Overall goal/objectives:
Grouping and read-across are frequently used for human health and environmental endpoints. The quality of this hazard assessment methodology, however, needs to improve as registrants often do not provide enough scientific evidence to support their read-across case. Critical to the robustness of a ‘read-across’ prediction is the formation of a group (or category) of similar substances, where traditionally the similarity is based on their structural parameters. The objective of this case study is to establish a grouping/read-across workflow which allows a New Approach Methodology – specifically molecular mechanistic data measured using omics technologies – to substantiate (or not) the grouping hypothesis derived from conventional QSAR approaches, enabling more robust read-across.
Case Study Leader:
ECHA & University of Birmingham (Mark Viant)
Collaborators:
ECHA, UoB
Status: Completed
The metabolomics and transcriptomics study of 7 disperse azo dyes (1 target substance and 6 potential source substances for grouping/read-across) has been completed, as has the data analysis. The case study has demonstrated how multi-omics responses and molecular pathway perturbations can be quantitatively compared to select the most reliable source substance for a pre-defined target substance. With more scientifically robust category formation, the data gap for the target substance can be more adequately predicted, or read across, from the source.
Presentations or publications that have been publicly released:
- Manuscript in process
- ‘Substantiating Chemical Categories with Omics-derived Mechanistic Evidence’, Accelerating the Pace of Chemical Risk Assessment public webinar, 2020
- ‘Defining chemical categories for read-across using omics-derived mechanistic evidence from an invertebrate model’, British Toxicology Society, 14th April 2021