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863 Section 4 High Throughput (HTP) In Vitro Screening

864 4.1 Chemical Library and Procurement Process

865 This section will provide an overview of the chemical selection and procurement process used for the 

866 high throughput assay process. The high throughput data for the SDWA and inert ingredient chemicals 

867 (as described in section 2) being identified for EDSP prioritization are taken from two chemical 

868 libraries, called ToxCast (data generated by the EPA, ORD, and a series of contract laboratories) and 

869 Tox21 (data generated by the National Institutes Health (NIH) Chemical Genomics Center (NCGC) as a 

870 part of the cross-agency Tox21 consortium that includes the EPA, NTP, NCGC and the U.S. Food and 

Drug Administration).27 871

872 The ToxCast program is running >600 assays, of which approximately 60 directly or indirectly probe 

873 endocrine-related pathways. For the endocrine-related assays, ToxCast is analyzing a total of 

874 approximately 1,800 unique chemicals of which 767 are in the current 4,305 EDSP21 universe chemical 

875 set that has passed all of the current prioritization filters (18%). The Tox21 program is also running a 

876 large number of endocrine-related assays on a library of approximately 8,200 unique chemicals, of 

877 which 2,093 are in the current 4,305 EDSP21 universe (49%). A subset of these chemicals will be useful 

878 for expanding the ES domain of applicability. Additionally, there are other chemicals with ToxCast or 

879 Tox21 data that are not exact matches with the EDSP21 universe of chemicals, but are close structural 

880 neighbors and can also be useful in training and testing extensions of the ES. 

881 Details of ordering, processing and plating chemicals, as well as, chemical information quality review 

882 and analytical quality control (QC) procedures can be found in Appendix D. Here we summarize the 

883 main steps in selecting, procuring, QCing and using these chemicals.

884 1. Nominations for chemicals to include in the ToxCast and Tox21 libraries were taken from a 

885 variety of internal and external stakeholder groups. These included EPA program offices, EPA 

886 research groups, partners at the NIH and FDA, NGOs, university research groups and industry. 

887 Approximately 20,000 compounds were nominated. 

888 2. Chemical identity was checked and curated by the EPA ORD DSStox group following their 

Standard Operating Procedures.28 889

890 3. EPA/ORD filtered this list of chemicals for ones that would likely not be amenable to testing in 

891 high throughput mode. The main filters were for volatility and insolubility. 

892 4. The remaining list of chemicals was submitted to the EPA's chemical contractor (Compound 

893 Focus, Inc. / CFI). CFI was able to procure approximately 4,400 of the initial nominations. 

894 Certificates of analysis for all procured chemicals were obtained, and stated purity information 

895 was included in the chemical records. 

896 5. CFI attempted to solubilize this library, and 3,726 were successfully solubilized and made 

897 available for testing. 

898 6. This entire library was included in the Tox21 testing library of 8,200 unique chemicals, and is 

899 being run in all Tox21 assays. The remaining Tox21 chemicals were independently procured by 

900 NTP and NCGC. Because of these three independent procurement processes, there are a large 

901 number of chemicals that have two or three independent replicates in the Tox21 assays, and the 

902 concordance results for these will be described in section 6. 

 

27 http://www.epa.gov/ncct/dsstox/sdf_tox21s.html 28 http://www.epa.gov/ncct/dsstox 
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903 7. A subset of the EPA library was selected to run in the ToxCast assay battery. These are 

904 organized into three phases, called ToxCast Phase I, ToxCast Phase II and E1K. Phase I and II 

905 chemicals (approximately 1,000) are run in all ToxCast assays (including the endocrine-related 

906 subset), while the E1K set (approximately 800) are run in just the endocrine-related subset of 

907 assays. 

908 8. For each laboratory that runs any of the ToxCast or Tox21 assays, CFI provides master plates, 

909 typically at 20 mM. Groups running the assays then perform serial dilutions. 

910 9. As part of the Tox21 process, all chemical samples in the large library are undergoing analytical 

911 QC, using samples in 384-well 20 mM master plates. Currently, the collection of this data is only 

912 partial. In addition to performing the analytical QC on freshly made plates, a set of plates is 

913 being allowed to sit for four months, and the QC process is being rerun. This will allow us to 

914 gauge chemical stability in solution.

915 4.2 High Throughput (HTP) In Vitro Data Processing Pipeline Overview

916 The high throughput data being described in this paper comprise a portion of the large ToxCast/Tox21 

917 inventory and assays. To maintain the advantage of high throughput, methods are needed to enable fast 

918 data processing. To manage such a large amount of data coming from different sources in different 

919 formats, the EPA has developed a comprehensive computational workflow. This workflow software 

920 system takes raw laboratory data, processes it through a single pipeline, and generates consistently 

921 formatted data sets that can be used for further analyses. To get an idea on the magnitude of the data sets 

922 being managed by the pipeline, for the estrogen receptor assays alone involves examination of data from 

923 five sources, and processing close to 5,000,000 individual data points. There are several levels of data 

924 processing that are described in more detail in Appendix E. This section will discuss the statistical and 

925 analytical approaches used during final data processing.

926 4.3 Statistical Analysis of Concentration-Response Data

927 Once the received data have been initially processed (i.e., reached Level 4 as described in the workflow 

928 in Appendix E), the curve fitting/hit-calling/filtering (Appendix E - i.e. Level 5-Level 8) portion of the 

workflow begins. All workflow software is implemented in the open R language29 929 and is available from 

930 the EPA/NCCT, as is all source data. 

931 General fit/hit Procedure

932 A four parameter Hill function is fit to the concentration-response data using the non-linear least square 

933 (NLS) package in R (release 2.14.2) and the port algorithm. For each chemical-assay pair, the 

934 concentration inducing a half-maximal response (AC50) is derived from the fitted model. For a given 

assay, a chemical is considered a hit if (1) it achieves an AC50, (2) the R2 935 of the model is at least rsq.

936 threshold, and (3) the Emax and T parameter are greater than the response threshold. For samples tested 

937 in technical replicate, the response values are pooled and fit with a single Hill slope to obtain one 

938 summary assay call for each substance. A summary of the automated curve-fitting and hit-calling 

939 procedures for each assay is provided in Table 4.3.1. 

 

29 http://cran.r-project.org/ 
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940 4.4 Outlier Detection

941 The data from each sample-assay pair are subjected to a series of outlier detection algorithms used to 

942 identify problem data points. The primary methodology used is a robust nonlinear regression outlier 

943 detection routine adopted from Motulsky and Brown (2006) which relies on robust non-linear least 

944 squares methods or nonlinear regression methods not unduly affected by outliers or other small 

945 departures from model assumptions. The outlier detection routine identifies any data points with obvious 

946 loss of signal at high concentrations due to cytotoxicity and other reasons, which simply relies on a gain 

947 of signal followed by a significant loss in signal. The remaining data points are then modeled using a 

948 four parameter robust Hill model and the residuals, or distance from the model line to the data points, 

949 are collected. The distribution of the residuals, total sample size, and false discovery rate are used to 

950 determine the optimal residual threshold based on the P values (Table 5.3.1) determined to be significant 

951 (Benjamin and Hochberg 1995). All points with absolute residuals greater than the optimal residual 

952 threshold are considered outliers. These points are flagged as a value of one. This method was used 

953 across all eight ER assays described in this paper. Additional outlier detection routines were run on the 

954 Tox21 (NCGC) ER BLA and BG1 LUC assays and included the pre-removal of sample technical 

955 replicates for chemical assay plates known to have cross-contamination. Additionally, a bad sample 

956 detection routine was developed to identify single technical replicates (1 out of 3 in the vast majority of 

957 cases) that departed significant from the response profile of the other two replicates.

958 4.5 Cell stress filter (Attagene-Specific)

959 Following the initial post-outlier/cytotoxicity fits, a meta-filter was applied to negate hit calls arising 

960 from non-specific, promiscuous activity which is specific to the Attagene detection technology (see 

961 section 5). This is a multiplexed reporter gene system that detects activity of 53 (CIS) and 25 (TRANS) 

962 endpoints concurrently and can display promiscuous activity across reporter genes associate with near963 cytotoxic levels of chemicals. Building on experience gained from ToxCast Phase I, a meta-filter was 

964 developed to augment confidence in results labeled as hits. This filter (See rule 4, section 5.2) negates 

965 assay-chemical hits with evidence of non-specific activity. The proposed filter has the following steps, 

966 which form a decision tree, described in the following steps. Figure 4.5.1 illustrates the approach. 

967 1. For each chemical, create a histogram of assay hits. For many chemicals, there can be a burst of 

968 activity at high concentration (typically in the range of 10-100 μM). Create a confidence function 

969 which is represented as a Hill curve with the 50% point being 0.25-log lower in concentration 

970 than the peak of the high-concentration activity burst and with a Hill-slope of 3. The top of the 

971 curve is 1 and the bottom depends on the total number of hits for the chemical. For n ≤ 5, bottom 

972 is 0 and for n > 10, the bottom is 0. The bottom is a linear function of n for n = [6...9]. This is 

973 called ConfHit(chemical, concentration). 

974 2. Create a second confidence function based on the cell stress assays (ATG_NRF2_ARE_CIS, 

975 ATG_MRE_CIS and ATG_CRE_CIS). If two or three of these are active, create a Hill function 

976 with midpoint = 0.25-log units lower in concentration than the highest concentration of the stress 

977 assays and a Hill-slope of 3. The top is set to 1 and the bottom to 0.5 if two cell-stress assays are 

978 hit, or to 0 if three cell-stress assays are hit. This function is called ConfStress(chemical, 

979 concentration). 

980 3. The overall confidence value for each assay is then the product ConfHit(chemical, AC50) x 

981 ConfStress(chemical, AC50).

982 4. A hit is accepted if the overall confidence value is > 0.5
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December 2012 52 | Page

log(AC50 – uM) 

Figure 4.5.1: Illustration of the Attagene-specific oxidative-stress promiscuity filter

983 The black line curve / histogram shows the number of hits (AC50 values) as a function of –

984 log(AC50-uM) (high concentration to the left, low to the right). The black triangles show the –log AC50

985 values for the cell stress assays. The red and blue dotted curves are the filter functions for the 

986 promiscuity and cell stress filters, and the green curve is the composite filter. The green data points 

987 show the AC50 values for the two Attagene Estrogen receptor assays (ATG ERα TRANS and ATG ERE 

988 CIS), which are significantly lower in concentration than where the filter (green curve) reaches 0.5, so 

989 are accepted as active.
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990 Section 5 Overview of the HTP ER Binding and Transactivation Assays 

991 5.1 Attributes of the eight HTP Assays for Identifying Estrogen Agonists 

992 Although there are a number of HTP assays relevant to the estrogen signaling pathway, many of these 

993 assays are still in the evaluation phase. The major focus of this section is on the use and interpretation of 

994 data from those HTP assays designed to detect ER binding and transactivation activity of chemicals that 

995 are relevant to expanding the ES. Thus, this analysis focuses on two types of assays (e.g., ER binding 

996 and gene transactivation/expression) that are mechanistically and functionally similar to the assays used 

997 to develop the ES and those currently used in the EPA's EDSP Tier 1 Screening Battery. However, it is 

998 important to emphasize that the analytical approaches presented in this document should serve as a 

999 template for evaluating other assays for ER and additional endocrine pathways at a later date. The 

1000 balance of this section will focus on eight HTP assays that were conducted as described in Appendix F 

1001 using either the ToxCast chemical library or the Tox21 chemical library. The eight ER assays presented 

1002 in this document include three cell-free binding assays (representing ERs for human, bovine, mouse) and 

1003 5 reporter gene transactivation assays that utilize a variety of cell lines (HepG2, BG1Luc4E2, HEK293, 

1004 CHOK1). While all of the assays are conducted with the assistance of robotics, the well capacity per 

1005 plate ranges from 96 (NVS ER binding assays), 384 (OT LUC), to 1,536 for the Tox21 BG1 LUC and 

1006 Tox21 BLA assays. Although the two Attagene assays (ATG ERα TRANS and ATG ERE CIS) are 

1007 conducted in duplicate 12-well plates, the multiplexed format of these assays allows a rapid assessment 

1008 of any impacts on an array of gene regulatory networks by assessing the activities of multiple 

1009 transcription factors in a single experiment. Tables 5.1.1 and 5.1.2 provide an overview of each of the 

1010 eight HTP assays, and more detailed descriptions of the assays are provided in Appendix F. 
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December 2012 54 | Page

1011 Table 5.1.1: Summary of parameters for the 5 ER reporter gene transactivation assays 

Assay

Tox21 BG1 LUC ER TA 

Agonist Tox21 BLA Agonist ATG ERa TRANS ATG ERE CIS

Odyssey Thera TA OTERα ERE LUC Agonist

Assay type reporter gene reporter gene reporter transcript reporter transcript reporter gene

Agonist or Antagonist 

Mode 

Agonist Agonist Agonist Agonist Agonist

Cell Source or Cell line BG1Luc4E2 HEK293T HepG2 HepG2 CHOK1

Cell or Tissue Origin human ovarian cancer human embryonic 

kidney

human hepatocellular 

carcinoma

human hepatocellular 

carcinoma

Chinese Hamster Ovary

Receptor (s) endogenous α & β rec hERα LBD GAL4-DBD GAL4 human ER-αLigand Binding Domain 

(transient transfection)

rec full length human 

ER-α (transient 

transfection)

rec full length human 

ERα plus any 

endogenous CHO ERα & 

ERß et al. in the cells

Endogenous Gene or 

Response element

ERE pGudLuc7.0 (4 EREs) 5X-UAS-ßLactamase + 

GAL4 hERα LBD = ßlactamase

Reporter Transcription 

Unit with Gal4 

promoter: 5X-UAS-TATA 

promoter (transient 

transfection)

Reporter Transcription 

Unit with 5X ERE 

synthetic response 

element (transient 

transfection)

5-ERE's MMTV promoter 

Luc

Endpoint luciferase fluorescence PCR / capillary 

electrophoresis

PCR / capillary 

electrophoresis

luciferase

Data as Rel Light Units blue: green ratio: 

green=background; 

converts to blue for 

activity

relative quantity of 

cDNA transcripts

relative quantity of 

cDNA transcripts

Rel Light Units

Expressed and graphed 

as 

normalized to DMSO 

and max E2, then 

converted to %E2 max

normalized to DMSO 

and max E2, then 

converted to %E2 max

normalized to DMSO 

and max E2, then 

converted to %E2 max

normalized to DMSO 

and max E2, then 

converted to %E2 max

normalized to DMSO 

and max E2, then 

converted to %E2 max

Carrier DMSO DMSO DMSO DMSO DMSO

Carrier Conc. 0.5% 0.5% 0.5% 0.5% 0.5%

Carrier Control yes yes yes yes yes

Reference Control E2 E2 E2 E2 E2

No. of Reference 

Concentrations 

16 pt curve (4.6E-6 to 

3.2E-15M) (-5.3 to -

14.5logM)

16 pt stnd curve 3.8E-6 

to 2.7E-13M (-5.4 to -

12.5logM)

1 pt @ 0.2 μM (-

6.7logM)

1 6 (1e-8 to 3.2 e-11M)
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Assay

Tox21 BG1 LUC ER TA 

Agonist Tox21 BLA Agonist ATG ERa TRANS ATG ERE CIS

Odyssey Thera TA OTERα ERE LUC Agonist

Cell Viability Cell Titer-Fluor. Run in 

parallel assay concurrent 

with ER antagonist 

format screen.

Cell Titer-Glo. Run in 

parallel assay concurrent 

with ER antagonist 

format screen

XTT, 200μM, 5-step 0.1 

dilution series, n=3; and 

visual inspection of cells. 

Also consider other 

stress response genes 

identified in multiplex 

assays

XTT, 200μM, 5-step 0.1 

dilution series, n=3; and 

visual inspection of cells. 

Also consider other 

stress response genes 

identified in multiplex 

assays

None

Test Chemical

Stock Concentration in 

DMSO

20 mM 20 mM 20 mM 20 mM 20 mM

Test Chemical Conc. 

Range (μM)

0.006 to 92 μM 0.006 to 92 μM 0.09 to 200 μM 0.09 to 200 μM 0.3 to 100 μM

Test Chemical Conc. 

Range (logM)

-8.2 to -4.0logM -8.2 to -4.0logM -7.0 to -3.7logM -7.0 to -3.7logM -6.5 to -4 logM

Test Chem Conc. 

Response curve. 

1 well /plate; 1 plate at 1 

conc.; 15 plates = 15pt 

conc response curve

1 well /plate; 1 plate at 1 

conc.; 15 plates = 15pt 

conc response curve

8 pt conc-resp curve 8 pt conc-resp curve 6-pt conc. resp. curve

Sample Replication 3 rep plates / 

concentration

3 rep plates / 

concentration

Single replicate Single replicate 3 

Test Replication 3 chemical source 

replicates

3 chemical source 

replicates

Single replicate Single replicate Single replicate

Solubility no solubility check no solubility check visual inspection of wells visual inspection of wells no solubility check

Analytical Analytical chemistry 

performed post testing

Analytical chemistry 

performed post testing

Analytical chemistry 

performed post testing

Analytical chemistry 

performed post testing

Analytical chemistry 

performed post testing

Plate Format 1536-well 1536-well 12-well 12-well 384-well

Cells per well 4000 5000 300,000 300,000 5500

Exposure Duration 24h 18h 24h 24h 24h

Media pH 7.0 7.0 7.2 - 7.4 7.2 - 7.4 7.2 - 7.4

Metabolic Activity unknown considered low for HEK293 cellsyes; this clonal line was 

selected for higher 

activity

yes; this clonal line was 

selected for higher 

activity

little or no metabolic 

activity for CHO cells
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1012 Table 5.1.2: Summary parameters for the 3 ER binding assays 

Protocol Novascreen: NVS_NR_hER Novascreen: NVS_NR_bER Novascreen: NVS_NR_mERa

Assay type competitive binding - [

3

H]E2 competitive binding - [

3

H]E2 competitive binding - [

3

H]E2

Receptor Source Human: MCF7 breast cancer 

cell line lysate

Bovine: uterine membranes Mouse: recombinant 

expression

Receptor Endogenous ERs Endogenous ERs affinity purified, 6-his-tag 

was cloned at the N-terminal 

mER-α Ligand Binding 

Domain (aa 297 to 585)

Ki (nM) 0.07 0.7 8

Bmax (fmol/mg) 3844 406 710000

total protein in assay well 

(mg/ml)

0.284 5.6 0.00028

Reference assay activity: E2 

IC50 (nM)

0.3 0.2 5 

Reference assay activity: 

R1881 IC50 (nM)

2200 -- --

Detection Method Separation of bound & free 

[

3

H]-E2

Separation of bound & free 

[

3

H]-E2

Separation of bound & free 

[

3

H]-E2

Assay format 96-well plate 96-well plate 96-well plate

[

3

H]-E2 (nM) 0.1 0.7 1.0

Carrier DMSO DMSO DMSO

Conc. 0.25% 0.25% 0.25%

Control yes yes yes

Vehicle Control reps 3/plate 3/plate 3/plate

Reference Control E2 E2 E2

Reference Conc. 0.1, 0.3, 1 nM 0.1, 0.3, 1 nM 0.1, 0.3, 1 nM

reps / plate 1/plate 1/plate 1/plate

replicate plates yes; n=2 yes; n=2 yes; n=2

Test chemical

Stock Conc. 20 mM 20 mM 20 mM

Test Chemical Range 

(General ranges, although 

some specific chemicals 

were tested in different 

ranges. These are noted on 

individual graphs)

Initial 25 μM single conc. in 

duplicate; then conc-resp for 

positives with 50 μM as 

upper limit

Initial 25 μM single conc. in 

duplicate; then conc-resp for 

positives with 50 μM as 

upper limit

Initial 25 μM single conc. in 

duplicate; then conc-resp for 

positives with 50 μM as 

upper limit

Test Chem Replication n=1 / concentration n=1 / concentration n=1 / concentration

Assay Buffer pH 7.4 7.4 7.4

Assay Temp 0-4°C 0-4°C 0-4°C

Incubation period 18h 18h 18h

Solubility Stock chem solubility at 

20mM in DMSO checked 

visually

Stock chem solubility at 

20mM in DMSO checked 

visually

Stock chem solubility at 

20mM in DMSO checked 

visually

Analytical ID, Purity, Conc of stock 

solutions w/in 1yr of prep

ID, Purity, Conc of stock 

solutions w/in 1yr of prep

ID, Purity, Conc of stock 

solutions w/in 1yr of prep

1013 With some exceptions to be noted below, all assays are conducted using a concentration-response 

1014 format. Figure 5.1.1 shows an example of a concentration-response plot with accompanying table 

1015 displaying analysis parameters specific for each chemical-assay pair. All subsequent concentration1016 response plots for the eight assays will follow a similar pattern with the exception that the curves for the 

ER binding assays will reflect a competitive inhibition of the radiolabeled ligand (3 1017 H-E2). The chemical 
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December 2012 57 | Page

1018 is identified by name with the CASRN in the plot and table shown in Figure 5.1.1. The table also 

1019 provides the sample_id which can be traced back to the vendor and lot/batch of chemical. The data for 

1020 test substances are shown as black points with confidence intervals (mean±1 SD) and the fitted Hill 

1021 curve. The AC50 and confidence intervals are indicated by a black bar and whiskers at 50% of maximal 

1022 response, respectively. Outliers detected by the robust outlier detection routine described in section 4 are 

1023 indicated by gray X's. The corresponding points and Hill-curve for the reference chemical are given in 

1024 red. Where cytotoxicity data are available, it is indicated by gold points and whiskers. The assay-wise 

1025 response threshold is shown as a thick dashed gray line, while half this value is given by a thin dashed 

1026 gray line. A response is only considered significant if the mean value at a concentration is greater than 

1027 the response threshold. The LEC (Lowest Effective Concentration) is the lowest tested concentration for 

1028 which the mean response value is greater than the response threshold. The LEC is indicated by a green 

1029 bar. The table provides the expected activity for reference chemicals; the four Hill curve parameters 

1030 (AC50 (i.e., converted 50% maximal concentration (M) of chemical from Hill model), top (T), bottom 

1031 (B), and Hill slope (W) and their confidence intervals; the relative AC50 (i.e., concentration where the 

1032 fitted curve for the chemical produces a response equal to 50% of the E2 maximal response); the 

1033 minimum and maximum tested concentrations for this chemical; the R-squared value and p-value for the 

1034 chemical-Hill-curve fit; the analytical QC grade for this sample and its purity from the Certificate of 

1035 Analysis (COA); and an overall Level 7 activity call with any optional notes (explained below). 

Figure 5.1.1 Example of concentration-response plot and accompanying table with analysis 

parameters depicting an increase in activity.

1036 5.2 Summary of Curve Fitting, Hit Calling and Assay QC Process for the ER assays 

1037 This section provides a detailed description of the data processing, curve fitting and hit calling process 

1038 used to analyze the results from each of the ER assays. Key steps pertaining to QC of the data as well as 

1039 the overall approach for the analyses and interpretation the data are summarized here and in Table 5.2.1. 

1040 1. Assay data are processed through the Level 3 of the pipeline as described in section 5 and 

1041 Appendix E. Key points for this step are as follows: 

1042 a. Chemical identity and sample information are assigned to each well on the assay plates.

1043 b. Chemical concentration values are assigned to each well.
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1044 c. Normalization of the response is carried out based on plate-wise positive and negative 

1045 control wells.

1046 2. Plates with data that do not meet the assay-specific criteria (see individual Assay Descriptions, 

1047 Appendix F) or the QC standards due to such occurrences as apparent contamination of wells or 

1048 missing control data are identified and deleted. 

1049 3. Assay data from each plate is then processed to Level 5 (see Appendix E). Key points are: 

1050 a. Replicates at each concentration tested are combined for curve fitting.

1051 b. Outlier masking is performed.

1052 4. Assay data are fit to Hill curves, yielding Level 6 data. Key points are:

1053 a. Chemicals meeting all of the assay-specific fitting criteria and that produce a concentration 

1054 response curve with data points that exceed the response threshold (e.g., level of 

1055 significance) are set to "Active" and all others to set to an "Inactive" classification. 

1056 b. For active samples, values of the Hill parameters including AC50, Bottom (B), Top (T) and 

1057 Hill slope (W) are derived, along with confidence intervals. 
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1058 Table 5.2.1: Assay-specific parameters for curve fitting and hit calling for the eight ER assays and 4 data sources. 

Assay Name Tox21 BLA Tox21 BG1 LUC ATG ERa TRANS ATG ERE CIS OT LUC NVS hER NVS bER NVS mERa

Raw Data 

Format

Concatenated 

Plate View Files

Concatenated 

Plate View Files

Preprocessed 

and Normalized 

Fold Induction 

Value 

Transposed by 

Assay Target; 

Control data 

delivered as 

separate file

Preprocessed 

and Normalized 

Fold Induction 

Value 

Transposed by 

Assay Target; 

Control data 

delivered as 

separate file

Processed Raw 

Values with 

plate and well 

information

Processed Raw 

Values with 

plate and well 

information

Processed 

Raw Values 

with plate and 

well 

information

Processed Raw 

Values with 

plate and well 

information

Plate Footprint 1536 1536 24 24 384 96 96 96

Concentration 

Response & 

General 

Technical 

Replication 

Format

15 point 

concentration 

response in 

triplicate; 1 

concentration 

per plate (15 

plate comprises 

a single 

technical 

replicate 

concentration 

response run)

15 point 

concentration 

response in 

triplicate; 1 

concentration 

per plate (15 

plate comprises 

a single 

technical 

replicate 

concentration 

response run)

8 point 

concentration 

response on a 

single plate (no 

technical 

replicates)

8 point 

concentration 

response on a 

single plate (no 

technical 

replicates)

6 point 

concentration 

response on a 

single plate in 

triplicate; 

technical 

replicates run 

on the same 

plate

Initial single 

screen in 

duplicate with 

follow-up 8 

point 

concentration 

response (no 

technical 

replicates)

Initial single 

screen in 

duplicate with 

follow-up 8 

point 

concentration 

response (no 

technical 

replicates)

Initial single 

screen in 

duplicate with 

follow-up 8 

point 

concentration 

response (no 

technical 

replicates)

Positive & 

Negative 

Controls

E2 & DMSO on 

each plate; Full 

16 point E2 

concentration 

response in 

duplicate on 

each plate; 

additional 36 

wells 

committed to 

higher 

concentration 

E2

E2 & DMSO on 

each plate; Full 

16 point E2 

concentration 

response in 

duplicate on 

each plate; 

additional 36 

well committed 

to higher 

concentration 

E2

E2 & other 

positive controls 

on separate 

plate (by run; 

single 

concentration); 

DMSO on each 

plate

E2 & other 

positive controls 

on separate 

plate (by run; 

single 

concentration); 

DMSO on each 

plate

E2 & DMSO on 

each plate; 3 

Concentration

s of E2 tested 

on each plate

E2 & DMSO on 

each plate; 3 

concentrations 

of E2 on each 

plate

E2 & DMSO 

on each plate; 

3 

concentration

s of E2 on 

each plate

E2 & DMSO on 

each plate; 2 

concentrations 

of E2 on each 

plate
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Assay Name Tox21 BLA Tox21 BG1 LUC ATG ERa TRANS ATG ERE CIS OT LUC NVS hER NVS bER NVS mERa

Normalization 

Method

Plate-wise; % 

Activity = (Test 

Chemical – 

NegControl) / 

(Pos ControlNegative 

Control) *100; 

Negative 

Control=Media

n plate raw 

value; 

PosControl=2n

d Bimodal Peak 

of all control 

wells (1st 4 

columns of 

plate) or 3 SD 

of all plate raw 

values from 

Median 

(whichever 

gives greatest 

dynamic range)

Plate-wise; % 

Activity = (Test 

Chemical - 

NegControl)/(P

osControlNegControl)*1

00; 

NegControl=M

edian plate raw 

value; 

PosControl=2n

d Bimodal Peak 

of all control 

wells (1st 4 

columns of 

plate) or 3 SD 

of all plate raw 

values from 

Median 

(whichever 

gives greatest 

dynamic range)

Run-wise; % 

Activity 

=log2(Test 

Chemical/NegCo

ntrol)/log2(PosC

ontrol/NegContr

ol)*100; 

NegControl=DM

SO fold change; 

PosControl=E2 

fold change

Run-wise; % 

Activity 

=log2(Test 

Chemical/NegCo

ntrol)/log2(PosC

ontrol/NegContr

ol)*100; 

NegControl=DM

SO fold change; 

PosControl=E2 

fold change

Plate-wise; % 

Activity = (Test 

Chemical - 

NegControl)/(

PosControlNegControl)*1

00; 

NegControl=M

edian of plate 

raw values; 

PosControl=M

edian of E2 

plate response 

at highest E2 

concentration

Plate-wise; % 

Activity = (Test 

Chemical - 

NegControl)/(

PosControlNegControl)*1

00; 

NegControl=Pl

ate DMSO 

Average raw 

value; 

PosControl=Pl

ate E2 Average 

Raw value at 

pre-selected 

concentration

Plate-wise; % 

Activity = 

(Test 

Chemical - 

NegControl)/(

PosControlNegControl)*

100; 

NegControl=Pl

ate DMSO 

Average raw 

value; 

PosControl=Pl

ate E2 

Average Raw 

value at preselected 

concentration

Plate-wise; % 

Activity = (Test 

Chemical - 

NegControl)/(P

osControlNegControl)*1

00; 

NegControl=Pla

te DMSO 

Average raw 

value; 

PosControl=Pla

te E2 Average 

Raw value at 

pre-selected 

concentration

Baseline 

Correction 

Performed

TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Robust Outlier 

Detection 

Performed 

(Mask Value)

TRUE; False 

Discovery Rate 

(FDR)=0.001 (1)

TRUE; 

FDR=0.001 (1)

TRUE; FDR=0.01 

(1)

TRUE; FDR=0.01 

(1)

TRUE; 

FDR=0.01 (1)

TRUE; 

FDR=0.01 (1)

TRUE; 

FDR=0.01 (1)

TRUE; 

FDR=0.01 (1)
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December 2012 61 | Page

Assay Name Tox21 BLA Tox21 BG1 LUC ATG ERa TRANS ATG ERE CIS OT LUC NVS hER NVS bER NVS mERa

Other Outlier 

Detection/ 

Masking (Mask 

Value)

Removal of 

contaminated 

plate (full 

concentration 

series 

removed) (3); 

Removal of 

inconsistent 

data for 

replicate 

source of a test 

chemical 'bad 

samples', based 

on discordance 

with other 2 

runs (full 

concentration 

series removed 

if 5 out of 15 

data points 

were more 

than 3SD away 

from median at 

each 

concentration 

(2)

Removal of 

contaminated 

plate (full 

concentration 

series 

removed) (3); 

Removal of 

inconsistent 

data for 

replicate 

source of a test 

chemical 'bad 

samples', based 

on discordance 

with other 2 

runs (full 

concentration 

series removed 

if 5 out of 15 

data points 

were more 

than 3SD away 

from median at 

each 

concentration) 

(2)

None None None None None None
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December 2012 62 | Page

Assay Name Tox21 BLA Tox21 BG1 LUC ATG ERa TRANS ATG ERE CIS OT LUC NVS hER NVS bER NVS mERa

Curve Fitting 

Parameters

Bottom Limits = 

-10% to 10% 

with a fixed 0% 

if responses 

started above 

the response 

threshold; Top 

Limits only 

limited by 

observed 

values; W (hill 

slope) limits = 1 

to 8; logAC50

limits = 3 fold 

below lowest 

concentration 

tested and 

highest 

concentration 

tested (no 

extrapolation)

Bottom Limits = 

-10% to 10% 

with a fixed 0% 

if responses 

started above 

the response 

threshold; Top 

Limits only 

limited by 

observed 

values; W (hill 

slope) limits = 1 

to 8; logAC50

limits = 3 fold 

below lowest 

concentration 

tested and 

highest 

concentration 

tested (no 

extrapolation)

Bottom Limits = -

10% to 10% with 

a fixed 0% if 

responses 

started above 

the response 

threshold; Top 

Limits only 

limited by 

observed values; 

W (hill slope) 

limits = 1 to 8; 

logAC50 limits = 3 

fold below 

lowest 

concentration 

tested and 

highest 

concentration 

tested (no 

extrapolation)

Bottom Limits = -

10% to 10% with 

a fixed 0% if 

responses 

started above 

the response 

threshold; Top 

Limits only 

limited by 

observed values; 

W (hill slope) 

limits = 1 to 8; 

logAC50 limits = 3 

fold below 

lowest 

concentration 

tested and 

highest 

concentration 

tested (no 

extrapolation)

Bottom Limits 

= -10% to 10% 

with a fixed 

0% if 

responses 

started above 

the response 

threshold; Top 

Limits only 

limited by 

observed 

values; W (hill 

slope) limits = 

1 to 8; logAC50

limits = 3 fold 

below lowest 

concentration 

tested and 

highest 

concentration 

tested (no 

extrapolation)

Bottom Limits 

= -10% to 10% 

with a fixed 

0% if 

responses 

started above 

the response 

threshold; Top 

Limits only 

limited by 

observed 

values; W (hill 

slope) limits = 

1 to 8; logAC50

limits = 3 fold 

below lowest 

concentration 

tested and 

highest 

concentration 

tested (no 

extrapolation)

Bottom Limits 

= -10% to 10% 

with a fixed 

0% if 

responses 

started above 

the response 

threshold; 

Top Limits 

only limited 

by observed 

values; W (hill 

slope) limits = 

1 to 8; 

logAC50 limits 

= 3 fold below 

lowest 

concentration 

tested and 

highest 

concentration 

tested (no 

extrapolation)

Bottom Limits = 

-10% to 10% 

with a fixed 0% 

if responses 

started above 

the response 

threshold; Top 

Limits only 

limited by 

observed 

values; W (hill 

slope) limits = 1 

to 8; logAC50

limits = 3 fold 

below lowest 

concentration 

tested and 

highest 

concentration 

tested (no 

extrapolation)

Secondary 

Filters (see 

section 6.2)

In flare region 

with 

submaximal 

response; 

failed sample 

QC; Evidence of 

cytotoxicity 

induced 

responses; 

Fluorescence 

activity in 

AutoFluor 

assays

In flare region 

with 

submaximal 

response; 

failed sample 

QC; Evidence of 

cytotoxicity 

induced 

responses; 

Fluorescence 

activity in 

AutoFluor 

assays

Failed sample 

QC; Non-specific 

oxidative stress 

induced 

response (based 

on distribution 

of hits across 

other 

transcription 

factors)

Failed sample 

QC; Non-specific 

oxidative stress 

induced 

response (based 

on distribution 

of hits across 

other 

transcription 

factors)

Failed sample 

QC

Failed sample 

QC

Failed sample 

QC

Failed sample 

QC

1059
Prioritization of the EDSP Universe of Chemicals

December 2012 63 | Page

1060 All of the steps from three to six are fully automated and are largely independent of each respective 

1061 assay. (Exceptions are noted above in Table 5.2.1). The next step, Level 7, implements a series of 

1062 automated filters that have been developed for specific assays, and are intended to address a series of 

1063 assay-specific technical issues that require the manual review of the chemical classification. These 

1064 computable filters initially examine one chemical at a time, and use the target assay concentration1065 response profile plus auxiliary information (e.g., cytotoxicity, autofluorescence, etc.) to flag potential 

1066 factors that may elicit an incorrect classification of the chemical as either an ACTIVE for ER agonist 

1067 activity or an INACTIVE. Table 5.3.1 lists the filters that have been developed and evaluated for their 

1068 intended use by the agency. Following are examples of the type of data that each filter is intended to flag 

1069 (Figures 5.2.2 – 5.2.9), and a listing of rules and three-step final hit call scheme (Figure 5.3.1) to guide 

1070 the consistent classification (ACTIVE or INACTIVE) across large groups of test chemicals.

Figure 5.2.2: Example of an active assay-sample pair.

The blue box along the baseline is the AC50 for one of the blue-channel autofluorescence assays 

described below. For this chemical, because the blue signal rises at concentrations significantly above 

the AC50, no change in the call is made.
Prioritization of the EDSP Universe of Chemicals

December 2012 64 | Page

Figure 5.2.3: Example of an inactive assay-sample pair

1071 Chemical Purity (Rules 1, 2): As described in section 4, all chemical samples are undergoing analytical 

1072 QC in addition to having to meet purity criteria when they were initially purchased. Rule 1 discards any 

1073 sample with analytical purity codes F, X, or W (see Appendix D for codes) which indicate that no 

1074 sample is present (nothing is in the well) or that the correct compound is not detected. These samples are 

1075 discarded for all assays. In Rule 2, all samples that were ACTIVE and have correct molecular weights, 

1076 but purity of 50-90% (B=75-90%; C=50-75% purity) are set aside. Data from these samples might be 

1077 used to support results from other, independently procured samples of the same chemical. Note that 

1078 samples in the B, C and D classes that are INACTIVE are not changed. The rationale is that if the target 

1079 chemical was active, even at 50% of the target concentration, it would show up active in the assay unless 

1080 its AC50 was very near the upper concentration tested.

1081 Automated Curve has active points but data do not fit to Hill curve (Rule 3): In a few cases, we see data 

1082 that shows one or more "active" points but the response profile is noisy or non-sigmoidal. These meet 

1083 the basic criteria for being called active, but are given a modifier of "<>" by the Level 6 curve fitting 

1084 routine. These ACTIVE curves are then classified as SETASIDE.
Prioritization of the EDSP Universe of Chemicals

December 2012 65 | Page

Figure 5.2.4: Example of a non-sigmoidal response that was called active at Level 6.

1085 Cell stress indications in Attagene assays (Rule 4): As described in section 5.2, the highly multiplexed 

1086 Attagene assays can show a burst of non-specific (non-receptor mediated) activity due to oxidative stress 

1087 near the cytotoxicity threshold. We have implemented a filter using all of the other non-ER readouts and 

1088 specific cell-stress markers to determine this threshold of cell stress. Any ACTIVE calls where the ER 

1089 AC50 is >0.75 of the cell stress AC50 is changed to INACTIVE. 

Figure 5.2.5: Example of an assay-sample pair set aside for Attagene oxidative stress filter.

1090 Cytotoxicity in Tox21 transactivation assays (Rules 5, 6): In any cell-based assay, cytotoxicity can lead 

1091 to apparent activity in the assay that is not receptor mediated. The Attagene assays are dealt with in Rule 

1092 4. The Tox21 BG1 LUC assay appears to be insensitive to cytotoxicity near or below the AC50, so 

1093 ACTIVE calls are not changed, but a note is added. The Tox21 BLA assay readout is actually a ratio of 
Prioritization of the EDSP Universe of Chemicals

December 2012 66 | Page

1094 two channels, and cytotoxicity can alter one of the channels (the denominator) so that the ratio 

1095 anomalously rises. Therefore, ACTIVE calls in Tox21 BLA below or near the cytotoxicity AC50 are 

1096 changed to SETASIDE. 

Figure 5.2.6: Example of an assay-sample pair set aside for cytotoxicity.

1097 Autofluorescence in Tox21 transactivation assays (Rules 7, 8): In assays with a fluorescent -based 

1098 readout, fluorescent compounds can give a false signal that is not receptor mediated. To account for this, 

1099 NCGC ran all compounds through a concentration-response assay using cells without the reporter gene, 

1100 and one with no cells at all. They then looked for signal in the red, green and blue channels. This results 

1101 in 6 autofluorescence assay readouts per chemicals from which AC50 values were derived. The Tox21 

1102 BG1 LUC assay should not be affected by autofluorescence, so ACTIVE calls are not changed if there is 

1103 possible autofluorescence, but a note is added. The Tox21 BLA assay can be affected, so ACTIVE calls 

1104 are changed to SETASIDE.
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December 2012 67 | Page

Figure 5.2.7: Example of an assay-sample pair set aside for possible autofluorescence.

1105 This chemical (9-cyanoanthracene) is a known fluorescent compound. 

1106 "Flare" in Tox21 assays (Rules 9, 10, 11): The Tox21 assays have the drawback that very strong 

1107 compounds (here meaning bright in the assay readout channel) can cause neighboring wells to appear to 

1108 be at least partially active – the strong chemical causes a bright "flare" that shows up in neighbor wells. 

1109 This is exacerbated by the use of 1,536 well plates and is most common in the Tox21 BG1 LUC assay. 

1110 Additionally, some of the test plates contain series of positive compounds that can lead to large truly 

1111 bright regions of a plate. Because this was a known problem, all assays are run in triplicate with the 

1112 arrangement of samples on the replicate plates slightly altered, so that a sample will have different 

1113 neighbors in the three runs. Despite this, some compounds (all three replicates) appear to be active 

1114 solely because they are next to bright compounds ("in the flare"). Figure 5.2.8 shows a plate with this 

1115 flare.
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December 2012 68 | Page

Figure 5.2.8: Plate from the Tox21 BG1 LUC assays showing a flare region. 

Wells that are positive are green and negative are red. Bright green wells show the strongest positive 

response. Wells with absolute response <10% E2 are colored white. As described in section 5 and in the 

Tox21 BG1 LUC assay description (Figure 5.2.6), plates are baseline corrected so that the plate median 

is zero. The detection signal associated with highly active chemicals in wells that may be clustered close 

together within an area of the plate may shadow neighboring wells. Flare regions (indicated by the gray 

background) have many continuous wells with statistically significant response above background. Note 

that for these assays, the concentration-response series is performed across plates and that this is an 

example shown from a plate testing the highest concentration (92 uM) for one set of 1,408 chemicals. 

Based on the flare detection algorithm that has been developed to detect areas of clustered strong 

signals, each sample-concentration pair can be labeled as being in a flare region or not.

1116 Based on this background, the rule for classifying flare-associated samples can be explained. If a 

1117 concentration-response curve starts high and decreases almost continuously, and the low concentration 

1118 points are in the flare, this chemical is classified as SETASIDE. If a standard Hill curve is present and 

1119 the Emax is large (>60% E2), it is assumed that the sample was a cause of the flare rather than the result, 

1120 so the call remains ACTIVE, but a note is added. If the Emax is <50, there is a strong chance that the 

1121 positive result is due to the flare, so the call is changed to SETASIDE. It is important to note that the 

1122 flare detection and handling algorithms are still under development. 
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December 2012 69 | Page

Figure 5.2.9: Example of an assay-sample pair set aside for flare. 

Points in a flare region are highlighted with a red star. Points at very high values (~100%) at high 

concentration are automatically masked by the robust outlier detection method, and appear to be caused 

in one of the three technical replicates by direct flare. The activity in the black (sample) curve is due to 

residual flare affecting the high concentration of one of the remaining two technical replicates.

Figure 5.2.10: Example of an assay-sample pair set aside for flare-related reverse slope. 

1123 There is only flare on the low-concentration plates, likely due to some contamination issue. 

1124 Low T curves (Rule 12): The standard hit calling routine sets a call to ACTIVE if the mean value at any 

1125 concentration (Emax) is greater than the response threshold. This has the potential to lead to a number of 
Prioritization of the EDSP Universe of Chemicals

December 2012 70 | Page

1126 false positive calls for samples where Emax is greater than the response threshold, but where the Hill1127 curve T is not. Consequently, a criterion has been added that T needs to be above the response threshold. 

Figure 5.2.11: Example of an assay-sample pair with T < response threshold.

1128 5.3 Summary of the 3-Step Process for Making Final Hit calls (Active or Inactive) for Each 

1129 Chemical

1130 This 3-step process of filtering data (Level 6 hit-calling, Level 7 chemical specific filtering and Level 7 

1131 chemical class filtering as described above) are illustrated in Figure 5.2.1. A final set of filters being 

1132 developed (but not discussed further in this document) combines data across assays. One example 

1133 involves a class of chemicals that can give discrepant activity across assays, while being a "true" result 

1134 in both cases. Chemicals which require metabolic activation will show activity in metabolically 

1135 competent assays but no activity in non-competent assays. One further level (Level 8) is a manual final 

1136 "hit-call" step that can override the automated calls in Levels 6 and 7. This is just now being 

1137 implemented and will not be discussed further. 
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December 2012 71 | Page

Figure 5.2.1: Illustration of the 3-step process of making initial hit calls and then using a variety 

of auxiliary information exclude or change some calls

1138 The following computable Level 7 rules produce a final call for the sample-assay pair that can be either 

1139 ACTIVE, INACTIVE, SETASIDE, or DISCARD, and can override the automated Level 6 ACTIVE or 

1140 INACTIVE calls. In addition to adding a final call, Level 7 can add optional notes, listed below. The 

1141 four classes of calls are defined as:

1142 • ACTIVE: All evidence supports a call that the chemical is active in a specific ER-dependent 

1143 in vitro assay.

1144 • INACTIVE : All evidence supports a call that the chemical is not active in a specific ER1145 dependent in vitro assay.

1146 • SETASIDE: The data indicates ER activity, but some significant question is left unanswered. 

1147 This might lead us to retest the sample in the current or other assays, or to compare the current 

1148 assay data with that of other assays. The exact rules will be case dependent.

1149 • DISCARD: indicates the sample purity is so low that no trust can be put in the data. 

1150 Table 5.3.1: Summary of Level 7 Rules Used to Create the Final Active/Inactive Call

Rule ID Assay Criteria Final Call Note

1 Any Analytical QC (F,X,W) DISCARD Unacceptable sample quality

2 Any ACTIVE and Analytical QC (B,C,D) SETASIDE Note: Low sample purity

3 Any ACTIVE and AC50 mod="<>" SETASIDE Bad Fit

4 Attagene ACTIVE and AC50>0.75 cell stress AC50 INACTIVE Attagene OS Filter

5 Tox21 BG1 LUC ACTIVE and AC50>cytotoxicity AC50/5 ACTIVE Cytotoxicity<AC50

ACTIVE

INACTIVE

ACTIVE

INACTIVE

SETASIDE

DISCARD

ACTIVE

INACTIVE

Levels 1...5

Level 6 calls

•One chemical at a time

•Use ER assay data only

Level 7 calls

•One chemical at a time

•Use auxiliary data (chemical 

purity, cytotoxicity, flare, 

autofluorescence, etc.)

Chemical 

Clusters SETASIDE

Level 7 calls

•Look across chemicals

•Use chemical class / 

category information
Prioritization of the EDSP Universe of Chemicals

December 2012 72 | Page

Rule ID Assay Criteria Final Call Note

6 Tox21 BLA ACTIVE and AC50>cytotoxicity AC50/5 SETASIDE Cytotoxicity<AC50

7 Tox21 BG1 LUC ACTIVE and AC50>autofluorescence 

AC50/5

ACTIVE Possible autofluorescence

8 Tox21 BLA ACTIVE and AC50>autofluorescence 

AC50/5

SETASIDE Possible autofluorescence

9 Tox21 BG1 LUC or BLA ACTIVE and flare is seen and T<60% E2 SETASIDE Activity may be due to flare

10 Tox21 BG1 LUC or BLA ACTIVE and flare is seen and T>=60% E2 ACTIVE Activity may be due to flare

11 Tox21 BG1 LUC ACTIVE and low concentration slope is 

negative due to flare

SETASIDE Reverse slope

12 Any ACTIVE and T<response threshold INACTIVE T < Response threshold

1151 5.4 Identification of potential technical issues that can lead to false positive or negative results 

1152 Table 5.3 summarizes several issues that were observed while reviewing all the empirical data from each 

1153 of the eight HTP ER assays. Many of these issues are associated with a particular technical component 

1154 (e.g., response signal, well capacity of plates, etc.), and while all are not necessarily exclusive to HTP 

1155 technology, due to the volume of data undergoing analysis it is essential to incorporate overview of 

1156 these technical issues within the classification procedure. This information is included here for 

1157 informational purposes, and is not necessarily comprehensive, for either issues or potential solutions. 
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1158 Table 5.4.1: Summary of potential technical issues that can impact the accuracy of classification of test chemicals and potential 

solutions.a 1159

Issuea Description Potential solutions Assays affected

Cytotoxic or 

cytostatic 

compounds 

Compounds that are cytotoxic or cytostatic yield high-quality 

response curves but are not always detected in assay-specific 

accompanying cytotoxicity assays. Example classes of 

compounds are anticancer drugs and organometallics (mercury, 

tin) 

Develop structural class filters that indicate cytotoxicity; 

Look for corresponding activity in other assays using the 

same cell and readout system but different targets; 

retest compounds in agonist mode with E2; For 2-

channel assays, implement robust informatic filter to 

classify compounds as cytotoxic but not receptor-active.

All

Surfactants Surfactant compounds appear to be active in some assays 

without accompanying cytotoxicity

Develop chemical class filters specifically for surfactant 

molecules. Use same solution as for cytotoxic 

compounds.

All

Chelators Chelators appear to be active in some assays, but they appear to 

be too large to be classic estrogen receptor actives

Develop chemical class filters specifically for chelator 

molecules. Use same solution as for cytotoxic 

compounds.

All

Flare In the 1,536-well NCGC assays, very potent compounds can 

cause a flare effect so that neighboring wells appear to be 

active. This effect is most prominent in the Tox21 BG1 LUC 

assay.

If assay is to be run again, perform better randomization 

of replicates; develop better spatial filter for detector. 

For current data set, improve flare detection algorithm.

Observed in 1,536 plates, 

Tox21 BLA and Tox21 BG1 

LUC TA Assays 

Contamination The NCGC assays suffered a one-time contamination of one 384-

well plate (out of 26 total in the Tox21 library). Although these 

samples could be removed from this analysis informatically 

analysis, they caused flare-contamination of other wells

Rebuild sample plates and rerun assays at a later date. Tox21 BLA and Tox21 BG1 

LUC TA Assays

Background noise 

level and partial 

agonists

There are classes of compounds that reproducibly yield partial 

agonist behavior, i.e. their Emax is significantly less than that of 

E2, sometimes as low as 20%. This is seen in even some of the 

standard positive reference compounds. This low level of 

efficacy is sometimes difficult to distinguish from background 

noise. 

Combine data across assays in a meta-analysis to look for 

low but consistent efficacy; Reduce activity threshold 

once other sources of false positives have been further 

reduced. 

Tox21 BG1 LUC 

ATG ERa TRANS

ATG ERE CIS

Discrepant curves 

within an assay for 

the same chemical 

Different samples (separately procured, different vendor, lot, 

batch) of a chemical can yield significantly different results. This 

is most pronounced across the Tox21 library simply because of 

the larger number of independently-procured samples

Once final analytical QC data is available, discard failing 

samples. Some remaining discrepancies may be due to 

flare, so improve the flare detection algorithm.

All, but most pronounced in 

the Tox21 assays since the 

design on those experiment 

included 3 separately

procured samples of each 

chemical.

Activity of weak 

compounds

No assays were run to the limit of solubility so activity of lowpotency chemicals is not detectedIf assays were to be rerun, use higher concentrations. 

However, it is not possible to run all compounds up to 

the limit of solubility in a high throughput mode. One 

alternative would be to combine assays in a metaanalysis to look for consistent but weak activity at higher 

concentrations. These few compounds could then be 

rerun at higher concentrations in selected assays. 

All
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Issuea Description Potential solutions Assays affected

Solubility Compounds are not appreciably soluble in DMSO Develop one or more assays that can use another 

solvent. This is technically feasible, but virtually all high 

throughput assays are optimized to use samples in 

DMSO.

All

Volatility Compounds that are volatile Methods have been developed to run in vitro assays for 

volatile compounds, but these are low throughput and 

still largely experimental.

All

Metabolic capacity Compounds require metabolic activation to be active or are 

metabolically deactivated in vivo. 

Run assays both with and without metabolic capacity. 

The current assay set contains both classes, although 

none of the assays have in vivo levels of metabolic 

activity. High throughput ER assays using +/- S9 fraction 

are being evaluated.

All

a

Issues are based upon observations made when reviewing extensive volumes of empirical data from each of the eight HTP ER assays; Most issues associated with a particular 

technical component (response signal, well capacity of plates, etc., or characteristic of chemical tested (e.g, autofluorescence)). 

1160
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1161 Section 6 Performance Evaluation of the HTP ER Binding and Transcriptional Activation Assays 

1162 6.1 Selection of Reference Chemicals

1163 Because these HTP ER Binding and Transcriptional Activation Assays have not previously been used in 

1164 a prioritization or regulatory decision making context, this SAP review is focused on assay performance 

1165 for the purposes of chemical prioritization. An important aspect of chemical prioritization for the EDSP 

1166 is to minimize false negatives to increase confidence that all potential active chemicals are being 

1167 detected. However, this objective does need to be balanced with respect to the number of false positives, 

1168 which when excessive would impact priority setting efficacy. Thus, the performance of each of the eight 

1169 assays has been evaluated using a set of reference chemicals (Table 6.1.1) and other performance 

1170 metrics that take into account several parameters that are unique to HTP technology (section 6.3). 

1171 A set of 37 reference chemicals is shown in Table 6.1.1. These chemicals, representing the chemical 

1172 classes and range of potencies commonly associated with ER agonist activity, have been classified as 

1173 "actives" or "inactives" based upon strong historical documentation in published literature as tabulated 

1174 in the Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) 

1175 Background Review Documents (BRDs) for ER Binding and TA test methods (ICCVAM 2002a,b, 

1176 2003, 2011), as well as more recent demonstrations of concordance among in vitro ER binding and 

1177 transactivation assays, and the in vivo uterotrophic assay (Kanno 2001, 2003a,b; Takeyoski 2006, 

1178 Freyberger 2010, Yamasaki 2004, Hartman 1983, Ohta 2012). The list is harmonized with reference 

1179 chemicals used during the validation of in vitro and in vivo assays that are now test guidelines for the 

1180 detection of ER agonist activity in the agency's Tier 1 Screening Battery (U.S. EPA, 2008). In addition, 

1181 the list is comprised of chemicals that are internationally recognized as ER agonists, and includes a 

1182 majority of the reference chemicals recommended in the OECD updated Test Guideline 455, 

1183 Performance-Based Test Guideline for Transfected Transactivation In vitro Assays to Detect Estrogen 

Agonists,

30 1184 and accompanying Performance Standards for Stably Transfected Transactivation In vitro

Assays to Detect Estrogen Agonists for TG 455. Series on Testing and Assessment, No. 173.31 1185 Overall, 

1186 these reference chemicals cover a range of chemical classes and potencies (e.g., strong actives typically 

exhibit an EC50 >1x10-11 M; moderate 1x10-9 to 1x10-8 M; weak 1x10-7 to 1x10-6 1187 M, and very weak > 

1x10-5 1188 M). The current reference list includes 11% strong, 11% moderate, 46% weak, and 9% very weak 

1189 actives, as well as 23% inactive chemicals.

 

30 http://iccvam.niehs.nih.gov/SuppDocs/FedDocs/OECD/OECD-TG455-2012-508.pdf 31 http://search.oecd.org/officialdocuments/displaydocumentpdf/?cote=env/jm/mono(2012)18&doclanguage=en 
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1190 Table 6.1.1: Estrogen Receptor (ER) Reference Chemicals for ER Agonist Activity (28 Actives, 9 Inactives) 

# CASRN Chemical Name

Expected 

Potencya Chemical Classd (MeSH) Product Classe

1 50-28-2 17beta-Estradiolb,c Strong Steroid Pharmaceutical, Veterinary Agent

2 56-53-1 Diethylstilbestrol (DES)b,c Strong Hydrocarbon (Cyclic) Pharmaceutical, Veterinary Agent

3 57-63-6 17alpha-Ethinyl estradiolb,c Strong Steroid Pharmaceutical, Veterinary Agent

4 84-16-2 meso-Hexestrolb,c Strong-Moderate Hydrocarbon (Cyclic) Pharmaceutical, Veterinary Agent

5 53-16-7 Estroneb,c Moderate Steroid Pharmaceutical, Veterinary Agent

6 57-91-0 17alpha-Estradiolb,c Moderate Steroid Pharmaceutical, Veterinary Agent

7 68-23-5 or 

68-22-4 

Norethynodrelb ,c or 19-Norethindrone Moderate Steroid Pharmaceutical

8 131-55-5 2,2',4,4'-Tetrahydroxybenzophenonec Moderate Benzophenone Cosmetics, Ultraviolet Absorbent

9 140-66-9 4-(1,1,3,3-Tetramethylbutyl)phenolb,c 

(4-tert-Octylphenol)

Weak Phenol Chemical Intermediate

10 84852-15-3 Nonylphenol (branched)c Weak Phenol Industrial Chemical

11 80-05-7 Bisphenol Ab,c Weak Phenol Chemical Intermediate

12 1478-61-1 Bisphenol AFc Weak Phenol

13 77-40-7 Bisphenol Bb,c Weak Phenol Chemical Intermediate

14 599-64-4 4-Cumylphenolb,c Weak Phenol Chemical Intermediate

15 143-50-0 Keponeb Weak Hydrocarbon (Halogenated) Pesticide

16 434-22-0 19-Nortestosteroneb Weak Steroid Pharmaceutical, Veterinary Agent

17 58-18-4 17alpha-Methyltestosteroneb Weak Steroid Pharmaceutical, Veterinary Agent

18 446-72-0 Genisteinb,c Weak Flavonoid, Heterocylic 

compound

Natural Product, Pharmaceutical

19 479-13-0 Coumestrolb,c Weak Flavonoid, Heterocyclic 

Compound

Natural Product

20 520-18-3 Kaempferolb,c Weak Flavonoid, Heterocylic 

compound

Natural Product

21 520-36-5 Apigeninb Weak Flavonoid, Heterocyclic 

Compound

Dye, Natural Product, Pharmaceutical 

Intermediate

22 486-66-8 Daidzeinb,c Weak Flavonoid, Heterocylic 

compound

Natural Product

23 789-02-6 o,p'-DDTc (1,1,1 trichloro2,2-Bis(p-chlorophenyl) ethane)Weak Organochlorine Pesticide

24 94-26-8 Butylparabenc (n-Butyl 4 Hydroxybenzoate) Weak Paraben

25 120-47-8 Ethylparabenb,c Very Weak Carboxylic Acid, Phenol, 

Paraben

Pharmaceutical, Preservative

26 85-68-7 Butylbenzyl phthalateb Very weak Carboxylic Acid, Ester, 

Phthalic Acid

Plasticizer, Industrial Chemical
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December 2012 77 | Page

# CASRN Chemical Name

Expected 

Potencya Chemical Classd (MeSH) Product Classe

27 72-43-5 Methoxychlorb,c Very Weak Hydrocarbon (Halogenated) Pesticide, Veterinary Agent

28 1912-24-9 Atrazineb Inactive Heterocyclic Compound Herbicide

29 330-55-2 Linuronb Inactive Phenylurea Herbicide

30 50-06-6 or

57-30-7 

Phenobarbitalb or PhenobarbitalSodium Inactive Heterocyclic Compound, 

Pyrimidine

Pharmaceutical

31 52-01-7 Spironolactoneb Inactive Lactone, Steroid Pharmaceutical

32 50-22-6 Corticosteroneb,c Inactive Steroid Pharmaceutical

33 57-83-0 Progesteronec Inactive Steroid Pharmaceutical

34 52-86-8 Haloperidolb Inactive Butyrophenone Pharmaceutical

35 65277-42-1 Ketoconazoleb Inactive Heterocyclic Compound Pharmaceutical

Abbreviations: CASRN= CAS Registry Number (American Chemical Society); MeSH=Medical Subject Headings (U.S. National Library of Medicine)

a Classification as 'actives' or 'inactives' based upon strong historical documentation as ER agonists or negatives (inactive) as tabulated in the ICCVAM Background 

Review Documents (BRDs) for ER Binding and TA test methods (ICCVAM 2002a,b, 2003, 2011), as well as concordance among in vitro ER binding and 

transactivation assays, and the in vivo uterotrophic assay (Kanno 2001, 2003a,b; Takeyoski 2006, Freyberger 2010, Yamasaki 2004, Hartman 1983, Ohta 2012). 

Chemicals cover a range of chemical classes and potencies (e.g., strong actives typically exhibit an EC50 >1x10-11 M; moderate 1x10-9 to 1x10-8 M; weak 1x10-7 to 

1x10-6 M, and very weak > 1x10-5 M. The current reference list includes 11% strong, 11% moderate, 46% weak, 9% very weak actives, and 23% inactive chemicals. 

b OECD(ENV/JM/TG)2012)18), Performance Standards for Stably Transfected Transactivation in vitro Assays to Detect Estrogen Agonists For TG 455. Series on 

Testing and Assessment, No. 173. Test Guidelines Programme, OCED, Paris, France.32 See Annex 2, Table 1, Comparison of results from STTA and BG1Luc ERTA 

Assays for 34 chemicals tested in both assays and classified as positive or negative ER agonists. 

c Denotes chemicals used to develop and validate the ER Expert System 

d Substances were assigned into one or more chemicals classes using the U.S. National Library of Medicine's Medical Subject Headings (MeSH), an internationally 

recognized standardized classification scheme.33

e Substances were assigned into one or more product classes using the U.S. National Library of Medicine's Hazardous Substances Database.34

1191 6.2 Results for Reference Chemicals Tested in the Eight HTP Assays 

1192 A summary of results for these reference chemicals when tested in each of the eight assays is shown in Table 6.2.1. 

 

32 http://search.oecd.org/officialdocuments/displaydocumentpdf/?cote=env/jm/mono(2012)18&doclanguage=en 33 http://www.nlm.nih.gov/mesh/ 34 http://toxnet.nlm.nih.gov/cgi-bin/sis/htmlgen?HSDB 
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1193 Table 6.2.1: Summary of results for the reference chemicals when tested in the Eight ER (agonists) HTP assays 

CASRN Name

Expected 

Potencya

Tox21 BG1 LUCTox21 BLAATG ERa TRANSATG ERE CISOT LUCNVS_NR_hERNVS_NR_bERNVS_NR_mERa

50-28-2 17beta-estradiol Strong A A A A A A A A 

56-53-1 Diethylstilbestrol (DES) Strong A A Ab Ab Ab Ab Ab Ab

57-63-6 17alpha-ethinyl estradiol Strong A A A A A A A A 

84-16-2 meso-Hexestrol Strong-Moderate A A A A A A A A 

53-16-7 Estrone Moderate A A A A A A A A 

57-91-0 17alpha-Estradiol Moderate A A A A A A A A

68-23-5 Norethynodrel Moderate A A 

68-22-4 Norethindrone Moderate A A A A A A A A 

131-55-5 2,2',4,4'-Tetrahydroxy-benzophenone Moderate A A A A A A A A 

140-66-9 4-tert-Octylphenol Weak A A A A A A A A 

84852-15-3 Nonylphenol (branched) Weak A A A A A A A A 

80-05-7 Bisphenol A Weak A A A A A A A A 

1478-61-1 Bisphenol AF Weak A A A A A A A A 

77-40-7 Bisphenol B Weak A I A A I

i A A A 

599-64-4 4-Cumylphenol Weak A A A A A A A A 

143-50-0 Kepone Weak A A A A I

i A I A 

434-22-0 19-Nortestosterone Weak A A 

58-18-4 17alpha-Methyltestosterone Weak A A A A I

i A I I 

446-72-0 Genistein Weak A A A A A A A 

479-13-0 Coumestrol Weak A Ac

520-18-3 Kaempferol Weak A A A A I I 

520-36-5 Apigenin Weak A A A A A I I 

486-66-8 Daidzein Weak A A A A A A A A

789-02-6 o,p'-DDT Weak A A A A I

j I I I 

94-26-8 Butylparaben Weak A A A A A A A A 

120-47-8 Ethylparaben Very Weak A Ad A A I

i A I I 

85-68-7 Butylbenzyl phthalate Very weak A A A I

i I I I 

72-43-5 Methoxychlor Very Weak Ae I A A I

i Ah I I 

1912-24-9 Atrazine Inactive I I I I I I I I 

330-55-2 Linuron Inactive I I I I I I I I 

50-06-6 Phenobarbital Inactive I I 

57-30-7 Phenobarbital Sodium Inactive I I I I I I I I 

52-01-7 Spironolactone Inactive I I I I I I I I 

50-22-6 Corticosterone Inactive I I Ak,g Ag I I I I 
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CASRN Name

Expected 

Potencya

Tox21 BG1 LUCTox21 BLAATG ERa TRANSATG ERE CISOT LUCNVS_NR_hERNVS_NR_bERNVS_NR_mERa

57-83-0 Progesterone Inactive I I Ag Ag I A A I 

52-86-8 Haloperidol Inactive I I I A I I I I 

65277-42-1 Ketoconazole Inactive I I I I I I I I 

A=Active; I=Inactive; Grey shaded areas indicates chemicals not tested in assay; Red shaded areas indicate test results that were inconsistent with the expected 

activity call for the reference chemical.

a Classification as 'actives' or 'inactives' based upon documentation as an ER agonists or negative (inactive) as tabulated in the ICCVAM Background Review 

Documents (BRDs) for ER Binding and TA test methods (ICCVAM 2002a,b, 2003, 2011) as well as information obtained from publications published and reviewed 

after the completion of the ICCVAM BRDs (Kanno 2001, 2003a,b; Takeyoski 2006, Freyberger 2010, Yamasaki 2004, Hartman 1983, Ohta 2012).

b Clear activity but chemical purity in Class B (70-90%).

c Clear dose-response activity but blue-channel autofluorescence assay is active below AC50. 

d Two samples tested. Classification as 'active' based upon plot with highest concentration tested for this chemical.

e Clear activity but flare is observed and T<flare threshold.

f Some activity but data are noisy.

g Assay is designed to assess the activities of multiple transcription factors in a single experiment (including ER and Glucocorticoid receptor), and results may 

indicate possible cross-talk between two pathways when testing steroids at higher concentrations.

h Triplicate sample from current experiment shows clear activity. Single sample from legacy data shows no activity. 

i Maximum dose tested: 1e-4

M 

j Maximum concentration tested: 9.5e-5

 M 

k Maximum concentration tested: 2e-4

 M 
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December 2012 80 | Page

1194 Summary of the performance (accuracy) of the eight assays when testing the set of Reference 

1195 Chemicals for ER Agonists: The following tables quantify the agreement between the expected 

1196 activity of the 37 reference chemicals (Table 6.2.1) and what was observed in each of the eight assays 

1197 for ER agonist activity (Table 6.2.2). 

1198 Table 6.2.2: Results for each assay when testing reference chemicals

Assay: Tox21 BG1 LUC

Chemicals evaluated: 28 actives, 9 inactives

Assay Activity

Active Inactive Total

Expected 

Activity

Active 28 0 28

Inactive 0 9 9 

Total 28 9 37

Assay: Tox21 BLA

Chemicals evaluated: 27 actives, 9 inactives

Assay Activity

Active Inactive Total

Expected 

Activity

Active 25 2 27

Inactive 0 9 9 

Total 25 11 36

Assay: ATG ERα TRANS

Chemicals evaluated: 23 actives, 8 inactives

Assay Activity

Active Inactive Total

Expected 

Activity

Active 23 0 23

Inactive 2 6 8 

Total 25 6 31

Assay: ATG ERE CIS

Chemicals evaluated: 24 active, 8 inactives

Assay Activity

Active Inactive Total

Expected 

Activity

Active 24 0 24

Inactive 3 5 8 

Total 27 5 32

Assay: OT LUC

Chemicals evaluated: 25 active, 8 inactives

Assay Activity

Active Inactive Total

Expected 

Activity

Active 18 7 25

Inactive 0 8 8 

Total 18 15 31

Assay: NVS_NR_hER

Chemicals evaluated: 25 active, 8 inactives

Assay Activity

Active Inactive Total

Expected 

Activity

Active 23 2 25

Inactive 1 7 8 

Total 24 9 33

Assay: NVS_NR_bER

Chemicals evaluated: 25 active, 8 inactives

Assay Activity

Active Inactive Total

Expected 

Activity

Active 17 8 25

Inactive 1 7 8 

Total 18 15 33

Assay: NVS_NR_mERa 

Chemicals evaluated: 24 active, 8 inactives

Assay Activity

Active Inactive Total

Expected 

Activity

Active 17 7 24

Inactive 0 8 8 

Total 17 15 32

1199 Table 6.2.3 provides the summary statistics for each of the assays using standard metrics of assay 

1200 performance to include the overall accuracy (i.e., concordance, the proportion of all reference chemicals 

1201 that were correctly classified), sensitivity (i.e., the proportion of all active (positive) reference chemicals 

1202 that were correctly classified), and specificity (i.e., the proportion of all inactive (negative) reference 

1203 chemicals correctly classified). The two Tox21 (BG1Luc and BLA) and the two Attagene TA assays 

1204 displayed a high level of overall accuracy, correctly predicting 91 to 100% of the active and inactive 

1205 reference chemicals. The Odyssey Thera TA assay was less sensitive (overall accuracy, 79%), and 

1206 curves tended to be right-shifted as compared with other TA assays. Of the three ER binding assays, the 

1207 NVS_NR_hER performed the best with an overall accuracy of 91%, while the bovine and mouse ER 
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December 2012 81 | Page

1208 binding assays showed a lower overall accuracy (73-78%) and sensitivity (68-71%), in particular 

1209 missing the weak to very weak reference chemicals.

1210 Table 6.2.3: Summary statistics for the predictive capacity of each assay when testing the 

1211 reference chemicals (ER agonist activity)

Tox21 BG1 LUCTox21 BLAATG ERa TRANSATG ERE CISOT LUCNVS_NR_hER NVS_NR_bER NVS_NR_mERa

Reference Chemicals Tested (Actives/Inactives) 28/9 27/9 23/8 24/8 25/8 24/8 25/8 25/8

Concordance1,4 1 0.94 0.94 0.91 0.79 0.91 0.73 0.78

Sensitivity2 1 0.93 1 1 0.72 0.92 0.68 0.71

Specificity3 1 1 0.75 0.63 1 0.88 0.88 1 1 Overall accuracy (e.g., the proportion of correct outcomes) between the test method results and the expected results for 

reference chemicals.

2 The proportion of all active (positive) chemicals that were correctly classified by the assay.

3 The proportion of all inactive (negative) chemicals that were correctly classified by the assay. 4

 Template for Accuracy Analysis:

Assay Activity

Active Inactive Total

Expected 

Activity

Active a c a + c

Inactive b d b + d

Total a + b c + d a + b + c + d 

a = positive in both new assay and by reference test classification

b = positive in new assay and negative by reference test classification

c = negative in new assay and positive by reference test classification

d = negative in both new assay and by reference test classification

Concordance = ([a+d]/[a+b+c+d])

Sensitivity = (a/[a+c])

Specificity = (d/[b+d])

1212 To evaluate the intra-laboratory reproducibility of the assays, standard deviations (SDs) were calculated 

1213 from the assay response (normalized to 100% E2 response) for groups of replicates at each 

1214 concentration tested for a given chemical. The mean of those SDs across the full concentration response 

1215 for a particular chemical is reported for each assay (Table 6.2.4). Overall the mean SDs ranged from 1.2 

1216 to 23.7% relative to the maximal response of 100%, with the highest SDs observed among replicates in 

1217 the ATG ERα Trans TA assay. 
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December 2012 82 | Page

Table 6.2.4: Mean Standard Deviationsa 1218 across the technical replicates for the reference chemicals in the eight ER HTP assays. 

CASRN Name

Expected 

Potencyb

Tox21 BG1 LUCTox21 BLAATG ERa TRANSATG ERE CISOT LUCNVS_NR_hERNVS_NR_bERNVS_NR_mERa

50-28-2 17beta-estradiol Strong 8.1a 4.4 7.8 6.0 9.6 1.2 2.7 -

c

56-53-1 Diethylstilbestrol (DES) Strong 10.1 7.9 19.3 10.0 11.7 - 2.5 1.2

57-63-6 17alpha-ethinyl estradiol Strong 8.5 4.9 23.7 3.8 12.1 - 2.1 1.6

84-16-2 meso-Hexestrol Strong-Moderate 8.4 4.6 8.2 6.4 12.9 1.1 1.9 0.7

53-16-7 Estrone Moderate 9.9 4.5 16.4 5.6 12.7 1.2 3.8 4.6

57-91-0 17alpha-Estradiol Moderate 9.0 5.8 8.5 6.7 12.8 0.8 2.8 0.8

68-23-5 Norethynodrel Moderate 13.9 3.5 - - - - - - 

68-22-4 Norethindrone Moderate 7.8 2.8 11.6 6.4 7.1 16.2 6.2 8.4

131-55-5 2,2',4,4'-Tetrahydroxy-benzophenone Moderate 9.3 2.4 9.1 5.5 9.8 1.7 7.0 11.6

140-66-9 4-tert-Octylphenol Weak 6.5 3.1 8.8 3.3 5.2 5.2 7.2 6.9

84852-15-3 Nonylphenol (branched) Weak 5.4 3.2 7.9 3.8 4.0 11.8 7.7 12.1

80-05-7 Bisphenol A Weak 6.2 2.4 4.8 2.1 4.0 8.8 6.8 6.5

1478-61-1 Bisphenol AF Weak 9.5 5.2 7.5 6.7 4.0 17.9 6.8 5.8

77-40-7 Bisphenol B Weak 9.8 3.7 14 9.7 1.8 8.8 12.4 3.2

599-64-4 4-Cumylphenol Weak 5.9 3.2 13.3 10.6 3.0 10.1 8.3 10.7

143-50-0 Kepone Weak 5.9 2.8 7.3 5.0 1.6 7.3 8.5 13.7

434-22-0 19-Nortestosterone Weak 8.0 3.6 - - - - - - 

58-18-4 17alpha-Methyltestosterone Weak 6.6 2.6 7.6 7.2 1.7 9.7 6.8 6.0

446-72-0 Genistein Weak 10.1 2.6 13.7 1.9 6.2 4.8 14 17.5

479-13-0 Coumestrol Weak 8.1 3.8 - - - - - - 

520-18-3 Kaempferol Weak 6.8 1.6 - - 2.9 - - - 

520-36-5 Apigenin Weak 5.4 4.7 - - 1.5 - - - 

486-66-8 Daidzein Weak 13 3.6 18.6 7.3 7.8 15.6 8.8 6.6

789-02-6 o,p'-DDT Weak 8.4 4.8 14.1 4.9 1.7 6.6 8.0 9.2

94-26-8 Butylparaben Weak 6.7 2.0 14.7 3.7 3.7 6.0 4.6 11.1

120-47-8 Ethylparaben Very Weak 4.1 1.8 14.0 6.7 2.4 8.5 6.1 10.3

85-68-7 Butylbenzyl phthalate Very weak 5.7 1.9 3.0 5.4

72-43-5 Methoxychlor Very Weak 6.1 2.1 3.3 6.1 1.2 6.3 5.7 18.1

1912-24-9 Atrazine Inactive 3.3 2.3 11 1.9 1.3 4.6 5.5 8.2

330-55-2 Linuron Inactive 5.3 2.1 11.3 2.4 1.5 5.2 4.0 11.2

50-06-6 Phenobarbital Inactive 3.8 1.2 - - - - - - 

57-30-7 Phenobarbital Sodium Inactive 4.2 1.6 - - 1.5 - - - 

52-01-7 Spironolactone Inactive 5.3 1.5 5.0 5.0 2.2 7.1 7.5 19.6

50-22-6 Corticosterone Inactive 3.6 1.8 6.2 8.0 1.3 5.6 7.5 8.8
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December 2012 83 | Page

CASRN Name

Expected 

Potencyb

Tox21 BG1 LUCTox21 BLAATG ERa TRANSATG ERE CISOT LUCNVS_NR_hERNVS_NR_bERNVS_NR_mERa

57-83-0 Progesterone Inactive 4.4 2.1 12 5.9 2.2 7.6 11.8 18.4

520-86-8 Haloperidol Inactive 4.4 1.1 - - 1.6 - - - 

65277-42-1 Ketoconazole Inactive 3.6 1.3 - - 2.8 - - - 

a Standard deviations were calculated from the assay response (normalized to 100%) for groups of replicates at each concentration tested for a given chemical. The 

mean of those SDs across the concentration dose response for a chemical is reported for each assay.

b Classification as 'actives' or 'inactives' based upon documentation as an ER agonists or negative (inactive) as tabulated in the ICCVAM Background Review 

Documents (BRDs) for ER Binding and TA test methods (ICCVAM 2002a,b, 2003, 2011) as well as information obtained from publications published and reviewed 

after the completion of the ICCVAM BRDs (Kanno 2001, 2003a,b; Takeyoski 2006, Freyberger 2010, Yamasaki 2004, Hartman 1983, Ohta 2012).

c - No technical replicate for chemical.
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December 2012 84 | Page

1219 6.3 Additional assay performance metrics for the HTP ER assays 

1220 In traditional assay validation, a number of standard performance metrics are used. These typically come 

1221 from a formal validation process that includes establishing quantitative values for the reproducibility and 

1222 accuracy of an assay both within and across laboratories. Using the traditional validation process as a 

1223 guide, the performance of the eight HTP assays described within this document were evaluated and 

1224 include both traditional performance metrics (e.g., accuracy as quantified using a set of reference 

1225 chemicals, and intra-laboratory replication), as well as new metrics that are being developed exclusively 

1226 for HTP technology. As part of the evaluation of assay performance, a number of QC metrics are being 

1227 carefully monitored as described in Table 6.3.1, and may ultimately be used as performance standards 

1228 for various assays or groups of assays. In addition, an advantage of HTP technology is that by testing 

1229 large numbers of compounds in a variety of assays, additional performance information can be provided 

1230 that is not typically available in traditional validation processes. For example, assessments of the intra1231 laboratory replication of independently procured samples of the same chemical, and the plate-to-plate 

1232 replication using large sets of standard test chemicals are provided for some of the eight assays. Finally, 

1233 to facilitate sharing of HTP data among collaborators and the research community at large, the 

1234 transferability of the assay data in a unified format as well as full documentation of any data analysis are 

1235 necessary. Thus, assessment of the process for transferring HTP data is an essential metric that is 

1236 currently being developed for the ER assays. 

1237 Table 6.3.1: List of additional assay performance metrics. 

Potential QC Metrics Available Where Described or Note of Not

Plate-wise Z' factor Yes Section 6.6

Reagent stability issues Underway Section 4 and Appendix F

Signal interference issues Yes Section 5.2

Acceptance criteria (signal window, variability, 

plate spatial uniformity, replication, and inter-plate 

variance)

Partial per 

assay

Acceptance criteria are defined under each assay 

description in Appendix F.

Plate uniformity with regard to signal and location 

of chemical placement on plate (i.e., spatial 

uniformity)

Partial per 

assay

Section 6.3

Consistency of assay performance over time No Assays are typically conducted during a single, 

relatively short, time period. However, evaluations 

of positive controls and sets of QC chemicals could 

be routinely tested and metrics developed over 

longer periods of time. 

Historical positive control data Available for 

E2, NVS_ER 

Binding assays

Appendix F

Metrics for Predictive Capacity, Reproducibility, 

and Transferability of Data

Reference Chemicals Yes Section 6.2

Intra-laboratory replication for technical replicates 

for all chemicals tested

Yes Section 6.3

Intra-laboratory replication for independently 

procured samples of same chemical

Yes Section 6.3
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December 2012 85 | Page

Potential QC Metrics Available Where Described or Note of Not

Transfer of data and consistency in classification of 

chemicals when multiple groups evaluate data 

using same or similar data analysis procedure and 

classification criteria.

Pilot Study 

Ongoing

Data from the Tox21 BG1 LUC and Tox21 BLA TA 

assays are currently being independently 

evaluated and analyzed by Federal Agencies 

within the Tox21 consortium. Results from this 

pilot study are expected to be published mid2013. 

1238 6.3.1 Replicate Analysis 1: Concentration-wise response for technical replicates.

1239 For each of the eight HTP assays, the assay response (normalized to 100% Emax) for groups of 

1240 technical replicates for each sample (e.g., same chemical vendor, lot and batch) at each concentration 

1241 evaluated have been compared. This analysis included the complete set of chemicals tested. The assay 

1242 response for pairs of technical replicates, concentration-by-concentration, were plotted and a R2 value 

1243 was calculated across all samples for the assay. Additionally, the distribution of the differences between 

1244 samples was calculated and the standard deviation was calculated. An example plot is shown in Figure 

1245 6.3.1. Table 6.3.1 gives the R2 and standard deviation values for the eight assays. 

1246 Table 6.3.1: Statistical parameters for the technical replicates concentration response correlation.

Assay R2 SD

Tox21 BLA 0.83 0.07

Tox21 BG1 LUC 0.70 0.11

ATG ERa TRANS 0.77 0.19

ATG ERE CIS 0.84 0.15

OT LUC 0.82 0.14

NVS_NR_hER 0.81 0.16

NVS_NR_bER 0.88 0.14

NVS_NR_mERa 0.65 0.25

1247
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Figure 6.3.1: Example of a plot showing reproducibility of response value for an assay

1248 This example is from the ATG ERE CIS assay. Each point corresponds to a single sample and 

1249 concentration. The x and y values are the response values for two technical replicates for the same 

1250 sample, in units of %E2 response. The R2 value is derived from using a linear model fitting y=x. The 

1251 standard deviation is calculated from the vector of differences between the responses from the pairs of 

1252 technical replicates.

1253 6.3.2 Replicate Analysis 2: Plate-to-plate replication using large sets of standard technical 

1254 replicates

1255 As part of the QC process for all of the NCGC Tox21 assays, a set of 88 test chemicals (included six 

1256 known ER agonists) was selected to be tested on all master 1,408-sample plates. This resulted in a 

1257 maximum of 39 technical replicates which allows examination of plate-to-plate variation among the 

1258 array of NCGC Tox 21 assays. NCGC runs assays for many targets and this set of 88 chemicals was 

1259 selected because they are known to be active against many of the targets (not just ER) associated with 

1260 those assays. An analysis is ongoing to evaluate the entire group of 88 test chemicals. However, a 

1261 preliminary evaluation of the subset of chemicals with ER agonist activity, has demonstrated that the 

1262 plate to plate variation for the 39 technical replicates is quite low. Figure 6.3.2 shows examples of 

1263 chemicals that are active or inactive for the two Tox21 ER TA assays.
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December 2012 88 | Page

Figure 6.3.2 Examples of the replication of set of 88 test chemicals on every plate used for the two 

(BLA and Luc BG1) Tox21 TA assays. 

1264 6.3.3 Replicate Analysis 3: Hit-calling and AC50 replication for multiple, independently 

1265 procured, chemicals

1266 For selected assays, multiple, independently procured, samples of the same chemical were tested. Each 

1267 of these generated a set of concentration-response values, a curve fit, and a hit call. AC50 values and the 

1268 hit calls were compared for each matched pair of samples for the same chemical. Table 6.3.3 compares 

1269 the number of samples that agree (active or inactive) or disagree for each of the assays. Samples that 

1270 were placed in the SETASIDE or DISCARD bins are not included. Figure 6.3.3 compares the AC50

1271 values, and annotates whether the samples were reference chemicals. As can be seen from the figures, 

1272 most of the replicate pairs that did not give the same hit call (one active, one inactive) had AC50 values 

1273 close to the upper concentration tested. It is expected that some of this sample-to-sample discrepancy 

1274 will be explained by samples that fail the analytical QC tests, which are only about one third complete at 

1275 this time.

1276 Table 6.3.3: Comparison of results from independently procured samples after Level 6 hit calling 

1277 and Level 7 filtering

Assay Both Active Sample 1 Active Sample 2 Active Neither Active Concordance

Tox21 BLA 86 30 57 1949 0.96

Tox21 BG1 LUC 104 50 69 1823 0.94

ATG ERa TRANS 7 2 1 24 0.91

ATG ERE CIS 3 3 1 27 0.88

NVS_NR_hER 3 2 0 19 0.92

NVS_NR_bER 4 0 0 17 1 

NVS_NR_mERa 2 0 0 3 1 

1278
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Figure 6.3.3: Plot showing the correlation between the AC50s and hit calls between independent 

samples of the same chemical

1279 Plotted are the log(AC50) values for the two samples (x and y). For samples that were inactive, the AC50

1280 was arbitrarily set to 0.01 M, and a small random increment was added to keep points from lying on top 

1281 of one another. For reference chemicals, we indicate the activity class by colors. The values are the 

1282 number of chemicals for which the replicates are (both active), (replicate 1 active, replicate 2 inactive), 
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1283 (replicate 2 active, replicate 1 inactive), (neither active). AC50 values with an AC50 modifier of ">=" 

1284 were set to 200 μM for graphical purposes. 

1285 6.3.4 Replicate Analysis 4: Comparing pairs of assays 

1286 For both the Attagene (TRANS and CIS) and NCGC/Tox21 (BLA and BG1 LUC) assay pairs, AC50

1287 calls were compared between assays. Each plot below shows the correlation between the AC50 values 

1288 for each pair of assays. This figure (Figure 6.3.4) show discrepant calls and the linearity of the potency 

1289 scale between assays. 

1290 For the Attagene assays, the TRANS assay seems to produce more positive calls not replicated in the 

1291 CIS than vice versa (comparison of clusters of points both to the left and below of the large cluster of 

1292 negative chemical sample points in both assays in the upper right hand corner of Figure 6.3.4a). There 

1293 are only seven chemical samples where the TRANS assay shows activity below 1 μM and the CIS assay 

1294 shows no activity. The quantitative relationship between AC50 values where the curve is fully resolved 

1295 is very linear and close to 1:1. 

1296 For the NCGC/Tox21 assays, the BG1 LUC assay shows a significant number of chemical samples 

1297 being positive when the BLA assay shows no activity. There are several dozen chemicals (only 0.4% of 

1298 total) where the BG1 LUC AC50 is less than 1 μM and the BLA assay is inactive (Figure 6.3.4b). There 

1299 are only 9 chemical samples where the BLA AC50 is less than 1 μM and the BG1 LUC assay is inactive. 

1300 AC50 values for the same chemical sample are approximately 10-fold lower in the Tox21 BG1 LUC 

1301 assay than in the Tox21 BLA assay. This increased sensitivity in the Tox21 BG1 LUC assay may 

1302 explain some of the extra "Active" sample calls over the Tox21 BLA results.
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Figure 6.3.4 Plot showing the sample by sample correspondence between the AC50 hit calls for the 

two a) Attagene (CIS and TRANS), and b) Tox21 (BG1 LUC and BLA) ER assays

1303 Plotted are the log(AC50) values for the two assays (x and y). For samples that were inactive, the AC50

1304 was arbitrarily set to 0.01 M, and a small random increment was added to keep points from lying on top 

1305 of one another. Samples with borderline activity (AC50 modifier of ">=") have their AC50 value set to 

1306 200 μM for visualization purposes. These samples then form the bands between the inactive and fully 

1307 active groups. For reference chemicals, we indicate the activity class by colors. 
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1308 Section 7 Analysis of ES TrSet and HTP Assay Data 

1309 This section provides a preliminary assessment of the potential for HTP assay data to expand the ES. 

1310 This analysis compares a subset of the HTP ER assays and data used to train the ES. The assays that 

1311 underlie the ES have been optimized and the data thoroughly analyzed, and provide a high-quality data 

1312 set against which new assays can be compared. The HTP data have been generated using available 

1313 technologies that have not undergone the type of rigorous optimization that the ES assays have to 

1314 capture low affinity chemicals, but the HTP assays are providing empirical data on 1,000's of chemicals. 

1315 Additionally, the analysis of the HTP assays is still ongoing, for instance on understanding how to deal 

1316 with technology-specific false positives and negatives. The goal of this section is to demonstrate an 

1317 approach for performing this comparison. However, both because only a subset of the HTP assays are 

1318 included in this analysis, and because of time pressures, any conclusions of this analysis should be 

1319 treated as preliminary. 

1320 7.0 Assays utilized in developing the ER Expert System

1321 The development of the ES was based on the information derived from two in vitro assays optimized to 

1322 detect low affinity binders that were the only types of ER Binders likely to be found on the FI and AM 

1323 chemical lists as presented in the 2009 SAP paper (SAP, 2009a). The first assay is a classic competitive 

1324 receptor binding assay that utilizes a rainbow trout liver cytosol as the source of ERs. This assay 

1325 establishes whether a chemical can displace the endogenous hormone from the ER, and the affinity for 

1326 this interaction is determined relative to the binding affinity of 17-beta-estradiol (E2). The second assay 

1327 detects the ability of a chemical to activate an estrogen-responsive gene at the tissue level along the ER1328 mediated reproductive impairment AOP (section III). For the ES, the gene activation (GA) assay 

1329 measures the induction of the mRNA for vitellogenin, which is an egg yolk precursor protein. The assay 

1330 utilizes precision cut liver slices from immature rainbow trout which have very low or no vitellogenin 

1331 expressed; but when exposed to E2 or other chemicals that activates the ER, vitellogenin mRNA is 

1332 significantly induced. The low background activity of this gene, the large dynamic range of the 

1333 response, and the sensitivity of quantitative real-time PCR as the endpoint makes this a very sensitive 

1334 assay for detecting ER activation, thus giving confidence in detecting very low affinity ER binders. The 

1335 principle assay components of the rtER binding and liver slice vitellogenin assay are provided in 

1336 summary tables in Appendix H (Table H-1 and Table H-2).

1337 The results of the response to a chemical in these two assays are taken together and used to interpret 

1338 whether a chemical has the potential to be active or inactive for interacting with an ER-mediated 

1339 pathway. An initial assessment is done in the rtER binding assay to assess whether the chemical can 

1340 displace E2 from the receptor. The results of the binding data, in combination with any previous 

1341 information derived for other chemicals in the ES TrSet in the same chemical group, determines whether 

1342 the chemical is tested further in the trout liver slice assay for verification of activity. Generally, a trout 

1343 slice assay is run as a follow-up to the binding assay, except when there already is compelling evidence 

1344 in the ES TrSet from previous testing that provides adequate mechanistic understanding and chemical 

1345 group-based evidence to ensure that the activity call can be based on the binding data alone when adding 

1346 this new chemical to the training set. Supporting information is also collected to determine if parameters 

1347 specific to each chemical could be impacting the apparent assay results. In the binding assays this 

1348 includes assessment of the formation of precipitates, and/or changes in media pH. In the slice assay, 

1349 cytotoxicity (usually determined by LDH leakage from the slices, but occasionally other cytotoxicity 

1350 assays), chemical insolubility (from visual observation of the formation of precipitates or other 

1351 indications such as light scattering by nephelometery), measured changes in media pH, calculated pKa, 
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December 2012 93 | Page

1352 among others are used to evaluate the performance of the assay. As knowledge is gained on activity of 

1353 individual chemicals, and of the activity of chemicals in the same ES group, the classification of a 

1354 chemical as potentially active or inactive (Binder or Nonbinder) is made with greater confidence. Note 

1355 that this complete analysis, e.g. comparing assays against one another, looking at chemicals class 

1356 information, etc. has not yet been completed for the HTP assays. 

1357 Decision diagrams and accompanying text showing the process for data analysis and interpretation of 

1358 activity of ES TrSet chemicals in the rtER binding and trout liver slice Vtg mRNA induction assays are 

1359 included in Appendix G: ES TrSet Decision Criteria for Binder, Nonbinder and Data Processing 

1360 Details.

1361 7.1 ER Expert System TrSet and HTP In vitro Assays 

1362 Analysis of the concordance in responses between the assays used in developing the ES and a subset of 

1363 five of the HTP assays was conducted. The five HTP assays were selected because they utilized in vitro

1364 assay formats which correspond to the technologies used in the lower-throughput TrSet assays f. The 

1365 remaining three assays were not included in this current analysis primarily due to lack of time to 

1366 perform a complete analysis. The three NVS ER binding assays all utilize the measurement of 

displacement of [3 1367 H]-E2 from the receptor, which is the same detection method utilized in the trout liver 

1368 cytosol assay. The NVS assays are conducted up to a maximum chemical concentration limit of 50 μM 

1369 if a prescreen up to 25 μM was positive, whereas the rtER cytosolic binding assay is conducted up to 

1370 chemical solubility. The two reporter gene assays, the Tox21 BG1-Luc and Tox21 BLA utilize standard 

1371 ER responsive reporter gene assays with luciferase or fluorescence, and again are well reported when 

1372 run in a lower-throughput mode. They were conducted up to 100 μM maximum test concentration. The 

1373 trout GA assay was conducted up to chemical solubility or cytotoxicity (whichever was noted first) with 

1374 initial test concentrations selected based upon rtER binding activity results.

1375 The analysis that follows was done with a set of 296 chemicals that were selected in June 2011 to be run 

1376 as part of the chemical selection process for HTP testing in endocrine related assays to address the 

1377 question of if and how HTP data can be used to expand the ES (section 3.1.1, goal 5). This set of 

1378 chemicals was selected to include the ES chemical groups covered by the TrSet to enable comparison of

1379 the assay results or 'hit calls' of the ES TrSet and the HTP assays (Appendix H, Tables H-3 to H-14). 

1380 Activity could be compared directly between the trout based assays and the HTP assays for these 296 ES 

1381 TrSet chemicals.

1382 7.2 Binding Assays: Trout Liver Cytosolic ER and HTP ER Binding Assays 

1383 A comparison of the activity determinations for the 296 chemicals in the ES TrSet binding assay and the 

1384 three HTP binding assays follow. It is important to note a key design criterion in generating the HTP 

1385 binding assay data set used here. These assays were run as part of a broad survey of chemicals against 

1386 potential targets using a collection of cell-free assays. The goal was to finding relatively potent 

1387 chemicals against close to 300 targets. In order to do this analysis in a cost-effective manner, chemicals 

1388 were prescreened against each target at a single screening concentration of 25 μM. Only those chemicals 

1389 showing appreciable activity in the single screen run were reevaluated in concentration-response mode, 

1390 and then only up to 50 μM. Therefore the HTP ER binding assay data set being compared here was not 

1391 run up to the limit of solubility in a way that allows a one-to-one comparison with the low-through TrSet 

1392 assays, so no final conclusions about absolute assay sensitivity can be made. 
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1393 For those chemicals that are Nonbinders (Inactive) in the trout binding assays there is generally good 

1394 agreement in the HTP binding assays (Table 7.2.1). This is shown by the 88% of the 172 Non-binders 

1395 from the ES TrSet also being called Inactive in all three of the HTP binding assays. One aspect to take 

1396 into account in this comparison of the agreement between the Inactive chemical calls, however, is that a 

1397 chemical concentration maximum of 25 μM was used as a single concentration prescreen in the HTP 

1398 binding assays and only if that was positive was the chemical further tested in a full dose-response up to 

1399 50 μM concentration maximum. This dose selection in itself will produce a higher number of negatives 

1400 than the rtER binding assay that was run at higher concentrations and often up to chemical solubility if 

1401 no effect was seen at lower concentrations. As the maximal concentration tested in any assay is limited 

1402 to lower concentrations, there will be fewer chemicals, particularly lower affinity chemicals, identified 

1403 as active. Thus, when making a direct comparison of Inactive determinations among assays, it is 

1404 important to interpret the results within this context.

1405 Table 7.2.1: Comparison of ES TrSet with HTP hER, bER and mERa binding assay calls. 

Comparison of Inactives hER Inactive bER Inactive mERa Inactive

All three NVS ER 

binding Inactive

172 chemicals in TrSet 

with RBA=0 (Inactive)

158 (92%) 164 (96%) 161 (94%) 152 (88%)

Comparison of Actives: hER Active bER Active mERa Active

Any HTP ER 

Active

124 chemicals in TrSet 

with RBA > 0 (Active)

42 (34%) 31 (25%) 29 (23%) 44 (35%)

1406 Most of the discordance between the ES TrSet benchtop assay results and HTP binding assays occurred 

1407 when the chemical was active in the rtER binding assay but was inactive in the HTP binding assay. For 

1408 those chemicals active in the rtER binding assay, there were 35% that were active in at least one of the 

1409 three HTP binding assays. As expected, given the screening approach described above, all of these 

1410 mismatches appear to be due to the 50 μM maximum test concentration cutoff used in the HTP assays. 

1411 The four chemicals below in the 2-hydroxybenzophenone chemical group (Figure 7.2.1), that were 

1412 tested in the rtER and HTP binding assays are compared to illustrate the point (Figure 7.2.2 and 7.2.3). 

1413 
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Figure 7.2.1: 2-Hydroxybenzophenones

1414 The two bottom chemicals in Figure 7.2.1 (2-hydroxybenzophenone and 2-hydroxy1415 4methoxybenzophenone) did not produce significant displacement from the hER, bER, or mERa in the 

1416 HTP 25 μM pre-screen. Thus, they were not tested in the full dose-response mode and were called 

1417 inactive in the HTP assays. The other two chemicals were active in at least two of the three HTP binding 

1418 assays (Figure 7.2.2). 
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Figure 7.2.2: Binding curves for two hydroxybenzophenones in HTP assays. 

The NVS binding assays for each receptor are shown side by side: hER (A,B), NVS bER (C,D), and NVS mERalpha (E,F). The left panels (A, C, E) of three sets of curves show 2,2',4,4'-tetrahydroxybenzophenone [131-50-5] 

and the right panels (B,D,F) are 2,4-dihydroxybenzophenone [131-56-6]. The red symbols and line are the E2 

positive controls run in the assay, the black symbols are the test chemical.

A B

C D

E F
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1419 From Figure 7.2.3 one can also see that if the trout cytosolic binding assay were conducted with the 

1420 same maximal cutoff concentration (red dashed line in figure is at 50 μM) for this class of chemicals as 

1421 in the HTP assays, then two of the four hydroxybenzophenones (C and D) would have been called 

1422 inactive, matching what was found with the HTP assays. Similar relationships between differences in 

1423 calls in activity and this cutoff concentration were seen within other chemical classes analyzed including 

1424 the alkylphenols, alkylanilines, and other chemical groups active in the trout cytosolic binding assay. 

Figure 7.2.3. Results of binding assays of four hydroxybenzophenones in rtER binding assay. 

The positive control curves for E2 are the left most curves, and two to three replicate curves for each 

hydroxybenzophenone are the right most curves in these graphs. The dotted red line at -4.3logM is the 

50 μM maximal concentration used in the HTP binding assays for a point of reference. 

1425 In general, the HTP competitive binding assays as conducted tend to call the low affinity chemicals in 

1426 the ES TrSet as Inactive due to the concentration cut-off, but they do well identifying the chemicals with 

1427 greater affinity for the receptor. This is illustrated in Table 7.2.2 where the ES TrSet Binders are binned 

1428 into groups of decreasing RBA (relative to E2 as 100%), and the number of chemicals identified as 

A B

C D

0

10

20

30

40

50

60

70

80

90

100

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CTRL

2,2',4,4'-Tetrahydroxybenzophenone

E2

 Log Concentration (M)

[3H]-E2 Binding (%)

131-55-5

0

10

20

30

40

50

60

70

80

90

100

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CTRL

2,4-Hydroxybenzophenone

E2

 Log Concentration (M)

[3H]-E2 Binding (%)

131-56-6

0

10

20

30

40

50

60

70

80

90

100

110

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CTRL

E2

2-HydroxyBenzophenone

 Log Concentration (M)

[3H]-E2 Binding (%)

117-99-7

0

10

20

30

40

50

60

70

80

90

100

110

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CTRL

E2

2-Hydroxy-4-methoxybenzophenone

 Log Concentration (M)

[3H]-E2 Binding (%)

131-57-7
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1429 Active in the HTP ER binding assays are also shown. As binding affinity increases, the number of 

1430 reported Active chemicals in HTP assays increase in each group. In the range of RBA from 0.01 to 0.1% 

1431 the hER HTP assay identified 10 of 18 ES TrSet Binders; in the 0.0001 to 0.01% range 10 of 33 were 

1432 found; in the next lower range there were 6 of 36. In the lowest RBA category for the TrSet Binders 

1433 there were no Actives in the hER, bER, or mERa assays. Separate tables for each RBA bin have been 

1434 included in Appendix H listing the individual chemicals and their corresponding activity in the assays 

1435 (Tables H15 to H19). 

1436 Table 7.2.2: Comparison of activity of binding assays based upon HTP positive matches to ES 

1437 TrSet positives grouped by relative binding affinity (RBA) in the rtER assay. 

ES TrSet RBA (%)

Number of TrSet 

Actives

Number of Chemicals Matching TrSet

NVS-hER NVS-bER NVS-mERa

RBA > 1* 7 6 6 7 

0.1 ≤ RBA < 1 7 7 7 6 

0.01 ≤ RBA < 0.1 18 10 6 7 

0.001 ≤ RBA < 0.01 33 10 7 6 

0.0001 ≤ RBA < 0.001 36 6 3 2 

0.00001 ≤ RBA < 0.0001 22 0 0 0 * Group excludes E2 

1438 A noted exception to the trend is the one chemical in the high affinity range, RBA>1%, that was not 

1439 active in the NVS hER or bER, assays- triphenylethylene (3PE). In rtER binding 3PE exhibits relatively 

1440 strong affinity for the receptor (RBA = 3.0%), however it did not show any indication of competitive 

1441 binding in hER or bER HTP assays (Figure 7.2.4). It did appear to bind in the NVS mERa assay, which 

1442 utilizes the ligand binding domain of the mouse ER-alpha. It is unclear why this chemical with high 

1443 affinity in the rtER binding assay, and known mammalian ER affinity (Blair et al. 2000) would not 

1444 exhibit a competitive binding curve in the HTP hER and bER assays. 

Triphenylethylene

cyto rtER binding

0

10

20

30

40

50

60

70

80

90

100

-10 -9 -8 -7 -6 -5 -4 -3 -2

E2

3PE

CTRL

[3H]-E2 Binding (%)

Log Concentration (M)

Figure 7.2.4: Binding displacement of triphenylethylene (3PE) in rtER and mERa. 

The dotted red line at -4.3logM in the cyto rtER binding assay (figure middle) is the 50 μM maximal 

concentration used in the HTP binding assays for a point of reference. The HTP NVS mERa binding 

result is shown on the right.

1445 In the group of ES TrSet chemicals in the RBA range from 0.1 – 1.0% one chemical was called inactive 

1446 in just one of the HTP assays. This was HPTE, which is the estrogenic metabolite of methoxychlor, and 

1447 it was called Inactive in the mERa binding assay. This was not active in the 25 μM single concentration 

Triphenylethylene
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1448 pre-screen so was not carried forward to a full dose-response analysis. HPTE was active in both the hER 

1449 and bER binding assays. 

1450 Among the 18 ES TrSet chemicals in the RBA range from 0.01 – 0.1% there were four alkylphenols and 

1451 these had the highest affinity of all of the alkylphenols in the ES TrSet. All four were tested in full dose 

1452 response mode in the HTP binding assays. Both p-n-nonylphenol and p-n-heptylphenol were Inactive in 

1453 these HTP binding assays. The NVS hER, NVS bER, and NVS mERa identified the branched p1454 nonylphenol as an Active binder, and p-dodecylphenol, (a mixture of branched isomers) was identified 

1455 as active in the NVS hER assay. There were five DDT-related chemicals in this RBA range that were all 

1456 active in the ES TrSet where their binding displacement curves were from 50% to 80%. In the HTP 

1457 binding assays, the NVS mERa assay produced a binding curve for the o,p'-DDD. The other DDT1458 related chemicals were Inactive in the HTP binding assays. And finally of note in this RBA range from 

1459 0.01 – 0.1% were five Mixed Phenols, of which there were three on the reference chemical list 

1460 (bisphenol A, bisphenol B, and the flavonoids daidzein; section 6) and phenolphthalien which were all 

1461 active in all three HTP binding assays (Figure 7.2.5). 3,3',5,5'-Tetrabromobisphenol A is an Active ES 

1462 TrSet Mixed Phenol in this RBA range that was Inactive in the NVS hER, NVS bER, and NVS mERa 

1463 binding assays when tested in a single replicate (Figure 7.2.6). This chemical was run in four replicates 

1464 in the rtER binding assay using different fish liver cytosol ER sources for each replicate. Three 

1465 replicates agreed very closely while a fourth gives considerable variability beyond what is normally seen 

1466 with this assay and considered an outlier, although still yielded a positive response. 
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Figure 7.2.5: Four Mixed Phenols in the ES TrSet with RBA from 0.01 – 0.1% that were active in 

all three HTP binding assays and the rtER binding assay. 

Structures are shown in A-D. Below each is first the cytosolic rtER binding curve result (E-H) and 

below that is the corresponding chemical in the HTP hER binding assay. The bER and mERa HTP 

binding assays are not shown.

1467 

OH OH

3CH CH3

OH

3CH

3CH

OH

OH

O

OH O OH OH

O

A B C D O

E F G H

I J K L
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Figure 7.2.6: Tetrabromobisphenol A (TBBA) was Active in the rtER binding assay (A) but 

Inactive in all three HTP Assays (B: hER; C: bER; D: mERa). 

Graph of TBBA in rtER (panel A) shows 4 replicate runs for this chemical in three different cytosol 

preparations. Three were nearly identical, and one was quite different. This 'outlier' curve was not used 

to calculate the RBA for TBBA.

1468 There were five ES TrSet chemicals in the next lower affinity RBA range (0.001 to 0.01%) that were 

1469 also Active in all three HTP binding assays. This included two Alkylphenols (4-t-octylphenol and 4-

1470 cumylphenol), one Mixed Phenol (2-Benzyl-4-chlorophenol, and two Parabens (butyl-4-

1471 hydroxybenzoate and Benzyl 4-hydroxy-benzoate). However, there were three salicylates that were ES 

1472 TrSet Actives that were Inactive in the 25 μM pre-screen for all three HTP binding assays so were not 

1473 tested in full concentration response mode. These were benzylsalicylate, hexylsalicylate, and Benzoic 

1474 acid, 2,4-dihydroxy-3,6-dimethyl-, methyl ester. Also Inactive in the three HTP binding assays but 

1475 active in the rtER binding assay were two alkylanilines: 4-hexylaniline and 4-pentylaniline. The 

1476 demonstration of the likely miss of these two chemicals and a whole series of alkylanilines Active in the 

1477 rtER binding assays due to the use of the 50 μM cutoff is shown in Figure 7.2.7. This left graph shows 

1478 that if the 50 μM maximal test concentration were applied to the rtER binding assay, neither the pentyl1479 or hexylaniline would be Binders. The right graph shows that the entire series of these 4-alkylanilines in 

1480 the ES TrSet would also appear to have flatline non-binding curves. This chemical series is the lead 

1481 series for B-Type Binders in the ERES. 

A B

C D

Br

OH Br

3CH CH3

Br

OH

Br
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0

10

20

30

40

50

60

70

80

90

100

110

120

-10 -9 -8 -7 -6 -5 -4 -3 -2

Hexylaniline

Pentyaniline

CTRL

Pentyaniline and Hexylaniline

rtER Binding

E2

A

Log Concentration (M)

[3H]-E2 Binding (%)

Figure 7.2.7: The binding curves in the rtER binding assay for 4-pentyl- and 4-hexyl-aniline 

(panel A) and the full series of 4-alkylanilines tested in the ES TrSet (Panel B) are shown with a

red dashed line at the 50 μM cutoff concentration used in the HTP binding assays. 

The binding curves in panel BA include the following 4-alkylanilines: aniline (ANL); methylaniline 

(PT); ethylaniline (EA), n-propylaniline (PA); n-butylaniline (BA), t-butylaniline (TBA); npentylaniline (AAN); n-hexylaniline (HAL) and n-octylanilne (OA). for the anilines shown in panel B 

are pentyl aniline (AAN; yellow diamonds) and the hexylaniline (HAL; red diamond symbol) are also 

on the full ES TrSet graph.

1482 The second to lowest ER affinity group (with RBAs from 0.0001 to 0.001%) contained chemicals across 

1483 a wide range of chemical groups. Many of those were called Inactive by the HTP binding assays. There 

1484 were two of 36 ES TrSet Actives that were Active in all three HTP binding assays. These were phenol 

1485 red and fluorescein. Fluorescein had an RBA of 0.00065% and phenol red was 0.00010%. Both the hER 

1486 and bER called 4-tert-pentylcyclohexanone as an Active chemical, but this was not detected as active in 

1487 the mERa binding assay (Figure 7.2.8). The hER HTP binding assay alone identified ethyl-4-

1488 hydroxybenzoate, morin, and diphenylphosphite as Active within this RBA range. Other chemicals of 

1489 note in this TrSet RBA range that were Inactive in the HTP binding assays were five alkylphenols with 

1490 LogKow values ranging from 3.0 to 3.5 and perfluorooctanoic acid (PFOA). 

1491 Finally, none of the TrSet chemicals in the lowest RBA range of ES TrSet Actives were identified as 

1492 Actives in the HTP binding assays. This group included two benzoates, two alkoxyanilines, two 

1493 alkylphenols, two alkoxyphenols, two phenones, two phthalates and two salicylates, and several 

1494 chemicals in the Mixed Organics category.

1495 One chemical group for which there was noticeable agreement between the rtER activity and the HTP 

1496 assays was for the ES 'Parabens (non-benzoate)' group (Appendix I, Figure I-1). The non-benzoate 

1497 designation in paraben and salicylates in the ES indicates there is no benzene bound directly to the ether 

1498 oxygen of the paraben or salicylate. For example, replacing the methyl group of the methylparaben 

1499 shown in Appendix I, Figure I-1 with a benzene ring would change the chemical classification from 

1500 Paraben (non-benzoate) to Paraben (benzoate). The hER HTP binding assay calls were consistent with 

1501 the ES TrSet activity of these 'Parabens (non-benzoate)' calling six of the seven as Active. The one HTP 

1502 Inactive was methyl-4-hydroxybenzoate, which was the lowest affinity in this TrSet group (RBA = 

1503 0.00015%). The Active chemicals called by the hER HTP assay ranged from n-heptyl-4-

0

10

20

30

40

50

60

70

80

90

100

110

120

-10 -9 -8 -7 -6 -5 -4 -3 -2

BA

E2

TBA

HAL

AAN

OA

ANL

EA

PA

PT

CTRL

B

Log Concentration (M)

[3H]-E2 Binding (%)
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1504 hydroxybenzoate with rtER RBA = 0.015% down to the ethyl-4-hydroxybenzoate with rtER RBA = 

1505 0.00080%. The bER and the mERa called 2 and 3 'Parabens (non-benzoate)' as Active, respectively.

Figure 7.2.8: Examples of chemicals detected as active from the ES TrSet chemicals with RBAs 

from 0.0001 to 0.001%

1506 In contrast to the 'Parabens (non-benzoate)' where the HTP assays largely agreed with the TrSet results, 

1507 the HTP assays did not identify the 'Salicylates (non-benzoate)' to be Active. The salicylates are a 

1508 similar structural group to the parabens (salicylates having ortho- instead of para-OH substitution). Yet 

1509 all of the TrSet Active 'Salicylates (non-benzoate)' were Inactive in the HTP binding assays (Appendix I, 

1510 Figure I-1). Again, there is an apparent assay difference as some of these chemicals in the 'Salicylates 

1511 (non-benzoate)' group have been shown active with the hER in the literature (for example, see Miller et 

1512 al., 2001; Schreurs et al., 2002). 

1513 The 25 μM single concentration pre-screen

1514 The use of the 25 μM single concentration cutoff as a pre-screen tool in the NVS binding assays appears 

1515 to play a role in the discordance between HTP and ES TrSet binding assays. Some of the chemicals that 

1516 were carried forward to test in full dose-response mode, i.e. were positive at 25 μM in the pre-screen, 

1517 gave flat line responses when run in dose-response mode. Out of the 296 chemicals tested in these 

1518 assays, 110, 115, and 96 of these chemicals gave an active signal in the 25 μM pretest with the NVS 

1519 bER, NVS hER, and NVS mERa, respectively, but were then negative in the full dose-response run. 

1520 Thus, the initial pre-screen may not be accurately assessing the activity of the chemicals. It is the 

1521 opposite scenario that is particularly important, where the chemical was negative in the 25 μM single 

1522 concentration pre-screen but might have been positive if tested in full dose-response mode. 

CH3

3CH O

3CH

OH

O

O

CH3

O PH

O

O

4-t-cyclohexanone ethyl-4-hydroxybenzoate morin diphenylphosphite

4-t-cyclohexanone [16587-71-6]

cyto rtER binding

0

10

20

30

40

50

60

70

80

90

100

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1

E2

TPCH

CTRL

 Log Concentration (M)

[3H]-E2 Binding (%)

OH

OH

O OH

OH

OH

O

HTP hER binding rtERbinding 
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December 2012 104 | Page

1523 To assess this issue, the number of chemicals that were inactive in the 25 μM prescreen for the NVS 

1524 binding assays but were active in the ES TrSet was determined. For the NVS mERa, there were 50 

1525 chemicals Inactive due to the pre-screen but ES TrSetActive. The most potent of those missed was 

1526 HPTE, the hydroxylated metabolite of methoxychlor, which in the trout assays has an RBA of 0.52%. 

1527 Similarly, there were 31 TrSet chemicals with RBAs from 0.00003% to 0.023% that were not tested in 

1528 dose-response mode in NVS hER, and 44 TrSet chemicals missed in the NVS bER assay. Overall, these 

1529 negatives due to the 25 μM prescreen were not specific to only one ES chemical group but were found 

1530 across many chemical classes. For example, chemicals that were inactive in the NVS mERa assay due to 

1531 the pre-screen were from: alkylphenols, benzoates, hydroxybenzophenones mentioned earlier, 

1532 phthalates, salicylates, phenones and many from Mixed Organics and Mixed Phenol groups. 

1533 7.3. Gene Activation Assays: Trout Vtg mRNA and HTP Transactivation Assays 

1534 A comparison was conducted of the 296 ES TrSet chemicals with two of the HTP TA assays (the Tox21 

1535 assays). The resulting agreement and discordant calls are shown in Table 7.3.1. This table shows the 

1536 majority of the chemicals were in agreement with Active and Inactive calls. The discordance mostly 

1537 occurred where the TrSet chemicals are categorized as Binders and the HTP assay hit call was Inactive, 

1538 as was the case in the comparison of the binding data. A large part of this difference can be attributed to 

1539 the lower concentrations and cutoffs used in the HTP TA assays compared to testing in the rtER binding 

1540 and liver slice Vtg mRNA expression assays which go up to limits of chemical solubility or cytotoxicity, 

1541 whichever came first. Another reason that different assays may produce discordant results is due to 

1542 differences in sensitivity. The 4-alkylanilines can be used to demonstrate this phenomenon. 

1543 Table 7.3.1: Concordance of activity between TrSet and HTP assays. Comparison of the 296 

1544 TrSet Chemicals with the HTP TA Assay Hit Calls

HTP 

Assayed

Agree 

Active

Agree 

Inactive

Disagree 

TrSet (+) & HTP (-) 

Disagree 

TrSet (-) & HTP (+)

BLA_ERa_fluor ratio 296 46 158 76 16

BG1_ERa_Luc 296 46 158 77 15

1545 7.3.1 Effects of Assay Sensitivity and Test Concentration Limits 

1546 As seen with binding in Figure 7.2.7, discrepancies between the 4-alkylanilines in the rtER GA assay 

1547 versus the Tox21 TA assays might be in part due to assay sensitivity differences. The 4-alkylanilines are 

1548 Inactive in BG1 and BLA assays but active in the ES TrSet. As mentioned earlier in section 7 the 

1549 determination of the Vtg mRNA expression by real time quantitative PCR is a very sensitive method for 

1550 detecting activity. Significant changes in Vtg mRNA expression can be detected for weakly active 

1551 chemicals.

1552 Figure 7.3.1 shows an example of one of the experimental runs for 4-pentylaniline in the trout Vtg assay 

with the red dashed line showing the concentration maximum of 9.2x10-5 1553 M (-4.0logM) used in the HTP 

1554 assays. This shows that the maximum concentration used in the BLA or BG1 assays may have been 

1555 sufficient for these Tox21 TA assays to detect this aniline as Active with one significant data point 

1556 indicating TA, assuming all other factors are equal. Testing the chemical to higher concentrations or to 

1557 its solubility limit in the assay could reduce uncertainties in interpreting the assay results. If there are 

1558 differences in sensitivity between the assays it would be expected to take greater concentrations of test 

1559 chemical to detect an active chemical. Assay sensitivity might be influenced by factors such as the free 
Prioritization of the EDSP Universe of Chemicals

December 2012 105 | Page

1560 fraction of chemical available to the cells or differences in metabolism. So understanding how assay 

1561 parameters can affect interpretation of the resultant data is critical.

Figure 7.3.1: Examples of activity of 4-pentylaniline in responses in trout Vtg mRNA assay tested to 

-2.3logM, BLA assay tested to -4.1logM (7.7x10-5M), and BG1 assay tested to -4.0logM (9.2x10-5M)

1562 Discordance in calls in the other direction, i.e., TrSet Inactive and HTP TA assay active, are due to other 

1563 reasons than differences in maximum concentration used or potential sensitivity differences between 

1564 assays. Of the chemicals in the "TrSet Inactive/HTP Active" category, there are 27 chemicals, of which 

1565 16 and 15 were active for the BLA and BG1 assays respectively, as shown in Table 7.3.1 above. There 

1566 were four chemicals that were Active in both assays indicating that the two assays were not equally 

1567 responsive across the chemical structure space. These four shared chemicals were two ES TrSet 'Mixed 

1568 Organics' (1,2-benzendicarboxaldehyde and methyltestosterone), 4-hydroxybenzoic acid phenyl ester 

1569 which is a 'paraben (benzoate)', and the 'triazine', terbuthylazine. 

1570 7.3.2 Interpreting Responses Where Replicates Disagree

1571 For those chemicals that were active in either the BG1 or the BLA assay but not both, these remaining 

1572 23 chemicals were spread across 18 chemical groups (Appendix H, Table H-20). There were three ES 

1573 TrSet Inactives in the LogKow Cut-off group that were active in the BLA assay but inactive in the BG1 

1574 assay; niacinamide, m-Xylenediamine, and hydroquinone. For each of these chemicals there was one of 

1575 the three replicate runs in which it was called Active (Figure 7.3.2). The response for niacinamide and 

1576 1,3-benzenedimethanmide could be questioned due to the high variability around the single positive 

1577 point. However, the result of the hydroquinone curve is more indicative of an Active response. For 

1578 hydroquinone the analytical QC on one of the Inactive replicates and on the single Active replicate 

1579 indicated the chemical purity of >90% was confirmed. For the second Inactive replicate the analytical 

1580 chemistry provided no information on purity or identity. So the analytical chemistry information does 

1581 not suggest a reason to disregard the Active or Inactive replicate response. Information on other possible 

1582 interferences should be examined (e.g., "flare" effects, fluorescent interferences, metabolism).

1.0×105

1.0×106

1.0×107

1.0×108

-10 -9 -8 -7 -6 -5 -4 -3 -2

AAN

E2

CONTROL

CTRL

n-Amylaniline (pentylaniline)

trout liver slice Vtg mRNA

Log Concentration (M)

VTG mRNA copies/400 ng total RNA

A B C
Prioritization of the EDSP Universe of Chemicals

December 2012 106 | Page

Figure 7.3.2: Three chemicals that were ES TrSet Inactives, BG1 Inactives, but BLA Actives. 

Panels A, B, and C show the replicates called Active for niacinamide, 1,3-benzendimethanimine, and 

hydroquinone, respectively. The two other replicate runs for these three chemicals were all flat line 

Inactive curves (not shown).

1583 The noted potential variability between replicates does raise concern over the interpretation of an Active 

1584 or Inactive call when chemicals are tested in one replicate. In those cases there is a need to be certain the 

1585 chemical quality is acceptable, the correct chemical was tested, and there were no other potential 

1586 interferences that may affect the response, especially to have high confidence in negative results. Note 

1587 however that there is always a trade-off between being absolutely certain of the results and testing a 

1588 large number of chemicals in a rapid and economical manner to carry out a prioritization process. 

1589 The discordant calls that were ES TrSet inactive and HTP TA active (27 of the 296) can be divided into 

1590 three categories. Some of these may be differences in metabolic capacity of the assays. For some the TA 

1591 activity calls is inconsistent with knowledge of activity of other chemicals in that group. For instance, 

1592 the acyclic alcohol, 2-ethyl-1-hexanol gave apparent rtER binding but was inactive in slices, but in the 

1593 BLA assay was weakly active. This chemical warrants further examination and confirmation using other 

1594 lines of evidence. Chemical groups that may give false negatives are described in more detail in section 

1595 7.4.1. And finally, there are a few discordant results between the ES TrSet chemicals and the HTP 

1596 assays for which no explanation could be offered at the time of this assessment with the information that 

1597 was available. It is especially important to follow-up on these Tox21(+)/TrSet(-) chemicals because they 

1598 indicate potential false negatives in the ES applicability domain. It also shows the potential value of 

1599 including the HTP data throughout the EDSP21 chemicals universe. 

1600 7.4 Interpretation of Combined ER Binding and Gene Activation Assay Results

1601 Finally an initial analysis of the ES TrSet assays and the activity calls of the combined HTP assay data 

1602 was conducted. Note that any conclusions from this analysis need to be treated as preliminary because 

1603 data from all of the assays have not been included. Two different ways of categorizing Actives and 

1604 Inactives in the HTP assays were used. In the first case, a chemical was considered Active in the HTP 

1605 assays if automated scoring indicated 'Active' in at least one of the 5 HTP assays. In the second case, a 

1606 chemical was considered Active if it was scored Active in at least one binding HTP assay and one 

1607 transactivation HTP assay in any combination. Across the list of 296 ES TrSet chemicals in this 

1608 comparison, 124 of the 296 chemicals had measureable ER binding (RBA > 0.00001%), and 172 of this 

1609 set were Nonbinders (RBA <0.00001%). For the HTP assays, there were a total of 191 of the 296 that 

1610 were Inactive in all five assays. There were 43 chemicals that were Active in at least one HTP binding 

A B C
Prioritization of the EDSP Universe of Chemicals

December 2012 107 | Page

1611 assay and active in the BG1 or BLA HTP assays. There were 62 chemicals that were Active in either an 

1612 HTP binding or an HTP transactivation assay (Figure 7.4.1). 

Figure 7.4.1: Comparison of Active and Inactive Calls within the 296 chemicals in the ES TrSet. 

Overall results of the ES TrSET assays are shown on the left, and HTP assay results are shown on the 

right (three competitive binding and two reporter gene transactivation (TA) assays out of 5 TA assays 

from section 5). 

1613 The overall proportions of Active and Inactive calls in this set of 296 chemicals across these two 

1614 classification schemes are similar. However, there are larger differences in distribution of the specific 

1615 chemicals beyond what the difference of 19 more Inactives in the HTP assays indicated by these pie

1616 charts would suggest. This further breakout is shown in the following Table 7.4.1. 

1617 Table 7.4.1: Breakout of activity calls between HTP* and ES TrSet data 

HTP Call ES TrSet Active ES TrSet Inactive

INACTIVE (All 5 HTP Assays) 191 57 134

ACTIVE (1 Binding and 1 TA) 43 34 9 

ACTIVE (1 Binding or 1 TA) 62 33 29

Sum 296 124 172

* Five HTP assays: NVS bER, NVS hER, NVS mERa, Tox21 BLA, Tox21 BG1_Luc

1618 Of the 191 chemicals that were Inactive in all five HTP assays, 134 of those were also called Inactive in 

1619 the ES assay. This was a 70% agreement rate on Inactive calls in the HTP assays with the ES TrSet data 

1620 calls within this subset of chemicals. Of those 57 Inactive chemicals in the HTP assays that were Active 

1621 in the ES TrSet assays, they represent 21 different chemical groups and had RBAs that ranged from 

1622 0.00001 to 0.02 (Table 7.4.2). For the most part, those chemicals that were Inactive in the HTP assays 

1623 but were Active in the ES TrSet assays had RBAs less than 0.001%, indicating that the ES TrSet assays 

1624 were identifying more low activity chemicals than the HTP assays. In this set of chemicals, the DDT1625 like group was the group with the highest RBAs (0.016 to 0.023%) that were called Inactive in the HTP 

1626 assays.

1627 There were no apparent overall trends in LogKow of these 61 chemicals that were Active in the ES TrSet 

1628 assays but Inactive in the five HTP assays. The LogKow of this subset ranged from 1.51 for phenol to 

1629 6.79 for pp-DDT. The alkylphenols represent the chemical category with the largest number of 

1630 discordant calls in this subset with 11 chemicals. They ranged in LogKow from near the lowest LogKow

1631 for known Active chemicals (LogKow = 1.51 for phenol) to 5.33 for 2,4-di-tert-butylphenol, and they 

1632 had RBA values determined in the trout binding assay across a 100-fold range from 0.000025 to 

1633 0.0035%. 
Prioritization of the EDSP Universe of Chemicals

December 2012 108 | Page

1634 Table 7.4.2. Chemicals Classified as Inactive in All Five HTP Assays but Active in ES TrSet 

1635 Assays 

Chemical Class No. of chemicals RBA (%) Range LogKow range

2-;4-; or 2,4,6-Benzoates 2 0.00005 – 0.0001 2.87

2-Hydroxy Benzophenones 1 0.00097 3.44

4-Alkoxyaniline 1 0.000064 2.63

4-Alkylaniline 3 0.00007 – 0.0015 1.62 – 4.08

Acyclic_Perfluoro 1 0.00052 6.30

Alkoxyphenols 2 0.000025 – 0.00079 1.59 – 3.50

Alkylchlorobenzenes 2 0.00016 – 0.00018 4.66 – 5.15

Alkylcyclohexanols 2 0.00024 – 0.00030 3.53 – 4.32

Alkylphenols 11 0.000026 – 0.0035 1.51 – 5.33

Cyclohexanones (#cycles=1) 1 0.00011 2.53

DDT-like Exact Match 3 0.016 – 0.023 5.87 – 6.79

Mixed Organics - Exact Match 11 0.000029 – 0.0026 2.05 – 5.02

Mixed Phenol - Exact Match 2 0.00019 – 0.00079 1.91 – 2.94

Parabens (non-benzoate) 1 0.00015 2.00

Phenones (Branched) 2 0.00007 – 0.00009 2.58 – 3.04

Phenones cyclo-substituted 1 0.00013 2.96

Phosphoric Acid Esters 2 0.0012 – 0.0019 4.70 – 6.30

Phthalates 1.6<LogKow<6.6 4 0.000066 – 0.00046 2.65 – 6.57

Salicylates (non-Benzoate) 4 0.000062 – 0.0017 2.60 – 6.16

Thiophosphate Esters 1 0.00067 3.44

1636 The most restrictive of the Active call categories utilized in Table 7.4.1 was the case in which there 

1637 needed to be at least one HTP binding assay and one of the two HTP TA assays scoring as Active. There 

1638 were 43 chemicals of the 296 chemical set that met these criteria. Of these 43 chemicals, 34 also fall 

1639 within the Active classification of the ES TrSet assays. These included an alkoxyphenol, three 

1640 alkylphenols, a DDT-like chemical, two mixed organics, 16 mixed phenols which included estradiol and 

1641 related steroids, a multicyclic hydrocarbon, six parabens, and two phenylphenols. The other nine of 

1642 these 43 chemicals that were Active in both an HTP binding and TA assay were Nonbinders in the ES 

1643 TrSet. These were also a mix of diverse chemicals across a range of chemical groups. A discussion of 

1644 the discordance in the activity calls for some of these nine chemicals in the context of their chemical 

1645 group and some potential causes for this disparity are addressed in section 7.4.1. 

1646 7.4.1 Interpreting results in the context of structural similarity and chemical group activity 

1647 When using assays in a chemical category-based approach for prioritizing chemicals it is important to 

1648 interpret the results in the context of what is known about the activity of similar chemicals. This 

1649 provides a sound basis for defining the chemical category and understanding the structure-activity 

1650 relationships for the activity of the chemicals in that category. The following example with the 

1651 alkylbenthiol group illustrates this point.

1652 The alkylbenzthiol group is a negative group in the ES TrSet. The two chemicals in this group are 

1653 p-toluene thiol and 4-t-butylthiophenol (4TB) (Figure 7.4.2 below). p-Toluene thiol is structurally 

1654 similar to 4-t-butlylthiophenol (4TB) but has a methyl group in place of the t-butyl. The p-Toluene thiol 

1655 produced a "ski jump" shaped curve in the rtER binding assay nearly identical to the binding curve of 
Prioritization of the EDSP Universe of Chemicals

December 2012 109 | Page

1656 4TB shown below in Figure 7.4.3. This chemical was also scored as Inactive across all five of the HTP 

1657 assays. Thus, this was a consensus Inactive chemical between the ES TrSet data and HTP assays 

1658 considered here.

1659 The chemical 4TB gave a "ski-jump" shaped binding curve in the 

1660 trout ER binding assay with precipitates reported at -3logM (Figure 

1661 7.4.3). The detection of precipitates is often associated with this 

1662 upward shape of the binding curve. In all three of the HTP binding 

1663 assays, this chemical produced binding curves of approximately 

75% displacement of [3 1664 H]-E2 (only the bER and hER binding 

1665 curves are shown). This chemical was not run in the trout liver 

1666 slice assays to determine Vtg mRNA expression because it was not 

1667 active in the ER binding assay and the inactivity of the structurally 

1668 similar p-toluene thiol in this chemical group provided strong 

1669 evidence that this was an inactive chemical. In the HTP BLA assay 

1670 4TB was Inactive, but in the BG1 assay it produced a response 

1671 greater than 50% of the E2 max (Figure 7.4.4). 

1672 The discrepancy in the responses between these two structurally similar chemicals in the HTP assays 

1673 was unexpected, especially when 4TB was active in both the HTP binding and BG1 assay. Because of 

1674 the high structural similarity between the p-toluene thiol and MTB, it would be expected that their 

1675 responses should be consistent. This suggests that before final activity calls on these chemicals are made 

1676 that it would be appropriate to examine whether analytical purity or other factors might be producing 

1677 this discordance in activity. 

Figure 7.4.3: Binding activity for 4-tert-butylbenzenthiol in the rtER assay (left panel), the HTP 

bER binding assay (middle panel) and hER binding assay (right panel). 

The vertical red dashed line in the left panel indicates where precipitates were detected in the assay.

1678

CH3

CH3

SH CH3

SH CH3

p-toluene thiol

4-t-butylbenzenethiol

Figure 7.4.2: 

Alkylbenthiols in ES TrSet

4-t-Butylthiophenol

rtER Binding

0

50

100

150

-10 -9 -8 -7 -6 -5 -4 -3

200

225

250

Log Concentration (M)

E2

4-t-butylthiophenol

CRTL

[3H]-E2 Binding (%)
Prioritization of the EDSP Universe of Chemicals

December 2012 110 | Page

Figure 7.4.4: HTP BG1 agonist assay showing Active response of the assay to 4-tertbutylbenzenethiol.

1679 7.4.2 Understanding chemical parameters that can potentially lead to false activity 

1680 As described in the decision pathway for the collection and interpretation of assay data used in 

1681 development of the ES (Appendix G, Figures G-1 to G-8) there are many factors inherent in the assays 

1682 that can lead to false or misleading activity. The following two examples help to illustrate how 

1683 assessment of multiple assay parameters can provide alerts to when apparent activity may not be true.

1684 Example 1. Alkylaromatic Sulfonic Acids. The Alkylaromatic Sulfonic Acid dodecylbenezenesulfonic 

1685 acid (DBS) was a Nonbinder in the ES TrSet but was an apparent active in the HTP assays. Note that the 

1686 TrSet "Nonbinder" call was arrived at after follow-up analyses, but that the initial concentration1687 response profile of the Trout binding assay (shown below) shows activity, just as is seen in the HTP 

1688 assays. A discussion of the potential for Alkylaromatic Sulfonic Acid groups to give false positive 

1689 signals was discussed in the 2009 SAP (SAP, 2009b). Chemicals in this group can yield apparent 

1690 binding curves due to low pH and protein denaturation, but produce no gene activation. Verification of 

1691 whether binding is truly competitive binding can be determined by Ki determinations and the pKa can 

1692 provide further information on whether these chemicals are charged species at test solution pH. This 

1693 additional mechanistic information obtained for the octylbenzenesulfonic acid presented in the 2009 

1694 SAP (SAP, 2009a,b) supported the handling of charged species in this chemical group as Non-Binders. 

1695 The octylbenzensulfonic acid figures are included in Appendix I (Figure I-2 to I-4).

1696 Dodecylbenzenesulfonic acid which has an apparent binding curve with the rtER was an apparent active 

1697 chemical in the three HTP binding assays (Figure 7.4.5). This chemical was inactive in the BG1 assay. 

1698 The single high concentration where it was active in the BLA was also at concentrations that correspond 

1699 to evidence of increasing cytotoxicity 
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December 2012 111 | Page

0

10

20

30

40

50

60

70

80

90

100

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL

Dodecylbenzenesulfonic acid

rtER Binding

E2

 Log Concentration (M)

[3H]-E2 Binding (%)

Figure 7.4.5: Activity of Dodecylbenzenesulfonic acid in rtER binding assay (top left panel), HTP 

bER binding assay (top middle panel), HTP hER binding assay (top right panel), HTP mERa 

binding assay (lower left panel), and HTP BLA assay (lower right panel). 

In each panel the curves to the right with the squares are estradiol standards for comparison.

1700 In the trout liver slice assay there was no indication of agonism or antagonism (Figure 7.4.6). 

1701 Cytotoxicity as determined by LDH leakage was evident at -3.3 and -3.0logM indicating the drop in the 

1702 Vtg mRNA induction in the antagonism test was due to toxicity not antagonism. 

Dodecylbenzenesulfonic acid

trout Vtg Gene Expression

PCR_8.28.6

1.0×103

1.0×104

1.0×105

1.0×106

1.0×107

-10 -9 -8 -7 -6 -5 -4 -3 -2

DBS

E2

CTRL

CTRL

Log Concentration (M)

VTG mRNA copies/400 ng total RNA

Dodecylbenzensulfonic Acid

rtER Gene Expression:Antagonist Mode

1.0×104

1.0×105

1.0×106

-10 -9 -8 -7 -6 -5 -4 -3 -2

DBS + E2 at -8 log M

E2

Control

CTRL

LDH

Log Concentration (M)

VTG mRNA copies/400 ng total RNA

LDH Leakage

LDH 8.28.6

0

25

50

75

100

125

150

175

200

-10 -9 -8 -7 -6 -5 -4 -3 -2

DBS 48h

E2 48h

Ctrl 48h

CTRL

Log Concentration (M)

Leakage % of Total LDH

Figure 7.4.6: Response of trout liver slice Vtg assay to dodecylbenzenesulfonic acid. 

Left panel shows assay run in agonist mode showing no significant Vtg activation, middle panel is assay 

run in antagonist mode showing drop off in signal occurring at same concentration as toxicity is detected 

by LDH (right panel and shown as vertical red dashed line in middle panel).

1703 The activity of the dodecylbenzene sulfonic acid is very similar between the HTP and ES TrSet binding 

1704 and TA assays. Based upon what is known about this chemical group, these assays indicate a false 

1705 positive binding and no gene induction and potential cytotoxicity at higher chemical concentrations. 

1706 This demonstrates the need to interpret these curves in the context of what is known about the chemical 

1707 group such as charge effects and pKa, and to be able to utilize accessory assays such as cytotoxity and 

1708 Ki determinations to help understand the mechanism behind the discordant responses. 
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December 2012 112 | Page

1709 Example 2. Chemical affects on media pH. This example uses the results for 8-hydroxyquinoline sulfate 

1710 (8HQ) which was also classified as a Mixed Phenol. In the HTP assays it was active in the hER binding 

1711 assay with a single concentration showing displacement and was active in the BLA assay (Figure 7.4.7). 

1712 It exhibited no indication of binding in the other two assays or activation in the BG1 assay. 

Figure 7.4.7: HTP binding assay with the hER (left panel) and HTP BLA agonist assay (right 

panel) showing significant Activity at the highest test concentration.

1713 Although in the rtER binding assay 8HQ exhibited a very steep, full displacement binding curve this 

1714 chemicals was classified as inactive in the ES TrSet because this was determined to be an artifact of a 

1715 drop in assay pH at these concentrations (right panel Figure 7.4.8). 

0

20

40

60

80

100

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CTRL

8-Hydroxyquinoline Sulfate

rtER Binding

E2

8HQ

 Log Concentration (M)

[3H]-E2 Binding (%)

pH Data

8HQ in cytosol

-11

4

5

6

7

8

9

-4 -3 -2 -1

Time: 0h

Time: 20h

Log Concentration (M)

pH

Figure 7.4.8: Binding activity for 8-hydroxyquinoline sulfate in rtER binding assay (left panel) 

and pH of the binding assay cytosol with graded concentrations of the test chemical (right panel).

1716 Further evidence that the apparent binding was not true competitive binding was produced in the trout 

1717 liver slices. 8HQ did not exhibit any agonist activity and in antagonist mode it appeared to be toxic near 

1718 -4logM. This interpretation of toxicity comes from a comparison of the two antagonism curves that use 

1719 E2 at -6 or -9logM with graded concentrations of 8HQ. These two curves show a drop in activity at the 

1720 same concentrations of 8HQ (Figure 7.4.9). If it was an antagonism effect the drop in activity should be 

1721 shifted to the right with the higher concentration of E2. This chemical also produced a pH drop in the 

1722 slice media that would be predicted to impair Vtg mRNA expression (below right).
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December 2012 113 | Page

8-Hydroxyquinoline Sulfate

liver slice Vtg mRNA_agonist

PCR 8HQ

1.0×103

1.0×104

1.0×105

1.0×106

1.0×107

1.0×108

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL

8HQ

E2

control

Log Concentration (M)

VTG mRNA copies/400 ng total RNA

8-Hydroxyquinoline Sulfate

Vtg mRNA _ antagonist

PCR 8HQ

1.0×103

1.0×104

1.0×105

1.0×106

1.0×107

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL

8HQ+E2-9

8HQ+E2-6

E2

control

Log Concentration (M)

VTG mRNA copies/400 ng total RNA

pH of Slice Media

8-Hydroxyquinoline Sulphate

6.6

6.8

7.0

7.2

7.4

7.6

7.8

8.0

8.2

-5 -4 -3 -2

Expt. 0524

Expt. 0907

Expt. 0927

CTRL

Concentration (log M)

pH of Slice Media

Figure 7.4.9: Effect of 8-hydroxyquinoline in trout liver slice Vtg assay. 

The left panel shows the assay run in agonist mode and no evidence of Vtg mRNA activation is present. 

In the middle panel the assay was run in antagonist mode with two different concentrations of E2 (-6 and 

-9logM) with graded concentrations of 8HQ.

1723 7.4.3 Strength of Evidence for Inactive Parent but Active Metabolite

1724 As described in section III, placing assays within the context of an AOP framework provides greater 

1725 confidence for making the linkage between the activity of the chemical and the molecular initiating 

1726 event used to develop the structure-activity relationships. Whether the assay endpoint is measuring at the 

1727 molecular initiating event (MIE), or further downstream such as a gene activation response downstream 

1728 from a receptor binding MIE, having confidence that the activity is due to parent chemical and not an 

1729 active metabolite structure interacting at the receptor is critical for establishing sound predictive 

1730 chemical categories based on these structure-activity relationships. The following two examples 

1731 illustrate the types of assay responses that can be indicative of metabolism and the level of information 

1732 that can inform whether a parent chemical is called active.

1733 Example 1. The Benzotriazole Phenols. This is another group classified as Non-Binders in the ES TrSet. 

1734 The chemical 2-(2 ́-hydroxy-5 ́-methylphenyl)benzotriazole produced no sign of binding in the rtER 

1735 binding assay, no binding in the bovine or human HTP assays, but it gave nearly a 70% displacement 

1736 curve in the HTP mouse ER-alpha LBD assay (Figure 7.4.10). 

1737 This chemical was not tested in the trout liver slice vitellogenin mRNA induction assay because of the 

1738 flatline response in the rtER binding assay and further evidence for this class as inactive by the flatline 

1739 rtER binding curve of 2-(2'-hydroxy-5'-t-octylphenyl)benzotriazole, a structurally similar benzotriazole 

1740 phenol in the ES TrSet (Figure 7.4.11b). In addition, this t-octylphenyl substituted benzotriazole was 

1741 also Inactive in all five of the HTP assays, further supporting this as an Inactive group. 
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Figure 7.4.10: Activity of 2-(2 ́-Hydroxy-5 ́-methylphenyl)benzotriazole in binding assays 

(A) rtER, (B) HTP bER, (C) HTP hER, (D) HTP mERa.

Figure 7.4.11: Structure comparison of two Benzotriazole Phenols in ES TrSet.

1742 The methylphenyl benzotriazole was Inactive in the BLA assay in three replicate experiments, but gave 

1743 a response equivalent in efficacy to estradiol in the BG1 assay in three replicate experiments (Figure 

1744 7.4.12). 

2-(2-Hydroxy-5-methylphenyl)benzotriazole

rtER Competitive Binding

-11 -10 -9 -8 -7 -6 -5 -4 -3 -2

0

10

20

30

40

50

60

70

80

90

100

110

120

E2

Competitor (M)

Specific Binding (%)

A B

C D
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Figure 7.4.12: Activity of -(2 ́-Hydroxy-5 ́-methylphenyl)benzotriazole in the BG1 agonist assay 

showing strong reproducible Activity in three replicate experiments each with chemical from a 

different source library.

1745 When considering the results across all of the HTP assays being compared, this chemical gives an 

1746 equivocal signal. The lack of binding data in three of the binding assays, the shallow slope of the 

1747 binding curve in the mERa binding assay and the strong indication of activity in the BG1 assay might 

1748 suggest that the activity is due to metabolic activation. The BG1 responses were obtained from three 

1749 different chemical sources, thus chemical purity is likely not the cause of discrepancy with BLA. The 

1750 evidence shown above in the binding assays strongly suggests that the parent chemical is not an ER 

1751 binder. Metabolic activation should be investigated as a source of discrepancy in activity to verify 

1752 whether the chemical structure of the parent chemical is responsible for this activity.

1753 Example 2. Methyltestosterone. Methyltestosterone was classified as a nonbinder Mixed Phenol in the 

1754 ES TrSet while its metabolite methylestradiol was a binder. Methyltestosterone was classified as active 

1755 exhibiting partial displacement of E2 in the hER binding assay using cell homogenates (Figure 7.4.13), 

1756 but negative in the NVS bER and NVS mERa. 

Figure 7.4.13: HTP binding curves for Methyltestosterone. Binding curve on the left is the hER 

assay showing nearly 50% displacement. 

The bER (middle panel) and mERa (right panel)

1757 Methyltestosterone was very active in both the BG1 and BLA assays, giving activation responses near 

1758 that of the E2 max in the three replicate runs with the BG1 assay and from 40-60% of E2 max in all 

1759 three replicate runs of the BLA assay (Figure 7.4.14). This chemical is known to be activated via 

1760 aromatase to the more potent 17-methylestradiol, thus the activity in the metabolically competent cell1761 based assays is not surprising. 

1762 The panels below show examples of one of three replicates of each of the BG1 assay and BLA assay 

1763 results for methyltestosterone showing significant activity.
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Figure 7.4.14: Panels showing examples of one of the three replicate runs of the BG1 assay (left 

panel) and BLA assay (right panel) for methyltestosterone. 

Both assays showed significant activity.

1764 Methyltestosterone (MT) was tested in rtER binding assay in four replicate runs and gave minimal 

displacement of 3 1765 H-E2 from the receptor so was classified as inactive (Figure 7.4.15). Although the 

1766 parent chemical is inactive, this chemical is metabolized in fish to 17-methylestradiol which has far 

1767 greater affinity for the receptor than MT (RBA = 68.3% in fathead minnow; Hornung et al. 2004). The 

1768 formation of this active metabolite in the fish was verified by analytical chemistry to establish the 

1769 mechanism for this activity. No Vtg slice assays were conducted as it was determined that enough 

1770 information was available to be confident that slice assays would be positive but Vtg induction would be 

1771 due to the 17-methylestradiol produced from MT and not to direct ER binding and activation by MT. 

1772 Methyltestosterone was classified as a Non-binder in the ES TrSet while its metabolite 

1773 17-methylestradiol is classified as a Binder.

Methyltestosterone

rtER Binding

-11 -10 -9 -8 -7 -6 -5 -4 -3 -2

0

20

40

60

80

100

120

E2

MT

Competitor (M)

Specific

Binding (%)

Figure 7.4.15: Methyltestosterone in rtER binding assay showing a maximum of 30% 

displacement of 3H-E2 from the receptor at the highest test concentration.

1774 7.4.4. Potential Assay Signal Artifacts in the Context of Other Evidence 

1775 In this final example, identifying potential artifacts based upon the detection endpoint of the assay can 

1776 influence whether a chemical is determined to be active. The potential for fluorescence based assays to 

1777 show flare responses has been described earlier in section 4, but whether these are detected or not, other 

1778 indicators may be suggestive that some signal artifact is occurring. Looking at responses of the 
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December 2012 117 | Page

1779 fluorescence assays in the context of the strength of the information from other assays can also help to 

1780 determine the confidence in interpreting the results of the assay for a given chemical.

1781 n-Dodecylgallate was positive in the mER-alpha LBD binding assay and the BLA assay (Figure 7.4.16-

1782 7.4.18). This chemical was conducted in single replicate in the NVS ER assays. The NVS bER and hER 

1783 assays showed flatline binding curves with no indication of binding, whereas the NVS mERa assays 

1784 showed about 50% displacement leading to a call of Active in this assay. Due to the limited number of 

1785 control E2 concentrations in the NVS mERa assay it does not allow for generation of a full dose 

1786 response curve for E2; thus the shape of the apparent binding curve for the test chemical cannot be 

1787 compared to that of E2. 

Figure 7.4.16: HTP binding curves for dodecylgallate. 

bER is on the left, mERa is shown in the middle panel, and the hER binding curve showing no binding 

is on the right.

1788 In the HTP TA assays (see Figure 7.4.11), the BLA assay showed a single significant point above the hit 

1789 call threshold). The BLA assay is a fluorescence based assay so this spike in response at the high test 

1790 concentration may indicate a fluorescence interference or other signal based artifact, especially as these 

1791 responses at this high concentration appear nearly identical between the two replicates. The bottom two 

1792 panels below show the two replicate runs of dodecylgallate in the BG1 assay showing no response and a 

1793 reproducible drop-off of background activity above 1e-6 M which might be indicative of toxicity; 

1794 although the cytotoxicy data points (yellow symbols) do not indicate toxicity at these concentrations.
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Figure 7.4.17: HTP TA assays run for dodecyl gallate. 

Two replicate runs in the BLA assay in the top two panels and two replicate runs in the BG1 assay in the 

bottom two panels.

1795 In the trout binding assay, there is some apparent displacement of E2 from the receptor, but this is 

1796 confounded by significant precipitation occurring at those concentrations (Figure 7.4.11; top left). 

Ignoring those concentrations at which precipitation was evident, the displacement of 3 1797 H-E2 from the 

1798 receptor was 10-30% at -4logM. In the trout liver slice assay, there was no indication of Vtg mRNA 

1799 induction in the two replicate runs (Figure 7.4.12; two bottom figures), thus, taken together, this 

1800 evidence clearly supported calling dodecylgallate as a Non-binder in the ES TrSet. 
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December 2012 119 | Page

0

10

20

30

40

50

60

70

80

90

100

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL

E2

Dodecylgallate

rtERBinding

ppt

 Log Concentration (M)

[3H]-E2 Binding (%)

 

Solubility of Dodecylgallate in Cytosol

0

10

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1

100

200

300

400

Control-0h EtOH

DDG-0h

Control-20h EtOH

DDG-20h

H2O blank

Ctrl

Log Concentration (M)

Solubility (Nephelometry Values)

Dodecylgallate trout Vtg mRNA

PCR 3.4.8

1.0×103

1.0×104

1.0×105

1.0×106

1.0×107

1.0×108

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL

DDG

E2

control

Log Concentration (M)

VTG mRNA copies/400 ng total RNA

Dodecylgallate: trout Vtg mRNA

PCR 4.15.8

1.0×104

1.0×105

1.0×106

1.0×107

1.0×108

1.0×109

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL

DDG

E2

control

Log Concentration (M)

VTG mRNA copies/400 ng total RNA

Figure 7.4.18: Dodecylgallate responses in ES TrSet assays. 

Top left shows rtER binding results with vertical dashed line indicating where ppt formation occurred as 

shown by nephelometry in the upper right panel. The two lower panels show replicate runs in agonist 

mode showing no significant Vtg mRNA induction.

1801 The combined interpretation of the HTP and ES TrSet assays would suggest that this is likely an 

1802 Inactive chemical. Because the NVS binding assays were conducted in limited replication in these runs 

1803 it does not provide the strongest evidence that this chemical is an ER active and before calling the spiked 

1804 response in the BLA assay as an active, it should be further investigated to make sure this is not due to 

1805 some fluorescent interference or signal artifact.

1806 7.5 Findings: ES TrSet and HTP Binding and TA Assays

1807 In general, all of the data gathered to make a determination of whether to call a chemical active or 

1808 inactive needs to be done by keeping in mind all of the data obtained about the chemical and the assay, 

1809 and interpreted with information about the activity of the chemical group that the chemical is in or the 

1810 activity of similar structural neighbors. When evaluating discordant calls between ES TrSet and HTP 

1811 assay data it is important to consider all reasons for discordance. Many of the factors associated with 

1812 discordance in the examples were related to the activity of the chemical in one way or another. These 

1813 types of factors include charge effects and pH effects, which may give false positive. The potential for 

1814 the chemical to produce cytotoxicity needs to be taken into account to understand the lack of activity of 
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December 2012 120 | Page

1815 an apparent binder. Discordance between assay responses can also be due to metabolism, and here it is 

1816 important to understand if the activity or lack of activity is due to the parent chemical or likely to be a 

1817 metabolite. In the context of building a category rule-based approach it is important to understand if the 

1818 activity is attributed to a metabolite. If so, then additional information to confirm the formation of the 

1819 metabolite in the system under study is warranted. Interpretation of any disparate results between assays 

1820 also needs to be done in the context of the concentration of chemical in each of the assays. The HTP 

1821 assays can raise the potential of positives, but should be followed up with some additional types of tests. 

1822 Thus, where possible, information should be gathered to understand differences specific to components 

1823 of each assay, such as those that may affect the free concentration of chemical available to interact with 

1824 the intended biological target. 

1825 A preliminary analysis of the HTP assays and the ES TrSet assays was conducted to enable an 

1826 understanding of HTP results for consideration in expanding the ES. 

1827 The major findings relative to Goal 5 (If and how HTP data could expand the ER training set to cover 

1828 the UnkBP chemicals) are as follows:

1829 Part 1. The data from the HTP assays as conducted have some strengths but also some limitations for 

1830 expansion of the ES. 

1831 HTP assays have the ability to test larger numbers of chemicals in a relatively short amount of time. 

1832 Utilizing ER assays with different means of signal detection can help indicate when a test chemical 

1833 might be interfering with the detection endpoint and yielding erroneous results, (e.g., BG1 and BLA ER 

1834 transactivation assays that utilize luciferase and fluorescence readouts, respectively). For instance, an 

1835 autofluorescent chemical giving a misread in the BLA assays should not erroneously affect the BG1 

1836 luciferase activity. Controls to detect chemical interferences at assay readout wavelengths are helpful to 

1837 detect the potential for misreads. 

1838 In the subset of HTP assays examined here there was a consistent lack of detection of chemicals that 

1839 have low but significant activity in the ES TrSet assays, across a variety of chemicals classes. This 

1840 increases uncertainty around the degree of confidence that can be placed in identifying the lowest 

1841 affinity binders as well as the degree of confidence that an inactive call is an inactive, and not a missed 

1842 low affinity active. 

1843 Another uncertainty relates to the lack of replication within assays, especially in the situation where only 

1844 one measurement point at the highest concentration tested indicates activity. Confirmation of the 

1845 response within an assay through replicate agreement, whether active or inactive, provides greater 

1846 confidence in the result. Replication is important in resolving discordant results among assays. 

1847 Part 2. An important programmatic goal of the EDSP prioritization is to have confidence that "inactive" 

1848 is truly negative and to identify potential active chemicals that trigger the molecular initiating event. The 

1849 following are areas that deserve attention during data interpretation (discussed also in Sections 5 and 6): 

1850 • Assay Sensitivity: As an initial step for prioritizing chemicals is to determine whether they have 

1851 potential to initiate or trigger the AOP , the sensitivity of an assay for weak responses should be 

1852 optimized to reduce the likelihood that chemicals with any potential for activity will be missed in 

1853 the analysis. Thus, the assays need to be optimized to detect low affinity or low potency 

1854 chemicals as was in the case of the ES TrSet assays.
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1855 • Flare Effects: Flare effects were found with the HTP results and are likely to compromise the 

1856 detection of the true result of adjacent wells or wells in close proximity. This could potentially be 

1857 resolved by greater spatial randomization of treatment wells across multiple plates. Alternatively, 

1858 where serious and obvious flare effects occur on large portions of plates, the data from those 

1859 plates should be discarded to avoid any potential for carrying undetected flare effects forward in 

1860 the data analysis.

1861 • Replication: Replication was an issue with the HTP assays, especially when interpreting the 

1862 weaker responses. For example, where one data point for the highest concentration measured 

1863 within the assay could potentially change the overall 'activity call', the lack of any replication 

1864 causes uncertainty in the interpretation. The uncertainty is magnified where discordant responses 

1865 were found among assays (among HTP assays, or HTP in comparison with ES TrSet assays) or 

1866 in instances where the apparent 'activity call' was suspect based on measured results for other 

1867 members of a chemical group. Conducting replication in a completely separate run would help to 

1868 resolve the uncertainty and increase confidence in each individual assay call. Allowing for data 

1869 inspection and interpretation between replicate runs provides an opportunity to detect and correct 

1870 for other potential problems (e.g., flares, dose ranges, etc).

1871 • Single Concentration Pre-screening: Examples provided in the section 7.2 demonstrated the 

1872 potential for inaccurate assay calls using the single concentration pre-screen of 25 μM in HTP 

1873 ER binding assays. Based on the number of prescreen hits that were later negative in full dose1874 response mode, and the number of prescreen negatives that would have been binders if run in full 

1875 dose-response mode would argue that the prescreening procedure be reassessed for 

1876 improvements or that running in a full dose-response mode would be more appropriate for a 

1877 prioritization approach. An initial dose-response followed by interpretation and resetting of the 

1878 dose range for a replicate dose-response run is recommended. 

1879 • Positive Controls. Positive controls, specifically a full E2 standard curve, should be run with 

1880 each assay. This would give greater confidence in the performance of the assays, especially the 

1881 binding assays, where the three point estradiol controls limited data interpretation.

1882 We have shown a preliminary assessment of the potential for HTP assay data to expand the ES using a 

1883 subset of the HTP and ES TrSet assays across 296 chemicals. Notable is that the non-coherence is 

1884 observed in both directions – some chemicals are active in the low-throughput assays but in none of the 

1885 HTP assays, while some chemicals are active in multiple HTP assays, but not in the low-throughput 

1886 ones. A more comprehensive comparison of these assay sets is already underway that includes all data 

1887 from the HTP assays. We fully expect that there will remain some chemicals for which the two assay 

1888 sets disagree, and by following up on those, we can help improve both the HTP assays and the 

1889 predictivity of the ES.

1890 Next steps may include the following specific analyses:

1891 1. Chemical-by-chemical comparisons of all discrepant chemicals across all assays to see if there are 

1892 chemical-structure trends that can help explain discrepancies. 

1893 2. Experimental follow-up, including reordering chemical samples for some of the discrepant 

1894 chemicals and running them in either the current assays or variants that can act as confirmatory. For 

1895 instance, some of the HTP(+)/ES(-) chemicals could be run in a mode that can be used to confirm 

1896 whether activity is due to binding to the ER binding pocket rather than through another mechanism.
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1897 Section 8 Category-Based Approach and Strategic Testing to Cover Unknowns

1898 A testing approach incorporating the principles used in the category-based approach for building the 

1899 original ES to cover the FI and AM inventories, and then used to expand coverage to the larger NF 

1900 inventory, can be applied again to cover the remaining EDSP Universe unknowns after application of 

1901 ESv2. The 'strategic testing' strategy presented in this section illustrates an approach that is relevant to 

1902 any in vitro assay data that is applicable for measuring the endpoint under study. This specific example 

1903 utilizes information on the chemicals class and LogKow ranges within classes where HTP data are 

1904 available to demonstrate the concept that a minimal amount of chemical testing (~100-200 chemicals) if 

1905 done in a 'strategic testing' chemical category-based approach can cover the remaining 1,700 unknowns 

1906 avoiding the need to test ~1,000 remaining chemicals for which no training set data are available.

1907 8.1 HTP coverage of the 29% ESv2 UNKs 

1908 There were 1,704 inventory chemicals of the 5,975 that were outside of the ESv2 expanded domain, and 

1909 thus, assigned UnkBP. Table 8.1.1 shows the 1,704 chemicals distributed across ESv2 groups, with the 

1910 chemical group names shown (column 1), the total number of chemicals in the group (column 2), the % 

1911 of 1,704 unknowns in the group (column 3). The table also shows (column 4) what % of all chemicals in 

1912 a group (% of column 2 chemicals) that have HTP data available, and whether there is HTP for eight 

1913 assays (column 5) of just the 2 TA assays (column 6). The remaining columns in Table 8.1.1 (columns 7 

1914 & 8) show the %, and number, respectively, of chemicals in that group that do not any HTP data 

1915 available. 

1916 Overall, HTP data was found for 44% or 744 chemicals (sum of columns 5 & 6 in Table 8.1.1) of the 

1917 total 1,704 UnkBP. Further, assessment of the 744 chemicals shows that 509 chemicals (68%) have HTP 

1918 data for the 2 NCGC ERTA assays, while 235 more UNKs have HTP data for all eight ER assays. As 

1919 mentioned in a previous section, the vast majority of these UnkBP chemicals are 'Mixed Organics' 

1920 (1,368 chemicals; 80%) and another 133 (8%) are 'Mixed Phenols'. The next largest UNK grouping is 

1921 'MultiCyclic Hydrocarbons' with 67 chemicals or 4% of all the UNKs. The remaining chemical UNKs 

1922 are from 21 ES chemical groups with 1 to 27 chemicals found within a single group (Table 8.1.1). The 

1923 HTP data coverage of the large MO class is 43%, with 41% coverage of the MP class, and 69% of 67 

1924 MultiCyclic Hydrocarbons with HTP data available from 2 or eight HTP ER binding and ERTA assays. 

1925 The HTP data coverage for the entire EDSP inventory is 44% overall consistent with the coverage of 

1926 MO and MP groups. 
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December 2012 123 | Page

1927 Table 8.1.1: Distribution of 1,704 chemicals of UnkBP (from the 5,975 EDSP inventory prior to final physical-chemical property 

1928 filtering) across ESv2 chemical groups as defined in the decision tree (Figure3-3). 

1929 The total number of chemicals in a class and % of 1,704 Total UNKs is listed, as well as number and % of chemicals within a class with 

1930 either TC&T21 (eight assays), 2 HTP assays (T21 only), or no HTP. Mixed Organics represent 80% of UNKs, and Mixed Phenols are 

1931 8%. Of the UnkBP chemicals, 744 (44%) have HTP data available, with 235 (32%) of chems with HTP having data from eight assays (3 

1932 binding, 5 TA), and 509 (68%) of chems with HTP having data from 2 (T21) ERTA assays. 

Chem Class # chem

% of 

1704

HTP

% chems

TC&T21

# chems

T21 only

# chems

no HTP

% 

chems

# 

chems

I. Acyclic_Perfluoro

Acyclic Perfluoro (Unknown Binding Potential) 16 0.9 6 0 1 94 15

III. Belongs to a known Inactive class

Mono-cyclic Hydrocarbons [LogKow<2.5,>6.4] 13 0.8 31 2 2 69 9 

Triazines [LogKow>4.1] 9 0.5 22 1 1 78 7 

Benzoates (non-ring Subst) [LogKow<3.0,>4.3] 6 0.4 67 1 3 33 2

Sorbitans [LogKow<3.1,>5.9] 4 0.2 25 0 1 75 3 

Imidazolidines [LogKow>0] 3 0.2 67 2 0 33 1 

Benzamides [LogKow<2.1,>2.8] 1 0.1 0 0 0 100 1 

Bis-Anilines [LogKow<2.1,>6.4] 1 0.1 0 0 0 100 1 

Phenones (n-alkyl) [LogKow>4.2] 1 0.1 100 0 1 0 0 

Pyrrolidiones [LogKow>3.4] 1 0.1 100 1 0 0 0 

III. Known Inactives Subtotal= 39 2.3 38 7 8 62 24

V. Special Rule applies

Multi Cyclic Hydrocarbons Unknown Binding Potential 67 3.9 69 21 25 31 21

Thiophosphate Esters Unknown Binding Potential 14 0.8 71 3 7 29 4 

Alkylbromobenzene 7 0.4 29 0 2 71 5 

V. Special Rules Subtotal= 88 5.2 66 24 34 34 30

VI. Site "A": Contains a Phenol fragment

Mixed Phenol - Unknown Binding Potential 133 7.8 41 19 36 59 78

Alcohols (LogKow>3.4) 27 1.6 11 2 1 89 24

Alkoxyphenols [LogKow >4.1] 1 0.1 100 1 0 0 0 

VI. Site "A" Subtotal= 161 9.4 37 22 37 63 102
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Chem Class # chem

% of 

1704

HTP

% chems

TC&T21

# chems

T21 only

# chems

no HTP

% 

chems

# 

chems

VII. Site "B": Contains "Specified" fragment

Mixed Organics - Unknown Binding Potential 1368 80.3 

2-Alkylanilines 7 0.4 43 175 414 57 779

Mixed Acetanilides (LogKow>1.3) 7 0.4 86 4 2 14 1 

Phenones cyclo-substituted - Unknown Binding 

Potential

6 0.4 29 0 2 71 5 

Multi subst. Alkylanilines 5 0.3 67 0 4 33 2 

Alkylcyclohexanols LogKow>4.4 or >2.6 and <3.5 4 0.2 100 1 4 0 0 

3-Alkylanilines 2 0.1 50 1 1 50 2 

2-;4-; or2,4,6-Benzoates Unknown Binding Potential 1 0.1 100 1 1 0 0 

VII. Site "B" Subtotal= 1400 82.2 100 0 1 0 0 

Unknown Binding Potential Total= 1704 44 235 509 56 960
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1933 An additional assessment beyond just the exact number of UnkBP chemicals for which HTP data are 

1934 available is warranted. Employing the ES conceptual approach, it is appropriate to assess where HTP 

1935 test data occurs with respect to the total number of inventory chemicals within a chemical class, and 

1936 with respect to the LogKow range represented. This further analysis also results in a proposal for 

1937 strategic testing to reduce the number of UnkBP without testing all of them. The approach consisted of 

1938 two parts: 

1939 Part 1: A systematic approach similar to that used to build the initial ES can be taken for UnkBP 

1940 chemicals that are classified in ES chemicals groups.

1941 Part 2: A separate approach is needed for chemicals in the two large mixed functional group classes 

1942 (MP and MO).

1943 Testing Proposal: Part 1_Grouped Chemicals. For those chemicals that could be assigned to ESv2 

1944 chemical groups, an assessment was done of the available HTP data, what type of data, i.e., eight assays 

1945 from the ToxCast and the Tox21 research programs described in section 4 or 2 assays from the Tox21 

1946 only, and where the data occurred with respect to the LogKow range of all chemicals in the class (Table 

1947 8.1.2). Table 8.1.2 includes columns noting if further testing is needed, the LogKow range not well 

1948 covered, and the number of chemicals recommended to test to cover the class. This approach uses the 

1949 mechanistic rationale that RBA is correlated with LogKow across ES chemical classes. For the purpose 

1950 of this exercise, it was assumed that any HTP ER binding or ERTA data available (whether only for the 

1951 two NCGC ERTA assays (Tox21 only') or for all 8 ToxCast and Tox21' assays were appropriate to 

1952 assess the ER binding potential of all chemicals regardless of the ES chemical group. Some insight into 

1953 that assumption is presented in sections 6 and 7 of this document. There are 203 total UnkBP chemicals 

1954 in Table 8.1.2, with 41 having 'TC & T21' HTP data, and 77 with 'T21 only' data, leaving 126 more 

1955 needing information. All 126 could be tested, or, based on where the HTP data occurs with respect to the 

1956 total Log Kow range of a chemical class, using the ES concepts described in previous sections the 

1957 number of additional chemicals recommended for testing to expand the ES to cover all 203 is 35 

1958 chemicals. 

1959 Testing Proposal: Part 2_Defining new chemical classes within Mixed Organics and Mixed Phenols. 

1960 The second approach examined the chemical structures represented in mixed groups (MO and MPs) 

1961 representing 88% of all UNKS remaining after application of ESv2 to the EDSP inventory. An 

1962 inspection of chemicals structures was done to search for common functional groups which will serve as 

1963 the basis of new chemical classes. Structural fragments for 16 proposed new classes were found (Figure 

1964 8.1.1). The approach is consistent with the iterative approach used to build the ESv1 and the expansion 

1965 to ESv2 described in section 3.1c.
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1966 Table 8.1.2. Summary of UNK chemicals within ESv2 chemicals classes, LogKow ranges, number of chemicals with HTP ToxCast 

1967 or Tox21 data and LogKow range covered, and recommendations for additional number LogKow ranges to test. Recommendations 

1968 to test are based on assuming all available HTP data is acceptable for the respective chemical class where available. Mixed 

1969 Organics and Mixed Phenols are excluded. Tox21*= total of ToxCast&Tox21 plus Tox21 only to assess total LogP range covered 

1970 by Tox21 ERTA assays. 

Total Class HTP HTP Testing needed or recommend

ESv2 Chemical Class

# 

chem

LogP 

range

ToxCast & 

Tox21 # chems

LogP 

range 

covered

Tox21*# 

chems

LogP range 

covered

HTP 

coverage 

Adequate?

LogP range 

to test

# chems to 

test

I. Acyclic_Perfluoro 

Acyclic Perfluoro UnkBP 16 -2.48-9.98 0 1 6.02 test to 

bound 

range

-2.48-4.99; 

>6.02-9.98

5 to ?

III. Belongs to a known Inactive class

Mono-cyclic Hydrocarbons 

[LogKow<2.5,>6.4]

13 1.99-2.47; 

6.83-9.42

2 2.25;7.94 2 1.99-

2.25;7.38-

7.94

-- 2.47;6.83-

7.38;

7.94-9.42

3 to 4

Triazines [LogKow>4.1] 9 5.12-

36.54

1 5.27 2 5.12-5.27 -- >5.27-36.54 3 to 4

Benzoates (non-ring 

Subst)[LogKow<3.0,>4.3]

6 1.83-2.74;

4.30-5.93

1 2.32 4 1.83-

2.74;5.84

-- 4.30;5.93 2 

Sorbitans [LogKow<3.1,>5.9] 4 5.39-

22.35

0 -- 1 6.10 -- 5.39-

<6.10;>6.10-

22.35

3 

Imidazolidines [LogKow>0] 3 0.15-2.16 2 0.22-2.16 2 0.22-2.16 -- 0.15 1 

Benzamides [LogKow<2.1,>2.8] 1 1.71 0 -- 0 -- -- 1.71 1 

Bis-Anilines [LogKow<2.1,>6.4] 1 8.81 0 -- 0 -- -- 8.81 1 

Phenones (n-alkyl)[LogKow>4.2] 1 4.68 0 -- 1 4.68 T21 

covered

-- --

Pyrrolidiones [LogKow>3.4] 1 5.30 1 5.30 1 5.30 TC&T21 

covered

-- --

III. Known Inactives Subtotal= 39 7 13

V. Special Rule applies

Multi Cyclic Hydrocarbons UnkBP 67 3.16-

17.62

21 3.16-6.70 25 3.16-17.62 T21 

covered

-- --

Thiophosphate Esters UnkBP 14 0.72-6.47 3 4.08-5.65 10 1.99-5.65 -- 0.72;6.47 2 

Alkylbromobenzene 7 2.88-7.48 0 -- 2 2.88-3.43 -- 3.43-7.48 2 to 3

V. Special Rules Subtotal= 88 24 37

VI. Site "A": Contains a Phenol fragment
Prioritization of the EDSP Universe of Chemicals

December 2012 127 | Page

Total Class HTP HTP Testing needed or recommend

ESv2 Chemical Class

# 

chem

LogP 

range

ToxCast & 

Tox21 # chems

LogP 

range 

covered

Tox21*# 

chems

LogP range 

covered

HTP 

coverage 

Adequate?

LogP range 

to test

# chems to 

test

Alcohols (LogKow>3.4) 27 3.42-7.62 2 5.63-7.62 3 3.46-7.62 -- 3.42-5.62 2 to 4

Alkoxyphenols [LogKow >4.1] 1 1.34 1 1.34 1 1.34 TC&T21 

covered

-- --

VI. Site "A" Subtotal= 28 3 4 

VII. Site "B": Contains "Specified" fragment

2-Alkylanilines 7 1.62-3.99 4 1.62-3.15 6 1.62-3.99 TC&T21 

covered

-- --

3-Alkylanilines 2 1.62-2.17 1 1.62 2 1.62-2.17 T21 

covered

-- --

Multi subst. Alkylanilines 5 2.17 but 

multisites

1 2.17 

dimethyl 

only

5 2.17 all-sites 

tested

T21 

covered

-- --

Mixed Acetanilides (LogKow>1.3) 7 0.01-4.44 0 -- 2 0.89-2.05 -- 2.05-4.44 1 to 2

Phenones cyclo-substituted - UnkBP 6 2.60-4.73 0 4 2.60-4.53 -- 4.53-4.73 1

Alkylcyclohexanols LogKow>4.4 or 

>2.6 & <3.5

4 2.96-

3.42;5.0-

5.4

1 3.42 2 2.96-3.42 -- 5.0-5.4 2 

2-;4-; or2,4,6-Benzoates UnkBP 1 2.38 0 -- 1 2.38 T21 

covered

-- --

VII. Site "B" Subtotal= 32 7 22

Unk Binding Potential Total= 203 41 77 Max # chems to test= 35

1971
Prioritization of the EDSP Universe of Chemicals

December 2012 128 | Page

1972 The analogs shown earlier in Figures 3-12A to 3-12F (section 3.1c) were chosen to better cover the 2009 

1973 NF list (section 3.1c) and will be combined with the proposed new classes resulting from the application 

1974 of ESv2 to the EDSP inventory (Figure 8.1.1) to provide the basis for future expansion of the ESv2 to 

1975 ESv3. The expansion to ESv2 to cover the NF 1,423 list is relevant to increased coverage of the EDSP 

1976 inventory because 1,205 of the 1,423 NF structures are also part of the EDSP inventory. As described 

1977 previously in this paper, the EDSP inventory is comprised of Inerts (FI and NF, and Frag), and SDWA 

1978 chemicals. The analogs in the figure from the section 3 (Figure 3-12A to 3-12F) primarily covered FI 

1979 and NF Inerts, while the proposed new classes (Figure 8.1.1), cover predominately SDWA chemicals 

1980 and Fragrances. Thus they should complement each other for a proposed ESv3 expansion to cover a 

1981 greater portion of the EDSP inventory. A testing strategy to efficiently generate training set data and 

1982 accomplish this is further described below. 

Figure 8.1.1: Structural fragments common to multiple chemicals classified as Mixed Organics or 

Mixed Phenols when ESv2 was applied to EDSP inventory. 

These fragments provide the basis of new chemical groupings for development of ER ESv3. 

1983 The 16 new chemical groups proposed for ESv3 expansion to cover additional Frag and SDWA 

1984 chemicals are listed in Table 8.1.3. Classes composed of a majority of Frag chemicals include 

1985 aldehydes, ketones, carboxy esters, cyclohexanols and cyclohexanones, with several in the Pyridines and

1986 Quinolines group. Not surprisingly, the most number of UnkBP chemicals are found in the Frag classes 

1987 given that the ES was not built to cover these classes and only 48 % of Fragrances overall were covered 

1988 by ESv2. Classes composed entirely or largely of SDWA chemicals include anthraquinones, 

1989 benzothiazoles, chlorinated cyclic hydrocarbons, halobenzenes, halophenols, isocyanates, 

1990 nitroaromatics, nitrophenols, siloxanes, and sulfonamides. 
Prioritization of the EDSP Universe of Chemicals

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1991 The search queries defined for the 16 new groups proposed for ESv3 expansion (Figure 8.1.1) were 

1992 applied to the 5,975 EDSP inventory to assess the number of chemicals that currently have information 

1993 available in the ES TrSet or would be covered as new testing is done within the 16 groups as 

1994 recommended in this section. Table 8.1.3 shows how many chemicals are found within a new group 

1995 (sum of columns 2, 3, & 4). It also includes information on where predictions can currently be made, 

1996 i.e., number of chemicals predicted to RBA<0.00001% (Nonbinders) and RBA>0.00001% (Binders) 

1997 within each class based on current TrSet data incorporated into ESv2. For example, for Ketones there 

1998 are five chemicals falling within this class that were tested; one Non-binder and four Binders, and 

1999 another 49 UnkBP. These results would indicate the ketones are a class of concern since there are 

2000 several Binders found in the group. Strategic testing of a few more representatives structures from 

2001 within the 49 UnkBP chemicals, should facilitate establishment of additional logic rules for predictionof 

2002 activity within the class. Other groups of higher priority due to presence of Binders would be aldehydes, 

2003 cyclohexanols/cyclohexanones, and sulfonamides. A strategic testing plan that builds from where 

2004 current TrSet data is available within a class can be developed, similar to that used in expanding the 

2005 TrSet to allow development of ESv2. 

2006 Table 8.1.3: Number of chemicals within the 5,975 EDSP Universe found in the 16 new groups 

2007 proposed to expand to ESv3, and the current coverage of ESv2 within each chemical group 

2008 displayed as the number of chemicals within the group predicted to be a Nonbinder 

2009 (RBA<0.00001%) or a Binder (RBA>0.00001%). For example, 2 aldehydes are predicted 

2010 Nonbinders, 1 is a predicted Binder, and 55 UnkBP are out of the model domain and cannot be 

2011 predicted.

Number of Chemicals

RBA<0.00001% RBA>0.00001% UnkBP 

Aldehydes 2 1 55

Carboxy Esters 12 0 151

Ketones 1 4 49

Cyclohexanols 2 0 32

Cyclohexanones 4 2 19

Pyridines & Quinolines 14 0 35

Anthraquinones 1 0 29

Benzothiazoles 5 0 5 

Chlorinated Cyclic Hydrocarbons 0 0 18

Halo Benzenes 0 0 21

Halo Phenols 0 0 25

Isocyanates 3 0 18

Nitro Aromatics 1 0 24

Nitro Phenols 0 0 13

Siloxanes 1 0 7 

Sulfonamides 19 1 9 

2012 While all five prioritized ketone chemicals were tested in the example given above, this is not to imply 

2013 that all active or inactive chemicals in the table were tested but rather some test values were combined 

2014 with other exclusion rules, e.g., LogKow <1.3, to make a determination. In fact, for the 12 carboxy esters 

2015 of RBA<0.00001%, four chemicals were tested negative and the remaining eight were below the 

2016 LogKow <1.3 cutoff. The aldehydes, ketones, carboxy esters, cyclohexanols, and cyclohexanones are all 
Prioritization of the EDSP Universe of Chemicals

December 2012 130 | Page

2017 important for expanded coverage of the ES as they predominantly make-up the contribution of Frag to 

2018 the EDSP inventory, and if percentages hold as seen for other Frag among the chemicals within the 

2019 model domain it might be expected that 10% of these will be Binders (Table 3-12B). 

2020 Similar to what was done for Part 1 using defined ES groups, an assessment was done for the 16 new 

2021 groups established on the MO and MPs to develop an approach for use of available HTP assay data 

2022 found appropriate to each ES group (section 7). The number of EDSP inventory chemicals found in each 

2023 new proposed group from Table 8.1.3 was reported along with the LogKow range covered by the 

2024 chemicals (Table 8.1.4). The HTP data availability for chemicals within the group, and the LogKow

2025 range of test data relative to the entire chemical group was collated. Again, the number of chemicals 

2026 having 'ToxCast and Tox21' HTP data (eight assays) and the LogKow ranges covered, and the number 

2027 with 'T21 only' (2 assays) HTP data and LogKow coverage is reported in the table. The LogKow range 

2028 not well covered, whether testing is needed, and how many additional chemicals would likely need to be 

2029 tested is included. As before, assuming all currently available HTP data can be used for the new 

2030 chemical classes, then testing of 10 to 22 additional chemicals to expand the ES to cover these classes 

2031 should allow predictions for the 506 UnkBP in Table 8.1.4, even though HTP data was available for less 

2032 than half the chemicals (i.e., 69 chemicals with data from eight assays, and 229 chemicals with data 

2033 from two to eight assays). The discussion here is meant to illustrate the approach to iterative 

2034 development of new chemical classes and testing within those classes, but is not inclusive of all new 

2035 classes possible. Additional assessments should be done of the remaining MO and MP chemicals 

2036 looking for new chemical groupings. 

2037 The remaining 995 UnkBP chemicals in MO and MP groups that were not included in the 16 new 

2038 proposed groups or earlier ESv2 chemical groups 65% are from the SDWA list (with some overlap with 

2039 NF + Frag), 'Frag only', NF, FI inert classes. More could likely be assigned to classes with additional 

2040 scrutiny, or a few chemicals may be determined not to need testing such as Vitamin K 

2041 [CASNR = 12001-79-5] and chlorophyll a [CASNR = 479-61-8]. Of the 995, 125 chemicals have TC& 

2042 T21 data, and 294 more have 'T21only' HTP data, leaving 576 or 34% of all UNKs not assigned to new 

2043 classes and with no HTP data (Table 8.1.5). These 576 UNKs are 65% from the SDWA list (with some 

2044 overlap to Frag & NF), 27% Frag, 7% NF and <1% Food use INERTS.
Prioritization of the EDSP Universe of Chemicals

December 2012 131 | Page

2045 Table 8.1.4: Proposed new chemical groups for testing to cover 30% of UnkBP Mixed Organics and Mixed Phenols

Proposed New 

Chem Group

Use/Occurrence 

Class

Total Class 

# chem

Total Class 

LogP 

range

HTP TC

& T21

LogP range 

covered HTP T21

LogP range 

covered

Testing 

needed or 

HTP found

LogP range 

to test

# 

chems 

to test

Aldehydes Fragr 55 1.34-5.27 3 1.34-4.36 17 1.34-4.36 need testing 4.36-5.27 1 to 3

Carboxy Esters Fragr, some SDWA 151 1.58-

15.51

11 1.51-8.63 57 1.51-15.51 TC&T21 

covered

Ketones Fragr, few SDWA or 

NF

49 1.96-6.35 4 2.04-6.35 15 1.96-6.35 T21 covered

Cyclohexanols 

(more to add)

Fragr, some SDWA or 

NF

28 2.85-

10.44

4 2.85-10.44 11 2.85-10.44 TC&T21 

covered

Cyclohexanones 

(more to add)

Fragr, some SDWA 20 1.54-5.48 1 2.94 9 2.25-3.81 need testing 1.54-2.25;

3.81-5.48

1 to 3

Pyridines & 

Quinolines

SDWA, many Fragr 35 -0.67-5.95 5 1.35-4.04 14 1.35-4.04 need testing 4.09-5.95 1 to 3

Anthraquinones all SDWA, w/ 1 NF 29 3.16-9.02 1 2.43 11 2.43-9.02 T21 covered

Benzothiazoles all SDWA 5 2.56-5.95 0 4 2.56-5.95 T21 covered

Chlorinated Cyclic 

Hydrocarbons

all SDWA 18 3.36-

11.27

6 4.26-7.35 9 4.26-7.35 need testing 3.36-3.81;

11.27

1 to 3

Halo Benzenes all SDWA 21 3.08-7.46 8 3.08-4.57 17 3.08-7.33 need testing 7.33-7.46 1 

Halo Phenols all SDWA 25 1.71-5.96 7 2.16-4.18 15 1.71-5.96 T21 covered

Isocyanates all SDWA 18 2.59-6.11 2 3.7-4.75 12 3.06-6.11 need testing 2.59-3.7 1 

Nitro Aromatics all SDWA 24 1.45-5.39 10 1.63-2.91 19 1.63-5.39 need testing 1.45-1.63 1 

Nitro Phenols all SDWA 12 1.54-4.88 4 1.73-3.67 8 1.73-4.88 need testing 1.54-1.73 1 

Siloxanes SDWA, some NF 7 5.71-

14.82

2 5.71-6.51 2 5.71-6.51 need testing 6.51-14.82 1 to 3

Sulfonamides SDWA, some NF& F 9 1.87-4.33 1 2.86 3 1.87-2.86 need testing 2.86-4.33 1 to 3

Total= 506 69 223 Recommended 

# chems to test=

10 to 

22
Prioritization of the EDSP Universe of Chemicals

December 2012 132 | Page

2046 Table 8.1.5. Remaining Ungrouped MO and MPs UnkBP chemicals and HTP data availability. 

166 Ungrouped MO & MP with Both TC&T21 

Mixed Organics - Unknown Binding Potential 117

Mixed Phenol - Unknown Binding Potential 8 

TC&T21 Total= 125

353 Ungrouped MO & MP, or ESv2 Grouped UnkBP Chems with 'T21 only' 

by mixed class

Mixed Organics - Unknown Binding Potential 276

Mixed Phenol - Unknown Binding Potential 18

T21 only Total = 294

679 Ungrouped MO & MP or ESv2 Grouped UnkBP with no HTP

Mixed Organics - Unknown Binding Potential 518

Mixed Phenol - Unknown Binding Potential 58

No HTP Total= 576
Prioritization of the EDSP Universe of Chemicals

December 2012 133 | Page

2047 Section 9 Summary and Key Goals for EDSP Prioritization 

2048 An EDSP universe of 10,341 unique chemicals (including pesticide active and inert ingredients 

2049 including fragrances, and drinking water contaminants) was identified by the agency for sorting and 

2050 priority setting for purposes of EDSP Tier 1 screening (U.S. EPA, 2012). The Tier 1 battery consists of 

2051 eleven in vitro and in vivo assays. Based on the experience with test orders issued in 2009 on 67 

2052 chemicals, the current trajectory with the current suite of Tier 1 assays would require decades to screen 

2053 the 10, 341 chemicals, requiring significant animal use and millions of dollars in testing and data 

2054 reviews. Thus, it is important that the agency reliably discerns which of the 10,341 chemicals do not 

2055 need to be subjected to EDSP Tier 1 screening and which ones should be screened. By fully optimizing 

2056 the use of both new and existing tools, the agency believes it can effectively achieve this goal. 

2057 The agency plans to implement a prioritization scheme that will use both exposure and effect based 

2058 metrics. This prioritization methodology draws on several fundamental concepts critical to the 

2059 development of computational tools: 

2060 • Use of existing knowledge, including the understanding of inherent chemical properties and 

2061 chemical structure and bioactivity relationships, 

2062 • Use of category-effects based and read across methods to maximize incorporation of existing 

2063 knowledge and effectively enable chemical extrapolation to avoid unnecessary testing (i.e., 

2064 strategic testing regardless of whether it's in vitro or in vivo),

2065 • Use of the adverse outcome pathway (AOP) concept to guide the development and application of 

2066 in vitro testing including lower or higher throughput methods with computer-based simulations, 

2067 and

2068 • Use of principles and practices to ensure scientific transparency, structure and rigor. 

2069 In applying these concepts, section 2 demonstrated how effective the use of physiochemical properties 

2070 are in reducing a large inventory by removing compounds untestable or unlikely to present a systemic 

2071 effect (i.e., ~ 10,000 chemical inventory reduced to ~ 5,000 or less). Section 3 illustrated the use of the 

2072 AOP construct in targeting the molecular initiating event with additional information gathered at a 

2073 higher level of biological organization along the pathway. The expert system was developed based on 

2074 measuring and understanding chemical structure - ER binding and ER-mediated gene activation 

2075 relationships by strategically testing in vitro. Two assays are used: (i) an ER (rainbow trout) 

2076 competitive binding assay to directly measure chemical-biological interaction and (ii) a trout liver slice 

2077 assay in which the consequences of ER activation or inhibition are measurable as a result of chemical2078 tissue uptake and partitioning in the presence of xenobiotic metabolism. Thus, the sequential application 

2079 of two in vitro assays along an AOP can be used to understand the consequence of different types of 

2080 chemical-receptor interactions. This understanding was important to the efficient use of chemical 

2081 extrapolation in addressing a large inventory for purposes of prioritization (i.e., greater than 70 percent 

2082 of approximately 6,000 chemicals were prioritized). Sections 4 and 5 present the high throughput 

2083 technology intended to expand effectively the knowledge of the chemical-receptor interactions for 

2084 purposes of covering the remaining 1,700 chemicals that cannot be predicted by the current ER expert 

2085 system. Sections 6 and 7 provided information on reference and training set chemicals to further the 

2086 understanding of strengths and limitations around the HTP in vitro data. There will be uncertainties in 

2087 any method, and it is important to characterize the nature of these uncertainties and objectively 

2088 determine how to use appropriately the data for the intended purpose, i.e., priority setting. 

2089 Understanding the type and degree of uncertainty tolerated in decision-making will help chart the 
Prioritization of the EDSP Universe of Chemicals

December 2012 134 | Page

2090 research and incremental application to decision making. In the case of priority setting for EDSP, it is 

2091 important to have confidence that negative findings are likely to indicate inactivity for the endpoint 

2092 measured/predicted. Section 8 provides a strategic testing approach based on understanding of the 

2093 chemical structures/properties to address the 1,700 unknown chemicals in a chemical category-based 

2094 approach. Rather than testing all 1,700, only a fraction needs to be tested in vitro (i.e., ~15%) to 

2095 understand and predict the chemical-receptor activity. Thus, the iterative process of in vitro testing and 

2096 chemical extrapolation lead to a more focused testing strategy.

2097 By taking advantage of both existing and emerging technologies, a scientifically robust and 

2098 comprehensive chemical prioritization process is being proposed. 

2099 While the agency has not displayed the full prioritized list of chemicals, it has demonstrated the general 

2100 methodology and concepts in which the chemical prioritization scheme would be applied for the ER 

2101 pathway, in sequence and chemical coverage. Dependent on the recommendations from the FIFRA SAP 

2102 expert panel, the agency plans to exercise the following steps in its completion of the prioritization 

2103 process:

2104 • Apply all physico-chemical exclusion criteria, exposure filters and engage in expert chemistry 

2105 review to ensure a "reality" check for scientific soundness and biological plausibility. Finalize 

2106 the analysis of additional HTP assays and select a subset that are of sufficient quality and 

2107 accuracy to use in expanding the ES. 

2108 • Expand ER expert system to version 3 to capture the remaining universe of chemicals currently 

2109 covered by applicable HTP data. 

2110 • For those chemicals not covered by the ES domain of applicability and by HTP data, generate 

2111 the appropriate training set data to complete coverage.

2112 • Prior to officially advancing any chemical for EDSP Tier 1 screening, the agency must make the 

2113 exposure finding as stated in the 1996 SDWA Amendment, section 1457 and excluding 

2114 chemicals no longer produced or used in the United States. 

2115 As the agency has envisaged and demonstrated a prioritization process for the ER pathway, the agency 

2116 plans to apply the same general concepts and decision logic for the remaining androgen receptor and 

2117 thyroid pathways, acknowledging that the androgen pathway may be more similar to the estrogen 

2118 receptor pathway than for the thyroid pathways. As the agency develops the additional prioritization 

2119 tools for the remaining pathways, the androgen and thyroid based prioritization schemes will be applied 

2120 to those chemicals that pass the physico-chemical exclusion criteria filters.
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2196 Schmieder, P.; Tapper, M.; Linnum, A.; Denny, J.; Kolanczyk , R.; and Johnson, R. 2000. Optimization 

2197 of a precision-cut trout liver tissue slice assay as a screen for vitellogenin induction: comparison 

2198 of slice incubation techniques. Aquatic Toxicology. 49, 251-268. 

2199 Schmieder, P.K., M.A. Tapper, J.S. Denny, R.C. Kolanczyk, B.R. Sheedy, T.R. Henry and G.D. Veith. 

2200 2004. Use of trout liver slices to enhance mechanistic interpretation of estrogen receptor binding 

2201 for cost-effective prioritization of chemicals within large inventories. Environmental Science & 

2202 Technology 38. 6333-6342. 

2203 Schreurs R., Lanser, P., Seinen, W., van der Burg, B. 2002. Estrogenic activity of UV filters determined 

2204 by an in vitro reporter gene assay and an in vivo transgenic zebrafish assay. Archives 

2205 Toxicology. 76, 257-261 

2206 U.S. EPA. 1999. Joint SAP/SAB Meeting Concerning the EPA's Position on the Testing of Endocrine 

2207 Disruptors. April 1999. http://www.epa.gov/scipoly/sap/meetings/1999/033099_mtg.htm 

2208 U.S. EPA. 2008. Technical Review Document, Endocrine Disruptor Screening Program, Proposed Tier 

2209 1 Screening Battery. http://www.epa.gov/scipoly/sap/meetings/2008/march/technical_review.pdf 

2210 U.S. EPA. 2009a. Final Contaminant Candidate List 3 Chemicals: Identifying the Universe. EPA 815-

2211 R-09-006. August 2009. 

2212 http://water.epa.gov/scitech/drinkingwater/dws/ccl/upload/CCL3_Chemicals_Universe_08-31-

2213 09_508_v3.pdf 

2214 U.S. EPA. 2009b. Final Contaminant Candidate List 3 Chemicals: Screening to a PCCL. EPA 815-R2215 09-007. August 2009. 

2216 http://water.epa.gov/scitech/drinkingwater/dws/ccl/upload/CCL3Chem_Screening_to_PCCL_08-

2217 31-09_508v2.pdf 

2218 U.S. EPA. 2012. The EDSP Universe of Chemicals and General Validation Principle. November 2012. 

2219 http://www.epa.gov/endo/pubs/edsp_chemical_universe_and_general_validations_white_paper_

2220 11_12.pdf 

2221 Takeyoshi, M. (2006), Draft Report of Pre-validation and Inter-laboratory Validation For Stably 

2222 Transfected Transcriptional Activation (TA) Assay to Detect Estrogenic Activity - The Human 

2223 Estrogen Receptor Alpha Mediated Reporter Gene Assay Using hER-HeLa-9903 Cell Line, 

2224 Chemicals Evaluation and Research Institute (CERI): Japan. p. 1-188. 

2225 Yamasaki K et al., (2004). Comparative study of the uterotrophic potency of 14 chemicals and their 

2226 receptor binding affinity. Tox Letters 146:111-120. 
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2227 List of Appendices

2228 Appendix A: EDSP Universe of Chemicals and General Validation Principles

2229 Appendix B: An Effects-based Expert System to Predict Estrogen Receptor Binding Affinity for Food 

2230 Use Inert Ingredients and Antimicrobial Pesticides: Application in a Prioritization 

2231 Scheme for Endocrine Disruptor Screening

2232 Appendix C: SAP Minutes on "An Effects-based Expert System to Predict Estrogen Receptor Binding 

2233 Affinity for Food Use Inert Ingredients and Antimicrobial Pesticides: Application in a 

2234 Prioritization Scheme for Endocrine Disruptor Screening"

2235 Appendix D: Section 4 Supplemental, Details of Chemical Library

2236 Appendix E: Section 5 Supplemental, HTP Data Processing Pipeline 

2237 Appendix F: Section 6 Supplemental, Descriptions of HTP Assays for the Detection of Estrogen 

2238 Receptor Agonists 

2239 Appendix G: Section 7 Supplemental, ES TrSet Decision Criteria for Binder/Non binder and Data 

2240 Collection and Processing Details

2241 Appendix H: Section 7 Supplemental, Tables 

2242 Appendix I: Section 7 Supplemental, Figures