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Updated: Dec 28, 2025

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
Published on: March 14, 2019
Potential of ToxCast Data in the Safety Assessment of Food Chemicals
Ans Punt1, James Firman2, Alan Boobis3
1Wageningen Food Safety Research, 6700 AE Wageningen, The Netherlands.
Abstract:
Tox21 and ToxCast are high-throughput in vitro screening programs coordinated by the U.S. National Toxicology Program and the U.S. Environmental Protection Agency, respectively, with the goal of forecasting biological effects in vivo based on bioactivity profiling. The present study investigated whether mechanistic insights in the biological targets of food-relevant chemicals can be obtained from ToxCast results when the chemicals are grouped according to structural similarity. Starting from the 556 direct additives that have been identified in the ToxCast database by Karmaus et al. [Karmaus, A. L., Trautman, T. D., Krishan, M., Filer, D. L., and Fix, L. A. (2017). Curation of food-relevant chemicals in ToxCast. Food Chem. Toxicol. 103, 174-182.], the results showed that, despite the limited number of assays in which the chemical groups have been tested, sufficient results are available within so-called "DNA binding" and "nuclear receptor" target families to profile the biological activities of the defined chemical groups for these targets. The most obvious activity identified was the estrogen receptor-mediated actions of the chemical group containing parabens and structurally related gallates, as well the chemical group containing genistein and daidzein (the latter 2 being particularly active toward estrogen receptor β as a potential health benefit). These group effects, as well as the biological activities of other chemical groups, were evaluated in a series of case studies. Overall, the results of the present study suggest that high-throughput screening data could add to the evidence considered for regulatory risk assessment of food chemicals and to the evaluation of desirable effects of nutrients and phytonutrients. The data will be particularly useful for providing mechanistic information and to fill data gaps with read-across.
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