It's difficult, but important, to make negative predictions.
Regul Toxicol Pharmacol
; 76: 79-86, 2016 Apr.
Article
em En
| MEDLINE
| ID: mdl-26785392
ABSTRACT
At the confluence of predictive and regulatory toxicologies, negative predictions may be the thin green line that prevents populations from being exposed to harm. Here, two novel approaches to making confident and robust negative in silico predictions for mutagenicity (as defined by the Ames test) have been evaluated. Analyses of 12 data sets containing >13,000 compounds, showed that negative predictivity is high (â¼90%) for the best approach and features that either reduce the accuracy or certainty of negative predictions are identified as misclassified or unclassified respectively. However, negative predictivity remains high (and in excess of the prevalence of non-mutagens) even in the presence of these features, indicating that they are not flags for mutagenicity.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
/
DNA Bacteriano
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Modelos Moleculares
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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
/
Prognostic_studies
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Risk_factors_studies
Limite:
Animals
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Humans
Idioma:
En
Revista:
Regul Toxicol Pharmacol
Ano de publicação:
2016
Tipo de documento:
Article