A comparison of classical and 21st century genotoxicity tools: A proof of concept study of 18 chemicals comparing in vitro micronucleus, ToxTracker and genomics-based methods (TGx-DDI, whole genome clustering and connectivity mapping).

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.

Use of connectivity mapping to support read across: A deeper dive using data from 186 chemicals, 19 cell lines and 2 case studies.

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.

Grouping 34 Chemicals Based on Mode of Action Using Connectivity Mapping.

Connectivity mapping is a method used in the pharmaceutical industry to find connections between small molecules, disease states, and genes. The concept can be applied to a predictive toxicology paradigm to find connections between chemicals, adverse events, and genes. In order to assess the applicability of the technique for predictive toxicology purposes, we performed gene array experiments on 34 different chemicals: bisphenol A, genistein, ethinyl-estradiol, tamoxifen, clofibrate, dehydorepiandrosterone, troglitazone, diethylhexyl phthalate, flutamide, trenbolone, phenobarbital, retinoic acid, thyroxine, 1α,25-dihydroxyvitamin D3, clobetasol, farnesol, chenodeoxycholic acid, progesterone, RU486, ketoconazole, valproic acid, desferrioxamine, amoxicillin, 6-aminonicotinamide, metformin, phenformin, methotrexate, vinblastine, ANIT (1-naphthyl isothiocyanate), griseofulvin, nicotine, imidacloprid, vorinostat, 2,3,7,8-tetrachloro-dibenzo-p-dioxin (TCDD) at the 6-, 24-, and 48-hour time points for 3 different concentrations in the 4 cell lines: MCF7, Ishikawa, HepaRG, and HepG2 GEO (super series accession no.: GSE69851). The 34 chemicals were grouped in to predefined mode of action (MOA)-based chemical classes based on current literature. Connectivity mapping was used to find linkages between each chemical and between chemical classes. Cell line-specific linkages were compared with each other and to test whether the method was platform and user independent, a similar analysis was performed against publicly available data. The study showed that the method can group chemicals based on MOAs and the inter-chemical class comparison alluded to connections between MOAs that were not predefined. Comparison to the publicly available data showed that the method is user and platform independent. The results provide an example of an alternate data analysis process for high-content data, beneficial for predictive toxicology, especially when grouping chemicals for read across purposes.

A novel transcriptomics based in vitro method to compare and predict hepatotoxicity based on mode of action.

High-content data have the potential to inform mechanism of action for toxicants. However, most data to support this notion have been generated in vivo. Because many cell lines and primary cells maintain a differentiated cell phenotype, it is possible that cells grown in culture may also be useful in predictive toxicology via high-content approaches such as whole-genome microarray. We evaluated global changes in gene expression in primary rat hepatocytes exposed to two concentrations of ten hepatotoxicants: acetaminophen (APAP), ß-naphthoflavone (BNF), chlorpromazine (CPZ), clofibrate (CLO), bis(2-ethylhexyl)phthalate (DEHP), diisononyl phthalate (DINP), methapyrilene (MP), valproic acid (VPA), phenobarbital (PB) and WY14643 at two separate time points. These compounds were selected to cover a range of mechanisms of toxicity, with some overlap in expected mechanism to address the question of how predictive gene expression analysis is, for a given mode of action. Gene expression microarray analysis was performed on cells after 24h and 48h of exposure to each chemical using Affymetrix microarrays. Cluster analysis suggests that the primary hepatocyte model was capable of responding to these hepatotoxicants, with changes in gene expression that appear to be mode of action-specific. Among the different methods used for analysis of the data, a combination method that used pathways (MOAs) to filter total probesets provided the most robust analysis. The analysis resulted in the phthalates clustering closely together, with the two other peroxisome proliferators, CLO and WY14643, eliciting similar responses at the whole-genome and pathway levels. The Cyp inducers PB, MP, CPZ and BNF also clustered together. VPA and APAP had profiles that were unique. A similar analysis was performed on externally available (TG-GATES) in vivo data for 6 of the chemicals (APAP, CLO, CPZ, MP, MP and WY14643) and compared to the in vitro result. These results indicate that transcription profiling using an in vitro assay may offer pertinent biological data to support predictions of in vivo hepatotoxicity potential.