AIMT12: NGRA uncertainty assessment: iterating a conceptual model through increasingly complex case studies and comparison to in vivo-based assessments

Description

The overarching goal is to test the hypothesis that in some situations new approach method (NAM)-based assessments bear less uncertainty whereas in others, animal-data based assessments will have less uncertainty. To achieve such, the specific project objectives include:

• Performing probabilistic and deterministic quantitative uncertainty analyses for up to 4 case examples that utilize different hazard characterisation tools used in non-animal approaches.
• Comparing hazard characterisation up to derivation of safe exposure levels via traditional and next generation risk assessment (NGRA) approaches for each case study.
• Demonstrating feasibility and utility of using a diverse toolbox of NAMs, including artificial intelligence (AI) and machine learning (ML) techniques, to assist in assessment of uncertainty.
• Synthesizing results and learnings in context of building on guidance from EFSA and WHO/IPCS on uncertainty knowledge by describing influencing factors, source, and size of uncertainties in NAM-based assessments.