Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained.
Regul Toxicol Pharmacol
; 76: 7-20, 2016 Apr.
Article
em En
| MEDLINE
| ID: mdl-26708083
ABSTRACT
The relative wealth of bacterial mutagenicity data available in the public literature means that in silico quantitative/qualitative structure activity relationship (QSAR) systems can readily be built for this endpoint. A good means of evaluating the performance of such systems is to use private unpublished data sets, which generally represent a more distinct chemical space than publicly available test sets and, as a result, provide a greater challenge to the model. However, raw performance metrics should not be the only factor considered when judging this type of software since expert interpretation of the results obtained may allow for further improvements in predictivity. Enough information should be provided by a QSAR to allow the user to make general, scientifically-based arguments in order to assess and overrule predictions when necessary. With all this in mind, we sought to validate the performance of the statistics-based in vitro bacterial mutagenicity prediction system Sarah Nexus (version 1.1) against private test data sets supplied by nine different pharmaceutical companies. The results of these evaluations were then analysed in order to identify findings presented by the model which would be useful for the user to take into consideration when interpreting the results and making their final decision about the mutagenic potential of a given compound.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Mutagênese
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Relação Quantitativa Estrutura-Atividade
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Testes de Mutagenicidade
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Mutação
Tipo de estudo:
Etiology_studies
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Prognostic_studies
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Qualitative_research
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Risk_factors_studies
Limite:
Animals
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Humans
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article