ArticleRegulatory toxicology and pharmacology : RTP2019
Principles and procedures for handling out-of-domain and indeterminate results as part of ICH M7 recommended (Q)SAR analyses.
Article in Regulatory toxicology and pharmacology : RTP, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Bridging science and curriculum: preparing future leaders in computational toxicology.Frontiers in toxicology · 2025Review
- Implementation ofComputational toxicology (Amsterdam, Netherlands) · 2022Article
- Use of In Silico Methods for Regulatory Toxicological Assessment of Pharmaceutical Impurities.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A cross-industry collaboration to assess if acute oral toxicity (Q)SAR models are fit-for-purpose for GHS classification and labelling.Regulatory toxicology and pharmacology : RTP · 2021Article
- Management of pharmaceutical ICH M7 (Q)SAR predictions - The impact of model updates.Regulatory toxicology and pharmacology : RTP · 2020Article
- Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses.Regulatory toxicology and pharmacology : RTP · 2019Article
- In silico toxicology: From structure-activity relationships towards deep learning and adverse outcome pathways.Wiley interdisciplinary reviews. Computational molecular scienceReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
44 authors.
Funding
Abstract
The International Council for Harmonization (ICH) M7 guideline describes a hazard assessment process for impurities that have the potential to be present in a drug substance or drug product. In the absence of adequate experimental bacterial mutagenicity data, (Q)SAR analysis may be used as a test to predict impurities' DNA reactive (mutagenic) potential. However, in certain situations, (Q)SAR software is unable to generate a positive or negative prediction either because of conflicting information or because the impurity is outside the applicability domain of the model. Such results present challenges in generating an overall mutagenicity prediction and highlight the importance of performing a thorough expert review. The following paper reviews pharmaceutical and regulatory experiences handling such situations. The paper also presents an analysis of proprietary data to help understand the likelihood of misclassifying a mutagenic impurity as non-mutagenic based on different combinations of (Q)SAR results. This information may be taken into consideration when supporting the (Q)SAR results with an expert review, especially when out-of-domain results are generated during a (Q)SAR evaluation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.