18 chemical mixture results in dose-dependent greater-than-additive effects on T4 concentra- tions at the highest mixture doses. This con- clusion 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 (Figure 1) by the additivity model (Figure 1) is not large. On a dose basis, the underestimation is about 2.5-fold for the three highest doses of the mix- ture (Figure 1). 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 pre- sumed low statistical power to detect differ- ences in this area of the dose response. A signiﬁcant ﬁnding in the present experi- ment is that the mixture actually caused decreases in T4 concentrations. This occurred even though the individual chemical concen- trations in the mixture were below effective doses. For example, at the second highest mix- ture 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 chemi- cals in the mixture with one exception, PCB-126. The dose of PCB-126 in the high- est dose of the mixture caused about a 16% decrease in T4. These data clearly demonstrate the principle that simple mathematical addi- tion of effects (i.e., effect addition) of individ- ual 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, 2000). Although all the chemicals used here decrease circulating T4 concentra- tions, 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 glu- curonidate 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 recep- tor (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 path- ways (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 versus T3 (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, catabo- lism, 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 alter- ing thyroid transporters (Guo et al. 2002). In addition, Khan and Hansen (2003) and col- leagues have demonstrated decreased pituitary sensitivity to thyroid-stimulating hormone by two PCB congeners. Therefore, the synergistic effect may be the result of activation of multi- ple pathways by the mixture, with the meas- ured effect, T4, a common downstream end point for these pathways. The curve fits to the individual chemical data revealed three levels of maximum efﬁcacy (Table 4). 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 maxi- mal effect (e.g., PCB-28, PCB-52, PCB-169). The data do support, with a number of excep- tions, 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 explana- tion 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 Environmental Health Perspectives • VOLUME 113 | NUMBER 11 | November 2005 Cumulative risk of thyroid disruptors Berg et al. 1994), yield potential pharmacoki- netic differences that may confound extrapo- lation of these results. Pharmacokinetic differences between short-term and steady- state exposures may also include differences in saturation of induction and metabolite gener- ation. Thus, extrapolation of our present ﬁndings to chronic exposures should be mod- erated 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 sul- fotransferases, 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 dif- ficult (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 out- comes (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 equiva- lency 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 mod- els 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 individ- ual 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. Conclusions The present work demonstrates that the cumu- lative effect of a mixture of TDCs is predicted by additivity at low doses and synergy at high doses. These data suggest that low doses of het- erogeneous TDCs that alter thyroid homeosta- sis should be considered together when calculating the risk of exposures to mixtures. 1553