From toxicogenomics data to cumulative assessment groups: a framework for chemical grouping.

Area of Science:

• Toxicology
• Computational Biology
• Genomics

Background:

• Chemical grouping is crucial for regulatory toxicology, reducing data gaps, and cumulative risk assessments.
• Existing methods may not fully capture complex molecular and phenotypic effects of diverse chemical classes.
• Integrating toxicogenomics data is key for advancing risk assessment methodologies.

Purpose of the Study:

• To introduce a novel framework for chemical grouping using chemical-gene-phenotype-disease (CGPD) tetramers.
• To leverage publicly available toxicogenomics data for identifying and clustering chemicals with similar effects.
• To support evidence-based regulatory decision-making and the transition to next-generation risk assessment.

Main Methods:

• Utilized chemical-gene-phenotype-disease (CGPD) tetramers derived from the Comparative Toxicogenomics Database (CTD).
• Integrated publicly available toxicogenomics data for diverse chemical groups (pesticides, pharmaceuticals, industrial chemicals).
• Validated the framework by comparing CGPD tetramer-based clusters with established cumulative assessment groups (CAGs) from EFSA.

Main Results:

• Demonstrated strong overlap between CGPD tetramer-based clusters and established pesticide cumulative assessment groups (CAGs).
• Identified additional compounds relevant for risk assessment, including those linked to endocrine disruption and metabolic disorders.
• The framework effectively bridges omics-derived molecular data with phenotypic and disease endpoints.

Conclusions:

• The novel CGPD tetramer framework provides a comprehensive tool for chemical grouping.
• This approach supports regulatory toxicology and facilitates cumulative risk assessments.
• The study advances the transition towards next-generation risk assessment methodologies by integrating diverse data types.