Variability and uncertainty of data from genotoxicity Test Guidelines: What we know and why it matters

Abstract This review comprehensively examines the variability and uncertainty associated with test guideline (TG)-conform genotoxicity data and explores the respective implications for the integration of non-animal-methods (NAMs) into regulatory frameworks. Historical amendments to OECD TGs are mapped to reveal the method’s evolution that improves the scientific quality of the data but also explains data heterogeneity within available databases. An analysis of the major genotoxicity databases ECVAM, ISSMIC, and OASIS demonstrates substantial variability in genotoxicity calls. Using the EFSA genotoxicity database, which currently harbours the best-curated (meta-) data, we estimate that 22–77% of compounds exhibit similarity of replicate results below 85%, depending on the assay. The potentially most important variables statistically explaining variability and sensitivity were analysed. The practical limitations to identify them with high reliability and to define their optimum needs to be accepted as a qualitative baseline uncertainty. These findings underscore the necessity of contextualizing NAM performance evaluations within the intrinsic variability and uncertainty of animal and in vitro reference data. We propose that this variability is explicitly considered in the development and validation of NAM-based Integrated Approaches for Testing and Assessment (IATAs). This review provides a critical foundation for regulators and scientists aiming to enhance the acceptance and utility of NAMs in genotoxicity assessment. Competing Interest Statement The authors have declared no competing interest. Footnotes GR: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Resources, Validation, Visualization, Writing – original draft (mainly sections 6, 7, supplement 2), Writing – review & editing TP: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft (mainly sections 6, 7, supplement 2), Writing – review & editing CLB: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Resources, Validation, Visualization, Writing – original draft (mainly section 3), Writing – review & editing CB: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Resources, Validation, Visualization, Writing – original draft (mainly section 3), Writing – review & editing VH: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft (mainly section 2 & supplement 1), Writing – review & editing DN: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft (mainly section 2 & supplement 1), Writing – review & editing EC: Conceptualization, Data curation, Investigation, Writing – original draft (mainly section 2 & supplement 1), Writing – review & editing OT: Conceptualization, Data curation, Resources, Funding acquisition, Writing – original draft (mainly section 4), Writing – review & editing CAG: Conceptualization, Funding acquisition, Project administration, Writing – review & editing MA: Conceptualization, Funding acquisition, Project administration, Writing – review & editing BM: Conceptualization, Funding acquisition, Project administration, Writing – review & editing MP: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft (mainly sections 1, 5, 8, 9), Writing – review & editing