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NAMs Strategy

Which carcinogenicity study should I use? Automated identification of reliable studies

Automating Study Selection for ICH M7 Class 1 Impurities Selecting the most reliable carcinogenicity study to derive acceptable intakes (AIs) under ICH M7 has historically been a subjective, time-consuming, and inconsistent process. In this paper, Jessica Halliday (Application Scientist at Lhasa Limited) and Dr David Ponting (Senior Principal Scientist at Lhasa Limited), as well as

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In silico prediction of Ames mutagenicity for organosilicon compounds: Exploring and enhancing chemical space boundaries

Organosilicon chemistry, a subsection of organic chemistry with unique chemical properties, is typically underrepresented in the training sets of (Q)SAR models and often outside the applicability domain. Using bacterial reverse mutation as an endpoint for a proof-of-concept study, we compiled a peer-reviewed reference dataset with publicly available reliable Ames tests of more than 100 organosilicon

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Translating Ambition Into Reality: A Collaborative Industry-led Framework For The UK Government Replacing Animals In Science Strategy

This whitepaper examines how the UK Government’s Replacing Animals in Science strategy can be translated into practical implementation across regulatory and industrial contexts. Drawing on a cross-sector discussion, it identifies key priorities for building confidence in non-animal New Approach Methodologies (NAMs), including regulatory submission confidence, Context of Use-led validation, modern benchmarking, trusted data infrastructure and international alignment.

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