Utilizing Omics Data for Chemical Grouping

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 introduce G/RAx to nonexperts, its importance, its terminology, the legislation through which it operates, and, most importantly, its current limitations. To introduce omics technologies to regulatory scientists, as applied in the context of G/RAx, explaining the value of applying these molecular assays to quantitatively group chemicals. To introduce the reader to some grouping case studies that use omics data, thereby increasing awareness of how these approaches have been used to group chemicals. To describe some challenges to advancing the incorporation of omics data into chemical grouping and identify next steps toward accelerating this implementation. 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. Read-across is routinely used to predict an apical endpoint of a chemical by interpolating or extrapolating the toxicity data of analogous chemicals that are similar in some manner, for example, chemical structure, shared metabolism, and/or mode of action (MoA; which defines a functional cellular change), thereby avoiding further testing. A schematic summarizing the concepts of conventional G/RAx is shown in Figure 1, and relevant terminology is introduced in Textbox 1. It is based on an assumption that a physicochemical, (eco)toxicological, or environmental fate property of a “target” compound can be inferred from test data for the same property of similar “source” compounds (OECD, 2017a). There are two distinct approaches to read-across: an analogue approach describes read-across from a single or very small number of source chemicals to a target chemical, whereas a category approach is used when data from a larger group of source chemicals are read-across to the target(s). Read-across predictions are endpoint-specific; for example, in quantitative read-across a known value(s) of a single endpoint for a source chemical(s) is used to infer a quantitative value of the same endpoint for the target chemical. Grouping—process of forming groups (or categories) of chemicals that have similar (or follow a regular pattern of) physicochemical, (eco)toxicological, and/or toxicokinetic properties. Read-across—alternative method for obtaining toxicity data (i.e., endpoint information) for one chemical—the target—by using data from the same endpoint from another chemical(s)—the source chemical(s), also referred to as an analogue—where the source and target chemicals lie within the same group. Endpoint—definition depends on the context of use. In REACH information requirements, endpoints are described either as a toxicological property (e.g., skin irritation, long-term toxicity to aquatic organisms) or as a type of study (e.g., carcinogenicity study, Daphnia chronic assay). Grouping hypothesis—description of the proposed membership of one (or more) chemical groups, based on the similarities of structural (or other physicochemical), (eco)toxicological, and/or toxicokinetic properties. Category justification—reasoning and associated evidence to verify the scientific validity of the grouping hypothesis for three or more chemicals. For the specific case of a single source and single target substance, this reasoning would be termed an analogue justification. Bridging studies—comparable studies on the source and target chemicals that allow a direct side-by-side comparison of the chemicals for a particular toxicological property (OECD, 2017a). Existing global regulations endorse the use of grouping as the basis for reading across existing toxicity data to fill data gaps for industrial chemicals. For example, in the United States, the TSCA requires consideration of chemical grouping, stating in Section 4(h) that as part of reducing and replacing vertebrate animal testing of chemicals it encourages “the grouping of 2 or more chemical substances into scientifically appropriate categories in cases in which testing of a chemical substance would provide scientifically valid and information on the chemical substances in the (USEPA, 2018). the the routinely chemical grouping such as the classification applied in the new chemical and analogue to fill data gaps in the assessment of new chemical also for data filling of existing chemicals and in such as and has to group chemicals to direct testing with these practices, the is for how to and use analogue data in in of animal data. In it is that TSCA Section 4(h) that information from and the of be to a or a for testing using vertebrate a toward molecular data for chemical these also exists for how to the of chemicals as a as chemicals. is by the of the which in to human health and the environment from chemicals using the is also to to animal testing when for chemical safety can be particular to our study is the on Grouping of Chemicals on Assessment which on the of chemicals as a group or in it in to include on analogue and category quantitative and and using include molecular for grouping chemicals. 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