The present study tested the hypothesis that a mixture of TDCs affect T4 concentrations in a dose-additive manner. We designed the mixture so that highest mixture-dose levels of the individual chemicals were at or below their no observed effect levels. The FSCR additivity model analyses demonstrate cumulative effects of low doses of the mixture and synergistic cumulative effects of the highest dosages of the mixture. These data advocate consideration of cumulative risk approaches when assessing the risk of exposures to chemical mixtures that contain TDCs.
The single-chemical and mixture data were modeled successfully using the FSCR model. Results demonstrate a very wide range of effective doses of PHAHs that decrease TH concentrations. These findings confirm previous work demonstrating that short-term exposure to TCDD (Craft et al. 2002
), and some individual PCB congeners for example, PCB congeners 47, 95, 101, and 153 (Craft et al. 2002
; Khan et al. 2002
; Saeed and Hansen 1997
) cause hypothyroxinemia in the rat. Our present work expands these findings by providing dose–response data and relative potencies for 2 dioxins, 4 furans, and 12 PCB congeners. OCDF was not effective at the doses used in this animal dose model. This was expected because of the limited absorption of this fully chlorinated dibenzofuran (Birnbaum and Couture 1988
; DeVito et al. 1998
). In addition the ED30
estimates provide a basis for establishing relative potency values for these chemicals.
Analyses of the mixtures data demonstrated a dose-dependent synergy. The additivity model underestimated the actual toxic effect of the mixture at the three highest doses tested (). Effects of the three lowest doses of the mixture were not significantly different than that predicted by the additivity model. These conclusions are based on the use of the FSCR method (Gennings et al. 2004
). These data were also analyzed using an SCR method (Gennings et al. 2002
). Although the SCR model provided significant evidence of a greater-than-additive effect (data not shown), this model was not appropriate for use with these data because of significant lack of fit to the data. The SCR model assumes a similar asymptote for all single chemicals and the mixture, a condition not satisfied in the present data set. Use of the FSCR model allowed for multiple asymptotic levels and dose thresholds and resulted in a model with no overall lack of fit.
Three conclusions are apparent from these data. The first is that exposure to the 18 chemical mixture results in dose-dependent greater-than-additive effects on T4 concentrations at the highest mixture doses. This conclusion is supported by the FSCR analysis. The second conclusion is that although the greater-than-additive effects are statistically significant, the magnitude of underestimation of the experimental data () by the additivity model () is not large. On a dose basis, the underestimation is about 2.5-fold for the three highest doses of the mixture (). This suggests that, even in the high mixture-dose region, the effects of this mixture are predicted by additivity with a fair degree of accuracy. The third conclusion is that departure from additivity was not detected in the low-dose region. Although this suggests that dose additivity predicts effects on T4 at low exposures, it is tempered by a presumed low statistical power to detect differences in this area of the dose response.
A significant finding in the present experiment is that the mixture actually caused decreases in T4 concentrations. This occurred even though the individual chemical concentrations in the mixture were below effective doses. For example, at the second highest mixture dose there was a 38% decrease in T4. The individual dose of PCB-153 at this mixture dose was approximately 254 μg/kg/day. The lowest effective dose of PCB-153 administered alone is much greater than 2,000 μg/kg/day. This relationship was similar for all the chemicals in the mixture with one exception, PCB-126. The dose of PCB-126 in the highest dose of the mixture caused about a 16% decrease in T4. These data clearly demonstrate the principle that simple mathematical addition of effects (i.e., effect addition) of individual chemicals will not predict the effects of these TDCs in a mixture.
The biologic reasons for the greater-than-additive effect of this mixture are currently unknown. Risk assessment approaches to additivity assume, where data are lacking otherwise, that chemicals with similar modes of action act in a dose-additive fashion (U.S. EPA 1986
). Although all the chemicals used here decrease circulating T4
concentrations, they may do so via a number of different mechanisms. One postulated mechanism for the reduction in T4
concentrations is the up-regulation of hepatic UGT isoforms that glucuronidate T4
, leading to biliary elimination (Capen 1997
; DeVito et al. 1999
; Hill et al. 1998
; McClain et al. 1989
). Evidence suggests that UGT1A1 and UGT1A6 are responsible for T4
glucuronidation in the rat (Vansell and Klaassen 2002
; Visser et al. 1993
). These UGT isoforms are induced by aryl hydrocarbon receptor (AhR), constitutive androstane receptor (CAR), and pregnane-X receptor (PXR) agonists. The dioxins, furans, and coplanar PCBs (e.g., PCB-77, PCB-126) all activate AhR (Wilson and Safe 1998
), whereas the more non-coplanar PCBs (e.g., PCB-52, PCB-138, PCB-153) act via CAR/PXR pathways (Connor et al. 1995
; Tabb et al. 2004
). Some of the chemicals tested (e.g., PCB-105, PCB-118) are agonists for AhR, PXR, and CAR. Activation of these UGTs through the different nuclear receptors may play a role in the synergistic effects. Differential regulation of microsomal enzymes that glucuronidate T4
(triiodothyronine) may also be responsible (Hood and Klaassen 2000a
). There are a number of other postulated mechanisms for altering circulating and tissue levels of THs. Hydroxylated metabolites of PCBs displace T4
from transthyretin, a major serum transport protein in rats (Brouwer et al. 1998
). This mechanism has been hypothesized to decrease bound T4
, resulting in greater uptake, catabolism, and elimination of T4
(Van den Berg et al. 1991
). PCBs also alter deiodinases and therefore iodination of THs (Hood and Klaassen 2000b
; Morse et al. 1993
). There is some evidence that PCBs increase uptake of T4
into the liver (Martin 2002
), possibly by altering thyroid transporters (Guo et al. 2002
). In addition, Khan and Hansen (2003)
and colleagues have demonstrated decreased pituitary sensitivity to thyroid-stimulating hormone by two PCB congeners. Therefore, the synergistic effect may be the result of activation of multiple pathways by the mixture, with the measured effect, T4
, a common downstream end point for these pathways.
The curve fits to the individual chemical data revealed three levels of maximum efficacy (). Because of the limited number of chemicals, it is difficult to quantitatively describe the structure–activity relationship for maximal T4 decreases. In addition the dose–response determinations were not designed to allow prediction of the asymptotic efficacy but instead aimed to characterize the low end of the dose–response functions. In some cases the maximal efficacy was driven by the highest dose tested, which did not demonstrate a clear maximal effect (e.g., PCB-28, PCB-52, PCB-169). The data do support, with a number of exceptions, a rough separation of chemicals into the more dioxin-like chemicals at the 50% point, and mono- and di-ortho substituted chemicals having an asymptote at 14%. A likely explanation for the different efficacies is that the PHAHs act through a variety a mechanisms, as discussed above, and the interaction of these mechanisms differentially affects T4 levels.
The significance of these findings for environmental exposures is tempered by some uncertainty. In our present study we used a weanling animal model with a short (i.e., 4-day) exposure duration. Short exposure durations, coupled with differences in half-lives of the chemicals in the mixture that vary from a few weeks to many months (Van den Berg et al. 1994
), yield potential pharmacokinetic differences that may confound extrapolation of these results. Pharmacokinetic differences between short-term and steady-state exposures may also include differences in saturation of induction and metabolite generation. Thus, extrapolation of our present findings to chronic exposures should be moderated by these uncertainties.
Extrapolation of our present work in rats to humans is tempered by the uncertainty in how the mode(s) of action of the TDCs may differ between species. Current hypotheses on the mechanisms by which PHAHs decrease T4
include up-regulation of hepatic UGTs and sulfotransferases, direct effects on the thyroid gland, and displacement of T4
from serum transport proteins (Brouwer et al. 1998
). Cross-species extrapolation of these mechanisms is difficult (Crofton 2004
). In addition one must consider the degree of TH disruption that will lead to adverse outcomes. Small decreases (~25%) in maternal T4
during the early fetal period will lead to adverse neurofunctional outcomes (i.e., IQ scores) in humans (Haddow et al. 2002
; Morreale de Escobar et al. 2000
). Limited data in animals suggest that T4
decreases need to exceed 50% before adverse outcomes can be detected (Crofton 2004
A limited number of studies have examined the effects of complex mixtures of endocrine-disrupting chemicals (EDCs) (Desaulniers et al. 2003
; Tinwell and Ashby 2004
; Wade et al. 2002
). Desaulniers et al. (2003)
examined the effects of a mixture of 16 coplanar PCBs, PCDDs, and PCDFs on T4
concentrations in neonatal rats. Decreases in T4
were associated with dioxin equivalents using the toxic equivalency factor methodology (Desaulniers et al. 2003
; Van den Berg et al. 1998
). Consistent with our present findings, Wade et al. (2002)
found that effects on thyroid histopathology and hormones were underpredicted based on additivity of published health advisories (e.g., RfDs and ADIs). Evaluation of different models for determining the effects of a mixture of seven EDCs on uterotrophic responses led to a conclusion that the most expedient method is to bioassay the mixture rather than test individual chemicals (LeBlanc and Olmstead 2004
; Tinwell and Ashby 2004
). These studies lack, either by study design or statistical approach, the ability to test for additivity. The present work expands the previous work by applying a rigorous statistical analysis to test for additivity.