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In a joint effort involving scientists from academia, industry and regulatory agencies, ECETOC’s activities in Omics have led to conceptual proposals for: (1) A framework that assures data quality for reporting and inclusion of Omics data in regulatory assessments; and (2) an approach to robustly quantify these data, prior to interpretation for regulatory use. In continuation of these activities this workshop explored and identified areas of need to facilitate robust interpretation of such data in the context of deriving points of departure (POD) for risk assessment and determining an adverse change from normal variation. ECETOC was amongst the first to systematically explore the application of Omics methods, now incorporated into the group of methods known as New Approach Methodologies (NAMs), to regulatory toxicology. This support has been in the form of both projects (primarily with CEFIC/LRI) and workshops. Outputs have led to projects included in the workplan of the Extended Advisory Group on Molecular Screening and Toxicogenomics (EAGMST) group of the Organisation for Economic Co-operation and Development (OECD) and to the drafting of OECD Guidance Documents for Omics data reporting, with potentially more to follow on data transformation and interpretation. The current workshop was the last in a series of technical methods development workshops, with a sub-focus on the derivation of a POD from Omics data. Workshop presentations demonstrated that Omics data developed within robust frameworks for both scientific data generation and analysis can be used to derive a POD. The issue of noise in the data was discussed as an important consideration for identifying robust Omics changes and deriving a POD. Such variability or “noise” can comprise technical or biological variation within a dataset and should clearly be distinguished from homeostatic responses. Adverse outcome pathways (AOPs) were considered a useful framework on which to assemble Omics

The predominantly animal-centric approach of chemical safety assessment has increasingly come under pressure. Society is questioning overall performance, sustainability, continued relevance for human health risk assessment and ethics of this system, demanding a change of paradigm. At the same time, the scientific toolbox used for risk assessment is continuously enriched by the development of “New Approach Methodologies” (NAMs). While this term does not define the age or the state of readiness of the innovation, it covers a wide range of methods, including quantitative structure–activity relationship (QSAR) predictions, high-throughput screening (HTS) bioassays, omics applications, cell cultures, organoids, microphysiological systems (MPS), machine learning models and artificial intelligence (AI). In addition to promising faster and more efficient toxicity testing, NAMs have the potential to fundamentally transform today’s regulatory work by allowing more human-relevant decision-making in terms of both hazard and exposure assessment. Yet, several obstacles hamper a broader application of NAMs in current regulatory risk assessment. Constraints in addressing repeated-dose toxicity, with particular reference to the chronic toxicity, and hesitance from relevant stakeholders, are major challenges for the implementation of NAMs in a broader context. Moreover, issues regarding predictivity, reproducibility and quantification need to be addressed and regulatory and legislative frameworks need to be adapted to NAMs. The conceptual perspective presented here has its focus on hazard assessment and is grounded on the main findings and conclusions from a symposium and workshop held in Berlin in November 2021. It intends to provide further insights into how NAMs can be gradually integrated into chemical risk assessment aimed at protection of human health, until eventually the current paradigm is replaced by an animal-free “Next Generation Risk Assessment” (NGRA).

The gut microbiome has become a burgeoning field of research and is proven to play a role in in many host physiological aspects, also contributing to the blood metabolome that is otherwise produced by tissues of the host organism. In order to increase the mechanistic understanding of how the microbiome influences its host health, there is a need to integrate knowledge of the composition of the gut microbiome with its functionality in terms of microbiota-mediated metabolic processes. The aim of this project was to obtain detailed insight in the mammalian-microbiome co-metabolism of endogenous metabolites and the extent to which the microbiome influences the plasma metabolome observed. This was achieved with the help of the MetaMap®Tox database in which the metabolome and toxicity data of more than 800 compounds are stored. To elucidate this interaction, metabolites produced by the gut microbiome in the blood of our animal model, Wistar rats, had to be determined. Therefore, antibiotics were used to modulate the microbial communities of Wistar-rats. After 28-day oral administration, metabolomics of plasma, feces, and cecum-content was done. Additionally, DNA was extracted from rat feces and the 16S subunit was sequenced to perform a core diversity analysis. Specific plasma metabolome patterns were established, and microbiome-related metabolites identified as key metabolites in MetaMap®Tox. Metabolites such as hippuric acid, indole derivates and glycerol appeared to be microbiome-derived or -associated plasma metabolites. In general, most changes were observed in metabolites belonging to the class of bile acids, complex lipids, fatty acids and related metabolites, as well as amino acids and related metabolites. Especially abrupt changes observed in the bile acid pool after antibiotic administration evidenced a strong influence of the microbiome on bile acid metabolism. In both feces and cecum-content, where almost all plasma metabolites could be determined as well, a t

A generic Transcriptomics Reporting Framework (TRF) is presented that lists parameters that should be reported in ‘omics studies used in a regulatory context. The TRF encompasses the processes from transcriptome profiling from data generation to a processed list of differentially expressed genes (DEGs) ready for interpretation. Included within the TRF is a reference baseline analysis (RBA) that encompasses raw data selection; data normalisation; recognition of outliers; and statistical analysis. The TRF itself does not dictate the methodology for data processing, but deals with what should be reported. Its principles are also applicable to sequencing data and other ‘omics. In contrast, the RBA specifies a simple data processing and analysis methodology that is designed to provide a comparison point for other approaches and is exemplified here by a case study. By providing transparency on the steps applied during ‘omics data processing and analysis, the TRF will increase confidence processing of ‘omics data, and regulatory use. Applicability of the TRF is ensured by its simplicity and generality. The TRF can be applied to all types of regulatory ‘omics studies, and it can be executed using different commonly available software tools.

This survey by the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) highlights that ‘omics technologies are generally not yet applied to meet standard information requirements during regulatory hazard assessment. While they are used within weight-of-evidence approaches to investigate substances’ modes-of-action, consistent approaches for the generation, processing and interpretation of ‘omics data are not applied. To date, no ‘omics technology has been standardised or validated. Best practices for performing ‘omics studies for regulatory purposes (e.g., microarrays for transcriptome profiling) remain to be established. Therefore, three frameworks for (i) establishing a Good-Laboratory Practice-like context for collecting, storing and curating ‘omics data; (ii) ‘omics data processing; and (iii) quantitative WoE approaches to interpret ‘omics data have been developed, that are presented in this journal issue. Application of the frameworks will enable between-study comparison of results, which will facilitate the regulatory applicability of 'omics data. The frameworks do not constitute prescriptive protocols precluding any other data analysis method, but provide a baseline for analysis that can be applied to all data allowing ready cross-comparison. Data analysis that does not follow the frameworks can be justified and the resulting data can be compared with the Framework-based common analysis output.

‘Omics technologies are gaining importance to support regulatory toxicity studies. Prerequisites for performing ‘omics studies considering GLP principles were discussed at the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) Workshop Applying ‘omics technologies in Chemical Risk Assessment. A GLP environment comprises a standard operating procedure system, proper pre-planning and documentation, and inspections of independent quality assurance staff. To prevent uncontrolled data changes, the raw data obtained in the respective ‘omics data recording systems have to be specifically defined. Further requirements include transparent and reproducible data processing steps, and safe data storage and archiving procedures. The software for data recording and processing should be validated, and data changes should be traceable or disabled. GLP-compliant quality assurance of ‘omics technologies appears feasible for many GLP requirements. However, challenges include (i) defining, storing, and archiving the raw data; (ii) transparent descriptions of data processing steps; (iii) software validation; and (iv) ensuring complete reproducibility of final results with respect to raw data. Nevertheless, ‘omics studies can be supported by quality measures (e.g., GLP principles) to ensure quality control, reproducibility and traceability of experiments. This enables regulators to use ‘omics data in a fit-for-purpose context, which enhances their applicability for risk assessment.

Prevailing knowledge gaps in linking specific molecular changes to apical outcomes and methodological uncertainties in the generation, storage, processing, and interpretation of 'omics data limit the application of 'omics technologies in regulatory toxicology. Against this background, the European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) convened a workshop Applying 'omics technologies in chemicals risk assessment that is reported herein. Ahead of the workshop, multi-expert teams drafted frameworks on best practices for (i) a Good-Laboratory Practice-like context for collecting, storing and curating 'omics data; (ii) the processing of 'omics data; and (iii) weight-of-evidence approaches for integrating 'omics data. The workshop participants confirmed the relevance of these Frameworks to facilitate the regulatory applicability and use of 'omics data, and the workshop discussions provided input for their further elaboration. Additionally, the key objective (iv) to establish approaches to connect 'omics perturbations to phenotypic alterations was addressed. Generally, it was considered promising to strive to link gene expression changes and pathway perturbations to the phenotype by mapping them to specific adverse outcome pathways. While further work is necessary before gene expression changes can be used to establish safe levels of substance exposure, the ECETOC workshop provided important incentives towards achieving this goal.

The mouse embryonic stem D3 (ES-D3) cell differentiation assay is based on the morphometric measurement of cardiomyocyte differentiation and is a promising tool to detect developmental toxicity of compounds. The BeWo transport model, consisting of BeWo b30 cells grown on transwell inserts and mimicking the placental barrier, is useful to determine relative placental transport velocities of compounds. We have previously demonstrated the usefulness of the ES-D3 cell differentiation assay in combination with the in vitro BeWo transport model to predict the relative in vivo developmental toxicity potencies of a set of reference azole compounds. To further evaluate this combined in vitro toxicokinetic and toxicodynamic approach, we combined ES-D3 cell differentiation data of six novel triazoles with relative transport rates obtained from the BeWo model and compared the obtained ranking to the developmental toxicity ranking as derived from in vivo data. The data show that the combined in vitro approach provided a correct prediction for in vivo developmental toxicity, whereas the ES-D3 cell differentiation assay as stand-alone did not. In conclusion, we have validated the combined in vitro approach for developmental toxicity, which we have previously developed with a set of reference azoles, for a set of six novel triazoles. We suggest that this combined model, which takes both toxicodynamic and toxicokinetic aspects into account, should be further validated for other chemical classes of developmental toxicants