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Predictive Modeling of a Mixture of Thyroid Hormone Disrupting Chemicals that Affect Production and Clearance of Thyroxine
Citation:
FLIPPIN, J. L., J. M. HEDGE, M. J. DEVITO, G. A. LEBLANC, AND K. M. CROFTON. Predictive Modeling of a Mixture of Thyroid Hormone Disrupting Chemicals that Affect Production and Clearance of Thyroxine. INTERNATIONAL JOURNAL OF TOXICOLOGY. Taylor & Francis, Inc., Philadelphia, PA, 28(5):368-381, (2009).
Impact/Purpose:
A manuscript summarizes the effects of a mixture of 18 polyhalogenated hydrocarbons and 3 pesticides would be predicted using an integrated addition model. Thyroid hormone (TH) disrupting compounds interfere with both thyroidal and extrathyroidal mechanisms to decrease circulating thyroxine (T4). This research tested the hypothesis that serum T4 concentrations of rodents exposed to a mixture of both TH synthesis inhibitors (pesticides) and stimulators of T4 clearance in the liver (polyhalogenated aromatic hydrocarbons, PHAHs) could be best predicted by an integrated addition model. Three additivity model predictions (dose addition, effect addition, and integrated addition) were generated based on single chemical data, and the results were compared. Effect addition overestimated the effect produced by the combination of all 21 chemicals. The predictions of the dose- and integrated-addition models were similar, but the empirical data was best modeled by integrated addition in which responses to the pesticides and PHAHs were separately evaluated according to dose additivity and the response to the two doses of chemicals were summed according to effect additivity. These results suggest that the use of integrated models may lead more accurate predictions of the effects of complex mixtures.
Description:
Thyroid hormone (TH) disrupting compounds interfere with both thyroidal and extrathyroidal mechanisms to decrease circulating thyroxine (T4). This research tested the hypothesis that serum T4 concentrations of rodents exposed to a mixture of both TH synthesis inhibitors (pesticides) and stimulators of T4 clearance in the liver (polyhalogenated aromatic hydrocarbons, PHAHs) could be best predicted by an integrated addition model. Female Long-Evans rats, 23 days of age, were dosed with dilutions of a mixture of 18 PHAHs (2 dioxins, 4 dibenzofurans, and 12 PCBs, including dioxin-like and non-dioxin like PCBs) and a mixture of 3 pesticides (thiram, pronamide, and mancozeb) for four consecutive days. Serum was collected 24 hours after the last exposure and T4 concentrations were measured by radioimmunoassay. Animals exposed to the highest dose of the mixture experienced a 45% decrease in serum T4. Three additivity model predictions (dose addition, effect addition, and integrated addition) were generated based on single chemical data, and the results were compared. Effect addition overestimated the effect produced by the combination of all 21 chemicals. The predictions of the dose- and integrated-addition models were similar, but the empirical data was best modeled by integrated addition in which responses to the pesticides and PHAHs were separately evaluated according to dose additivity and the response to the two doses of chemicals were summed according to effect additivity. These results suggest that the use of integrated models may lead more accurate predictions of the effects of complex mixtures.
