Prediction of Estrogenic Bioactivity of Environmental Chemical

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Prediction of Estrogenic Bioactivity of Environmental Chemical Metabolites Caroline Lucia Pinto, Kamel Mansouri, Richard S. Judson, and Patience Browne Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/acs.chemrestox.6b00079 • Publication Date (Web): 10 Aug 2016 Downloaded from http://pubs.acs.org on August 13, 2016

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Chemical Research in Toxicology

Prediction of Estrogenic Bioactivity of Environmental Chemical Metabolites Caroline L. Pinto†§*, Kamel Mansouri‡§, Richard Judson‡, Patience Browne† Affiliation: (†) US Environmental Protection Agency, Office of Chemical Safety and Pollution Prevention, Washington, DC. (§) Oak Ridge Institute for Science and Education, Oak Ridge, TN. (‡) US Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC. Author to whom correspondence should be addressed: *Caroline L Pinto, PhD ORISE Postdoctoral Fellow U.S. Environmental Protection Agency OCSPP/Office of Science Coordination and Policy 1200 Pennsylvania Ave. NW Washington, DC 20460 Phone: +1 202-564-0479 Email: [email protected]

Keywords: metabolism, estrogenic activity, in silico, bioactivation, QSAR

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Table of Contents Graphic


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Abstract The US Environmental Protection Agency's (EPA) Endocrine Disruptor Screening Program (EDSP) is using in vitro data generated from ToxCast/Tox21 high-throughput screening assays to assess the endocrine activity of environmental chemicals. Considering that in vitro assays may have limited metabolic capacity, inactive chemicals that are biotransformed into metabolites with endocrine bioactivity may be missed for further screening and testing. Therefore, there is a value in developing novel approaches to account for metabolism and endocrine activity of both parent chemicals and their associated metabolites. We used commercially available software to predict metabolites of 50 parent compounds, out of which 38 chemicals are known to have estrogenic metabolites, and 12 compounds and their metabolites are negative for estrogenic activity. Three ER QSAR models were used to determine potential estrogen bioactivity of the parent compounds and predicted metabolites, the outputs of the models were averaged and the chemicals were then ranked based on the total estrogenicity of the parent chemical and metabolites. The metabolite prediction software correctly identified known estrogenic metabolites for 26 out of 27 parent chemicals with associated metabolite data, and 39 out of 46 estrogenic metabolites were predicted as potential biotransformation products derived from the parent chemical. The QSAR models estimated stronger estrogenic activity for the majority of the known estrogenic metabolites compared to their parent chemicals. Finally, the three models identified a similar set of parent compounds as top ranked chemicals based on the estrogenicity of putative metabolites. This proposed in silico approach is an inexpensive and rapid strategy for the detection of chemicals with estrogenic metabolites and may reduce potential false negative results from in vitro assays.


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1. Introduction The US Environmental Protection Agency’s (EPA) Endocrine Disruptor Screening Program (EDSP) was established to assess the potential estrogenic bioactivities of pesticides and chemicals found in sources of drinking water, and later expanded to include androgen and thyroid activities.1 To evaluate the endocrine activity of chemicals, the EDSP developed a two-tiered approach that consists of five in vitro and six in vivo Tier 1 screening assays to identify potential activity in the estrogen, androgen and thyroid pathways,2 and definitive Tier 2 in vivo tests to characterize adverse outcomes and establish dose-response relationships.1 To establish screening priorities for approximately 10,000 chemicals in the EDSP universe, EPA implemented a strategic approach based on bioactivity in ToxCast/Tox21 in vitro high throughput screening (HTS) assays and computational models,3 and is accepting these data as alternatives for selected Tier 1 in vitro and in vivo screening assays.4 Eighteen ToxCast/Tox21 estrogen receptor (ER) HTS assays were integrated into a network model whose results strongly correlate with reported potencies of reference estrogenic chemicals.5 The model also accurately predicts in vivo responses in uterotrophic studies and results of selected EDSP Tier 1 assays.4,6 However, in vitro assays may not always predict in vivo outcomes due to their limited coverage of metabolic processes present in a whole organism. This is especially important for compounds that undergo bioactivation, as these chemicals can be false negatives when tested in assays without metabolic activity.7 The Organization for Economic Cooperation and Development (OECD) noted the urgent need to validate and implement in vitro testing strategies to assess the effect of metabolism on the endocrine activity of chemicals.8 OECD did not propose a specific approach, but recommended the use of currently available in vitro tools and exploration of in silico models to address the impact of metabolism on chemical bioactivity.8 Computational modeling methods are alternatives to


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address the insufficient knowledge about chemicals with little or no experimental data, and quantitative structure activity relationships (QSARs) have been developed to predict putative metabolites of chemicals and estimate chemical interaction with the ERs.9-12 Herein, we demonstrate an in silico approach combining metabolite prediction with ER agonist QSAR models to identify parent chemicals that are converted into estrogenic metabolites. The proposed approach is demonstrated on methoxychlor and other chemicals with metabolites known to have greater estrogenic activity than their respective parent chemicals. Our objective was to demonstrate that this in silico analysis can be used as a rapid and inexpensive approach to detect environmental chemicals with estrogenic metabolites, reducing potential false negative results from HTS assays. 2. Methods To demonstrate an in silico approach for identifying chemicals with estrogenic metabolites, compounds with metabolites known to have estrogenic activity were selected from the literature. The primary and secondary metabolites of the chemicals were predicted and the activity of both parent chemicals and metabolites were estimated using three QSAR models that provide quantitative predictions (scores) for estrogen agonist activity. The scores for all predicted primary and secondary metabolites were averaged and the predictions from the three models were combined. The chemicals were then ranked according to the total estimated estrogenicity of the metabolites. The approach is illustrated in Figure 1 and further detailed below. 2.1 Identification of parent chemicals with estrogenic metabolites Parent chemicals with more potent estrogenic metabolites were identified from peer-reviewed literature and used as reference chemicals to demonstrate the proposed in silico approach for


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identifying compounds with estrogenic metabolites. The chemicals were selected from published studies reporting increased activity of the parent chemicals in in vitro assays measuring estrogenic activity following incubation with liver S9 or microsome fractions.13-28 Chemicals for which no S9 or microsome studies were identified, but with data indicating that metabolites displayed greater estrogenic activity than the precursor chemical form were also included in the analysis (Table 1 and Figure 2). 15, 18, 21-25, 29-38 These parent chemicals were first run through metabolite prediction software to determine if metabolites with known estrogenic activity were correctly predicted. Both parent chemicals and metabolites were then run through ER QSAR models to assess whether the estrogenic activity of metabolites were estimated to be greater than the parent chemicals. 2.2 Selection of parent chemicals and metabolites negative for estrogenic activity Chemicals with no or very weak estrogenic activity and without indications of being converted into estrogen-active metabolites were identified from in vitro S9 literature studies and used to demonstrate the performance of the approach for potential negative chemicals.17, 39 To qualify as a potential negative, the chemicals had to satisfy the following criteria: an in vitro study indicating no estrogenic activity of the chemical after incubation with S9 fractions and only negative uterotrophic (UT) results according to studies compiled from a curated uterotrophic bioassay database.6 The negative chemicals for bioactivation are indicated in Figure 3 and Table S1. 2.3 In silico models Putative primary and secondary metabolites of the parent chemicals were predicted using the metabolism module of the ADMET Predictor™ software v. 8.0 (Simulation Plus, Inc., Lancaster, CA, USA). This software predicts atomic sites of metabolic oxidation and generates putative


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metabolites for nine human cytochrome P450 enzymes (CYP450s) involved in Phase I metabolic transformations of xenobiotics: CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP2A6, CYP2B6, CYP2C8 and CYP2E1. The simplified molecular-input line-entry system (SMILES) notations of the parent chemicals were used as inputs for predicting primary metabolites, and the resulting primary metabolites were used as inputs to predict secondary metabolites. The SMILES notations of the parent chemicals and metabolites used to generate the ER QSAR scores are included for reference (Tables S1 and S2 and Files S1 and S2). The chemical names of the predicted primary and secondary metabolites were verified using the publicly available web platforms:

Distributed

Structure-Searchable

Toxicity

Database

(DSSTox;

http://www.epa.gov/chemical-research/distributed-structure-searchable-toxicity-dsstox-database) or CADD (Computer-Aided Drug Design) Group Chemoinformatics Tools and User Services (CACTUS; http://cactus.nci.nih.gov/chemical/structure). The ER agonist bioactivities of the parent chemicals and their primary and secondary metabolites were predicted using three QSAR models: the Online Chemical Database with Modeling Environment (OCHEM) Consensus CERAPP ER agonist model publicly available through the OCHEM web platform at (https://ochem.eu/home/show.do), an ER agonist model developed by Lockheed Martin (LM), and the ER model developed by the Laboratory of Chemoinformatics at University

of

Strasbourg,

France

(UNISTRA)

available

at

(http://infochim.u-

strasbg.fr/webserv/VSEngine.html).40 These ER QSARs were trained on results of 18 high throughput ER assays for approximately 1600 ToxCast chemicals, which included known reference ER ligands and a wide array of chemicals with known estrogen-like activity covering different chemical structure classes.5, 40 The results of the 18 ER assays are integrated into an ER model which produces area under the curve (AUC) values and has been validated with estrogenic


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reference chemicals.5, 40 The ER model AUC values range from 0 (no activity) to 1.0 (activity of 17-estradiol) and inversely correlate with AC50s (activity concentration at half-maximal response): the stronger the potency, the lower the AC50 and generally the higher the ER QSAR/AUC score. The output of the ER QSAR scores are equivalent to ER AUC values. A regression plot demonstrating the relationship between ER AUC/QSAR scores and AC50s is shown in Figure S1, and the corresponding AC50 values for parent chemicals and all putative metabolites are shown in Files S1 and S2. Further details of the ER AUC model can be found in Judson et al., 2015. For ranking the estrogenic activity of parent chemicals and metabolites, the mean QSAR score for all putative primary and secondary metabolites was calculated for each ER QSAR model. To overcome limitations associated with predictive capability of individual QSAR models, the ER QSAR scores were further averaged, resulting in one single score that combined the predictions from the three models. The parent chemicals were then ranked according to the estimated activity of the putative metabolites. 3. Results Chemical selection for in silico prediction of estrogenic metabolites Thirty-eight parent chemicals with 46 metabolites reported to have greater estrogenic activity than their respective parent compound were selected from published literature (Figure 2 and Table 1). Studies demonstrating increased in vitro estrogenic activity after S9 or microsome incubation were identified for 30 of the listed parent chemicals (Table 1). No S9 or microsome studies were identified for seven parent chemicals, but they were included as reference chemicals as the in vitro estrogenic activity of metabolites was greater than the parent chemical form (Table 1). Parent


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chemicals included methoxychlor, known to be biotransformed into the more estrogenically active 2, 2-bis (p-hydroxyphenyl)-1,1,1-trichloroethane

(HPTE), the pharmaceutical mestranol,

metabolized to 17α-ethinylestradiol, and the phytoestrogens biochanin A and formononetin. Compounds with very weak or no estrogenic activity, such as trans-stilbene, diphenyls, styrenes and their related compounds, pyrethroids, polycyclic aromatic hydrocarbons (PAHs) and benzophenones were also identified as chemicals converted into more estrogenic metabolites. Most of the listed parent compounds have very weak (AC50s > 10 µM, approximately) or no estrogenic activity, comprising 35 of the 38 parent chemicals assessed (Table 1). These compounds were used as reference chemicals to demonstrate the utility of in silico tools to: 1) predict the formation of estrogenic metabolites from the parent chemical, 2) estimate the estrogenic activity of the parent compounds and their respective estrogenic metabolites, and ultimately, 3) rank the parent chemicals based on the total estimated estrogenicity of predicted primary and secondary metabolites. In silico prediction of estrogenic metabolites The initial step in this proposed in silico approach utilizes metabolite prediction software to identify putative biotransformation products derived from a precursor molecule. Using the 27 reference chemicals with associated estrogenic metabolite information identified from the literature, we compared the putative metabolites generated by the ADMET™ Predictor software to their reported estrogenic metabolites (Table 2). The software predicted 39 out of the 46 listed estrogenic metabolites (85%) as potential biotransformation products derived from the parent reference chemicals. For 26 of 27 reference chemicals with metabolite information (96%), estrogenic metabolites were correctly predicted, and for 21 of 27 chemicals (78%), all listed estrogenic metabolites were correctly identified (Table 2). The software failed to predict any


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estrogenic metabolites only for cypermethrin. In addition, the 3-hydroxydiphenyl metabolite of diphenyl, 3-phenoxybenzylalcohol and 3-(4’-hydroxyphenoxy)-benzylalcohol metabolites of permethrin, the 9-phenanthrol metabolite of phenanthrene, the 2,3,4-trihydroxybenzophenone metabolite of benzophenone 3 and the 2-hydroxy metabolite of chalcone were not predicted (Table 2). The chemical structures of all putative metabolites are depicted in File S1, and predictions of CYP450 isoform substrate specificity for the parent chemicals and pharmacokinetic parameters (Km, Vmax and CLint) are reported in File S3. Uterotrophic activity of parent chemicals and metabolites Uterotrophic assays from literature studies were used to assess whether the chemicals identified from in vitro studies have evidence of in vivo estrogenic activity (Table S3 and File S4). Uterotrophic assays were identified for 16 of the 38 parent chemicals expected to undergo bioactivation, and 12 chemicals showed positive effects (increased uterine weight) in at least one study: mestranol, biochanin A, formononetin, methoxychlor, benzophenone 3, benzophenone, trans-stilbene, benz[a]anthracene, benzopyrene, fluoranthene, permethrin and cypermethrin. Only one study was identified for 2,4-diphenyl-1-butene, diphenyl, ortho-phenylphenol and trans-1,2diphenylcyclobutane, and these chemicals did not elicit uterotrophic effects (Table S3 and File S4). In vivo estrogenic effects for twenty of the 46 estrogenic metabolites were also assessed in uterotrophic assays, and positive results were associated with 17 metabolites, indicating that metabolites with in vitro ER activity are also estrogenic in vivo. Uterotrophic activity was identified for metabolites of 2,2,-diphenylpropane, azobenzene, biochanin A, benzophenones, diphenyl, diphenylmethane, mestranol, formononetin, benzo[a]pyrene, methoxychlor and transstilbene (Table S3 and File S4).


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ER QSAR model predictions of ER agonist activity of parent chemicals and their reported estrogenic metabolites We next assessed the ability of the ER QSAR models to differentiate the activity of the parent chemicals and their reported estrogenic metabolites. The ER QSAR scores are equivalent to ER AUC scores, which are associated to an in vitro potency (AC50). Each of the ER QSAR models correctly predicted greater estrogenic activity for the metabolites than their respective parent compound for the majority of the metabolites (Table 2 and Figure 4A-C). For example, benzophenone was estimated a score of 0.036, 0.032 and 0 by OCHEM, LM and Unistra’s models, respectively, indicating very weak/no activity (AC50s higher than 390 µM, approximately). 4hydroxybenzophenone, a metabolite of benzophenone, was estimated an ER QSAR score of 0.224, 0.259 and 0.150 by OCHEM, LM and Unistra’s models, respectively, which corresponds to approximate AC50s of 19 µM, 13 µM and 45 µM (File S1). The estrogenic metabolites whose activity was not predicted to be greater than their respective parent chemical were 1-hydroxypyrene, predicted by OCHEM to have a similar ER score to pyrene (Table 2 and Figure 4A); 7-hydroxy-2-nitrofluorene, the three chalcone metabolites, 2hydroxychrysene,

3-hydroxychrysene,

daidzein,

17α-ethinylestradiol,

2-phenanthrol,

9-

phenanthrol, trans-4-(4-hydroxyphenyl)-3-buten-2-one, trans-4-hydroxystilbene and trans-4,4’dihydroxystilbene, which were predicted by LM to be less active or have similar activity to their respective parent chemicals (Table 2 and Figure 4B); and the pyrethroid metabolites 3phenoxybenzaldehyde and 3-phenoxybenzylacohol, which were estimated by Unistra to have slightly lower or similar activities than permethrin and cypermethrin (Table 2 and Figure 4C). Metabolites for a total of 11 of the 27 parent compounds (41%) failed to be unanimously predicted


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by the models as being more estrogenic than their respective parent chemical while experimental data indicated greater activity than the parent compound. The three QSARs were trained with a similar set of ToxCast chemicals, but the models have inherent limitations in their predictive capability due to different parameters used in their modeling techniques. Consensus approaches that weight or average outputs of different QSARs have been used to improve prediction accuracy of single models.40, 41 As the ER QSAR models varied slightly in numeric outputs, the scores for parent chemicals and metabolites were averaged in order to derive a more robust estimate of estrogen agonist activity. Averaging the ER QSAR scores from the three models resulted in all estrogenic metabolites, except 2-hydroxychalcone, displaying stronger activity than their respective parent chemicals (Figure 4D and Table 2), in accordance with experimental data. In silico approach to identify and rank chemicals with bioactivation potential As previously shown, the averaged outputs from the ER QSAR models correctly estimated higher scores for most reported estrogenic metabolites compared to their parent chemicals. We next tested an approach to evaluate potential bioactivation of chemicals that includes all of the putative metabolites. The parent chemicals and all of their primary and secondary metabolites predicted by the ADMET software were run in the three ER QSAR models (File S1 and Figure S2A-C), and the QSAR scores for the primary and secondary metabolites were averaged, resulting in each chemical having an ER QSAR prediction of the estrogenicity of the parent, primary and secondary metabolites (Table 3 and Figure 5A-C). This in silico approach was applied to 37 of the 38 parent chemicals as no metabolites were predicted for nonylphenol ethoxylate.


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A total of 22, 27 and 24 chemicals were predicted to have metabolite scores  0.1 (AC50s lower than 100 µM, approximately) by OCHEM, LM and Unistra’s models, respectively (Table 3 and Figure 5A-C), with an overlap of 19 chemicals among the three models (Figure S3). Twelve out of the 19 parent chemicals with metabolite scores  0.1 were unanimously estimated to be bioactivated by all models: 1,3-diphenylpropane, 2,4-dipheny-1-butene, 2,2-diphenylpropane, 4,4dimethoxystilbene, benzophenone, benzophenone 3, dibenzyl, diphenyl, diphenylacetylene, methoxychlor, ortho-phenylphenol and trans-1,2-diphenylcyclobutane (Figure 5A-C and Figure S3). All three models failed to predict bioactivation of mestranol and biochanin A, which were estimated to have higher or similar scores than their metabolites. Additionally, formononetin, 2methyl-6-tert-butylphenol, 1-(N-phenylamino) naphthalene were predicted to have higher or similar scores than their metabolites by OCHEM (Figure 5A), and 4-tert-butylphenylsalycilate was predicted to be more active than its metabolites by Unistra (Figure 5C). LM was the model with the lowest ability to predict bioactivation: 2-nitrofluorene, trans-alpha-methylstilbene, chalcone, trans-stilbene, ortho-benzylphenol and formononetin were estimated to have higher or similar scores than their metabolites (Figure 5B). The scores of the parent chemicals, primary and secondary sets of metabolites predicted by each ER QSAR model (Figure 5A-C and Table 3) were further averaged, generating an overall measure of the estrogenic bioactivation potential of the chemicals (Figure 6). The combined approach predicted bioactivation of 22 out of the 25 parent chemicals with metabolite scores  0.1, with the exception of mestranol, biochanin A and ortho-benzylphenol. The top ranked chemicals based on metabolite scores included mestranol, methoxychlor, phytoestrogens, benzophenones, styrenes and diphenyls (metabolites scores  0.1), followed by PAHs (metabolite scores  0.05; AC50s


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lower than 250 µM, approximately). Metabolites of 2-nitrofluorene, cypermethrin and permethrin were predicted to have the weakest estrogenic activity (scores < 0.05). In silico approach with potential negative chemicals for bioactivation To demonstrate the performance of the in silico approach with negative chemicals, we selected 12 parent compounds with no or very weak estrogenic activity and that do not have indications of being converted into more estrogen-active metabolites. These chemicals lack in vitro estrogenic activity after S9 treatment and do not elicit uterotrophic effects in vivo (Table S1 and File S4). Similarly to the approach used with reference chemicals for bioactivation, the ER QSAR scores of all primary and secondary metabolites were predicted for negative chemicals (Figure S4 and File S2) and their scores averaged (Figure 7). The QSAR models estimated higher activity for metabolites of some negative chemicals, but in general, the difference in activity between parent and metabolites was minimal (Figure 7 and Table S4). Chemicals showing the most significant differences was octyl-dimethyl-PABA, whose metabolites were estimated by OCHEM and Unistra models to have slightly higher metabolite scores than the parent chemical (Figure 7A,C), and the LM model clearly estimated metabolites of 2,4-di-tert-butylphenol, butylbenzylphthalate and 2mercaptobenzothiazole to be more active than the parent chemicals (Figure 7B). The combined approach also estimated a modest tendency for higher metabolite scores for negative chemicals, such as 2-mercaptobenzothiazole, 2, 4-di-tert-butylphenol, butylbenzylphthalate and octyl-dimethyl-PABA (Figure 7D and Table S4). However, the difference between parent and metabolite scores for negative chemicals was negligible when compared to the difference in parent and metabolite activity for most chemicals expected to undergo bioactivation (Figure 6). 4. Discussion


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There is an urgent need to develop in vitro and in silico strategies for predicting effects of metabolism on the endocrine activity of chemicals. The use of S9 fractions coupled with in vitro assays designed to detect endocrine activity have limited throughput, and issues associated with availability and variability of S9 sources, non-specific protein binding and target cell toxicity may hinder their use and standardization in endocrine testing. Alternatively, in silico methods are readily available and can be used for high throughput prediction of metabolism on chemical bioactivity. Herein, we have demonstrated an approach using existing in silico models to identify chemicals that are potentially converted to estrogenic metabolites via CYP450-mediated metabolism. This in silico approach was demonstrated using methoxychlor, a well-known parent chemical with estrogenic metabolites, and other chemicals selected from literature studies indicating greater in vitro estrogenic activity of metabolites compared to the precursor chemical. The ADMET software predicted estrogenic metabolites of 96% (26 of 27) reference chemicals. Overall, 85% (39 of 46) known estrogenic metabolites were predicted (Table 2), and the majority of the reported estrogenic metabolites were correctly estimated to have stronger activity than the parent chemical by each of the ER QSAR models (Figure 4). These results indicate that in silico tools can be used to predict endocrine-active metabolites of parent compounds and to estimate and differentiate estrogenic bioactivity of parent chemical and respective metabolites. Our proposed in silico approach for predicting effects of metabolism on estrogen bioactivity estimates the estrogenic activity of all predicted primary and secondary metabolites, and compares the metabolites with the estimated activity of the parent chemical. Slight differences were observed in the scores from the three ER QSAR models used to evaluate estrogen activity of parent chemicals and their respective metabolites. These divergences are likely attributed to variables used in the modeling procedure of the QSARs, such as different model descriptors and machine


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learning techniques.40 Nonetheless, there was good agreement among all three models in indicating the same chemicals as most active based on the averaged estrogenic activity of the metabolites (Figure 5 and Figure S3). Combining model predictions can help overcome limitations of single models and improve their overall predictive accuracy.40 Indeed, while most reported estrogenic metabolites in the literature were estimated to be more active than the parent chemicals by single QSAR models (Figure 4AC and Table 3), the averaged model predictions identified all reported estrogenic metabolites, except 2-hydroxychalcone, as more active than the respective parent form (Fig. 4D). Trans-stilbene has been reported in several studies to undergo bioactivation in the presence of S9 or microsome fractions.17, 19, 20 However, while the OCHEM and Unistra models predicted greater activity of the metabolites (Figure 5A,C), the LM ER QSAR score indicated metabolites of trans-stilbene were expected to be relatively less estrogenic than the parent compound (Figure 5B), though still identified potential activity of the metabolites. The combined model approach indicated bioactivation of trans-stilbene (Figure 6), in accordance with published literature. This in silico approach combining the scores from the three models failed to predict bioactivation of ortho-benzylphenol, biochanin A and mestranol (Figure 6). Inability of the approach to predict bioactivation of mestranol may be due to limitations of the ER QSAR/AUC models in distinguishing the activity of potent estrogenic chemicals, as the response may already be saturated for strong agonists at the lowest concentration tested in ToxCast assays. In a regulatory context, however, these chemicals would certainly be prioritized for further screening based on their intrinsic estrogenic activity. Slightly higher metabolite scores were identified for some of the negative chemicals (Figure 7). However, the difference between parent and metabolite scores was minor when compared to the


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difference of activity between parent and metabolites for most chemicals expected to undergo bioactivation (Figure 6 and Figure 7). Prioritization decisions based on this in silico approach may focus on chemicals with clear indications of undergoing bioactivation (metabolite scores higher than 0.1) or when pronounced differences are observed between parent and metabolite scores. To help confirm in silico findings, the estrogenic activity of parent chemicals could be assessed in ER-based in vitro assays after incubation with S9 or microsome fractions and/or compared among ER-reporter cell lines with and without metabolic competence. In order to improve the performance of QSAR models in predicting metabolite estrogenicity, selected metabolites could be synthesized/tissue-extracted and their estrogenic activity directly assessed in vitro, applying the results to refine the ER QSAR models. The structural feature associated with the increased estrogenic activity of the metabolites is a phenolic hydroxyl group attached para to a hydrophobic core, which is crucial for hydrogen-bond interactions in the ligand binding pocket of the ERs. Hydroxylation reactions adding a hydroxyl group at the para position of a ring (for example, 4hydroxylation of diphenyl yielding 4-hydroxydiphenyl) or O-demethylation reactions exposing hydroxyl groups (O-demethylation of methoxychlor, benzophenone 3 and formononetin) are expected to increase the estrogenicity of the compounds. A phenolic ring and an additional hydroxyl group within a distance in a similar range to the hydroxyls of 17-estradiol is associated with even stronger interaction with the ERs. Chemicals with hydroxyl substitutions at other positions in an aromatic ring may also bind the ERs, but binding affinity tends to decrease with the change of hydroxyl substitution from para to meta to ortho-positions due to decreased accessability of the hydroxyl group for H-bond interactions.42 In vivo uterotrophic data demonstrates the utility of the proposed in silico approach. Several positive uterotrophic studies were identified for benzophenone (parent score = 0.02 and metabolite


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scores > 0.1) and also for its metabolite 4-hydroxybenzophenone (Table S3 and File S4). While benzophenone displays clear in vivo estrogenicity, it would likely have been missed or ranked lower in prioritization based solely on the estrogenic activity of the parent chemical. Uterotrophic activity was also observed for 11 other parent chemicals, including benzophenone 3, trans-stilbene, methoxychlor, benzanthr[a]cene, benzo[a]pyrene and pyrethroids. The lowest observed effect level (LOEL) of trans-stilbene for eliciting uterotrophic effects in ovariectomized mice was 100 mg/kg/day, and the metabolites 4-hydroxystilbene and 4,4’-dihydroxystilbene increased uterine weight at 1 mg/kg/day.43 Only one uterotrophic study was identified for diphenyl, orthophenylphenol and the styrenes 2,4-diphenyl-1-butene and trans-1,2-diphenylcyclobutane. These chemicals displayed metabolite scores > 0.1, but did not elicit uterotrophic activity. Diphenyl was tested at one single dose of 8 mg/rat in the uterotrophic study.44 However, the metabolites 4hydroxydiphenyl and 4, 4’-dihydroxydiphenyl have been shown to be active in rats with LOELs of 10 and 60 mg/kg/day, respectively,45-47 and it is possible that the diphenyl dose used (8 mg/rat) was insufficient to produce significant amounts of estrogen-active metabolites. On the other hand, the highest tested dose for ortho-phenylphenol in the uterotrophic study was 1000 mg/kg/day.48 Phase II conjugates have been identified as major urinary metabolites of ortho-phenylphenol in rodents and humans (approximately 70% to 90%), followed by conjugates of its hydroxylated metabolites (2-phenylhydroquinone sulfate/glucuronide and 2,4’-dihydroxybiphenyl sulfate).49 The minor hydroxylated metabolites with potential estrogenic activity may not have been produced at sufficient amounts and/or quickly undergone Phase II conjugation and excretion, which would explain the lack of uterotrophic effect in the study. The metabolite prediction software used in the analysis generates putative metabolites for nine human Phase I CYP450s: CYP1A2, 2C9, 2C19, 2D6, 3A4, 2A6, 2B6, 2C8, 2E1, which represent


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over 70% of total hepatic CYP content,50 and are responsible for metabolizing over 90% of xenobiotics.51 However, the software may fail to predict metabolites specifically generated by alternative CYP450s or non-CYP-mediated reactions. For example, the ester hydrolysis of permethrin to the metabolite 3-phenoxybenzylalcohol is mainly catalyzed by Phase I carboxylesterases in humans,52 which explains the inability of the software in identifying 3phenoxybenzylalcohol and its hydroxylated derivative (3-(4’-hydroxyphenoxy)-benzylalcohol) as potential biotransformation products. Moreover, the metabolite prediction software missed metabolites known to be formed via CYP-mediated reactions. Metabolism of chalcone to 2hydroxychalcone is catalyzed by CYP1A1 and CYP3A1 only, whereas CYP1A1, 1A2, 2B1 and 2D6 are involved in the formation of the 4- and 4’-hydroxychalcones. 27 The software, as expected, correctly predicted the para-substituted metabolites, but failed to predict the 2-hydroxylated chalcone likely because predictions specific for the CYP1A1 and 3A1 isoforms are not included in the software. The formation of the metabolite 9-hydroxyphenanthrene has been shown to be preferentially catalyzed by human CYP2E and CYP1A1/2.53, 54 However, the position C9 of phenanthrene was not predicted to be a site of oxidation for human CYP2E1 or CYP1A2. The formation of the 2,3,4-trihydroxybenzophenone metabolite is mainly catalyzed by CYP2D6 activity in humans,24 and it is unclear why the software missed it as a potential metabolite of benzophenone 3. Diphenyl metabolism is induced via CYP1A/2B reactions, and while 3hydroxydiphenyl has been identified as a liver microsomal oxidation product in several mammals,55 it has not yet been detected in human-derived samples.56 It is possible that human CYP isoforms are unable to catalyze the C3 regio-specific hydroxylation of diphenyl or that the software only predicts para-hydroxylation of aryl moieties, as CYP-catalyzed meta- and orthohydroxylations occur less frequently.57 Additionally, metabolites with potential endocrine activity


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derived from extra-hepatic metabolism, such as the prostaglandin H synthase system, or from gut microbiota reactions, will not likely be predicted by the model. Moreover, due to interspecies differences in the structure and activity of CYP450s, the model may potentially miss endocrineactive biotransformation products specific to other animals, and extrapolation of the predictions to other species should be considered carefully. Noteworthy, the majority of chemicals used to train metabolite prediction tools are drug-like compounds, focusing on enzymes known to play major roles in the metabolism of the pharmaceutical chemical space. The software predictions could potentially lead to a significant number of false-negatives or false-positives when used across the broad chemical space relevant for the EDSP. In the lack of an ideal tool, however, the software is useful to provide exploratory information until a more appropriate model trained on a broader chemical space is developed. Another limitation of the approach is the assumption that all predicted metabolites are formed, and at equivalent proportions, which is unlikely in the in vivo scenario. Metabolites with higher predicted scores, for example, may not be produced at sufficient levels to exert a biological effect and/or might quickly undergo Phase II metabolism and rapid excretion. Prediction of major Phase I metabolites and the effect of Phase II conjugation and Phase III transport on endocrine activity could also be included in future in silico strategies to improve predictions of metabolism on endocrine bioactivity of environmental chemicals. OECD has addressed the need to incorporate testing strategies to assess the effect of metabolism on the endocrine activity of chemicals,8 and this need has been recognized by the US EPA’s EDSP. In the present study we have demonstrated an in silico approach that can be readily applied for identifying environmental chemicals with the potential to be biotransformed into metabolites with


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estrogenic activity. The proposed in silico approach is highly relevant to screening chemicals which may have limited estrogenic activity, but become more active as they are metabolized. For regulatory purposes, this in silico analysis can help gain a better insight of the bioactivation potential of chemicals in the EDSP universe and can be used as an initial screening tool for potential false negative compounds. Funding information This project was supported in part by an appointment to the Research Participation Program at the Office of Science Coordination and Policy, U.S. Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA. Acknowledgements The authors would like to thank Dr. Jean Lou Dorne (European Food Safety Authority; Parma, Italy) and Dr. Stephen Ferguson (National Institute of Environmental Health Sciences; NIEHS) for valuable comments on the manuscript; Dr. Ilya A. Balabin (Lockheed Martin Information Technology, Supporting the EPA, RTP, USA), Dr. Igor Tetko (Institute of Structural Biology, Helmholtz Zentrum Muenchen-German Research Center for Environmental Health, Neuherberg, Germany) and Prof. Alexandre Varnek (Laboratoire de Chemoinformatique, University of Strasbourg, Strasbourg, France) for the ER QSAR predictions used in the paper. Supporting information List of potential negative chemicals for bioactivation (Table S1); SMILES notation of parent chemicals and estrogenic metabolites (Table S2); Uterotrophic studies for parent chemicals and estrogenic metabolites (Table S3); Averaged ER QSAR scores for predicted primary and


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secondary sets of metabolites for negative chemicals (Table S4); Graphic plot of ER AUC scores vs AC50 values (Figure S1); Structures, SMILES notations and ER QSAR scores for parent chemicals expected to undergo bioactivation and all their predicted primary and secondary metabolites (File S1); Structures, SMILES notations and ER QSAR scores for parent chemicals not expected to undergo bioactivation and all their predicted primary and secondary metabolites (File S2); Predicted CYP450 isoform substrate specificity for parent chemicals and pharmacokinetic parameters (Km, Vmax and CLint) (File S3); Uterotrophic studies for parent chemicals and estrogenic metabolites (File S4). This material is available free of charge via the Internet at http://pubs.acs.org. Abbreviations AC50: Activity concentration at half-maximal response AUC: Area under the Curve bGal - Beta-galactosidase CACTUS: CADD Group Chemoinformatics Tools and User Services CADD: Computer-Aided Drug Design CAT- Cloramphenicol acetyltransferase CLint : Intrinsic clearance CYP450: Cytochrome P450 DSSTox: Distributed Structure-Searchable Toxicity E2: 17-estradiol


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EDSP: Endocrine Disruptor Screening Program (E)GFP – (Enhanced) Green Fluorescent Protein EPA: Environmental Protection Agency ER: Estrogen Receptor ER: Estrogen Receptor beta ERE: Estrogen Response Element hERα: Human Estrogen Receptor alpha HPLC: High-performance liquid chromatography HPTE: 2, 2-bis (p-hydroxyphenyl)-1,1,1-trichloroethane HTS: High-throughput screening LBD - Ligand Binding Domain LM: Lockheed Martin LOEL: Lowest Observed Effect level Luc – Luciferase NA - Non active ND - Not determined OCHEM: Online Chemical database OECD: Organization for Economic Cooperation and Development


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Testa, B., and Kramer, S. D. (2007) The biochemistry of drug metabolism - An introduction - Part 2. Redox reactions and their enzymes. Chem. Biodivers. 4, 257-405. Vinggaard, A. M., Korner, W., Lund, K. H., Bolz, U., and Petersen, J. H. (2000) Identification and quantification of estrogenic compounds in recycled and virgin paper for household use as determined by an in vitro yeast estrogen screen and chemical analysis. Chem. Res. Toxicol. 13, 1214-1222. Delfosse, V., Grimaldi, M., Cavailles, V., Balaguer, P., and Bourguet, W. (2014) Structural and Functional Profiling of Environmental Ligands for Estrogen Receptors. Environ. Health Perspect. 122, 1306-1313. Fujimoto, T., Kitamura, S., Sanoh, S., Sugihara, K., Yoshihara, S., Fujimoto, N., and Ohta, S. (2003) Estrogenic activity of an environmental pollutant, 2-nitrofluorene, after metabolic activation by rat liver microsomes. Biochem. Biophys. Res. Commun. 303, 419-426. Kunz, P. Y., and Fent, K. (2006) Multiple hormonal activities of UV filters and comparison of in vivo and in vitro estrogenic activity of ethyl-4-aminobenzoate in fish. Aquat. Toxicol. 79, 305324. Molina-Molina, J. M., Escande, A., Pillon, A., Gomez, E., Pakdel, F., Cavailles, V., Olea, N., AitAissa, S., and Balaguer, P. (2008) Profiling of benzophenone derivatives using fish and human estrogen receptor-specific in vitro bioassays. Toxicol. Appl. Pharmacol. 232, 384-395. Kuiper, G. G. J. M., Lemmen, J. G., Carlsson, B., Corton, J. C., Safe, S. H., van der Saag, P. T., van der Burg, P., and Gustafsson, J. A. (1998) Interaction of estrogenic chemicals and phytoestrogens with estrogen receptor beta. Endocrinology 139, 4252-4263. Lemaire, G., Mnif, W., Mauvais, P., Balaguer, P., and Rahmani, R. (2006) Activation of alpha- and beta-estrogen receptors by persistent pesticides in reporter cell lines. Life Sci. 79, 1160-1169. Saito, K., Tomigahara, Y., Ohe, N., Isobe, N., Nakatsuka, I., and Kaneka, H. (2000) Lack of significant estrogenic or antiestrogenic activity of pyrethroid insecticides in three in vitro assays based on classic estrogen receptor alpha-mediated mechanisms. Toxicol. Sci. 57, 54-60. Paris, F., Balaguer, P., Terouanne, B., Servant, N., Lacoste, C., Cravedi, J. P., Nicolas, J. C., and Sultan, C. (2002) Phenylphenols, biphenols, bisphenol-A and 4-tert-octylphenol exhibit alpha and beta estrogen activities and antiandrogen activity in reporter cell lines. Mol. Cell. Endocrinol. 193, 43-49. Hasenbrink, G., Sievernich, A., Wildt, L., Ludwig, J., and Lichtenberg-Frate, H. (2006) Estrogenic effects of natural and synthetic compounds including tibolone assessed in Saccharomyces cerevisiae expressing the human estrogen alpha and beta receptors. FASEB J. 20, 1552-+. Bovee, T. F. H., Helsdingen, R. J. R., Koks, P. D., Kuiper, H. A., Hoogenboom, R. L. A. P., and Keijer, J. (2004) Development of a rapid yeast estrogen bioassay, based on the expression of green fluorescent protein. Gene 325, 187-200. Escande, A., Pillon, A., Servant, N., Cravedi, J. P., Larrea, F., Muhn, P., Nicolas, J. C., Cavailles, V., and Balaguer, P. (2006) Evaluation of ligand selectivity using reporter cell lines stably expressing estrogen receptor alpha or beta. Biochem. Pharmacol. 71, 1459-1469. Pillon, A., Boussioux, A. M., Escande, A., Ait-Aissa, S., Gomez, E., Fenet, H., Ruff, M., Moras, D., Vignon, F., Duchesne, M. J., Casellas, C., Nicolas, J. C., and Balaguer, P. (2005) Binding of estrogenic compounds to recombinant estrogen receptor-alpha: Application to environmental analysis. Environ. Health Perspect. 113, 278-284. Legler, J., van den Brink, C. E., Brouwer, A., Murk, A. J., van der Saag, P. T., Vethaak, A. D., and van der Burg, P. (1999) Development of a stably transfected estrogen receptor-mediated luciferase reporter gene assay in the human T47D breast cancer cell line. Toxicol. Sci. 48, 55-66.


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Gaido, K. W., Leonard, L. S., Maness, S. C., Hall, J. M., McDonnell, D. P., Saville, B., and Safe, S. (1999) Differential interaction of the methoxychlor metabolite 2,2-bis-(p-hydroxyphenyl)-1,1,1trichlor with estrogen receptors alpha and beta. Endocrinology 140, 5746-5753.

Notes

The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency. Mention of trade names or commercial products does not constitute endorsement for use. Tables

Table 1. In vitro ER agonist activity of parent chemicals and estrogenic metabolites. Table 2. ER QSAR scores for parent chemicals and estrogenic metabolites. Table 3. Averaged ER QSAR scores for the predicted primary and secondary sets of metabolites for chemicals expected to undergo bioactivation.


29


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Page 30 of 47

Table 1. In vitro ER agonist activity of parent chemicals and estrogenic metabolites a Parent compound

S9 or Microsome study b (Ref)

AC50 (µM) c

In vitro assay

Ref

1-(N-phenylamino) naphthalene

17

>1000 ●

Yeast_hERαLBD-bGal (Y2H)

17

1,3- Diphenylpropane

26

NA

Yeast_hERα_ERE_bGal

2,4-Diphenyl-1-butene

26

NA

2,2-Diphenylpropane

25

>1000

2-Methyl-6-tertbutylphenol

17

>1000 ●

2-Nitrofluorene

60

Estrogenic Metabolite

AC50 (µM) c

In vitro assay

Ref

ND

—

—

—

26

ND

—

—

—


26

ND

—

—

—

MCF7_ERE_Luc

34

2-(4-Hydroxyphenyl)-2phenylpropane 4,4'-Dihydroxydiphenylpropane

0.15

MCF7_ERE_Luc

34

0.64

HepG2_hERα_ERE_Luc

30

0.8


58

0.51

HeLa_hERα_ERE_Luc

59

0.63

MCF7_ERE_Luc

34

—

—


17

ND

—

NA


60

7-Hydroxy-2-nitrofluorene

1‡


60

NA

MCF7_ERE_Luc

60

3‡

MCF7_ERE_Luc

60

MCF7_ERE_Luc

18

ND

—

—

—


17

ND

—

—

—

MCF7_ERE_Luc

18

4,4'-Dimethoxystilbene

18

NA

4-Tertbutylphenylsalicylate

17

>1000 ●

Azobenzene

18

NA

MCF7_ERE_Luc

18

4-Hydroxyazobenzene

ND

NA§


31

3-Hydroxybenz[a]anthracene

0.0042 §


31


§


31

0.21

T47D_ERE_Luc

23

1.2

MCF7_hERαLBD_Gal4-Luc

29

T47D_ERE_Luc

23

0.7


29

8-Hydroxybenzo[a]pyrene

0.26

T47D_ERE_Luc

23


3.25

T47D_ERE_Luc

23

Benz[a]anthracene Benzo[a]pyrene

23 #

9.21 1

T47D_ERE_Luc

23


29

3-Hydroxybenzo[a]pyrene 9-Hydroxybenzo[a]pyrene


0.11 0.0075

0.047

30

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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


Benzophenone

21

8.13 30 >1000

●

MCF7_ERE_Luc

37

2.7

CHO-K1_hERα_ERE_Luc

32


32

0.72

MCF7_ERE_Luc

37


33

1.82


61


33

4-Hydroxybenzophenone

●

4.5 Benzophenone 3

21, 24


32

1.26

MCF7_ERE_Luc

37

20.3

MCF7_ERE_Luc

62

9.2

MCF7_ERE_Luc

62

> 30

HeLa_hERα_ERE_Luc

62

8.5

HeLa_hERα_ERE_Luc

62

19.5

MCF7_ERE_Luc

37

1.5


32

NA


21

1.15


61

●


33


21

26

2,4-Dihydroxybenzophenone

660

●


33

2.2

†


24

0.65

3.3 †

CHO-K1_hER_ERE_Luc

24

0.099 †


24

†


24

MCF7_ERE_Luc

37


32

1.8

●

0.033 2,3,4-Trihydroxybenzophenone

11.8 18


33

6.8

●


21

2.6

†


24

2.2 †


24

○

HEK293_hERα_ERE_Luc

63

182 ○

HEK293_hER_ERE_Luc

63

9

Biochanin

A¶

Chalcone

Chrysene

28

27

23 #

36 ○


63

53 ○


63

NA

MCF7_ERE_Luc

27

NA

T47D_ERE_Luc

23

NA


31

Genistein

●

198

4-Hydroxychalcone

1‡

MCF7_ERE_Luc

27

4'-Hydroxychalcone

1‡

MCF7_ERE_Luc

27

MCF7_ERE_Luc

27

T47D_ERE_Luc

23


31

0.315

T47D_ERE_Luc

23

0.49

T47D_ERE_Luc

38


31

‡

2-Hydroxychalcone

20

1-Hydroxychrysene

1.72 0.00042

2-Hydroxychrysene

§

0.0042 § Cypermethrin

ND

3-Hydroxychrysene

1.1

T47D_ERE_Luc

23

3-Phenoxybenzaldehyde

4.8


35

NA

MCF7_ERE_Luc

22

NA

HeLa_hERα_ERE_Luc

64

NA

HeLa_hERα_ERE_Luc

65

4,4'-Dihydroxydibenzyl

1

MCF7_ERE_Luc

18

4-Hydroxydiphenyl

3

HeLa_hERα_ERE_Luc

66

Dibenzyl

18

NA

MCF7_ERE_Luc

18

Diphenyl

25

NA

MCF7_ERE_Luc

25


31


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

HeLa_hERα_ERE_Luc

> 50

Page 32 of 47

1


58

3-Hydroxydiphenyl

10

HeLa_hERα_ERE_Luc

66

4,4'-Dihydroxydiphenyl

0.5

HeLa_hERα_ERE_Luc

66

—

66

Diphenylacetylene

18

NA

MCF7_ERE_Luc

18

ND

Diphenylmethane

25

>1000

MCF7_ERE_Luc

34

4-Hydroxydiphenylmethane 4,4'-Dihydroxydiphenylmethane

—

—

0.32

MCF7_ERE_Luc

25

1.5

HepG2_hERα_ERE_Luc

30

MCF7_ERE_Luc

34

1 Fluoranthene

ND

NA


31

3-Hydroxyfluoranthene

0.00042 §


31

Fluorene

ND

NA


31

2-Hydroxyfluorene

0.00056 §


31

Formononetin¶

28

6○


63

Daidzein

97 ○


63

2○


63

80 ○


63

0.0158

Yeast_hERα_ERE_GFP

67

0.00054

Yeast_hERα_ERE_GFP

67

0.0091

Yeast_hERα_ERE_EGFP

68

8E-07

HeLa_hERα_ERE_Luc

69

MCF7_ERE_Luc

70

Mestranol

14

17α-Ethynylestradiol

7.14E-06 Methoxychlor

13, 15

4.45


15

Mono-Hydroxymethoxychlor

0.198

HepG2_hERα_ERE_Luc

30

5.72

T47D_ERE_Luc

71

HPTE

0.128


15


13

0.051

HepG2_hERα_ERE_Luc

72

0.053

HeLa_hERα_ERE_Luc

59

5.38

Hs578T_ER_ERE_Luc

36

35

T47D_ERE_Luc

38

12

Naphthalene

ND

NA


31

NA

Hs578T_ERα_ERE_Luc

36

1-Naphthol


36

2-Naphthol

17

T47D_ERE_Luc

38

Nonylphenol ethoxylate

17

>1000

●


17

ND

—

—

—

ortho-Benzylphenol

17

>1000 ●


17

ND

—

—

—

ortho-Phenylphenol

16

NA

BG1_ERE_Luc

16

ND

—

—

16, 22

NA

HeLa_hERα_ERE_Luc

64

NA

Permethrin

18.8 NA NA

†

MCF7_ERE_Luc

22

HeLa_hERα_ERE_Luc

65

3-Phenoxybenzylalcohol 3-(4’-Hydroxyphenoxy)-benzylalcohol

ND

NA


31

35

MCF7_ERE_Luc

22


35

MCF7_ERE_Luc

22

4.8


35

NA

MCF7_ERE-Luc

22


31

†

6.75 2.48


Phenanthrene

Yeast_hERα_ERE_b-Gal

19.97

16

BG1_ERE_Luc

— 6.67

2-Phenanthrol


†

0.00075 §

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Pyrene


ND

NA

Hs578T_ERα_ERE_Luc

36

NA


36

9-Phenanthrol

NA


31

1-Hydroxypyrene

NA


36

T47D_ERE_Luc

38


36

T47D_ERE_Luc

38


36


31

0.1

MCF7_ERE_Luc

18

Trans-4,4'-Dihydroxystilbene

0.077

MCF7_ERE_Luc

18

ND Δ


26

50 ‡

MCF7_ERE_Luc

27

—

—

0.021

MCF7_ERE_Luc

18

3.7  6.8 6.9 0.89 0.00024

Trans-stilbene

Trans-1,2diphenylcyclobutane Trans-4-phenyl-3buten-2-one Trans-stilbene oxide Trans-α-methylstilbene

17, 19, 20

>100

18

MCF7_ERE_Luc

Trans-4-Hydroxystilbene

26

NA


26

ND

NA

MCF7_ERE_Luc

27

19

>100

MCF7_ERE_Luc

19

Trans-1-(4-hydroxyphenyl)-2phenylcylobutane Trans-4-(4-hydroxyphenyl)-3-buten2-one ND

18

NA

MCF7_ERE_Luc

18

4,4'-Dihydroxy-α-methylstilbene

—

§

a

Measured activity of parent chemicals and estrogenic metabolites in ER-based in vitro assays. b Studies indicating higher estrogenic activity of parent chemicals in ER-based in vitro assays after incubation with S9 or microsome fractions. C Estrogenic activity corresponds to AC50 values unless otherwise noted. CAT- Cloramphenicol acetyltransferase; bGal - Beta-galactosidase; (E)GFP – (Enhanced) Green Fluorescent Protein; LBD - Ligand Binding Domain; Luc – Luciferase; NA - Non active or marginally active at high concentrations; ND - Not determined; Y2H - Yeast Two Hybrid Assay (measurement of coactivator recruitment to hERα LBD) # Increased displacement of radiolabeled estradiol from ER of uterus cytosol after chemical incubation with microsomes. † Reported value corresponds to EC20 (µM) (concentration of compound showing 20% of the maximal response of estradiol (E2)) § Relative effective estrogenic activity (inverse value of the concentration of the test compound that resulted in the same activity of E2) ● Reported value corresponds to 10% relative effective concentration (concentration of compound showing 10% of the maximal activity of E2) ○ Relative transactivation activity of 1µM chemical compared to Estradiol (transactivation activity of Estradiol set at 100) ‡ EC50 not reported; potency value is an approximation from graph ¶ Study indicating that more potent phytoestrogens are products of microsomal incubation with biochanin A and formononetin Δ Estrogenic activity detected for HPLC fraction corresponding to trans-1-(4-hydroxyphenyl)-2-phenylcylobutane


33


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Page 34 of 47

Table 2. ER QSAR scores for parent chemicals and estrogenic metabolites a ER QSAR Score

Parent compound

ER QSAR Score

Estrogenic Metabolite

OCHEM

LM

Unistra

Avgb

OCHEM

LM

Unistra

Avgb

St Dev

1-(N-phenylamino) naphthalene

0.054

0.068

0.050

0.057

0.009

ND

—

—

—

—

—

1,3- Diphenylpropane

0.024

0.008

0.100

0.044

0.049

ND

—

—

—

—

—

2,4-Diphenyl-1-butene

0.055

0.024

0.180

0.086

0.083

ND

—

—

—

—

—

2,2-Diphenylpropane

0.027

0.259

0.376

0.300

0.344

0.040

0.139

0.117

2-(4-Hydroxyphenyl)-2-phenylpropane

0.357

0.130

4,4'-Dihydroxydiphenylpropane

0.473

0.408

0.420

0.434

0.035

2-Methyl-6-tert-butylphenol

0.018

0.030

0.070

0.039

0.027

ND

—

—

—

—

—

2-Nitrofluorene

0.011

0.004

0.000

0.005

0.005

7-Hydroxy-2-nitrofluorene

0.023

0.004

0.010

0.012

0.010

4,4'-Dimethoxystilbene

0.031

0.000

0.070

0.034

0.035

ND

—

—

—

—

—

4-Tert-butylphenylsalicylate

0.077

0.153

0.160

0.130

0.046

ND

—

—

—

—

—

Azobenzene

0.006

0.005

0.040

0.017

0.020

4-Hydroxyazobenzene

0.026

0.164

0.110

0.100

0.070


0.068

0.183

0.090

0.114

0.061

Benz[a]anthracene

0.011

0.009

0.050

0.023

0.023


0.034

0.030

0.090

0.051

0.033


0.045

0.027

0.050

0.041

0.012


0.050

0.013

0.050

0.037

0.021


0.077

0.190

0.050

0.106

0.074


0.031

0.029

0.050

0.037

0.012

4-Hydroxybenzophenone

0.224

0.259

0.150

0.211

0.055

2,4-Dihydroxybenzophenone

0.393

0.370

0.300

0.354

0.048

2,3,4-Trihydroxybenzophenone †

0.225

0.283

0.280

0.263

0.033

Genistein

0.548

0.561

0.490

0.533

0.038

4-Hydroxychalcone

0.177

0.195

0.180

0.184

0.010

4'-Hydroxychalcone

0.231

0.289

0.220

0.247

0.037

2-Hydroxychalcone †

0.113

0.107

0.180

0.133

0.041

1-Hydroxychrysene

0.036

0.037

0.090

0.054

0.031

2-Hydroxychrysene

0.106

0.014

0.090

0.070

0.049

Benzo[a]pyrene

0.012

0.007

0.030

0.016

St Dev

0.012

Benzophenone

0.036

0.032

0.000

0.023

0.020

Benzophenone 3

0.078

0.245

0.100

0.141

0.091

Biochanin A

0.501

0.535

0.390

0.475

0.076

Chalcone

Chrysene

0.028

0.012

0.406

0.024

0.070

0.050

0.168

0.028

0.207

0.020


34

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3-Hydroxychrysene

0.054

0.013

0.090

0.052

0.038

Cypermethrin

0.008

0.000

0.030

0.013

0.016

3-Phenoxybenzaldehyde †

0.032

0.071

0.030

0.044

0.023

Dibenzyl

0.017

0.007

0.080

0.035

0.040

4,4'-Dihydroxydibenzyl

0.295

0.383

0.160

0.279

0.112

4-Hydroxydiphenyl

0.230

0.259

0.110

0.200

0.079

3-Hydroxydiphenyl †

0.165

0.197

0.110

0.157

0.044

4,4'-Dihydroxydiphenyl

0.277

0.213

0.130

0.207

0.074

—

—

—

—

—

4-Hydroxydiphenylmethane

0.130

0.228

0.070

0.143

0.080

4,4'-Dihydroxydiphenylmethane

0.229

0.087

0.090

0.135

0.081

Diphenyl

0.004

0.163

0.060

0.076

0.081

Diphenylacetylene

0.031

0.092

0.080

0.068

0.033

Diphenylmethane

0.010

0.007

0.030

0.016

0.012

Fluoranthene

0.002

0.000

0.030

0.011

0.017

3-Hydroxyfluoranthene

0.058

0.033

0.060

0.050

0.015

Fluorene

0.002

0.004

0.010

0.005

0.004

2-Hydroxyfluorene

0.067

0.050

0.040

0.052

0.014

Formonometin

0.334

0.491

0.210

0.345

0.141

Daidzein

0.407

0.466

0.410

0.428

0.034

Mestranol

0.691

0.809

0.610

0.703

0.100

17α-ethynylestradiol

0.724

0.791

0.750

0.755

0.034

Mono-hydroxymethoxychlor

0.278

0.307

0.290

0.292

0.015

Methoxychlor

0.200

0.242

0.150

0.197

0.046

HPTE

0.401

0.392

0.350

0.381

0.027

1-Naphthol

0.044

0.046

0.060

0.050

0.009

Naphthalene

0.001

0.009

0.030

0.014

0.015

2-Naphthol

0.031

0.195

0.060

0.095

0.088

Nonylphenol ethoxylate

0.007

0.329

0.050

0.129

0.175

ND

—

—

—

—

—

ortho-Benzylphenol

0.038

0.329

0.070

0.146

0.159

ND

—

—

—

—

—

ortho-Phenylphenol

0.054

0.212

0.110

0.125

0.080

ND

—

—

—

—

—

3-Phenoxybenzylalcohol †

0.059

0.079

0.030

0.056

0.025

3-(4’-Hydroxyphenoxy)-benzylalcohol †

0.128

0.079

0.070

0.092

0.031


0.032

0.071

0.030

0.044

0.023

2-Phenanthrol

0.083

0.014

0.090

0.062

0.042

9-Phenanthrol †

0.007

0.024

0.090

0.041

0.044

1-Hydroxypyrene

0.003

0.024

0.060

0.029

0.029

Trans-4-hydroxystilbene

0.267

0.190

0.130

0.196

0.069

Trans-4,4'-dihydroxystilbene

0.350

0.103

0.170

0.208

0.128

Permethrin

0.006

0.000

0.060

0.022

0.033

ND

Phenanthrene

0.002

0.023

0.050

0.025

0.024

Pyrene

0.003

0.000

0.030

0.011

0.016

Trans-stilbene

0.017

0.261

0.080

0.119

0.127

Trans-1,2-diphenylcyclobutane

0.095

0.101

0.140

0.112

0.024

Trans-1-(4-hydroxyphenyl)-2-phenylcylobutane

0.337

0.321

0.430

0.363

0.059

Trans-4-phenyl-3-buten-2-one

0.004

0.107

0.030

0.047

0.054

Trans-4-(4-hydroxyphenyl)-3-buten-2-one

0.056

0.102

0.090

0.083

0.024

Trans-stilbene oxide

0.020

0.010

0.000

0.010

0.010

ND

—

—

—

—

—

Trans-α-methylstilbene

0.094

0.506

0.150

0.250

0.223

4,4'-Dihydroxy-α-methylstilbene

0.592

0.710

0.490

0.597

0.110


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ER QSAR scores for parent chemicals and estrogenic metabolites predicted by OCHEM, LM and Unistra’s models. bAveraged scores represent the mean value from OCHEM, LM and Unistra ER QSAR model scores. † : indicates metabolites failed to be predicted by the metabolism prediction software ADMET™. a

ND: not determined


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Table 3. Averaged ER QSAR scores for predicted primary and secondary sets of metabolites a

OCHEM

LM

Unistra

Chemical Nonylphenol ethoxylate* 2-Nitrofluorene Cypermethrin Permethrin Pyrene 2-Methyl-6-tert-butylphenol Fluorene Fluoranthene Naphthalene Benzo[a]pyrene Benz[a]anthracene Phenanthrene Chrysene Azobenzene 1-(N-phenylamino) naphthalene Diphenylmethane Trans-4-phenyl-3-buten-2-one Trans-stilbene oxide o-Benzylphenol 4,4'-Dimethoxystilbene 4-Tert-butylphenylsalicylate Diphenylacetylene 1,3-Diphenylpropane Dibenzyl Ortho-phenylphenol Trans-stilbene Benzophenone Diphenyl Trans-1,2-diphenylcyclobutane Chalcone 2,4-Diphenyl-1-butene Benzophenone 3 2,2-Diphenylpropane Methoxychlor Trans-alpha-methylstilbene Biochanin A Formonometin Mestranol

P

M1

M2

P

M1

M2

P

M1

M2

Avg

0.007 0.011 0.008 0.006 0.003 0.018 0.002 0.002 0.001 0.012 0.011 0.002 0.012 0.006 0.054 0.010 0.004 0.020 0.038 0.031 0.077 0.031 0.024 0.017 0.054 0.017 0.036 0.004 0.095 0.028 0.055 0.078 0.027 0.200 0.094 0.501 0.334 0.691

— 0.013 0.018 0.020 0.003 0.007 0.050 0.055 0.038 0.047 0.041 0.038 0.065 0.026 0.039 0.106 0.037 0.070 0.088 0.217 0.098 0.150 0.130 0.100 0.143 0.267 0.156 0.230 0.150 0.196 0.163 0.290 0.357 0.278 0.281 0.325 0.301 0.687

— 0.031 0.035 0.039 0.041 0.014 0.079 0.092 0.028 0.089 0.097 0.057 0.108 0.055 0.044 0.130 0.038 0.132 0.111 0.216 0.086 0.171 0.174 0.155 0.127 0.257 0.201 0.277 0.197 0.215 0.176 0.234 0.287 0.401 0.357 0.277 0.313 0.579

0.329 0.004 0.000 0.000 0.000 0.030 0.004 0.000 0.009 0.007 0.009 0.023 0.024 0.005 0.068 0.007 0.107 0.010 0.329 0.000 0.153 0.092 0.008 0.007 0.212 0.261 0.032 0.163 0.101 0.406 0.024 0.245 0.259 0.242 0.506 0.535 0.491 0.809

— 0.002 0.008 0.021 0.024 0.000 0.026 0.021 0.121 0.057 0.045 0.026 0.021 0.164 0.056 0.114 0.111 0.172 0.090 0.008 0.163 0.117 0.200 0.193 0.278 0.190 0.269 0.259 0.174 0.279 0.250 0.305 0.376 0.307 0.487 0.532 0.501 0.788

— 0.004 0.017 0.020 0.023 0.085 0.030 0.021 0.065 0.054 0.080 0.114 0.079 0.010 0.174 0.116 0.208 0.139 0.166 0.118 0.280 0.167 0.209 0.266 0.274 0.110 0.169 0.213 0.185 0.252 0.296 0.285 0.320 0.392 0.441 0.537 0.504 0.758

0.050 0.000 0.030 0.060 0.030 0.070 0.010 0.030 0.030 0.030 0.050 0.050 0.050 0.040 0.050 0.030 0.030 0.000 0.070 0.070 0.160 0.080 0.100 0.080 0.110 0.080 0.000 0.060 0.140 0.070 0.180 0.100 0.130 0.150 0.150 0.390 0.210 0.610

— 0.005 0.048 0.062 0.060 0.073 0.020 0.060 0.060 0.050 0.091 0.090 0.090 0.110 0.063 0.035 0.060 0.030 0.097 0.130 0.146 0.133 0.095 0.080 0.125 0.130 0.125 0.110 0.227 0.193 0.270 0.267 0.300 0.290 0.280 0.323 0.330 0.465

— 0.050 0.063 0.073 0.092 0.064 0.080 0.084 0.058 0.088 0.100 0.109 0.108 0.128 0.094 0.072 0.075 0.087 0.122 0.163 0.134 0.163 0.144 0.118 0.138 0.160 0.234 0.130 0.306 0.263 0.296 0.258 0.327 0.350 0.366 0.255 0.413 0.540

0.129 0.005 0.013 0.022 0.011 0.039 0.005 0.011 0.013 0.016 0.023 0.025 0.029 0.017 0.057 0.016 0.047 0.010 0.146 0.034 0.130 0.068 0.044 0.035 0.125 0.119 0.023 0.076 0.112 0.168 0.086 0.141 0.139 0.197 0.250 0.475 0.345 0.703


P St Dev 0.175 0.005 0.016 0.033 0.017 0.027 0.004 0.017 0.015 0.012 0.023 0.024 0.019 0.020 0.009 0.013 0.054 0.010 0.159 0.035 0.046 0.033 0.049 0.040 0.080 0.127 0.020 0.081 0.024 0.207 0.083 0.091 0.116 0.046 0.223 0.076 0.141 0.100

AC50 (µM) 59.37 3402.35 1062.68 612.86 1370.70 359.79 3402.35 1370.70 1062.68 832.90 578.77 510.59 462.21 783.31 193.28 832.90 277.86 1524.71 46.85 411.00 58.32 162.48 308.58 400.76 63.57 69.87 578.77 140.08 77.22 38.28 112.07 48.80 49.58 26.98 14.59 1.50 5.20 0.15

OCHEM, LM & Unistra M1 St AC50 Avg Dev (µM) — — — 0.007 0.006 1986.75 0.024 0.021 544.68 0.034 0.024 411.00 0.029 0.029 462.21 0.027 0.040 482.70 0.032 0.016 431.49 0.045 0.021 298.34 0.073 0.043 148.48 0.051 0.005 236.89 0.059 0.028 187.68 0.052 0.034 226.65 0.059 0.035 187.68 0.100 0.070 89.82 0.053 0.012 216.41 0.085 0.043 114.87 0.070 0.038 156.88 0.090 0.073 100.87 0.092 0.004 98.23 0.118 0.105 70.92 0.135 0.034 53.07 0.133 0.016 55.17 0.142 0.054 48.41 0.124 0.060 64.62 0.182 0.084 32.83 0.196 0.069 27.37 0.183 0.076 32.44 0.200 0.079 25.81 0.184 0.039 32.05 0.223 0.049 18.76 0.228 0.057 17.98 0.287 0.019 9.47 0.344 0.040 5.28 0.292 0.015 9.10 0.349 0.119 4.96 0.394 0.120 3.49 0.377 0.108 4.05 0.647 0.165 0.28

Avg — 0.028 0.038 0.044 0.052 0.055 0.063 0.066 0.050 0.077 0.092 0.093 0.098 0.064 0.104 0.106 0.107 0.120 0.133 0.166 0.167 0.167 0.176 0.180 0.180 0.176 0.201 0.207 0.229 0.243 0.256 0.259 0.311 0.381 0.388 0.356 0.410 0.626

M2 St Dev — 0.023 0.023 0.027 0.036 0.037 0.028 0.039 0.020 0.020 0.011 0.031 0.017 0.059 0.065 0.030 0.089 0.028 0.029 0.049 0.101 0.004 0.032 0.077 0.082 0.075 0.033 0.074 0.067 0.025 0.069 0.026 0.021 0.027 0.046 0.156 0.095 0.116

AC50 (µM) — 472.46 370.04 308.58 226.65 198.88 176.48 168.08 247.13 137.28 98.23 97.18 91.92 173.68 85.62 83.52 82.47 68.82 55.17 39.06 38.67 38.67 35.16 33.61 33.61 35.16 25.42 23.09 17.83 15.67 13.67 13.21 7.70 3.92 3.69 4.73 2.97 0.35

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a

The mean of ER QSAR scores for all putative primary and secondary metabolites were calculated for individual models, and the results were averaged in order to obtain a prediction of estrogenicity for parent chemicals, primary and secondary metabolites combining the outputs from the three QSAR models. P = Parent Chemical; M1 = Primary Metabolites; M2 = Secondary Metabolites. M1 and M2 represent the averages of the ER QSAR scores for all predicted primary and secondary metabolites, respectively. The average (Avg) indicates the calculated mean values obtained from OCHEM, LM and Unistra models scores for P, M1 and M2 *No metabolites were predicted for nonylphenol ethoxylate


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Figure legends

Figure 1. Flow diagram demonstrating the overall approach for identifying chemicals with estrogenic metabolites. Figure 2. Chemical structures of molecules converted into metabolites with greater estrogenic activity than the parent chemical form. Figure 3. Chemical structures of molecules not expected to be converted into metabolites with greater estrogenic activity than the parent chemical form. Figure 4. Estrogenic metabolites are generally predicted to have higher ER QSAR scores than parent chemicals. ER QSAR scores of parent chemicals and estrogenic metabolites reported in the literature are indicated for OCHEM (A), LM (B) and Unistra (C) ER QSAR models, and the averaged scores from the three QSAR models is shown in (D). Chemicals are ordered alphabetically (black circles – parent chemicals, white circles – estrogenic metabolites). Figure 5. Averaged ER QSAR scores of parent chemicals and all predicted primary and secondary metabolites. The averaged ER QSAR scores of primary and secondary sets of metabolites are generally higher than the score of the parent chemical as indicated by OCHEM (A), LM (B) and Unistra (C) averaged model scores. Chemicals are ordered ascendingly based on the metabolite score (black circles – parent chemicals, gray circles – primary metabolites, white circles – secondary metabolites). No metabolites were generated for nonyphenol ethoxylate. Figure 6. Combined ER QSAR model scores for parent chemicals, primary and secondary metabolites indicating bioactivation of parent chemicals. The combined ER QSAR model scores indicate that primary and secondary sets of metabolites are generally predicted to have higher scores


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Page 40 of 47

than the 38 parent chemicals assessed. Chemicals are ordered ascendingly based on the metabolite score (black circles – parent chemicals, gray circles – averaged primary metabolites, white circles – averaged secondary metabolites). No metabolites were generated for nonyphenol ethoxylate. Figure 7. In silico approach with chemicals not expected to undergo bioactivation. The averaged ER QSAR scores of parent chemicals and all predicted primary and secondary metabolites are indicated for OCHEM (A), LM (B) and Unistra (C) ER QSAR models. The combined ER QSAR model scores are shown in panel D.


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Figure 1


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Figure 2


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Figure 3


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A

B

C

D

Page 44 of 47

Figure 4


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A

B

C

Figure 5


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Figure 6


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A

B

C

D

Figure 7


47

Prediction of Estrogenic Bioactivity of Environmental Chemical

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