Validation of the performance of TIMES genotoxicity models with EFSA pesticide data.

This study validates the performance of the TIssue MEtabolism Simulator (TIMES) genotoxicity models with data on pesticide chemicals included in a recently released European Food Safety Authority (EFSA) genotoxicity database. The EFSA database is biased towards negative chemicals. A comparison of substances included in the EFSA database and TIMES genotoxicity databases showed that the majority of the EFSA pesticides is not included in the TIMES genotoxicity databases and, thus, out of the applicability domains of the current TIMES models. However, the EFSA genotoxicity database provides an opportunity to expand the TIMES models. Where there is overlap of substances, consistency between EFSA and TIMES databases for the chemicals with documented data is found to be high (>80%) with respect to the Ames data and lower than the Ames data with respect to chromosomal aberration (CA) and mouse lymphoma assay (MLA) data. No conclusion for consistency with respect to micronucleus test and comet genotoxicity data can be provided due to the limited number of overlapping substances. Specificity of the models is important, given the prevalence of negative genotoxicity data in the EFSA database. High specificity (>80%) is obtained for prediction of the EFSA pesticides with Ames data. Moreover, this high specificity of the TIMES Ames models is not dependant on pesticides being within the domains. Specificity of the TIMES CA and MLA models is lower (>40%) to pesticides for out of domain. Sensitivity of TIMES in vitro and in vivo models cannot be properly estimated due to the small number of positive chemicals in the EFSA database.

Integrated approach to testing and assessment for predicting rodent genotoxic carcinogenicity.

We investigated the performance of an integrated approach to testing and assessment (IATA), designed to cover different genotoxic mechanisms causing cancer and to replicate measured carcinogenicity data included in a new consolidated database. Genotoxic carcinogenicity was predicted based on positive results from at least two genotoxicity tests: one in vitro and one in vivo (which were associated with mutagenicity categories according to the Globally Harmonized System classification). Substances belonging to double positives mutagenicity categories were assigned to be genotoxic carcinogens. In turn, substances that were positive only in a single mutagenicity test were assigned to be mutagens. Chemicals not classified by the selected genotoxicity endpoints were assigned to be negative genotoxic carcinogens and subsequently evaluated for their capability to elicit non-genotoxic carcinogenicity. However, non-genotoxic carcinogenicity mechanisms were not currently included in the developed IATA. The IATA is docked to the OECD Toolbox and uses measured data for different genotoxicity endpoints when available. Alternatively, the system automatically provides predictions by SAR genotoxicity models using the OASIS Tissue Metabolism Simulator platform. When the developed IATA was tested against the consolidated database, its performance was found to be high, with sensitivity of 74% and specificity of 83%, when measured carcinogenicity data were used along with predictions falling within the models' applicability domains. Performance of the IATA would be slightly changed to a sensitivity of 80% and specificity of 72% when the evaluation by non-genotoxic carcinogenicity mechanisms was taken into account. Copyright © 2016 John Wiley & Sons, Ltd.

A feasibility study: Can information collected to classify for mutagenicity be informative in predicting carcinogenicity?

Carcinogenicity is a complex endpoint of high concern yet the rodent bioassay still used is costly to run in terms of time, money and animals. Therefore carcinogenicity has been the subject of many different efforts to both develop short-term tests and non-testing approaches capable of predicting genotoxic carcinogenic potential. In our previous publication (Mekenyan et al., 2012) we presented an in vitro-in vivo extrapolation workflow to help investigate the differences between in vitro and in vivo genotoxicity tests. The outcomes facilitated the development of new (Q)SAR models and for directing testing. Here we have refined this workflow by grouping specific tests together on the basis of their ability to detect DNA and/or protein damage at different levels of biological organization. This revised workflow, akin to an Integrated Approach to Testing and Assessment (IATA) informed by mechanistic understanding was helpful in rationalizing inconsistent study outcomes and categorizing a test set of carcinogens with mutagenicity data on the basis of regulatory mutagenicity classifications. Rodent genotoxic carcinogens were found to be correctly predicted with a high sensitivity (90-100%) and a low rate of false positives (3-10%). The insights derived are useful to consider when developing future (non-)testing approaches to address regulatory purposes.

Investigating the relationship between in vitro-in vivo genotoxicity: derivation of mechanistic QSAR models for in vivo liver genotoxicity and in vivo bone marrow micronucleus formation which encompass metabolism.

Strategic testing as part of an integrated testing strategy (ITS) to maximize information and avoid the use of animals where possible is fast becoming the norm with the advent of new legislation such as REACH. Genotoxicity is an area where regulatory testing is clearly defined as part of ITS schemes. Under REACH, the specific information requirements depend on the tonnage manufactured or imported. Two types of test systems exist to meet these information requirements, in vivo genotoxicity assays, which take into account the whole animal, and in vitro assays, which are conducted outside the living mammalian organism using microbial or mammalian cells under appropriate culturing conditions. Clearly, with these different broad experimental categories, results for a given chemical can often differ, which presents challenges in the interpretation as well as in attempting to model the results in silico. This study attempted to compare the differences between in vitro and in vivo genotoxicity results, to rationalize these differences with plausible hypothesis in concert with available data. Two proof of concept (Q)SAR models were developed, one for in vivo genotoxicity effects in liver and a second for in vivo micronucleus formation in bone marrow. These "mechanistic models" will be of practical value in testing strategies, and both have been implemented into the TIMES software platform ( http://oasis-lmc.org ) to help predict the genotoxicity outcome of new untested chemicals.