A comparison of transgenic rodent mutation and in vivo comet assay responses for 91 chemicals.

A database of 91 chemicals with published data from both transgenic rodent mutation (TGR) and rodent comet assays has been compiled. The objective was to compare the sensitivity of the two assays for detecting genotoxicity. Critical aspects of study design and results were tabulated for each dataset. There were fewer datasets from rats than mice, particularly for the TGR assay, and therefore, results from both species were combined for further analysis. TGR and comet responses were compared in liver and bone marrow (the most commonly studied tissues), and in stomach and colon evaluated either separately or in combination with other GI tract segments. Overall positive, negative, or equivocal test results were assessed for each chemical across the tissues examined in the TGR and comet assays using two approaches: 1) overall calls based on weight of evidence (WoE) and expert judgement, and 2) curation of the data based on a priori acceptability criteria prior to deriving final tissue specific calls. Since the database contains a high prevalence of positive results, overall agreement between the assays was determined using statistics adjusted for prevalence (using AC1 and PABAK). These coefficients showed fair or moderate to good agreement for liver and the GI tract (predominantly stomach and colon data) using WoE, reduced agreement for stomach and colon evaluated separately using data curation, and poor or no agreement for bone marrow using both the WoE and data curation approaches. Confidence in these results is higher for liver than for the other tissues, for which there were less data. Our analysis finds that comet and TGR generally identify the same compounds (mainly potent mutagens) as genotoxic in liver, stomach and colon, but not in bone marrow. However, the current database content precluded drawing assay concordance conclusions for weak mutagens and non-DNA reactive chemicals.

It's difficult, but important, to make negative predictions.

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 (∼90%) 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.

Evaluation of a statistics-based Ames mutagenicity QSAR model and interpretation of the results obtained.

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.

Evaluation of a modified CD71 MicroFlow method for the flow cytometric analysis of micronuclei in rat bone marrow erythrocytes.

The aim of this study was to evaluate a modified flow cytometric method for the quantification of micronuclei in rat bone marrow reticulocytes. The method identified uses the erythrocyte pure fraction from cellulose filtered bone marrow with slight modifications to the widely published MicroFlow(®) method developed by Litron Laboratories, Rochester, NY for the detection of micronuclei in peripheral blood. A number of experiments were conducted to compare the micronucleus induction measured by flow cytometry with traditional microscopic analysis in male rats treated daily for 2 days with appropriate vehicle controls or various doses of cyclophosphamide (CP), mitomycin C (MMC), vinblastine sulfate (VBS), 1,2-dimethylhydrazine (DMH), etoposide (ETO), colchicine (COL), or 4-nitroquinoline-1-oxide (4NQO). In addition, for a subset of chemical we compared the induction of micronuclei in bone marrow and peripheral blood. The results from this study showed a very good correlation of micronucleus frequencies in bone marrow between microscopic analysis and the flow cytometry as well as between blood and bone marrow. In general, micronucleus frequencies of test compound treated animals and inter-animal variability were slightly lower by flow cytometric analysis compared to manual slide analysis. The data presented in this study support the use of the CD71 flow method for the analysis of micronuclei in rat bone marrow and also suggest that peripheral blood may be equally as sensitive as bone marrow in detecting a micronucleus response in short term studies.