Next generation risk assessment (NGRA) aims to enable transparent, reproducible chemical safety assessments based on human-relevant, animal-free new approach methodologies (NAMs). The Alternative Safety Profiling Algorithm (ASPA) was developed within the ASPIS cluster to provide an algorithmic workflow that structures problem formulation, evidence integration, and decision-making across three main pillars – hazard, ADME (toxicokinetics), and exposure. A stakeholder workshop was organized to refine ASPA. Four breakout groups systematically reviewed corresponding workflow sections, identifying strengths, conceptual gaps, and opportunities for harmonization. Across groups, participants endorsed ASPA’s modular, technology-neutral nature and its focus on standardizing processes rather than prescribing specific test batteries. The hazard pillar discussions emphasized a sensitive, hypothesis-generating Tier 1, complemented by a specific, mechanistic Tier 2, capable of deriving points of departure (PoDs). ADME experts supported a physiologically based kinetic (PBK) modelling strategy, advancing from generic towards more complex models, using mechanistic information and experimental data. The exposure group proposed refinements for transparent, tiered exposure modelling, with emphasis on realistic worst-case scenarios and explicit uncertainty communication. Cross-pillar discussions highlighted the importance of feedback loops among all pillars, and the documentation of decision points to achieve consistency and defensi­bility. The workshop outcomes informed three parallel developments: (i) algorithmic refinement and re-design toward the next ASPA version, (ii) the creation of detailed guidance for each building block, and (iii) the establishment of practical case studies to demonstrate workflow implementation. This report already contains a first case study (developmental neurotoxicity assessment of desnitro-imidacloprid). These advances increase the operability, transparency, and regulatory readiness of ASPA.

Daphnia are keystone species of freshwater habitats used as model organisms in ecology and evolutionary biology. Their small size, wide geographic distribution, and sensitivity to chemicals make them useful as environmental sentinels in regulatory toxicology and chemical risk assessment. Biomolecular (-omic) assessments of responses to chemical toxicity, which reveal detailed molecular signatures, become more powerful when correlated with other phenotypic outcomes (such as behavioral, physiological, or histopathological) for comparative validation and regulatory relevance. However, the lack of histopathology or tissue phenotype characterization of this species presently limits our ability to assess cellular mechanisms of toxicity. Here, we address the central concept that interpreting aberrant tissue phenotypes requires a basic understanding of species normal microanatomy. We introduce the female and male DaphniaHistology Reference Atlas (DaHRA) for the baseline knowledge of Daphnia magna microanatomy. We also include developmental stages of female D. magna in the atlas. This interactive web-based resource of adult D. magna features overlaid vectorized demarcation of anatomical structures whose labels comply with an anatomical ontology created for this atlas. We demonstrate the potential utility of DaHRA for toxicological investigations by presenting aberrant phenotypes of acetaminophen-exposed D. magna. We envision DaHRA to facilitate the future integration of molecular and phenotypic data from the scientific community as we seek to understand how genes, chemicals, and environment interactions determine organismal phenotype.

Effective species conservation and management requires comprehensive biomonitoring, enhanced by combining traditional and newer methodologies, such as environmental DNA (eDNA) analyses. A seasonal pulse of spawning adult Atlantic salmon (Salmo salar) was detected by normalised eDNA 12S reads from metabarcoding, which facilitated estimation of spatial patterns in salmon biomass. A strong relationship was found between normalised reads in the lower section of the River Conwy (Wales, UK) and whole-river adult biomass (estimated from rod catch data and a fish counter), explaining 61% of the variation in a linear regression. Moreover, the positive linear relationship between adult biomass and partial effect on normalised reads occurred after the biomass estimate exceeded 1500 kg, indicating a threshold where normalised reads become representative of biomass. The relationship observed between normalised reads and biomass, as well as the unique profiles of normalised reads at each of the sites, supports the hypothesis of limited eDNA transport among sampling sites that were 2-4 km apart. River pH showed a significant non-linear relationship with normalised reads, with a peak in partial effect on normalised reads at pH 6.5. Partial effect on normalised reads also showed a positive linear relationship with flow (discharge), while also peaking at the highest average monthly air temperatures (14°C). These trends are contrary to what would be expected from eDNA decay, dilution or transport, demonstrating that metabarcoding is robust to such influences, reinforcing the interpretation of trends driven by Atlantic salmon ecology and physiology. For example, pH effects reflect beneficial conditions for eggs and perhaps habitat preference for spawners, flow effects reflect the annual return of salmon during higher flows which aid upstream migration, tributary entry and spawning, and, finally, temperature effects reflect higher metabolic rates and greater shedding of eDNA.

By grouping structurally similar chemicals, toxicity endpoints from data-rich substances can be read across to data-poor substances, supporting environmental and human health risk assessment without animal testing. However, structural similarity alone is insufficient, and additional supporting data can strengthen a grouping justification. This study aimed to demonstrate how multi-omics bioactivity data can increase confidence in a grouping hypothesis, where the bioactivity profiles can reflect a chemical's mode(s) of action. We investigated three structurally similar phthalates and three uncouplers of oxidative phosphorylation, applying structure-based grouping approaches and short-term exposures of the ecotoxicological test species Daphnia magna to generate multi-omics data. Bioactivity similarities between the 'omics responses to chemical exposure were assessed using t-statistics comparing treated samples to controls and visualised using hierarchical cluster analysis. Conventional structure-based grouping did not assign the phthalates and uncouplers into two anticipated categories, with the structurally more diverse uncouplers often assigned into multiple groups. Following bioactivity thresholding, which removed one uncoupler as it induced minimal molecular responses, bioactivity profile-based grouping of the remaining five substances correctly separated them into two chemical classes with high replicability confidence. However, a plausible toxicological interpretation of the reduced set of functionally annotated molecular features driving the grouping was attempted, although of limited success. This study demonstrates how multi-omics bioactivity profiles can increase confidence in chemical grouping and investigates a potential strategy for plausibly interpreting 'omics data.

Next generation risk assessment (NGRA) strategies use animal-free new approach methodol­ogies (NAMs) to generate information concerning chemical hazard, toxicokinetics (ADME), and exposure. The information from these major pillars of data gathering is used to inform risk assessment and classification decisions. While the required types of data are widely agreed upon, the pro­cesses for data collection, integration and reporting, as well as several decisions on the depth and granularity of required data, are poorly standardized. Here, we present the Alternative Safety Pro­filing Algorithm (ASPA), a broad-purpose, transparent, and reproducible risk assessment workflow that allows documentation and integration of all types of information required for NGRA. ASPA aims to make safety assessments fully traceable for the recipient (e.g., a regulator), delineating which steps and decisions have led to the final outcome and why certain decisions were made. An overarching objective of ASPA is to ensure that identical data input yields identical outcomes in the hands of independent assessors. Therefore, ASPA is not just a data gathering workflow; it also considers data interdependencies and requires precise justification of intermediate decisions. This includes the monitoring and assessment of uncertainties. To assist users, the ASPA-assist software was developed. It formalizes the reporting process in a reproducible and standardized fashion. By guiding an operator step-by-step through the ASPA workflow, a complete and comprehensive report is assembled, whereby all data, methods, operator activities, and intermediate decisions are recorded. Practical examples illustrating the broader applicability of ASPA across various regula­tions and problem formulations are provided through case studies.

Benchmark dose (BMD) modeling estimates the dose of a chemical that causes a perturbation from baseline. Transcriptional BMDs have been shown to be relatively consistent with apical end point BMDs, opening the door to using molecular BMDs to derive human health-based guidance values for chemical exposure. Metabolomics measures the responses of small-molecule endogenous metabolites to chemical exposure, complementing transcriptomics by characterizing downstream molecular phenotypes that are more closely associated with apical end points. The aim of this study was to apply BMD modeling to in vivo metabolomics data, to compare metabolic BMDs to both transcriptional and apical end point BMDs. This builds upon our previous application of transcriptomics and BMD modeling to a 5-day rat study of triphenyl phosphate (TPhP), applying metabolomics to the same archived tissues. Specifically, liver from rats exposed to five doses of TPhP was investigated using liquid chromatography-mass spectrometry and 1H nuclear magnetic resonance spectroscopy-based metabolomics. Following the application of BMDExpress2 software, 2903 endogenous metabolic features yielded viable dose-response models, confirming a perturbation to the liver metabolome. Metabolic BMD estimates were similarly sensitive to transcriptional BMDs, and more sensitive than both clinical chemistry and apical end point BMDs. Pathway analysis of the multiomics data sets revealed a major effect of TPhP exposure on cholesterol (and downstream) pathways, consistent with clinical chemistry measurements. Additionally, the transcriptomics data indicated that TPhP activated xenobiotic metabolism pathways, which was confirmed by using the underexploited capability of metabolomics to detect xenobiotic-related compounds. Eleven biotransformation products of TPhP were discovered, and their levels were highly correlated with multiple xenobiotic metabolism genes. This work provides a case study showing how metabolomics and transcriptomics can estimate mechanistically anchored points-of-departure. Furthermore, the study demonstrates how metabolomics can also discover biotransformation products, which could be of value within a regulatory setting, for example, as an enhancement of OECD Test Guideline 417 (toxicokinetics).

Perfluorooctanoic acid (PFOA) is a persistent environmental contaminant that can accumulate in the human body due to its long half-life. This substance has been associated with liver, pancreatic, testicular and breast cancers, liver steatosis and endocrine disruption. PFOA is a member of a large group of substances also known as “forever chemicals” and the vast majority of substances of this group lack toxicological data that would enable their effective risk assessment in terms of human health hazards. This study aimed to derive a health-based guidance value for PFOA intake (ng/kg BW/day) from in vitro transcriptomics data. To this end, we developed an in silico workflow comprising five components: (i) sourcing in vitro hepatic transcriptomics concentration-response data; (ii) deriving molecular points of departure using BMDExpress3 and performing pathway analysis using gene set enrichment analysis (GSEA) to identify the most sensitive molecular pathways to PFOA exposure; (iii) estimating freely-dissolved PFOA concentrations in vitro using a mass balance model; (iv) estimating in vivo doses by reverse dosimetry using a PBK model for PFOA as part of a quantitative in vitro to in vivo extrapolation (QIVIVE) algorithm; and (v) calculating a tolerable daily intake (TDI) for PFOA. Fourteen percent of interrogated genes exhibited in vitro concentration-response relationships. GSEA pathway enrichment analysis revealed that “fatty acid metabolism” was the most sensitive pathway to PFOA exposure. In vitro free PFOA concentrations were calculated to be 2.9% of the nominal applied concentrations, and these free concentrations were input into the QIVIVE workflow. Exposure doses for a virtual population of 3,000 individuals were estimated, from which a TDI of 0.15 ng/kg BW/day for PFOA was calculated using the benchmark dose modelling software, PROAST. This TDI is comparable to previously published values of 1.16, 0.69, and 0.86 ng/kg BW/day by the European Food Safety Authority. In conclusion, this study demonstrates the combined utility of an “omics”-derived molecular point of departure and in silico QIVIVE workflow for setting health-based guidance values in anticipation of the acceptance of in vitro concentration-response molecular measurements in chemical risk assessment.

The assessment and regulation of chemical toxicity to protect human health and the environment are done one chemical at a time and seldom at environmentally relevant concentrations. However, chemicals are found in the environment as mixtures, and their toxicity is largely unknown. Understanding the hazard posed by chemicals within the mixture is critical to enforce protective measures. Here, we demonstrate the application of bioactivity profiling of environmental water samples using the sentinel and ecotoxicology model species Daphnia to reveal the biomolecular response induced by exposure to real-world mixtures. We exposed a Daphnia strain to 30 sampled waters of the Chaobai River and measured the gene expression response profiles. Using a multiblock correlation analysis, we establish correlations between chemical mixtures identified in 30 water samples with gene expression patterns induced by these chemical mixtures. We identified 80 metabolic pathways putatively activated by mixtures of inorganic ions, heavy metals, polycyclic aromatic hydrocarbons, industrial chemicals, and a set of biocides, pesticides, and pharmacologically active substances. Our data-driven approach discovered both known bioactivity signatures with previously described modes of action and new pathways linked to undiscovered potential hazards. This study demonstrates the feasibility of reducing the complexity of real-world mixture toxicity to characterize the biomolecular effects of a defined number of chemical components based on gene expression monitoring of the sentinel species Daphnia.

Grouping/read-across is widely used for predicting the toxicity of data-poor target substance(s) using data-rich source substance(s). While the chemical industry and the regulators recognise its benefits, registration dossiers are often rejected due to weak analogue/category justifications based largely on the structural similarity of source and target substances. Here we demonstrate how multi-omics measurements can improve confidence in grouping via a statistical assessment of the similarity of molecular effects. Six azo dyes provided a pool of potential source substances to predict long-term toxicity to aquatic invertebrates (Daphnia magna) for the dye Disperse Yellow 3 (DY3) as the target substance. First, we assessed the structural similarities of the dyes, generating a grouping hypothesis with DY3 and two Sudan dyes within one group. Daphnia magna were exposed acutely to equi-effective doses of all seven dyes (each at 3 doses and 3 time points), transcriptomics and metabolomics data were generated from 760 samples. Multi-omics bioactivity profile-based grouping uniquely revealed that Sudan 1 (S1) is the most suitable analogue for read-across to DY3. Mapping ToxPrint structural fingerprints of the dyes onto the bioactivity profile-based grouping indicated an aromatic alcohol moiety could be responsible for this bioactivity similarity. The long-term reproductive toxicity to aquatic invertebrates of DY3 was predicted from S1 (21-day NOEC, 40 µg/L). This prediction was confirmed experimentally by measuring the toxicity of DY3 in D. magna. While limitations of this ‘omics approach are identified, the study illustrates an effective statistical approach for building chemical groups.

AbstractAnthropogenically forced changes in global freshwater biodiversity demand more efficient monitoring approaches. Consequently, environmental DNA (eDNA) analysis is enabling ecosystem-scale biodiversity assessment, yet the appropriate spatio-temporal resolution of robust biodiversity assessment remains ambiguous. Here, using intensive, spatio-temporal eDNA sampling across space (five rivers in Europe and North America, with an upper range of 20–35 km between samples), time (19 timepoints between 2017 and 2018) and environmental conditions (river flow, pH, conductivity, temperature and rainfall), we characterise the resolution at which information on diversity across the animal kingdom can be gathered from rivers using eDNA. In space, beta diversity was mainly dictated by turnover, on a scale of tens of kilometres, highlighting that diversity measures are not confounded by eDNA from upstream. Fish communities showed nested assemblages along some rivers, coinciding with habitat use. Across time, seasonal life history events, including salmon and eel migration, were detected. Finally, effects of environmental conditions were taxon-specific, reflecting habitat filtering of communities rather than effects on DNA molecules. We conclude that riverine eDNA metabarcoding can measure biodiversity at spatio-temporal scales relevant to species and community ecology, demonstrating its utility in delivering insights into river community ecology during a time of environmental change.

In the European regulatory context, rodent in vivo studies are the predominant source of neurotoxicity information. Although they form a cornerstone of neurotoxicological assessments, they are costly and the topic of ethical debate. While the public expects chemicals and products to be safe for the developing and mature nervous systems, considerable numbers of chemicals in commerce have not, or only to a limited extent, been assessed for their potential to cause neurotoxicity. As such, there is a societal push toward the replacement of animal models with in vitro or alternative methods. New approach methods (NAMs) can contribute to the regulatory knowledge base, increase chemical safety, and modernize chemical hazard and risk assessment. Provided they reach an acceptable level of regulatory relevance and reliability, NAMs may be considered as replacements for specific in vivo studies. The European Partnership for the Assessment of Risks from Chemicals (PARC) addresses challenges to the development and implementation of NAMs in chemical risk assessment. In collaboration with regulatory agencies, Project 5.2.1e (Neurotoxicity) aims to develop and evaluate NAMs for developmental neurotoxicity (DNT) and adult neurotoxicity (ANT) and to understand the applicability domain of specific NAMs for the detection of endocrine disruption and epigenetic perturbation. To speed up assay time and reduce costs, we identify early indicators of later-onset effects. Ultimately, we will assemble second-generation developmental neurotoxicity and first-generation adult neurotoxicity test batteries, both of which aim to provide regulatory hazard and risk assessors and industry stakeholders with robust, speedy, lower-cost, and informative next-generation hazard and risk assessment tools.

Daphnia produce genetically identical males and females; their sex is determined by environmental conditions. Recently, Kato et al. identified isoform switching events in Daphnia as a gene regulatory mechanism for sex-specific development. This finding uncovers the impact of alternative usage of gene isoforms on this extreme phenotypic plasticity trait.

Prymnesins produced by an algal bloom of Prymnesium parvum led to the death of several hundred tons of freshwater fish in the Oder River in summer 2022. We investigated effects on aquatic life and human cell lines from exposure to extracts of contaminated water collected during the fish kill. We detected B-type prymnesins and >120 organic micropollutants. The micropollutants occurred at concentrations that would cause the predicted mixture risk quotient for aquatic life to exceed the acceptable threshold. Extracts of water and filters (biomass and particulates) induced moderate effects in vivo in algae, daphnids and zebrafish embryos but caused high effects in a human neuronal cell line indicating the presence of neurotoxicants. Mixture toxicity modelling demonstrated that the in vitro neurotoxic effects were mainly caused by the detected B-type prymnesins with minor contributions by organic micropollutants. Complex interactions between natural and anthropogenic toxicants may underestimate threats to aquatic ecosystems.

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 useful information on the chemical substances in the category" (USEPA, 2018). Under the TSCA, the USEPA already routinely uses chemical grouping techniques such as the classification applied in the Ecological Structure–Activity Relationship (ECOSAR) software, new chemical categories, and analogue identification to fill data gaps in the assessment of new chemical submissions. The USEPA also considers analogues for data gap filling of existing chemicals and in special cases, such as per- and polyfluoroalkyl substances, has developed rules to group chemicals to direct national testing strategies. Associated with these practices, the USEPA is drafting guidance for how to select and use analogue data in ecotoxicology in place of animal data. In parallel, it is noteworthy that TSCA Section 4(h) states that information from "high throughput screening methods and the prediction models of those methods" should be considered "prior to making a request or adopting a requirement for testing using vertebrate animals," providing a legal route toward utilizing molecular (including omics) data for chemical grouping. Complementing these regulations, extensive international guidance also exists for describing how to assess the hazards of related chemicals as a group, rather than as individual chemicals. Foremost is guidance published by the Chemical Safety Programme of the OECD, which assists member countries in their efforts to protect human health and the environment from hazardous chemicals using the best available science. The OECD is also committed to promoting alternatives to animal testing when suitable methods for evaluating chemical safety can be demonstrated. Of particular note to our study is the OECD Guidance on Grouping of Chemicals (Series on Testing & Assessment No. 194; 2017a), which focuses on considering the hazards of chemicals as a group or category. First published in 2007, it was updated in 2014 to include sections on analogue and category approaches, quantitative and qualitative read-across, justifying read-across, and using "bioprofiling" results (that include molecular data) for grouping chemicals. A new edition of this guidance document is now being prepared, including more extensive guidance on the use of omics data for chemical grouping. A further example of guidance related to chemical grouping, in this case specifically grouping and read-across, is the RAAF published by the ECHA (2017). This document provides both a framework and guidance for describing how G/RAx should be used and presented in a registration dossier in the context of meeting the REACH information requirements. It facilitates the consistent evaluation of the elements within a read-across case, including the grouping hypothesis and category (or analogue) justification. Case studies represent a further important contributor to defining future regulatory landscapes by helping to establish common and best practices for the use of novel methods for assessing chemicals, including as groups. For example, case studies form the Accelerating the Pace of Chemical Risk Assessment initiative, which is an international government-to-government activity whose aim is to promote collaboration and dialogue on the scientific and regulatory needs for the application and acceptance of NAMs in regulatory decision making (https://apcra.net/). These include a case study applying multi-omics to chemical grouping (Gruszczynska et al., 2024). The OECD Integrated Approaches for Testing and Assessment (IATA) case studies project aims to provide a forum to exchange information and grow confidence in the application of NAMs to assess chemical hazards in specific regulatory contexts (OECD, n.d.). A total of 34 IATA case studies have been reviewed, discussed, and published on the IATA OECD website (https://www.oecd.org/chemicalsafety/risk-assessment/iata/). Of these, 20 case studies have included aspects related to grouping and read-across, of which two have used transcriptomics data (OECD, 2017b; OECD, 2020). Structured reporting templates are critical tools for ensuring that standardized information is consistently described, facilitating both the evaluation of chemical toxicity and ecotoxicity data by regulators and data sharing. They also allow end users to assess if the approach may be suitable for other geographical regions, chemical sectors, or regulatory contexts and most importantly allow regulators to become familiar with data derived from NAMs. Reporting formats for documenting conventional chemical grouping and read-across—including the context of use, target chemical(s)/category definition, endpoint, grouping hypothesis, and justification for filling data gaps—are available (see, e.g., Chapter 7 in OECD, 2017a). An OECD project has developed the Omics Reporting Framework (OORF) for describing the acquisition, processing, and analysis of omics data for a range of applications (OECD, 2023). The OORF is currently being extended to include a reporting template for the specific application of molecular (omics) data to chemical grouping. The OECD states that "the most compelling evidence in support of a read-across hypothesis is information on a common mode of action of the substances and a mechanistic rationale for their common biological behaviour" (OECD, 2017a). Conventional G/RAx approaches are based on the hypothesis that structurally similar chemicals elicit similar biological responses. Yet there are many groups of chemicals for which this assumed relationship between structure and function does not hold. For example, thalidomide exists as two enantiomers that produce distinct biological responses: one is a sedative, and the other causes fetal malformations. Such "activity cliffs"—where structurally similar compounds have different MoA and/or potencies—demonstrate that structural similarity does not always lead to similar biological responses. By contrast, fentanyl and morphine are structurally dissimilar compounds, yet both share the same MoA, thus producing similar effects. The fact that structure does not always reliably predict function was well articulated by Wallqvist et al. (2006), who reported that "the connection between structure and biological response is not symmetric, with biological response better at predicting chemical structure than vice versa. Structurally and functionally similar compounds can have distinguishable biological responses reflecting different mechanisms of action." Furthermore, such differences in the toxicodynamic properties (i.e., MoA) of analogous compounds may also manifest as different acute and/or chronic toxicity outcomes. This limitation is acknowledged in ECHA's RAAF, which states "structural similarity alone is not sufficient to justify the possibility to predict properties of the target substance by read-across." It is therefore no surprise that Schultz et al. (2019) identified one of the main sources of uncertainty in read-across as the category justification (i.e., justifying group membership). If the source chemical(s) is not sufficiently similar to the target chemical(s) in terms of structure, to support similarity in their MoA, read-across will not be justifiable. For this reason, uncertainty surrounding the category justification must be addressed because it weakens confidence in the grouping, discussed further below. Under REACH, if read-across within a registration dossier does not provide sufficient evidence for a robust grouping of source(s) and target(s) chemicals, the dossier could be deemed noncompliant. Specifically, the ECHA has reported that G/RAx studies can be rejected in the absence of supporting data to substantiate the grouping hypothesis, for example, lack of knowledge of MoA and/or bridging studies between the source and target chemicals (ECHA, 2020). A second cause of registration dossiers that incorporate G/RAx being rejected is when the rationale for the read-across is missing or weak; for example, no explanation is provided linking structural similarity with the predicted toxicological endpoint. Another common problem when forming chemical groups based on ECOSAR and other quantitative structure–activity relationship ([Q]SAR) predictions is when many chemicals fall into multiple groups or into none at all. This is a particular problem where there are no or too few measured data to enable any benchmarking of the predicted values. There is clearly a need to improve our confidence in the formation of chemical groups or categories such as those efforts undertaken by the Board of Scientific Counselors in updating the TSCA (USEPA, 2022) and to quantify that level of confidence to facilitate the use of grouping in various risk-assessment contexts. The use of biological effects data as a basis for providing evidence to support group (or category) formation—via calculating the similarity of responses to chemical exposure—is widely viewed as a feasible solution for reducing uncertainty in grouping. However, we must ensure that the biological data will provide sufficient confidence for this application. It is important to emphasize that the generation of meaningful biological data requires a consideration of both the biological test system and the applied molecular assay. Several options are available for measuring molecular responses to exposure. For the generalized case of a data-poor target (i.e., limited experimental toxicity data), a biological effects comparison of source and target chemicals should examine a broad range of possible MoAs or mechanisms of action (MechoAs) to ensure that any significant toxicological effects are covered. In contrast, if the question is whether a target chemical behaves similarly to a group of chemicals with a relatively well-defined MoA or MechoA (which defines a specific target or pathway), a greater weighting could be placed on assessing the similarity of biological effects specific to that MechoA/MoA. Molecular measurements from omics technologies can be used to help address both of these situations, as illustrated in Figure 2, with introductory terminology presented in Textbox 2. For the former case, "untargeted" omics technologies measure a broad range of molecular responses (thus informing a broad range of MechoAs/MoAs); for the latter, a defined panel of "key event" molecular biomarkers could be measured that, for example, were derived from an adverse outcome pathway (AOP) describing the MechoA/MoA (https://aopwiki.org/). This first step in the application of omics technologies to chemical grouping is depicted in Figure 3 (right side). Ome—a broad collection of biomolecules, for example, genome (all genetic material), transcriptome (all gene transcripts), proteome (all proteins), and metabolome (all small-molecule metabolites). Omics—technologies that can be used to measure a broad range of molecular responses in the genome, transcriptome, proteome, or metabolome of a biological test system following chemical exposure. Transcriptomics—systematic study of expression of many genes in a cell, tissue, or organism, providing information on the molecular responses following chemical exposure. Metabolomics—systematic study of levels of many small-molecule metabolites and the biochemical processes that they are involved in, within a cell, tissue, or organism, providing information on downstream functional molecular responses to exposure. Targeted assay—measurement method that predefines the analytes, for example, to measure a specific MechoA/MoA (if few analytes are targeted) or multiple MechoAs/MoAs (if many analytes are targeted). Measurements using omics technologies can be targeted (see Table 1). Untargeted assay—measurement method that does not predefine the analytes. This approach attempts to measure the broadest range of molecular responses and hence gain insights into multiple MechoAs/MoAs simultaneously. Measurements using omics technologies can be untargeted (see Table 1). Key event molecular biomarker—a measurable marker that serves as an indicator of a specific MechoA/MoA, for example, derived from an AOP. Molecular effects data—a type of NAM data derived from either targeted or untargeted molecular assays that is used to calculate the bioactivity similarity of two or more chemicals and which can provide insights into MechoA(s)/MoA(s). Bioactivity similarity—a quantitative measure of similarity of two or more chemicals that is derived by comparing the molecular effects data from omics assays. It is important to highlight that measuring and using molecular data in regulatory toxicology are not new. Of the three measurement strategies for grouping, described in Table 1, the first—targeted molecular measurements—is already part of internationally accepted OECD test guidelines. The second strategy multiplexes (or parallelizes) many targeted molecular measurements, and the third approach additionally introduces untargeted measurements. Examples are provided in Table 1 to support the reader's understanding of these measurement strategies for a generalized MechoA/MoA assessment. Having generated either targeted or untargeted omics data in a relevant biological test system, the next step is to calculate the bioactivity similarity of the molecular responses to the source and target chemicals (see definitions in Textbox 2), in what can be referred to as a bridging study (Textbox 1). This typically involves applying multivariate statistical analyses and can enable assigning a quantitative level of confidence to the grouping hypothesis (Figure 3, right side). The third step is to interpret the molecular data in an attempt to identify the MechoA/MoA/AOP to build evidence for the grouping hypothesis; that is, if the omics data identify shared MechoA/MoA/AOP(s) that are perturbed as a result of exposure to a series of chemicals, then this provides a mechanistic justification to group those chemicals (Figure 3, right side). Minimally, evidence of shared molecular effects (without a clear understanding of mechanism) can be used to provide some justification for the chemical category. Here, resources that associate molecular changes with pathway perturbations, MechoA(s)/MoA(s)/AOP(s), and/or hazard are important parts of the toolkit. Many open-access and commercial resources exist, including the AOP Wiki, the Comparative Toxicogenomics Database, the S1500+ gene panel, the MTox700+ metabolite panel, BASF's MetaMapTox, and Qiagen's Ingenuity Pathway Analysis. By using omics data to substantiate a grouping hypothesis, the conventional G/RAx workflow (e.g., OECD, 2017a) need only be slightly altered to include this additional supporting evidence (Figure 3, left side). The initial steps—identifying the target compound and selecting source chemicals based on structural and physicochemical similarity—can remain largely the same. This satisfies current requirements for read-across to be based on structural similarity between source and target chemicals. The next step would typically be to refine the categories using mechanistic (MechoA/MoA/AOP) and/or endpoint-specific profilers (e.g., using the OECD [Q]SAR Toolbox). It is at this stage that the omics data can be introduced; that is, in addition to using these profilers, chemical categories are also refined based on bioactivity similarity using omics-derived molecular data. Source chemicals that are shown through a bridging study to not be biologically similar to the target can be excluded from the group. This approach should provide more confidence that the source and target chemicals share a common MechoA/MoA/AOP and thus elicit similar toxicological endpoints. Once the final list of source chemicals has been produced, the available toxicity data can be read across to fill the data gap for the target chemical. In effect, this is a weight-of-evidence approach that utilizes structural and bioactivity similarity to derive the chemical categories. In the future, if a structure-based chemical group is inadequate, it may be useful to create an alternative grouping hypothesis based solely on omics-derived molecular data. Such a change to regulatory practice would first require greater confidence in bioactivity profile–based grouping using omics data than exists today and would be best facilitated by the availability of a database of omics responses to enable comparison to data generated for the target substance. Several bioactivity profile–based grouping (and read-across) studies using omics data have been published, demonstrating the interest in evaluating this approach for supporting effective decision-making. We selected five studies and mapped them against a series of questions to highlight what each study sought to achieve, what approaches were used including whether any guidelines were followed, and to what extent uncertainties were considered to enable the reporting of the confidence of the grouping (Supporting Information, Table S1). Although such a mapping exercise is helpful for highlighting emerging "acceptable practice," in our study we focus on examining the benefits (this section) and challenges (next section) of incorporating omics data into chemical grouping (summarized in Textbox 3). Summary of top five benefits and challenges of incorporating omics data into chemical grouping. To provide targeted measurements of a broad range of MechoAs/MoAs simultaneously and an untargeted assessment of uncharacterized MechoAs/MoAs (dependent on the biological test system). To enable grouping by quantitatively comparing the similarities and differences of omics bioactivity profiles across chemicals, using statistically derived probability estimates, as a complement to structural similarity. In addition, the potential to provide mechanistic insights alongside statistically derived bioactivity-based grouping, together contributing to the category (or analogue) justification to support read-across to fill data gaps. Can be applied in high-throughput in vitro studies to screen and group the effects of multiple substances, enabling the triggering of higher-tier testing of a few sentinel substances. Can be used to group substances even when their structures are ill-defined, for example, UVCBs and polymers. A complex type of molecular data that requires specialist expertise for data analysis and interpretation. Currently, level(s) of bioactivity similarity has not been determined to assess "how similar is similar enough" to place two or more substances into the same group, nor have bioactivity thresholds been determined to delineate a molecular effect from no effect. It can be difficult to interpret the molecular changes and associate them to a MechoA/MoA. Currently, there has been only a limited assessment of the reliability of bioactivity profile–based grouping using omics data across laboratories, although there is ongoing work to address this challenge. There is currently no agreed "best practice" for grouping using omics data, although progress is underway at the OECD. The key benefit is that grouping using omics data does offer a solution to the well-recognized problem that grouping hypotheses typically do not include biological effects data; that is, they lack sufficient mechanistic underpinning. Furthermore, by measuring a broad swathe of biological response space (i.e., multiple MechoAs/MoAs/AOPs) simultaneously, and without bias, the untargeted omics measurements enable a data-driven assessment of not only which chemicals group together but also what molecular perturbations are driving that grouping. This knowledge could provide insights into hazards and, consequently, could aid the selection of further targeted assays. Further benefits of bioactivity profile–based grouping using omics data arise from the inherent capabilities of the technologies and computational approaches being applied. For example, metabolomics analyses can be conducted on biofluids, thereby enabling repeated measurements of some biological test systems, which could be valuable in cases where extensive metabolism or bioactivation occurs. Furthermore, the data analysis workflows are able to provide quantitative measures of similarity between two (or more) omics profiles, and confidence in the grouping can be based on statistical probabilities, facilitating robust decision-making (Gruszczynska et al., 2024). Although we are proposing that bioactivity profile–based grouping be considered as part of the weight of evidence toward a grouping hypothesis based on structural similarity, this assumes that structures can be defined for the test substances. However, there are many substances, for example, those of unknown or variable composition, complex reaction products and biological materials (UVCBs), that are poorly characterized in terms of component chemicals and their proportions, for which structure-based grouping is difficult, if not impossible. Applying an untargeted omics assay to an appropriate biological test system and calculating the bioactivity similarity from the biolo

We developed a simple screening system for the evaluation of neuromuscular and general toxicity in zebrafish embryos. The modular system consists of electrodynamic transducers above which tissue culture dishes with embryos can be placed. Multiple such loudspeaker-tissue culture dish pairs can be combined. Vibrational stimuli generated by the electrodynamic transducers induce a characteristic startle and escape response in the embryos. A belt-driven linear drive sequentially positions a camera above each loudspeaker to record the movement of the embryos. In this way, alterations to the startle response due to lethality or neuromuscular toxicity of chemical compounds can be visualized and quantified. We present an example of the workflow for chemical compound screening using this system, including the preparation of embryos and treatment solutions, operation of the recording system, and data analysis to calculate benchmark concentration values of compounds active in the assay. The modular assembly based on commercially available simple components makes this system both economical and flexibly adaptable to the needs of particular laboratory setups and screening purposes.

Human industries generate hundreds of thousands of chemicals, many of which have not been adequately studied for environmental safety or effects on human health. This deficit of chemical safety information is exacerbated by current testing methods in mammals that are expensive, labor-intensive, and time-consuming. Recently, scientists and regulators have been working to develop new approach methodologies (NAMs) for chemical safety testing that are cheaper, more rapid, and reduce animal suffering. One of the key NAMs to emerge is the use of invertebrate organisms as replacements for mammalian models to elucidate conserved chemical modes of action across distantly related species, including humans. To advance these efforts, here, we describe a method that uses the fruit fly, Drosophila melanogaster, to assess chemical safety. The protocol describes a simple, rapid, and inexpensive procedure to measure the viability and feeding behavior of exposed adult flies. In addition, the protocol can be easily adapted to generate samples for genomic and metabolomic approaches. Overall, the protocol represents an important step forward in establishing Drosophila as a standard model for use in precision toxicology.