Alistair B. A. Boxall, Rob Collins, John L. Wilkinson, Caroline Swan, Alejandra Bouzas-Monroy, Josh Jones, Emily Winter, Jessie Leach, Ursula Juta, Alex Deacon, Ian Townsend, Peter Kerr, Rachel Paget, Michael Rogers, Dave Greaves, Dan Turner, Caitlin Pearson
{"title":"The DIKW of Transcriptomics in Ecotoxicology: Extracting Information, Knowledge, and Wisdom From Big Data.","authors":"Jessica A Head, Jessica D Ewald, Niladri Basu","doi":"10.1002/etc.5954","DOIUrl":"https://doi.org/10.1002/etc.5954","url":null,"abstract":"","PeriodicalId":11793,"journal":{"name":"Environmental Toxicology and Chemistry","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141916397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mark R. Viant, Rosemary E. Barnett, Bruno Campos, John K. Colbourne, Marianne Barnard, Adam D. Biales, Mark T. D. Cronin, Kellie A. Fay, Kara Koehrn, Helen F. McGarry, Magdalini Sachana, Geoff Hodges
<p>Historically, regulatory decisions on the safety of chemicals to both humans and the environment have relied primarily on the availability of in vivo toxicity data to inform hazard and ultimately risk assessment. However, increasing recognition of the benefits of more mechanistically based scientific understanding, together with changing ethical and societal concerns, are driving the development of new approach methodologies (NAMs) that can support robust safety decision-making without animal testing. Grouping and read-across (G/RAx) is one of the most commonly used alternative approaches to animal testing in chemical risk assessment for filling data gaps with existing in vivo toxicity data (European Chemicals Agency [ECHA], <span>n.d</span>.; Organisation for Economic Co-operation and Development [OECD], <span>2017a</span>). As such, it exemplifies the efficient use of existing data and in some cases new nonanimal data. For example, under REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals regulation) Annex XI, information from one or more analogous (or “source”) chemicals can be used to predict missing endpoint data for one or more “target” chemicals (European Commission, <span>2006</span>). With approximately 100,000 chemicals listed on the European inventory (ECHA, <span>2023</span>) and approximately 85,000 chemicals listed in the US Environmental Protection Agency's (USEPA's) Toxic Substances Control Act (TSCA) inventory (<span>2024a</span>), the use of G/RAx (described as chemical “categories” under the TSCA; USEPA, <span>2010</span>) is becoming an increasingly viewed option for addressing regulatory requirements for filling data gaps in chemical safety dossiers for human health and environmental endpoints. Furthermore, grouping of chemicals can facilitate other hazard-assessment practices, for example, the harmonized classification of multiple substances within a group in accordance with the classification, labeling, and packaging regulation (Swedish Chemicals Agency, <span>2020</span>).</p><p>There are numerous approaches for defining groups of chemicals, most often based on chemical similarity (Patlewicz et al., <span>2018</span>). Notable examples in a regulatory context include the approach documented in the ECHA Read-Across Assessment Framework (RAAF; ECHA, <span>2017</span>), supporting REACH, and within the TSCA (USEPA, <span>2010</span>). These existing schemes are traditionally and primarily based on firstly grouping “source” and “target” chemicals into categories based on structural and other physicochemical parameters and, secondly, reading across existing toxicity data (i.e., an apical endpoint) from one or more “source” chemical(s) to predict the toxicity of one or more “target” chemical(s). However, most grouping dossiers still fail to incorporate and utilize absorption, distribution, metabolism, and excretion (ADME)/toxicokinetic and toxicodynamic similarities, with the strong reliance on structu
本文介绍了化学品 G/RAx 作为化学品危害评估的一种替代测试方法的重要性,包括一般概念、术语和该方法可通过的立法。此外,还向广大读者介绍了 omics 技术和术语,以便分几个步骤介绍基于生物活性特征的分组:第一,设计研究,包括选择生物测试系统和 omics 检测方法;第二,生成 omics 数据;第三,通过对 omics 数据进行统计分析,计算化学品之间的生物活性相似性,并将这些结果用于证明分组假设的合理性;第四,尝试对 omics 数据进行合理的毒理学解释,以便与包括化学结构在内的其他数据源一起,为类似物或类别的合理性提供更有力的证据。一个可选的额外步骤是将在一个测试物种中进行的omics 研究得出的分组假设复制到(一个)其他物种中,其依据是有令人信服的证据表明,定义类别的MechoA/MoA 的分子途径在所考虑的物种中是一致的。然后,我们介绍了将全息图学应用于分组的几个好处,主要是解决了一个公认的问题,即基于化学结构的分组假说不够稳健,也就是说,通过引入共同的分子效应和潜在的机理基础,提供了严谨性。不过,仍存在一些挑战,包括需要确保用于化学分组的 omics 数据的相关性和可靠性,包括定义适合目的的分级验证标准。虽然与解释基于生物活性特征的分组结果相关的一些挑战依然存在,但之前发现的其他障碍正在通过当前的几项活动积极解决,包括更新经合组织关于化学品分组的主要指南(经合组织关于测试与样品的系列文件;评估编号:194;2017a),正在开展的一项研究活动(经合组织,2017b),以及在全球范围内开展的一项研究活动(经合组织,2017c)。194; 2017a);经合组织正在开展一个项目,以确定如何在 G/RAx 法规研究中报告 omics 数据;扩展 MATCHING 项目,以更深入地研究如何从代谢组学分组数据中得出 "合理的毒理学解释";以及欧盟化学品风险评估伙伴关系倡议内的项目,等等。总之,使用 omics 数据进行基于生物活性特征的分组的前景非常令人鼓舞,需要继续开展案例研究,以建立对这种方法的信心。辅助信息可在 Wiley 在线图书馆查阅:https://doi.org/10.1002/etc.5959.Professors Mark Viant 和 John Colbourne 是伯明翰大学的雇员。他们还是 Michabo Health Science 公司的创始人和董事,该公司是伯明翰大学的一家分拆公司,专门从事'omics'技术和计算毒理学研究,为非杀伤性武器提供科学咨询服务。本出版物的内容以及在其中表达的观点和采用的论据均为作者个人观点,并不一定反映经合组织或其成员国政府、美国环境保护局或健康与安全执行局的官方观点或政策:Mark R. Viant, Geoff Hodges: Conceptualization; Investigation; Supervision; Writing-original draft.罗斯玛丽-E-巴尼特:构思;调查;可视化;写作-原稿。布鲁诺-坎波斯、约翰-K-科尔本概念化、调查、写作-原稿。玛丽安-巴纳德:写作-原稿。Adam D. Biales, Mark T.D. Cronin、Kellie A. Fay、Kara Koehrn、Helen F. McGarry、Magdalini Sachana:调查;写作-原稿。
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Gina Lintern, Alan G. Scarlett, Marthe Monique Gagnon, John Leeder, Aydin Amhet, Damian C. Lettoof, Victor O. Leshyk, Alexandra Bujak, Jonathan Bujak, Kliti Grice