| Literature DB >> 30189015 |
Alexander Amberg1, Lennart T Anger1, Joel Bercu2, David Bower3, Kevin P Cross3, Laura Custer4, James S Harvey5, Catrin Hasselgren6, Masamitsu Honma7, Candice Johnson3, Robert Jolly8, Michelle O Kenyon9, Naomi L Kruhlak10, Penny Leavitt4, Donald P Quigley3, Scott Miller3, David Snodin11, Lidiya Stavitskaya10, Andrew Teasdale12, Alejandra Trejo-Martin12, Angela T White5, Joerg Wichard13, Glenn J Myatt3.
Abstract
(Quantitative) structure-activity relationship or (Q)SAR predictions of DNA-reactive mutagenicity are important to support both the design of new chemicals and the assessment of impurities, degradants, metabolites, extractables and leachables, as well as existing chemicals. Aromatic N-oxides represent a class of compounds that are often considered alerting for mutagenicity yet the scientific rationale of this structural alert is not clear and has been questioned. Because aromatic N-oxide-containing compounds may be encountered as impurities, degradants and metabolites, it is important to accurately predict mutagenicity of this chemical class. This article analysed a series of publicly available aromatic N-oxide data in search of supporting information. The article also used a previously developed structure-activity relationship (SAR) fingerprint methodology where a series of aromatic N-oxide substructures was generated and matched against public and proprietary databases, including pharmaceutical data. An assessment of the number of mutagenic and non-mutagenic compounds matching each substructure across all sources was used to understand whether the general class or any specific subclasses appear to lead to mutagenicity. This analysis resulted in a downgrade of the general aromatic N-oxide alert. However, it was determined there were enough public and proprietary data to assign the quindioxin and related chemicals as well as benzo[c][1,2,5]oxadiazole 1-oxide subclasses as alerts. The overall results of this analysis were incorporated into Leadscope's expert-rule-based model to enhance its predictive accuracy.Entities:
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Year: 2019 PMID: 30189015 PMCID: PMC6402318 DOI: 10.1093/mutage/gey020
Source DB: PubMed Journal: Mutagenesis ISSN: 0267-8357 Impact factor: 3.000