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Multi-Omics Integration Into Adverse Outcome Pathway Framework: Principles, Progress, and Prospects for
Rajesh Pamanji1, Ragothaman Prathiviraj2, Gisha Sivan3
1Department of Microbiology, Pondicherry University, Puducherry, India.
Environmental and Molecular Mutagenesis
|June 3, 2026
Summary
Multi-omics technologies enhance adverse outcome pathway (AOP) construction by integrating diverse data layers. This approach improves mechanistic understanding and quantitative utility for next-generation chemical risk assessment.
Area of Science:
- Mechanistic toxicology
- Computational biology
- Biomarker discovery
Background:
- Adverse outcome pathway (AOP) framework aids mechanistic toxicology but traditionally uses limited, single-layer data.
- This limitation restricts mechanistic resolution and quantitative application in regulatory risk assessment.
- Multi-omics technologies offer rich, multi-scale molecular data to enhance AOPs.
Purpose of the Study:
- To systematically review how different omics layers (transcriptomics, proteomics, metabolomics, epigenomics) contribute to AOP development.
- To discuss emerging frameworks for integrating these omics data into AOP networks.
- To assess quantitative AOP (qAOP) strategies and identify knowledge gaps for regulatory acceptance.
Main Methods:
- Systematic review of multi-omics technologies (transcriptomics, proteomics, metabolomics, epigenomics, single-cell) in AOP development.
- Examination of transcriptome-guided key event (KE) identification and proteomic confirmation of KE-to-KE relationships (KERs).
- Assessment of metabolomics for phenotypic linkage, epigenomics for persistent effects, and single-cell approaches for resolution.
Main Results:
- Each omics layer offers unique contributions: transcriptomics for KE identification, proteomics for KERs, metabolomics for phenotypic linkage, and epigenomics for long-term effects.
- Transcriptomic points of departure (tPODs) from short-term exposures show concordance with chronic apical endpoints.
- Single-cell omics can overcome limitations of bulk assays by providing cellular resolution.
Conclusions:
- Integrating multi-omics data significantly enhances AOPs' mechanistic resolution and quantitative utility for chemical risk assessment.
- Key knowledge gaps include incomplete KE annotation, lack of standardized bioinformatics pipelines, and regulatory hurdles for omics-derived values.
- Further research and standardized frameworks are needed to accelerate the regulatory acceptance of multi-omics-informed AOPs.
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