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Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics
KLEINSTREUER, N. C., A. M. SMITH, P. R. WEST, K. CONARD, B. FONTAINE, A. M. WEIR-HAUPTMAN, J. PALMER, T. B. KNUDSEN, D. J. DIX, E. L. DONLEY, AND G. G. CEZAR. Identifying developmental toxicity pathways for a subset of ToxCast chemicals using human embryonic stem cells and metabolomics . TOXICOLOGY AND APPLIED PHARMACOLOGY. Elsevier Science Inc., Burlington, MA, Online:1-10, (2011).
A strong association between the glutathione pathway, oxidative stress and developmental defects was predicted by the environmental chemical test set examined here, and is well sup- ported by the ToxCast data and the existing literature. Further study of biomarkers of toxicant exposure or effect within these and other pathways will broaden the domain of applicability of metabolomics analysis and improve prioritization and classification of a wide variety of chemicals for human developmental toxicity potential.
Metabolomics analysis was performed on the supernatant of human embryonic stem (hES) cell cultures exposed to a blinded subset of 11 chemicals selected from the chemical library of EPA's ToxCast™ chemical screening and prioritization research project. Metabolites from hES cultures were evaluated for known and novel signatures that may be indicative of developmental toxicity. Significant fold changes in endogenous metabolites were detected for 83 putatively annotated mass features in response to the subset of ToxCast chemicals. The annotations were mapped to specific human metabolic pathways. This revealed strong effects on pathways for nicotinate and nicotinamide metabolism, pantothenate and CoA biosynthesis, glutathione metabolism, and arginine and proline metabolism pathways. Predictivity for adverse outcomes in mammalian prenatal developmental toxicity studies used ToxRefDB and other sources of information, including Stemina Biomarker Discovery's predictive DevTox®model trained on 22 pharmaceutical agents of known developmental toxicity and differing potency. The model initially predicted developmental toxicity from the blinded ToxCast compounds in concordance with animal data with 73% accuracy. Retraining the model with data from the unblinded test compounds at one concentration level increased the predictive accuracy for the remaining concentrations to 83%.These preliminary results on a 11-chemical subset of the ToxCast chemical library indicate that metabolomics analysis of the hES secretome provides information valuable for predictive modeling and mechanistic understanding of mammalian developmental toxicity.
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Record Details:Record Type: DOCUMENT (JOURNAL/PEER REVIEWED JOURNAL)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL CENTER FOR COMPUTATIONAL TOXICOLOGY