Building and Applying Quantitative Adverse Outcome Pathway Models for Chemical Hazard and Risk Assessment.

Area of Science:

• Toxicology
• Computational Biology
• Risk Assessment

Background:

• Improving chemical hazard and risk assessment speed, accuracy, and applicability is crucial.
• Integrating in vitro assays with biological pathway information offers a promising approach.

Purpose of the Study:

• To examine the utility of the adverse outcome pathway (AOP) framework for developing pathway-based quantitative models (qAOPs).
• To assess the applicability of qAOPs for regulatory chemical safety assessment.

Main Methods:

• Utilizing AOPs as conceptual models and the AOP knowledge base for data on key event relationships.
• Applying diverse computational modeling methods (statistical, Bayesian networks, regression, ODEs, individual-based models) to develop qAOPs.
• Discussing the integration of toxicokinetic models for exposure linkages and in vitro-in vivo/across-species extrapolation.

Main Results:

• Demonstrated that qAOP models can be developed using various computational approaches, not necessarily mirroring the original AOP structure.
• Highlighted the importance of selecting appropriate modeling methods based on research questions and data availability.
• Emphasized the need for toxicokinetic models to bridge exposure data with qAOPs and facilitate extrapolation.

Conclusions:

• qAOP models offer a robust framework for regulatory chemical safety assessment.
• Transparent documentation and adherence to best practices are essential for building confidence in qAOP models for regulatory use.
• The integration of AOPs and computational modeling advances toxicological risk assessment methodologies.