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 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.