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