High Accuracy Classification of Developmental Toxicants by In Vitro Tests of Human Neuroepithelial and Cardiomyoblast Differentiation

Cells. 2022 Oct 27;11(21):3404. doi: 10.3390/cells11213404.

Abstract

Human-relevant tests to predict developmental toxicity are urgently needed. A currently intensively studied approach makes use of differentiating human stem cells to measure chemically-induced deviations of the normal developmental program, as in a recent study based on cardiac differentiation (UKK2). Here, we (i) tested the performance of an assay modeling neuroepithelial differentiation (UKN1), and (ii) explored the benefit of combining assays (UKN1 and UKK2) that model different germ layers. Substance-induced cytotoxicity and genome-wide expression profiles of 23 teratogens and 16 non-teratogens at human-relevant concentrations were generated and used for statistical classification, resulting in accuracies of the UKN1 assay of 87-90%. A comparison to the UKK2 assay (accuracies of 90-92%) showed, in general, a high congruence in compound classification that may be explained by the fact that there was a high overlap of signaling pathways. Finally, the combination of both assays improved the prediction compared to each test alone, and reached accuracies of 92-95%. Although some compounds were misclassified by the individual tests, we conclude that UKN1 and UKK2 can be used for a reliable detection of teratogens in vitro, and that a combined analysis of tests that differentiate hiPSCs into different germ layers and cell types can even further improve the prediction of developmental toxicants.

Keywords: alternative testing strategies; developmental and reproductive toxicity; drug screening; gene expression; in vitro test; induced pluripotent stem cells; toxicogenomics; transcriptomics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Differentiation
  • Humans
  • In Vitro Techniques
  • Stem Cells
  • Teratogens* / toxicity
  • Toxicity Tests*

Substances

  • Teratogens

Grants and funding

This work was supported by the Project SysDT (031L0117A-D) and (in part) by the Research Training Group “Biostatistical Methods for High-Dimensional Data in Toxicology” (RTG 2624) that were funded by the BMBF (German Ministry of Education and Research) and the DFG (German Research Foundation—Project Number 427806116), respectively. Furthermore, the study was partially supported by CEFIC, the DK-EPA (MST-667-00205), the Land-BW (NAM-ACCEPT), and by the European Union’s Horizon 2020 research and innovation program under grant agreements No 964537 (RISK-HUNT3R), No. 964518 (ToxFree), and No. 825759 (ENDpoiNTs).