Utilizing Omics Data for Chemical Grouping

IF 3.6 4区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Environmental Toxicology and Chemistry Pub Date : 2024-08-08 DOI:10.1002/etc.5959
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
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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 structure-based similarity often leading to a rejection of the proposed read-across arguments, potentially resulting in regulatory noncompliance. For example, solely relying on structural similarity as the justification for a read-across introduces the potential to misevaluate the hazard of the target because structural similarity does not strongly infer equivalent levels of toxicity. This has prompted new efforts, such as the National Institute of Environmental Health Sciences workshop on clustering and classification (<span>2022</span>), to increase the confidence and consistency of chemical grouping by integrating molecular responses, and ideally a mechanistic understanding, into this process (Escher et al., <span>2019</span>; Pestana et al., <span>2021</span>).</p><p>While NAMs span a wide range of approaches from in vitro testing and novel bioanalytical assays to in silico methods, in our study we focus on the application of omics technologies to generate molecular data that can be used to quantitatively determine group membership, thereby offering a solution to a significant limitation of conventional structure-based G/RAx approaches. This approach to forming chemical groups involves quantitatively comparing “profiles” of biological response data, derived from omics technologies such as transcriptomics (measuring gene expression) or metabolomics (measuring downstream metabolic biochemistry), and in concept is not unlike the widely used approaches for comparing structural fingerprints such as Tanimoto similarity (Sperber et al., <span>2019</span>). Furthermore, with metabolomics possessing the capability to measure substance metabolism, there exists the potential to utilize both ADME/toxicokinetic and toxicodynamic similarities to build reliable groups from this data type. 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引用次数: 0

Abstract

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], n.d.; Organisation for Economic Co-operation and Development [OECD], 2017a). 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, 2006). With approximately 100,000 chemicals listed on the European inventory (ECHA, 2023) and approximately 85,000 chemicals listed in the US Environmental Protection Agency's (USEPA's) Toxic Substances Control Act (TSCA) inventory (2024a), the use of G/RAx (described as chemical “categories” under the TSCA; USEPA, 2010) 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, 2020).

There are numerous approaches for defining groups of chemicals, most often based on chemical similarity (Patlewicz et al., 2018). Notable examples in a regulatory context include the approach documented in the ECHA Read-Across Assessment Framework (RAAF; ECHA, 2017), supporting REACH, and within the TSCA (USEPA, 2010). 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 structure-based similarity often leading to a rejection of the proposed read-across arguments, potentially resulting in regulatory noncompliance. For example, solely relying on structural similarity as the justification for a read-across introduces the potential to misevaluate the hazard of the target because structural similarity does not strongly infer equivalent levels of toxicity. This has prompted new efforts, such as the National Institute of Environmental Health Sciences workshop on clustering and classification (2022), to increase the confidence and consistency of chemical grouping by integrating molecular responses, and ideally a mechanistic understanding, into this process (Escher et al., 2019; Pestana et al., 2021).

While NAMs span a wide range of approaches from in vitro testing and novel bioanalytical assays to in silico methods, in our study we focus on the application of omics technologies to generate molecular data that can be used to quantitatively determine group membership, thereby offering a solution to a significant limitation of conventional structure-based G/RAx approaches. This approach to forming chemical groups involves quantitatively comparing “profiles” of biological response data, derived from omics technologies such as transcriptomics (measuring gene expression) or metabolomics (measuring downstream metabolic biochemistry), and in concept is not unlike the widely used approaches for comparing structural fingerprints such as Tanimoto similarity (Sperber et al., 2019). Furthermore, with metabolomics possessing the capability to measure substance metabolism, there exists the potential to utilize both ADME/toxicokinetic and toxicodynamic similarities to build reliable groups from this data type. However, progress incorporating omics data into G/RAx has been hampered by a range of factors, including siloing of new scientific developments from regulatory science, to more specific issues such as a lack of standardized assays, reporting templates, and well-constructed case studies.

To achieve our goal of introducing chemical grouping based on omics data across a range of contexts of use and regulatory jurisdictions, this article is necessarily generalized in places.

The importance of chemical G/RAx as an alternative test method for the hazard assessment of chemicals has been introduced, including general concepts, terminology, and the legislation through which this approach can operate. In addition, omics technologies and terminology have been introduced to a wide audience, allowing bioactivity profile–based grouping to be described in several steps: first, designing the study, including the choice of biological test system and omics assays; second, generating the omics data; third, calculating the bioactivity similarity between chemicals via statistical analysis of the omics data and contributing these results toward justifying a grouping hypothesis; and fourth, attempting to provide a plausible toxicological interpretation of the omics data, toward building stronger evidence for the analogue or category justification along with other data sources including chemical structure. An optional additional step is to duplicate the grouping hypothesis derived from an omics study in one test species to (an)other species, based on compelling evidence that the molecular pathways underpinning the MechoA/MoA defining the category are conserved across the species being considered. We have then described several benefits of applying omics to grouping, primarily by providing a solution to the well-recognized problem that a chemical structure–based grouping hypothesis is insufficiently robust, that is, by providing rigor through introducing shared molecular effects and, potentially, a mechanistic underpinning. However, several challenges remain, including the need to ensure the relevance and reliability of omics data for chemical grouping, including to define fit-for-purpose tiered validation criteria. While some challenges associated with interpreting bioactivity profile–based grouping results remain, other barriers that were identified previously are actively being addressed through several current activities, including updating the OECD's principal guidance on chemical grouping (OECD Series on Testing & Assessment No. 194; 2017a), an active OECD project to define how to report omics data in a G/RAx regulatory study, extension of the MATCHING project to more thoroughly investigate how a “plausible toxicological interpretation” can be derived from metabolomics grouping data, and projects within the EU Partnership for the Assessment of Risks from Chemicals initiative, to name a few. In conclusion, the outlook for the future of bioactivity profile–based grouping using omics data is highly encouraging, with a need for continuing case studies to build confidence in this approach.

The Supporting Information is available on the Wiley Online Library at https://doi.org/10.1002/etc.5959.

Professors Mark Viant and John Colbourne are employees of the University of Birmingham. They are also founders and directors of Michabo Health Science, a spin-out company of the University of Birmingham that provides scientific consultancy services in NAMs specializing in ‘omics technologies and computational toxicology.

The contents of this publication and the opinions expressed and arguments employed herein are those of the authors and do not necessarily reflect the official views or policy of the OECD or of the governments of its member countries, the US Environmental Protection Agency, or the Health and Safety Executive.

Mark R. Viant, Geoff Hodges: Conceptualization; Investigation; Supervision; Writing—original draft. Rosemary E. Barnett: Conceptualization; Investigation; Visualization; Writing—original draft. Bruno Campos, John K. Colbourne: Conceptualization; Investigation; Writing—original draft. Marianne Barnard: Writing—original draft. Adam D. Biales, Mark T. D. Cronin, Kellie A. Fay, Kara Koehrn, Helen F. McGarry, Magdalini Sachana: Investigation; Writing—original draft.

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利用 Omics 数据进行化学分组。
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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来源期刊
CiteScore
7.40
自引率
9.80%
发文量
265
审稿时长
3.4 months
期刊介绍: The Society of Environmental Toxicology and Chemistry (SETAC) publishes two journals: Environmental Toxicology and Chemistry (ET&C) and Integrated Environmental Assessment and Management (IEAM). Environmental Toxicology and Chemistry is dedicated to furthering scientific knowledge and disseminating information on environmental toxicology and chemistry, including the application of these sciences to risk assessment.[...] Environmental Toxicology and Chemistry is interdisciplinary in scope and integrates the fields of environmental toxicology; environmental, analytical, and molecular chemistry; ecology; physiology; biochemistry; microbiology; genetics; genomics; environmental engineering; chemical, environmental, and biological modeling; epidemiology; and earth sciences. ET&C seeks to publish papers describing original experimental or theoretical work that significantly advances understanding in the area of environmental toxicology, environmental chemistry and hazard/risk assessment. Emphasis is given to papers that enhance capabilities for the prediction, measurement, and assessment of the fate and effects of chemicals in the environment, rather than simply providing additional data. The scientific impact of papers is judged in terms of the breadth and depth of the findings and the expected influence on existing or future scientific practice. Methodological papers must make clear not only how the work differs from existing practice, but the significance of these differences to the field. Site-based research or monitoring must have regional or global implications beyond the particular site, such as evaluating processes, mechanisms, or theory under a natural environmental setting.
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Correction. Spotlights are papers selected by editors published in peer-reviewed journals that may be more regionally specific or appearing in languages other than English Issue Information - Cover Editorial Board and Table of Contents Detection and Prediction of Toxic Aluminum Concentrations in High-Priority Salmon Rivers in Nova Scotia.
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