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Mutagenesis ; 34(1): 111-121, 2019 03 06.
Artigo em Inglês | MEDLINE | ID: mdl-30281100


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%.

Simulação por Computador , Mutagênese/efeitos dos fármacos , Testes de Mutagenicidade , Mutagênicos/química , Animais , Humanos , Mutagênicos/toxicidade , Projetos de Pesquisa , Sensibilidade e Especificidade
Mutagenesis ; 34(1): 25-32, 2019 03 06.
Artigo em Inglês | MEDLINE | ID: mdl-30346596


While high-level performance metrics generated from the validation of quantitative structure-activity relationship (QSAR) systems can provide valuable information on how well these models perform and where they need to be improved, they require appropriate interpretation. There is no universal performance metric which will answer all of the questions a user might ask relating to a model, and therefore, a combination of metrics should usually be considered. Furthermore, results may vary according to the chemical space being used to validate a model, and, in some cases, it may be the validation data which is lacking or ambiguous rather than the prediction being made. Finally, users also need to consider the interpretability of the predictions being made, alongside the accuracy of the predictions. In this paper, we will discuss these important considerations in more detail within the context of the results obtained at Lhasa Limited as part of the National Institute of Health Sciences (NIHS) QSAR challenge project.

Mutagênese/efeitos dos fármacos , Mutagênicos/toxicidade , Relação Quantitativa Estrutura-Atividade , Técnicas In Vitro , Mutagênese/genética , Testes de Mutagenicidade
Mutagenesis ; 34(1): 3-16, 2019 03 06.
Artigo em Inglês | MEDLINE | ID: mdl-30357358


The International Conference on Harmonization (ICH) M7 guideline allows the use of in silico approaches for predicting Ames mutagenicity for the initial assessment of impurities in pharmaceuticals. This is the first international guideline that addresses the use of quantitative structure-activity relationship (QSAR) models in lieu of actual toxicological studies for human health assessment. Therefore, QSAR models for Ames mutagenicity now require higher predictive power for identifying mutagenic chemicals. To increase the predictive power of QSAR models, larger experimental datasets from reliable sources are required. The Division of Genetics and Mutagenesis, National Institute of Health Sciences (DGM/NIHS) of Japan recently established a unique proprietary Ames mutagenicity database containing 12140 new chemicals that have not been previously used for developing QSAR models. The DGM/NIHS provided this Ames database to QSAR vendors to validate and improve their QSAR tools. The Ames/QSAR International Challenge Project was initiated in 2014 with 12 QSAR vendors testing 17 QSAR tools against these compounds in three phases. We now present the final results. All tools were considerably improved by participation in this project. Most tools achieved >50% sensitivity (positive prediction among all Ames positives) and predictive power (accuracy) was as high as 80%, almost equivalent to the inter-laboratory reproducibility of Ames tests. To further increase the predictive power of QSAR tools, accumulation of additional Ames test data is required as well as re-evaluation of some previous Ames test results. Indeed, some Ames-positive or Ames-negative chemicals may have previously been incorrectly classified because of methodological weakness, resulting in false-positive or false-negative predictions by QSAR tools. These incorrect data hamper prediction and are a source of noise in the development of QSAR models. It is thus essential to establish a large benchmark database consisting only of well-validated Ames test results to build more accurate QSAR models.

Mutagênese/efeitos dos fármacos , Mutagênicos/toxicidade , Relação Quantitativa Estrutura-Atividade , Simulação por Computador , Bases de Dados Factuais , Humanos , Japão , Testes de Mutagenicidade
Regul Toxicol Pharmacol ; 88: 77-86, 2017 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-28549899


The identification of impurities with mutagenic potential is required for any potential pharmaceutical. The ICH M7 guidelines state that two complementary in silico toxicity prediction tools may be used to predict the mutagenic potential of pharmaceutical impurities. An expert review of the resulting in silico predictions is required, and numerous publications have been released to guide the expert review process. One such publication suggests that literature-based structural alerts (LBSAs) may provide a suitable aid in the expert review process. This publication provides a study of the effect of using one such set of LBSAs for the expert review of mutagenicity predictions from two complementary in silico tools. The analysis was performed using an Ames test dataset of 2619 compounds, and required interpretation of the LBSAs which proved to be a subjective process. Globally the LBSAs produced many more false positives than the in silico systems; whilst some exhibited a predictive performance comparable to the in silico systems, the majority were overly sensitive at the cost of accuracy. Use of LBSAs as part of an expert review process, without considering mitigating factors, could result in many more false positives and potentially the need to carry out additional and unnecessary Ames tests.

Contaminação de Medicamentos , Testes de Mutagenicidade , Mutagênicos/toxicidade , Simulação por Computador , DNA/efeitos dos fármacos , Conjuntos de Dados como Assunto , Reações Falso-Positivas , Guias como Assunto
Regul Toxicol Pharmacol ; 76: 7-20, 2016 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-26708083


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.

Modelos Estatísticos , Mutagênese , Testes de Mutagenicidade/estatística & dados numéricos , Mutação , Relação Quantitativa Estrutura-Atividade , Algoritmos , Animais , DNA Bacteriano/efeitos dos fármacos , DNA Bacteriano/genética , Bases de Dados Factuais , Técnicas de Apoio para a Decisão , Humanos , Reprodutibilidade dos Testes , Medição de Risco , Software
Mutagenesis ; 31(1): 17-25, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-26142242


While the in vivo genotoxicity of a compound may not always correlate well with its activity in in vitro test systems, for certain compound classes a good overlap may exist between the two endpoints. The difficulty, however, lies in establishing the cases where this relationship holds true and selecting the most appropriate protocol to highlight any potential in vivo hazard. With this in mind, a project was initiated in which existing structural alerts for in vitro chromosome damage in the expert system Derek Nexus were assessed for their relevance to in vivo activity by assessing their predictivity against an in vivo chromosome damage data set. An expert assessment was then made of selected alerts. Information regarding the findings from specific in vivo tests was added to the alert along with any significant correlations between activity and test protocol or mechanism. A total of 32 in vitro alerts were updated using this method resulting in a significant improvement in the coverage of in vivo chromosome damage in Derek Nexus against a data set compiled by the mammalian mutagenicity study group of Japan. The detailed information relating to in vivo activity and protocol added to the alerts in combination with the mechanistic information provided will prove useful in directing the further testing of compounds of interest.

Aberrações Cromossômicas , Simulação por Computador , Dano ao DNA , Mutagênicos/toxicidade , Software , Animais , Cromossomos/efeitos dos fármacos , Humanos , Mamíferos/genética , Testes de Mutagenicidade