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Author Manuscript
Environ Sci Technol. Author manuscript; available in PMC 2010 May 27.

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Published in final edited form as:
Environ Sci Technol. 2007 June 15; 41(12): 4354–4360.

Mathematical Model Developed for Environmental Samples:
Prediction of GC/MS Dioxin TEQ from XDS-CALUX Bioassay Data
David J. Brown†,‡, Jean Orelien§, John D. Gordon†, Andrew C. Chu†, Michael D. Chu∥,
Masafumi Nakamura⊥, Hiroshi Handa⊥, Fujio Kayama#, Michael S. Denison+, and George C.
Clark*,††Xenobiotic Detection Systems, 1601 East Geer Street, Suite S, Durham, North Carolina 27704‡Marietta College, 215 5th Street, Marietta, Ohio 45750§SciMetrika, LLC, 2 Davis Drive, Research Triangle Park, North Carolina 27709∥Analytical Perspectives, 2714 Exchange Drive, Wilmington, North Carolina 28405

NIH-PA Author Manuscript⊥Hiyoshi Corporation, 908 Kitanosho-cho, Omihachiman, Shiga 523-8555 Japan#Department of Environmental Medicine, Center for Community Medicine, Jichi Medical University,

Tochigi 329-0498, Japan+Department of Environmental Toxicology, Meyer Hall University of California–Davis, Davis,

California 95616

Abstract

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Remediation of hazardous waste sites requires efficient and cost-effective methods to assess theextent of contamination by toxic substances including dioxin-like chemicals. Traditionally, dioxinlike contamination has been assessed by gas chromatography/high-resolution mass spectrometry(GC/MS) analysis for specific polychlorinated dibenzo-p-dioxins, dibenzofurans, and biphenyl
congeners. Toxic equivalency factors for these congeners are then used to estimate the overall dioxin
toxic equivalency (TEQ) of complex mixtures found in samples. The XDS-CALUX bioassay
estimates contamination by dioxin-like chemicals in a sample extract by measuring expression of asensitive reporter gene in genetically engineered cells. The output of the XDS-CALUX assay is a
CALUX-TEQ value, calibrated based on TCDD standards. Soil samples taken from a variety ofhazardous waste sites were measured using the XDS-CALUX bioassay and GC/MS. TEQ andCALUX-TEQ from these methods were compared, and a mathematical model was developed
describing the relationship between these two data sets: log(TEQ) = 0.654 × log(CALUX-TEQ) +0.058-(log(CALUX-TEQ))2. Applying this equation to these samples showed that predicted and GC/
MS measured TEQ values strongly correlate (R2 = 0.876) and that TEQ values predicted fromCALUX-TEQ were on average nearly identical to the GC/MS-TEQ. The ability of XDS-CALUX
bioassay data to predict GC/MS-derived TEQ data should make this procedure useful in risk
assessment and management decisions.

Introduction
Hazardous waste site remediation is a complex and expensive process, at least in part because
the sites are typically contaminated with a large number of diverse chemical substances (1,2).

© 2007 American Chemical Society*
Corresponding author phone: (919)688-4804; fax: (919)688-4404; georgeclark@dioxins.com.

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Initial characterization of these sites requires rapid identification of the nature and amount ofcontaminants, and improved technologies for this purpose would significantly expedite theinitial phases of remediation. Because dioxins and dioxin-like chemicals (DLCs) contaminate
many hazardous waste sites under remediation, the United States Environmental Protection
Agency (U.S. EPA) Superfund Innovative Technology (SITE) Program has been evaluatingnovel technologies and comparing them to the established high-resolution gas
chromatography/mass spectrometry (GC/MS) technology for quantifying DLCs.

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DLCs refer to a subset of halogenated aromatic hydrocarbons, specifically 2,3,7,8-chlorosubstituted polychlorinated dibenzo-p-dioxins and dibenzofurans and 3,3′,4,4′-chlorosubstituted biphenyls that produce a common spectrum of Ah receptor-dependent toxicologicaland biological effects (3,4). These compounds induce similar toxicological effects in animals
including weight loss, thymic atrophy, immune suppression, hepatoxicity, porphyria,
chloracne and related dermal lesions, cancer, and reproductive toxicity (4–7). The spectrum of
toxicological effects in humans may not include all of the effects observed in animal studies(8). The prototypical member of the dioxin family is 2,3,7,8-tetrachlorodibenzo-p-dioxin(TCDD). While the exact mechanism by which dioxins and related DLCs exert their toxic
effects is not known, what is clear is that the alterations in gene expression induced by thesecompounds are mediated by their ability to bind to and activate the aryl hydrocarbon receptor(AhR), a soluble intracellular ligand-dependent transcription factor. Following binding to theAhR, the DLC/AhR complex migrates from the cytosol into the cell’s nucleus, wherein it isconverted into its DNA binding form and stimulates transcription of a battery of genes (9–14). It is believed that the toxicity of TCDD and related DLCs is related to the ability of thesechemicals to induce persistent expression of target genes.

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The toxicity of a dioxin congener is related to the ability of the congener to bind to and activate
the AhR, and the difference in relative potency between congeners can be several orders ofmagnitude. To assist with quantifying the overall impact of a mixture of congeners, the World
Health Organization (WHO) established toxic equivalency factors (TEFs) for 17
polychlorinated dibenzo-p-dioxin (PCDD) and polychlorinated dibenzofuran (PCDF)
congeners and 12 polychlorinated biphenyl (PCB) congeners. TCDD, the most potent
congener, is arbitrarily assigned a TEF of 1.0, and the TEF for less toxic congeners is <1.0
(15,16). Reevaluation of the 1998 WHO TEFs was recently reported, and minor changes weremade in some of the TEF values (17). In a typical analytical study, the concentration of DLCs
in a complex chemical mixture is determined by GC/MS, the amount of each congener present
in the mixture is multiplied by the congener’s TEF to obtain a toxic equivalent quotient (TEQ)
for that congener, and the sum of the individual TEQs represents the overall toxic potency ofthe chemical mixture. The TEQ estimate can be used for risk assessment studies involving
different complex mixtures (15–17).
The XDS-CALUX bioassay, developed by Xenobiotic Detection Systems, is based on amechanistic understanding of the ability of dioxin to activate gene expression, a step criticalin the toxic and biological effects of dioxins and related DLCs. XDS-CALUX uses a geneticallyengineered murine cell line that carries a firefly luciferase reporter gene driven by an AhRdependent promoter that is activated when the cells are exposed to dioxin or DLCs (18,19).
The magnitude of the induction response is directly proportional to the concentration of DLC
in the sample and the degree of AhR receptor occupancy and activation. When specific sample
processing procedures are used to eliminate the many non-dioxin-like AhR ligands/agonists(20,21) present in a sample extract, this bioassay can detect dioxin and DLCs with a highspecificity and sensitivity in extracts from various matrices (22–24). Furthermore, the XDSCALUX bioassay is highly quantitative, and using a TCDD standard curve, the reporter genesignal can be converted to a CALUX-toxic equivalency quotient (CALUX-TEQ). TheCALUX-TEQ is a surrogate for the standard TEQ generated by GC/MS. For the purpose ofEnviron Sci Technol. Author manuscript; available in PMC 2010 May 27.

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this paper, TEQ will refer to GC/MS estimates of toxic equivalency, and CALUX-TEQ willrefer to XDS-CALUX estimates of TCDD equivalency calculated from a TCDD standard
curve.
The present modeling procedure was evolved from a SITE study sponsored by the U.S. EPA.
The SITE study analyzed environmental soil and sediment samples from 10 hazardous waste
sites contaminated with complex mixtures of dioxin-like chemicals. Samples were analyzed
both by XDS-CALUX and by GC/MS, and CALUX-TEQ and GC/MS-TEQ values were
calculated and compared. Mathematical modeling was employed to determine the relationshipbetween these two data sets. Subsequently, the equation determined by mathematical modelingof the SITE data sets was applied to a second unrelated data set of environmental samples (partof an EPA SW846 validation study) to predict GC/MS-TEQ. Predicted TEQ and GCMS TEQ
values were compared to assess the validity of the modeled equation. The relevance of thecurrent results for future dioxin risk assessment and risk management studies is discussed.

Materials and Methods
Chemicals

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PCDD and PCDF standards were purchased from Wellington Laboratories (Guelph, Ontario,
Canada). Solvents and chromatography matrices for sample extraction and cleanup werepurchased from Fisher Scientific and were ACS grade or higher (Pittsburgh, PA).
Environmental Samples

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The U.S. EPA, the Battelle Corporation, and XDS conducted a U.S. EPA SITE study of 209
soil and sediment samples, including spiked samples and standard extracts, in March 2004.
Samples were obtained from 10 hazardous waste sites undergoing remediation and included
Warren County PCB Landfill in North Carolina; Tittabawassee River Flood Plain in Michigan;
residential sites in Midland, MI; Winona Post site in Winona, MO; Solutia site in Nitro, WV;
New York/New Jersey Harbors; Newark Bay in New Jersey; two sites on the Saginaw Riverin Michigan; and the Brunswick Wood Preserving Site in Glynn County, GA (25). Samples
contained various contaminants including dioxins, PCBs, PCP, and PAHs. A complete
description of the sites and samples is available in a U.S. EPA report (25). GC/MS analysis
was performed by Axys Analytical Services (Sidney, British Columbia, Canada), while XDSCALUX analysis was carried out by XDS, in a mobile laboratory stationed in Saginaw, MI(40 samples),or at XDS, in Durham, NC (169 samples). Thirty-six soil samples and eight ash
samples (part of an SW846 validation study) were used to test the proposed mathematicalmodel. Soil and ash samples collected in Japan were analyzed by GC/MS at Hiyoshi
Corporation (ash and soil) (Omihachiman, Shiga, Japan), soil samples collected at a U.S. EPA
remediation site in Hawaii were analyzed by GC/MS at Southwest Laboratories (Tempe, AZ),
and XDS-CALUX analysis was conducted at XDS, in Durham, NC. XDS was blinded to thesample design and GC/MS data in the U.S. EPA SITE study as well as to the GC/MS data in
the SW846 validation study (GC/MS data were sent to an independent statistician).
XDS-CALUX Sample Preparation
Sample extraction and cleanup were conducted as described previously (26–28). Briefly,
aliquots of each sample were extracted with toluene and then purified by acid silica column
chromatography and a patented carbon column chromatography procedure (29). Two fractions
were subsequently collected from the carbon column. The first fraction contained chlorinatedbiphenyls, and the second fraction contained primarily PCDD/Fs (27,29). Isolated fractions
were exchanged into dimethylsulfoxide (DMSO), for measurement of CALUX-TEQ in the
XDS-CALUX bioassay. Both fractions were analyzed for CALUX-TEQ; however, in thispaper, results for only the PCDD/Fs fraction are presented and analyzed.
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XDS-CALUX Bioassay Analysis

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XDS-CALUX was conducted in H1L6.1c2 cells, a mouse hepatoma (hepa1c1c7) cell line
stably transfected with the pGudLuc6.1 reporter plasmid that carries an AhR responsive mouse
mammary tumor virus promoter and firefly luciferase gene (18,19). Samples in DMSO weresuspended in cell culture medium (RPMI 1640 supplemented with 8% fetal calf serum and 1%
Pen/Strep antibiotics) and added to monolayer cultures of the H1L6.1c2 cell line in 96 wellplates. Each assay was calibrated with a TCDD standard curve and included positive controls(solutions of TCDD and PCB 126) and negative controls (DMSO and solvent blanks). Plates
were incubated for 20 h in a humidified 5% CO2 atmosphere at 37 °C, the medium wasremoved, and the luciferase activity was quantified (Promega luciferase assay kit, Promega,
Madison, WI) using a Berthold Orion Microplate Luminometer (Oak Ridge, TN). All sample
measurements were made in the linear range of the assay (i.e., values near the middle of thestandard curve). Quality control criteria used for the analysis of XDS-CALUX bioassay results
were as described in Brown et al. (26). The TCDD standard curve was modeled to a fourparameter Hill equation using the least-squares best fit method, and the resulting standard curve
was used to calculate CALUX-TEQ for unknown samples (26,27). CALUX-TEQ and TEQ
data were collected and analyzed in a double-blinded manner with statistical analysis conductedby an independent statistician. The 209 EPA SITE samples were extracted and analyzed in thescreening mode of analysis in the XDS-CALUX method that includes a single extraction of asample. Upon un-blinding of the SITE study design, it became evident that there were 49 total
samples within the study. A summary of the breakdown of the sample classes was 32 samples
of soil and sediment analyzed in quadruplicate (124 samples), 12 performance evaluation
samples analyzed in quadruplicate except for two samples that were replicated seven times (58samples), and five extracts that were replicated from four to eight times within the 209 total
samples (25). The performance evaluation samples and solutions contained reference materials
with certified concentrations of dioxin, furans, and/or PCBs, spiked samples with a certified
concentration of dioxin and/or other contaminants, and blank samples. A complete descriptionof the SITE study design has been described (25). Analysis of the samples by GC/MS was notblinded, and the replicate criteria for generation of TEQ by GC/MS are not available to XDS.
SW846 samples were extracted and analyzed in triplicate for CALUX-TEQ determination andpresented as mean values, and GC/MS TEQ analysis was based on single instrumental
determinations of DLCs by GC/MS determination of TEQ.
Mathematical Modeling

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GC/MS TEQ and CALUX-TEQ values were fit to a statistical model. Initially, a generalized
estimating equation (GEE) (30) that accounted for the correlation due to replicates using a base
10 logarithmic transformation of the two variables was used. However, the final model was
based on the mean of the replicates. Input data for mathematical modeling initially included
all samples. Subsequently, in developing a model for dioxins, seven samples were eliminatedthat were PCB standards, and data were limited to samples with TEQ values between 1 and3000 pg/g with the model being generated on the means of 34 samples. Data presented are on
42 samples without elimination of the samples below 1 pg/g or greater than 3000 pg/g.
TEF/REP Conversion Factor
Calculation of conversion factors for samples based on differences between WHO TEF values
and XDS-CALUX relative potency (REP) values was conducted as described previously(31). TEQ values for SW846 samples were recalculated using GC/MS congener specificconcentrations and XDS-CALUX REP values (32) (see Table 1). GC/MS measurements forsome congeners in SW846 samples were reported without non-detect (ND) negative control
values. Therefore, for this study, all ND values were set to zero. Conversion factors (or averaged
conversion factors, where n > 1) were calculated by dividing the TEQ for the sample by theEnviron Sci Technol. Author manuscript; available in PMC 2010 May 27.

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TEQ calculated using CALUX REP values. The TEF/REP corrected estimate of TEQ was
derived by multiplying this factor by the XDS-CALUX measured value for CALUX-TEQ.

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Results
Comparison of GC/MS-TEQ and CALUX-TEQ

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GC/MS and XDS-CALUX assays were performed on 209 SITE samples as described in the
Materials and Methods. During data analysis, sample replicates within the data set wereidentified, the data presented in the graphs represent the average for each of 42 samples
analyzed in quadruplicate, and the mean was calculated for the samples after un-blinding of
the data. GC/MS-TEQ and CALUX-TEQ values calculated from GC/MS and XDS-CALUX,
respectively, are compared graphically in Figure 1. The results show that GC/MS-TEQ andCALUX-TEQ values are highly correlated (R2 = 0.892). Variance is observed between the two
methods at very low or high GC/ MS-TEQs or CALUX-TEQs. This may indicate that GC/MSand XDS-CALUX assays demonstrate increased variance at or near the limits of detection ofboth analytical procedures. The variance is also greater at extremely high concentrations of
dioxin-like chemicals. This is probably due to the XDS-CALUX bioassay detecting additional
compounds that activate AhR but are not detected or measured by GC/MS methods. The
extreme dilution of the sample to allow detection in the linear range of the standard curve also
contributes to the variance in estimating the CALUX-TEQ for the sample. CALUX-TEQ
values are consistently higher than TEQ values (Figure 1), with CALUX-TEQs averaging 9.4
times higher than TEQ values. This is consistent with what has been observed previously forenvironmental samples (22,33).
Modeling the Relationship between GC/MS-TEQ and CALUX-TEQ
The data in Figure 1 (excluding samples with TEQ values ≤1 or ≥3000 pg of TCDD/g) wereused to derive a statistical model for the relationship between GC/MS-TEQ and CALUX-TEQ
as follows:(1)

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This model was evaluated by predicting TEQ from CALUX-TEQ using input CALUX-TEQ
data from the SITE study (Figure 1). The results showed a linear relationship with a correlationof 0.876 (Figure 2), which is slightly lower than the direct correlation between GC/MS-TEQ
and CALUX-TEQ. However, since the data trend line almost coincides with the dasheddiagonal line through the origin, the predicted TEQ values are very close to the TEQ values
measured by GC/MS. For these data, CALUX predicted TEQ was on average 0.997 times the
TEQ measured by GC/MS. As observed previously, the discrepancy between GC/MS
measured TEQ and CALUX predicted TEQ was greater for samples with very low or highTEQ values.
Testing the Mathematical Model
The mathematical model was tested with XDS-CALUX data from an independent data set,
SW846, that included 36 dioxin contaminated soil samples and eight ash samples. CALUXTEQ and GC/MS-TEQ values for these samples had correlation values of 0.857 and 0.935 forash and soil samples, respectively (Figure 3). As observed previously, CALUX-TEQ values
for the SW846 samples were consistently higher than TEQ values. For these data, CALUXTEQ was on average 12.6-fold higher than TEQ (3.4-fold for ash samples and 14.6–fold forsoil samples).

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On the basis of our mathematical model and eq 1, CALUX-TEQ values were used to predict
TEQ values, and the predicted and measured GC/MS-TEQ values for the SW846 data set werecompared (Figure 4). Correlation coefficients were 0.817 for ash samples and 0.922 for soil
samples. Predicted TEQ was on average 6.5-fold higher than measured GC/MS-TEQ (1.3-foldfor ash and 7.7-fold for soil). Note that these values change when samples with extreme GC/
MS-TEQ values (i.e., ≤1 or ≥3000 pg/g) were excluded from the analysis (see Table 2 andDiscussion).
Prediction of SW846 Data using TEF REP Conversion Factors
The XDS-CALUX bioassay has been used to define congener specific toxic equivalencies
called REP values (32) based on the method described in refs 34 and 35. REP is an XDSCALUX specific equivalent of TEF, as determined by the WHO using GC/MS. TEF REP
conversion factors were calculated for each sample in the SW846 data set; the result for arepresentative sample is presented in Table 1, and average results for all ash and soil samples
are summarized in Table 3. Results for individual samples are summarized graphically in
Figure 5. By using TEF REP conversion factors, the overall discrepancy between CALUXTEQ and TEQ is reduced but not eliminated (also see Table 2), with predicted TEQ on average
7.0–fold higher than TEQ (2.3-fold for ash samples and 8.0-fold for soil samples). Thus,
applying a TEF REP conversion eliminates approximately half of the difference between
CALUX-TEQ and GC/MS-TEQ.

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Discussion

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The XDS-CALUX bioassay is a sensitive method for measuring the bioactivity of dioxin anddioxin-like chemicals. Using a recombinant cell line containing a stably transfected AhR
responsive promoter and firefly luciferase reporter gene, the XDS-CALUX bioassay estimatesthe concentration of DLCs by measuring the ability of a sample to activate AhR-dependent
luciferase gene expression. While the XDS-CALUX bioassay generates a CALUX-TEQ value,
which reflects the relative bioactivity of DLCs in the sample, the traditional GC/MS analyticalmethod for measuring dioxin-like compounds generates a TEQ value by physical measurementof the concentrations of individual chlorinated dioxin congeners and multiplication by a TEF
and summation to generate a TEQ in the mixture. While both methods are efficient andaccurate, they are based on different methods with differences in the underlying mechanisms.
CALUX-TEQ and GC/MS-TEQ values are not expected to be identical due to differences in
the TEF and REP values, measurement of other DLCs, or non-additive interactions that may
occur in the XDS-CALUX method. The goal of this study was to define the relationshipbetween CALUX-TEQ and GC/MS-TEQ. To this end, CALUX-TEQ and GC/MS-TEQ values
were measured and compared for two data sets, a mathematical model was derived using thefirst data set, and the model was tested with a second data set. The results suggest that XDSCALUX can be a useful and relatively accurate method for estimating the concentration of
DLCs in complex environmental samples and that CALUX-TEQ and GC/MS-TEQ data canbe compared.
CALUX-TEQ and GC/MS-TEQ were calculated and compared for 209 SITE samples that
represented 32 soil and sediment samples with quadruplicate analysis by GC/MS after uncoding of the blinded data, allowing comparison to XDS-CALUX determinations. During data
analysis, replicates of samples were identified, and some samples (blanks, PCB standards,
TCDD standards, and other DLCs to evaluate limits of detection and specificity) were
eliminated from the final data used for modeling. On average, CALUX-TEQ values were 9.4fold higher than TEQ values, and these values demonstrated greater variance at extreme endsof the CALUX-TEQ/ TEQ distribution. Mathematical modeling of these data (excluding
samples with TEQ values ≤1 and ≥3000 pg of TCDD/g) produced a model for the relationship

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between CALUX-TEQ and TEQ (eq 1). Applying eq 1 to SITE samples showed that predicted
and GC/MS measured TEQ values are strongly correlated (R2 = 0.876) and that TEQ values
predicted from CALUX-TEQ values were on average nearly identical to the GC/MS-TEQ(multiple of 0.997). The model was further tested using soil and ash samples from an
independent data set, SW846. When eq 1 was applied to this data set, a strong correlation wasagain observed between predicted and TEQ measured values by GC/MS (R2 = 0.922 for soil
samples and R2 = 0.817 for ash samples). Prior to transformation, CALUX-TEQ was on average
12.6-fold higher than TEQ, but after transformation using eq 1, the difference was reduced by
52% (Table 2). If the analysis was limited to samples with TEQ values in the range of 1–3000
pg of TCDD/g, predicted TEQ was 1.6-fold higher than TEQ. Thus, the model accounted forapproximately 82% of the systematic difference between CALUX-TEQ and GC/MS-TEQ for
this data set. Table 2 also shows that the difference between CALUX-TEQ and GC/MS-TEQ
is generally higher in soil than in ash samples. While the reason for this difference is not entirely
clear, it suggests that CALUX-TEQ and GC/MS-TEQ may be more similar for some sample
types/matrices than others. This also suggests that the use of mathematical modeling should
be conducted on a matrix-by-matrix basis. Analysis comparing XDS-CALUX and GC/MS ona matrix basis should aid in estimating TEQ since it would provide an estimate of thecontribution of other halogenated dioxins/ furans and biphenyls and other active compounds
such as chlorinated napthalenes that may contribute to CALUX-TEQ but not to GC/MS
estimates of TEQ.

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A previous study proposed a way of comparing GC/MS-TEQ and CALUX-TEQ results based
solely on the difference between WHO TEF values and bioassay REP values (31). While thismethod takes into account only one source of variation between GC/MS-TEQ and CALUXTEQ results, in some sample matrices, it appears to provide acceptable results. XDS-CALUX
REP values, which are a measure of the relative potency of specific dioxin congeners, aresimilar but non-identical to WHO estimates of TEF values (32). To determine how much of
the observed variation between GC/MS-TEQ and CALUX-TEQ values resulted fromdifferences in the relative potency for specific PCDD and PCDF congeners, this method of
conversion was also conducted on the SW846 data set. Table 3 and Figure 5 show that thisconversion reduced the average fold difference between CALUX-TEQ and GC/MS-TEQ from
12.6 to 7.0 (all samples) or 4.2 to 2.3 (all samples in the range of 1–3000 pg of TCDD/g). Thus,
TEF REP conversion accounts for approximately half of the difference between CALUX-TEQ
and TEQ. Given that the TEF values themselves may possess an order of magnitude in variancein establishing these values, the point estimates between CALUX-TEQ and TEQ correlate
remarkably well (15–17).

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On the basis of the previous analysis and results from prior studies, we propose that at least
three factors account for the observed differences between CALUX-TEQ and GC/ MS-TEQ:(i) XDS-CALUX may detect bioactivity of AhR agonists that are not normally measured by
GC/MS such as polychlorinated naphthalenes (34,35) or brominated dioxin-like compounds(36,37); (ii) XDS-CALUX, but not GC/MS, can detect non-additive interactions between AhR
ligands (refs 38–40 and reviewed in ref 41); and (iii) specific dioxin-like chemicals havedifferent relative potency values using GC/MS and XDS-CALUX (i.e., TEF and REP,
respectively). The use of conversion factors based on the difference between TEF and REP
values can correct for the third factor, but for the ash and soil samples in the SW846 study, this
only accounted for about half of the observed differences. In contrast, mathematical modelingas conducted in this study can take into account all three factors. The model provides an averagebasis for estimation of GC/MS-derived TEQ from CALUX-TEQ based upon sampling of 10
sites that are under remediation for these contaminates.
Studies are ongoing with the U.S. EPA and the Battelle Corporation to identify the
hypothesized DLCs and other chemical entities in these sample extracts that are acting as AhR

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agonists. The patented sample processing method (U.S. Patent 6,720,431 (29)) has been
demonstrated to remove greater than 99.8% of 14 different non-halogenated aromatichydrocarbons that are AhR agonists (28). Two aromatic hydrocarbons (beno (b) fluorantheneand indeno (1,2,3 cd) pyrene) were removed from sample extracts to a lesser extent (94.3 and97.5%, respectively). This suggests that non-halogenated aromatic hydrocarbons that are AhR
agonists are probably not responsible for the higher estimates of CALUX-TEQ observed in
the samples from the SITE study. However, this possibility cannot be completely discounted
if extremely high concentrations of aromatic hydrocarbon AhR agonists are present in thesamples. Brominated and/or mixed bromo/chloro dioxins and furans and halogenatednaphthalenes are two classes of chemicals that could contribute to the higher XDS-CALUX
activity.

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The previous analysis also suggests that XDS-CALUX and GC/MS agree more closely forsamples falling in the midrange of detection limits for these analytical methods (i.e., samples
with TEQ >1 and <3000 pg of TCDD/g in this study). The variance observed with very diluteor concentrated samples in this study was likely due to threshold and saturation effects that are
typical for most analytical methods. It is also possible that for samples with low levels of dioxin
contamination, the bioactivity of other AhR agonists that are not measured by GC/MS could
be relatively more important contributors to the overall biological response. Because of its
broader specificity, XDS-CALUX may provide a better assessment of relative potency inextremely complex samples.
In summary, this study demonstrates that mathematical modeling can expand our ability toquantify the toxic potential of complex environmental samples containing dioxin and dioxinlike chemicals and compares the results obtained through different analytical methods. While
GC/MS is used in current U.S. EPA regulatory requirements for quantifying dioxin
contamination, XDS-CALUX is an important alternative method that can provide data for risk
assessment and remediation decisions. Because of its high capacity and broad specificity, XDSCALUX may be the preferred method (due to lower cost and time) for high-throughput
screening or to accommodate samples with high or unknown complexity. The results presented
here make it possible to compare analytical studies that use XDS-CALUX and GC/MS. Thus,
the XDS-CALUX bioassay, coupled with the model presented here, may have important futureapplications in risk assessment and risk management associated with dioxin contamination
and/or hazardous waste site remediation.

Acknowledgments
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This work was supported by a grant from the National Institutes of Environmental Health Sciences, Small BusinessInitiated Research (SBIR) Grant ES08372 and an NIEHS Superfund Basic Research Grant (M.S.D., ES04699). The
authors acknowledge the valuable contributions of Dr. Miriam Sanders of Page One editorial services in revising themanuscript.

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FIGURE 1.

Comparison of GC/MS-TEQ and CALUX-TEQ for SITE samples. TEQ was determined by
GC/MS, CALUX-TEQ was determined by XDS-CALUX as described, and the average resultsfor each quadruplicate analysis of 42 soil and sediment samples are graphed. Solid line is thedata trend line; for comparison, the dashed line represents y = x(slope = 1 and intercept = 0).
The regression line equation for the data is y = 3.356x1.0067.

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FIGURE 2.

Modeling the relationship between predicted GC/MS-TEQ and GC/MS-TEQ. The TEQ was
predicted from CALUX-TEQ values for SITE samples using eq 1. Solid line is the data trendline; for comparison, the dashed line represents y = x(slope = 1 and intercept = 0). The regression
line equation for the data is y = 1.7789x0.9384.

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FIGURE 3.

Comparison of GC/MS-TEQ and CALUX-TEQ for SW846 samples. TEQ was determined by
GC/MS, and CALUX-TEQ was determined by XDS-CALUX as described. Solid line is thedata trend line; for comparison, the dashed line represents y = x(slope = 1 and intercept = 0).
Sample matrix was ash (■) or soil (△).

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FIGURE 4.

Predicting GC/MS-TEQ from CALUX-TEQ for SW846 samples. TEQ was predicted fromCALUX-TEQ determinations using eq 1. Solid line is the data trend line; for comparison, thedashed line represents y = x(slope = 1 and intercept = 0). Sample matrix was ash (■) or soil(△). The regression line equation for ash is y = 6.49x0.82 and for soil is y = 5.86x0.97.

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FIGURE 5.

Predicted CALUX-TEQ by TEF/REP conversion comparison with GC/MS TEQ. TEQ was
calculated from XDS-CALUX data using TEF REP conversion factors as described previously(ref 32; also see Tables 1 and 3). Solid line is the data trend line; for comparison, the dashedline represents y = x(slope = 1 and intercept = 0). Sample matrix was ash (■) or soil (△). The
regression line for ash is y = 4.49x0.82 and for soil is y = 3.88x0.97.

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7100
2.5
10
13
30
34
4.3
82
270
44
380

OCDD

2378-TCDF

12378-PeCDF

23478-PeCDF

123478-HxCDF

123678-HxCDF

123789-HxCDF

234678-HxCDF

1234678-HpCDF

1234789-HpCDF

OCDF

0.0001

0.01

0.01

0.1

0.1

0.1

0.1

0.5

0.05

0.1

0.00010.01

0.0016

0.044

0.024

0.31

0.11

0.14

0.13

0.58

0.14

0.067

0.00034

0.031

0.061

0.098

0.075

0.73

1

CALUX-REP

0.608
71.37637.688

1.936

6.48

25.42

0.473

4.76

3.9

7.54

1.4

0.1675

2.414

9.92

0.793

1.666

0.54

3.358

CALUX-TEQ(pg of TCDD/g)

0.038

0.44

2.7

8.2

0.43

3.4

3.0

6.5

0.5

0.25

0.71

3.2

1.3

1.7

0.72

4.6

WHO-TEQ(pg of TCDD/g)

TEF and REP values are shown for PCDD and PCDF congeners in a representative SW846 soil sample. Conversion factor was calculated as described previously (31).

a3201234678-HpCDD

0.1

0.1

0.1

1

1

WHO-TEF

TEQ/CALUX-TEQ = 37.688/71.376 = 0.528

13

7.2

123478-HxCDD
17

4.6

12378-PeCDD

123789-HxCDD

ND

2378-TCDD

123678-HxCDD

GC/MS(pg/g)

congener

TABLE 1

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TEF REP Conversion for Representative Soil Samplea

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2.3
(57%)

1.6
(82%)

4.2

2.3
(45%)

1.3
(89%)

3.4

ash samples

1.3
(68%)

1.1
(88%)

1.9

ash samples
in range(1–3000 pg/g)

8.0
(48%)

7.7
(51%)

14.6

soil samples

3.0
(55%)

1.7
(83%)

5.4

soil samples
in range(1–3000 pg/g)

Average fold difference between CALUX-TEQ and TEQ is shown for all samples or sample subsets as indicated. TEQ values were derived using no transformation, mathematical modeling (eq 1), or TEF
REP conversion, as indicated. Values in parentheses show what fraction of the discrepancy between CALUX-TEQ and TEQ is accounted for by mathematical modeling or TEF REP conversion, respectively.

a

7.0
(48%)

6.5
(52%)

mathematical model

TEF REP conversion factor

12.6

all samples

none

transformation method

all samples
in range(1–3000 pg/g)

Summary of Overall Discrepancy between CALUX-TEQ and TEQ Values for SW846 Samplesa

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TABLE 2
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N

8

36

matrix

ash

soil

0.35

0.52

min

0.75

0.80

max

0.53

0.69

median

0.55

0.69

av

0.10

0.11

SD

18

16

rel SD (%)

Average TEF/REP Conversion Factors for Soil and Ash Samples in SW846 Data Set

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TABLE 3
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