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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.

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.