In Vitro and in Silico Derived Relative Effect Potencies of Ah-Receptor

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In vitro and in silico derived relative effect potencies of Ah-receptor mediated effects by PCDD/Fs and PCBs in rat, mouse and guinea pig CALUX cell lines Mehdi Ghorbanzadeh, Karin van Ede, Malin Larsson, Majorie van Duursen, Lorenz Poellinger, Sandra Lücke, Miroslav Machala, Katerina Pencikova, Jan Vondracek, Martin van den Berg, Michael S. Denison, Tine Ringsted, and Patrik L. Andersson Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/tx5001255 • Publication Date (Web): 05 Jun 2014 Downloaded from http://pubs.acs.org on June 9, 2014

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Chemical Research in Toxicology

In vitro and in silico derived relative effect potencies of Ah-receptor mediated effects by PCDD/Fs and PCBs in rat, mouse and guinea pig CALUX cell lines

Mehdi Ghorbanzadeh†¤, Karin I van Ede‡¤, Malin Larsson†, Majorie BM van Duursen‡, Lorenz Poellinger§, Sandra Lücke-Johansson§, Miroslav Machala#, Kateřina Pěnčíková#, Jan Vondráček#, Martin van den Berg‡, Michael S Denison┴, Tine Ringsted†, Patrik L Andersson†*

†

Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden

Endocrine Toxicology Group, Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80177, NL3508 TD Utrecht, The Netherlands

‡

§

Department of Cell and Molecular Biology, Karolinska Institute, SE-171 77 Stockholm, Sweden

Department of Chemistry and Toxicology, Veterinary Research Institute, 621 32 Brno, Czech Republic

#

┴

Department of Environmental Toxicology, University of California, Davis, California 95616

¤

These authors contributed equally to this article

*To whom correspondence should be addressed. Tel: +46-90-786-5266. Fax: +46-90-786-7655. Email: [email protected]

Mehdi Ghorbanzadeh: [email protected], [email protected] Karin I van Ede: [email protected] Malin Larsson: [email protected] Majorie BM van Duursen: [email protected] Lorenz Poellinger: [email protected] Sandra Lücke: [email protected] Miroslav Machala: [email protected] Kateřina Pěnčíková: [email protected] Jan Vondráček: [email protected] Martin van den Berg: [email protected] Michael S Denison: [email protected] Tine Ringsted: [email protected] Patrik L Andersson: [email protected]

1 ACS Paragon Plus Environment


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Abstract. For a better understanding of species-specific relative effect potencies (REPs), responses of dioxin-

like compounds (DLCs) were assessed. REPs were calculated using chemical-activated luciferase gene expression assays (CALUX) derived from guinea pig, rat and mouse cell lines. Almost all 20 congeners tested in the rodent cell lines were partial agonists and less efficacious than 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). For this reason, REPs were calculated for each congener using concentrations at which 20% of the maximal TCDD response was reached (REP20TCDD). REP20TCDD values obtained for PCDD/Fs were comparable with their toxic equivalency factors assigned by the World Health Organization (WHO-TEF) while those for PCBs were in general lower than the WHO-TEF values. Moreover, the guinea pig cell line was the most sensitive as indicated by the 20% effect concentrations of TCDD of 1.5, 5.6 and 11.0 pM for guinea pig, rat and mouse cells, respectively. A similar response pattern was observed using multivariate statistical analysis between the three CALUX assays and the WHO-TEFs. The mouse assay showed minor deviation due to higher relative induction potential for 2,3,7,8-tetrachlorodibenzofuran and 2,3,4,6,7,8-hexachlorodibenzofuran and lower for 1,2,3,4,6,7,8heptachlorodibenzofuran and 3,3’,4,4’,5-pentachlorobiphenyl (PCB126). 2,3,7,8-tetrachlorodibenzofuran was more than two times more potent in the mouse assay as compared with rat and guinea pig cells while measured REP20TCDD for PCB126 was lower in mouse cells (0.05) as compared with guinea pig (0.2) and rat (0.07). In order to provide REP20TCDD values for all WHO-TEF assigned compounds, quantitative structure-activity relationship (QSAR) models were developed. The QSAR models showed that specific electronic properties and molecular surface characteristics play important roles in the AhR-mediated response. In silico derived REP20TCDD values were generally consistent with the WHO-TEFs with a few exceptions. The QSAR models indicated that e.g. 1,2,3,7,8-pentachlorodibenzofuran and 1,2,3,7,8,9-hexachlorodibenzofuran were more potent than given by their assigned WHO-TEF values and the non-ortho PCB 81 was predicted, based on the guinea-pig model, to be one order of magnitude above its WHO-TEF value. By combining in vitro and in silico approaches, REPs were established for all WHO-TEF assigned compounds (except OCDD), which will provide future guidance in testing AhR mediated responses of DLCs and to increase our understanding of species variation in AhRmediated effects.



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Introduction

Polychlorinated

dibenzo-p-dioxins

(PCDDs),

polychlorinated

dibenzofurans

(PCDFs)

and

polychlorinated biphenyls (PCBs) include a range of highly toxic and persistent environmental pollutants originating from industrial products and combustion activities. In total, there are theoretically 209 PCB and 210 PCDD/F congeners based on the number of chlorine atoms and their positions on the aromatic rings. Owing to their chemical characteristics, high resistance to biodegradation and high lipophilicity, these compounds are widely distributed in the environment and human food chain.1-3 Exposure to PCDDs, PCDFs and dioxin-like PCBs can cause a wide variety of adverse health effects including (neuro)developmental defects, endocrine disruption, skin toxicity, immune deficiencies and carcinogenic responses.4-10 Most, if not all, biological effects of these dioxin-like compounds (DLCs) are mediated through a common mechanism of action initiated by binding to and activation of the aryl hydrocarbon receptor (AhR).11-16 Risk assessment of DLCs is challenging since these compounds exist in the environment as complex mixtures. In order to simplify risk assessment for this class of compounds the toxic equivalency (TEQ) concept has been developed. The TEQ value of a sample reflects the overall toxicity due to DLCs and is the sum of congener-specific toxic equivalency factors (TEFs) multiplied by the concentration in a matrix, such as feed, food or blood. In total 29 PCDDs, PCDFs and PCBs have been assigned with a TEF value by the World Health Organization (WHO).17-18 This means that those compounds must (1) have some similarity in structure to the 2378-substituted PCDDs and PCDFs, (2) bind to and activate the AhR, (3) be persistent and accumulate in the food chain, and (4) show AhR-mediated biological/toxic response.18-19 Each WHO-TEF value is derived from multiple toxic and biologic relative effect potencies (REPs) of an individual DLC compared to the most potent congener, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD).17 The DR CALUX® (Chemically-Activated LUciferase eXpression) bioassay is an assay increasingly being used by research and commercial laboratories for the screening of samples for dioxins and DLCs. The recombinant cell line contains a stably transfected dioxin (AhR)-responsive firefly luciferase reporter gene.20 After activation of the AhR, the receptor-ligand complex is transported into the nucleus where it binds to a dioxin responsive element (DRE) in the DNA. The cells respond with an increased and dose-related production of luciferase. There are several recombinant cell lines that have been developed from a variety of species.20 However, the majority of the screening analyses for DLCs are performed using either mouse (Hepa1c1c7) or rat (H4IIE) hepatoma cells which are stably transfected with the AhR-responsive reporter plasmids pGudLuc1.1 or pGudLuc6.1.21-22 In general there is a good correlation between bioanalytical equivalents (BEQs) calculated with CALUX and TEQs


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calculated from chemical analysis data (GC/HRMS) and WHO-TEF values.23-27 However, in particular for dioxin-like PCBs, an underestimation has been seen between BEQs based on CALUX-derived REPs and GC/HRMS TEQ calculations.28-29 Also, in a recently published study by Lee et al., differences were seen between TEQ calculations based on CALUX-derived REPs and those based on the WHO-TEFs.30 To improve risk assessment, using bioassays such as the DR-CALUX, more knowledge regarding bioassay specific REPs is very important. In this study, the potency of 20 selected PCDDs, PCDFs and PCBs were examined in rat, mouse and guinea pig CALUX bioassays. Variation in species sensitivity was studied using effect concentration ratio plots and multivariate statistics. Furthermore, quantitative structure-activity relationship (QSAR) models were developed based on derived data to predict the REPs for the non-tested DLCs assigned with a WHO-TEF value. QSAR modeling is applied in many disciplines, such as risk assessment and toxicity prediction, and represents statistical models that quantify the relationship between the variation in chemical properties of the compounds and their biological activity. The model aims at providing predictions of the activity of structurally similar but untested compounds as well as discovering structural analogies that influence the activity of a group of compounds. A number of QSAR models have been reported to estimate different biochemical and toxicological responses for PCDDs, PCDFs and PCBs but to our knowledge no other research group has combined all three chemical classes into the same model.31-46 By this approach, we aimed at achieving a better understanding of the structural requirements behind the AhR mediated response of the entire group of DLCs. Thus, the experimentally derived REP values were complemented with the QSAR based values and compared and discussed in relation to their assigned WHO-TEF values.

Materials and Methods Chemicals. A set of four PCDDs, six PCDFs and ten PCBs (including two non-dioxin like (NDL)

PCBs, i.e. PCB74 and PCB153), were selected based on WHO-TEF values, number of chlorine atoms, substitution pattern, and environmental abundance. Selected compounds are displayed in Figure S1 of the Supporting Information. 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), 1,2,3,7,8-pentachlorodibenzo-p-dioxin (12378-PeCDD), 1,2,3,6,7,8-hexachlorodibenzo-p-dioxin (123678-HxCDD), 1,2,3,4,6,7,8-heptachlorodibenzop-dioxin (1234678-HpCDD), 2,3,7,8-tetrachlorodibenzofuran (TCDF), 2,3,4,7,8,-pentachlorodibenzofuran (23478-PeCDF), (234678-HxCDF),

1,2,3,4,7,8-hexachlorodibenzofuran(123478-HxCDF), 1,2,3,4,6,7,8-heptachlorodibenzofuran

2,3,4,6,7,8-hexachlorodibenzofuran

(1234678-HpCDF),


1,2,3,4,7,8,9-


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heptachlorodibenzofuran (1234789-HpCDF) and 3,3’,4,4’,5-pentachlorobiphenyl (PCB126) were purchased from Wellington Laboratories Inc. (Guelph, Ontario, Canada). 2,3’,4,4’,5-pentachlorobiphenyl (PCB118), 2,3,3’,4,4’,5-hexachlorobiphenyl (PCB156) and 2,2’,4,4’,5,5’-hexachlorobiphenyl (PCB153) were purchased from

Cerilliant

tetrachlorobiphenyl

Corp.

(Round

Rock,

TX,

USA).

2,4,4',5-tetrachlorobiphenyl

(PCB77), 2,3,3',4,4'-pentachlorobiphenyl

(PCB74),

3,3',4,4'-

(PCB105), 2,3',4,4',5,5'-hexachlorobiphenyl

(PCB167), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB169), 2,3,3',4,4',5,5'-heptachlorobiphenyl (PCB189) were purchased from Larodan Fine Chemicals (Malmö, Sweden). All congeners had a purity > 99% except for 1234678-HpCDD (98.7%). The congeners were dissolved and diluted in dimethyl sulfoxide (DMSO) (SigmaAldrich, Stockholm, Sweden). Molecular descriptors. The 3D molecular structures of the compounds were constructed using the

software Scigress.47 All molecular structures were geometrically optimized using the Austin Model 1 (AM1), a semi empirical method incorporated in the MO-G application of the software Scigress. Prior to the geometry optimization the initial dihedral angle was set; 44° for non-ortho (no) PCBs and 50° for mono-ortho PCBs based upon crystallographic data of the PCBs.42,48 The 2,3,7,8-substituted PCDD/Fs were optimized with the same procedure, but with a planar structure. The chemical descriptors included in the current study are related to molecular size as starting point, conformation, connectivity, hydrophobicity, and electronic properties. Detailed information on all 98 calculated and measured descriptors has been descripted earlier by Larsson et al.48and only a brief summary will be given here. The two-dimensional molecular descriptors size, conformation and connectivity were calculated in MOE

49

and the octanol-water partition coefficient (log Kow) from KowWIN

(www.epa.gov). Included three-dimensional molecular descriptors were dipole moments, molecular orbital (MO) energies, atom-specific electron density coefficients of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), atomic electrostatic potential charges, atom-specific nucleophilic, electrophilic and radical susceptibility. Note that the atom-specific descriptors are calculated for the lateral positions of the three chemical classes, i.e. positions 2,3,7,8 and 345/3'4'5' for the PCDD/Fs and PCBs, respectively. This was done to compare these three groups of compounds (due to the structural differences in the chemical skeletons shown in Figure S1 of the Supporting Information) and to capture atom specific characteristics of the lateral positions, which are critical for AhR mediated responses.4 Due to the different number of lateral positions for these chemical classes, the highest and lowest values concerning these positions were used as descriptors. Calculations for the electronic descriptors were performed in Scigress using AM1 (MO energies, dipole moment, susceptibilities) and in the Gaussian 09 suite of programs using B3-LYP 6-31G** (MO


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energies, dipole moment, atomic ESP charges).50 From the MO energies, the differences between the two highest HOMO (EHOMO, EHOMO-1) energies and the LUMO (ELUMO) energy were created (GAP and GAP-1). The experimental digitalized ultraviolet (UV) absorption spectra were previously measured in our laboratory for all studied compounds in the range from 200 to 350 nm and used as descriptors to describe molecular size and substitution pattern related properties.48,51 The earlier not published UV spectra of PCBs 74 and 153 are included in Supporting Information (Figure S2). Biological data. The rat hepatoma (H4IIe) cells and guinea pig intestinal adenocarcinoma cells

(GPC16) contain the stably transfected plasmid pGudLuc 1.1, whereas, the mouse hepatoma (Hepa1c1c7) cells contain the stably transfected plasmid pGudLuc 6.1.21-22 The names of the rat, guinea pig and mouse clonal cell lines are H4L1.1c4, G16L1.1c8 and H1L6.1c2, respectively. The pGudLuc1.1/6.1 plasmids contain the luciferase reporter gene under AhR-dependent control of 4 xenobiotic responsive elements. The cell lines were cultured in alpha-MEM culture medium (Gibco / Invitrogen, Breda, The Netherlands) supplemented with 10% fetal bovine serum (FBS) (Gibco / Invitrogen, Breda, The Netherlands), 50 IU/mL penicillin and 50 µg/mL streptomycin (Gibco / Invitrogen, Breda, The Netherlands). The cell lines were grown confluent in white clearbottomed 96 well microplates (Costar, Cambridge, MA, USA) at 37°C in a humidified 5% CO2 atmosphere. Standard curves of the 20 selected PCDDs, PCDFs and PCBs were prepared in culture medium containing twice the desired concentration. For exposure, 100µl was added in triplicate to the 96MW-plate containing 100µl medium. The outer edge of the MW-plate was filled with medium only to avoid concentration differences due to evaporation. The final DMSO concentration was 0.1% v/v with the following concentrations of the congeners: TCDD, PeCDD and 23478-PeCDF (0.0005 – 1 nM), 2378-TCDF, 123478-HxCDF, 234678-HxCDF and PCB126 (0.005 – 10 nM), 123678-HxCDD (0.005 – 25 nM), 1234678-HpCDD, 1234678-HpCDF and 1234789HpCDF (0.05 – 100 nM), PCB169 (0.005 – 1000 nM), PCB77 and PCB189 (10 – 5000 nM), PCB74, PCB105, PCB118, PCB153, PCB156, PCB167 (10 – 10000 nM). For the G16L1.1c8 cell line, some congeners were exposed with a different concentration range; TCDF, 123478-HxCDF, 234678-HxCDF, 1234678-HpCDD, 1234678-HpCDF, 1234789-HpCDF (0.0005 – 1 nM), PCB169 (0.05 – 50 nM) and PCB77 (0.5 – 500 nM). In each experiment a reference curve of TCDD was included. After an exposure period of 24 h, cells were washed with phosphate buffered saline (PBS) and lysed with lysis reagent (Promega, Fitchburg, WI, USA, pH 7.8). Luciferase activity was measured 20 minutes after the cells were lysed using the Luminostar Optima from BMG Labtech (Offenburg, Germany).



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Dose-response modeling. The dose-response curves were fitted by a four-parameter log-logistic

model52 in GraphPad Prism version 6.00 (GraphPad Software, La Jolla California USA, www.graphpad.com). The equation used in GraphPad was the “log (agonist) vs. response -Variable slope” with a fixed bottom plateau set to 0. It should be noted that not all congeners had a similar Ymax or Hill slope as seen for TCDD. This difference has a profound influence on the EC50 calculations, which generally form the basis for a REP determination. Therefore, it was decided to calculate the concentration needed for a congener to reach a benchmark response (BMR) of 20% and 50% of the maximum TCDD response (BMR20TCDD and BMR50TCDD).53 Prerequisites for BMR20TCDD and / or BMR50TCDD calculation; •

For BMR20TCDD, Ymax had to reach at least 25% of TCDD maximum response.

•

For BMR50TCDD, Ymax for a given congener had to reach at least 55% of TCDD maximum response.

•

If maximum response did not reach a clear Ymax, top plateau was fixed at the Ymax of TCDD.

•

Coefficient of determination (R2) value of above 0.80.

The dose response curves of the 20 selected PCDDs, PCDFs and PCBs were defined by taking the average of two independent experiments in which each concentration was tested in triplicate (with the exception of PCB169 in rat and mouse where only four concentration levels were tested twice and 12 concentration levels were tested once due to experimental circumstances). The BMR was then calculated from the averaged dose response curve. To exclude the background luciferase activity, the DMSO blank response was subtracted from the compound response. Multivariate data analysis. In order to develop QSAR models the multivariate OPLS method was

applied, which uses the descriptor matrix X to predict the response matrix Y.54-55 It is a modification of the partial least squares (PLS) method and it divides the systemic variation of X into two parts; one predictive variation correlated to Y and one orthogonal variation uncorrelated to Y. Compared to PLS, OPLS does not change the predictive power but improves model interpretation and reduces model complexity. The response values used to build QSAR models were log BMR20TCDD based REP (log REP20TCDD) and log BMR50TCDD based REP (log REP50TCDD). By definition the REP value was set to 1 for TCDD in every experimental model. The developed QSARs were evaluated by internal and external validation tests, and then applied to predict the response values of non-tested compounds. In addition, principal component analysis (PCA) was applied to choose the training set compounds and to analyze the variation in the measured responses. With PCA one single


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matrix (X) is decomposed into the product of two smaller matrices, scores (T) and loadings (P), plus a matrix of residuals (E): (1)

= ´ +

The scores express the systemic behavior of the objects (here, compounds) and the loadings comprise information on variables. The plot of orthogonal vectors of scores and loadings reflect the variation between the compounds and variables, respectively. The PCA and OPLS calculations were done using SIMCA version 13.0 software (Umetrics AB, Umea, Sweden).55 Training and validation sets. The studied set of 20 PCDD/Fs and PCBs was split into a training set

and a validation set. The training set of 12 compounds was selected based on the chemical diversity of the compounds as analyzed using PCA on the compiled set of chemical descriptors. Figure S3 of the supporting information displays the PCA score plot of the 12 training set compounds, 8 validation compounds and 11 nontested compounds (covering the compounds with assigned WHO-TEF value) where each group of chemicals shows a chemical variety. All calculated descriptors (listed in Table S1) were used for the PCA, which resulted in a model with two significant principal components (PC) explaining 36% and 24%, of the variation in the data set, respectively. In order to have a diverse training set covering the whole chemical space of the WHO-TEF assigned compounds, the congeners were selected from all three classes of compounds. In addition, compounds were selected from the different areas of the score plot including compounds with high and low PC1 and PC2 scores to reach representatives with different number of chlorine atoms and from each chemical class. The training set consisted of six PCBs, four PCDFs and two PCDDs. As shown in Figure S3 the compounds of the training set were representative of each chemical class. The remaining eight tested compounds, including four PCBs, two PCDDs and two PCDFs, were used as validation set. The training set participated in the modeling process and the validation set was used to evaluate the predictive capacity of the resulting QSAR models. Development and validation of QSAR models. Models were developed including both responding

and non-responding compounds. Non-responding compounds, which are not AhR agonists, were given REP values one order of magnitude lower than the lowest REP calculated in the corresponding assay.46,56 The nonresponding compounds were not given REPs of zero due to the transformation of REPs to log scale which was performed for modeling purpose. This procedure was done to model the chemistry of non-activity. The same training and validation sets were applied to develop and validate all QSAR models. The fitting of the models was assessed by the coefficient of determination (R2) and the root mean square error (RMSE). The ability to predict



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new compounds was evaluated by internal cross-validation and by using the external validation set. With crossvalidation, a group of compounds is excluded from the model development process and the developed model predicts the target values corresponding to the removed compounds. This procedure is repeated several times until each observation has been removed once and the predictive ability of the model is expressed as crossvalidated explained variation (Q2). A calculated Q2 value larger than 0.5 indicates that the developed model could be regarded as predictive.57 In addition, the root mean square error of cross-validation (RMSEcv) was calculated. Based on the predictions of the validation set, the root mean square error of prediction (RMSEP) was also calculated. RMSEP is a measure of the predictive power of the developed model and is calculated as the standard deviation of the predicted residuals. Outliers in the models were searched using the model membership probability. If a compound had a membership probability of less than 5%, then it was considered as a moderate outlier. Note that predictions at 99% confidence level were reported but indicated as extrapolations. Variable influence on projection (VIP) was used to show the importance of each chemical descriptor in the models. In order to find how the descriptors influenced the developed models, the correlation plot for each important descriptor and the corresponding response value was investigated. The applicability domain of the developed models was analyzed as recommended by the organization for economic cooperation and development (OECD).58 The approach used to determine the applicability domain of the models was based on the membership probability. According to this method, a compound with a membership probability higher than 0.05 is considered as being inside the applicability domain of the model.

Results and discussion Measured REPs. In the rat and mouse CALUX assays, all congeners except the mono-ortho

substituted PCB189 and the NDL PCB153 induced AhR-mediated luciferase activity. In the guinea pig CALUX assay, all congeners, except the NDL PCB153, were able to induce an AhR-mediated luciferase activity (Figure 1). Although most congeners caused a dose-dependent induction in luciferase activity, this induction was not always as high as the maximum response of TCDD. Differences in maximum response or Hill slope can significantly affect the calculation of REPs.59 For this reason, it was decided to calculate effect concentrations of the 20 selected congeners with a benchmark approach as has been earlier described by Van Ede et al.53 Effect concentrations were calculated at the concentration where the congeners reached 20% and 50% of maximum response of TCDD (BMR20TCDD and BMR50TCDD). The BMR20TCDD and BMR50TCDD as well as Ymax, for the rat, mouse and guinea pig CALUX assays are listed in Table 1.


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In Figure 2 AhR-activation by TCDD and PCB126 are compared, which clearly illustrates that the guinea pig was more sensitive than the rat and mouse cell lines. When BMR20TCDD ratios were compared between two species for all congeners tested, it showed that BMR20TCDD concentrations for guinea pig were up to one order of magnitude lower for the PCDDs and PCDFs tested and up to two orders of magnitude lower for the PCBs tested compared to rat and mouse (Figure S4a-b). Variation in the BMR20TCDD concentrations in the rat and mouse CALUX assay were within one order of magnitude. Generally, the BMR20TCDD for PCDDs, PCDFs and PCB77 were lower in mice when compared to rat (Figure S4c). From the data presented in Figures 2 and S4, the rank order in sensitivity for the different CALUX assays is guinea pig > mouse ~ rat. Differences in REP values between CALUX assays may be due to biochemical, pharmacological and/or species-/cell-specific differences, such as variations in AhR ligand-binding affinity and specificity, differences in the binding of and regulation by cofactors (e.g. ARNT) or chaperone proteins, AhR DNA binding, and recruitment of transcriptional cofactors and/or differences in other factors that may modulate AhR functionality.60-

61

Furthermore, differences in

response due to plasmid differences between the cell lines can also partly cause variation in sensitivity between the cell lines. Han et al. describes that mouse H1L1.1c2 has a higher EC50 for TCDD exposure compared to the mouse H1L6.1c2 cell line.22 REP values were calculated using BMR20TCDD and BMR50TCDD concentrations (REP20TCDD and REP50TCDD) obtained in each CALUX cell line and REP20TCDD are shown in Table 2. In Figure 3, the ratio between the calculated REP20TCDD values and the WHO-TEFs are illustrated for the different species. A ratio of 1 indicates that a derived REP20TCDD is comparable to its WHO-TEF. This graph shows that in general REP20TCDD values for PCDDs and PCDFs in the rat, mouse and guinea pig cell lines were similar to or somewhat higher than the WHO-TEFs but still within the half order of magnitude of uncertainty around the WHO-TEF value17 (Figure 3a). Exceptions are 1234678-HpCDD and 1234789-HpCDF for which, for all species, REP20TCDD values were calculated outside the uncertainty range and up to 20-fold higher compared to the WHO-TEFs. In contrast to the PCDDs and PCDFs, REP20TCDD values for the different PCBs were generally below the WHO-TEF and even outside the uncertainty range for some of the PCBs in the rat and mouse cell lines (Figure 3b). The guinea pig cell line showed a wide variation around the WHO-TEFs for the different PCBs, with PCBs 77, 126, 105 and 156 having higher and PCBs 169, 118 and 167 having lower REP20TCDD values than their respective WHO-TEFs. These data suggest that current WHO-TEF values, at least for PCB169, PCB118 and PCB167, might be overestimated.



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The variations in REP20TCDD values among the different congeners tested in the rat, mouse and guinea pig CALUX assays were further investigated using PCA. A first analysis including all tested congeners showed only minor variations in induction profiles (see Figure S5). A second PCA was calculated on the eleven most potent congeners capturing 95% of the variation by two PCs; 87% by PC1 and 8% by PC2 (Figure 4). In PC1 the congeners were positioned in order of their potency, such that the most potent ones (i.e. TCDD, 12378-PeCDD and 23478-PeCDF) were located on the right side of the score plot and the least potent ones (i.e. 1234678HpCDF and 1234678-HpCDD) on the left side (Figure 4a). The second PC was mainly related to pattern differences in the mouse assay response where TCDF shows relatively higher response and 1234678-HpCDF and PCB126 showed lower response. For example TCDF showed a REP20TCDD for mouse of 0.8 as compared to its WHO-TEF of 0.1. The loading plot (Figure 4b) indicates that the REP20TCDD value profiles calculated from rat and guinea pig CALUX assays are the closest to the WHO-TEF pattern. QSAR modeling. REPs derived from rat, guinea pig and mouse assays were used to create rat, guinea

pig and mouse QSAR models, respectively. Internal and external predictivity of the QSAR models was higher when using log REP20TCDD values for each assay than those based on log REP50TCDD (results not shown). Therefore, QSAR models were based on log REP20TCDD and showed Q2 and RMSECV values from 0.81 to 0.85 and from 0.92 to 1.02, respectively (Table 3). Q2 values larger than 0.5 obtained from internal validation of each model generally mean that the developed models are predictive. In addition, the differences between obtained R2 and Q2 do not exceed 0.3 indicating that there was no over-fitting in the model development.57 The plots of the predicted values versus the experimentally measured values showed in general a good agreement for all CALUX assays. The residuals obtained in the QSAR models for all compounds were plotted against the experimental values (Figure S6). The residuals of the training set compounds were randomly distributed indicating that no systematic error exists in the models. However, the residuals of some compounds in the validation set were large indicating that they may be outliers. Details on the applicability domain of the models including outlier identification are found in the Supporting Information. The most significant descriptors in the QSAR models were studied using VIPs and correlation plots in order to gain insights in structure-specific related differences in their induction potencies (Table 3 and Figure S7). The differences in LUMO and HOMO energy (GAP), total positive van der Waals surface area (PEOE_VSA_POS), Balaban's connectivity topological index (Balaban's index), UV descriptors and shape indices (e.g. Kier3) were the most significant descriptors. The correlation plots indicate that highly potent compounds have low chemical stability (low GAP), low number of chlorine atoms, are more symmetric (low


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Balaban's index value), and have large positive van der Waals surface area. Interestingly, GAP and Balaban's index are the only ones (of these descriptors) that can differentiate PCB126 from the less potent PCBs and at the same time indicate its lower potency as compared with most PCDD/Fs. It has previously been demonstrated that the GAP descriptor is of importance in modeling the properties and activities of halogenated organic compounds and to be negatively correlated with REPs.62-65 Furthermore, McKinney et al. showed that a chemical with greater ability to accept electrons through charge-transfer interaction could bind to the Ah-receptor with greater affinity than those with lower electron acceptor properties.66-67 A more detailed explanation of the most important descriptors and how they affect AhR mediated response is found in the Supporting Information. Predicted REPs versus WHO-TEFs. WHO-TEFs are based on consensus decisions using mainly in

vivo rodent studies but also in vitro information. Here, we compared the REP20TCDD values calculated by our three QSAR models with their WHO-TEF values. Almost all QSAR-predicted REP20TCDD values for PCDDs including the non-tested ones based on guinea pig, rat and mouse CALUX data were within one order of magnitude of the WHO-TEF values (Table 2, Figure S8). Notably, several PCDDs were found outside the applicability domain including 1234678-HpCDD and OCDD that were even outliers at 99% confidence interval. The non-tested 123478-HxCDD and 13789-HxCDD showed predicted REP20TCDD close to their WHO-TEFs. The QSAR-predicted REP20TCDD values for all PCDFs were mainly found within one order of magnitude in relation to their WHO-TEFs. TCDF and OCDF showed larger prediction errors, however the predictions are uncertain as these are defined as weak outliers in the models. The predicted REP20TCDD values for the non-tested 123678-HxCDF were found in the range of its WHO-TEF value, whereas 12378-PeCDF and 123789-HxCDF deviated (Table 2). The predicted REP20TCDD values for 12378-PeCDF were 0.6, 0.09 and 0.7 in guinea pig, rat and mouse respectively, which are three to twenty times higher than expected based on its WHO-TEF value (0.03). However, it should be noted that this congener is very fast metabolized in vivo and only very low concentrations are found in blood.68 In vitro and in silico models might give an overestimation of the potency due to an overload of the in vitro system with high concentrations of the congener and the fact that toxicokinetics are not taken into account in these models. 123789-HxCDF (WHO-TEF 0.1), which is in vivo slower metabolized compared to TCDD69, was predicted to be the most potent congener among the non-tested with predicted REP20TCDD values of 0.8, 0.1 and 1 in the guinea pig, rat and mouse, respectively which indicate a activity up to ten times the WHO-TEF. The REP20TCDD values predicted by the QSAR models for the non-tested non-ortho PCB81, range from 0.0001 for rat to 0.002 for guinea pig. Only in the guinea pig model, the predicted value differed from its WHO-



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TEF indicating a stronger induction. Among the tested PCBs, the REP of PCB126 was close to its WHO-TEF but showed large prediction errors between experimental and QSAR based values (Table 2, Figure 5a-c). This deviation can be explained by the fact that measured REP20TCDD values for PCB126 are more similar to those of PCDD/Fs compared to other PCBs, whereas its structure and chemistry resembles PCBs. QSAR models predict the potency of a congener based on chemical properties and their measured effect in an assay. As a consequence, developed QSAR models in this study tend to predict a considerably lower REP20TCDD for PCB126, as has been determined based on the CALUX assays. The prediction error could also be caused by lack of chemical descriptors that describe the critical chemical features of PCB126 for the studied response. Similar findings have been observed by Larsson et al.48 It is worthy to note that QSAR models where PCB126 was excluded showed lower prediction errors. Among non-tested mono-ortho PCBs, predictions of PCB114 and PCB123 were found outside the uncertainty range of their WHO-TEFs for some of the species. PCB114 showed a REP20TCDD value nearly 10 times higher than its current WHO-TEF in the guinea pig model, while the values were close to WHO-TEF for rat and mouse. Compared to literature data, which showed that PCB114 has a REP20TCDD value of 0.00004 in rat hepatoma H4IIE cells70-71 and 0.000068 in a DR-CALUX assay72 our REP20TCDD value in the rat model is very similar (0.0003) and identical to the current WHO-TEF value. On the contrary, PCB123 had much lower REP20TCDD than present WHO-TEF (more than ten times lower) for both rat and mouse models whereas the predicted value of the guinea pig model was in range. PCB123 has in a similar CALUX study obtained a REP20TCDD of 0.000035 which is closer to the existing WHO-TEF rather than our prediction (0.000001).72 In conclusion, the difference between the measured and the predicted values was in general higher for PCBs than for PCDD/Fs.

Conclusion. REPs derived in the CALUX assays followed, in general, nicely the WHO-TEFs for the studied set of PCDD/Fs and PCBs. The combination of in vitro and in silico based methods resulted in complete REP sets for all DLCs having a WHO-TEF value, except for one model outlier (OCDD). The guinea pig cell line was found to be the most sensitive as indicated by the 20% effect concentrations of TCDD that were 1.5, 5.6 and 11 pM for guinea pig, rat and mouse, respectively. Multivariate statistical analysis indicated only minor variation in the response pattern for the three studied species. The pattern was as well similar to the WHO-TEFs with mouse showing minor variation due to higher induction potential for TCDF and 234678-HxCDF, and lower for 1234678-HpCDF and PCB126. The measured REP20TCDD value for TCDF in mouse cells was more than two


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times higher than that for rat and guinea pig cells while measured REP20TCDD for PCB126 was lower in mouse cells (0.05) as compared with guinea pig (0.2) and rat (0.07). The predictive performance of the QSAR models was generally high with few exceptions. The prediction error for PCB126 was large probably related to imperfect chemical descriptors that were not able to capture its high induction potential. REPs derived with the QSAR models showed that 123789-HxCDF was the most potent non-tested congener in most assays. In addition the predicted REP20TCDD values for 12378-PeCDF were three to twenty times higher as compared with its WHOTEF value (0.03). In vitro and in silico derived data from the present study for all WHO-TEF assigned congeners could be used as a guidance for future testing but also as a basis for a better understanding of species variations and risk assessments of DLCs.

Funding Sources This study was financially supported by the European Union through the project entitled “The development, validation and implementation of human systemic Toxic Equivalencies (TEQs) as biomarkers for dioxin-like compounds (SYSTEQ)” (226694-FP7-ENV-2008-1).

Supporting Information The supporting information includes molecular structures of the studied compounds, a short description of the chemical descriptors, an analysis of the chemical diversity of the studied compounds, membership probability values of the developed QSAR models, UV spectra of PCB74 and PCB153, graphs depicting variation in sensitivity between the species and variation analysis of REP20TCDD values, plots illustrating the correlation between selected chemical descriptors and log REP20TCDD, correlation plots between log TEF and predicted log REP20TCDD values, and the residual values of the models against log REP20TCDD values. This material is available free of charge via the Internet at http://pubs.acs.org.

Abbreviations CALUX, Chemical-activated luciferase gene expression assays; QSAR, quantitative structure−activity relationship; PCDD, polychlorinated dibenzo-p-dioxins; PCDF, polychlorinated dibenzofurans; PCB, polychlorinated biphenyls; NDL PCB, non dioxin-like PCB, AhR, aryl hydrocarbon receptor; BMR, benchmark response; TEQ, toxic equivalency; TEF, toxic equivalency factor; REP, relative effect potency; TCDD, 2,3,7,8tetrachlorodibenzodioxin; TCDF, 2,3,7,8-tetrachlorodibenzofuran; 12378-PeCDD, 1,2,3,7,8-pentachlorodibenzop-dioxin; 123678-HxCDD, 1,2,3,6,7,8-hexachlorodibenzo-p-dioxin; 1234678-HpCDD, 1,2,3,4,6,7,8heptachlorodibenzo-p-dioxin; 123478-HxCDD, 1,2,3,4,7,8-hexachlorodibenzo-p-dioxin; 123789-HxCDD, 1,2,3,7,8,9-hexachlorodibenzo-p-dioxin; OCDD, Octachlorodibenzo-p-dioxin; 23478-PeCDF, 2,3,4,7,8,pentachlorodibenzofuran; 123478-HxCDF, 1,2,3,4,7,8-hexachlorodibenzofuran; 234678-HxCDF, 2,3,4,6,7,8hexachlorodibenzofuran; 1234678-HpCDF, 1,2,3,4,6,7,8-heptachlorodibenzofuran; 1234789-HpCDF, 1,2,3,4,7,8,9-heptachlorodibenzofuran; 12378-PeCDF, 1,2,3,7,8- pentachlorodibenzofuran; 123678-HxCDF, 1,2,3,6,7,8-hexachlorodibenzofuran; 123789-HxCDF, 1,2,3,7,8,9-hexachlorodibenzofuran; OCDF, Octachlorodibenzofuran; PCB118, 2,3’,4,4’,5-pentachlorobiphenyl; PCB156, 2,3,3’,4,4’,5-hexachlorobiphenyl;



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PCB153, 2,2’,4,4’,5,5’-hexachlorobiphenyl; PCB157, 2,3,3',4,4',5'-Hexachlorobiphenyl; PCB74, 2,4,4',5tetrachlorobiphenyl; PCB77, 3,3',4,4'-tetrachlorobiphenyl; PCB81, 3,4,4',5-Tetrachlorobiphenyl; PCB123, 2',3,4,4',5-Pentachlorobiphenyl; PCB114, 2,3,4,4',5-Pentachlorobiphenyl; PCB105, 2,3,3',4,4'pentachlorobiphenyl; PCB126, 3,3’,4,4’,5-pentachlorobiphenyl; PCB167, 2,3',4,4',5,5'-hexachlorobiphenyl; PCB169, 3,3',4,4',5,5'-hexachlorobiphenyl ; PCB189, 2,3,3',4,4',5,5'-heptachlorobiphenyl; PCA, principal component analysis; PLS, partial least squares; OPLS, orthogonal projection to latent structures; HOMO, highest occupied molecular orbital, LUMO, lowest unoccupied molecular orbital, RMSE, root mean square error; RMSEP, root mean square error of the prediction; RMSEcv, root mean square error of cross validation; R2: determination coefficient, Q2, cross-validated R2.


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(40) Dynes, J. D., Gottschalk, K. E., Pedersen, L.G. (1985) Interring dihedral angles in polychlorinated biphenyls from photoelectron spectroscopy. Can. J. Chem. 63, 1292-1299. (41) Mekenyan, O. G., Veith, G. D., Call D. J., Ankley, G. T. (1996) A QSAR evaluation of Ah receptor binding of halogenated aromatic xenobiotics. Environ. Health Perspect.104, 1302-1310. (42) Shaikh, N. S., Parkin, S., Luthe, G., Lehmler, H. J. (2008) The three-dimensional structure of 3,3',4,4'tetrachlorobiphenyl, a dioxin-like polychlorinated biphenyl (PCB). Chemosphere 70, 1694–1698. (43) Tuppurainen, K., Ruuskanen, J. (2000) Electronic eigenvalue (EEVA): a new QSAR/QSPR descriptor for electronic substituent effects based on molecular orbital energies. A QSAR approach to the Ah receptor binding affinity of polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs). Chemosphere 41, 843-848. (44) Van der Burght, A. S. A. M., Clijsters, P. J., Horbach, G. J., Andersson, P. L., Tysklind, M., Van den Berg, M., (1999) Structure-dependent induction of CYP1A by polychlorinated biphenyls in hepatocytes of Cynomolgus monkeys (Macaca fascicularis). Toxicol. Appl. Pharmacol. 155, 13–23. (45) Van der Burght, A. S. A. M., Tysklind, M., Andersson, P. L., Horbach, G. J., Van den Berg M. (2000) Structure-dependent induction of CYP1A by polychlorinated biphenyls in hepatocytes of male castrated pig. Chemosphere 41, 1697-1708. (46) Andersson, P. L., Van der Burgh, A. S. A. M., Van de Burgh, M., Tysklind, M. (2000) Multivariate modeling of polychlorinated biphenyl-induced CYP1A activity in hepatocytes from three different species: ranking scales and species differences. Environ. Toxicol. Chem. 19, 1454-1463. (47) Scigress Version 2.2.0. Fujitsu Limited, Tokyo, Japan, (2008) software available at http://www.fqs.pl/chemistry_materials_life_science/products/scigress. (48) Larsson, M., Mishra, B., Tysklind, M., Jonsson, A. L., Andersson, P. L. (2013) On the use of electronic descriptors for QSAR modeling of PCDDs, PCDFs, and dioxin-like PCBs. SAR QSAR Environ. Res. 24, 461479. (49) MOE 2006.08. Chemical Computing Group, Quebec, Canada, (2008) software available at http://www.chemcomp.com.



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(50) Gaussian 09 Revision A.1. Gaussian, Inc., Wallingford CT, 2009; software available at http://www.gaussian.com. (51) Andersson, P., Haglund, P., Rappe, C., Tysklind, M. (1996) Ultraviolet absorption characteristics and calculated semi-empirical parameters as chemical descriptors in multivariate modelling of polychlorinated biphenyls. J. Chemometr. 10, 171-185. (52) Ritz, C. (2010) Toward a unified approach to dose-response modeling in ecotoxicology. Environ. Toxicol. Chem. 29, 220-229. (53) van Ede, K. I., Andersson, P. L., Gaisch, K. P. J., van den Berg, M., van Duursen, M. B. M. (2013) Comparison of intake and systemic relative effect potencies of dioxin-like compounds in female mice after a single oral dose. Environ. Health Perspect. 121, 847-853. (54) Trygg, J., Wold, S. (2002) Orthogonal projections to latent structures (OPLS). J. Chemometr. 16, 119-128. (55) Eriksson, L., Rosen, J., Johansson, E., Trygg, J. (2012) Orthogonal PLS (OPLS) modeling for improved analysis and interpretation in drug design. Mol. Inf. 31, 414 – 419. (56) Harju, M., Hamers, T., Kamstra, J.H., Sonneveld, E., Boon, J.P., Tysklind, M., Andersson, P.L. (2007) Quantitative structure-activity relationship modeling on in vitro endocrine effects and metabolic stability involving 26 selected brominated flame retardants. Environ. Toxicol. Chem. 26, 816-826. (57) Golbraikh, A., Tropsha, A. (2002) Beware of q2! J. Mol. Graph. Model. 20, 269–276. (58)

OECD.

Quantitative

Structure-Activity

Relationships

Project

[(Q)SARs].

Available

online:

http://www.oecd.org/document/23/0,3746,en_2649_34377_33957015_1_1_1_1,00.html. (59) Villeneuve, D. L., Blankenship, A. L., Giesy, J. P. (2000) Derivation and application of relative potency estimates based on in vitro bioassay results. Environ. Toxicol. Chem. 19, 2835-2843. (60) Carlson, E. A., McCulloch, C., Koganti, A., Goodwin, S. B., Sutter, T. R., Silkworth J. B. (2009) Divergent transcriptomic responses to aryl hydrocarbon receptor agonists between rat and human primary hepatocytes. Toxicol. Sci. 112, 257-272.


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(61) Connor, K. T., Aylward, L. L. (2006) Human response to dioxin: Aryl hydrocarbon receptor (AhR) molecular structure, function, and dose-response data for enzyme induction indicate an impaired human AhR. J. Toxicol. Environ. Health. Part. B 9, 147-171. (62) Kobayashi, S., Saito, A., Ishii, Y., Tanaka, A., Tobinaga, S. (1991) Relationship between the biological potency of polychlorinated dibenzo-para-dioxins and their electronic states. Chem. Pharm. Bull. 39, 2100-2105. (63) Niu, J. F., Huang, L. P., Chen, J. W., Yu, G., Schramm, K. W. (2005) Quantitative structure-property relationships on photolysis of PCDD/Fs adsorbed to spruce (Picea abies (L.) Karst.) needle surfaces under sunlight irradiation. Chemosphere 58, 917-924. (64) Diao, J. X., Li, Y., Shi, S. Q., Sun, Y. (2010) QSAR models for predicting toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans using quantum chemical descriptors. Bull. Environ. Contam. Toxicol. 85, 109-115. (65) Arulmozhiraja, S., Morita, M. (2004) Structure-activity relationships for the toxicity of polychlorinated dibenzofurans: Approach through density functional theory-based descriptors. Chem. Res. Toxicol. 17, 348-356. (66) Waller, C. L., McKinney, J. D. (1995) Three-dimensional quantitative structure-activity relationships of dixoins and dioxin-like compounds: model validation and Ah receptor characterization. Chem. Res. Toxicol. 8, 847-858. (67) McKinney, J. D., Pedersen, L. G. (1986) Biological activity of polychlorinated biphenyls related to conformational structure. Biochem. J. 240, 621-622. (68) Brewster, D. W., Birnbaum, L. S. (1988) Disposition of 1,2,3,7,8-pentachlorodibenzofuran in the rat. Toxicol. Appl. Pharmacol. 95, 490-498. (69) Van den Berg, M., De Jongh, J., Poiger, H., Olson, J. R., (1994) The toxicokinetics and metabolism of polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) and their relevance for toxicity. Crit. Rev. Toxicol. 24, 1-74. (70) Sawyer, T., Safe, S. (1982) PCB isomers and congeners: Induction of aryl hydrocarbon hydroxylase and ethoxyresorufin o-deethylase enzyme activities in rat hepatoma cells. Toxicol. Lett. 13, 87–94.



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(71) Hahn, M. E., and Chandran, K. (1996) Uroporphyrin accumulation associated with cytochrome P4501A induction in fish hepatoma cells exposed to aryl

hydrocarbon receptor agonists, including 2,3,7,8-

tetrachlorodibenzo-p-dioxin and planar chlorobiphenyls. Arch. Biochem. Biophys. 329, 163–174. (72) Behnisch, P. A., Hosoe, K., Sakai, S. I. (2003) Brominated dioxin-like compounds: in vitro assessment in comparison to classical dioxin-like compounds and other polyaromatic compounds. Environ. Int. 29, 861-877.


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48


Table 1. Benchmark response (BMR) concentrations in nM or µM (mono-ortho PCBs only) and efficacy (Ymax) in percentage relative to TCDD

Compounda

Guinea pig G16L1.1c8 BMR BMR Ymax

20TCDD

50TCDD

0.0015 0.0018 0.014 0.019

0.0049 0.0066 0.044 0.052

TCDF 23478-PeCDF 123478-HxCDF 234678-HxCDF 1234678-HpCDF 1234789-HpCDF Non-ortho PCB

0.0056 0.0012 0.0050 0.0059 0.020 0.013

PCB77 PCB126 PCB169 Mono-ortho PCBs PCB74 PCB105 PCB118 PCB156 PCB167 PCB189 Di-ortho PCB PCB153

PCDD TCDD 12378-PeCDD 123678-HxCDD 1234678-HpCDD PCDF

a

Rat H4L1.1c4 BMR BMR 20TCDD

50TCDD

100 106 92 104

0.0056 0.012 0.052 0.20

0.027 0.057 0.30 1.1

0.022 0.0050 0.022 0.024 0.081 0.033

79 96 85 86 -c 92

0.10 0.036 0.075 0.096 0.38 0.15

0.71 0.0081 0.11

2.8 0.039 0.47

94 100 95

1.1 0.012 0.13 0.011 0.12 0.12

7.0 0.11 0.74 0.037 0.59 1.1

-b

-b

Ymax

Mouse H1L6.1c2 BMR BMR Ymax

20TCDD

50TCDD

100 100 91 94

0.011 0.0091 0.030 0.095

0.029 0.024 0.092 0.57

100 99 82 71

0.97 0.21 0.40 0.58 1.9 0.83

-c 101 88 86 91 94

0.013 0.0096 0.025 0.018 0.43 0.066

0.057 0.026 0.086 0.054 2.8 1.2

81 90 87 82 68 59

40 0.076 2.6

286 0.43 11

101 86 79

6.0 0.24 21

1073 0.99 -b

-c 76 46

-c 98 80 94 -c -c

4.2 1.3 1.6 0.048 1.5 -b

15 7.7 10 0.36 12 -b

-c -c -c 76 -c -b

4.5 6.1 2.9 0.28 7.8 -b

30 38 15 2.3 39 -b

-c -c -c 62 -c -b

-b

-b

-b

-b

-b

-b

-b

b

The names of compounds are abbreviated according to Materials and Methods. No or too low induction to calculate BMR20TCDD and/or BMR50TCDD (see Materials and Methods). Congener did not reach a clear top plateau, the top plateau was fixed at the Ymax of TCDD (100) .

c

25

ACS Paragon Plus Environment


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Table 2. Relative effect potencies calculated based on measured data (REP) and predicted by developed QSAR models (REPQSAR), based on BMR20TCDD determined in rat H4L1.1c4, mouse H1L6.1c2 and guinea pig G16L1.1c8 CALUX along with the WHO-TEFs.17 Compound PCDD TCDDv 12378-PeCDDt 123478-HxCDDnt 123678-HxCDDv 123789-HxCDDnt 1234678-HpCDDt OCDDnt PCDF TCDFt 12378-PeCDFnt 23478-PeCDFt 123478-HxCDF t 123678-HxCDFnt 123789-HxCDFnt 234678-HxCDFv 1234678-HpCDFv 1234789-HpCDFt OCDFnt Non-ortho PCB PCB77t PCB81nt PCB126v PCB169v Mono-ortho PCB PCB74v PCB105t PCB114nt PCB118t PCB123nt PCB156t PCB157nt PCB167t PCB189v

Guinea Pig

REP 1 0.8 0.1 0.08

Rat

Mouse

REPQSAR

REP

REPQSAR

REP

REPQSAR

0.6ex 0.3 0.2 0.2ex 0.2ex

1 0.5

0.4 0.1 0.1 0.2 0.2

1 1.2

2ex 0.4 0.3 0.4ex 0.4ex

0.1

na

na

0.03

na

0.3 1 0.3

0.3 0.07 0.1

0.002 0.2 0.01

0.000001 0.0001 0.00001 0.0001 0.00001 0.00001

0.4 0.1

na

2ex 0.6 0.5 0.4 0.2 0.8 0.3 0.1 0.2 0.03ex

0.06

0.002 0.002 0.0009

0.0001

0.2 0.07

0.06 0.01 0.04

0.07 0.002

na

0.0002 0.00006 0.0002 0.00006 0.000009 0.00004 0.00004 0.000003 0.000004

0.000001 0.000004 0.000003 0.0001 0.000004

0.3ex 0.09 0.07 0.08 0.04 0.1 0.05 0.03 0.04 0.01ex

0.00009 0.0001 0.00007 na

0.00003 0.000008 0.00003 0.00001 0.000001 0.00001 0.00001 0.000001 0.000002

WHO-TEF

na na

0.8 1 0.4

0.6 0.02 0.2

0.002 0.05 0.0005

0.000002 0.000002 0.000004 0.00004 0.000001

7ex 0.7 0.6 0.4 0.2 1 0.3 0.09 0.1 0.04ex

0.1 0.03 0.3 0.1 0.1 0.1 0.1 0.01 0.01 0.0003

0.0007 0.0005 0.0003

0.0001 0.0003 0.1 0.03

na

0.00005 0.000007 0.00004 0.00001 0.000001 0.000006 0.000008 0.0000005 0.0000007

Training set; v Validation set; nt Non-tested; na not analyzed due to being an outlier; ex Values are extrapolated from the applicability domain determined using 95% confidence level but within the 99% confidence interval.

t


1 1 0.1 0.1 0.1 0.01 0.0003

0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003 0.00003

Page 27 of 39

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Table 3. Statistics of calculated OPLS models for each species. R2tra

R2totb

RMSEc

RMSEPd

Q2e

RMSEcvf

Significant descriptorsg

Guinea pig

0.94

0.84

0.64

1.21

0.78

1.04

Gap_B3LYP; Gap_AM1; Balaban’s index; Kier3; 215; PEOE_VSA_POS; 240; 210; PEOE_PC+; PEOE_PC-; PEOE_VSA_FNEG; PEOE_VSA_FPOS; PEOE_RPC-

Rat

0.94

0.83

0.61

1.32

0.81

0.95

Gap_B3LYP; Gap_AM1; 240; Kier3; Balaban’s index; 215; PEOE_VSA_POS; PEOE_PC+; PEOE_PC-; 235; PEOE_VSA_FNEG; PEOE_VSA_FPOS; 210

Mouse

0.96

0.92

0.58

0.97

0.84

1.03

Gap_B3LYP; Gap_AM1; Balaban’s index; kier3; 215; PEOE_VSA_POS; 240; PEOE_VSA_FNEG; PEOE_VSA_FPOS; PEOE_RPC-; PEOE_PC+; PEOE_PC-; 210; 235

R2tr.: determination coefficient for the training set. bR2tot : determination coefficient for the whole data set. cRMSE: root mean square error. dRMSEP: root mean square error of prediction. eQ2: cross-validation R2. fRMSEcv: root mean square error of cross-validation. gThe most significant descriptors in order of VIP value. The meaning of each descriptor is given in Table S1. a

27



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure Legends Figure 1. Dose-response curves of the studied 18 dioxin-like and 2 non-dioxin like compounds determined in the rat H4L1.1c4 (A and B), mouse H1L6.1c2 (C and D), and guinea pig G16L1.1c8 (E, F and G) CALUX cell line. Luciferase activity was determined after 24h exposure. Results are expressed as the mean ± SD of 2 independent experiments in which each concentration was tested in triplicate. Figure 2. Dose-response curves of (A) TCDD and (B) PCB126 for rat H4L1.1c4, mouse H1L6.1c2, guinea pig G16L1.1c8 cells. Figure 3. Symbols represent the ratios between REPs based on BMR20TCDD concentrations determined in the present study (Table 2) and their assigned WHO-TEFs. A ratio of 1 indicates that a derived REP20TCDD is comparable to its WHO-TEF. Ratios were determined for various PCDDs and PCDFs (A) or PCBs (B) in the rat, mouse and guinea pig CALUX assays. Gray shaded area represents the half log uncertainty range around the WHO-TEFs. Figure 4. Principal component analysis calculated for the tested compounds based on REP20TCDD and REP50TCDD of each species and their TEF values. The first two principal components (PC) are shown as (A) score plot of PC1 versus PC2 and (B) loading plot of loading vector (LV) 1 versus LV2. Figure 5. The plots of QSAR-predicted (pred.) log relative effect potency (REP20TCDD) values against experimental (exp) log REP20TCDD values for (A) rat H4L1.1c4, (B) mouse H1L6.1c2 and (C) guinea pig G16L1.1c8 cells. The blue triangles refer to the training set compounds and red circles to the validation set compounds. Included compounds were all found within the 95% confidence interval of the model.


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Page 29 of 39

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Figure 1.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 2.

A)


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Page 31 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


B)



Figure 3.

A)

Ratio REP20TCDD / WHO-TEFs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Guinea Pig

Rat

Mouse


Page 32 of 39

Page 33 of 39

B)

Ratio REP20TCDD / WHO-TEFs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


100 Guinea Pig

Rat

Mouse

10

1

0.1

0.01

7 B7 PC

26 B1 C P

B PC

169

B PC

105

B1 PC

18

56 B1 C P

67 B1 C P



Figure 4.

A)

2

1.5 TCDF 1 234678-HxCDF 23478-PeCDF 0.5

123478-HxCDF

1234678-HpCDD PC2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1234789-HpCDF

0

123678-HxCDD -0.5

12378-PeCDD

1234678-HpCDF

-1

TCDD PCB126

-1.5 -2 -6

-4

-2

0 PC1


2

4

6

Page 35 of 39

B)

0.8 REP20TCDD Mouse

0.6

REP50TCDD Mouse 0.4

0.2 LV2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


REP20TCDD Guinea pig

REP50TCDD Guinea pig

0 TEF

-0.2

-0.4

REP50TCDD Rat

REP20TCDD Rat

-0.6

-0.8 0.3

0.35

0.4 LV1


0.45


Figure 5.

A) 1 12378-PeCDD

0

TCDD

23478-PeCDF

123678-HxCDD 123478-HxCDF 234678-HxCDF TCDF 1234789-HpCDF 1234678-HpCDD 1234678-HpCDF

PCB126

-1

Exp. log REP20TCDD

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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-2 -3 PCB156 PCB77

-4 PCB167

-5

PCB105

PCB118 PCB74

-6 PCB153

PCB189

-7 -7

-6

-5

-4

-3

-2

-1

Pred. log REP20TCDD


0

1

Page 37 of 39

B) 1

Mouse 12378-PeCDD 234678-HxCDF

0

1234678-HpCDD

-1 PCB126

Exp. log REP20TCDD

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


23478-PeCDF TCDF 123478-HxCDF

124789-HpCDF 1234678-HpCDF

-2 PCB77

-3 -4

PCB156

-5

PCB118 PCB74

PCB167

-6

PCB105 PCB153

-7 -7

PCB189

-6

-5

-4

-3

-2

-1

Pred. log REP20TCDD


0

1


C) 1

Guinea Pig

23478-PeCDF

0

12378-PeCDD 234678-HxCDF

PCB126

TCDF 123478-HxCDF

1234789-HpCDF 1234678-HpCDD 1234678-HpCDF

-1

Exp. log REP20TCDD

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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-2 PCB77

-3 PCB105

-4

PCB156 PCB189

-5

PCB118

PCB167 PCB74

-6 PCB153

-7 -7

-6

-5

-4

-3

-2

-1

Pred. log REP20TCDD


0

1

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420x297mm (300 x 300 DPI)


In Vitro and in Silico Derived Relative Effect Potencies of Ah-Receptor

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