定量有害结局路径(qAOPs)评估环境化学物质毒性的研究进展Ⅰ：模型构建与应用案例
Research Advance of Quantitative Adverse Outcome Pathways (qAOPs) in Environmental Chemicals Toxicity Assessment Ⅰ: Model Building and Application Cases
-
摘要: 近年来，有害结局路径（adverse outcome pathway，AOP）框架逐渐发展成熟，将生物信息组织成一种可用于评估化学品对人体健康和生态环境生物毒性的新方法，其开发的目的是用于化学品的评估和监管工作，包括优先级评估和危害性预测，最终实现风险评估并服务于管理决策。尽管AOP框架取得了巨大进展，但将其有效应用于化学品监管需要对分子启动事件、关键事件和有害结局之间的关系进行定量描述，因此发展定量AOPs （quantitative AOPs，qAOPs）至关重要。本文首先概述了AOP框架的现状，包括AOP数据库（AOP Knowledge Base）、定性AOPs （qualitative AOPs）和qAOPs。其次主要介绍了qAOPs构建的基本框架与步骤、方法模型，现阶段已构建的qAOPs案例及其应用现状。最后论述了当前qAOPs发展中存在的问题与潜在解决方案，并展望了未来的发展趋势与潜在应用。Abstract: Over the past decade, the development of adverse outcome pathway (AOP) framework has matured significantly, which has been considered as a new approach for organizing biological information into a format and applicable method for chemical safety evaluation in both human health and ecological contexts. Ultimately, it is developed for use in the assessment and regulation of chemicals, including the priority assessment and hazard prediction. Based on above, AOP will contribute to the realization of the risk assessment and application in regulatory decision making. Although the development of AOP frameworks has made great progress, its effective application to chemical regulation requires a detailed quantitative description of the relationship among the molecular priming events, key events and the adverse outcome. Consequently, quantitative AOPs (qAOPs) is critical for AOP application. At first, this review summarizes the development status of AOP framework, including AOP knowledge base (AOP KB), qualitative AOPs and quantitative AOPs. Secondly, this review describes how qAOP models can be developed and provides examples of how they could be used in a hazard or risk assessment context. Finally, the most important issues and potential solutions in the current qAOPs development process are discussed in this review, the future research and application of qAOPs in the hazard assessment of environment chemicals and environmental mixtures are also prospected.
-
-
Union Nations Environment Programme. Global Chemicals Outlook Ⅱ from Legacies to innovative solutions:Implementing the 2030 Agenda for sustainable development[R]. Geneva:Union Nations Environment Programme, 2019 赵静, 王燕飞, 蒋京呈, 等. 化学品环境风险管理需求与战略思考[J]. 生态毒理学报, 2020, 15(1):72-78 Zhao J, Wang Y F, Jiang J C, et al. Study on chemical environmental risk management needs and strategy[J]. Asian Journal of Ecotoxicology, 2020, 15(1):72-78(in Chinese)
European Chemicals Agency (ECHA). Guidance for identification and naming of substances under REACH and CLP. Version 2.1, May 2017.[R/OL]. (2017-06-07)[2020-10-24]. https://echa.europa.eu/documents/10162/23036412/substance_id_en.pdf/ee696bad-49f6-4fec-b8b7-2c3706113c7d United States Environmental Protection Agency (US EPA). Endocrine disruptor screening program[R/OL]. (2020-09-15)[2020-10-24]. http://www.epa.gov/endo/ 中华人民共和国环境保护部. 中国现有化学物质名录[S]. 北京:中华人民共和国环境保护部, 2013 Krewski D, Acosta D, Andersen M, et al. Toxicity testing in the 21st Century:A vision and a strategy[J]. Journal of Toxicology and Environmental Health, Part B, 2010, 13(2-4):51-138 National Research Council of the National Academies. Toxicity testing in the 21st Century:A vision and a strategy[R]. Washington DC:National Academies Press, 2007 Scientific Committee on Emerging and Newly Identified Health Risks (SCENIHR). Addressing the new challenges for risk assessment:Discussion paper approved for public consultation in view of receiving feedback from stakeholders for its further development[R/OL]. (2016-08-17)[2020-10-24]. http://ec.europa.eu/health/scientific_committees/emerging/docs/scenihr_o_037.pdf Brockmeier E K, Hodges G, Hutchinson T H, et al. The role of omics in the application of adverse outcome pathways for chemical risk assessment[J]. Toxicological Sciences:An Official Journal of the Society of Toxicology, 2017, 158(2):252-262 Ankley G T, Bennett R S, Erickson R J, et al. Adverse outcome pathways:A conceptual framework to support ecotoxicology research and risk assessment[J]. Environmental Toxicology and Chemistry, 2010, 29(3):730-741 Perkins E J, Ashauer R, Burgoon L, et al. Building and applying quantitative adverse outcome pathway models for chemical hazard and risk assessment[J]. Environmental Toxicology and Chemistry, 2019, 38(9):1850-1865 Kramer V J, Etterson M A, Hecker M, et al. Adverse outcome pathways and ecological risk assessment:Bridging to population-level effects[J]. Environmental Toxicology and Chemistry, 2011, 30(1):64-76 Wheeler J R, Weltje L. In Response:Adverse outcome pathways-An industry perspective[J]. Environmental Toxicology and Chemistry, 2015, 34(9):1937-1938 Conolly R B, Ankley G T, Cheng W Y, et al. Quantitative adverse outcome pathways and their application to predictive toxicology[J]. Environmental Science & Technology, 2017, 51(8):4661-4672 Wittwehr C, Aladjov H, Ankley G, et al. How adverse outcome pathways can aid the development and use of computational prediction models for regulatory toxicology[J]. Toxicological Sciences:An Official Journal of the Society of Toxicology, 2017, 155(2):326-336 Edwards S W, Tan Y M, Villeneuve D L, et al. Adverse outcome pathways-organizing toxicological information to improve decision making[J]. The Journal of Pharmacology and Experimental Therapeutics, 2016, 356(1):170-181 Jaworska J, Dancik Y, Kern P, et al. Bayesian integrated testing strategy to assess skin sensitization potency:From theory to practice[J]. Journal of Applied Toxicology, 2013, 33(11):1353-1364 Carriger J F, Martin T M, Barron M G. A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors[J]. Aquatic Toxicology, 2016, 180:11-24 Shoemaker J E, Gayen K, Garcia-Reyero N, et al. Fathead minnow steroidogenesis:In silico analyses reveals tradeoffs between nominal target efficacy and robustness to cross-talk[J]. BMC Systems Biology, 2010, 4:89 Cheng W Y, Zhang Q, Schroeder A, et al. Editor's highlight:Computational modeling of plasma vitellogenin alterations in response to aromatase inhibition in fathead minnows[J]. Toxicological Sciences:An Official Journal of the Society of Toxicology, 2016, 154(1):78-89 Baker C T H, Bocharov G A, Paul C A H. Mathematical modelling of the interleukin-2 T-cell system:A comparative study of approaches based on ordinary and delay differential equation[J]. Journal of Theoretical Medicine, 1997, 1(2):117-128 Hallam T G, Clark C E, Lassiter R R. Effects of toxicants on populations:A qualitative approach Ⅰ. Equilibrium environmental exposure[J]. Ecological Modelling, 1983, 18(3-4):291-304 Grimm V, Railsback S F. Individual-base Modeling and Ecology[M]. Princeton:Princeton University Press, 2005:480 Jager T, Barsi A, Hamda N T, et al. Dynamic energy budgets in population ecotoxicology:Applications and outlook[J]. Ecological Modelling, 2014, 280:140-147 Kooijman B. Basic Concepts[M]//Dynamic Energy Budget Theory for Metabolic Organisation. Cambridge:Cambridge University Press:1-23 Martin B T, Jager T, Nisbet R M, et al. Extrapolating ecotoxicological effects from individuals to populations:A generic approach based on Dynamic Energy Budget theory and individual-based modeling[J]. Ecotoxicology, 2013, 22(3):574-583 Kramer V J, Etterson M A, Hecker M, et al. Adverse outcome pathways and ecological risk assessment:Bridging to population-level effects[J]. Environmental Toxicology and Chemistry, 2011, 30(1):64-76 Barnthouse L W. Quantifying population recovery rates for ecological risk assessment[J]. Environmental Toxicology and Chemistry, 2004, 23(2):500-508 Miller D H, Jensen K M, Villeneuve D L, et al. Linkage of biochemical responses to population-level effects:A case study with vitellogenin in the fathead minnow (Pimephales promelas)[J]. Environmental Toxicology and Chemistry, 2007, 26(3):521-527 Railsback S F, Harvey B C, Jackson S K, et al. InSTREAM:The individual-based stream trout research and environmental assessment model[R]. Washington DC:U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, 2009 Doering J A, Villeneuve D L, Poole S T, et al. Quantitative response-response relationships linking aromatase inhibition to decreased fecundity are conserved across three fishes with asynchronous oocyte development[J]. Environmental Science & Technology, 2019, 53(17):10470-10478 Miller D H, Ankley G T. Modeling impacts on populations:Fathead minnow (Pimephales promelas) exposure to the endocrine disruptor 17beta-trenbolone as a case study[J]. Ecotoxicology and Environmental Safety, 2004, 59(1):1-9 Doering J A, Giesy J P, Wiseman S, et al. Predicting the sensitivity of fishes to dioxin-like compounds:Possible role of the aryl hydrocarbon receptor (AhR) ligand binding domain[J]. Environmental Science and Pollution Research International, 2013, 20(3):1219-1224 Doering J A, Wiseman S, Beitel S C, et al. Identification and expression of aryl hydrocarbon receptors (AhR1 and AhR2) provide insight in an evolutionary context regarding sensitivity of white sturgeon (Acipenser transmontanus) to dioxin-like compounds[J]. Aquatic Toxicology, 2014, 150:27-35 Doering J A, Wiseman S, Giesy J P, et al. A cross-species quantitative adverse outcome pathway for activation of the aryl hydrocarbon receptor leading to early life stage mortality in birds and fishes[J]. Environmental Science & Technology, 2018, 52(13):7524-7533 Moe J, Wayne L, Xie L, et al. Quantification of an adverse outcome pathway by Bayesian network modelling:Extrapolation from molecular events to demographic responses in Lemna minor[C]. Brussels:SETAC Europe 13th Special Science Symposium, 2018 Jeong J, Song T, Chatterjee N, et al. Developing adverse outcome pathways on silver nanoparticle-induced reproductive toxicity via oxidative stress in the nematode Caenorhabditis elegans using a Bayesian network model[J]. Nanotoxicology, 2018, 12(10):1182-1197 Pirone J R, Smith M, Kleinstreuer N C, et al. Open source software implementation of an integrated testing strategy for skin sensitization potency based on a Bayesian network[J]. Alternatives to Animal Experimentation, 2014, 31(3):336-340 Burgoon L D, Druwe I L, Painter K, et al. Using in vitro high-throughput screening data for predicting benzo[k]fluoranthene human health hazards[J]. Risk Analysis:An Official Publication of the Society for Risk Analysis, 2017, 37(2):280-290 Massart J, Begriche K, Moreau C, et al. Role of nonalcoholic fatty liver disease as risk factor for drug-induced hepatotoxicity[J]. Journal of Clinical and Translational Research, 2017, 3(Suppl 1):212-232 Gao M M, Ma Y J, Alsaggar M, et al. Dual outcomes of rosiglitazone treatment on fatty liver[J]. The AAPS Journal, 2016, 18(4):1023-1031 Zgheib E, Gao W, Limonciel A, et al. Application of three approaches for quantitative AOP development to renal toxicity[J]. Computational Toxicology, 2019, 11:1-13 Sachana M, Munn S, Bal-Price A. Binding of agonists to ionotropic glutamate receptors in adult brain causes excitotoxicity that mediates neuronal cell death, contributing to learning and memory impairment. AOP-Wiki.[R/OL]. (2018-06-28)[2020-10-24]. https://aopwiki.org/aops/48 Foran C M, Rycroft T, Keisler J, et al. A modular approach for assembly of quantitative adverse outcome pathways[J]. Alternatives to Animal Experimentation, 2019, 36(3):353-362 Hassan I, El-Masri H, Kosian P A, et al. Neurodevelopment and thyroid hormone synthesis inhibition in the rat:Quantitative understanding within the adverse outcome pathway framework[J]. Toxicological Sciences:An Official Journal of the Society of Toxicology, 2017, 160(1):57-73 Aardema M J, MacGregor J T. Toxicology and genetic toxicology in the new era of "toxicogenomics":Impact of "-omics" technologies[J]. Mutation Research, 2002, 499(1):13-25 Patlewicz G, Simon T W, Rowlands J C, et al. Proposing a scientific confidence framework to help support the application of adverse outcome pathways for regulatory purposes[J]. Regulatory Toxicology and Pharmacology, 2015, 71(3):463-477 Teeguarden J G, Tan Y M, Edwards S W, et al. Completing the link between exposure science and toxicology for improved environmental health decision making:The aggregate exposure pathway framework[J]. Environmental Science & Technology, 2016, 50(9):4579-4586 Escher B I, Stapleton H M, Schymanski E L. Tracking complex mixtures of chemicals in our changing environment[J]. Science, 2020, 367(6476):388-392 Bradley P M, Journey C A, Romanok K M, et al. Expanded target-chemical analysis reveals extensive mixed-organic-contaminant exposure in US streams[J]. Environmental Science & Technology, 2017, 51(9):4792-4802 Johnson A C, Jin X W, Nakada N, et al. Learning from the past and considering the future of chemicals in the environment[J]. Science, 2020, 367(6476):384-387 Brack W, Aissa S A, Backhaus T, et al. Effect-based methods are key. The European Collaborative Project SOLUTIONS recommends integrating effect-based methods for diagnosis and monitoring of water quality[J]. Environmental Sciences Europe, 2019, 31(1):1-6 张家敏, 彭颖, 方文迪, 等. 有害结局路径(AOP)框架在水体复合污染监测研究中的应用[J]. 生态毒理学报, 2017, 12(1):1-14 Zhang J M, Peng Y, Fang W D, et al. Application of adverse outcome pathways framework in monitoring of toxic chemicals from aquatic environments[J]. Asian Journal of Ecotoxicology, 2017, 12(1):1-14(in Chinese)
Zhang X W. Environmental DNA shaping a new era of ecotoxicological research[J]. Environmental Science & Technology, 2019, 53(10):5605-5612 -
点击查看大图
计量
- 文章访问数: 6725
- HTML全文浏览数: 6725
- PDF下载数: 435
- 施引文献: 0
下载:
