Substantiating chemical groups for read-across using molecular response profiles

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.