Substantiating chemical groups for read-across using molecular response profiles.

Area of Science:

• Environmental toxicology
• Computational toxicology
• Omics sciences

Background:

• Chemical grouping uses structural similarity to predict toxicity, aiding risk assessment and reducing animal testing.
• Structural similarity alone is often insufficient; additional data strengthens grouping justifications.
• Multi-omics data offers a way to assess chemical bioactivity and infer mode of action.

Purpose of the Study:

• To demonstrate how multi-omics bioactivity data can increase confidence in chemical grouping hypotheses.
• To investigate the utility of bioactivity profiles in reflecting chemical modes of action.
• To assess the performance of structure-based versus bioactivity-based grouping.

Main Methods:

• Investigated three phthalates and three uncouplers of oxidative phosphorylation.
• Applied structure-based grouping and short-term exposures of Daphnia magna to generate multi-omics data.
• Assessed bioactivity similarities using t-statistics and hierarchical cluster analysis.

Main Results:

• Conventional structure-based grouping failed to separate phthalates and uncouplers into distinct categories.
• Bioactivity profile-based grouping, after thresholding, correctly separated the remaining five substances into two chemical classes.
• High replicability confidence was achieved with the bioactivity profile-based grouping.

Conclusions:

• Multi-omics bioactivity profiles significantly increase confidence in chemical grouping for risk assessment.
• Bioactivity profile-based grouping demonstrates a viable strategy for classifying chemicals based on their molecular responses.
• Interpreting the specific molecular features driving 'omics-based grouping remains a challenge.