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A key step in the risk assessment process of a substance is the assessment of its genotoxic potential. Irrespective of the industry involved, current approaches rely on combinations of two or three in vitro tests and while highly sensitive, their specificity is thought to be limited. A refined in vitro genotoxicity testing strategy with improved predictive capacity would be beneficial and "3R" friendly as it helps to avoid unnecessary in vivo follow-up testing. Here, we describe a proof of concept study evaluating a balanced set of compounds that have in vivo negative or positive outcomes, but variable in vitro data, to determine if we could differentiate between direct and indirect acting genotoxicants. Compounds were examined in TK6 cells using an approach in which the same sample was used to evaluate both early genomic markers (Affymetrix analysis 4 hr post treatment), and the genotoxic outcome (micronuclei [MN] after 24 hr). The resulting genomic data was then analyzed using the TGx-DDI biomarker, Connectivity mapping and whole genome clustering. Chemicals were also tested in the ToxTracker assay, which uses six different biomarker genes. None of the methods correctly differentiated all direct from indirect acting genotoxicants when used alone, however, the ToxTracker assay, TGx-DDI biomarker and whole genome approaches provided high predictive capacity when used in combination with the MN assay (1/18, 2/18, 1/18 missed calls). Ultimately, a "fit for purpose" combination will depend on the specific tools available to the end user, as well as considerations of the unique benefits of the individual assays.

Flexible, rapid, and predictive approaches that do not require the use of large numbers of vertebrate test animals are needed because the chemical universe remains largely untested for potential hazards. Development of robust new approach methodologies and nontesting approaches requires the use of existing information via curated, integrated data sets. The ecological threshold of toxicological concern (ecoTTC) represents one such new approach methodology that can predict a conservative de minimis toxicity value for chemicals with little or no information available. For the creation of an ecoTTC tool, a large, diverse environmental data set was developed from multiple sources, with harmonization, characterization, and information quality assessment steps to ensure that the information could be effectively organized and mined. The resulting EnviroTox database contains 91 217 aquatic toxicity records representing 1563 species and 4016 unique Chemical Abstracts Service numbers and is a robust, curated database containing high‐quality aquatic toxicity studies that are traceable to the original information source. Chemical‐specific information is also linked to each record and includes physico‐chemical information, chemical descriptors, and mode of action classifications. Toxicity data are associated with the physico‐chemical data, mode of action classifications, and curated taxonomic information for the organisms tested. The EnviroTox platform also includes 3 analysis tools: a predicted‐no‐effect concentration calculator, an ecoTTC distribution tool, and a chemical toxicity distribution tool. Although the EnviroTox database and tools were originally developed to support ecoTTC analysis and development, they have broader applicability to the field of ecological risk assessment. Environ Toxicol Chem 2019;9999:1–12. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

A key step in the risk assessment process of a substance is the assessment of its genotoxic potential. Irrespective of the industry involved, current approaches rely on combinations of two or three in vitro tests and while highly sensitive, their specificity is thought to be limited. A refined in vitro genotoxicity testing strategy with improved predictive capacity would be beneficial and “3R” friendly as it helps to avoid unnecessary in vivo follow‐up testing. Here, we describe a proof of concept study evaluating a balanced set of compounds that have in vivo negative or positive outcomes, but variable in vitro data, to determine if we could differentiate between direct and indirect acting genotoxicants. Compounds were examined in TK6 cells using an approach in which the same sample was used to evaluate both early genomic markers (Affymetrix analysis 4 hr post treatment), and the genotoxic outcome (micronuclei [MN] after 24 hr). The resulting genomic data was then analyzed using the TGx‐DDI biomarker, Connectivity mapping and whole genome clustering. Chemicals were also tested in the ToxTracker assay, which uses six different biomarker genes. None of the methods correctly differentiated all direct from indirect acting genotoxicants when used alone, however, the ToxTracker assay, TGx‐DDI biomarker and whole genome approaches provided high predictive capacity when used in combination with the MN assay (1/18, 2/18, 1/18 missed calls). Ultimately, a “fit for purpose” combination will depend on the specific tools available to the end user, as well as considerations of the unique benefits of the individual assays.

Derivation of Predicted No Effect Concentrations (PNECs) for aquatic systems is the primary deterministic form of hazard extrapolation used in environmental risk assessment. Depending on the data availability, different regulatory jurisdictions apply application factors (AFs) to the most sensitive measured endpoint to derive the PNEC for a chemical. To assess differences in estimated PNEC values, two PNEC determination methodologies were applied to a curated public database using the EnviroTox Platform ( www.EnviroToxdatabase.org ). PNECs were derived for 3647 compounds using derivation procedures based on example US EPA and a modified European Union chemical registration procedure to allow for comparisons. Ranked probability distributions of PNEC values were developed and 5th percentile values were calculated for the entire dataset and scenarios where full acute or full chronic data sets were available. The lowest PNEC values indicated categorization based on chemical attributes and modes of action would lead to improved extrapolations. Full acute or chronic datasets gave measurably higher 5th percentile PNEC values. Algae were under-represented in available ecotoxicity data but drove PNECs disproportionately. Including algal inhibition studies will be important in understanding chemical hazards. The PNEC derivation logic flows are embedded in the EnviroTox Platform providing transparent and consistent PNEC derivations and PNEC distribution calculations.

We previously demonstrated that the Connectivity Map (CMap) (Lamb et al., 2006) concept can be successfully applied to a predictive toxicology paradigm to generate meaningful MoA-based connections between chemicals (De Abrew et al., 2016). Here we expand both the chemical and biological (cell lines) domain for the method and demonstrate two applications, both in the area of read across. In the first application we demonstrate CMap's utility as a tool for testing biological relevance of source chemicals (analogs) during a chemistry led read across exercise. In the second application we demonstrate how CMap can be used to identify functionally relevant source chemicals (analogs) for a structure of interest (SOI)/target chemical with minimal knowledge of chemical structure. Finally, we highlight four factors: promiscuity of chemical, dose, cell line and timepoint as having significant impact on the output. We discuss the biological relevance of these four factors and incorporate them into a work flow.