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 other Lhasa colleagues, introduce a new automated workflow implemented within the LCDB Plus platform, a carcinogenicity decision-support tool from Lhasa Limited.
By extending the Lhasa reliability grading scheme to include a new A+ category (optimized for dose-response modeling) and utilizing a Pareto optimization algorithm, this workflow successfully identifies the single most robust study in 97.6% of cases.
Why download this paper?
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Validated Results: See how the algorithm’s selections match up against expert-derived choices from the ICH M7 addendum.
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Supports the 3Rs: Learn how reproducible, non-animal decision frameworks align with modern regulatory strategies to replace animal testing.
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Efficiency: Discover how to reduce your study selection burden while ensuring regulatory compliance for Class 1 mutagenic impurities.
This workflow reduces study selection burden and supports the nationwide transition to reproducible, non-animal decision frameworks using New Approach Methodologies (NAMs) consistent with the 3Rs and recent UK, EU, and US regulatory strategies for replacing animals in science, while delivering recommendations aligned with experts, enabling reproducible acceptable intake derivation, and supporting submissions for ICH M7 Class 1 impurities.
Discover LCDB Plus for informed carcinogenicity assessments here.