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Two major endpoints for genotoxicity tests are gene mutation and chromosome damage (CD), which includes clastogenicity and aneugenicity detected by chromosomal aberration (CA) test or micronucleus (MN) test. Many in silico prediction systems for bacterial mutagenicity (i.e. Ames test results) have been developed and marketed. They show good performance for prediction of Ames mutagenicity. On the other hand, it seems that in silico prediction of CD does not progress as much as Ames prediction. Reasons for this include different mechanisms and detection methods, many false positives and conflicting test results. However, some (quantitative) structure–activity relationship ((Q)SAR) models (e.g. Derek Nexus [Derek], ADMEWorks [AWorks] and CASE Ultra [MCase]) can predict CA test results. Therefore, performances of the three (Q)SAR models were compared using the expanded Carcinogenicity Genotoxicity eXperience (CGX) dataset for understanding current situations and future development. The constructed dataset contained 440 chemicals (325 carcinogens and 115 non-carcinogens). Sensitivity, specificity, accuracy or applicability of each model were 56.0, 86.9, 68.6 or 89.1% in Derek, 67.7, 61.5, 65.2 or 99.3% in AWorks, and 91.0, 64.9, 80.5 or 97.7% in MCase, respectively. The performances (sensitivity and accuracy) of MCase were higher than those of Derek or AWorks. Analysis of predictivity of (Q)SAR models of certain chemical classes revealed no remarkable differences among the models. The tendency of positive prediction by (Q)SAR models was observed in alkylating agents, aromatic amines or amides, aromatic nitro compounds, epoxides, halides and N-nitro or N-nitroso compounds. In an additional investigation, high sensitivity but low specificity was noted in in vivo MN prediction by MCase. Refinement of test data to be used for in silico system (e.g. consideration of cytotoxicity or re-evaluation of conflicting test results) will be needed to improve performance of CD prediction. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Genotoxicity evaluation has been widely used to estimate the carcinogenicity of test substances during safety evaluation. However, the latest strategies using genotoxicity tests give more weight to sensitivity; therefore, their accuracy has been very low. For precise carcinogenicity evaluation, we attempted to establish an integrated testing strategy for the tailor-made carcinogenicity evaluation of test materials, considering the relationships among genotoxicity test results (Ames, in vitro mammalian genotoxicity and in vivo micronucleus), carcinogenicity test results and chemical properties (molecular weight, logKow and 179 organic functional groups). By analyzing the toxicological information and chemical properties of 230 chemicals, including 184 carcinogens in the Carcinogenicity Genotoxicity eXperience database, a decision tree for carcinogenicity evaluation was optimised statistically. A decision forest model was generated using a machine-learning method—random forest—which comprises thousands of decision trees. As a result, balanced accuracies in cross-validation of the optimised decision tree and decision forest model, considering chemical space (71.5% and 75.5%, respectively), were higher than balanced accuracy of an example regulatory decision tree (54.1%). Moreover, the statistical optimisation of tree-based models revealed significant organic functional groups that would cause false prediction in standard genotoxicity tests and non-genotoxic carcinogenicity (e.g. organic amide and thioamide, saturated heterocyclic fragment and aryl halide). In vitro genotoxicity tests were the most important parameters in all models, even when in silico parameters were integrated. Although external validation is required, the findings of the integrated testing strategies established herein will contribute to precise carcinogenicity evaluation and to determine new mechanistic hypotheses of carcinogenicity. [ABSTRACT FROM AUTHOR]

Genotoxicity evaluation has been widely used to estimate the carcinogenicity of test substances during safety evaluation. However, the latest strategies using genotoxicity tests give more weight to sensitivity; therefore, their accuracy has been very low. For precise carcinogenicity evaluation, we attempted to establish an integrated testing strategy for the tailor-made carcinogenicity evaluation of test materials, considering the relationships among genotoxicity test results (Ames, in vitro mammalian genotoxicity and in vivo micronucleus), carcinogenicity test results and chemical properties (molecular weight, logKow and 179 organic functional groups). By analyzing the toxicological information and chemical properties of 230 chemicals, including 184 carcinogens in the Carcinogenicity Genotoxicity eXperience database, a decision tree for carcinogenicity evaluation was optimised statistically. A decision forest model was generated using a machine-learning method-random forest-which comprises thousands of decision trees. As a result, balanced accuracies in cross-validation of the optimised decision tree and decision forest model, considering chemical space (71.5% and 75.5%, respectively), were higher than balanced accuracy of an example regulatory decision tree (54.1%). Moreover, the statistical optimisation of tree-based models revealed significant organic functional groups that would cause false prediction in standard genotoxicity tests and non-genotoxic carcinogenicity (e.g., organic amide and thioamide, saturated heterocyclic fragment and aryl halide). In vitro genotoxicity tests were the most important parameters in all models, even when in silico parameters were integrated. Although external validation is required, the findings of the integrated testing strategies established herein will contribute to precise carcinogenicity evaluation and to determine new mechanistic hypotheses of carcinogenicity., (© The Author(s) 2018. Published by Oxford University Press on behalf of the UK Environmental Mutagen Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© The Author 2015. Published by Oxford University Press on behalf of the UK Environmental Mutagen Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)