NAMs: Beta testing needed

• Published in: Current opinion in toxicology Vol. 39; p. 100490
• Main Authors: Bloch, Denise, Marx-Stoelting, Philip, Tralau, Tewes
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-09-2024
• Subjects: Acute toxicity; New approach methodologies; Regulatory implementation of alternatives to animal testing; Safe harbour approach; Short-term toxicity; Safe harbour approach; Regulatory implementation of alternatives to animal testing; New approach methodologies; Short-term toxicity; Acute toxicity
• ISSN: 2468-2020; 2468-2020
• DOI: 10.1016/j.cotox.2024.100490

A variety of new approach methodologies (NAMs) have already been developed for acute systemic and short-term toxicity, including in vitro, in silico, and omics methods. To advance their regulatory implementation, we suggest that beta testing of these methods in regulatory settings is urgently needed. There are several limitations to the use of NAMs for acute systemic and short-term toxicity, such as the lack of definitions for applicability domains, skewed reference data for validation, and the absence of representation of kinetic processes and multi-organ complexity. These limitations may lead to risks associated with the ordinary regulatory implementation, such as the application of methods to substances outside of their intended applicability domain or reduced predictivity due to a lack of mechanistic information or consideration of kinetics. We argue that this could be avoided by beta testing. Further benefits of beta testing would be the filling of in vivo data gaps and potentially improved validation with regard to human relevance of methods. In order to enhance the improvement, familiarisation, and acceptance of NAMs in the near future, it is essential for such concept of beta testing to rely on feedback loops between method testers and developers.
•Beta Testing new approach methodologies will accelerate their acceptance.•Acute systemic and short-term toxicity are ready for beta testing.•Advantages include familiarisation and identification of data gaps.•Feedback loops ensure constant method improvement.