Crofton et al. (Table 4). The data were also modeled with a single-chemical-required (SCR) approach that requires similar asymptotes (Casey et al. 2004). This model proved to be inadequate with signiﬁcant lack of ﬁt because of clearly different asymptotes (p < 0.0001). The FSCR model proved more appropriate as it allowed for three Table 3. Summary statistics for individual chemicals, and the ED30 and 95% conﬁdence intervals for each chemical. ED30 Chemical (µg/kg/day) 95% Conﬁdence α Asymptote interval estimatea aPercentage of control (e.g., α = 14 represents an 86% decrease in T4 concentration relative to the control mean). Table 4. Parameter estimates from the FSCR model. 0.15 1.51 4.65 15.6 27.5 — 76,103.0 33,025.0 852.0 4,833.0 1,031.0 1,289.0 1.33 8,001.0 12,696.0 760.0 227.0 30,541.0 (0.08, 0.22) (1.10, 1.92) (1.90, 7.40) (10.17, 21.01) (17.05, 38.01) — (50,142, 102,064) (20,958, 45,092) (655, 1,049) (3,819, 5,847) (861, 1,200) (1,103, 1,475) (0.77, 1.88) (6,692, 9,310) (10,659, 14,732) (629, 891) (167, 286) (23,122, 37,960) Estimate 50.26 30.74 14.33 42.29 –0.0608 –0.0378 –0.000119 –0.000833 –0.000696 –0.719000 –0.000054 –0.000034 –0.000567 –0.002616 –0.000021 –0.000012 –0.000028 –0.000666 –0.374900 –6.505000 –0.331000 –0.000872 0.389 3.094 76.410 513.1 669.5 0.043 9.812 3,227.3 0.004 1.859 49.5 SE 1.45 1.95 1.03 14.78 0.0164 0.0142 0.000020 0.000171 0.000109 0.225 0.000006 0.000003 0.000061 0.000549 0.000005 0.000003 0.000006 0.000102 0.0639 2.414 0.274 0.000467 1.500 3.927 398.449 147.9 147.5 0.130 25.104 5,581.7 0.020 0.977 74.3 TCDD PCDD TCDF 1-PCDF 4-PCDF OCDF PCB-28 PCB-52 PCB-77 PCB-101 PCB-105 PCB-118 PCB-126 PCB-138 PCB-153 PCB-156 PCB-169 PCB-180 Parameter α1,2,6,12,13,16,17 α3,10,11,14,15 α4,5,7,8,9 αmix β1 (1-PCDF) β2 (4-PCDF) β3 (PCB-101) β4 (PCB-105) β5 (PCB-118) β6 (PCB-126) β7 (PCB-138) β8 (PCB-153) β9 (PCB-156) β10 (PCB-169) β11 (PCB-180) β12 (PCB-28) β13 (PCB-52) β14 (PCB-77) β15 (PCDD) β16 (TCDD) β17 (TCDF) θmix δ1 δ2 δ3 δ4 δ5 δ6 δ10 δ11 δ16 δ17 δmix 1552 50 31 50 50 50 — 50 50 31 31 14 14 50 14 14 14 31 31 p-Value < 0.001 < 0.001 < 0.001 0.004 < 0.001 0.008 < 0.001 < 0.001 < 0.001 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.007 0.228 0.062 0.795 0.431 0.848 0.001 < 0.001 0.742 0.696 0.563 0.855 0.057 0.506 different asymptotic levels, dose thresholds, and no overall lack of ﬁt (p > 0.05). Table 5 presents summary statistics for the mixture data. The data reveal a mixture- dose–dependent decrease in T4 concentrations that produced a maximal decrease of approxi- mately 50%. The experimental mixture data and the fits of the empirical and additivity models to the mixture data are shown in Figure 1. Comparison of the dose–response curve for the mixture under the hypothesis of additivity (Figure 1, dashed line) to the ﬁt to the empirical data (Figure 1, solid line) illustrate the dose-dependent nature of the nonadditive effects of the mixture (Figure 1). A quasi-likeli- hood ratio test rejected the hypothesis of addi- tivity (p < 0.001). Table 6 lists the results of an overall (6 df) test of additivity where the hypothesis is that the true mean is equivalent to the predicted mean from the FSCR model. From this test, there is statistically signiﬁcant evidence of departure from additivity (p < 0.001). Associated with this test are the six comparisons at each mixture-dose group (Table 6). The T4 mean for the mixtures at the three highest mixture-dose groups (667, 1,335, and 2,002 µg/kg/day) were each signiﬁcantly different from that predicted under additivity. Because the sample means are below that pre- dicted under additivity, there is evidence of an interaction (greater than additivity) at these dose groups. The difference between the addi- tivity model and empirical data (Figure 1) at the three highest mixture doses (i.e., area of maximal difference) was approximately 15% in terms of T4 concentration or 2.5-fold on a Table 5. Effects of PHAH mixture on serum T4 con- centrations. Mixture dose Test solution (µg/kg/day) (% stock) T4 mean (% control ± SD) Sample size Table 6. Test results for the hypothesis that mean T4 values for the mixture dose are equal to those pre- dicted under the additivity model. 0 20.0 66.7 200.3 667.5 1,335.1 2,002.6 — 0.33 1.1 3.3 11 22 33 Statistical test Overall F-test (df 6, 1,305) Individual F -tests (df 1, 1,305) 100.0 ± 11.8 106.8 ± 22.7 98.5 ± 21.7 94.6 ± 22.9 73.7 ± 14.6 62.9 ± 10.8 52.2 ± 15.1 Mixture dose (µg/kg/day) — 20.0 66.7 200.3 667.5 1,335.1 2,002.6 Statistic 3.43 1.65 0.03 0.21 8.76 7.91 10.10 12 12 12 12 12 12 12 p-Value 0.002 0.200 0.862 0.647 0.003* 0.005* 0.002* microgram per kilogram per day dose basis. There was no evidence for signiﬁcant departure from additivity at the three lowest doses of the mixture (Table 6). Discussion The present study tested the hypothesis that a mixture of TDCs affect T4 concentrations in a dose-additive manner. We designed the mix- ture 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 effec- tive doses of PHAHs that decrease TH concen- trations. These ﬁndings conﬁrm previous work demonstrating that short-term exposure to TCDD (Craft et al. 2002), and some individ- ual PCB congeners for example, PCB con- geners 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 establish- ing relative potency values for these chemicals. Analyses of the mixtures data demon- strated a dose-dependent synergy. The additiv- ity model underestimated the actual toxic effect of the mixture at the three highest doses tested (Figure 1). Effects of the three lowest doses of the mixture were not signiﬁcantly dif- ferent 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 *Dose groups where the mean T4 response is signiﬁcantly different (p > 0.05) from that predicted under additivity. VOLUME 113 | NUMBER 11 | November 2005 • Environmental Health Perspectives