QSAR Modeling and Prediction of the Endocrine-Disrupting Potencies

Apr 21, 2010 - Weihua Yang , Shide Shen , Lailong Mu , Hongxia Yu. Environmental Toxicology and Chemistry 2011 30 (10.1002/etc.v30.11), 2431-2439 ...
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Chem. Res. Toxicol. 2010, 23, 946–954

QSAR Modeling and Prediction of the Endocrine-Disrupting Potencies of Brominated Flame Retardants Ester Papa,* Simona Kovarich, and Paola Gramatica QSAR Research Unit in EnVironmental Chemistry and Ecotoxicology, DBSF, UniVersity of Insubria, Via J.H. Dunant 3, 21100 Varese, Italy ReceiVed February 2, 2010

In the European Union REACH regulation, the chemicals with particularly harmful behaviors, such as endocrine disruptors (EDs), are subject to authorization, and the identification of safer alternatives to these chemicals is required. In this context, the use of quantitative structure-activity relationships (QSAR) becomes particularly useful to fill the data gap due to the very small number of experimental data available to characterize the environmental and toxicological profiles of new and emerging pollutants with ED behavior such as brominated flame retardants (BFRs). In this study, different QSAR models were developed on different responses of endocrine disruption measured for several BFRs. The multiple linear regression approach was applied to a variety of theoretical molecular descriptors, and the best models, which were identified from all of the possible combinations of the structural variables, were internally validated for their performance using the leave-one-out (Q2LOO ) 73-91%) procedure and scrambling of the responses. External validation was provided, when possible, by splitting the data sets in training and test sets (range 2 of QEXT ) 76-90%), which confirmed the predictive ability of the proposed equations. These models, which were developed according to the principles defined by the Organization for Economic Co-operation and Development to improve the regulatory acceptance of QSARs, represent a simple tool for the screening and characterization of BFRs. Introduction 1

In the past decade, brominated flame retardants (BFRs) have been recognized as an emerging class of organic pollutants. BFRs are incorporated into inflammable polymers to increase the resistance to fire of a variety of consumer products, such as electronic devices, building materials, and textiles. Among the large number of brominated flame retardant compounds, the three most marketed high production volume (HPV) products are tetrabromobisphenol A (TBBPA), hexabromocyclododecane (HBCD), and three commercial formulations of polybrominated diphenyl ethers (PBDEs) (penta-, octa-, and deca-BDE). The * To whom correspondence should be addressed. E-mail: ester.papa@ uninsubria.it. 1 Abbreviations: BFRs, brominated flame retardants; HPV, high production volume; TBBPA, tetrabromobisphenol A; HBCD, hexabromocyclododecane; PBDEs, polybrominated diphenyl ethers; PCBs, polychlorinated biphenyls; AhR, aryl hydrocarbon receptor; ED, endocrine disrupting; TH, thyroid hormone; T4, thyroxin; OH-BDE, hydroxylated PBDE; REACH, Registration, Evaluation, Authorisation and Restriction of Chemicals; CMR, carcinogenic, mutagenic and toxic to reproduction; PBT, persistent, bioaccumulative, and toxic; QSAR, quantitative structure-activity relationships; CH3O-BDE, methoxylated PBDE; OECD, Organization for Economic Cooperation and Development; TBBPA-DBPE, tetrabromobisphenol-A-bis(2,3)dibromopropyl ether; 246-TBP, 2,4,6-tribromophenol; EC50, median effective concentration; IC50, median inhibition concentration; RBA, AhR relative binding affinity; TCDD, 2,3,7,8-tetrachloro-dibenzop-dioxin; ERODind, ethoxyresorufin-O-deethylase induction potency; DRag, AhR agonism; ERag, estrogen receptor agonism; PRant, progesterone receptor antagonism; T4-TTRcomp, T4-transthyretin competition; T4REP, T4-TTR relative competition; E2SULTinh, estradiolsulfotransferase inhibition; E2SULTREP, estradiolsulfotransferase relative inhibition; PCP, pentachlorophenol; DBDE, decabromodiphenylethane; EBTPI, ethylenebistetrabromo phthalimide; TBE, bis(tribromophenoxy)ethane; OLS, ordinary least-squares; 2 2 QLOO , Q2 leave-one-out; R2, coefficient of determination; QBOOT , Q2 bootstrap; R/Q2YS, R/Q2 scrambled; Q2EXT, Q2 external; RMSET/P, root-meansquare of errors for training/prediction sets; AD, applicability domain; HBB, hexabromobenzene; PLS, partial least-squares; SVM, support vector machine.

widespread production and use of BFRs in the last 40 years has caused them to disperse into the environment. These substances have been found in different organisms of both aquatic and terrestrial ecosystems, and the high concentrations measured at the top levels of different food chains (e.g., birds, whales, polar bears, and human breast milk) have been related to bioaccumulation processes (1–3). Therefore, the ubiquitous presence of BFRs in all environmental compartments (4–6), caused by their high lipophilicity, bioaccumulation, and persistence properties, has gradually increased the concerns regarding health risks to man and the environment (7, 8). Moreover, the structural similarity of BFRs to other classes of organohalogenated compounds, such as polychlorinated biphenyls (PCBs) and dioxins, has raised concerns about their potential endocrine disruption activity. An endocrine-disrupting (ED) chemical has been defined as “an exogenous agent which interferes with the synthesis, secretion, transport, binding, action, or elimination of natural hormones in the body which are responsible for the maintenance of homeostasis, reproduction, development or behaviour” (9). In recent years, many studies, both in vivo and in vitro, have been conducted to investigate the endocrine disruption activity of BFRs. Experimental evidence has shown that some BFRs may interfere, as agonists and/or antagonists, with steroid receptors (estrogens, androgens, etc.) and interact with the aryl hydrocarbon receptor, AhR (“dioxin-like-activity”) (10–14). The strongest ED activity of BFRs was represented by the effects on the thyroid hormone (TH) system, where thyroid hyperplasia was induced and the TH transport and metabolism was altered (15, 16). These effects could be explained by the structural resemblance of BFRs to the TH thyroxin (T4), especially of those BFRs containing an -OH group, like hydroxylated metabolites of PBDE (OH-BDEs). Several risk assessments have

10.1021/tx1000392  2010 American Chemical Society Published on Web 04/21/2010

QSAR Prediction of the ED Potencies of BFRs

been performed for different BFRs (i.e., penta-, octa-, and decaBDE formulations, TBBPA, and HBCD), which have partially contributed to the implementation of local legal actions, such as the European ban of PBDEs from electrical and electronic applications (EU RoHS Directive) (17) or the “Voluntary Industrial Program for the Reduction and Control of BFR production and use” (18). Despite the fact that no clear harmonized legislation regarding the marketing and use of BFRs has been available so far, the EU REACH regulation for the Registration, Evaluation, Authorisation and Restriction of Chemicals (19) will increase the level of control upon these substances through the registration and the authorization process, of which the last is applied to those BFRs that are carcinogenic, mutagenic and toxic to reproduction (CMR), ED or persistent, bioaccumulative, and toxic (PBT). However, at this moment, the number of experimental data available to characterize the physicochemical properties and ecotoxicological behavior of these compounds is very limited. In particular, only a small number of toxicological data is available for hydroxylated and methoxylated metabolites of PBDEs, which are found to be just as ubiquitous in the environment as their parent compounds (20, 21). In this context, the development of in silico models based on quantitative structure-activity relationships (QSAR) is among the successful strategies that can maximize the value of existing data, using them to predict unknown activities for existing or even not yet synthesized chemicals and to design safer alternatives that can substitute unsafe chemicals. The development and application of in silico approaches are being financially supported by the European Commission, through the seventh Framework Programme for Research, to predict lacking experimental data as well as to perform risk assessment of several classes of compounds of interest, including, among others, BFRs (22). The aim of this work was to develop QSAR models for the prediction of the endocrine disruption potency and dioxin-like activity for a set of 243 BFRs, which included all 209 PBDEs and several hydroxy- (OH-) and methoxy- (CH3O-) BDE congeners, TBBPA and analogues, HBCD, and other BFRs. Because of the limited amount of experimental data available from the literature, particular attention was paid to develop as simple as possible models (parsimony principle) (23), which have been validated and have a clearly defined applicability domain (AD) according to the Organization for Economic Cooperation and Development (OECD) principles for regulatory acceptability of QSARs (24). Also proposed is a comparison with existing QSARs (25–29), developed for the same end points as those presented in this study. Moreover, an analysis of the activity profile of alternatives to BFRs, which are already listed in EU regulation, is also proposed.

Materials and Methods Experimental Data. The experimental data related to endocrine disruption potencies of BFR were available for several PBDE and OH-BDE congeners, TBBPA (tetrabromobisphenol-A), TBBPADBPE [tetrabromobisphenol-A-bis(2,3)dibromopropyl ether], 246TBP (2,4,6-tribromophenol), and HBCD (12, 13, 30). The selected responses included RBA (Ah receptor relative binding affinity) [RBA ) EC50(2,3,7,8-tetrachloro-dibenzo-p-dioxin, TCDD) µM/ EC50(BFR) µM], EROD induction potency (EC50ERODind µM), Ah receptor agonism (EC50DRag µM), estrogen receptor agonism (EC50ERag µM), progesterone receptor antagonism (IC50PRant µM), T4-TTR relative competition [T4REP ) IC50(T4) µM/IC50(BFR) µM], and estradiol sulfotransferase relative inhibition {E2SULTREP ) IC50[pentachlorophenol (PCP)] µM/IC50(BFR) µM}. All of the

Chem. Res. Toxicol., Vol. 23, No. 5, 2010 947 responses were converted into logarithmic units. Moreover, to obtain increasing trends of toxicity, the experimental values for the responses EC50ERODind, EC50DRag, EC50ERag, and IC50PRant were transformed into the logarithm of the inverse micromolar concentrations. In addition, other BFRs with structures similar to those listed above were included in our data set to be screened for their endocrine activity potencies. Moreover, decabromodiphenylethane (DBDE), ethylenebistetrabromo phthalimide (EBTPI), and bis(tribromophenoxy)ethane (TBE), which were recently introduced as safer alternatives to the traditional BFR (31), were also added to the structural data set. The experimental data set and predicted values for all of the 209 PBDE congeners and the other 34 BFRs considered in this study are available in the Supporting Information (Table S1). Molecular Descriptors. The input files for descriptor calculation, containing information relative to the minimum energy conformation of the chemicals, were calculated by the semiempirical AM1 Hamiltonian method for geometry optimization available in the HYPERCHEM package (32). The molecular descriptors (mono-, di-, and tridimensional) were then computed by the software DRAGON (33). Constant or near-constant values and descriptors found to be correlated pairwise (one of any two descriptors with a correlation greater than 0.98) were excluded in a preliminary step to reduce redundant and nonuseful information. As a result of this prereduction procedure, a final set of 712 DRAGON descriptors was obtained. In addition, four quantum-chemical descriptors, calculated by the HYPERCHEM program, were included in the modeling procedure: highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), HOMO-LUMO gap, and ionization potential. A total of 716 descriptors were then used as the input variables in the QSAR model development. QSAR Modeling. Multiple linear regression analysis and variable selection were performed by the software MOBY-DIGS (34) using, respectively, the ordinary least squares (OLS) regression and the All Subset Selection method that verifies the modeling ability of all of the possible combinations of the available descriptors. All of the regression models were developed by maximizing the cross2 validated Q2 leave-one-out (QLOO ). In addition, to avoid multicollinearity with “apparent” prediction power (chance correlation), the regressions were calculated only for variable subsets with an acceptable multivariate correlation with response, by applying the QUIK rule (Q under influence of K) (35). This procedure excludes models that have a K multivariate correlation index of the [X] variable block greater than the correlation within the [X + Y] block variables, where Y is the response variable. The coefficient of determination (R2) was reported as a measure of the total variance of the response explained by the regression models (fitting). The robustness of the models was then evaluated by applying the 2 bootstrap method and calculating the QBOOT (5000 iterations). The models were also analyzed for the absence of chance correlation (models where the independent variables are randomly correlated to the response variables) by applying the Y-scrambling technique. Evidence that the models were well founded, and not obtained by chance, was provided by obtaining new models on the set with a randomized response (i.e., by assigning to each object a response randomly selected from the true responses); these models had significantly lower R2 and Q2 values than the original proposed 2 2 model. The averaged R2 and Q2 scrambled (RYS and QYS ) were calculated after 500 scrambling iterations. External Validation. To generate QSAR models that are also able to give reliable prediction for new chemicals (compounds not participating in model development), external validation was performed (36, 37). Thus, when a sufficient number of experimental data were available (nobj > 16), the original data sets were preliminarily split into a training set, on which the models were developed, and a prediction set, on which the developed models were verified. The splitting was carried out by random selection through activity sampling: that is, the whole range of properties was sorted in ascending order, after which 50% of the compounds was assigned to the prediction set. The external predictivity was 2 then quantified by the calculation of external Q2 (Qext ) (38):

0.88 0.88

0.93 0.90

) ) ) ) ) ) ) ) ) ) ) 10

8

9

Log1/EC50ERODind Log1/EC50DRag Log1/EC50ERag

LogE2SULTREP

LogT4REP

Log1/IC50PRant

a For each model, the number of objects included in each training/test set, the equations, and the statistical parameters that quantify the internal and external predictivity and robustness are indicated. S-R50, split model (splitting random 50%); F, full model.

0.36

0.47

0.15 0.86

0.84

0.27 0.31 0.14 0.14 0.29 0.35 0.37 0.36 0.29 0.15 0.06 0.73 0.76

0.22 0.11 0.24 0.11 0.33 0.10 0.23 0.11 0.26 0.27 0.18 0.22 0.19 0.23 0.12 0.28 0.12 0.21 0.10 0.14 0.15 0.16 0.78 0.73 0.72 0.83 0.87 0.89 0.73 0.82 0.79 0.87 0.76 0.79 0.73 0.78 0.82 0.91 0.91 0.73 0.84 0.75 0.85 0.88 0.9 0.82 0.89 0.87 0.96 0.94 0.87 0.88 0.85 0.91 0.95

RMSET 2 QEXT 2 QYS 2 RYS 2 QBOOT 2 QLOO

R2 equations

Y Y Y Y Y Y Y Y Y Y Y 8

10 18 10 19 9 17 11 21 8 8 8 1S-R50 1F 2S-R50 2F 3S-R50 3F 4S-R50 4F 5F 6F 7F LogRBA

Development of the QSAR Models. The QSAR approach was applied to model the selected end points related to endocrine disruption potencies of BFRs. Models for LogRBA, Log1/ IC50PRant, LogT4REP, and LogE2SULTREP were first of all developed on training sets generated by a priori random splitting of the available experimental data. Models developed for Log1/ EC50ERODind, Log1/EC50DRag, and Log1/EC50PRant were developed on all of the available experimental data, since their numbers were too small to perform meaningful splitting and external validation of the models. The performance of the best models, developed for the seven endocrine toxicity end points, is reported in Table 1. The plots of the experimental versus predicted values are reported in Figures 1 and 2. All of the proposed models had high fitting power and satisfactory internal predictive ability, the last being confirmed 2 2 and QBOOT values to R2 for each by the closeness of the QLOO model. When the models were applied to the prediction sets, 2 , which they demonstrated high predictivity, verified by Qext ranged from 0.76 to 0.90. The absence of chance correlation was checked with the Y-scrambling procedure and confirmed 2 2 and QYS . The quality of the split models by low values of RYS proposed in Table 1 was further verified by comparing RMSET and RMSEP values. These two parameters confirmed the ability of the proposed QSAR split models to predict each end point with similar accuracy for chemicals included in training or prediction sets. According to the results presented in Table 1,

test obj.

Results and Discussion

Table 1. List of the Models Developed in This Paper for the Studied End Points of Endocrine Activity of BFRsa

where the summation in the numerator runs over the external test set while that in the denominator runs over the training set; yˆi is the predicted value for the i-th compound in the prediction set, yi is the observed value, and jy is the average value of the training set responses. The RMSE value (root mean squares of errors) summarizes the overall error of a model and was used as an additional measure of the accuracy of the proposed QSARs. It is calculated as the square root of the sum of squared errors in prediction divided by their total number. This parameter was used to compare the accuracy and the stability of our models in the training (RMSET) and in the prediction (RMSEP) sets. Chemical AD. In general, QSAR models are developed on a defined domain of compounds with known activities and structures (training set). For this reason, they cannot be applied to every new chemical for predictive purposes (36, 37). Quantitative measures of a model AD are needed to evaluate the degree of extrapolation and for the identification of problematic compounds. In this study, the AD was defined by the identification of response outliers (i.e., compounds with cross-validated standardized residuals greater than 2.5 standard deviation units) and of chemicals that were structurally very influential in determining the parameters of the models and fell outside the structural chemical domain of the training set. These compounds had the leverage value h (diagonal element of the Hat matrix) (39) greater than 3 p′/n (h*), where p′ is the number of model variables plus one and n is the number of the objects used to calculate the model (39). The AD was also verified graphically in the Williams plot, which is the plot of hat values (h) versus standardized residuals. It is to note that data predicted for high leverage chemicals in the prediction set are extrapolated and could be unreliable.

2 REXT

(yi - jyTR)2 /nEXT

i)1

-11.33((1.19) + 0.92((0.13) L1v + 11.56((1.87) Mor22u -10.31((0.92) + 0.79((0.10) L1v + 8.89((1.54) Mor22u -3.60((0.67) + 0.01((0.002) RDF045m + 2.58((0.71) GATS4m -3.67((0.51) + 0.01((0.001) RDF045m + 2.69((0.54) GATS4m -9.07 ((0.74) + 40.76((3.65) qpmax +3.93 ((1.01) MATS6v -8.60((0.62) + 38.23((3.03) qpmax + 2.89((0.80) MATS6v -0.56((0.35) + 2.10((0.3) B08[C-O] - 2.77((1.04) GGI7 -0.61((0.23) + 2.11((0.19) B08[C-O] - 2.53((0.63) GGI7 11.07((1.84) - 0.12((0.02) piID -0.27((0.06) - 2.74((0.35) Mor08e 0.99((0.19) - 0.50((0.05) RGyr

RMSEP

] ]

(yˆi - yi)2 /nEXT

train obj.

QEXT2 ) 1 -

i)1 nTR

model IDa

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nEXT

Papa et al. 0.42

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response

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QSAR Prediction of the ED Potencies of BFRs

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Figure 1. Plots of experimental vs predicted values for the split and the full models developed for the responses LogRBA, Log1/IC50PRant., LogT4REP, and LogE2SULTREP, reported in Table 1 (dots, training set; empty triangles, prediction set).

all of the variables selected in the QSAR models developed on random training sets proved to be reliable for the prediction of the studied ED activities. Therefore, for those end points for which the splitting procedure was used, the same variables were used to model all of the experimental data available for the end points LogRBA, Log1/IC50PRant, LogT4REP, and LogE2SULTREP. This was performed in order to use all of the available structural and experimental information that could be useful to extend the domain of applicability of our models. Also, the equations and performances of the full models obtained for the seven end points of ED activity are reported in Table 1. The modeling variables reported in Table 1 are as follows: RDF045m from Radial Distribution Function descriptors (40); the Weighted Holistic Invariant Molecular (WHIM) descriptor L1v (41); Mor22u and Mor08e from the 3D-MoRSE group (42); the 2D Autocorrelation descriptors GATS8e (43) and MATS6v (44); RGyr, which is among the geometrical descriptors; piID from the path and walk counts descriptors; Qpmax and GGI7 from, respectively, the charge descriptors and the topological charge indices; and B08[C-O] from the 2D binary fingerprints group

(33, 45). These descriptors, which describe mainly 3D features and take into account different atomic characteristics such as van der Waals volumes, electronegativity, and molar masses, were all necessary to reach an adequate predictivity of the models. It should be noted that the use of mono- and bidimensional descriptors only, which are considered to be simpler than 3D descriptors, did not yield satisfactory models. This fact highlights the relevance of 3D structural information to find significant QSAR relationships for the studied end points, which has also been documented in literature (25–29). In more detail, the Radial Distribution Function descriptors (40) are based on the distance distribution in a molecule and explain the probability distribution of finding an atom in a spherical volume of radius R (where a step size for R equal to 0.5 Å is used to define 30 different RDF descriptors). The selected RDF045m describes spheres of radius of 4.5 Å and provides information on the interatomic distances in the entire molecule weighted by atomic masses. WHIM descriptors are calculated as statistical indices of the atoms projected onto the three principal components obtained

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Papa et al. Table 2. Levels of Activity Predicted by the Full QSAR Models Proposed in Table 1 for DBDE, EBTPI, and TBEa end point

DBDE

EBTPI

TBE

LogRBA Log1/EC50ERODind Log1/EC50DRag Log1/IC50PRant Log1/EC50ERag LogT4REP LogE2SULTREP

high* low* low moderate low low low

low* low* high* moderate low* high* high

high* low* low* low low low* moderate

a Predictions out of the AD of the models are indicated with the symbol *. Criteria for classification of endocrine potencies of BFRs: (1) RBA (13): high, RBA > 1 × 10-2; moderate, 1 × 10-3 < RBA < 1 × 10-2; and low, RBA < 1 × 10-3. (2) ERODind (13): high, EC50 < 100 nM; moderate, 100 nM < EC50 < 1000 nM; and low, EC50 > 1000 nM. (3) DRag, PRant, ERag,T4-TTRcomp, E2SULTinh (12): high, E(I)C50 < 1 µM; moderate, 1 µM < E(I)C50 < 10 µM; and low, E(I)C50 > 10 µM.

Figure 2. Plots of experimental vs predicted values for the full models developed for the responses Log1/EC50ERODind, Log1/EC50DRag, and Log1/EC50ERag, reported in Table 1.

from weighted covariance matrices of the atomic coordinates (41). L1v is defined as the first component size directional WHIM index weighted by atomic van der Waals volumes and provides information about the distribution of the molecular size along the first principal direction of the molecule. Mor22u (Morse signal no. 22 unweighted) and Mor08e (Morse signal no. 08 weighted by the Sanderson electronegativity) provide 3D information related to the weights of the atoms in the structure, as viewed by an angular scattering function (42). The values of these functions are calculated at 32 evenly distributed values of scattering angles of 0-32 Å-1 from the 3D atomic coordinates of a molecule (42). Two-dimensional autocorrelation descriptors (43, 44) give information on the distribution of the atomic properties along the topological structure and are calculated by summing the products of atom weights of the terminal atoms of all of the paths of the considered path length. The weight properties are, for GATS4m, the atomic masses and, for MATS6v, the atomic van der Waals volumes and encode for dimensional information along the molecule. RGyr is the radius of gyration and is a geometrical size descriptor that describes the distribution of atomic masses in a molecule and measures the molecular compactness (small values are obtained when most of the atoms are close to the center of mass) (33, 45). The descriptor piID is the conventional bond order ID number, which is calculated as the number of atoms plus the half sum of all of the weighted paths over all of the graph vertices of a molecule (33, 45). piID values are directly related to molecular size and complexity.

The charge descriptors describe electronic aspects of the whole molecule: GGI7 describes the distribution of the net charges of pairs of vertices with a topological distance equal to 7; the total positive charge (Qpos) is the sum of all of the positive atomic charges of a molecule (33, 45). Finally, the descriptor B08[C-O] is the structural molecular fragment (33) that counts the number of pairs of C and O atoms located at a topological distance 8. To be noted is the importance of variables related to electronic properties such as electronegativity or the charge distribution along the molecules, already reported in the literature as relevant for the modeling of various ED activities of BFR (25–29). In fact, the information encoded in these variables is related to different aspects of the structure where variations imply the modification of the local potentiality of interaction with the receptor, with a consequent alteration of the ED response. Analysis of the Trends of Endocrine Activity Predicted by QSAR. An analysis of the trend of the predictions made for each end point by the full models proposed in Table 1 allowed the following considerations, of which the criteria for the classification of the ED potency of BFRs are reported in Table 2. Models for the Binding to and the Interaction with the Ah Receptor. The application in prediction of the model developed for the response LogRBA showed that the binding activity to AhR of the PBDEs is at least 50-100 times lower than that of the reference toxicant TCDD (13). Moreover, among the studied compounds, TBBPA analogues were predicted as the most active. BDEs with meta- and para-substitutions, in addition to one or two ortho-bromines in position 2 and 2′, presented larger RBAs than those with more ortho-bromines. This is in agreement with what was observed in the literature where larger RBA values were found for meta- and para- BDE analogous of coplanar PCBs (i.e., PCBs 77 and 126) than for non-planar congeners (13). Furthermore, we observed that for the different grades of bromination, in particular from tri- to nona-BDEs, the largest values of RBA and the largest values of the descriptor L1v, which describes the longest molecules along the direction of maximum length, occurred for para- and meta-substituted BDEs. OH-BDEs showed similar activities as non-hydroxylated BDEs, while methoxylated PBDE (CH3O-BDEs) were predicted as low active. The model developed to predict the ability of BFR to induce EROD activity showed activity values that were about 1000 times lower than those induced by TCDD [reference value for EROD activation by TCDD ) 0.014 nM (13)]. In general, it was observed that small BDEs (from 1 to 5 bromine atoms)

QSAR Prediction of the ED Potencies of BFRs

without an ortho-substituent induce larger EROD activity than ortho-substituted compounds, with a progressive decrease of predicted activity in the presence of two or more ortho-bromines. Only 4′-OH-BDE-17, 2′-OH-BDE-28, 4′-OH-BDE-30, 4′-HOBDE-69, 4′-HO-BDE-121, and BFRs with a single ring in their structure were predicted with larger activity than the other studied BFRs, which were not PBDEs. Predictions made for the DR-agonist response (ability of a chemical to interact as an agonist with the Ah receptor) were compared to the criteria for BFR classification based on in vitro toxicity results reported by Hamers (12). According to these criteria, the activity of 64 out of the 243 BFRs included in this study could be classified as low, while the majority of the chemicals were associated with moderate or high activity. Chemicals predicted with high DR-agonist potency were mainly BDEs with non-ortho-, mono-, or di-ortho-bromines (in position 2,2′), with various substitution patterns up to nine bromines. Other active compounds were EBTPI, 4-phenoxyphenol, bisphenol A (BPA), mono- and dibromo bisphenol A (MBBPA and DiBBPA), 6-OH-BDE-99, 3-OH-BDE-47 and 4-OH-BDE-42. All of the other BFR were predicted as having a moderate or low potency. Models for the Interaction with Progesterone (PR) and Estrogen (ER) Receptors. Only two chemicals were predicted as very active considering their PR antagonist activity: BDE 19 and 4-bromophenol. However, it should be noted that, in agreement with the experimental results (12), these activities are about 4000 times lower than that of the reference compound RU-486. Low activity according to Hamers’ classes (12) was predicted for non-ortho-BDEs and for some mono- and di-orthoBDEs with a small number of bromines (1-4) or with uneven distribution of the substitutions on the two rings. The other BFRs not being PBDEs were also predicted as having moderate or low activity; in particular, OH-BDEs with a hydroxyl group in position 4 or 6 were related to moderate PR-antagonist activity as compared to the other OH-PBDE metabolites, which were predicted with low activity. The model developed for ER-agonistic activity did not predict any compound as having high estrogenic activity; however, moderate activities (12) were associated with PBDEs with various patterns of ortho substitutions in the absence of para4,4′-bromines. The only exceptions were observed for BDE100 and BDE-155, which have, respectively, 2,2′,6- and 2,2′,6,6′-bromine atoms together with 4,4′-substitution, and were predicted as moderate ER agonists. It was also observed that all of the PBDEs with 3,3′,4,4′-bromines were predicted to have low estrogenic activity. Only OH-BDEs up to penta-bromine and with a hydroxyl group in position 4 or 6 were predicted as moderately active, with the exception of 4-OH-BDE-121, which was predicted to have low estrogenic activity. Among the other BFRs different from PBDEs, larger ER agonistic activities were predicted for mono-, tri-, and penta-bromophenols, hexabromobenzene (HBB), HBCD, BPA, and mono-, di-, and tribromo bisphenol A. Models for T4-TTR Relative Competing Potency and E2SULT Relative Inhibiting Potency. The application of the full model developed for the end point T4REP resulted in high predicted potency for BFRs different from PBDEs and for OHBDEs. In particular, the binding activity to TTR, higher or comparable than the reference compound T4 (IC50 ) 0.055 µM) (12), was predicted for all of the studied OH-BDEs and, in particular, for those with hydroxyl groups in positions paraand meta-, as well as for bromophenols and mono- to tetrabromobisphenols. This result is in agreement with observations

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reported in the literature that the phenolic group is required to increase T4-TTR binding capacity (29) because the natural ligand T4 is also hydroxylated. Moderate potencies were predicted for tetra to octa-PBDEs with ortho-2,2′,6,6′- or 2,2′,6bromines mainly in absence of 3,3′ substitutions, while low potencies were predicted for the other PBDEs and in particular for non-ortho, mono-, and di-ortho-PBDEs. In a similar way, most of the PBDEs were predicted as moderately active (12) for their inhibiting potency on E2 sulphonyl transferase, and a decrease of the PBDEs activity was observed in relation to an increase in the bromination level. Only BDE 100, 155, 207, and 208 were associated with low activity. Non-BDE compounds were predicted as having a high inhibiting activity. In particular 3-, 4-, and 5-OH-BDEs, phenols, and bisphenols were predicted with an activity comparable to the reference compound PCP (0.15 µM). The fact that OHBDEs with hydroxyl groups in positions para- and meta- were predicted as more active than ortho- OH-BDEs is in agreement with results from Kester et al. (46) who suggested that inhibition of E2-SULT could be favored by planar hydroxylated polyhalogenated derivatives of PCBs, polychlorinated dibenzodioxins and furans (PCDD/Fs), PBDEs, tetrabromo- and tetrachlorobisphenol A. Analysis of the AD. The analysis of the AD is an important step in the characterization of models, in particular when they are developed on a small number of experimental data (36, 37). No response outliers (chemicals with standardized residual >2.5σ) were identified by the analysis of standardized residuals in prediction, performed for each full QSAR model reported in Table 1. The wideness of the structural AD was verified for the 209 PBDEs in addition to the 34 structurally heterogeneous BFRs; it was measured for each model with the leverage distance (h) (39) and was quantified as a percentage. It is interesting to note that the full models reported in Table 1 covered a large part of the studied structural domain with a high percentage of reliable predictions in a range from 75 to 100%. In greater detail, from the analysis of these structural ADs, we could verify that: mono, di-, nona-, and deca-BDEs as well as HBB, DBDE, EBTPI, TBE, HBCD, bromophenols, and BPA analogues fell outside the structural AD of the proposed full LogRBA model, which covered 75% of the studied domain; all of the BFRs, different from PBDEs, CH3O-BDEs, OH-BDEs, and BPA analogues, fell outside the structural AD of the proposed Log1/EC50ERODind model, which covered 93% of the studied domain; some tetra-, penta-, and hexa-BDEs, as well as some BPA analogues, CH3OBDEs, HBCD, EBTPI, and TBE fell outside the structural AD of the proposed Log1/EC50DRag model (percentage of the studied domain covered ) 81%); 4-bromophenol, EBTPI, and TBBPADBPE fell outside the structural AD of the proposed Log1/ EC50ERag model (percentage of the studied domain covered ) 99%); 14 low brominated PBDEs, 4-bromophenol, and bisphenol A fell outside the structural AD of the proposed Log1/ EC50PRant model, which covered 93% of the studied domain; BDE-4, BDE-19, and BDE-54 in addition to HBCD, EBTPI, and TBE fell outside the domain calculated for T4REP full model, which covered 98%; finally, none of the studied chemicals fell outside the AD of the model for E2SULTREP. Activity Profile of Alternatives to BFRs. The potential endocrine activity of DBDE, EBTPI, and TBE, which have already been listed in EU regulation as alternatives to existing BFRs (31), was screened by applying the full QSAR models proposed in Table 1. The levels of activity predicted for these

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Papa et al.

Table 3. Comparison of the Performances of the QSAR Models Developed in This Paper for the End Point Log1/RBA with Other Models Existing in Literature end point

Log1/RBA

models

modeling method

training (Test) obj.

no. of variables or PLS components

1 SpR50 (this paper) 1 F (this paper) Xu et al. (27) Wang et al. (26) Wang et al. (25)

MLR MLR MLR MLR CoMFA (PLS) CoMSIA (PLS) SVM3f SVM5f RBFN3f RBFN5f

10 (8) 18 18 18 18 18 15 (3) 15 (3) 15 (3) 15 (3)

2 2 4 4 6 6 7 7 7 7

Zheng et al. (28)

chemicals were defined according to the literature (12, 13) and are reported in Table 2. Unfortunately, the difference in structure of these chemicals with respect to the modeled training sets was the cause of extrapolation of some results (predictions out of the AD of the models). However, to show the possible application of our QSARs for screening purposes and because of the absence of any other experimental evidence for these specific end points, some comments can be made on the ED profile of DBDE, EBTPI, and TBE. According to our predictions, the three chemicals have a weak/moderate ED profile with some exceptions regarding their activity: DBDE and TBE had high binding affinity to AhR; EBTPI was associated to high DR agonist activity; DBDE and EBTPI are moderate PR antagonists; EBTPI had high T4-TTR competing potency; while both EBTPI and TBE were related to E2SULT inhibition. These predictions should be evaluated carefully, also bearing in mind that predicted values out of the domain of a specific model are less reliable. For instance, the high activity predicted for EBTPI for the response LogT4REP should be analyzed, taking also into account the high E2SULT inhibition potency, which was reliably predicted for the same compound (i.e., within the AD). In fact, according to experimental evidence as well as to our results, these two end points show similar trends. Therefore, even though the T4-TTR relative competing potency of EBTPI is probably a strong overestimation, it is expected that a BFR with high E2SULT inhibition potency will also have relevant T4-TTR competing potency. Therefore, according to our comments, EBTPI should be highlighted as the chemical with the most active profile, among the alternatives to BFRs considered in this study, in relation to the studied ED activities. This conclusion could reinforce the requirements for further assessment of this substance that was included, by the Norwegian Pollution Control Authority, in a priority list of 12 “new” BFR compounds relevant for further investigation and monitoring in the Norwegian environment (47). Comparison to Existing QSARs. To the best of our knowledge, from among the responses of ED activities presented in this study, only LogRBA could be compared to existing quantitative results. In fact, Harju and co-workers (29) proposed different partial least-squares (PLS) models for some ED properties of selected BFRs, such as metabolization rates and AR antagonism, which were not investigated in this study, and T4-TTR binding and E2SULT inhibition, which were described by Harju et al. (29) only by non-quantitative SAR results. A strict comparison between our models and the already published QSARs for LogRBA (25–28) was also not possible either, due to the different amounts of studied compounds or differences in the development of the models; however, some general considerations can be made. Table 3 shows that our LogRBA

R

0.80

R2 0.90 0.82 0.65 0.9 0.99 0.98

RCV

0.54

2 QLOO

2 Rext

0.79 0.73 0.29 0.84 0.58 0.68 0.89 0.90 0.85 0.91

0.73

0.99 0.95 0.76 0.97

model had comparable or, in some cases, better performances than other more complex QSARs. The general tendency that we observed in other existing models with only a few exceptions (28) was to use more than two variables and complex modeling techniques, such as PLS (29) or SVM (28), even for small data sets. Note that our approach was based on the parsimony principle, which implies, inter alia, that the ratio of observations to variables should be as high as possible and at least 5:1 (23, 48). This approach is efficient to reduce the chance of overfitting, which increases with the increase in the number of the variables included in the models and gives an overoptimistic idea of their predictive ability.

Conclusions In this study, new predictive regression QSAR models were developed ad hoc for several ED potencies evaluated for a set of more than 200 BFRs. Particular attention was paid to their validation, also external, and to the definition of the AD of these QSARs. The possibility to identify the reliability of predictions for the studied end points represents a crucial point that should always be taken into consideration, especially for models that are developed starting from a limited amount of experimental data. The chemical AD of the proposed models in this study was always higher than 75% when it was calculated for a wide set of BFRs. The utility of our models to be applied for predictive screening purposes was clearly shown through an analysis of the predictions obtained for the different responses of endocrine activity for the BFRs under study. This analysis was additionally useful to highlight some structural features that could be involved in the mechanism of ED activity. Moreover, the screening of the endocrine profile of three alternatives to BFRs, with the use of our models, highlighted some concerns regarding the endocrine activity of ethylene bis-tetrabromo phthalimide, which should be taken into consideration for further investigation. A comparison of the models proposed here with other QSARs, which was possible only for the response LogRBA, demonstrated that our models have comparable or higher fitting performance than the existing ones, which are in general more complex in terms of number of variables or modeling method, and not externally validated, with the exception only of the models by Zheng et al. (28), who proposed complex but externally validated QSARs. The QSAR models that were developed in this study are applicable to predict the endocrine potencies of other BFRs or similar compounds that fall in their AD. The analysis of a model domain was a necessary procedure that again highlights the importance of correct development and evaluation, according to the OECD principles, of QSAR models suggested for

QSAR Prediction of the ED Potencies of BFRs

predictive purposes. The proposed models can be used to fill data gaps for use in the new REACH regulation, facilitating the screening and prioritization of chemicals and the identification of more problematic compounds even before their synthesis, as well as for the design of safer alternatives. A web tool for the application of our QSAR models to new and existing BFRs will be made available for public access on the Internet, as an output of the FP7 EU Project CADASTER (22). Acknowledgment. Financial support by the European Union through the project CADASTER (FP7-ENV-2007-212668) is gratefully acknowledged. Supporting Information Available: Full data matrix containing experimental and individual model predictions (by full models) for all of the end points. This material is available free of charge via the Internet at http://pubs.acs.org.

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