You are here:
Computational Modeling of Thyroid Hormone Regulated Neurodevelopment for Chemical Prioritization (SOT)
Citation:
Watt, E., D. F. Kapraun, T. B. Knudsen, K. Crofton, AND R. Judson. Computational Modeling of Thyroid Hormone Regulated Neurodevelopment for Chemical Prioritization (SOT). Presented at SOT Annual Meeting, San Diego, CA, March 22 - 26, 2015.
Impact/Purpose:
poster presented at SOT annual meeting in San Diego, CA on March 24, 2015
Description:
Thyroid hormones (TH) are critical for normal brain development. Environmental chemicals may disrupt TH homeostasis through a variety of physiological systems including membrane transporters, serum transporters, synthesis and catabolic enzymes, and nuclear receptors. Current computational models provide rich descriptions of some aspects of TH regulation and transport, but do not address the complex cellular dynamics invoking downstream adverse outcomes. The goal of the current research is to develop a sophisticated computational model that predicts chemical effects on known signaling pathways of TH-mediated brain development coupled with estimated chemical exposures. The model has two major components: (1) a biologically-based dose-response/physiologically-based toxicokinetic-model used to calculate TH levels in both the euthyroid state and in the presence of xenobiotic thyroid disrupting chemicals; and (2) fetal TH levels were used as input into a cell-agent based model where TH levels regulate endothelial, glial, and neuronal interactions necessary for brain development. Chemicals used in the model included positive hits from ToxCast/Tox21 TH receptor and thyroid peroxidase assays. The model outputs the daily dose required to activate biological targets for both rat and human. These doses were compared to exposure estimates generated from ExpoCast (Wambaugh et al., 2013). For the majority of chemicals, the predicted exposure levels were much lower than doses predicted to trigger biological activity. Future work will include characterization of chemicals with both low and high margins-of-exposure to further validate the model. Computational modeling that combines both hazard and exposure hold great promise in facilitating efficient screening of thyroid disrupting chemicals. This abstract does not necessarily reflect the policy of the US EPA.