Developmental toxicity in vitro assays have hitherto been established as stand-alone systems, based on a limited number of toxicants. Within the embryonic stem cell-based novel alternative tests project, we developed a test battery framework that allows inclusion of any developmental toxicity assay and that explores the responses of such test systems to a wide range of drug-like compounds. We selected 28 compounds, including several biologics (e.g., erythropoietin), classical pharmaceuticals (e.g., roflumilast) and also six environmental toxicants. The chemical, toxicological and clinical data of this screen library were compiled. In order to determine a non-cytotoxic concentration range, cytotoxicity data were obtained for all compounds from HEK293 cells and from murine embryonic stem cells. Moreover, an estimate of relevant exposures was provided by literature data mining. To evaluate feasibility of the suggested test framework, we selected a well-characterized assay that evaluates ‘migration inhibition of neural crest cells.’ Screening at the highest non-cytotoxic concentration resulted in 11 hits (e.g., geldanamycin, abiraterone, gefitinib, chlorpromazine, cyproconazole, arsenite). These were confirmed in concentration–response studies. Subsequent pharmacokinetic modeling indicated that triadimefon exerted its effects at concentrations relevant to the in vivo situation, and also interferon-β and polybrominated diphenyl ether showed effects within the same order of magnitude of concentrations that may be reached in humans. In conclusion, the test battery framework can identify compounds that disturb processes relevant for human development and therefore may represent developmental toxicants. The open structure of the strategy allows rich information to be generated on both the underlying library, and on any contributing assay.Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-014-1231-9) contains supplementary material, which is available to authorized users.

Analysis of transcriptome changes has become an established method to characterize the reaction of cells to toxicants. Such experiments are mostly performed at compound concentrations close to the cytotoxicity threshold. At present, little information is available on concentration-dependent features of transcriptome changes, in particular, at the transition from noncytotoxic concentrations to conditions that are associated with cell death. Thus, it is unclear in how far cell death confounds the results of transcriptome studies. To explore this gap of knowledge, we treated pluripotent stem cells differentiating to human neuroepithelial cells (UKN1 assay) for short periods (48 h) with increasing concentrations of valproic acid (VPA) and methyl mercury (MeHg), two compounds with vastly different modes of action. We developed various visualization tools to describe cellular responses, and the overall response was classified as "tolerance" (minor transcriptome changes), "functional adaptation" (moderate/strong transcriptome responses, but no cytotoxicity), and "degeneration". The latter two conditions were compared, using various statistical approaches. We identified (i) genes regulated at cytotoxic, but not at noncytotoxic, concentrations and (ii) KEGG pathways, gene ontology term groups, and superordinate biological processes that were only regulated at cytotoxic concentrations. The consensus markers and processes found after 48 h treatment were then overlaid with those found after prolonged (6 days) treatment. The study highlights the importance of careful concentration selection and of controlling viability for transcriptome studies. Moreover, it allowed identification of 39 candidate "biomarkers of cytotoxicity". These could serve to provide alerts that data sets of interest may have been affected by cell death in the model system studied.

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The first in vitro tests for developmental toxicity made use of rodent cells. Newer teratology tests, e.g. developed during the ESNATS project, use human cells and measure mechanistic endpoints (such as transcriptome changes). However, the toxicological implications of mechanistic parameters are hard to judge, without functional/morphological endpoints. To address this issue, we developed a new version of the human stem cell-based test STOP-tox (UKN) . For this purpose, the capacity of the cells to self-organize to neural rosettes was assessed as functional endpoint: pluripotent stem cells were allowed to differentiate into neuroepithelial cells for 6 days in the presence or absence of toxicants. Then, both transcriptome changes were measured (standard STOP-tox (UKN) ) and cells were allowed to form rosettes. After optimization of staining methods, an imaging algorithm for rosette quantification was implemented and used for an automated rosette formation assay (RoFA). Neural tube toxicants (like valproic acid), which are known to disturb human development at stages when rosette-forming cells are present, were used as positive controls. Established toxicants led to distinctly different tissue organization and differentiation stages. RoFA outcome and transcript changes largely correlated concerning (1) the concentration-dependence, (2) the time dependence, and (3) the set of positive hits identified amongst 24 potential toxicants. Using such comparative data, a prediction model for the RoFA was developed. The comparative analysis was also used to identify gene dysregulations that are particularly predictive for disturbed rosette formation. This 'RoFA predictor gene set' may be used for a simplified and less costly setup of the STOP-tox (UKN) assay.

Despite the progress made in developmental toxicology, there is a great need for in vitro tests that identify developmental toxicants in relation to human oral doses and blood concentrations. In the present study, we established the hiPSC-based UKK2 in vitro test and analyzed genome-wide expression profiles of 23 known teratogens and 16 non-teratogens. Compounds were analyzed at the maximal plasma concentration ( C max ) and at 20-fold C max for a 24 h incubation period in three independent experiments. Based on the 1000 probe sets with the highest variance and including information on cytotoxicity, penalized logistic regression with leave-one-out cross-validation was used to classify the compounds as test-positive or test-negative, reaching an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.96, 0.92, 0.96, and 0.88, respectively. Omitting the cytotoxicity information reduced the test performance to an AUC of 0.94, an accuracy of 0.79, and a sensitivity of 0.74. A second method, which used the number of significantly deregulated probe sets to classify the compounds, resulted in a specificity of 1; however, the AUC (0.90), accuracy (0.90), and sensitivity (0.83) were inferior compared to those of the logistic regression-based procedure. Finally, no increased performance was achieved when the high test concentrations (20-fold C max ) were used, in comparison to testing within the realistic clinical range (1-fold C max ). In conclusion, although further optimization is required, for example, by including additional readouts and cell systems that model different developmental processes, the UKK2-test in its present form can support the early discovery-phase detection of human developmental toxicants.