ArticleRegulatory toxicology and pharmacology : RTP2019
Transitioning to composite bacterial mutagenicity models in ICH M7 (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 16 papers.
What it found
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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.
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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.
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Who cites it
16 citing papers in PubMed, 35 citations in OpenAlex.
- Assessment of mutagenic potential of puberulic acid contaminated in red yeast rice (beni-koji) health food supplements.Mutagenesis · 2026Article
- Potential for Computational Genotoxicity: A Report on Symposium 3 of the 53rd Annual Meeting of the Japanese Environmental Mutagen and Genome Society (JEMS), 2024.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2026Article
- Risk-based in silico mutagenic assessment of benzodiazepine impurities using three QSAR tools.Toxicology reports · 2025Article
- Mind your prevalence!Journal of cheminformatics · 2024Article
- Matrine and Oxymatrine: evaluating the gene mutation potential using in silico tools and the bacterial reverse mutation assay (Ames test).Mutagenesis · 2024Article
- Novel (Q)SAR models for prediction of reversible and time-dependent inhibition of cytochrome P450 enzymes.Frontiers in pharmacology · 2024Article
- Implementation ofComputational toxicology (Amsterdam, Netherlands) · 2022Article
- New science, drug regulation, and emergent public health issues: The work of FDA's division of applied regulatory science.Frontiers in medicine · 2022Review
- MultiCASE Platform for In Silico Toxicology.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Article
- Bacterial mutagenicity test data: collection by the task force of the Japan pharmaceutical manufacturers association.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2021Article
- In vivo and in vitro mutagenicity of perillaldehyde and cinnamaldehyde.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2021Article
- Development of a new quantitative structure-activity relationship model for predicting Ames mutagenicity of food flavor chemicals using StarDrop™ auto-Modeller™.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2021Article
- 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
- Screening for Ames mutagenicity of food flavor chemicals by (quantitative) structure-activity relationship.Genes and environment : the official journal of the Japanese Environmental Mutagen Society · 2020Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
Abstract
The International Council on Harmonisation (ICH) M7(R1) guideline describes the use of complementary (quantitative) structure-activity relationship ((Q)SAR) models to assess the mutagenic potential of drug impurities in new and generic drugs. Historically, the CASE Ultra and Leadscope software platforms used two different statistical-based models to predict mutations at G-C (guanine-cytosine) and A-T (adenine-thymine) sites, to comprehensively assess bacterial mutagenesis. In the present study, composite bacterial mutagenicity models covering multiple mutation types were developed. These new models contain more than double the number of chemicals (n = 9,254 and n = 13,514) than the corresponding non-composite models and show better toxicophore coverage. Additionally, the use of a single composite bacterial mutagenicity model simplifies impurity analysis in an ICH M7 (Q)SAR workflow by reducing the number of model outputs requiring review. An external validation set of 388 drug impurities representing proprietary pharmaceutical chemical space showed performance statistics ranging from of 66-82% in sensitivity, 91-95% in negative predictivity and 96% in coverage. This effort represents a major enhancement to these (Q)SAR models and their use under ICH M7(R1), leading to improved patient safety through greater predictive accuracy, applicability, and efficiency when assessing the bacterial mutagenic potential of drug impurities.
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What OpenQuestion holds
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.