Extrapolation of in vitro structural alerts for mutagenicity to the in vivo endpoint. (3rd October 2018)

• Record Type: Journal Article
• Authors: Tennant, Rachael E; Guesné, Sébastien J; Canipa, Steven; Cayley, Alex; Drewe, William C; Honma, Masamitsu; Masumura, Kenichi; Morita, Takeshi; Stalford, Susanne A; Williams, Richard V
• Is Part Of: Mutagenesis. Volume 34:Number 1(2019)
• Page Start: 111
• Page End: 121
• Publication Date: 2018-10-03
• Subjects: Mutagenesis -- Periodicals; Mutagenicity Tests -- Periodicals; Mutagens -- Periodicals; Mutagenesis; Periodicals; 576.542
• DOI: 10.1093/mutage/gey030
• Languages: English
• ISSNs: 0267-8357

Abstract

Abstract: As part of the hazard and risk assessment of chemicals in man, it is important to assess the ability of a chemical to induce mutations in vivo . Because of the commonalities in the molecular initiating event, mutagenicity in vitro can correlate well to the in vivo endpoint for certain compound classes; however, the difficulty lies in identifying when this correlation holds true. In silico alerts for in vitro mutagenicity may therefore be used as the basis for alerts for mutagenicity in vivo where an expert assessment is carried out to establish the relevance of the correlation. Taking this into account, a data set of publicly available transgenic rodent gene mutation assay data, provided by the National Institute of Health Sciences of Japan, was processed in the expert system Derek Nexus against the in vitro mutagenicity endpoint. The resulting predictivity was expertly reviewed to assess the validity of the observed correlations in activity and mechanism of action between the two endpoints to identify suitable in vitro alerts for extension to the in vivo endpoint. In total, 20 alerts were extended to predict in vivo mutagenicity, which has significantly improved the coverage of this endpoint in Derek Nexus against the data set provided. Updating the Derek Nexus knowledge base in this way led to an increase in sensitivity for this data set against this endpoint from 9% to 66% while maintaining a good specificity of 89%.