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Updated: Feb 8, 2026

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
Published on: March 14, 2019
High-throughput metabolism-induced toxicity assays demonstrated on a 384-pillar plate
Kyeong-Nam Yu1, Soo-Yeon Kang1, Stephen Hong1
1Department of Chemical and Biomedical Engineering, Cleveland State University, 455 Fenn Hall (FH), 1960 East 24th Street, Cleveland, OH, 44115-2214, USA.
Abstract:
The US Environmental Protection Agency (EPA) launched the Transform Tox Testing Challenge in 2016 with the goal of developing practical methods that can be integrated into conventional high-throughput screening (HTS) assays to better predict the toxicity of parent compounds and their metabolites in vivo. In response to this need and to retrofit existing HTS assays for assessing metabolism-induced toxicity of compounds, we have developed a 384-pillar plate that is complementary to traditional 384-well plates and ideally suited for culturing human cells in three dimensions at a microscale. Briefly, human embryonic kidney (HEK) 293 cells in a mixture of alginate and Matrigel were printed on the 384-pillar plates using a microarray spotter, which were coupled with 384-well plates containing nine model compounds provided by the EPA, five representative Phase I and II drug metabolizing enzymes (DMEs), and one no enzyme control. Viability and membrane integrity of HEK 293 cells were measured with the calcein AM and CellTiter-Glo® kit to determine the IC50 values of the nine parent compounds and DME-generated metabolites. The Z' factors and the coefficient of variation measured were above 0.6 and below 14%, respectively, indicating that the assays established on the 384-pillar plate are robust and reproducible. Out of nine compounds tested, six compounds showed augmented toxicity with DMEs and one compound showed detoxification with a Phase II DME. This result indicates that the 384-pillar plate platform can be used to measure metabolism-induced toxicity of compounds in high-throughput with individual DMEs. As xenobiotics metabolism is a complex process with a variety of DMEs involved, the predictivity of our approach could be further improved with mixtures of DMEs.
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