Prioritization of the EDSP Universe of Chemicals

December 2012 i | Page

Prioritization of the Endocrine Disruptor Screening 

Program Universe of Chemicals for an Estrogen 

Receptor Adverse Outcome Pathway Using 

Computational Toxicology Tools

U.S. Environmental Protection Agency

Endocrine Disruptor Screening Program

Jointly developed by: 

Office of Chemical Safety and Pollution Prevention (OCSPP)

Office of Science Coordination and Policy (OSCP)

Office of Pesticide Programs (OPP)

Office of Pollution Prevention and Toxics (OPPT)

Office of Water (OW)

Washington, DC 20460

Office of Research and Development (ORD)

National Environmental and Effects Health Research Laboratory (NEEHRL)

Mid-Continent Ecology Division (MED), Duluth, MN 55804

Toxicity Assessment Division (TAD), RTP, NC 27111

National Center for Computational Toxicology (NCCT)

Research Triangle Park, NC 27709

December 2012
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1 Preface

2 This document is intended to provide a general chemical prioritization methodology using the estrogen 

3 receptor pathway; the overall concepts and decision logic are designed to capture a generic approach 

4 that could be applied and adapted to address other endocrine pathways. Thus, the focus of this review by 

5 the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) Scientific Advisory Panel (SAP) is on 

6 the concepts, decision logic and methodologies and not on the exact number or list of chemicals that 

7 may be prioritized for the Endocrine Disruptor Screening Program (EDSP) Tier 1 screening. Consistent 

8 with this approach, that while the number of chemicals that remain after each step within the 

9 prioritization process is provided, these are only approximate values to demonstrate the potential impact 

10 of each prioritization step and application of computational tools. None of the steps described has been 

11 fully completed and finalized. It is the intention of the U.S. Environmental Protection Agency (EPA) to 

12 complete the prioritization methodology and process after receiving recommendations from the SAP on 

13 the proposed approach. 

14 None of the discussions surrounding any individual chemical substance in this paper, EPA presentations 

15 at the SAP meeting, or the meeting summary are intended to support any conclusions other than to aid in 

16 the development of potential priority-setting methods. None of the discussions, conclusions or examples 

17 presented should be taken as final decisions regarding potential hazards or risks of the substance or that 

18 the EPA has or will make a determination that any use of the substance necessarily will pose a risk. 

19 Further, the research results described are not designed to provide a scientific basis, by themselves, to 

20 allow the EPA to conclude that these substances are "endocrine disruptors." Measured or predicted 

21 estrogen receptor (ER) binding affinity data from in vitro assays for substances mentioned in this paper 

22 may prove to be useful in developing future prioritization scheme(s) for EDSP Tier 1 screening.

23 This document has been reviewed in accordance with EPA policy. Mention of trade names or 

24 commercial products do not constitute endorsement. 
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25 Table of Contents

26 Section 1 Background, Programmatic Goals, and Purpose of the FIFRA SAP Review ................. 1

27 Section 2 EDSP Universe of Chemicals and Prioritization Scheme .................................................. 4

28 2.1 Defining the EDSP Universe of Chemicals ................................................................................... 4

29 2.2 EDSP21 Prioritization Scheme using Computational Toxicology Tools...................................... 5

30 2.2.1 OPP Registration Review Schedule .................................................................................... 7

31 2.2.2 Exclusion of polymers, mixtures, and undefined substances ............................................. 7

32 2.2.3 Physico-Chemical Properties Considerations ..................................................................... 7

33 2.2.4 Prioritize SDWA Chemicals That Have Human Exposure Potential ............................... 14

34 Section 3 ER Expert System ............................................................................................................... 15

35 3.1 Introduction of ER Expert System and Five Specific Goals ....................................................... 15

36 3.1.1 Automation of ESv1 ......................................................................................................... 18

37 3.2 ESv2 ............................................................................................................................................ 18

38 3.2.1 Building ESv2 ................................................................................................................... 18

39 3.2.2 Assessing ESv1 and ESv2 Coverage of the EDSP Inventory........................................... 35

40 3.2.3 ESv2 Chemical Groups in Final Filtered EDSP Inventory of 4,957 Chemicals .............. 42

41 Section 4 High Throughput (HTP) In Vitro Screening ..................................................................... 49

42 4.1 Chemical Library and Procurement Process ............................................................................... 49

43 4.2 High Throughput (HTP) In Vitro Data Processing Pipeline Overview ....................................... 50

44 4.3 Statistical Analysis of Concentration-Response Data ................................................................. 50

45 4.4 Outlier Detection ......................................................................................................................... 51

46 4.5 Cell stress filter (Attagene-Specific) ........................................................................................... 51

47 Section 5 Overview of the HTP ER Binding and Transactivation Assays ..................................... 53

48 5.1 Attributes of the eight HTP Assays for Identifying Estrogen Agonists ...................................... 53

49 5.2 Summary of Curve Fitting, Hit Calling and Assay QC Process for the ER assays ..................... 57

50 5.3 Summary of the 3-Step Process for Making Final Hit calls (Active or Inactive) for Each 

51 Chemical ...................................................................................................................................... 70

52 5.4 Identification of potential technical issues that can lead to false positive or negative results .... 72

53 Section 6 Performance Evaluation of the HTP ER Binding and Transcriptional Activation 

54 Assays ................................................................................................................................... 75

55 6.1 Selection of Reference Chemicals ............................................................................................... 75

56 6.2 Results for Reference Chemicals Tested in the Eight HTP Assays ............................................ 77

57 6.3 Additional assay performance metrics for the HTP ER assays ................................................... 84

58 6.3.1 Replicate Analysis 1: Concentration-wise response for technical replicates. .................. 85

59 6.3.2 Replicate Analysis 2: Plate-to-plate replication using large sets of standard technical 

60 replicates ........................................................................................................................... 86

61 6.3.3 Replicate Analysis 3: Hit-calling and AC50 replication for multiple, independently 

62 procured, chemicals .......................................................................................................... 88

63 6.3.4 Replicate Analysis 4: Comparing pairs of assays ............................................................ 90

64 Section 7 Analysis of ES TrSet and HTP Assay Data ....................................................................... 92
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65 7.0 Assays utilized in developing the ER Expert System ................................................................. 92

66 7.1 ER Expert System TrSet and HTP In vitro Assays ..................................................................... 93

67 7.2 Binding Assays: Trout Liver Cytosolic ER and HTP ER Binding Assays ................................ 93

68 7.3. Gene Activation Assays: Trout Vtg mRNA and HTP Transactivation Assays ....................... 104

69 7.3.1 Effects of Assay Sensitivity and Test Concentration Limits .......................................... 104

70 7.3.2 Interpreting Responses Where Replicates Disagree ....................................................... 105

71 7.4 Interpretation of Combined ER Binding and Gene Activation Assay Results .......................... 106

72 7.4.1 Interpreting results in the context of structural similarity and chemical group activity . 108

73 7.4.2 Understanding chemical parameters that can potentially lead to false activity .............. 110

74 7.4.3 Strength of Evidence for Inactive Parent but Active Metabolite .................................... 113

75 7.4.4. Potential Assay Signal Artifacts in the Context of Other Evidence ............................... 116

76 7.5 Findings: ES TrSet and HTP Binding and TA Assays ............................................................. 119

77 Section 8 Category-Based Approach and Strategic Testing to Cover Unknowns ....................... 122

78 8.1 HTP coverage of the 29% ESv2 UNKs ..................................................................................... 122

79 Section 9 Summary and Key Goals for EDSP Prioritization ......................................................... 133

80 References ............................................................................................................................................. 135

81 List of Appendices ................................................................................................................................. 138

82 Appendix A: EDSP Universe of Chemicals and General Validation Principles .................. 138

83 Appendix B: An Effects-based Expert System to Predict Estrogen Receptor Binding Affinity 

84 for Food Use Inert Ingredients and Antimicrobial Pesticides: Application in a 

85 Prioritization Scheme for Endocrine Disruptor Screening ............................. 138

86 Appendix C: SAP Minutes on "An Effects-based Expert System to Predict Estrogen Receptor 

87 Binding Affinity for Food Use Inert Ingredients and Antimicrobial Pesticides: 

88 Application in a Prioritization Scheme for Endocrine Disruptor Screening" .. 138

89 Appendix D: Section 4 Supplemental, Details of Chemical Library..................................... 138

90 Appendix E: Section 5 Supplemental, HTP Data Processing Pipeline ................................. 138

91 Appendix F: Section 6 Supplemental, Descriptions of HTP Assays for the Detection of 

92 Estrogen Receptor Agonists ............................................................................. 138

93 Appendix G: Section 7 Supplemental, ES TrSet Decision Criteria for Binder/Non binder and 

94 Data Collection and Processing Details ........................................................... 138

95 Appendix H: Section 7 Supplemental, Tables ....................................................................... 138

96 Appendix I: Section 7 Supplemental, Figures...................................................................... 138
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97 Section 1 Background, Programmatic Goals, and Purpose of the FIFRA SAP Review 

An important programmatic objective of the agency's endocrine disruptor screening program (EDSP)1 98 is 

99 to prioritize the thousands of chemicals as defined by the 1996 Food Quality Protection Act (FQPA) and 

100 Safe Drinking Water Act (SDWA). In 1998, the Endocrine Disruptor Screening and Testing Advisory 

101 Committee (EDSTAC) recommended that the agency, to the extent possible, use a prioritization scheme 

that combines exposure and effects information.2 102 With regard to effects information to support 

103 prioritization, the EDSTAC encouraged the agency to evaluate the use of high throughput (HTP) in vitro

104 assays and computational (in silico) models, including (quantitative) structure-activity relationships 

[(Q)SARs].3 105 EDSTAC further recommended that chemical inventories be sorted into four categories: 

106 1) chemicals considered unlikely to interact with hormone systems, such as certain polymers and strong 

107 mineral acids and bases; 2) chemicals without sufficient data to make a determination whether or not to 

108 proceed to Tier 2 testing or hazard/risk assessment; 3) chemicals with sufficient existing data to make a 

109 determination to proceed to Tier 2 testing (i.e., existing data that provided information consistent with 

110 the Tier 1 screening assays); and 4) chemicals with sufficient existing information to support a hazard 

111 assessment. The Science Advisory Board (SAB) and the Science Advisory Panel (SAP) [U.S. EPA, 

19994 112 ] supported sorting chemicals into the four categories and stressed the importance of having 

113 exposure and effects information to prioritize chemicals in category 2. The SAB/SAP, however, 

114 concluded at that time that HTP assays and (Q)SARs were not sufficiently developed for use in chemical 

115 priority setting. The SAB/SAP endorsed continued research to advance HTP and bench-level in vitro

116 assay systems as well as (Q)SARs to provide measured or predicted data concerning receptor binding 

and/or transcriptional activation that could be used for future prioritization efforts. 5 117

118 Since those reviews, research has advanced and the agency has developed a prioritization scheme 

119 consistent with the broader, general concept articulated in the National Academy of Sciences' (NAS) 

Toxicity Testing in the Twenty-first Century: A Vision and a Strategy.

6 120 The 2007 National Research 

121 Council (NRC) report emphasized the need to integrate all existing knowledge and employ new in silico

122 and in vitro technologies (particularly those that inform how chemicals perturb normal biological 

123 function) to effectively accelerate the pace of testing and assessment. A framework to integrate existing 

124 knowledge concerning the linkage between different biological levels of organization is provided in the 

Adverse Outcome Pathway (AOP) concept. Knowledge of adverse outcome pathways (AOPs7 125 ) have 

 

1 http://www.epa.gov/endo/ 2 U.S. EPA, Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC), Final Report, August 1998. 

http://www.epa.gov/endo/pubs/edspoverview/finalrpt.htm 3 In 1998, EDSTAC recommended, "that existing QSAR models be derived and supplemented with data from the HTPS 

assays, thereby expanding the predictive ability of these models."

4 EPA-SAB-EC-99-013, Review of the EPA's Proposed Environmental EDSP by a Joint Subcommittee of the Science 

Advisory Board and Scientific Advisory Panel, July 1999. http://www.epa.gov/scipoly/sap/meetings/1999/finalrpt.pdf 5 In 2009, "The [SAP] Panel was in strong agreement that the expert system is likely to be an important tool to support 

'sorting and prioritizing'...The Panel recommended that the Agency continue with its efforts to collect additional data to 

better understand the relationship of the predicted ERα binding at the cellular and tissue level..."

U.S. EPA, SAP Minutes No. 2009-08, A Set of Scientific Issues Being Considered by the EPA Regarding: The Use of 

Structure Activity Relationships of Estrogen Binding Affinity to Support Prioritization of Pesticide Inert Ingredients and 

Antimicrobial Pesticides for Screening and Testing, p. 57. 

http://www.epa.gov/scipoly/sap/meetings/2009/august/082509minutes.pdf 6 National Academy of Sciences, Toxicity Testing in the Twenty-first Century: A Vision and a Strategy (Report in Brief), 

2007, pp. 196. http://dels.nas.edu/resources/static-assets/materials-based-on-reports/reports-in brief/Toxicity_Testing_final.pdf 7 As defined by Ankley et al. 2010: An AOP is a conceptual construct that portrays existing knowledge concerning the 

linkage between a direct molecular initiating event (e.g., a molecular interaction between a xenobiotic and a specific 

biomolecule) and an adverse outcome at a biological level of organization relevant to risk assessment. AOPs are generally a 
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126 direct application to the development and application of computational toxicology methods in hazard 

127 and risk assessment and is an important organizing principle used in this document. 

128 The prioritization scheme provided within this document is consistent with the EDSTAC, SAB/SAP, 

129 and NRC recommendations and provides a scientifically based approach to chemical prioritization in the 

130 context of the agency's Endocrine Disruptor Screening Program (EDSP). This chemical prioritization 

131 approach includes the consideration of physico-chemical properties, structure- activity relationships, 

132 chemical extrapolation, exposure information and high throughput computational methods, and 

133 application of the AOP concept as an organizing principle. 

134 This document lays out a prioritization scheme to support determinations of the order in which 

135 chemicals may be subject to EDSP Tier 1 screening to determine the potential to interact with the 

136 endocrine system. The January 2013 SAP review will seek comment on the concepts, decision logic, and 

137 methods used in the proposed prioritization scheme. Although for this demonstration, the ER pathway is 

138 the focus of the effects-based prioritization method, the agency believes this SAP review on the concepts 

139 and decision logic will help establish a solid scientific foundation to advance the use of computational 

140 toxicology tools beyond prioritization based on ER, and beyond EDSP chemical prioritization, and into 

141 in vitro to in vitro assay replacement and targeted EDSP Tier 1 screening and subsequently, a complete 

142 Tier 1 data replacement.

143 The following sections of this document lay out in detail the methodology underlying the proposed 

144 prioritization scheme:

Section 2 Provides an overview of defining the chemical universe of regulatory interest, the 

overall prioritization scheme and describes the physico-chemical properties criteria to 

remove chemicals not testable or unlikely to interact with the endocrine system 

consistent with EDSTAC category 1. 

Section 3 Introduces the AOP concept and its application to the development of computational 

toxicology methods and provides background on the data used to build the original 

ER expert system (ESv1) (which was reviewed by the 2009 SAP8), the automation of 

ER ESv1 as recommended by the 2009 SAP, and how it has now been expanded to 

cover the larger inventory of interest (ESv2). 

Sections 4 and 5 Describe the HTP library, processing of HTP data and the assays relevant to 

evaluating the ER pathway.

Section 6 Provides an evaluation of the HTP performance based on selected reference 

chemicals.

 

sequential series of events that, by definition, span multiple levels of biological organization. The content connecting the 

initiating event to the outcome can take various forms, depending on the types and extent of biological information from 

which the AOP is derived or the risk context that it is being used to address. Relationships among levels of biological 

organization may be causal, mechanistic, inferential, or correlation based, and the information on which they are based may 

derive from in vitro, in vivo, or computational systems. Such linkages provide the critical foundation for greater use of 

predictive approaches.

8 U.S. EPA, SAP Minutes No. 2009-08, op. cit. http://www.epa.gov/scipoly/sap/meetings/2009/august/082509minutes.pdf 
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Section 7 Presents a preliminary comparative analysis of the in vitro training set data used to 

build the ER expert system with similar in vitro HTP assays. This comparative 

analysis was conducted to help further the understanding of HTP performance and 

interpretation.

Section 8 Provides a strategy for addressing the chemical structures in the EDSP universe that 

are outside the applicability domain of the ER expert system (ESv2) through use of 

the HTP data (ESv3). 

Section 9 Presents a summary of findings and key goals for the EDSP prioritization process.
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145 Section 2 EDSP Universe of Chemicals and Prioritization Scheme

146 One of the EPA highest priorities is to ensure that people and the environment are not exposed to 

147 dangerous levels of chemicals. The EPA developed the EDSP in response to the statutory mandate in the 

148 Federal Food, Drug, and Cosmetic Act (FFDCA) "to determine whether certain substances may have an 

149 effect in humans that is similar to an effect produced by a naturally occurring estrogen, or such other 

150 endocrine effects as the Administrator may designate." As part of the EDSP, the statute requires that all 

151 pesticide chemicals (active and inert ingredients) be screened, and also establishes the EPA's authority to 

152 "provide for the testing of any other substance that may have an effect that is cumulative to an effect of a 

153 pesticide chemical if the Administrator determines that a substantial population may be exposed to such 

154 a substance." In addition to FFDCA, the Safe Drinking Water Act (SDWA) provides the EPA with 

155 authority to provide for testing "of any other substance that may be found in sources of drinking water if 

156 the Administrator determines that a substantial population may be exposed to such substance." Beyond 

157 testing and determining endocrine effects, FFDCA also directs the EPA to take action: "In the case of 

158 any substance that is found...to have an endocrine effect...the Administrator shall, as appropriate, take 

159 action under such statutory authority as is available to the Administrator...to ensure the protection of 

160 public health." 

161 The scope of the authority established by the statutory provisions in the FFDCA and SDWA (i.e., 

162 pesticide chemicals and drinking water contaminants with exposure determinations for substantial 

163 populations) provides a chemical universe for the EDSP (U.S. EPA, EDSP Universe of Chemicals and 

General Validation Principles, November 20129 164 ).

165 2.1 Defining the EDSP Universe of Chemicals

166 The EDSP21 is a work plan that describes an approach for using computational or in silico models and 

167 molecular-based in vitro high throughput (HTP) assays to prioritize and screen chemicals to determine 

their potential to interact with the estrogen, androgen or thyroid (E, A, or T) hormonal systems.10 168 One 

169 step in the EDSP21 process is defining the list of chemicals that must be screened under EDSP, and 

170 hence these are the focus for the EDSP21 prioritization effort as stipulated in the FFDCA and SDWA. In 

171 November 2012, the agency published a white paper titled, EDSP Universe of Chemicals and General 

172 Validation Principles, which describes some history of the EDSP program and the rationale and process 

of generating a comprehensive list of substances which comprise the EDSP universe of chemicals.11 173 To 

174 develop the EDSP universe of chemicals list, the EPA Office of Pesticide Programs (OPP) provided 

175 current lists of chemicals for active and inert ingredients, and the EPA Office of Water (OW) provided 

176 the most recent Contaminant Candidate of Concern List (CCL3) universe of chemicals; links to these 

177 chemical lists are provided in the EDSP Universe of Chemicals and General Validation Principles. Each 

178 of these lists included chemical names, and where available, Chemical Abstract Registry Numbers 

179 (CASRN). These different lists (see Table 2.1.1) were combined to produce a non-redundant master list 

180 (Pesticide active ingredients = 1,449; Pesticide inert ingredients = 5,276; unique SDWA 

181 substances = 3,616). 

182 In order to prioritize this universe of chemicals, a prioritization scheme was developed to identify 

183 chemicals that may have a potential to interact with the endocrine system (with ER binding being the 

 

9 http://www.epa.gov/endo/pubs/edsp_chemical_universe_and_general_validations_white_paper_11_12.pdf 10 http://www.epa.gov/endo/pubs/edsp21_work_plan_summary%20_overview_final.pdf 11 http://www.epa.gov/endo/pubs/edsp_chemical_universe_list_11_12.pdf 
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184 first AOP) and may be elevated for EDSP Tier 1 Screening. This scheme is described more fully in 

185 section 2.2, and illustrated in Figure 2.2.1. 

186 Table 2.1.1: Number of Compounds on Each List Associated with the EDSP Universe of 

187 Chemicals

List Number of Compounds

Conventional pesticidal active ingredients 838

Antimicrobial active ingredients 324

Biochemical active ingredients 287

Non food use inert ingredients 2,211

Food use inert ingredients 1,536

Fragrances used as inert ingredients 1,529

SDWA chemicals 3,616

Total 10,341*

*Due to overlap among the different chemical regulatory inventories the total number, 

i.e., 10,341, in the universe is not equal to the sum of the individual inventories and should not 

be construed as a list of chemicals that will automatically be submitted for EDSP screening.

188 2.2 EDSP21 Prioritization Scheme using Computational Toxicology Tools 

189 In the U.S. Congressional House Committee Report accompanying the fiscal year (FY) 2012 House 

190 Appropriations Bill (H.R. 2584), the EPA was directed to accelerate the evaluation, validation, and 

191 implementation of the endocrine-relevant high throughput assays, including those from the EPA's 

ToxCastTM effort.12 192 The bill states the EPA shall "in future EDSP Test Orders, use a targeted approach 

193 and adjust individual Test Orders in response to scientifically credible requests by taking existing data 

194 into account, and using information from valid in vitro assays or computer models, including ToxCast, 

as appropriate..."13 195 In the report for fiscal year 2013, the "EPA is directed to provide a report to the 

196 Committee that details its current and future efforts to develop approaches to understand the toxicity of 

chemicals in terms of molecular 'groups' or 'families' based on the chemicals intrinsic properties..."14 197

198 In response, the EPA has developed a prioritization method that maximally benefits from the use of all 

199 extant scientific knowledge databases and technologies to better determine which chemicals should be 

200 evaluated earlier than other chemicals in the EDSP Tier 1 Screening. The proposed EDSP chemical 

201 prioritization scheme is built with multi-parameter filters to establish a biologically and toxicologically 

202 relevant and focused chemical list, followed by computational toxicology (e.g., in vitro and in silico

203 methods) tools to further prioritize chemicals for potential EDSP Tier 1 screening. 

204 While the methodology presented in this paper focuses on the ER AOP, the overall concept and decision 

205 logic are intended to capture a generic approach that could be adapted to address other endocrine 

206 pathways. The use of the AOP concept has direct application to development of other effect-based 

 

12 http://www.epa.gov/ncct/toxcast/ 13 U.S. Congress, House Appropriations Committee Report 112-151 (To accompany H.R. 2584), Department of the Interior, 

Environment, and Related Agencies Appropriation Bill, 2012, p. 59. 

http://www.gpo.gov/fdsys/pkg/CRPT-112hrpt151/pdf/CRPT-112hrpt151.pdf. 14 U.S. Congress, House Appropriations Committee Report 112-589 (To accompany H.R. 6091), Department of the Interior, 

Environment, and Related Agencies Appropriation Bill, 2013, p. 48. 

http://www.gpo.gov/fdsys/pkg/CRPT-112hrpt589/pdf/CRPT-112hrpt589.pdf. 
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207 models and simulation-based analyses. In addition, while numbers of chemicals are presented 

208 throughout this document, they are only approximate values intended only to demonstrate potential 

209 impact of each prioritization step. Following receipt of the SAP review, the prioritization methodology 

210 presented in this document will be fully completed. 

211 As illustrated in Figure 2.2.1, the EDSP proposed prioritization process optimizes the use of existing 

212 information and includes the following components, applied in the sequence listed below: 

213 1. Complete EDSP Universe of Chemicals (n = 10,341). 

214 2. Set aside pesticide active ingredients (n = 1,449) as they will be prioritized for EDSP screening 

based on the OPP registration review schedule,15 215 which will streamline the work flow process 

216 for pesticide registration review decisions.

217 3. Identify "discrete" chemicals by excluding polymers, mixtures and undefined substances. 

218 4. Apply remaining physico-chemical properties exclusion criteria.

219 5. Prioritize SDWA chemicals that have potential human exposure. 

220 6. Apply ESv2 predictions for current chemical domain of applicability. 

221 7. Extend the chemical coverage of ESv2 with ER and estrogen receptor transcriptional activation 

222 (ERTA) assay data where applicable (illustrated in Figure 2.2.1).

223 8. For those remaining EDSP chemicals not covered by the ESv2 and do not have empirical data, 

224 they will be strategically prioritized for future medium or high throughput in vitro testing and 

225 results will be incorporated into the ESv3 for expansion of the chemical domain for EDSP 

226 chemical prioritization.

227 The remainder of this document describes the prioritization methodology in the order of application as 

228 illustrated in Figure 2.2.1. 

Figure 2.2.1: Proposed EDSP21 Prioritization Scheme

 

15 http://www.epa.gov/oppsrrd1/registration_review/reg_review_process.htm 
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229 2.2.1 OPP Registration Review Schedule

230 Starting with the approximately 10,000 EDSP universe of chemicals, chemicals were separated based on 

231 whether there is an existing pesticide registration. Registered pesticide active ingredients are prioritized 

for EDSP screening based on the existing registration review schedule.16 232 As stipulated in the FFDCA 

233 408(p), all pesticide ingredients must be screened for potential to interact with the endocrine system. 

234 This differs from the Safe Drinking Water Act (SDWA) Amendment of 1996, section 1457 that requires 

235 drinking water contaminants with exposure to a substantial population be screened under the EDSP. 

236 Thus, the prioritization process described in this document will be applied for SDWA substances, 

237 pesticide inert ingredients, and fragrances that may be subject to EDSP screening and for which no OPP 

238 registration review timeline is available.

239 After removing the pesticide active ingredients, approximately 9,000 chemicals remain on the 

240 inventory for prioritization. 

241 2.2.2 Exclusion of polymers, mixtures, and undefined substances

Based on recommendations by the joint (FIFRA SAP/SAB) panels in 1999,17 242 mixtures, polymers and 

243 undefined substances are excluded or placed in the lower-priority category based on the following 

244 criteria:

245 • Mixtures of either defined or undefined chemicals. Mixtures of defined discrete chemical 

246 structures will be screened after the single chemicals have been addressed;

247 • Polymers with an average molecular weight >1,000 Daltons based on its lack of ability to 

248 interact and bind to the ER, initiate the ER pathway and initiate systemic effects are excluded; 

249 and, 

250 • Undefined substances (i.e., not having a defined chemical structure) are excluded. 

251 Reviewing the chemical universe that includes SDWA and inert ingredients, the initial physico-chemical

252 properties filter distinguished discrete chemicals from undefined chemicals, mixtures from single 

253 chemicals and polymers with molecular weight exceeding 1,000 Daltons. 

254 Based on these physico-chemical categories, approximately 5,600 discrete single chemicals, excluding 

255 pesticide active ingredients, with unique chemical structures and CAS numbers will advance into the 

256 remaining physico-chemical properties filter described in section 2.2.3. 

257 After removing polymers, mixtures and undefined substances, approximately 5,600 chemicals 

258 remain for further prioritization.

259 2.2.3 Physico-Chemical Properties Considerations

Consistent with the SAP/SAB (1999), in a 2005 Federal Register Notice,18 260 the agency had previously 

261 articulated its intention to use physico-chemical properties to exclude chemicals based on being a 

262 polymer, strong mineral acid and base as well as chemicals that were judged to be too reactive and 

263 would, therefore, not be anticipated to elicit systemic effects. In this prioritization scheme, physico-

 

16 http://www.epa.gov/oppsrrd1/registration_review/reg_review_process.htm 17 http://www.epa.gov/scipoly/sap/meetings/1999/033099_mtg.htm 18 http://www.epa.gov/fedrgstr/EPA-TOX/2005/September/Day-27/t19260.pdf 
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264 chemical (p-chem) properties were used to exclude additional chemicals on the list (e.g., strong acids 

265 and bases, highly reactive or unstable chemicals); these chemicals would be excluded or not be 

266 considered a high priority for EDSP screening. The p-chem filter is used to assess whether a chemical is 

267 stable enough for human or ecological species exposures, and if not, the chemical does not need to be 

268 further prioritized for potential EDSP Tier 1 Screening. Consideration is given as to whether the 

269 chemical is too acidic, or basic, or otherwise reactive, a stable charged species in solution, or too 

270 corrosive to elicit systemic toxicity. The p-chem property considerations used as exclusion criteria are 

271 further defined in sections below. 

272 It is important to recognize that while each property is considered and described separately, the 

273 consideration of p-chem properties is an integrated analysis and is completed as a whole. Some of the 

274 properties are tightly correlated and will be considered in an integrated fashion. It is highly 

275 recommended that when p-chem filters are applied there includes an expert judgment review by internal 

276 EPA senior chemists to determine whether the exclusion of a chemical by a p-chem property for EDSP 

277 screening is biologically plausible and scientifically supportable. (See section 3.2.) 

278 In the subsequent sections, the following p-chem properties are described in detail with proposed 

279 exclusion criterion: 

280 • Chemicals that are too acidic or basic

281 • Chemicals that are too reactive or unstable

282 • Chemicals that are charged species

283 • Chemicals that are too corrosive

284 2.2.3.1 Chemicals that are too acidic or basic 

285 For purposes of this exclusion (and the charged species exclusion; see section 2.2.3.3.), an acid is a 

286 substance that is a proton (H+) donor and a base is a proton acceptor:

Acid: HA  H+ + A- (e.g., hydrochloric acid, HCl  H+ + Cl- 287 ) 

Acid dissociation constant (Ka) = (H+)(A- 288 ) and pKa = - log(Ka) 

289 (HA)

Base: B + H+  BH+ (e.g., Ammonia, NH3 + H+  NH4

+ 290 ) 

Base dissociation constants (Kb) = (BH+ 291 ) and pKb = - log(Kb) 

(B)(H+ 292 ) 

293 As shown above, the dissociation of acids and bases is an equilibrium process and the equilibrium 

294 constant for acids is termed the acid dissociation constant and abbreviated Ka. The negative log10 of Ka

295 is often used to characterize the acidity of a chemical and is abbreviated pKa. Similarly the base 

296 dissociation constant is Kb and the negative log of Kb is pKb. The smaller the pKa or pKb value, the 

297 stronger the acid or base. Some molecules may have multiple acidic groups to donate protons, or 

298 multiple basic groups to accept protons, and so they will have multiple dissociation constants. 

Because acid and base dissociation is an equilibrium process, an anion (A- 299 ) formed by dissociation of 

the acid (HA) can accept a proton and is therefore, by definition, a base (A- 300 is termed the "conjugate" 

301 base of the acid HA). A cation formed from acceptance of a proton by a base is called a conjugate acid.
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302 As stated above the smaller the pKa or pKb, the stronger the acid or base, but there is an inverse 

303 relationship between pKa or pKb and their respective conjugate acids or bases. A strong acid such as 

304 hydrochloric acid will have a very low pKa (<0) because it readily donates protons, and a very high pKb

of the conjugate base (Cl- 305 ) because the chloride ion has no tendency to accept protons. Similarly, a 

306 strong base has a very low pKb, but a very high pKa of the conjugate acid. 

307 For the acid/base exclusion, strong acids (very low pKa's) and strong bases (very low pKb's = very high 

308 pKa's of the conjugate acids) will be excluded from EDSP Tier 1 screening. The exclusion of chemicals 

309 based on extreme pKa values (acid pKa's <2 or pKa's of conjugate acids >11.5) reflect the concern that 

310 strong mineral acids and bases are simply not testable because of local tissue destruction, and the lack of 

311 potential to interact with a receptor or molecular target related to endocrine disruption. It is important to 

312 note that most strong acids and strong bases will readily form salts (see section 2.2.3.3.) that are also not 

313 expected to be endocrine active chemicals.

314 2.2.3.2 Chemicals that are too reactive/unstable

315 Another p-chem exclusion criterion relates to the reactivity of chemicals in water. The U.S. 

316 Occupational and Safety Health Administration (OSHA) Hazard Communication Standard defines 

317 water-reactive material as chemicals that react with water to release a gas that is either flammable or 

318 presents a health hazard. This definition does not accurately reflect the physico-chemical property that 

319 the exclusion criterion is intended to capture; rather, it is intended to flag those chemicals that may 

320 degrade within an aquatic environment. Thus, a more appropriate exclusion criterion for EDSP 

321 prioritization should be based on the stability of the chemical in water. Stability is assessed based on the 

322 rate of hydrolysis at pH values that are normally found in environmental waters (typically pH between 5 

323 and 9) and reported in terms of hydrolysis half-lives.

324 The EPI (Estimation Programs Interface) SuiteTM is a Windows®-based suite of physical/chemical 

325 property and environmental fate estimation programs developed by the EPA's Office of Pollution 

326 Prevention Toxics and Syracuse Research Corporation (SRC). EPI SuiteTM uses a single input to run the 

327 following estimation programs: KOWWINTM, AOPWINTM, HENRYWINTM, MPBPWINTM, 

328 BIOWINTM, BioHCwin, KOCWINTM, WSKOWWINTM, WATERNTTM, BCFBAFTM, HYDROWINTM, 

329 KOAWIN and AEROWINTM, and the fate models WVOLWINTM, STPWINTM and LEV3EPITM. 

ECOSARTM, which estimates ecotoxicity, is also included in EPI SuiteTM.19 330 EPI Suite model appears to 

331 be an appropriate source for obtaining this type of data. The HYDROWIN module of EPI Suite provides 

332 estimates for aqueous hydrolysis rate constants and half-lives for some chemical classes: esters, 

333 carbamates, epoxides, halomethanes, selected alkyl halides and phosphorous esters. Literature 

334 environmental fate surveys and experimental data available from other sources (e.g., the EPA method 

335 development stability study results) can also be used to supplement the EPI Suite information, if 

336 necessary.

337 Initially, we recommend that priority be given to chemicals that have hydrolysis half-lives greater than 

338 40 days based on the belief that these will persist in the environment long enough to pose risks to the 

339 aquatic environment. However, chemicals with half-lives less than 40 days would clearly be excluded 

340 for EDSP Tier 1 screening by the filter.

 

19 http://www.epa.gov/opptintr/exposure/pubs/episuite.htm 
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341 2.2.3.3 Chemicals that are charged species

342 Expanding on the inclusion/exclusion criteria is the consideration of charged species. Chemicals that 

343 either have no charge or they have a net positive or negative charge (some chemicals such as amino 

344 acids bear both positive and negative charges). The common ER active chemical classes in ESv1 and 

345 ESv2, such as alkyl phenols, phthalates, polybrominated/chlorinated biphenyls, polybrominated 

346 biphenyls and others, bear no charge at neutral pH 7. This p-chem exclusion criteria, therefore, is based 

347 on identifying chemicals that are predominantly charged at pH 7 and therefore deemed unlikely to 

348 interact with receptor binding sites. 

349 Charged/Ionization. For some chemicals, whether or not a chemical is charged (ionized) depends on 

350 the pH. Acidic chemicals tend to be uncharged at low pH and lose protons to become negatively charged 

351 (anionic) at higher pH. Basic chemicals tend to be uncharged at high pH, but positively charged 

352 (cationic) at lower pH. The consideration of pH is important because some known endocrine active 

353 chemicals, such as the alkyl phenols, are ionizable. For example, p-octylphenol is a weak acid with a 

pKa of 10.4,20 354 which means that, if p-octylphenol were in an aqueous solution at a pH of 10.4, 50% of 

355 the chemical would be uncharged and 50% would have a negative charge. At pH lower than 10.4, less of 

356 the chemical is charged and above pH 10.4 more of the chemical is ionized. At a pH 7, only 0.04% is 

357 charged.

358 Exceptions. Some known endocrine active chemicals are charged at pH 7. For example perchlorate and 

359 thiocyanate anions are potent inhibitors of the sodium/iodide symporter which results in perturbation of 

the thyroid hormonal pathway. Perfluorooctanoic acid has a very low pKa

21 360 which means that the 

361 charged anionic form will predominate at pH 7. Heavy metals such as cadmium, mercury and lead will 

362 exist as cations at neutral pH. For the most part, the exceptions are known and understood. 

363 Identifying chemicals charged at pH 7. Some chemicals on the EDSP inventory are charged at all pHs 

364 such as quaternary ammonium compounds. Many other chemicals are ionizable and their degree of 

365 ionization depends on pH. For ionizable compounds there are available tools on the internet such as 

ChemAxon's "Chemicalize"22 366 that calculate acid and base equilibrium constants and thereby the 

367 predominant electronic species of almost any molecule at pH 7.4 (physiological pH). At this website, 

368 data entry of chemical identifiers such as name, CASRN, SMILES string, etc., results in a wealth of 

369 chemical information that is automatically calculated. For example 3,5-dichlorobenzoic acid (CASRN 

370 51-36-5) is on the EDSP chemical inventory and its properties are calculated by Chemicalize. At a pH of 

371 7.4, the predominant species is as follows: 

 

20 http://www.chemicaldictionary.org/dic/4/4-Octylphenol_1689.html 21 http://www.ec.gc.ca/lcpe-cepa/default.asp?lang=En&n=705376A7-1&offset=4&toc=show 22 http://www.chemicalize.org/. This is the source of pKa estimates for the acid-base and charged species exclusions in this 

section.
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372 The structure displayed is negatively charged, 

373 and another panel on the Chemicalize display 

374 indicates that at pH 7.0, 99.95% of the 

375 molecule is negatively charged. Accordingly, 

376 3,5-dichlorobenzoic acid would be considered 

377 eligible for the charged species exclusion from 

378 Tier 1 EDSP screening.

379 The whole list of discrete chemicals on the 

380 EDSP chemical inventory was analyzed using 

381 ChemAxon's Chemicalize tools for the 

382 potential ability to ionize. For chemicals that are acids or salts of acids up to 3 pKa's were estimated and 

383 for chemicals that are bases or salts of bases up to 3 pKa's of the conjugate acids of the bases were 

384 estimated. Some chemicals did not ionize at any pH and others were ionized at all pHs. 

385 Table 2.2.1 illustrates some examples of chemicals whose ionization is pH dependent. The first three 

386 chemicals are acids, the next three are bases and the last four have both acid and basic pKa's. The 

387 assumption in the table is that a chemical is eligible for the charged species exclusion if <1% of the 

388 electronic species at pH 7 are uncharged. The corresponding rule for this is that an ionizable chemical is 

389 eligible for the exclusion if the lowest pKa for an acidic chemical <5 or the highest pKa of the conjugate 

390 acid of the basic chemical is >9. The column with the header "Eligible for exclusion?" is the result of 

391 application of the rule. The column with the header "% Uncharged species at pH 7" is an exact 

392 calculation by Chemicalize of the charged species at pH 7. The column entries are in good agreement. 

393 Table 2.2.1 Examples of Chemicals whose Ionization is pH Dependent 

No. Chemical

Acid

pKa's

Lowest

a pKa

Basic 

pKa's

Highest

b pKa

Eligible for 

exclusion?

% Uncharged 

species at pH 7

Why 

ineligible?

1 4-Chlorobenzoic acid 4.07 4.07 Yes 0.12

2 Barbituric acid 3.95

7.80

10.95

3.95 Yes 0.08

3 Hexachlorophene 5.15

7.00

5.15 No 1.56 (>1%)

4 Trimethylamine 9.57 9.57 Yes 0.27

5 Auramine 9.01

3.78

3.07

9.01 Yes 0.97

6 C.I. Basic violet 14 8.85

3.62

2.85

8.85 No 1.65 (>1%)

7 Anthranilic acid 4.89 4.89 1.95 1.95 Yes 0.55

8 Albuterol 10.12

14.18

15.15

10.12 9.40 9.40 Yes 0.39

9 11-Aminoundecanoic 

acid

4.65 4.65 10.21 10.21 Yes* 0 

10 Ciprofloxacin 5.76 5.76 8.68

-0.22

8.68 No* 0 pKs out of 

range

* These form "inner salts."

Figure 2.2.3.3.1: Example of a Charged Chemical 
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394 Criteria for p-chem property exclusion. If the main criterion for exclusion is a charged species at pH 

395 7, decisions will have to be made on chemicals with dissociation constants close to pH 7. Essentially the 

396 decision is whether 50% charged species is sufficient, 90% is sufficient or perhaps 99.9% is sufficient. 

397 Therefore, the criterion for exclusion might be "a chemical is eligible for exclusion under this p-chem

398 criterion if 90% (or 50%, or 99%) or greater of the molecular species at pH 7 is either positively or 

399 negatively charged."

400 2.2.3.4 Chemicals that are too corrosive 

401 This exclusion category overlaps largely with the exclusion for chemical reactivity, though the term 

402 "corrosive" has the dual meaning of causing eye and skin damage, and of causing damage to metals. In 

403 most cases, flesh and metal will both be affected, but not always: phenols are only weakly acidic, but 

404 strongly affect the eyes, and quaternary alkylammonium compounds damage the eyes by a detergent 

405 effect rather than by chemical reactivity. Substances that corrode metals by facilitating electrochemical

406 corrosion (halide salts, some other electrolytes) often have little effect on biological materials. Mercuric 

407 chloride is corrosive to aluminum, magnesium and zinc because the amalgam that forms prevents the 

408 adhesion of the protective oxide coating that the metals ordinarily have.

409 Because of the different meanings of (and tests for) corrosivity, no single consistent standard is possible. 

410 Nevertheless, chemicals that react relatively rapidly with air or water are likely to affect tissues as well. 

411 Examples of corrosive chemicals include, but are not limited to:

412 • Acids (both inorganic and organic) and substances that react with water to produce acids: 

413 Anhydrides, acid halides, α-haloketones and -esters, reactive esters, allylic and benzylic halides, 

414 and various organic and inorganic materials with P-X, S-X, Si-X, B-X or Sb-X bonds, where 

415 X = halogen. 

416 • Bases (both organic and inorganic) and substances that react with water to produce bases: 

417 Hydroxides, oxides and amides of first-, second- and third-period metals; the analogous metal 

418 salts of aliphatic alcohols and amines; primary, secondary and tertiary aliphatic amines, and the 

419 ketimine and aldimine condensation products with them that readily hydrolyze to regenerate 

420 them; reactive organometallics such as alkyl- and aryllithium and -magnesium compounds. 

421 • Other reactive species, such as isocyanates, isothiocyanates and carbodiimides; epoxides; 

422 Michael-acceptors, such as acrylates and cyanoacrylates.

423 • Certain irritant compounds, such as capsaicin, whose mode of action does not seem to fit into 

424 any of the above categories.

425 It should be noted that while the corrosive effects of these materials may be attributed to the specific 

426 functional groups mentioned, those effects tend to be mitigated by the concentration of the groups. So, 

427 although formic and acetic acids (molecular weights 46 and 60 respectively) are quite corrosive to skin 

and eye, stearic acid (mw 284) is not. (In the Polymer Exemption23 428 this was taken into account by 

429 setting lower limits on the functional group equivalent weight; that is, the molecular weight divided by 

430 the number of functional groups.) It is also true that solid, relatively water-insoluble materials are less 

431 likely to be corrosive than liquids for physical reasons. 

 

23 http://www.epa.gov/oppt/newchems/pubs/polyexem.htm 
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432 The corrosive chemical filter is based on those chemicals currently on the U.S. Department of 

433 Transportation Class 8 list (DOT8) and on Material Safety Data Sheets (MSDS) for chemicals on the 

434 EDSP inventory contain warnings of corrosive properties of the chemical.

435 2.2.3.5. Summary Prioritization Assumption 

436 These exclusions are applied to chemicals that meet the following criteria: (1) not a FIFRA pesticide 

437 active ingredient, and (2) a discrete chemical. Chemicals can be excluded for more than one of these 

438 p-chem property criteria. In particular, all chemicals excluded for being too acid or too basic are also 

439 excluded for being charge at pH = 7. 

440 In summary, if a chemical is too acidic/basic, corrosive, reactive/unstable, or charged, it will be 

441 excluded for EDSP Tier 1 screening based on the lack of potential for endocrine activity resulting in 

442 systemic effects. The summary of these p-chem property exclusion criteria, parameters, data sources, 

443 cut-off boundaries and approximate chemicals excluded per filter are described in Table 2.2.2.1. 

444 Table 2.2.2.1: Summary Table of Physico-Chemical Ex Physico-Chemical Properties 

445 Considerations Exclusion Criteria 

Criterion Parameters Data sources Boundaries

Approx. # Chemicals 

excluded

Too acidic or 

basic

pKa of acids or conjugate 

acids of bases

Chemaxon's "Chemicalize" 

predictive software

pKa < 2 or pKa of 

conjugate acid >11.5

700

Too reactive or 

unstable in water

Hydrolysis half-life Epi Suite's HYDROWIN 

predictive module

Eligible for exclusion 

if hydrolysis half-life 

< 40 days

100

Charged at pH 7 pKa based net negative or 

positive charge at pH7

Chemaxon's"Chemicalize" 

predictive software

Lowest acid pKa < 5

or highest conjugate 

acid pKa >9

1,000

Too corrosive Member of a chemical 

class with reactive 

functional groups

DOT8 list; substructure 

search of the inventory for 

specific functional groups

Solid, water insoluble 

chemicals not eligible

200

446 Conclusion: The consideration of physico-chemical properties for chemical prioritization is not a novel 

447 concept and has been in practice for many decades. The use of these parameters is also not precedent 

setting for the EDSP as in September 27, 2005,24 448 when the agency had articulated its plan to exclude 

449 chemicals with similar inherent properties. This current description, however, starts with those general 

450 exclusions and expands on the previous considerations by providing explicit exclusion criteria cut-offs 

451 that are intended to prioritize the universe of chemicals for EDSP screening. It should be noted that the 

452 application of these physico-chemical properties filters should not be automated and applied in isolation; 

453 the results need to be reviewed by an expert group of agency scientists to ensure that chemicals excluded 

454 consistent with the best available data and interpretation and consistent with the intended application of 

455 the filters. (See section 3.2.) 

456 After applying, additional p-chem criteria (acid/base, stability, charge, corrosivity), approximately 

457 4,500 chemicals remain for further prioritization.

 

24 http://www.epa.gov/fedrgstr/EPA-TOX/2005/September/Day-27/t19260.pdf 
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458 2.2.4 Prioritize SDWA Chemicals That Have Human Exposure Potential 

459 The exposure filter is a continuing step in the prioritization process for the EDSP universe of chemicals 

460 that include the SDWA chemicals for potential Tier 1 screening. For the purposes of the EDSP, the 

461 exposure filtering process will determine whether the SDWA chemicals are known or anticipated to 

462 occur in public water systems. In preparation of the latest CCL3 universe of chemicals list (U.S. EPA, 

463 2009a; U.S. EPA, 2009b), EPA's OW considered four main types of occurrence information: 

464 • Public water system monitoring data (i.e., finished water data)

465 • Ambient concentrations in surface and ground waters (i.e., ambient water data)

466 • Environmental release data (i.e., Toxic Release Inventory)

467 • Production data (e.g., Inventory Update Rule)

468 For each of the main types of occurrence information, there are one or more databases identified as part 

469 of the CCL3 process. The complete list of databases used in creating the CCL3 universe of chemicals 

can be found in the, Final Contaminant Candidate List 3 Chemicals: Identifying the Universe. 25 470

471 Application of exposure filter

472 The exposure filter will be applied to SDWA chemicals that reach this stage of the prioritization process. 

473 If a chemical is identified in any of the databases/sources associated with the four main types of 

474 occurrence-type information, the chemical will proceed forward in the subsequent prioritization process. 

475 For those SDWA chemicals that are not detected in any of the sources of exposure used by OW in 

476 determining the CCL universe, they will move to a lower priority category. These chemicals may be 

477 prioritized or evaluated when additional exposure data become available. 

478 Summary Prioritization Assumption 

479 To be as inclusive as possible, it is assumed that if a chemical is listed in any one exposure source, it 

480 will have the potential for human exposure and will advance in the prioritization process. These 

481 exposure sources are consistent with the CCL universe determinations and are not subject to review. 

 

25 http://water.epa.gov/scitech/drinkingwater/dws/ccl/upload/CCL3_Chemicals_Universe_08-31-09_508_v3.pdf
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482 Section 3 ER Expert System

483 3.1 Introduction of ER Expert System and Five Specific Goals

484 The ER Expert System (ESv1) was the subject of a 2009 SAP, "An Effects-based Expert System to 

485 Predict Estrogen Receptor Binding Affinity for Food Use Inert Ingredients and Antimicrobial Pesticides: 

486 Application in a Prioritization Scheme for Endocrine Disruptor Screening" (SAP, 2009a, b). This expert 

487 system was also reviewed by the Organization for Economic Cooperation and Development (OECD, 

488 2009). A brief review of the basic concepts and context upon which it was built is provided in this 

489 section.

490 The ER-mediated AOP was the conceptual framework utilized (Figure 3.1.1). Briefly, the AOP starts at 

491 the point of chemical-biological interaction or the molecular initiating event (MIE), the pathway 

492 proceeds through a series of additional key events that are relevant measures of effects associated with 

493 an observed adverse outcome of regulatory concern. The assays used in developing the training set for 

494 the ESv1 include measurement of ER-binding as the MIE in the pathway with confirmation of the 

495 activity at a higher level of biological organization, the tissue level, using a liver slice assays for 

496 vitellogenin induction. The focus is on the MIE for prioritizing chemicals to then be tested in higher tier 

497 EDSP assays providing information on effects at higher levels of biological organization in the AOP up 

498 to the adverse outcome. In its 2010 report, Integrated Approaches to Testing and Assessment Strategies: 

499 Use of New Computational and Molecular Tools, the SAP supported the use of the AOP as a conceptual 

500 framework for the application of computational tools, and further, recommended the use of the OECD 

501 QSAR validation principles in evaluating acceptance and utility of computational tools (SAP, 2010). 

502 The ESv1 was developed following the OECD QSAR validation principles (OECD, 2004; U.S. EPA 

503 2012). One aspect of addressing the principles was developing the system to address a defined 

504 regulatory purpose, specifically for: 

505 1. Prioritizing chemicals from two specific inventories, food use pesticidal inerts (FI) and 

506 antimicrobial pesticides (AM) which do not include any chemicals with steroidal-type chemical 

507 structures, and thus not capable of higher affinity ER interactions; 

508 2. Building a training set (TrSet) of chemicals to cover the defined regulatory inventories using 

509 in vitro assays specifically optimized to detect any indication of binding and gene activation by 

510 testing up to chemical solubility or cytotoxicity within the assays to increase confidence that a 

511 chemical predicted negative is unlikely to bind and activate the ER; and,

512 3. Using a chemical class-based approach to allow extrapolation from a limited number of well513 characterized TrSet chemicals to a broader inventory of chemicals by employing effects-based 

514 chemical category and read-across concepts. 

515 The transparency (collected information, i.e., assays used and chemicals tested, and relevance to the 

516 chemical predictions) and usefulness of the system for the intended purpose (predictions provided for FI 

517 and AM chemicals) was emphasized in the approach to develop the ESv1. For example, the relationship 

518 between relative binding affinity (RBA) and LogKow that was identified for the ESv1 chemical groups 

519 was used within each group to ensure the predicted chemical was bounded by TrSet chemicals. 

520 Chemicals falling outside the boundaries of known ability to predict (whether active or inactive) were 

521 considered to have "Unknown Binding Potential" (UnkBP). The automated version of the ESv1 enables 

522 users to compare the predicted chemical to TrSet chemicals within each chemical group (i.e., the 

523 decision tree node).
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Figure 3.1.1: ER-mediated Reproductive Impairment Adverse Outcome Pathway (AOP) (from 

Schmieder et al., 2004). The pathway demonstrates typical responses that might be observed at various 

levels of biological organization and the types of assays (in vitro or in vivo) from which observations 

along the pathway can be made. The molecular initiating event, chemical binding to rtER, is measured 

in vitro, and verified using a bioassay at a higher level of biological organization, vitellogenin (Vtg) 

gene expression in livers of male rainbow trout. Together the mechanistic information is used within ES 

chemical groups to predict chemical potential to bind and initiate the AOP.

524 The ES is a logic rule-based decision tree that encodes the experts' mechanistic understanding with 

525 respect to both the chemical and biological aspects of the well-defined endpoint, i.e., the ER bioassay 

526 domain as described in 2009 SAP white paper (SAP, 2009a), and in keeping with the QSAR validation 

527 principles. Briefly, the well-defined endpoint with respect to biology ensures the assays used are 

528 optimized to detect binding activity for chemicals with properties similar to FI and AM chemicals, in 

529 this case, optimized to detect low affinity interactions by testing up to solubility of the chemical in the 

530 assay system and measuring gene activation in a metabolically competent assay system to verify binding 

531 translates to higher order biological activity. The well-defined endpoint (i.e., ER binding) with respect to 

532 physical-chemical properties includes understanding how the chemical is handled in the assay system 

533 (i.e., chemical form, chemical concentration and bioavailability), as well as, properties that alter assay 

534 conditions such as a change in pH causing protein denaturation resulting in apparent but not true binding 

535 displacement. The original ESv1 was built on a training set of measured chemical binding to the ER, but 

536 the interpretation of binding was informed by a series of additional factors (described in detail in 

537 section 7 and Appendix G Figures 1 to 8) to increase the confidence in minimizing the potential for ER 

538 active chemicals to be determined to be inactive. The ER binding and gene activation (GA) assays were 

539 optimized to find any indication of binding and GA within a chemical class by testing chemicals for any 

540 significant activity above background up to the limit of chemical solubility in the respective assay or 

541 cytotoxicity in cell-based assays, whichever limit was reached first. Additionally, the activity of a 
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542 chemical with respect to other chemicals within the same ESv1 chemical group was taken into account 

543 to assess reasonableness in the context of known activity of related structures. Additional factors 

544 considered in making final determination of ER activity for TrSet chemicals include: transformation of 

545 parent chemical into a more active metabolite; biological components of the assay system affecting 

546 chemical free concentration; chemical affecting the bioassay conditions (e.g., chemical dissociation 

547 leading to altered assay pH and protein denaturation), etc. A detailed discussion is presented in 

548 section 7. 

549 The individual components of quality assured data are evaluated in total to arrive at a final interpretation 

550 of chemical ER activity. Subsequent to data analysis the individual components of information are 

551 gathered and assessed together resulting in a final interpretation (activity determination) made for a 

552 chemical. Where there are conflicting results the chemical remains 'under assessment' until additional 

553 information can be obtained to verify or refute hypotheses and a final determination of binding potential 

554 made considering all the mechanistic information gathered. Additional information may include: 

555 (1) verification of the competitive nature of the binding interaction by determining the binding inhibition 

556 constant (Ki); (2) determination of binding using a recombinant (rec) receptor as the source of rtER (rec 

557 rtER); and (3) chemical analysis looking for disappearance of parent chemical during assay incubation 

558 and appearance of hypothesized metabolites where warranted and possible. The expert decisions made 

559 on the TrSet chemicals within the context of each ES chemical group is formulated into rules and coded 

560 into the ESv1 decision tree. Untested chemicals being assessed by the ESv1 that fall within the 

561 boundaries of the logic rules are assigned a predicted binding potential of: a) RBA>0.00001% for "ER 

562 binders" (0.00001% relative to 17beta-estradiol is the lowest binding affinity observed for any chemical 

563 across the entire TrSet); or, b) RBA<0.00001% for "non-binders"; or, c) 'Unknown Binding Potential

564 (UnkBP) for chemicals falling outside of the boundaries of the logic rules. 

565 The initial ESv1 was built using rainbow trout ER (rtER) binding and GA assays. A comparison of rtER 

566 data to that for rat and human ER indicated that trout responses are as or more sensitive than with 

567 mammalian systems. The 2009 SAP concurred with the conclusion that basing the ES development on 

568 trout data is an appropriate approach for prioritizing across taxa (SAP, 2009b). 

569 Subsequent to the 2009 SAP, the developers of the ESv1 expanded the domain to cover an additional 

570 and larger universe of non-food use (NF) inert ingredients. Details of the expansion of the system are 

571 described in section 3.1.c. The expanded system is referred to as ESv2. When the EDSP universe of 

572 chemicals (defined in section 2.1) was compiled, the ESv2 developers were asked to determine:

573 1. The extent to which ESv1 covers (i.e., can predict binding potential for) chemicals on the EDSP 

574 inventory to prioritize a list of chemicals for EDSP T1Screening;

575 2. The extent to which the expanded ESv2 covers chemicals on the EDSP inventory; 

576 3. Identify chemicals with Unknown Binding Potential (UnkBP); 

577 4. The extent to which available HTP data covers UnkBP chemicals; and

578 5. If and how HTP data could expand the ER training set to cover the UnkBP chemicals. 

579 Sections 3.1 to 3.3 address goals 1 to 4 and section 8 addresses goal 5 by recommending how HTP data 

580 found applicable for the types of chemical structures represented in the UnkBPs could be used to build 

581 an expanded ESv3, and how a minimal amount of 'strategic testing' could be undertaken to achieve 

582 complete coverage of the UnkBPs in the EDSP inventory. Section 6 provides an analysis of reference 

583 compounds and section 7 provides a comparison of ES TrSet data to HTP ER binding and GA data to 

584 help address goal 5. 
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585 3.1.1 Automation of ESv1 

586 As recommended by the 2009 SAP (SAP, 2009b), an automated stand-alone application profiler was 

587 implemented as the platform for the ES. The profiler was built through collaboration between the EPA, 

588 the OECD Secretariat and the OECD QSAR Application Toolbox developer (Laboratory of 

589 Mathematical Chemistry, Bourgas, Bulgaria). An ESv1 profiler is included in the most recent release of 

the OECD QSAR toolbox.26 590 The structural fragments, calculated parameters, and the TrSet of in vitro

591 test results for the rt-ER receptor binding, trout liver slice gene activation assay and associated measures 

592 (see section 7) were used to define rules in a yes/no decision based dendroid logic scheme consisting of 

593 nodes specific to ES chemical groups. As mentioned previously, ESv1 was developed to cover the 

594 specific EPA regulatory FI and AM domains. The ESv1 Profiler application follows the schematic as 

595 introduced in the expert system decision tree (Figure 3.1.2). 

596 The ESv1 profiler was designed to be transparent with respect to boundary definitions of the queries and 

597 display of the ES TrSet test results (chemical structures, LogKow, RBA) specific to each decision tree 

598 node. In addition, the logic tree was designed for facile expansion. When new inventories containing 

599 new chemical groups are encountered additional nodes with defined boundaries are added. The new 

600 groups are subject to the same strategy of linking structural and parametric attributes to new ES TrSet 

601 results for representative members of a new ES chemical class. This process systematically expands the 

602 domain, or coverage, of the model. A screenshot of the automated ES is shown in Figure 3.1.3A. The 

603 figure shows results of profiling the entire EDSP inventory displayed as the number of chemicals 

604 apportioned to each node of the tree. Further, within a single node, in this example of the alkoxyphenols, 

605 the processed structures within that node may be displayed in a pop-up box as those meeting the criteria 

606 (green) and those outside the limits of the boundary (red). Figure 3.1.3B shows the individual chemical 

607 mode for an example salicylate found in the EDSP inventory. For each chemical processed a series of 

608 questions or queries are asked in each individual node. If the criteria of the boundary are met then the 

609 box is colored dark green, if not then red. The structure passes through the dendroid scheme until a final 

610 answer is reached. In this screen shot there is a pop-up window containing the ES TrSet specific to the 

611 'salicylate (non-benzoate)' node of the tree. The structure, LogKow, and ES prediction for the target 

612 chemical (on the left side of the screen) can be directly compared to structures, LogKow, and RBA of the 

613 relevant TrSet chemicals. Therefore, users can determine for themselves the extent to which they agree 

614 with the coverage provided and the prediction made. 

615 3.2 ESv2 

616 3.2.1 Building ESv2

617 Following the 2009 SAP review, work began to extend ES coverage to a larger inventory of non-food 

618 use pesticidal inerts (NF). The strategic testing employed for this new set of chemicals to expand the ES 

619 TrSet was designed to allow for expansion of the previous ESv1 rules to additionally cover this larger 

620 chemical domain. As previously done for FI and AM in ESv1, the new inventory of NF inerts was first 

621 verified for CAS number and structure, and then sorted as to whether the chemical was a discrete or 

622 non-discrete (mixture, polymer, undefined) substance (see section 2.2). The sorting process was as 

623 previously described in the 2009 SAP and applied to the current EDSP inventory (section 2.2). Only the 

624 1,423 discrete substances on the NF list (i.e., defined as a single chemical with a well-defined structure) 

625 were considered for the model expansion.

 

26 http://www.oecd.org/chemicalsafety/assessmentofchemicals/theoecdqsartoolbox.htm 
Prioritization of the EDSP Universe of Chemicals

December 2012 19 | Page

626 When the ESv1 was applied to the NF 1,423 list, it successfully classified 85% of the list. There were 

627 215 discrete substances not in the model domain, and thus, classified as UnkBP. Of these, 156 were 

628 Mixed Organics, 40 were Mixed Phenols, and 12 were members of ESv1 defined classes but with 

629 LogKows outside of the previously tested range in the existing TrSet. There were eight more substances 

630 placed in the UnkBP category that were in Multi Cyclic Hydrocarbons or Thiophosphate Esters ES 

631 groups. Both of these groups contain both ES TrSet "Binders" and "Non-binders," without enough 

632 chemicals tested to establish a clear LogKow relationship. In these instances, the chemical is tested and 

633 test results are coded in the ES as an 'exact match' to the specific chemical structure. The knowledgebase 

634 within these groups will increase with additional testing allowing specific ES groups or sub-groups to be 

635 defined, additional ES rules to be established, and predictions to be made for untested chemicals in the 

636 group. Because the majority of NF UnkBP chemicals were Mixed Phenols (MP) and Mixed Organics 

637 (MO), this was a logical place to start to look for similar chemicals that could be used to hypothesize 

638 new chemical groups. Chemicals were then selected within the groups for testing to expand the ER ES 

639 TrSet to better cover NF unknowns as will be further described below. 
Prioritization of the EDSP Universe of Chemicals

December 2012 20 | Page

Figure 3.1.2: The expert system decision tree based on rainbow trout estrogen receptor binding affinity and trout liver slice 

transactivation knowledgebase. [2009 SAPa; EPA-HQ-OPP-2009-0322-0002]

Start Here:

Chemical List

e.g., Food Use Inerts,

Antimicrobials

Contains 

a Cycle

Yes Contains 2 OH,

or OH and =0, at 

Spec. Dist

Log KOW <1.3

“Special 

Rule”

Applies

Site “A”

Contains Phenol

Fragment 

Log Kow Range

4-Alkylanilines ( 1.4 - 5.1)

4-Alkoxy Anilines ( 2.6 - 3.7)

Phthalates ( 2.5 - 6.8)

Phenones-(branched) ( 2.7 - 4.8)

4-Alkyl cyclohexanols ( 2.4 - 3.8)

4-Alkyl cyclohexanones ( 2.4 - 3.8) *

2-; 4-; or 2,4,6-Benzoates

(substitution-dependent Log Kows)

IV

Possible Low Affinity

Site A-Type, Site B-Type

Site “B”

Contains “Specified”

Fragment

Log Kow Range

Alkylphenols (1.5 - 8.0)

Alkoxy phenols (2.3 - 4.3)

Parabens (1.9 - 5.4)

Salicylates (2.5 - 5.1)

Belongs to 

known Active

Sub-class

RBA > 0.00001%

Yes

No

VI

Unknown 

Binding Potential

No

Yes

Yes

No

Yes

Belongs to 

known Active

Sub-class

Belong to untested class

Unknown Binding Potential

Mixed Phenols

No

VII

No

Belongs to 

known Inactive 

Sub-class

Log Kow Range 

4-Alkylbenzthiols ( 2.9 - 4.2)

4-n-Alkylfluorobenzenes ( 2.3 - 4.9)

Alkylphenol (2,6-subst) ( 4.2)

Benzamides ( 1.7 - 2.1) 

Borate Esters (-0.5 - 1.0)

Benzoates (non-ring subst) ( 3.6 - 4.2)

Bis-anilines ( 1.6 - 6.2)

Hydrofurans (alcohol & ketone) ( 0.0 - 1.2)

Imidazolidines (-0.9 - 0.6) 

Isothiazolines ( 0.6 - 2.5)

Mono-cyclic Hydrocarbons ( 2.7 - 4.6)

Oxazoles (<0.3)

Phenones (n-alkyl) ( 1.6 - 4.1)

Pyrrolidiones (-0.4 - 3.3)

Sorbitans ( 3.2 - 5.9) 

Triazines (-0.4 - 3.4)

Yes

DDT-Like 

Tamoxifen-Like

Multicyclic hydrocarbons

Alkylchlorobenzenes

Thiophosphate Esters

Yes

No

No

Yes

RBA > 0.00001%

RBA < 0.00001%

I

RBA < 0.00001%

Yes

No

V

III

Contains

a Charge

No

Yes

II

Alkylaromatic Sulfonic Acids 

Log Kow (-0.6 - 5.7)

Sulfonic Acid Dyes 

Log Kow (-0.4 - 6.0)

Exact match to Mixed Phenols 

Training Set Structure and 

measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Unknown 

Binding Potential

Mixed Organics

No

Exact match to Mixed Organics 

Training Set Structure and 

measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Unknown 

Binding Potential

Exact match to Group 

Training Set Structure 

and measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Meets specific distance 

criteria for: 

Sites “A-B”,

Sites “A-C”, or

Sites “A-B-C”

Contains some 

attenuating feature,

steric or other

High Binding Affinity 

“A-B”;“A-C” or “A-B-C”

RBA < 0.00001%

Low Affinity Binder

“A-B”,“A-C” or “A-B-C”

Strength of 

attenuation factors 

Yes

No

Strong

Weak

Start Here:

Chemical List

e.g., Food Use Inerts,

Antimicrobials

Contains 

a Cycle

Yes Contains 2 OH,

or OH and =0, at 

Spec. Dist

Log KOW <1.3

“Special 

Rule”

Applies

Site “A”

Contains Phenol

Fragment 

Log Kow Range

4-Alkylanilines ( 1.4 - 5.1)

4-Alkoxy Anilines ( 2.6 - 3.7)

Phthalates ( 2.5 - 6.8)

Phenones-(branched) ( 2.7 - 4.8)

4-Alkyl cyclohexanols ( 2.4 - 3.8)

4-Alkyl cyclohexanones ( 2.4 - 3.8) *

2-; 4-; or 2,4,6-Benzoates

(substitution-dependent Log Kows)

IV

Possible Low Affinity

Site A-Type, Site B-Type

Site “B”

Contains “Specified”

Fragment

Log Kow Range

Alkylphenols (1.5 - 8.0)

Alkoxy phenols (2.3 - 4.3)

Parabens (1.9 - 5.4)

Salicylates (2.5 - 5.1)

Belongs to 

known Active

Sub-class

RBA > 0.00001%

Yes

No

VI

Unknown 

Binding Potential

No

Yes

Yes

No

Yes

Belongs to 

known Active

Sub-class

Belong to untested class

Unknown Binding Potential

Mixed Phenols

No

VII

No

Belongs to 

known Inactive 

Sub-class

Log Kow Range 

4-Alkylbenzthiols ( 2.9 - 4.2)

4-n-Alkylfluorobenzenes ( 2.3 - 4.9)

Alkylphenol (2,6-subst) ( 4.2)

Benzamides ( 1.7 - 2.1) 

Borate Esters (-0.5 - 1.0)

Benzoates (non-ring subst) ( 3.6 - 4.2)

Bis-anilines ( 1.6 - 6.2)

Hydrofurans (alcohol & ketone) ( 0.0 - 1.2)

Imidazolidines (-0.9 - 0.6) 

Isothiazolines ( 0.6 - 2.5)

Mono-cyclic Hydrocarbons ( 2.7 - 4.6)

Oxazoles (<0.3)

Phenones (n-alkyl) ( 1.6 - 4.1)

Pyrrolidiones (-0.4 - 3.3)

Sorbitans ( 3.2 - 5.9) 

Triazines (-0.4 - 3.4)

Yes

DDT-Like 

Tamoxifen-Like

Multicyclic hydrocarbons

Alkylchlorobenzenes

Thiophosphate Esters

Yes

No

No

Yes

RBA > 0.00001%

RBA < 0.00001%

I

RBA < 0.00001%

Yes

No

V

III

Contains

a Charge

No

Yes

II

Alkylaromatic Sulfonic Acids 

Log Kow (-0.6 - 5.7)

Sulfonic Acid Dyes 

Log Kow (-0.4 - 6.0)

Exact match to Mixed Phenols 

Training Set Structure and 

measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Unknown 

Binding Potential

Mixed Organics

No

Exact match to Mixed Organics 

Training Set Structure and 

measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Unknown 

Binding Potential

Exact match to Group 

Training Set Structure 

and measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Meets specific distance 

criteria for: 

Sites “A-B”,

Sites “A-C”, or

Sites “A-B-C”

Contains some 

attenuating feature,

steric or other

High Binding Affinity 

“A-B”;“A-C” or “A-B-C”

RBA < 0.00001%

Low Affinity Binder

“A-B”,“A-C” or “A-B-C”

Strength of 

attenuation factors 

Yes

No

Strong

Weak

Meets specific distance 

criteria for: 

Sites “A-B”,

Sites “A-C”, or

Sites “A-B-C”

Contains some 

attenuating feature,

steric or other

High Binding Affinity 

“A-B”;“A-C” or “A-B-C”

RBA < 0.00001%

Low Affinity Binder

“A-B”,“A-C” or “A-B-C”

Strength of 

attenuation factors 

Yes

No

Strong

Weak
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December 2012 21 | Page

Figure 3.1.3A: Screenshot of the Automated ES showing in detail a portion of the profile for the entire EDSP inventory. Nodes in 

purple indicate where chemicals are binned. Numbers indicate chemicals passing between nodes of the system. Window (pop-up) in lower 

right corner shows profiled structures within a specific node, in this case the alkoxyphenols, where green structures meet the criteria and 

red do not meet the criteria for the specific boundary. 
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December 2012 22 | Page

Figure 3.1.3B: Screenshot of the Automated ES showing in detail a portion of the profile for a single chemical found within the 

EDSP inventory. Nodes in dark green indicate where chemicals meet the criteria of the boundary. Those in red represent a 'no' answer to 

the question asked. Window (pop-up) in lower right corner shows the target profiled structure and comparison to the training set within 

the specific node for which the chemical was profiled; in this case the 'salicylates (non-benzoate)'. 

CAS 

65405-77-8

LogKow 4.28

cis-3-Hexenyl 

salicylate
Prioritization of the EDSP Universe of Chemicals

December 2012 23 | Page

640

641 Figure 3.2a.1: ESv2 showing location of added groups in red. 

642 New Groups from Mixed Phenols. The two-dimensional structures of the MP were assembled and sorted according to similar structural 

643 fragments. The following figure illustrates the common structural fragment for six new groups including two with subgroups.
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December 2012 24 | Page

Figure 3.2a.2: Structural fragments common to multiple chemicals classified as Mixed Phenols 

when ESv1 was applied to 1,423 NF list. These fragments provide the basis of new chemical groupings 

for development of ESv2. 

644 The next three figures will illustrate by example the process of grouping and the strategic testing 

645 employed to cover the new groups; Figure 3.2a.3 Benzotriazole Phenol, Figure 3.2a.4. 2-Hydroxy 

646 Benzophenones, and Figure 3.2a.5. Phenolsulfophthaleins. 

647 Given the importance of having confidence in the structure-bioactivity relationships, every attempt was 

648 made to procure chemicals for testing with purity >95%. Selection of representative structures for a 

649 given group was designed to cover the range of LogKow of NF inventory chemicals whenever possible. 

650 For example, two representative benzotriazole phenols (Figure 3.2a.3, HMP and BMBP) were tested and 

651 found to be rtER Non-binders. These two representative chemical structures nearly covered the LogKow

652 range of the group. Although the high end of the LogKow testing extended to 7.63, it was thought that 

653 coverage to the maximum of 8.00 was achieved considering the inherent error in LogKow estimators in 

654 this high range. Because only Non-binders were found in the group, it was concluded that this entire 

655 group would fall under part 'III' of the original ESv1 'Belongs to Known Inactive Subclass' 

656 (Figure 3.1.1). 
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Figure 3.2a.3: Benzotriazole Phenol structural fragment used to define this new group from 

within ER ESv1 Mixed Phenols , those chemicals tested to expand TrSet coverage of the new 

chemical group, and 1,493 NF inventory chemicals that are classed in this group. 

Green background = rtER non-binders; white background = untested. 

657 The 2-hydroxy benzophenones group provided mixed results with respect to rtER binding. All five 

658 members found on the 1,463 NF inventory were tested (Figure 3.2a.4: pink=binders, green=non659 binders). In addition to the inventory chemicals, the base structure was also tested [CAS = 117-99-7; 

660 HBO in the figure]. A wide range of LogKows were tested for binding affinity. The two inactives were 

661 the result of structure attenuation. The first of these and the lowest LogKow structure in this chemical 

662 group contains a sulfonic acid structural fragment shown to be consistent with presence of a charged 

663 chemical species at test pH (i.e., low pKa). Charged species are, in this case >99% dissociated, denature 

664 receptor proteins, as discussed in the 2009 SAP white paper (SAP 2009a). The highest LogKow structure 

665 in the series is also a Non-binder most likely due to some steric influence of the long alkyl chain of the 

666 alkoxy preventing binding. This 2-hydroxy benzophenones chemical group was placed into ESv2 under 

667 Part 'VI' or the 'Site-A: Contains a Phenol Fragment' branch (Figure 3.1.2). The structural boundaries 

668 coded in the automated decision tree were written with 'masks' to represent the exclusions described 

669 here.

670 The phenolsulfophthaleins (Figure 3.2a.5) were also placed into ESv2 under Part 'VI' under the 'Site-A: 

671 Contains a Phenol Fragment' branch of the tree. Again, a mixed result was observed for this group 

672 with two of three NF structures tested. It is assumed that the inactive structure is due to attenuation, i.e., 

673 steric interference with bulky groups (bromines) ortho to the phenol group on the aromatic ring, and 

674 thus, impeding hydrogen-bonding. A sub-class of hindered phenolsulfophthaleins was created within the 

675 ESv2 to capture this. 
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December 2012 26 | Page

Figure 3.2a.4: 2-Hydroxy Benzophenones structural fragment used to define this new group from 

within ESv1 Mixed Phenols, the chemicals tested to expand TrSet coverage of the new chemical 

group, and 1,493 NF inventory chemicals that are classed in this group. 

Green background = rtER non-binders; pink background = rtER binder. 
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December 2012 27 | Page

Figure 3.2a.5: Phenolsulfophthalein and Hindered Phenolsulfophthalein structural fragments 

used to define this new group from within ESv1 Mixed Phenols , the chemicals tested to expand 

TrSet coverage of the new chemical group, and 1,493 NF inventory chemicals that are classed in 

this group. Green background = rtER Non-binder; pink background = rtER Binder; white background = untested.

676 New Groups from Mixed Organics. The strategy used to increase model coverage for the Mixed Phenols 

677 was also applied to the Mixed Organics. Chemicals were grouped according to common structural 

678 fragment followed by strategic testing of representative structures to expand the ES TrSet. Rules were 

679 then established and nodes added to the ES as part of the expansion. Figure 3.2a.6 shows representative 

680 structural fragments for eight new groups originating from the Mixed Organics. 
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Figure 3.2a.6: Structural fragments common to multiple chemicals classified as Mixed Organics 

when ESv1 was applied to NF 1,423 list. These fragments provide the basis of new chemical groupings 

for development of ESv2. 

681 Examples for three of these new groups are shown in Figures 3.2a.7 to 3.2a.9. phosphorous acid esters 

682 are shown in Figure 3.2a.7. Two representative structures that were tested and found inactive adequately 

683 covered the LogKow range of inventory chemicals. This group is placed under part 'III' of ESv1 'Belongs 

684 to Known Inactive Subclass' group. Three representative structures tested for the amino 

685 anthracenediones (Figure 3.2a.9) and were found to be non-binders and the group was also placed under 

686 part 'III' of ESv1 'Belongs to Known Inactive Subclass'.

687 The phosphoric acid esters in Figure 3.2a.8, on the other hand, were found to bind rtER when tested. 

688 Structural requirements include the need to have at least one cycle on an esterified oxygen. Further all 

689 three oxygens need to be esterified. If not, the hydrogen is lost and the chemical species is >99% ionic at 

690 neutral pH, pKa=0.87. A chemical source could not be located for the structure in orange and a surrogate 

691 was tested. This group proved to be very interesting with respect to mode of receptor interaction as the 

692 atomic charges are not consistent with A-type (-0.25) or B-type (-0.32). Current testing of the yellow 

693 structure in Figure 3.2a.8 will serve to extend the LogKow coverage for this class. This group was placed 

694 under ES decision tree branch 'V' where 'Special Rules Apply'.
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December 2012 29 | Page

Figure 3.2a.7: Phosphorous Acid Esters structural fragment used to define this new group from 

within ER ESv1 Mixed Organics, the chemicals tested to expand TrSet coverage of the new 

chemical group, and NF inventory chemicals that are classed in this group. 

Green background = rtER non-binders; white background = untested. 
Prioritization of the EDSP Universe of Chemicals

December 2012 30 | Page

Figure 3.2a.8: Phosphoric Acid Ester structural fragment used to define this new group from 

within ESv1 Mixed Organics, the chemicals tested to expand TrSet coverage of the new chemical 

group, and NF inventory chemicals that are classed in this group. 

Green background = rtER non-binders; pink background = rtER binder; white background = untested; 

orange background = a surrogate of this chemical was tested; yellow background = on-going test.
Prioritization of the EDSP Universe of Chemicals

December 2012 31 | Page

Figure 3.2a.9: Amino Anthracenediones structural fragment used to define this new group from 

within ESv1 Mixed Organics, the chemicals tested to expand TrSet coverage of the new chemical 

group, and NF inventory chemicals that are classed in this group. 

Green background = rtER non-binders; white background = untested.

695 The 14 new groups described above, six from Mixed Phenols and eight from Mixed Organics, are 

696 summarized in Table 3.2a.1 along with their location in the expanded ES decision tree. The new rules 

697 were coded and an automated version of ESv2 built. It should be mentioned that the expansion of the ES 

698 to cover the NF list is still ongoing. The 14 new groups added during expansion of ESv1 to ESv2 did not 

699 completely cover all the 2009 NF Mixed Phenols and Mixed Organics. Additional groups or sub-groups 

700 within those currently proposed are likely to be found as the iterative process continues. 

701 Figure 3.2a.10A to 3.2a.10F shows examples of representative groupings found by inspection within the 

702 MO and MP UnkBP remaining after application of the newly coded ESv2 to the 2009 NF list. As with 

703 the examples shown above, and the new groups coded into ESv2 based on strategic testing that 

704 expanded the ES TrSet, these structural analogs in Figure 3.2a.10 could give rise to additional ES 

705 classes to better categorize the NF, as well as, the larger EDSP inventory (see section 3.3a). 
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December 2012 32 | Page

706 Table 3.2a.1: New groups covered in the ESv2 expansion 

New Group Decision Tree Location ER ES

Mixed Phenol Dimethylamino Phenols III. 'Belongs to Known Inactive Subclass'

Naphthalenol-Azophenyls III. 'Belongs to Known Inactive Subclass'

Benzotriazole Phenols III. 'Belongs to Known Inactive Subclass'

2-Hydroxy Benzophenones VI. 'Site A – Contains a Phenol Fragment'

Phenolsulfophthaleins VI. 'Site A – Contains a Phenol Fragment'

Fluoresceins VI. 'Site A – Contains a Phenol Fragment'

Mixed Organics Anhydrides II. 'Contains a Charge'

Sugars III. 'Belongs to Known Inactive Subclass'

Alcohols III. 'Belongs to Known Inactive Subclass'

Morpholines III. 'Belongs to Known Inactive Subclass'

Acetanilides III. 'Belongs to Known Inactive Subclass'

Amino Anthracenediones III. 'Belongs to Known Inactive Subclass'

Phosphorous Acid Esters III. 'Belongs to Known Inactive Subclass'

Phosphoric Acid Esters V. 'Special Rules Apply'
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December 2012 35 | Page

Figure 3.2a.10: Example of proposed further categorization of analog groups within ESv1 Mixed 

Orgaincs and Mixed Phenols. (A) Ascorbic Acid analogs; Caffeine analogs; (B) Aldehyde analogs; 

Ketone analogs; (C) Di-ketone analogs; Di-corboxy ester analogs; Epoxides; (D) Reactive 

Peroxides; Vitamin E analogs; (E) silicon; nitrogen heterocycles; (F) Mixed Phenol analogs.

707 3.2.2 Assessing ESv1 and ESv2 Coverage of the EDSP Inventory 

708 The distribution of the 5,975 EDSP inventory chemicals (before applying the final filtering based on 

709 p-chem properties described in section 2.2.3 by use class and SDWA occurrence is summarized in Table 

710 3.2b.1), the inventory includes 2,594 (43%) inert ingredients and 3,381 (57%) chemicals that are SDWA 

711 only, and 1,029 that overlap between SDWA and inert ingredients. Of the inert ingredients, there are FI 

712 inclusive of 50 chemicals that are also Fragrances (Frag) and 46 chemicals of these are also SDWA 

713 chemicals. The inert ingredients also include 1,141 NF with 152 chemicals that are also Frag and 73% of 

714 these also SDWA chemicals. There are 963 Frag chemicals that are not otherwise FI or NF but 227 

715 chemicals of these are also on the SDWA list. The number of chemicals unique to one listing can be 

716 parsed out. For example, there are 239 FI chemicals that are not Frag, NF, or on the SDWA list, and 545 

717 chemicals are unique to the NF list only, with 736 Frag that are on no other lists. 
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718 Table 3.2b.1: EDSP inventory of 5,975 summarized by number of chemicals found in each "Use 

719 Class" and on the SDWA occurrence list, and the overlap of chemicals across the lists.

Inerts 

Use Class # chems

SDWA also non-SDWA

# chems % FI NF Frag

FI only 440 201 46 239

FI & Frag 50 46 92 4 

FI total= 490

NF only 989 444 45 545

NF & Frag 152 111 73 41

NF Total= 1141

Frag only 963 227 24 736

Frag Total= 1165

All Inerts= 2594

Inerts & SDWA= 1029 40

Inerts non-SDWA= 1565

SDWA only= 3381

All SDWA= 4410

Total Inventory= 5975

720 A summary of the automated ESv1 predictions for the 5,975 EDSP inventory is found in Table 3.2b.2. 

721 The ES reviewed in 2009 (SAP, 2009a, b) was built to cover a chemical regulatory domain of ~400 FI 

722 and ~200 AM chemicals, and was found to predict 68% of the much larger number of 2,594 inert 

723 ingredients that includes more FIs, and many Frag and NF chemical structures that were not in the initial 

724 regulatory chemical domain. The ESv1 was also able to provide predictions for 69% (2,323) of 3,381 

725 chemicals unique to the SDWA list. Overall ESv1 covered 68% of the 5,975 inventory. When the ESv1 

726 was expanded to ER ESv2, based on testing in the 1,405 NF inventory (described in section 3.2.1) the 

727 coverage increased to 71% overall when inert ingredients (FI, NF, fragrances) and SDWA substances 

728 are considered.
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729 Table 3.2b.2: Overall results for 5,975 Inventory using ESv1 and ESv2 

ER ESv1 All Inerts SDWA only Inert + SDWA

5975 Inventory # chem % # chem % # chem % pred

RBA<0.00001% 1701 66 2282 67 3983 67

RBA>0.00001% 67 3 41 1 108 2 

Predicted= 1768 68 2323 69 4091 68

UnkBP 826 32 1058 31 1884 32

Totals = 2594 3381 5975

ER ESv2 All Inerts SDWA only Inert + SDWA

5975 Inventory # chem % # chem % # chem % 

RBA<0.00001% 1785 69 2321 69 4106 69

RBA>0.00001% 87 3 78 2 165 3 

Predicted= 1872 72 2398 71 4271 71

UnkBP 722 28 982 29 1704 29

Totals = 2594 3381 5975

730 A more detailed analysis of ES coverage of FI, NF and Frag chemicals on the 5,975 inventory is given 

731 in Table 3.2b.3 (A). As might be expected for the ESv1, which was built on FI type chemical structures, 

732 it provided predictions for 96% of FI chemicals. Additionally, it predicted 80% of NF and 69% of 

733 SDWA for a total coverage of 68% for the entire inventory. Table 3.2b.3 also shows that the poorest 

734 coverage is for 'Frag only' where only 40% of 963 chemicals unique to this Inert class are covered. This 

735 would indicate that there are many unique structures on the Frag list. 
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736 Table 3.2b.3: Summary of ES predictions for 5,975 Inerts and SDWA chemicals 

737 Inerts include: Food use Inerts (FI), Non-Food use (NF), Fragrances (Frag), and 1,029 SDWA chemicals that are also Inerts. The SDWA 

738 only chemicals are listed as well as totals of Inerts and SDWA. (A) The number and % of chemicals for which ER ESv1 provides 

739 predictions is presented. (B) The number and % of chemicals for which ER ESv2 provides predictions. 

740 (A)

ER ESv1 All Inerts Food Use Inerts (FI) Non Food Use (NF) Fragrances (Frag) SDWA Only Inert + SDWA

5975 Inventory

# 

chem % only

+ 

Frag Total

Total 

% only

+ 

Frag Total

Total 

% Only

Only 

% All All % # chem % # chem % 

RBA<0.00001% 1701 66 420 47 467 95 807 76 883 77 351 36 474 41 2282 67 3983 67

RBA>0.00001% 67 3 2 3 5 1 19 6 25 2 37 4 46 4 41 1 108 2 

Predicted= 1768 68 472 96 908 80 388 40 520 45 2323 69 4091 68

UnkBP 826 32 18 0 18 4 163 70 233 20 575 60 645 55 1058 31 1884 32

Totals = 2594 440 50 989 152 963 3381 5975

Use Class Total= 490 1141 1165

741 (B)

ER ESv2 All Inerts Food Use Inerts (FI) Non Food Use (NF) Fragrances (Frag) SDWA Only Inert + SDWA

5975 Inventory

# 

chem % only + Frag Total

Total 

% only

+ 

Frag Total

Total 

% Only

Only 

% All All % # chem % # chem % 

RBA<0.00001% 1785 69 425 47 472 96 854 84 938 82 375 39 506 43 2321 69 4106 69

RBA>0.00001% 87 3 2 3 5 1 29 14 43 4 39 4 56 5 78 2 165 3 

Predicted= 1872 72 477 97 981 86 414 43 562 48 2399 71 4271 71

UnkBP 722 28 13 0 13 1 106 54 160 14 549 57 603 52 982 29 1704 29

Totals = 2594 440 50 989 152 963 3381 5975

Use Class Total= 490 1141 1165

742
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743 Although only modest increases are observed (Table 3.2b.3 (B), the expanded ESv2 resulted in 

744 increased coverage of chemicals from all chemical list sources (i.e., use classes). The 'Frag only' list 

745 coverage increased from 40% to 43% leaving 57% of 'Frag only' structures that were outside of the 

746 ESv2 model domain, and thus, were reported as UnkBP. 

747 The chemicals within the ESv2 model domain are also reported in Table 3.2b.4 with calculated 

748 percentages of Binders and NonBinders out of the total chemicals with predictions (in the model 

749 domain). This emphasizes the small number, and %, of ER Binders identified overall. Binders predicted 

750 for 'Inerts + SDWA' using ESv1 are 3%, or 108 chemicals. Predictions from ESv2 with 71% EDSP 

751 inventory coverage indicate that 4%, or 165 chemicals have ER binding potential. This is consistent with 

752 the results from 2009 SAP paper (SAP, 2009a) where 4% of chemicals on the much smaller FI list were 

753 predicted to bind. It is interesting to note that the percentage of "Binders" predicted within the 'Frag 

754 only' category is 9 to 10%, twice of what is found in other use classes. Thus, this chemical list warrants 

755 further attention. There are chemical groups identified on the Frag list that can be targeted for strategic 

756 in vitro testing, which is further addressed in section 3.3.1, to expand the ESv2 domain of applicability. 

757 The relatively modest decrease in UnkBP chemicals from 1,884 with ESv1, to 1,704 with ESv2, is in 

758 part attributed to surprisingly good initial coverage of the EDSP inventory by ESv1, and the fact that the 

759 vast majority of remaining unpredicted chemicals contained mixed functional groups. As described in 

760 section 3.2. Building ESv2, there were 14 new groups added to ESv2 by investigating MO and MP of 

761 ESv1. The number of EDSP inventory chemicals found in the 14 new groups is listed in Table 3.2b.5, 

762 with the greatest number of chemicals in the 'Alcohols' group including "NonBinders" and "UnkBP". 

763 The largest number of "Binders" are found in the new 'phosporic acid esters' group, followed by 

764 '2-hydroxy benzophenones,' 'fluoresceins' and one 'phenolsulfophthalein'. A prediction cannot be made 

765 for all the newly classified chemicals but locating them within a group or subgroup serves as a basis for 

766 strategic testing to cover all remaining unpredicted members of the group. Table 3.2b.6 shows the 

767 number and percentage of all 1,704 UnkBP chemicals by ESv2 chemical class. Even with the addition of 

768 the new groups, there remains a large number of MO (80%) and MP (8%) chemicals with UnkBP. This 

769 is not surprising and can be addressed using the same strategies used to expand to ESv2. A detailed 

770 discussion is provided in section 3.3a. Additional tables showing distribution of 4,106 RBA<0.00001%

771 chemicals and 165 RBA>0.00001% across ESv2 chemical groups is provided in Appendix G. 
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772 Table 3.2b.4: Predicted percentage of Binders and NonBinders in the 5,975 Inventory from (A) ESv1, and (B) ESv2, shown by 

773 Inerts use class, SDWA only chemicals, and the combined inventory.

ER ESv1 All Inerts Food Use Inerts (FI) Non Food Use (NF) Fragrances (Frag) SDWA Only Inert + SDWA

5975 Inventory # chem % Only + Frag Total % Only + Frag Total % Only % All % # chem % # chem % 

RBA<0.00001% 1701 96 420 47 467 99 807 76 883 97 351 90 474 91 2282 98 3983 97

RBA>0.00001% 67 4 2 3 5 1 19 6 25 3 37 10 46 9 41 2 108 3 

Total= 1768 472 908 388 520 2323 4091 

ER ESv2 All Inerts Food Use Inerts (FI) Non Food Use (NF) Fragrances (Frag) SDWA Only Inert + SDWA

5975 Inventory #c hem % Only + Frag Total % Only + Frag Total % Only % All % # chem % # chem %

RBA<0.00001% 1785 95 425 47 472 99 854 84 938 96 375 91 506 90 2321 97 4106 96

RBA>0.00001% 87 5 2 3 5 1 29 14 43 4 39 9 56 10 78 3 165 4 

Total= 1872 477 981 414 562 2399 4271 

774
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775 Table 3.2b.5: Number of chemicals classified by ESv2 into the 14 new groups (and 3 subgroups) 

776 used to expand from ESv1 to ESv2. 

RBA<0.00001% RBA>0.00001% UnkBP 

# chems # chems # chems

II. 'Contains a Charge'

Anhydrides 15 0 0 

III. 'Belongs to Known Inactive Subclass'

Dimethylamino Phenols 5 0 0 

Naphthalenol-Azophenyls 21 0 0 

Benzotriazole Phenols 9 0 0 

Sugars 22 0 0 

Alcohols 29 0 27

Morpholines 19 0 0 

Acetanilides 4 0 0 

Mixed Acetanilides 7 0 7 

Amino Anthracenediones 13 0 0

Phosphorous Acid Esters 13 0 0 

V. 'Special Rules Apply'

Phosphoric Acid Esters 0 19 0 

VI. 'Site A – Contains a Phenol Fragment'

2-Hydroxy Benzophenones 0 6 0 

Phenolsulfophthaleins 0 1 0 

Hindered Phenolsulfophthaleins 2 0 0 

Fluoresceins 0 2 0 

Hindered Fluoresceins 3 0 0 

777 Table 3.2b.6: Results of ESv2 for 1,704 UnkBP of 5,975 EDSP Inventory. Chemicals are listed by 

778 ES decision tree section (Figure 3-1) and ES chemical group. 

Chem Class # chem % of 1704

I. Acyclic_Perfluoro 

Acyclic Perfluoro (Unknown Binding Potential) 16 0.9

III. Belongs to a known Inactive class

Mono-cyclic Hydrocarbons [LogKow<2.5,>6.4] 13 0.8

Triazines [LogKow>4.1] 9 0.5

Benzoates (non-ring Subst) [LogKow<3.0,>4.3] 6 0.4

Sorbitans [LogKow<3.1,>5.9] 4 0.2

Imidazolidines [LogKow>0] 3 0.2

Benzamides [LogKow<2.1,>2.8] 1 0.1

Bis-Anilines [LogKow<2.1,>6.4] 1 0.1

Phenones (n-alkyl) [LogKow>4.2] 1 0.1

Pyrrolidiones [LogKow>3.4] 1 0.1

III. Known Inactives Subtotal= 39 2.3
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Chem Class # chem % of 1704

V. Special Rule applies 

Multi Cyclic Hydrocarbons Unknown Binding Potential 67 3.9

Thiophosphate Esters Unknown Binding Potential 14 0.8

Alkylbromobenzene 7 0.4

V. Special Rules Subtotal= 88 5.2

VI. Site "A": Contains a Phenol fragment

Mixed Phenol - Unknown Binding Potential 133 7.8

Alcohols (LogKow>3.4) 27 1.6

Alkoxyphenols [LogKow >4.1] 1 0.1

VI. Site "A" Subtotal= 161 9.4

VII. Site "B": Contains "Specified" fragment 

Mixed Organics - Unknown Binding Potential 1368 80.3

2-Alkylanilines 7 0.4

Mixed Acetanilides (LogKow>1.3) 7 0.4

Phenones cyclo-substituted - Unknown Binding Potential 6 0.4

Multi subst. Alkylanilines 5 0.3

Alkylcyclohexanols LogKow>4.4 or >2.6 and <3.5 4 0.2

3-Alkylanilines 2 0.1

2-;4-; or2,4,6-Benzoates Unknown Binding Potential 1 0.1

VII. Site "B" Subtotal= 1400 82.2

Unknown Binding Potential Total= 1704

779 3.2.3 ESv2 Chemical Groups in Final Filtered EDSP Inventory of 4,957 Chemicals 

780 The EDSP inventory of 5,975 chemicals was filtered to remove additional chemicals based on p-chem

781 properties that make them untestable or unlikely to pose systemic toxicity as discussed in section 2, 

782 section 2.2c. These include: hydrolysis half-life indicating chemical breakdown in solution, corrosivity, 

783 and extreme pKa acid and base values that would leave the chemical completely dissociated at the pH of 

784 test assays and would be expected to alter pH of test systems. As was originally intended, the use of 

785 these parameters and how the filters are implemented with respect to the EDSP inventory is still under 

786 investigation by the EPA and will be further assessed after this SAP review and prior to final 

787 implementation. As stated earlier, the agency intends to apply the physico-chemical properties filter with 

788 expert review. In this context, the ES will continue to inform the p-chem property filters. An example of 

789 the application of the ES predictions as a tool to facilitate this analysis is presented below.

790 The p-chem property filtering exercise, as described in section 2, resulted in 1,018 chemicals being 

791 removed from the 5,975, for a total of 4,947 chemicals remaining on the EDSP inventory. The chemicals 

792 that were removed were characterized based on ESv2 chemical groupings. The results in Table 3.2c.1A 

793 show that 92% of chemicals removed by this filtering, or 949 chemicals, belong to ESv2 NonBinder 

794 groups. A detailed listing of all ES NonBinder groups with excluded chemicals is included in Table 

795 3.2c.1B. 

796 Filtered chemicals using procedures described in section 2 that were classified as Binders or having 

797 UnkBP were examined more closely. Three perfluorinated chemicals filtered out were ES predicted 

798 Binders (Acyclic_Perfluoro (RBA>0.00001%) and five more were placed in the 'Acyclic Perfluoro 

799 (UnkBP)' group (Table 3.2c.1A). The reason for excluding these chemicals was an acid pKa exclusion 
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800 rule (see section 2). However, due to perfluorinated chemicals having unique chemistries that make 

801 them highly persistent, it was determined this class of chemicals merits further attention, and thus all 

802 eight perfluorinated chemicals were added back to the Final EDSP Inventory. 

803 The Alkylphenols predicted as Binders in Table 3.2c.1A were also examined. The two chemicals 

804 excluded were forms of methylphenol (cresol) that were initially filtered due to a corrosivity rule. 

805 However, p-cresol (CASNR 106-44-5) was included in the ES TrSet where it was testable in aqueous 

806 media and found to be a Binder. Also, additional cresol forms were still found on the inventory after 

807 filtering. Thus, confirming that the implementation of filtering rules warrants additional examination to 

808 ensure consistency of application across chemicals. An iterative process is proposed where the expert 

809 system is applied to the inventory before filtering, and the resulting predictions are used to examine the 

810 chemicals under consideration for removal by exclusion filters to highlight chemicals needing further 

811 examination.

812 The remaining 69 UnkBP chemicals from MO, MP, and triazine groups (Table 3.2c.1A) remain filtered 

813 out along with 949 predicted NonBinders. From Table 3.2c.1B, it can be seen that there are some ES 

814 chemical groups for which all inventory chemicals were excluded after filtering, i.e., where '0' chemicals 

815 are found under the 'Remain in 4947' column for the following ES groups: Acyclic Phosponic Acids, 

816 Acyclic Sulfates, Alkylaromatic Sulfonic Acids, Phosphoric Acid Functional Group, and Sulfonic Acid 

817 Functional Groups. Also, Sulfonic Acid Dyes are largely removed with a few chemicals remaining in 

818 the inventory. 

819 In summary, the fact that 92% of the filtered chemicals were also predicted NonBinders by the ES adds 

820 additional confidence in the use of these filters, with many of these chemicals having ES TrSet data and 

821 additional testing that provides mechanistic rationale for the exclusion, as was presented in the 2009 

822 SAP, e.g., Alkylaromatic Sulfonic Acids. The final result of filtering of the 5,975 EDSP inventory was 

823 that of the 1,028 chemicals removed there were ten added back resulting in a final EDSP post-filtering 

824 inventory of 4,957. 

825 Table 3.2c.1 (A) (B): Summary of chemicals filtered out of 5,975 EDSP Inventory by applying 

826 physical-chemical property exclusion filters. The 1,028 chemicals excluded are summarized by the 

827 number of chemicals (# chem) and percent of 1,028 (%) excluded by their ESv2 chemical group and ES 

828 prediction. A) detailed information for filtered chemicals in ER Binders or UnkBP categories by class, 

829 with all remaining NonBinder groups summed together; and, B) detailed information on NonBinders ES 

830 chemical groups including number of chemicals within a group that were 'Excluded by Filter' and 

831 number Remain after Filter in the final post-filtered EDSP inventory of 4,947. 

A) ES Chemical Classification ES Prediction # chems %

Alkylphenols RBA>0.00001% 2 0.19

Acyclic_Perfluoro (RBA>0.00001%) RBA>0.00001% 3 0.29

Acyclic Perfluoro - UnkBP UnkBP 5 0.49

Mixed Organics - UnkBP UnkBP 63 6.13

Mixed Phenol - UnkBP UnkBP 4 0.39

Triazines [LogKow>4.1] UnkBP 2 0.19

Subtotal= 79 8 

All remaining Filtered Groups Chemicals RBA<0.00001% 949 92

Total = 1028 100
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Excluded by 

Filters Remain in 4947

B) ES Chemical Classification ES Prediction # chems # chems

Acyclic_Alcohol RBA<0.00001% 27 267

Acyclic_Amines RBA<0.00001% 19 98

Acyclic_Carboxy Ester RBA<0.00001% 54 393

Acyclic_Carboxylic Acid RBA<0.00001% 68 313

Acyclic_Phosphonic Acid RBA<0.00001% 6 0 

Acyclic_Sulfate RBA<0.00001% 52 0 

Acyclics_Quat Amine RBA<0.00001% 5 15

Acyclics_Other RBA<0.00001% 128 746

Alkylaromatic Sulfonic Acids RBA<0.00001% 45 0 

Alkylphenols (2,6-subst) RBA<0.00001% 1 35

Amino Anthracenedione (LogKow>3.0) RBA<0.00001% 1 12

Anhydrides RBA<0.00001% 1 14

Bis-Anilines [2.1<=LogKow<=6.4] RBA<0.00001% 1 8 

Carboxylic Acid Functional Group RBA<0.00001% 38 187

Hydrofurans [LogKow<=6.1] RBA<0.00001% 1 22

Imidazolidines [LogKow<=0] RBA<0.00001% 1 5 

Inorganic RBA<0.00001% 197 143

LogKow Cut-off <1.3 RBA<0.00001% 31 358

Miscellaneous Charged Atom RBA<0.00001% 2 19

Mixed Organics - Exact Match (RBA<0.00001%) RBA<0.00001% 2 52

Mixed Phenol - Exact Match (RBA<0.00001%) RBA<0.00001% 1 6 

Mono-cyclic Hydrocarbons [2.5<=LogKow<=6.4] RBA<0.00001% 2 64

Morpholine RBA<0.00001% 1 18

Naphthalenol_Azophenyl RBA<0.00001% 2 19

Phenolsulfonphthaleins hindered RBA<0.00001% 1 1

Phosphoric Acid Functional Group RBA<0.00001% 19 0 

Quat Amine Functional Group RBA<0.00001% 4 26

Sugars RBA<0.00001% 2 20

Sulfonic Acid Dyes RBA<0.00001% 135 8 

Sulfonic Acid Functional Group RBA<0.00001% 98 0 

Triazines [LogKow<=4.1] RBA<0.00001% 4 24

949

832 The distribution of the final 4,957 chemicals on the EDSP inventory by use class and SDWA occurrence 

833 is summarized in Table 3.2c.2. Similar to 5,975 inventory the Inerts (Fi, NF, Frag and overlaps with 

834 SDWA) comprise 42% of the total list while 'SDWA only' is 58%, thus the chemical exclusions did not 

835 appear to pertain preferentially to chemicals from any particular list. The results of the ESv1 and ESv2 

836 classification of the filtered inventory is shown in Table 3.2c.3 in detail by use class. The apparent 

837 overall coverage of 65% and 67% respectively for ESv1 and ESv2 (Table 3.2c.4) is less than the 

838 previous 72% coverage of the unfiltered inventory of 5,975. However, this is to be expected when 949 

839 predictions are removed from the predicted NonBinder class by the filters and only 69 are removed from 

840 UnkBP. Consistent with what was noted with the larger unfiltered EDSP inventory when ESv1 and 

841 ESv2 predictions are compared, the percentage of all predicted chemicals that were Binders in the 4,957 

842 inventory (Table 3.2c.5) was < 5% for most lists (2% for FI chemicals; increased from 4 to 6% of NF 
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843 predictions; and 2 to 4% of the 'SDWA only'). Again, the highest percentage of Binders are found in the 

844 Frag group at 10%. 

845 Table 3.2c.2: EDSP inventory of 4,957 summarized by number of chemicals in each Use Class and 

846 on the SDWA occurrence list

Inerts 

Use Class # chems

SDWA also non-SDWA

# chems % FI NF Frag

FI only 262 127 46 135

FI & Frag 46 43 92 3 

FI total= 308

NF only 687 332 45 355

NF & Frag 148 107 73 41

NF Total= 835

Frag only 937 222 24 715

Frag Total= 1131

All Inerts= 2080 831 40

Inerts non-SDWA= 1249

SDWA only= 2877

All SDWA= 3710

Total Inventory= 4957
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847 Table 3.2c.3 A: Summary of ESv1 Predictions for 4,957 Inerts and SDWA chemicals; Inerts include: Food Use Inerts (FI), Non848 Food Use (NF), Fragrances (Frag), and 831 SDWA chemicals that are also Inerts. 

849 The SDWA only chemicals are listed in the last column.

ER ESv1 All Inerts Food Use Inerts (FI) Non Food Use (NF) Fragrances (Frag) SDWA Only

4957 Inventory # chem % Only + Frag Total % Pred Only + Frag Total % Pred Only Only % ALL % Pred # chem % Pred

RBA<0.00001% 1214 58 243 43 286 93 518 72 590 71 338 36 453 40 1827 63

RBA>0.00001% 67 3 2 3 5 2 19 6 25 3 37 4 46 4 41 1 

Predicted= 1281 62 291 94 615 74 375 499 44 1868 65

UnkBP 799 38 17 0 17 150 70 220 562 632 1009 

Totals = 2080 262 46 687 148 937 2877 

Use Class Total= 308 835 1131

850 Table 3.2c.3 B: Summary of ESv2 Predictions for 4,957 Inerts and SDWA chemicals; Inerts include: Food Use Inerts (FI), Non851 Food Use (NF), Fragrances (Frag), and 831 SDWA chemicals that are also Inerts. 

852 The SDWA only chemicals are listed in the last column.

ER ESv2 All Inerts Food Use Inerts (FI) Non Food Use (NF) Fragrances (Frag) SDWA Only

4957 Inventory # chem % Only + Frag Total % Pred Only + Frag Total % Pred Only Only % ALL % Pred # chem % Pred

RBA<0.00001% 1293 62 248 43 291 94 560 80 640 77 362 39 485 43 1864 65

RBA>0.00001% 87 4 2 3 5 2 29 14 43 5 39 4 56 5 78 3 

Predicted= 1380 66 296 96 683 82 401 541 48 1942 67

UnkBP 700 34 12 0 12 98 54 152 536 590 935

Totals = 2080 262 46 687 148 937 2877

Use Class Total= 604 1518 1672

853
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854 Table 3.2c.4: Overall results for 4,957 Filtered Inventory using ESv1 and ESv2

ER ESv1 All Inerts SDWA only Inert + SDWA

4957 Inventory # chem % # chem % # chem % 

RBA<0.00001% 1214 58 1827 63 3041 61

RBA>0.00001% 67 3 41 1 108 2 

Predicted= 1281 62 65 64

UnkBP 799 38 1009 35 1808 36

Totals = 2080 2877 4957

ER ESv2 All Inerts SDWA only Inert + SDWA

4957 Inventory # chem % # chem % # chem % 

RBA<0.00001% 1293 62 1864 65 3157 64

RBA>0.00001% 87 4 78 3 165 3 

Predicted= 1380 66 68 67

UnkBP 700 34 935 32 1635 33

Totals = 2080 2877 4957 
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855 Table 3.2c.5A: Proportion of ESv1 predicted ER actives in 4,957 EDSP inventory found on the Inerts inventory and in each Use 

856 Class and among the chemicals on only the SDWA list. Predicted Binders are 5% of Inerts overall, 2% of SDWA, and 10% of 

857 Fragrances. 

ER ESv1

All Inerts Food Use Inerts (FI) Non Food Use (NF) Fragrances (Frag) SDWA Only

# chem % Only + Frag Total % Only + Frag Total % Only % All % # chem % 

RBA<0.00001% 1214 95 243 43 286 98 518 72 590 96 338 90 453 91 1827 98

RBA>0.00001% 67 5 2 3 5 2 19 6 25 4 37 10 46 9 41 2 

Totals = 1281 291 615 375 499 1868

858

859 Table 3.2c.5B: Proportion of ESv2 predicted ER actives in 4,957 EDSP Inventory found on the INERTS inventory and in each 

860 Use Class and among the chemicals on only the SDWA list. Predicted Binders are 6% of Inerts overall, 4% of SDWA and 10% of 

861 Fragrances.

ER ESv2

All Inerts Food Use Inerts (FI) Non Food Use (NF) Fragrances (Frag) SDWA Only

# chem % Only + Frag Total % Only + Frag Total % Only % All % # chem % 

RBA<0.00001% 1293 94 248 43 291 98 560 80 640 94 362 90 485 90 1864 96

RBA>0.00001% 87 6 2 3 5 2 29 14 43 6 39 10 56 10 78 4 

Totals = 1380 296 683 401 541 1942

862
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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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December 2012 53 | Page

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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December 2012 55 | Page

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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December 2012 56 | Page

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 
Prioritization of the EDSP Universe of Chemicals

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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December 2012 58 | Page

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. 
Prioritization of the EDSP Universe of Chemicals

December 2012 59 | Page

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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December 2012 60 | Page

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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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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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
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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.
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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.
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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 
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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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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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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 
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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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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
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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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# 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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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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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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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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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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December 2012 90 | Page

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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December 2012 94 | Page

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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December 2012 95 | Page

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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December 2012 96 | Page

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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December 2012 97 | Page

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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December 2012 98 | Page

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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December 2012 99 | Page

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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December 2012 100 | Page

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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December 2012 101 | Page

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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December 2012 102 | Page

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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December 2012 103 | Page

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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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 
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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
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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
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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%. 
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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 
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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 (%)
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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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December 2012 114 | Page

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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December 2012 115 | Page

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 
Prioritization of the EDSP Universe of Chemicals

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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December 2012 118 | Page

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 
Prioritization of the EDSP Universe of Chemicals

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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December 2012 122 | Page

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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December 2012 124 | Page

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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December 2012 125 | Page

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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December 2012 126 | Page

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
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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
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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

December 2012 129 | Page

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
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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.
Prioritization of the EDSP Universe of Chemicals

December 2012 135 | Page

2121 References 

2122 Benjamini Y, Y Hochberg. 1995. Controlling the false discovery rate: a practical and powerful 

2123 approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological). 

2124 57(1):289-300. 

2125 Blair, R.M., Fang, H., Branham, W.S., Hass, B.S., Dial, S.L., Moland, C.L., Tong, W., Shi, L., Perkins, 

2126 R., and Sheehan, D.M. 2000. The estrogen receptor binding affinities of 188 natural and 

2127 xenochemicals: Structural diversity of ligands. Toxicological Sciences, 54, 138-153. 

2128 Denny, J.S., M.A. Tapper, P.K. Schmieder, M.W. Hornung, K.M. Jensen, G.T. Ankley, and T.R. Henry. 

2129 2005. Comparison of relative binding affinities of endocrine active compounds to fathead 

2130 minnow and rainbow trout estrogen receptors. Environmental Toxicology and Chemistry 24:11. 

2131 2948-2953. 

2132 Freyberger A et al. (2010). Assessment of robust model protocol with accelerated throughput for a 

2133 human recombinant full length ER-alpha binding protocol. Reprod Tox 30:50-59. 

2134 Hartman RW et al. (1983). Ring-substituted 1,2-dialkylated 1,2-bis(hydroxyphenyl)ethanses. 1. 

2135 Synthesis and estrogen receptor binding affinity of 2,2'- and 3,3'-disubstituted hexestrols. Med 

2136 Chem., 26(8):1137-1344. 

2137 Hornung, M.W., Jensen, K., Korte, J., Kahl, M., Durhan, E., Henry, T., Denny, J. and Ankley, G. T. 

2138 2004. Mechanistic basis for estrogenic effects of methyltestosterone in fathead minnow: 

2139 Evidence for conversion of 17-methyltestosterone to 17-methylestradiol. Aquatic Toxicology, 

2140 66, 15-23 

2141 ICCVAM (2002a), Background Review Document: Estrogen Receptor Binding, Appendix D, 

2142 Substances Tested in the ER Binding Assay, NIH Publication Report No. 03-4504. Available at: 

2143 http://iccvam.niehs.nih.gov/docs/endo_docs/final1002/erbndbrd/ERBd034504.pdf

2144 ICCVAM (2002b), Background Review Document: Estrogen Receptor Transcriptional Activation (TA) 

2145 Assay. Appendix D, Substances Tested in the ER TA Assay, NIH Publication Report No. 03-

2146 4505. Available at: 

2147 http://iccvam.niehs.nih.gov/docs/endo_docs/final1002/erta_brd/ERTA034505.pdf

2148 ICCVAM (2003), ICCVAM Evaluation of In vitro Test Methods for Detecting Potential Endocrine 

2149 Disruptors: Estrogen Receptor and Androgen Receptor Binding and Transcriptional Activation 

2150 Assays. Available at: http://iccvam.niehs.nih.gov/docs/endo_docs/edfinalrpt0503/edfinrpt.pdf

2151 ICCVAM (2011), ICCVAM Test Method Evaluation Report on the LUMI-CELL® ER (BG1Luc ER 

2152 TA) Test Method, An In vitro Method for Identifying ER Agonists and Antagonists, National 

2153 Institute of Environmental Health Sciences: Research Triangle Park, NC.

2154 Kanno, J, et al. (2001), The OECD program to validate the rat uterotrophic bioassay to screen 

2155 compounds for in vivo estrogenic responses: Phase 1, Environ. Health Persp. 109:785-94. 

2156 Kanno J, et al. (2003a), The OECD program to validate the rat uterotrophic bioassay: Phase Two Dose -

2157 Response Studies, Environ. Health Persp. 111:1530-1549. 
Prioritization of the EDSP Universe of Chemicals

December 2012 136 | Page

2158 Kanno J, et al. (2003b), The OECD program to validate the rat uterotrophic bioassay: Phase Two – 

2159 Coded Single-Dose Studies, Environ. Health Persp. 111:1550-1558. 

2160 Kunz PY, HF Galicia, K Fent. 2006. Comparison of in vitro and in vivo estrogenic activity of UV filters 

2161 in fish. Toxicological Sciences, 90(2):349–361. PMID: 16403853 

2162 Martin MT, DJ Dix, RS Judson, RJ Kavlock, DM Reif, AM Richard, DM Rotroff, S Romanov, A 

2163 Medvedev, N Poltoratskaya, M Gambarian, M Moeser, SS Makarov, KA Houck. 2010. Impact 

2164 of environmental chemicals on key transcription regulators and correlation to toxicity end points 

2165 within EPA's ToxCast program. Chemical Research in Toxicology, 23(3):578-90. PMID: 

2166 20143881 

2167 Miller, D., Wheals, B.B., Beresford, N., and Sumpter, J.P. 2001. Estrogenic activity of phenolic 

2168 additives determined by in vitro yeast bioassay. Environmental Health Perspectives 109, 133-

2169 138. 

2170 Motulsky HJ, RE Brown. 2006. Detecting outliers when fitting data with nonlinear regression - a new 

2171 method based on robust nonlinear regression and the false discovery rate. BMC Bioinformatics. 

2172 7:123. 

2173 OECD, 2007. Guidance Document on the Validation of (Quantitative) Structure-Activity Relationships 

2174 [(Q)SARs] Models. Series on Testing and Assessment, No. 69. March 30, 2007. 

2175 ENV/JM/MONO(2007)2. http://search.oecd.org/officialdocuments/displaydocumentpdf/?cote=e

2176 nv/jm/mono(2007)2&doclanguage=en

2177 OECD, 2009. Report of the Expert Consultation to Evaluate an Estrogen Receptor Binding Affinity 

2178 Model for Hazard Identification. Series on Testing and Assessment, No. 111. August 21, 2009. 

2179 ENV/JM/MONO(2009)33. http://search.oecd.org/officialdocuments/displaydocumentpdf/?cote=

2180 env/jm/mono(2009)33&doclanguage=en 

2181 Ohta R et al. (2012). Ovariectomized mouse uterotrophic assay of 36 chemicals. J. Toxicol Sci. 

2182 37(5):879-889. 

2183 Romanov S, A Mededev, M Gambarian, N Poltoratskaya, M Moeser, L Medvedeva, M Gambarian, L 

2184 Diatchenko, S Makarov. Homogeneous reporter system enables quantitative functional 

2185 assessment of multiple transcription factors. Nature Methods 5(3):253-60. PMID: 18297081 

2186 SAP 2009a, FIFRA SAP. An Effects-based Expert System to Predict Estrogen Receptor Binding 

2187 Affinity for Food Use Inert Ingredients and Antimicrobial Pesticides: Application in a 

2188 Prioritization Scheme for Endocrine Disruptor Screening, Meeting Materials, 

2189 http://www.regulations.gov/#!docketDetail;D=EPA-HQ-OPP-2009-0322 

2190 SAP 2009b, FIFRA SAP. An Effects-based Expert System to Predict Estrogen Receptor Binding 

2191 Affinity for Food Use Inert Ingredients and Antimicrobial Pesticides: Application in a 

2192 Prioritization Scheme for Endocrine Disruptor Screening, Meeting Minutes, 

2193 http://www.epa.gov/scipoly/sap/meetings/2009/august/082509minutes.pdf 

2194 SAP 2011, FIFRA SAP. Integrated Approaches to Testing and Assessment Strategies: Use of New 

2195 Computational and Molecular Tools, May 24-26, 2011 (EPA-HQ-OPP-2011-0284-0054) 
Prioritization of the EDSP Universe of Chemicals

December 2012 137 | Page

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. 
Prioritization of the EDSP Universe of Chemicals

December 2012 138 | Page

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