. Science of the Total Environment, 2015, 524-525:416-426
    Yap C W. PaDEL-descriptor:An open source software to calculate molecular descriptors and fingerprints[J]. Journal of Computational Chemistry, 2011, 32(7):1466-1474
    Ambure P, Aher R B, Gajewicz A, et al." NanoBRIDGES"software:Open access tools to perform QSAR and nano-QSAR modeling[J]. Chemometrics and Intelligent Laboratory Systems, 2015, 147:1-13
    Gramatica P. Principles of QSAR models validation:Internal and external[J]. QSAR&Combinatorial Science, 2007, 26(5):694-701
    Yuan L L, Li J S, Zha J M, et al. Targeting neurotrophic factors and their receptors, but not cholinesterase or neurotransmitter, in the neurotoxicity of TDCPP in Chinese rare minnow adults ( Gobiocypris rarus )[J]. Environmental Pollution, 2016, 208(Pt B):670-677
    Trott O, Olson A J. AutoDock Vina:Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading[J]. Journal of Computational Chemistry, 2010, 31(2):455-461
    Adasme M F, Linnemann K L, Bolz S N, et al. PLIP 2021:Expanding the scope of the protein-ligand interaction profiler to DNA and RNA[J]. Nucleic Acids Research, 2021, 49(W1):W530-W534
    Hollas B. An analysis of the autocorrelation descriptor for molecules[J]. Journal of Mathematical Chemistry, 2003, 33(2):91-101
    Ibrahim Z Y, Uzairu A, Abechi S. Quantum Modelling of Some Potent, Non-Toxic Antimalarial Compounds[M]. Chisinau:Scholar Press, 2019:161-166
    Galvez J, Garcia R, Salabert M T, et al. Charge indexes. New topological descriptors[J]. Journal of Chemical Information and Computer Sciences, 1994, 34(3):520-525
    Caballero J, Garriga M, Fernández M. 2D autocorrelation modeling of the negative inotropic activity of calcium entry blockers using Bayesian-regularized genetic neural networks[J]. Bioorganic&Medicinal Chemistry, 2006, 14(10):3330-3340
    Adedirin O, Uzairu A, Shallangwa G A, et al. QSAR and molecular docking based design of some n-benzylacetamide as γ -aminobutyrate-aminotransferase inhibitors[J]. The Journal of Engineering and Exact Sciences, 2018, 4(1):65-84
    Shi Q P, Guo W, Shen Q C, et al. In vitro biolayer interferometry analysis of acetylcholinesterase as a potential target of aryl-organophosphorus flame-retardants[J]. Journal of Hazardous Materials, 2021, 409:124999
    Todeschini R, Consonni V. Molecular Descriptors for Chemoinformatics:Volume Ⅰ:Alphabetical Listing/Volume Ⅱ:Appendices, References[M]. John Wiley&Sons, 2009:41
    García-Domenech R, Galvez J, de Julian-Ortiz J V, et al. Some new trends in chemical graph theory[J]. Chemical Reviews, 2008, 108(3):1127-1169
    Guo J X, Wu J J, Wright J B, et al. Mechanistic insight into acetylcholinesterase inhibition and acute toxicity of organophosphorus compounds:A molecular modeling study[J]. Chemical Research in Toxicology, 2006, 19(2):209-216
    Shityakov S, F rster C. In silico predictive model to determine vector-mediated transport properties for the blood-brain barrier choline transporter[J]. Advances and Applications in Bioinformatics and Chemistry, 2014, 7:23-36
    Bennion B J, Lau E Y, Fattebert J L, et al. Modeling the binding of CWAs to AChE and BuChE[J]. Military Medical Science Letters, 2013, 82(3):102-114
    Jończyk J, Kukułowicz J, ątka K, et al. Molecular modeling studies on the multistep reactivation process of organophosphate-inhibited acetylcholinesterase and butyrylcholinesterase[J]. Biomolecules, 2021, 11(2):169
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  • 收稿日期:  2021-12-13

有机磷类化合物大鼠急性毒性QSAR模型构建与毒性机制研究

    通讯作者: 闫赛红, E-mail: shyan@rcees.ac.cn ;  查金苗, E-mail: jmzha@rcees.ac.cn
    作者简介: 郑子廷(1997-),男,硕士研究生,研究方向为环境毒理学,E-mail:zztsaigo@126.com
  • 1. 中国科学院生态环境研究中心, 环境水质学国家重点实验室, 北京 100085;
  • 2. 中国科学院大学, 北京 100049
基金项目:

国家重点研发计划项目(2019YFC1803403)

摘要: 有机磷化合物(OPs)广泛分布在各种环境介质中,并对各类生物的健康有潜在的危害。本研究采用基于逐步算法(SW)和遗传算法(GA)的多元线性回归(MLR)方法,收集并筛选出53种OPs的数据集并建立其关于大鼠急性口服毒性(LD50)的定量结构活性关系(QSAR)模型。构建的SW-MLR模型的参数决定系数(R2)、留一法交叉验证系数(Q2LOO)、外部检验系数(Q2F1Q2F2)分别为0.897、0.817、0.515和0.505,GA-MLR模型的参数分别为0.827、0.752、0.831和0.828。2个模型的统计参数表征了良好的预测能力。使用外部测试集对模型进行评估时,发现GA-MLR模型比SW-MLR模型具有更好的预测和泛化能力。此外,基于建立的模型预测了其他9种OPs的急性毒性,并辅以分子对接技术探究了其潜在的神经毒性。分子对接结果显示,其中8种OPs可以与人类丁酰胆碱酯酶结合。模型机理解释和分子对接结果显示,OPs取代基的高烷基化程度和支链长度的增加能够降低毒性。因此,建立的模型可以从OPs的分子结构上探索与毒性相关的信息,并为监管和筛选新的OPs提供基础数据。

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