### 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 (T AD), RTP, NC 27111

National Center for Computational Toxicology (NCCT) Research Triangle Park, NC 27709



# December 2012

### Prioritization of the EDSP Universe of Chemicals

December 2012 i | Page Preface 1



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

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

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

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

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

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

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

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

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

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

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

### the proposed approach. 13



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

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

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

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

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

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

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



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

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



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

### commercial products do not constitute endorsement. 24

### Prioritization of the EDSP Universe of Chemicals

December 2012 ii | Page 

## Table of Contents 25



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


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



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

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

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

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

### 2.2.3 Physico

- Chemical Properties Considerations ..................................................................... 7 32 

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


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



### 3.1 In troduction of ER Expert System and Five Specific Goals ....................................................... 15 35 

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

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

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

### 3.2.2 Assess ing ESv1 and ESv2 Coverage of the EDSP Inventory ........................................... 35 39 

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


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



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

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

### 4.3 Statistical Analysis of Concentration -Respon se Data ................................................................. 50 44 

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

### 4.5 Cell stress filter (Attagene

- Specific) ........................................................................................... 51 46


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



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

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

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

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

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


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

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



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

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

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

### 6.3.1 Replicate Analysis 1: Concentration

- wise response for technical replicates. .................. 85 58 

### 6.3.2 Replicate Analysis 2: Plate

- to

- plate replication using large sets of st andard technical 59 

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

### 6.3.3 Replicate Analysis 3: Hit

- calling and AC 50 

### replication for multiple, independently 61 

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

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


### Section 7 Analysis of ES TrSet and HTP Assay Data ....................................................................... 92 64

### Prioritization of the EDSP Universe of Chemicals

December 2012 iii | Page 7.0 Assays utilized in developing the ER Expert System ................................................................. 92 65 

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

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

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

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

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

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

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

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

### 7.4.3 Strength of Evidence for I nactive Parent but Active Metabolite .................................... 113 74 

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

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


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



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


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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

### Data Collection and P rocessing Details ........................................................... 138 94 

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

### Appendix I: Section 7 Supplemental, Figures ...................................................................... 138 96

### Prioritization of the EDSP Universe of Chemicals

December 2012 1 | Page Section 1 Background, Programmatic Goals, and Purpose of the FIFRA SAP Review 97



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

### is 98 

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

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

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

### that combines exposure and effects information. 2 

### With regard to effects information to support 102 

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

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

### [(Q)SARs]. 3 

### EDSTAC further recommended that chemical inventories be sorted into four categories: 105 

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

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

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

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

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

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

### 1999 4 

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

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

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

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

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

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



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

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

### Toxicity Testin g in the Twenty- first Century: A Vision and a Strategy

. 6 

### The 2007 National Research 120 

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

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

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

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

### Adverse Outcome Pathway (AOP) concept. Knowledge of adverse outcome pathways (AOPs 7 

### ) have 125

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

, Re view 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/19 99/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 t o 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 Estroge n Binding Affinity to Support Prioritization of Pesticide Inert Ingredients and Antimicrobial Pesticides for Screening and Testing , p. 57. http://www.epa.gov/scipoly/sap/me etings/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 existi ng 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

### Prioritization of the EDSP Universe of Chemicals

December 2012 2 | Page direct application to the development and application of computational toxicology methods in hazard 126 

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



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

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

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

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

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

### application of the AOP concept as an organizing principle. 133



### Th is document lays out a prioritization scheme to support determinations of the order in which 134 

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

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

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

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

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

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

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

### Tier 1 data replacement. 142



### The following sections of this document lay out in detail the me thodology underlying the proposed 143 

### prioritization scheme: 144



### 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 n ot 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 SAP 8 

### ), the automation of
ER ESv 1 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 HT P 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

### Prioritization of the EDSP Universe of Chemicals

December 2012 3 | Page Section 7 Presents a preliminary comparative analysis of the in vitro t raining 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 chemica l 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.

### Prioritization of the EDSP Universe of Chemicals

December 2012 4 | Page Section 2 EDSP Universe of Chemicals and Prioritization Scheme 145



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

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

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

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

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

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

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

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

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

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

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

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

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

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

### public health." 160



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

, 161 

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

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

### General Validation Principles , November 2012 9 

### ). 164



### 2.1 Defining the EDSP Universe of Chemicals 165



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

### molecular

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

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

### One 168 

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

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

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

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

### of generating a comprehensive list of substances which comprise the EDSP universe of c hemicals. 11 

### To 173 

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

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

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

### chemical lists are provided in the EDSP Universe of Chemicals and General Validation Principles

. Each 177 

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

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

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

### substances = 3,616). 181



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

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

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

### Prioritization of the EDSP Universe of Chemicals

December 2012 5 | Page first AOP) and may be elevated for EDSP Tier 1 Screening. This scheme is described more fully in 184 

### section 2.2, and illustrated in Figure 2.2.1. 185



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

### Chem icals 187


### 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.



### 2.2 EDSP21 Prioritization Scheme using Computational Toxicology Tools 188



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

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

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

### ToxCast TM effort. 12 

### The bill states the EPA shall "in future EDSP Test Orde rs, use a targeted approach 192 

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

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

### as appropriate..." 13 

### In the report for fiscal year 2013, the "EPA is directed to provide a report to the 195 

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

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



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

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

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

### prioritization scheme is built with multi

- parameter filters to establish a biologically and toxicologically 201 

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

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



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

### logic are intended to capture a gene ric approach that could be adapted to address other endocrine 205 

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

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, 20 13, p. 48. http://www.gpo.gov/fdsys/pkg/CRPT

- 112hrpt589/pdf/CRPT

- 112hrpt589.pdf

.

### Prioritization of the EDSP Universe of Chemicals

December 2012 6 | Page models and simulation -based analyses. In addition, while numbers of chemicals are presented 207 

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

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

### presented in this document will be fully completed. 210



### As ill ustrated in Figure 2.2.1, the EDSP proposed prioritization process optimizes the use of existing 211 

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



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

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

### based on the OPP registration review schedule, 15 

### which will streamline the work flow process 215 

### for pesticide registration review decisions. 216 

### 3. Identify "discrete" chemicals by excludin g polymers, mixtures and undefined substances. 217 

### 4. Apply remaining physico

- chemical properties exclusion criteria. 218 

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

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

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

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

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

### they will be strat egically prioritized for future medium or high throughput in vitro testing and 224 

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

### chemical prioritization. 226



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

### illustrated in Figure 2.2.1. 228



### Figure 2.2.1: Proposed EDSP21 Prioritization Scheme

15 http://www.epa.gov/oppsrrd1/registration_review/reg_review_process.htm

### Prioritization of the EDSP Universe of Chemicals

December 2012 7 | Page 2.2.1 OPP Registration Review Schedule 229



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

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

### for EDSP screening based on the existing registration review schedule. 16 

### As stipulated in the FFDCA 232 

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

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

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

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

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

### registration review timeline is available. 238



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

### inventory for prioritization. 240



### 2.2.2 Exclusion of polymers, mixtures, and undefined substances 241



### Based on recommendations by the joint (FIFRA SAP/SAB) panels in 1999, 17 

### mixtures, polymers and 242 

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

### criteria: 244



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

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



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

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

### and, 249



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



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

- chemical 251 

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

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



### Based on these physico

- chemical categories, approximately 5,600 discrete single chemicals, excluding 254 

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

### remaining physico

- chemical properties filter described in section 2.2.3. 256



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

### remain for further prioritization. 258



### 2.2.3 Physico

- Chemi cal Properties Considerations 259



### Consistent with the SAP/SAB (1999), in a 2005 Federal Register Notice, 18 

### the agency had previously 260 

### articulated its intention to use physico

- chemical properties to exclude chemicals based on being a 261 

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

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

- 263

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/20 05/September/Day

- 27/t19260.pdf

### Prioritization of the EDSP Universe of Chemicals

December 2012 8 | Page chemical (p- chem) properties were used to exclude additional chemicals on the list ( e.g. , strong acids 264 

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

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

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

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

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

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

### further defined in sections below. 271



### It is important to rec ognize that while each property is considered and described separately, the 272 

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

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

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

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

### screening is biologically plausible a nd scientifically supportable. ( See section 3.2.) 277



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

### exclusion criterion: 279



### • Chemicals that are too acidic or basic 280



### • Chemicals that are too reactive or unstable 281



### • Chemicals that are charged species 282



### • Chemicals that are too corrosive 283



### 2.2.3.1 Chemicals that are too acidic or basic 284



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

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



### Acid: HA  H + 

### + A - 

### ( e.g. , hydrochloric acid, HCl  H + 

### + Cl - 

### ) 287



### Acid dissociation constant (K
a 

### ) = (H + 

### )(A - 

### ) and pK
a 

### = - log(K
a 

### ) 288 

### (HA) 289



### Base: B + H + 

###  BH + 

### ( e.g. , Ammonia, NH3 + H + 

###  NH 4 + 

### ) 290



### Base dissociation constants (K b 

### ) = (BH + 

### ) and pK b 

### = - log(K b 

### ) 291 

### (B)(H + 

### ) 292



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

### constant for acids is termed the acid dissociation constant and abbreviated K
a 

### . The negative log 10 

### of K
a 294 

### is often used to chara cterize the acidity of a chemical and is abbreviated pK
a 

### . Similarly the base 295 

### dissociation constant is K b 

### and the negative log of K b 

### is pK b 

### . The smaller the pK
a 

### or pK b 

### value, the 296 

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

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



### Because acid and base dissociation is an equilibrium process, an anion (A - 

### ) formed by dissociation of 299 

### the acid (HA) can accept a proton and is therefore, by definition, a base (A - 

### is termed the "conjugate" 300 

### base of the acid HA). A cation formed from acceptance of a proton by a base is called a conjugate acid. 301

### Prioritization of the EDSP Universe of Chemicals

December 2012 9 | Page As stated above the smaller the pK
a 

### or pK b 

### , the stronger the acid or base, but there is an inverse 302 

### relationship between pK
a 

### or pK b 

### and their respective conjugate acids or bases. A strong acid such as 303 

### hydrochloric acid will have a very low pK
a 

### (<0) because it readily donates protons, and a very high pK b 304 

### of the conjugate base (Cl - 

### ) because the chloride ion has no tendency to accept protons. Similarly, a 305 

### strong base has a very low pK b 

### , but a very high pK
a 

### of the conjugate acid. 306



### For the acid/base exclusion, strong acids (very low pK
a 

### 's) and strong bases (very low pK b 

### ' s = very high 307 

### pK a 

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

### based on extreme pK
a 

### values (acid pK
a 

### 's <2 or pK
a 

### ' s of conjugate acids >11.5) reflect the concern that 309 

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

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

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

### expected to be endocrine active chemicals. 313



### 2.2.3.2 Chemicals that are too reactive/unstable 314



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

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



### water

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

### presents a health hazard. This definition does not accurately reflect the physico

- chemical property that 318 

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

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

### prioritization should be based on the stability of the chemical in wa ter. Stability is assessed based on the 321 

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

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



### The EPI (Estimation Programs Interface) SuiteTM is a Windo ws®

- based suite of physical/chemical 324 

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

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

### following estimation pr ograms: KOWWINTM, AOPWINTM, HENRYWINTM, MPBPWINTM, 327 

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

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

### ECOSARTM, which estimates ecotoxicity, is also included in EPI Suit eTM. 19 

### EPI Suite model appears to 330 

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

### estimates for aqueous hydrolysis rate constants and half

- lives for some chemical classes: esters, 332 

### carbamates, epoxides, hal omethanes, selected alkyl halides and phosphorous esters. Literature 333 

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

### development stability study results) can also be used to supplement the EPI Suite infor mation

, if 335



### necessary. 336



### Initially, we recommend that priority be given to chemicals that have hydrolysis half

- lives greater than 337 

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

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

### for EDSP Tier 1 screening by the filter. 340

19 http://www.epa.gov/opptintr/exposure/pubs/episuite.htm

### Prioritization of the EDSP Universe of Chemicals

December 2012 10 | Page 2.2.3.3 Chemicals that are charged species 341



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

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

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

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

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

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

### interact with receptor binding sites. 348



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

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

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

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

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

### pK a 

### of 10.4, 20 

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

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

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

### charged. 357



### Exceptions

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

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

### the thyroid hormonal pathway. Perfluorooctanoic acid has a very low pK
a 21 

### which means that the 360 

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

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



### Identifying chemicals charged at p H 7 . Some chemicals on the EDSP inventory are charged at all pHs 363 

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

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

### ChemAxon's "Chemicalize" 22 

### that calculate acid and base equilibrium constants and thereby the 366 

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

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

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

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

### 7.4, the predominant species is as follows: 371

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 pK
a estimates for the acid

- base and charged spe cies exclusions in this
section.

### Prioritization of the EDSP Universe of Chemicals

December 2012 11 | Page The structure displayed is negatively charged, 372 

### and another panel on the Chemicalize display 373 

### indicates that at pH 7.0, 99.95% of the 374 

### molecule is negatively charged. Accordingly, 375 

### 3,5-dichlorobenzoic acid would be considered 376 

### eligible for the charged species e xclusion from 377 

### Tier 1 EDSP screening. 378



### The whole list of discrete chemicals on the 379 

### EDSP chemical inventory was analyzed using 380 

### ChemAxon's Chemicalize tools for the 381 

### potential ability to ionize. For chemicals that are acids or salts of acids up to 3 pK a 

### ' s were estimated and 382 

### for chemicals that are bases or salts of bases up to 3 pK a 

### ' s of the conjugate acids of the bases were 383 

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



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

### chemicals are acids, the next three are bases and the last four have both acid and basic pK
a 

### 's. The 386 

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

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

### eligible for the exclusion if the lowest pK a 

### for an acidic chemical <5 or the highest pK a 

### of the conjugate 389 

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

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

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



### Table 2.2.1 Examples of Chemicals whose Ionization is pH Dependent 393

No. Chemical Acid pK a 's Lowest a pK a Basic pK a 's Highest b pK
a 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 pK s out of

range
* These form "inner salts." 

### Figure 2.2.3.3.1: Example of a Charged Chemical

### Prioritization of the EDSP Universe of Chemicals

December 2012 12 | Page Criteria for p- chem property exclusion

. If the main criterion for exclusion is a charged species at pH 394 

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

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

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

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

### negatively charged." 399



### 2.2.3.4 Chemicals that are too corrosive 400



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

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

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

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

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

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

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

### adhesion of the prot ective oxide coating that the metals ordinarily have. 408



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

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

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



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

### Anhydrides, acid halides, α

- haloketones and

- esters, reactive esters, allylic and benzylic halides, 413 

### and various organic and inorganic materials with P- X, S

- X, Si

- X, B

- X or Sb -X bonds, where 414 

### X = halogen. 415



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

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

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

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

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



### • Other reactive species

, such as isocyanates, isothiocyanates and carbodiimides; epoxides; 421 

### Michael

- acceptors

, such as acrylates and cyanoacrylates. 422



### • Certain irritant compounds

, such as capsaicin , whose mode of action does not seem to fit into 423 

### any of the above categories. 424



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

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

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

### and eye, stearic acid (mw 284) is not. (In the Polymer Exemption 23 

### this was taken into account by 428 

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

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

### likely to be corrosive than liquids for physical reasons. 431

23 http://www.epa.gov/oppt/newchems/pubs/polyex em.htm

### Prioritization of the EDSP Universe of Chemicals

December 2012 13 | Page Th e corrosive chemical filter is based on those chemicals currently on the U.S. Department of 432 

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

### EDSP inventory contain warnings of corrosive properties of the che mical. 434



### 2.2.3.5. Summary Prioritization Assumption 435



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

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



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

### excluded for being charge at pH = 7. 439



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

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

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



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



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

### Considerations Exclusion Criteri a 445

Criterion Parameters Data sources Boundaries Approx. # Chemicals

excluded

Too acidic or
basic pK a of acids or conjugate
acids of bases Chemaxon's "Chemicalize" predictive software pK a < 2 or pK a 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 pK a based net negative or positive charge at pH7 Chemaxon's"Chemicalize" predictive software Lowest acid pK
a < 5 or highest conjugate acid pK
a >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



### Conclusion : The consideration of physico

- chemical properties for chemical prioritization is not a novel 446 

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

### setting for the EDSP as in September 27, 2005, 24 

### when the age ncy had articulated its plan to exclude 448 

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

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

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

### application of these physico

- chemical properties filters should not be automated and applied in isolation; 452 

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

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

### the filters. ( See section 3.2.) 455



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

### 4, 500 chemicals remain for further prioritization. 457

24 http://www.epa.gov/fedrgstr/EPA

- TOX/2005/September/Day

- 27/t19260.pdf

### Prioritization of the EDSP Universe of Chemicals

December 2012 14 | Page 2.2.4 Prioritize SDWA Chemicals That Have Human Exposure Potential 458



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

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

### exposure filtering process will determine whether th e SDWA chemicals are known or anticipated to 461 

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

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



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



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



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



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



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

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

### can be found in the, Final Contaminant Candidate List 3 Chemicals: Identifying the Universe

. 25 470



### Application of exposure filter 471



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

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



### oc currence -type information, the chemical will proceed forward in the subsequent prioritization process. 474 

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

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

### prioritized or evaluated when additional exposure data become available. 477



### Summary Prioritization Assumption 478



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

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

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

25 

### http://water.epa.gov/scitech/drinkingwater/dws/ccl/upload/CCL3_Chemicals_Universe_08-31-09_508_v3.pdf

### Prioritization of the EDSP Universe of Chemicals

December 2012 15 | Page Section 3 ER Expert System 482



### 3.1 Introduction of ER Expert System and Five Specific Goals 483



### The ER Expert System (ESv1) was the subject of a 2009 SAP, "An Effects

- based Expert System to 484 

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

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

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

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

### section. 489



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

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

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

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

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

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

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

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

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

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

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

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

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

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

### regulatory purpose, specifically for: 504



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

### antimicrobial pesticides (AM) which do not include any chemicals with steroidal

- type chemical 506 

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

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

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

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

### chemical predicted negative is unlikely to bi nd and activate the ER; and, 511 

### 3. Using a chemical class -based approach to allow extrapolation from a limited number of well512 

### characterized TrSet chemicals to a broader inventory of chemicals by employing effects

- based 513 

### chemical category and read -across concepts. 514



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

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

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

### between relative binding affinity (RBA) and LogK ow 

### that was identified for the ESv1 chemical groups 518 

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

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

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

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

### decision tree node). 523

### Prioritization of the EDSP Universe of Chemicals

December 2012 16 | Page 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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

### cytotoxicity in cell

- based assays, whi chever limit was reached first. Additionally, the activity of a 541

### Prioritization of the EDSP Universe of Chemicals

December 2012 17 | Page chemical with respect to other chemicals within the same ESv1 chemical group was taken into account 542 

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

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

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

### chemical free concentration; chemical affecti ng the bioassay conditions ( e.g. , chemical dissociation 546 

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

### section 7. 548



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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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



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

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

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

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



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

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

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

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



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

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

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

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

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

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



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

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

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

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

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

### help address goal 5. 584

### Prioritization of the EDSP Universe of Chemicals

December 2012 18 | Page 3.1.1 Automation of ESv1 585



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

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

, 587 

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

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

### the OECD QSAR toolbox. 26 

### The structural fragments, calculated parameters, and the TrSet of in vitro 590 

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

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

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

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

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



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

### display of the ES TrSet test results (chemical structures, LogK ow 

### , RBA) specific to each decision tree 597 

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

### new chemical gr oups are encountered additional nodes with defined boundaries are added. The new 599 

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

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

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

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

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

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

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

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

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

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

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

### 'salicylate (non -benzoate)' node of the tree. The structure, Log K ow 

### , and ES prediction for the target 611 

### chemical (on the left side of the screen) can be directly compared to structures, LogK ow 

### , and RBA of the 612 

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

### with the c overage provided and the prediction made. 614



### 3.2 ESv2 615



### 3.2.1 Building ESv2 616



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

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

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

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

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

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

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

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

- defined structure) 624 

### were considered for the model expansion. 625

26 http://www.oecd.org/chemicalsafety/assessmentofchemicals/theoecdqsartoolbox.htm

### Prioritization of the EDSP Universe of Chemicals

December 2012 19 | Page When the ESv1 was applied to the NF 1, 423 list, it successfully classified 85% of the list. There were 626 

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

### Mixed Organics, 40 were Mixed Phenols, and 12 were members of ESv1 defin ed classes but with 628 

### LogK ow 

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

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

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

### chemicals tested to establish a clear LogK ow 

### relationship. In these instances, the chemical is tested and 632 

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

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

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

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

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

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

### TrSet to better cover NF unknowns as will be further described below. 639

### 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 K OW <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 c yclohexanols ( 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 K OW <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 c yclohexanols ( 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

### Prioritization of the EDSP Universe of Chemicals

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 struct ures meet the criteria and red do not meet the criteria for the specific boundary.

### Prioritization of the EDSP Universe of Chemicals

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 LogK ow 4.28

cis -3- Hexenyl salicylate

### Prioritization of the EDSP Universe of Chemicals

December 2012 23 | Page 640



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



### New Groups from Mixed Phenols. The two

- dimensional structures of the MP were assembled and sorted according to similar structural 642 

### fragments. The following figure illustrates the common structural fragment for six new groups including two with subgroups. 643

### Prioritization of the EDSP Universe of Chemicals

December 2012 24 | Page Figure 3.2a.2: Structural fra gments 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.

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

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

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



### Given the importance of having confidence in the structure- bioactivit y relationships, every attempt was 647 

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

### given group was designed to cover the range of LogK ow 

### of NF inventory chemicals whenever possible. 649 

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

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

### range of the group. Although the high end of the LogK ow 

### testing extended to 7.63, it was thought that 652 

### coverage to the maximum of 8.00 was achieved considering the inherent error in LogK ow 

### estimators in 653 

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

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

### (Figure 3.1.1). 656

### Prioritization of the EDSP Universe of Chemicals

December 2012 25 | Page 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.

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

### members found on the 1,463 NF inventory were tested (Figure 3.2a.4: pink=binders, green=non658 

### binders). In addition to the inventory chemicals, the base structure was also tested [CAS = 117-99-7; 659 

### HBO in the f igure]. A wide range of LogK ow 

### s were tested for binding affinity. The two inactives were 660 

### the result of structure attenuation. The first of these and the lowest LogK ow 

### structure in this chemical 661 

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

### chemical species at test pH ( i.e. , low pK
a 

### ). Charged species are, in this case >99% dissociated, denature 663 

### receptor proteins, as discussed in the 2009 SAP white paper (SAP 2009a). The highest LogK ow 

### structure 664 

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

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

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

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

### here. 669



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

- A: 670 

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

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

, 672 

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

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

### ESv2 to capture this. 675

### Prioritization of the EDSP Universe of Chemicals

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.

### Prioritization of the EDSP Universe of Chemicals

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 che micals that are classed in this group. Green background = rtER Non

- binder; pink background = rtER Binder; white background = untested.



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

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

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

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

### structural fragments for eight new groups originating from the Mixed Organics. 680

### Prioritization of the EDSP Universe of Chemicals

December 2012 28 | Page 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.

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

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

### covered the LogK ow 

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

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

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

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



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

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

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

### neutral pH, pK
a 

### =0.87. A chemical source could not be located for the structure in orange and a surrogate 690 

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

### atomic charges are not consistent with A

- type ( -0.25) or B- type ( -0.32). Current testing of the yellow 692 

### structure in Figure 3.2a.8 will serve to extend the LogK ow 

### coverage for this class. This group was placed 693 

### under ES decision tree branch 'V' where ' Special Rules Apply '. 694

### Prioritization of the EDSP Universe of Chemicals

December 2012 29 | Page Figure 3.2a.7: Phosphorous Acid Esters structural fragment used to define this new group fr om 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 thi s 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.

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

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

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

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

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

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



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

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

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

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

### classes to better categorize the NF, as well as, the larger EDSP inventory (see section 3.3a). 705

### Prioritization of the EDSP Universe of Chemicals

December 2012 32 | Page Table 3.2a.1: New groups covered in the ESv2 expansion 706 

### 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'

### Prioritization of the EDSP Universe of Chemicals

December 2012 33 | Page

### Prioritization of the EDSP Universe of Chemicals

December 2012 34 | Page

### Prioritization of the EDSP Universe of Chemicals

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.

3.2.2 Assessing ESv1 and ESv2 Coverage of the EDSP Inventory 707



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

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

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

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

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

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

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

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

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

### chemicals are unique to the NF list only, with 736 Frag that are on no other lists. 717

### Prioritization of the EDSP Universe of Chemicals

December 2012 36 | Page Table 3.2b.1: EDSP inventory of 5, 975 summarized by number of chemicals found in each "Use 718 

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



### 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

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

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

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

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

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

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

### was expanded to ER ESv 2, based on testing in the 1,405 NF inventory (described in section 3.2.1) the 726 

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

### are considered. 728

### Prioritization of the EDSP Universe of Chemicals

December 2012 37 | Page Table 3.2b.2: Overall results for 5,975 Inventory using ESv1 and ESv2 729


### 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

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

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

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

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

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

### would indicate that there are many unique structures on the Frag list. 735

### Prioritization of the EDSP Universe of Chemicals

December 2012 38 | Page Table 3.2b.3: Summary of ES predictions for 5,975 Inerts and SDWA chemicals 736 

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

### only chemicals are listed as well as totals of Inerts and SDWA. (A) The number and % of chemicals for which ER ESv 1 provides 738 

### predictions is presented. (B) The number and % of chemicals for which ER ESv 2 provides predictions. 739



### (A) 740

ER ESv1 All Inerts Food Use Inerts ( FI ) N on 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



### (B) 741

ER ESv2 All Inerts Food Use Inerts ( FI ) N on 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

### Prioritization of the EDSP Universe of Chemicals

December 2012 39 | Page Although only modest increases are observed (Table 3.2b.3 (B), the expanded ESv2 resulted in 743 

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

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

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



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

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

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

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

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

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

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

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

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

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



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

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

### vast majority of remai ning unpredicted chemicals contained mixed functional groups. As described in 759 

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

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

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

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

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

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

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

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

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

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

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

### chemicals and 165 RBA>0.00001% across ESv2 chemical groups is provided in Appendix G. 771

### Prioritization of the EDSP Universe of Chemicals

December 2012 40 | Page Table 3.2b.4: Predicted percentage of Binders and NonBinders in the 5 ,975 Inventory from (A) ESv1, and (B) ESv2, shown by 772 

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

ER ESv1 All Inerts Food Use Inerts ( FI ) N on 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 ESv 2 All Inerts Food Use Inerts ( FI ) N on 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

### Prioritization of the EDSP Universe of Chemicals

December 2012 41 | Page Table 3.2b.5: Number of chemicals classified by ESv2 into the 14 new groups (and 3 subgroups) 775 

### used to expand from ESv1 to ESv2. 776


### 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

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

### ES decision tree section (Figure 3 -1) and ES chemical group. 778 

### 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 [LogK ow 

### <2.5,>6.4] 13 0.8 Triazines [LogK ow 

### >4.1] 9 0.5 Benzoates (non

- ring Subst) [LogK ow 

### <3.0,>4.3] 6 0.4 Sorbitans [LogK ow 

### <3.1,>5.9] 4 0.2 Imidazolidines [LogK ow 

### >0] 3 0.2 Benzamides [LogK ow 

### <2.1,>2.8] 1 0.1

Bis

- Anilines [LogK ow 

### <2.1,>6.4] 1 0.1 Phenones (n

- alkyl) [LogK ow 

### >4.2] 1 0.1 Pyrrolidiones [LogK ow 

### >3.4] 1 0.1
III. Known Inactives Subtotal= 39 2.3

### Prioritization of the EDSP Universe of Chemicals

December 2012 42 | Page 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 (LogK ow 

### >3.4) 27 1.6 Alkoxyphenols [LogK ow 

### >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 (LogK ow 

### >1.3) 7 0.4 Phenones cyclo

- substituted - Unknown Binding Potential 6 0.4 Multi subst. Alkylanilines 5 0.3 Alkylcyclohexanols LogK ow 

### >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

3.2.3 ESv2 Chemical Groups in Final Filtered EDSP Inventory of 4 ,957 Chemicals 779



### The EDSP inventory of 5 ,975 chemicals was filtered to remove additional chemicals based on p- chem 780 

### properties that make them untestable or unlikely to pose systemic toxicity as discus sed in section 2

, 781 

### section 2.2c. These include: hydrolysis half-life indicating chemical breakdown in solution, corrosivity, 782 

### and extreme pK a 

### acid and base values that would leave the chemical completely dissociated at the pH of 783 

### test assays and would be expected to alter pH of test systems. As was originally intended, the use of 784 

### these parameters and how the filters are implemented with respect to the EDSP inventory is still under 785 

### investigation by the EPA and will be further assessed after this SAP review and prior to final 786 

### implementation. As stated earlier, the agency intends to apply the physico

- chemical properties filter with 787 

### expert review. In this context, the ES will continue to inform the p- chem property filters. An example of 788 

### the application of the ES predictions as a tool to facilitate this analysis is presented below. 789



### The p- chem property filtering exercise, as described in section 2, resulted in 1, 018 chemicals being 790 

### removed from the 5,975, for a total of 4, 947 chemicals remaining on the EDSP inventory . The chemicals 791 

### that were removed were characterized based on ESv2 chemical groupings. The results in Table 3.2c.1A 792 

### show that 92% of chemicals removed by this filtering, or 949 chemicals, belong to ESv2 NonBinder 793 

### groups. A detailed listing of all ES NonBinder groups with excluded chemicals is included in Table 794 

### 3.2c.1B. 795



### Filtered chemicals using procedures described in section 2 that were classi fied as Binders or having 796 

### UnkBP were examined more closely. Three perfluorinated chemicals filtered out were ES predicted 797 

### Binders (Acyclic_Perfluoro (RBA>0.00001%) and five more were placed in the 'Acyclic Perfluoro 798 

### (UnkBP)' group (Table 3.2c.1A). The reason for excluding these chemicals was an acid pK
a 

### exclusion 799

### Prioritization of the EDSP Universe of Chemicals

December 2012 43 | Page rule (see section 2). However, due to perfluorinated chemicals having unique chemistries that make 800 

### them highly persistent, it was determined this class of chemicals merits further attention, and th us all 801 

### eight perfluorinated chemicals were added back to the Final EDSP Inventory. 802



### The Alkylphenols predicted as Binders in Table 3.2c.1A were also examined. The two chemicals 803 

### excluded were forms of methylphenol (cresol) that were initially filtered due to a corrosivity rule. 804 

### However, p- cresol (CAS NR 106-44- 5) was included in the ES TrSet where it was testable in aqueous 805 

### media and found to be a Binder. Also, additional cresol forms were still found on the inventory after 806 

### filtering. Thus, confirming that the implementation of filtering rules warrants additional examination to 807 

### ensure consistency of application across chemicals. An iterative process is proposed where the expert 808 

### system is applied to the inventory before filtering, and the resulting predictions a re used to examine the 809 

### chemicals under consideration for removal by exclusion filters to highlight chemicals needing further 810 

### examination. 811



### The remaining 69 UnkBP chemicals from MO, MP, and triazine groups (Table 3.2c.1A) remain filtered 812 

### out along with 949 p redicted NonBinders. From Table 3.2c.1B, it can be seen that there are some ES 813 

### chemical groups for which all inventory chemicals were excluded after filtering, i.e. , where '0' chemicals 814 

### are found under the 'Remain in 4947' column for the following ES groups: Acyclic Phosponic Acids, 815 

### Acyclic Sulfates, Alkylaromatic Sulfonic Acids, Phosphoric Acid Functional Group, and Sulfonic Acid 816 

### Functional Groups. Also, Sulfonic Acid Dyes are largely removed with a few chemicals remaining in 817 

### the inventory. 818



### In summary, th e fact that 92% of the filtered chemicals were also predicted NonBinders by the ES adds 819 

### additional confidence in the use of these filters, with many of these chemicals having ES TrSet data and 820 

### additional testing that provides mechanistic rationale for the exclusion, as was presented in the 2009 821 

### SAP, e.g. , Alkylaromatic Sulfonic Acids. The final result of filtering of the 5 ,975 EDSP inventory was 822 

### that of the 1, 028 chemicals removed there were ten added back resulting in a final EDSP post- filtering 823 

### inventory of 4 ,957. 824



### Table 3.2c.1 (A) (B): Summary of chemicals filtered out of 5,975 EDSP Inventory by applying 825 

### physical- chemical property exclusion filters. The 1

, 028 chemicals excluded are summarized by the 826 

### number of chemicals ( # chem) and percent of 1,028 (%) excluded by their ESv2 chemical group and ES 827 

### prediction. A) detailed information for filtered chemicals in ER Binders or UnkBP categories by class, 828 

### with all remaining NonBinder groups summed together; and, B) detailed information on NonBinders ES 829 

### chemical groups including number of chemicals within a group that were 'Excluded by Filter' and 830 

### number Remain after Filter in the final post

- filtered EDSP inventory of 4 ,947. 831



### 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 [LogK
ow 

### >4.1] UnkBP 2 0.19
Subtotal= 79 8 All remaining Filtered Groups Chemicals RBA<0.00001% 949 92
Total = 1028 100

### Prioritization of the EDSP Universe of Chemicals

December 2012 44 | Page 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 (LogK
ow 

### >3.0) RBA<0.00001% 1 12 Anhydrides RBA<0.00001% 1 14

Bis

- Anilines [2.1<=LogK
ow 

### <=6.4] RBA<0.00001% 1 8 Carboxylic Acid Functional Group RBA<0.00001% 38 187 Hydrofurans [LogK
ow 

### <=6.1] RBA<0.00001% 1 22 Imidazolidines [LogK
ow 

### <=0] RBA<0.00001% 1 5 Inorganic RBA<0.00001% 197 143 Log K ow 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<=LogK
ow 

### <=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 [LogK
ow 

### <=4.1] RBA<0.00001% 4 24

949



### The distribution of the final 4,957 chemicals on the EDSP inventory by use class and SDWA occurrence 832 

### is summarized in Table 3.2c.2. Similar to 5,975 inventory the Inerts (Fi, NF, Frag and overlaps with 833 

### SDWA) comprise 42% of the total list while 'SDWA only' is 58%, thus the chemical exclusions did not 834 

### appear to pertain preferentially to chemicals from any particular list. The results of the ESv1 and ESv2 835 

### classif ication of the filtered inventory is shown in Table 3.2c.3 in detail by use class. The apparent 836 

### overall coverage of 65% and 67% respectively for ESv1 and ESv2 (Table 3.2c.4) is less than the 837 

### previous 72% coverage of the unfiltered inventory of 5,975. However, this is to be expected when 949 838 

### predictions are removed from the predicted NonBinder class by the filters and only 69 are removed from 839 

### UnkBP. Consistent with what was noted with the larger unfiltered EDSP inventory when ESv1 and 840 

### ESv2 predictions are co mpared, the percentage of all predicted chemicals that were Binders in the 4 ,957 841 

### inventory (Table 3.2c.5) was < 5% for most lists (2% for FI chemicals; increased from 4 to 6% of NF 842

### Prioritization of the EDSP Universe of Chemicals

December 2012 45 | Page predictions; and 2 to 4% of the 'SDWA only'). Again, the highest percentage of Binders are found in the 843 

### Frag group at 10%. 844



### Table 3.2c.2: EDSP inventory of 4, 957 summarized by number of chemicals in each Use Class and 845 

### on the SDWA occurrence list 846



### 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

### Prioritization of the EDSP Universe of Chemicals

December 2012 46 | Page Table 3.2c.3 A: Summary of ESv1 Predictions for 4,957 Inerts and SDWA chemicals; Inerts include: Food Use Inerts (FI), Non

847 

### Food Use (NF), Fragrances (Frag), and 831 SDWA chemicals that are also Inerts

. 848 

### The SDWA only chemicals are listed in the last column. 849

ER ESv1 All Inerts Food Use Inerts ( FI ) N on 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



### Table 3.2c.3 B: Summary of ESv2 Predictions for 4,957 Inerts and SDWA chemicals; Inerts include: Food Use Inerts (FI), Non

850 

### Food Use (NF), Fragrances (Frag), and 831 SDWA chemicals that are also Inerts

. 851 

### The SD WA only chemicals are listed in the last column. 852

ER ESv2 All Inerts Food Use Inerts ( FI ) N on 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

### Prioritization of the EDSP Universe of Chemicals

December 2012 47 | Page Table 3.2c.4: Overall results for 4,957 Filtered Inventory using ESv1 and ESv2 854


### 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

### Prioritization of the EDSP Universe of Chemicals

December 2012 48 | Page Table 3.2c.5A: Proportion of ESv1 predicted ER actives in 4,957 EDSP inventory found on the Inerts inventory and in each Use 855 

### Class and among the chemicals on only the SDWA list. Predicted Binders are 5% of Inerts overall, 2% of SDWA, and 10% of 856 

### Fragrances. 857

ER ESv1 All Inerts Food Use Inerts ( FI ) N on Food Use (NF) Fragrances ( Frag ) SDWA Only

# chem % Only + Frag Total % Only + Frag Total % Only % A ll % # 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



### Table 3.2c.5B: Proportion of ESv2 predicted ER actives in 4
, 957 EDSP Inventory found on the INERTS inventory and in each 859 

### Use Class and among the chemicals on only the SDWA list. Predicted Binders are 6% of Inerts overall, 4% of SDWA and 10% of 860 

### Fragrances. 861

ER ESv2 All Inerts Food Use Inerts ( FI ) N on Food Use (NF) Fragrances ( Frag ) SDWA Only

# chem % Only + Frag Total % Only + Frag Total % Only % A ll % # 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

### Prioritization of the EDSP Universe of Chemicals

December 20 12 49 | Page Section 4 High Throughput (HTP) In Vitro Screening 863



### 4.1 Chemical Library and Procurement Process 864



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

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

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

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

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

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

### Drug Administration). 27 871



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

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

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

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

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

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

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

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

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



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

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

### main steps in selecting, procuring, Q Cing and using these chemicals. 883



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

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

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

### Ap proximately 20,000 compounds were nominated. 887 

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

### Standard Operating Procedures. 28 889 

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

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

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

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

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

### was included in the chemical records. 895 

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

### available for testing. 897 

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

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

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

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

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

27 http://www.epa.gov/ncct/dsstox/sdf_tox21s.html

28 http://www.epa.gov/ncct/dsstox

### Prioritization of the EDSP Universe of Chemicals

December 20 12 50 | Page 7. A subset of the EPA library was selected to run in the ToxCast assay battery. These are 903 

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

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

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



### assays. 907 

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

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

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

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

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

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

### gauge chemical stability in solution. 914



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



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

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

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

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

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

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

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

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

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

### analytical approaches used during final data processing. 925



### 4.3 Statistical Analysis of Concentration- Response Data 926



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

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

### workflow begins. All workflow software is implemented in the open R language 29 

### and is available from 929 

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



### General fit/hit Procedure 931



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

### (NLS) package in R (release 2.14.2) and the port algorithm. For each chemical

- assay pair, the 933 

### concentration inducing a half

- maximal response (AC 50 

### ) is derived from the fitted model. For a given 934 

### assay, a chemical is considered a hit if (1) it achieves an AC 50 

### , (2) the R 2 

### of the model is at least rsq. 935 

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

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

### summary assay call for each substance. A summary of the automated cu rve

- fitting and hit

- calling 938 

### procedures for each assay is provided in Table 4.3.1. 939

29 http://cran.r

- project.org/

### Prioritization of the EDSP Universe of Chemicals

December 20 12 51 | Page 4.4 Outlier Detection 940



### The data from each sample

- assay pair are subjected to a series of outlier detection algorithms used to 941 

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

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

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

### departures fr om model assumptions. The outlier detection routine identifies any data points with obvious 945 

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

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

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

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

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

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

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

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

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

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

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

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



### 4.5 Cell stress filter (Attagene

- Specific) 958



### Following the initial post

- outlier/cytotoxicity fits, a meta

- filter was applied to negate hit calls arising 959 

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

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

### endpoints concurrently and can display promiscuous activity across reporter genes associate with near962 

### cytotoxic levels of chemicals . Building on experience gained from ToxCast Phase I, a meta- filter was 963 

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



### assay

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

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



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

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

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

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

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

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

### called Conf Hit 

### ( chemical, concentration). 973 

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

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

### with midpoint = 0.25-log units lower in concentration than the highest concentration of the s tress 976 

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

- stress assays are 977 

### hit, or to 0 if three cell -stress assays are hit. This function is called Conf Stress 

### (chemical, 978 

### concentration). 979 

### 3. The overall confidence value for eac h assay is then the product ConfHit(chemical, AC 50 

### ) x 980 

### Conf Stress 

### (chemical, AC 50 

### ). 981 

### 4. A hit is accepted if the overall confidence value is > 0.5 982

### Prioritization of the EDSP Universe of Chemicals

December 20 12 52 | Page log( AC 50 

### – uM)

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

The black line curve / histogram shows the number of hits (AC 50 

### values) as a function of – 983 

### log(AC 50 

### -uM) (high concentration to the left, low to the right). The black triangles show the – log AC 50 984 

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

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

### show the AC 50 

### values for the two Attagene Estrogen receptor assays (ATG ER α TRANS and ATG ERE 987 

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

### are accepted as active. 989

### Prioritization of the EDSP Universe of Chemicals

December 20 12 53 | Page Section 5 Overview of the HTP ER Binding and Transactivation Assays 990



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



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

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

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

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

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

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

### important to emphasize that the analytical approaches presented in this document sho uld serve as a 998 

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

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

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

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

### 5 reporter gene transactivation assays that utilize a variety of cell lines (HepG2, BG1Lu c4E2, HEK293, 1003 

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

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

### Tox21 BLA assays. Although the two Attagene as says (ATG ER α TRANS and ATG ERE CIS) are 1006 

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

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

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

### eight HTP assays, and more detailed descriptions of the assays are provided in Appendix F. 1010

### Prioritization of the EDSP Universe of Chemicals

December 2012 54 | Page Table 5.1.1: Summary of parameters for the 5 ER reporter gene transactivation assays 1011

Assay Tox21 BG1 LUC ER TA Agonist Tox21 BLA Agonist ATG ERa TRANS ATG ERE CIS Odyssey Thera TA OT

ERα ERE LUC Agonist
Assay type reporter gene reporter gene reporter transcript reporter transcript reporter gene Agonist or Antagonist
Mode Agonist Agonist Agonist Agonist Agonist

Cell Source or Cell line BG1Luc4E2 HEK293T HepG2 HepG2 CHOK1 Cell or Tissue Origin human ovarian cancer human embryonic kidney human hepatocellular

carcinoma human hepatocellular

carcinoma Chinese Hamster Ovary

Receptor (s) endogenous α & β rec hERα LBD GAL4

- DBD GAL4 human ER

- α

Ligand Binding Domain (transient transfection) rec full length human

ER

- α (transient transfection) rec full length human

ERα plus any endogenous CHO ERα &

ERß et al . in the cells Endogenous Gene or Response element ERE pGudLuc7.0 (4 EREs) 5X -UAS- ßLactamase +
GAL4 hERα LBD = ß

lactamase Reporter Transcription
Unit with Gal4 promoter: 5X

- UAS

- TATA

promoter (transient transfection) Reporter Transcription
Unit with 5X ERE synthetic res ponse element (transient transfection) 5- ERE's MMTV promoter

Luc

Endpoint luciferase fluorescence PCR / capillary electrophoresis PCR / capillary electrophoresis luciferase

Data as Rel Light Units blue: green ratio: green=background;

converts to blue for activity relative quantity of cDNA transcripts relative quantity of cDNA transcripts Rel Light Units

Expressed and graphed

as normalized to DMSO and max E2, then
converted to % E2 max normalized to DMSO and max E2, then
converted to % E2 max normalized to DMSO and max E2, then
converted to % E2 max normalized to DMSO and max E2, then
converted to % E2 max normalized to DMSO and max E2, then
converted to % E2 max

Carrier DMSO DMSO DMSO DMSO DMSO

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

Carrier Control yes yes yes yes yes Reference Control E2 E2 E2 E2 E2

No. of Reference

Concentrations 16 pt curve (4.6E

- 6 to

3.2E

- 15M) (

- 5.3 to

- 14.5logM) 16 pt stnd curve 3.8E

- 6

to 2.7E

- 13M (

- 5.4 to

- 12.5logM) 1 pt @ 0.2 μM (

- 6.7logM) 1 6 (1e

- 8 to 3.2 e

- 11M)

### Prioritization of the EDSP Universe of Chemicals

December 2012 55 | Page Assay Tox21 BG1 LUC ER TA Agonist Tox21 BLA Agonist ATG ERa TRANS ATG ERE CIS Odyssey Thera TA OT

ERα ERE LUC Agonist Cell Viability Cell Titer

- Fluor. Run in parallel assay concurrent with ER antagonist

format screen. Cell Titer

- Glo. Run in parallel assay concurrent with ER antagonist

format screen XTT, 200μM, 5

- step 0.1 dilution series, n=3; and visual inspection of cells.

Al so consider other
stress response genes identified in multiplex

assays XTT, 200μM, 5

- step 0.1 dilution series, n=3; and visual inspection of cells.

Also consider other
stress response genes identified in multiplex

assays None

Test Chemical

Stock Concentration in

DMSO 20 mM 20 mM 20 mM 20 mM 20 mM

Test Chemical Conc. Range (μM) 0.006 to 92 μM 0.006 to 92 μM 0.09 to 200 μM 0.09 to 200 μM 0.3 to 100 μM

Test Chemical Conc. Range (logM) - 8.2 to

- 4.0logM

- 8.2 to

- 4.0logM

- 7.0 to

- 3.7logM

- 7.0 to

- 3.7logM

- 6.5 to

- 4 logM

Test Chem Conc. Response curve. 1 well /plate; 1 plate at 1 conc.; 15 plates = 15pt

conc response curve 1 well /plate; 1 plate at 1 conc.; 15 plates = 15pt

conc response curve 8 pt conc

- resp curve 8 pt conc

- resp curve 6- pt conc. resp. curve

Sample Replication 3 rep plates /

concentration 3 rep plates /

concentration Single replicate Single replicate 3

Test Replication 3 chemical source replicates 3 chemical source replicates Single replicate Single replicate Single replicate

Solubility no solubility check no solubility check visual inspection of wells visual inspection of wells no solubility check Analytical Analytical chemistry performed post testing Analytical chemistry performed post testing Analytical chemistry performed post testing Analytical chemistry performed post testing Analytical chemistry performed post testing

Plate Format 1536

- well 1536

- well 12

- well 12

- well 384

- well Cells per well 4000 5000 300,000 300,000 5500 Exposure Duration 24h 18h 24h 24h 24h Media pH 7.0 7.0 7.2 - 7.4 7.2 - 7.4 7.2 - 7.4 Metabolic Activity unknown considered low for HEK

293 cells yes; this clonal line was selected for higher activity yes; this clonal line was selected for higher activity little or no metabolic activity for CHO cells

### Prioritization of the EDSP Universe of Chemicals

December 2012 56 | Page Table 5.1.2: Summary parameters for the 3 ER binding assays 1012

Protocol Novascreen: NVS_NR_hER Novascreen: NVS_NR_bER Novascreen: NVS_NR_mERa Assay type competitive binding

- [ 3 H]E2 competitive binding

- [ 3 H]E2 competitive binding

- [ 3 H]E2
Receptor Source Human: MCF7 breast cancer cell line lysate Bovine: uterine membranes Mouse: recombinant expression
Receptor Endogenous ERs Endogenous ERs affinity purified, 6

- his

- tag

was cloned at the N

- terminal

mER

- α Ligand Binding Domain (aa 297 to 585) Ki (nM) 0.07 0.7 8 Bmax (fmol/mg) 3844 406 710000 total protein in assay well (mg/ml) 0.284 5.6 0.00028

Reference assay activity: E2 IC50 (nM) 0.3 0.2 5

Reference assay activity: R1881 IC50 (nM) 2200

-- --

Detection Method Separation of bound & free [ 3 H]

- E2 Separation of bound & free [ 3 H]

- E2 Separation of bound & free [ 3 H]

- E2 Assay format 96

- well plate 96

- well plate 96

- well plate [ 3 H]

- E2 (nM) 0.1 0.7 1.0

Carrier DMSO DMSO DMSO

Conc. 0.25% 0.25% 0.25%

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

Reference Control E2 E2 E2

Reference Conc. 0.1, 0.3, 1 nM 0.1, 0.3, 1 nM 0.1, 0.3, 1 nM reps / plate 1/plate 1/plate 1/plate replicate plates yes; n=2 yes; n=2 yes; n=2

Test chemical

Stock Conc. 20 mM 20 mM 20 mM Test Chemical Range (General ranges, although some specific chemicals
were tested in different ranges. These are noted on individual graphs) Initial 25 μM single conc. in duplicate; then conc

- resp for positives with 50 μM as upper limit Initial 25 μM single conc. in duplicate; then conc

- resp for positives with 50 μM as upper limit Initial 25 μM single conc. in duplicate; then conc

- resp for positives with 50 μM as upper limit

Test Chem Replication n =1 / concentration n=1 / concentration n=1 / concentration Assay Buffer pH 7.4 7.4 7.4 Assay Temp 0- 4°C 0- 4°C 0- 4°C Incubation period 18h 18h 18h Solubility Stock chem solubility at
20mM in DMSO checked visually Stock chem solubility at

20mM in DMSO checked visually Stock chem solubility at
20mM in DMSO checked visually Analytical ID, Purity, Conc of stock solutions w/in 1yr of prep ID, Purity, Conc of stock solutions w/in 1yr of prep ID, Purity, Conc of stock solutions w/in 1yr of prep



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

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

### displaying analysis parameters specific for each chemical

- assay pair. All s ubsequent concentration1015 

### response plots for the eight assays will follow a similar pattern with the exception that the curves for the 1016 

### ER binding assays will reflect a competitive inhibition of the radiolabeled ligand ( 3 

### H- E2). The chemical 1017

### Prioritization of the EDSP Universe of Chemicals

December 2012 57 | Page is identified by n ame with the CASRN in the plot and table shown in Figure 5.1.1. The table also 1018 

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

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

### curve. The AC 50 

### and confidence intervals are indicated by a black bar and whiskers at 50% of maximal 1021 

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

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

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

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

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

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

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

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

### ( AC 50 

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

### (B), and Hill slope (W) and their confidence intervals; the relative AC 50 

### ( i.e. , concentration where the 1031 

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

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

### chemical

- Hill

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

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



### Figure 5.1.1 Example of concentration-response plot and accompanying table with analysis parameters depicting an increase in activity.

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



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

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

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



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

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



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

### b. Chemical concentration values are assigned to each well. 1043

### Prioritization of the EDSP Universe of Chemicals

December 2012 58 | Page c. Normalization of the response is carried out based on plate-wise positive and negative 1044 

### control wells. 1045 

### 2. Plates with data that do not meet the as say

- specific criteria (see individual Assay Descriptions, 1046 

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

### missing control data are identified and deleted. 1048 

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



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

### b. Outlier masking is performed. 1051 

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



### a. Chemicals meeting all of the assay

- specific fitting criteria and that produce a concentration 1053 

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

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

### b. For active samples, values of the Hill parameters including AC 50 

### , Bottom (B), Top (T) and 1056 

### Hill slope (W) are derived, along with confidence intervals. 1057

### Prioritization of the EDSP Universe of Chemicals

December 2012 59 | Page Table 5.2.1: Assay- specific parameters for curve fitting and hit c alling for the eight ER assays and 4 data sources. 1058 Assay Name Tox21 BLA Tox21 BG1 LUC ATG ERa TRANS ATG ERE CIS OT LUC NVS hER NVS bER NVS mERa

Raw Data

Format Concatenated

Plate View Files Concatenated

Plate View Files Preprocessed

and Normalized
Fold Induction

Value Transposed by Assay Target;
Control data

delivered as separate file Preprocessed

and Normalized
Fold Induction

Value Transposed by Assay Target;
Control data

delivered as separate file Processed Raw

Values with plate and well

information Pr ocessed Raw

Values with plate and well

information Processed

Raw Values with plate and

well
information Processed Raw

Values with plate and well

information

Plate Footprint 1536 1536 24 24 384 96 96 96

Concentration Response &
General

Technical Replication

Format 15 point

concentration

response in triplicate; 1

concentration per plate (15 plate comprises a single
technical replicate

concentration response run) 15 point

concentration

response in triplicate; 1

concentration per plate (15 plate comp rises a single
technical replicate

concentration response run) 8 point

concentration

response on a single plate (no

technical replicates) 8 point

concentration

response on a single plate (no

technical replicates) 6 point

concentration

response on a single plate in

triplicate;
technical replicates run

on the same plate Initial single

screen in duplicate with

follow

- up 8 point

concentration response (no

technical replicates) Initial single

screen in duplicate with

follow

- up 8 point

concentration response (no

technical replicates) Initial single

screen in duplicate with

follow

- up 8 point

concentration response (no

technical replicates)

Positive & Negative
Controls E2 & DMSO on each plate; Full

16 point E2

concentration

response in duplicate on each plate;

addi tional 36
wells

committed to higher

concentration

E2 E2 & DMSO on each plate; Full

16 point E2

concentration

response in duplicate on each plate;

additional 36
well committed to higher

concentration

E2 E2 & other positive controls

on separate plate (by run ; single concentration);

DMSO on each plate E2 & other positive controls

on separate plate (by run; single concentration);

DMSO on each plate E2 & DMSO on each plate; 3

Concentration
s of E2 tested on each plate E2 & DMSO on each plate; 3

concentrations
of E2 on each plate E2 & DMSO on each plate;

3

concentration

s of E2 on each plate E2 & DMSO on each plate; 2

concentrations
of E2 on each plate

### Prioritization of the EDSP Universe of Chemicals

December 2012 60 | Page Assay Name Tox21 BLA Tox21 BG1 LUC ATG ERa TRANS ATG ERE CIS OT LUC NVS hER NVS bER NVS mERa

Normalization

Method Plate

- wise; % Activity = (Test

Chemical – NegControl) / (Pos Control

Negative Control) *100; Negative
Control=Media

n plate raw value;

PosControl=2n

d Bimodal Peak

of all control wells (1st 4

columns of plate) or 3 SD of all plate raw

values from

Median (whichever gives greatest dynamic range) Plate

- wise; % Activity = (Test

Chemical - NegControl)/(P

osControl

NegControl)*1
00; NegControl=M edian plate raw value;
PosControl=2n

d Bimodal Peak

of all control wells (1st 4

columns of plate) or 3 SD of all plate raw

values from

Median (whichever gives greatest dynamic range) Run

- wise; % Activity =log2(Test

Che mical/NegCo ntrol)/log2(PosC ontrol/NegContr ol)*100; NegControl=DM SO fold change;

PosControl=E2

fold change Run

- wise; % Activity =log2(Test Chemical/NegCo ntrol)/log2(PosC ontrol/NegContr ol)*100; NegControl=DM SO fold change;

PosControl=E2

fold change Plate

- wis e; % Activity = (Test

Chemical - NegControl)/(

PosControl

NegControl)*1
00; NegControl=M edian of plate

raw values;
PosControl=M

edian of E2 plate response at highest E2

concentration Plate

- wise; % Activity = (Test

Chemical - NegControl)/(

PosControl

NegControl )*1
00; NegControl=Pl

ate DMSO Average raw value;

PosControl=Pl ate E2 Average

Raw value at

pre

- selected

concentration Plate

- wise; % Activity =

(Test

Chemical - NegControl)/(

PosControl

NegControl)*
100; NegControl=Pl

ate DMSO Average raw value;

PosControl=Pl

ate E2 Average Raw value at pre

selected

concentration Plate

- wise; % Activity = (Test

Chemical - NegControl)/(P

osControl

NegControl)*1
00; NegControl=Pla

te DMSO Average raw value;

PosControl=Pla te E2 Average
Raw value at

pre

- selected

concentration

Baseline

Co rrection
Performed TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Robust Outlier

Detection
Performed (Mask Value) TRUE; False Discovery Rate (FDR)=0.001 (1) TRUE; FDR=0.001 (1) TRUE; FDR=0.01 (1) TRUE; FDR=0.01 (1) TRUE; FDR=0.01 (1) TRUE; FDR=0.01 (1) TRUE; FDR=0.01 (1) TRUE; FDR=0.01 (1)

### Prioritization of the EDSP Universe of Chemicals

December 2012 61 | Page Assay Name Tox21 BLA Tox21 BG1 LUC ATG ERa TRANS ATG ERE CIS OT LUC NVS hER NVS bER NVS mERa

Other Outlier Detection/ Masking (Mask Value) Removal of

contaminated

plate (full

concentration

series removed) (3);

Removal of

inconsistent

data for replicate
source of a test

chemical 'bad samples', based

on discordance

with other 2

runs (full

concentration
series removed

if 5 out of 15 data points

were more than 3SD away
from median at

each

concentration (2) Removal of

contaminated

plate (full

concentration

series removed) (3);

Removal of

inconsistent

data for replicate
source of a test

chemical 'bad samples', based

on discordance

with other 2

runs (full

concentration
series removed

if 5 out of 15 data points

were more than 3SD away
from median at

each concentration) (2) None None None None None None

### Prioritization of the EDSP Universe of Chemicals

December 2012 62 | Page Assay Name Tox21 BLA Tox21 BG1 LUC ATG ERa TRANS ATG ERE CIS OT LUC NVS hER NVS bER NVS mERa Curve Fitting

Parame ters Bottom Limits =

- 10% to 10%
with a fixed 0%

if responses

started above the response threshold; Top

Limits only limited by

observed values; W (hill slope) limits = 1

to 8; logAC 50 limits = 3 fold

below lowest

concentration

tested and

highest

concentration

tested (no extrapolation) Bottom Limits =

- 10% to 10%
with a fixed 0%

if responses

started above the response threshold; Top

Limits only limited by

observed values; W (hill slope) limits = 1

to 8; logAC 50 limits = 3 fold

below lowest

concentration

tested and

highest

concentration

tested (no extrapolation) Bottom Limits =

- 10% to 10% with

a fixed 0% if

responses started above the response threshold; Top

Limits only limited by

observ ed values;

W (hill slope) limits = 1 to 8; logAC 50 limits = 3
fold below

lowest

concentration

tested and

highest

concentration

tested (no extrapolation) Bottom Limits =

- 10% to 10% with

a fixed 0% if

responses started above the response threshold; Top

Limi ts only limited by observed values;

W (hill slope) limits = 1 to 8; logAC 50 limits = 3
fold below

lowest

concentration

tested and

highest

concentration

tested (no extrapolation) Bottom Limits

=

- 10% to 10%

with a fixed
0% if

responses started above
the res ponse threshold; Top

Limits only limited by
observed values; W (hill slope) limits = 1 to 8; logAC 50 limits = 3 fold

below lowest

concentration

tested and

highest

concentration

tested (no extrapolation) Bottom Limits

=

- 10% to 10%

with a fixed
0% if

responses started above the response threshold; Top

Limits only limited by
observed values; W (hill slope) limits = 1 to 8; logAC 50 limits = 3 fold

below lowest

concentration

tested and

highest

concentration

tested (no extrapolation) Bottom Limits

=

- 10% to 10%

with a fixed
0% if

responses started above the response

threshold; Top Limits only limited by observed values; W (hill slope) limits =
1 to 8; logAC 50 limits
= 3 fold below

lowest

concentration

tested and

highest

concentration

tested (no extrapolation) Bot tom Limits =

- 10% to 10%
with a fixed 0%

if responses

started above the response threshold; Top

Limits only limited by

observed values; W (hill slope) limits = 1

to 8; logAC 50 limits = 3 fold

below lowest

concentration

tested and

highest

concentration

test ed (no extrapolation)

Secondary Filters (see section 6.2) In flare region

with
submaximal

response; failed sample QC; Evidence of

cytotoxicity

induced

responses; Fluorescence

activity in
AutoFluor

assays In flare region

with
submaximal

response; failed sa mple QC; Evidence of

cytotoxicity

induced

responses; Fluorescence

activity in
AutoFluor

assays Failed sample
QC; Non

- specific

oxidative stress

induced response (based

on distribution
of hits across

other transcription factors) Failed sample
QC; Non

- specific

oxidative stress

induced response (based

on distribution
of hits across

other transcription factors) Failed sample
QC Failed sample
QC Failed sample
QC Failed sample
QC

1059

### Prioritization of the EDSP Universe of Chemicals

December 2012 63 | Page All of the steps from three to six are fully automated and are largely independent of each respective 1060 

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

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



### assay

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

### computable filters initially examine one chemical at a time, and use the target a ssay concentration

1064 

### response profile plus auxiliary information ( e.g. , cytotoxicity, autofluorescence , etc.) to flag potential 1065 

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

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

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

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

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



### Figure 5.2.2: Example of an active assay- sample pair. The blue box along the baseline is the AC 50 

### for one of the blue- channel autofluorescence assays described below. For this chemical, because the blue signal rises at concentrations significantly above
the AC 50 

### , no change in the call is made.

### Prioritization of the EDSP Universe of Chemicals

December 2012 64 | Page Figure 5.2.3: Example of an inactive assay

- sample pa ir

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

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

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

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

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

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

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

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

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

### its AC 50 

### was very near the upper concentration tested. 1080



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

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

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

### routine. These ACTIVE curves are then classified as SETASIDE. 1084

### Prioritization of the EDSP Universe of Chemicals

December 2012 65 | Page Figure 5.2.4: Example of a non

- sigmoidal response that was called active at Level 6.

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

### Attagene assays can show a burst of non- specific (non

- receptor mediated) activity due to oxidative stress 1086 

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

### specific cell

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

### AC 50 

### is >0.75 of the cell stress AC 50 

### is changed to INACTIVE

. 1089



### Figure 5.2.5: Example of an assay

- sample pair set aside for Attagene oxidative stress filter.

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

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

### 4. The Tox21 BG1 LUC assay appears to be in sensitive to cytotoxicity near or below the AC 50 

### , so 1092 

### ACTIVE calls are not changed, but a note is added. The Tox21 BLA assay readout is actually a ratio of 1093

### Prioritization of the EDSP Universe of Chemicals

December 2012 66 | Page two channels, and cytotoxicity can alter one of the channels (the denominator) so that the ratio 1094 

### anomalously rises. Therefore, ACTIVE calls in Tox21 BLA below or near the cytotoxicity AC 50 

### are 1095 

### changed to SETASIDE. 1096



### Figure 5.2.6: Example of an assay

- sample pair set aside for cytotoxicity.

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

- based 1097 

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

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

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

### in 6 autofluorescence assay readouts per chemicals from which AC 50 

### values were derived. The Tox21 1101 

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

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

### are changed to SETASIDE. 1104

### Prioritization of the EDSP Universe of Chemicals

December 2012 67 | Page Figure 5.2.7: Example of an assay

- sample pair set aside for possible autofluorescence.

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



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

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

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

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

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

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

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

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

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

### flare. 1115

### Prioritization of the EDSP Universe of Chemicals

December 2012 68 | Page Figure 5.2.8: Plate from the Tox21 BG1 LUC assays showing a flare region. Wells that are positive are green and negative are red. Bright green wells show the strongest positive response. Wells with absolute response <10% E2 are colored white. As described in section 5 and in the Tox21 BG1 LUC assay description (Figure 5.2.6), plates are baseline corrected so that the plate median is zero. The detection signal associated with highly active chemicals in wells that may be clustered close together within an area of the plate may shadow neighboring wells. Flare regions (indicated by the gray background) have many continuous wells with statistically significant response above background. Note that for these assays, the concentration

- response series is performed across plates and that this is an example shown from a plate testing the highest concentration (92 uM) for one set of 1,408 chemicals. Based on the flare detection algorithm that has been developed to detect areas of clustered strong signals, each sample

- concentration pair can be la beled as being in a flare region or not.

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

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

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

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

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

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

### flare detection and handling algorithms are still under development. 1122

### Prioritization of the EDSP Universe of Chemicals

December 2012 69 | Page Figure 5.2.9: Example of an assay- sample pair set aside for flare. Points in a flare region are highlighted with a red star. Points at very high values (~100%) at high concentration are automatically masked by the robust outlier detection method, and appear to be caused in one of the three technical replicates by direct flare . The activity in the black (sample) curve is due to residual flare affecting the high concentration of one of the remaining two technical replicates.

Figure 5.2.10: Example of an assay

- sample pair set aside for flare

- related reverse slope.

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



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

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

### Prioritization of the EDSP Universe of Chemicals

December 2012 70 | Pagefalse positive calls for samples where Emax is greater than the response threshold, but where the Hill1126 

### curve T is not. Consequently, a criterion has been added that T needs to be above the response threshold. 1127



### Fig ure 5.2.11: Example of an assay

- sample pair with T < response threshold.

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

### Chemical 1129



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

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

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

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

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

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

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

### implemented and will not be discussed further. 1137

### Prioritization of the EDSP Universe of Chemicals

December 2012 71 | Page Figure 5.2.1: Illustration of the 3- step process of making initial hit calls and then using a variety of auxiliary information exclude or change some calls

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

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

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

### four classes of calls are defined as: 1141



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

### in vitro assa y. 1143



### • INACTIVE : All evidence supports a call that the chemical is not active in a specific ER

1144 

### dependent in vitro assay. 1145



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

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

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



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



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

Rule ID Assay Criteria Final Call Note

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

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

3 Any ACTIVE and AC 50 mod="<>" SETASIDE Bad Fit

4 Attagene ACTIVE and AC 50 >0.75 cell stress AC 50 INACTIVE Attagene OS Filter

5 Tox21 BG1 LUC ACTIVE and AC 50 >cytotoxicity AC 50 /5 ACTIVE Cytotoxicity<AC 50 

## ACTIVE

INACTIVE ACTIVE

INACTIVE SETASIDE

DISCARD ACTIVE

INACTIVE Levels 1...5

Level 6 calls

• One chemical at a time

• Use ER assay data only Level 7 calls

• One chemical at a time

• Use auxiliary data ( chemical

purity, cytotoxicity, flare

, autofluorescence, etc.) Chemical
Clusters SETASIDE

Level 7 calls

• Look across chemicals

• Use chemical class / category information

### Prioritization of the EDSP Universe of Chemicals

December 2012 72 | Page Rule ID Assay Criteria Final Call Note

6 Tox21 BLA ACTIVE and AC 50 >cytotoxicity AC 50 /5 SETASIDE Cytotoxicity<AC 50 7 Tox21 BG1 LUC ACTIVE and AC 50 >autofluorescence

AC 50 /5 ACTIVE Possible autofluorescence

8 Tox21 BLA ACTIVE and AC 50 >autofluorescence

AC 50 /5 SETASIDE Possible autofluorescence

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

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

11 Tox21 BG1 LUC ACTIVE and low concentration slope is negative due to flare SETASIDE Reverse slope

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



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



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

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

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

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

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

### informational purposes, and is not necessarily comprehensive, for either issues or potential solutions. 1157

### Prioritization of the EDSP Universe of Chemicals

December 2012 73 | Page Table 5.4.1: Summary of potential technical issues that can impact the accuracy of classification of test chemicals and pote ntial 1158 

### solutions. a 1159

Issue a Description Potential solutions Assays affected
Cytotoxic or cytostatic compounds Compounds that are cytotoxic or cytostatic yield high

- quality response curves but are not always detected in assay

- specific accompanying cytotoxicity assays. Example classes of compounds are anticancer drugs and organometallics (mercury, tin) Develop structural class filters that indicate cytotoxicity; Look for corresponding activity in other assays using the same cell and readout system but different targets; retest compounds in agonist mode with E2; For 2

- chan nel assays, implement robust informatic filter to classify compounds as cytotoxic but not receptor

- active. All

Surfactants Surfactant compounds appear to be active in some assays without accompanying cytotoxicity Develop chemical class filters specifically for surfactant molecules. Use same solution as for cytotoxic compounds. All

Chelators Chelators appear to be active in some assays, but they appear to be too large to be classic estrogen receptor actives Develop chemical class filters specifically for chelator molecules. Use same solution as for cytotoxic compounds. All

Flare In the 1,536

- well NCGC assays, very potent compounds can cause a flare effect so that neighboring wells appear to be active. This effect is most prominent in the Tox21 B G1 LUC

assay. If assay is to be run again, perform better randomization of replicates; develop better spatial filter for detector. For current data set, improve flare detection algorithm. Observed in 1,536 plates,

Tox21 BLA and Tox21 BG1 LUC TA Assays

Co ntamination The NCGC assays suffered a one

- time contamination of one 384

- well plate (out of 26 total in the Tox21 library). Although these samples could be removed from this analysis informatically analysis, they caused flare

- contamination of other wells R ebuild sample plates and rerun assays at a later date. Tox21 BLA and Tox21 BG1 LUC TA Assays

Background noise level and partial agonists There are classes of compounds that reproducibly yield partial agonist behavior, i.e. their Emax is significantly less than that of E2, sometimes as low as 20%. This is seen in even some of the standard positive reference compounds. This low level of efficacy is sometimes difficult to distinguish from background

noise. Combine data across assays in a meta

- analysis to look for low but consistent efficacy; Reduce activity threshold once other sources of false positives have been further
reduced. Tox21 BG1 LUC

ATG ERa TRANS

ATG ERE CIS

Discrepant curves within an assay for
the same chemica l Different samples (separately procured, different vendor, lot, batch) of a chemical can yield significantly different results. This is most pronounced across the Tox21 library simply because of the larger number of independently

- procured samples Once fin al analytical QC data is available, discard failing samples. Some remaining discrepancies may be due to flare, so improve the flare detection algorithm. All, but most pronounced in the Tox21 assays since the design on those experiment included 3 separately procured samples of each

chemical. Activity of weak compounds No assays were run to the limit of solubility so activity of low

potency chemicals is not detected If assays were to be rerun, use higher concentrations. However, it is not possible to run all compounds up to the limit of solubility in a high throughput mode. One alternative would be to combine assays in a meta

analysis to look for consistent but weak activity at higher concentrations. These few compounds could then be rerun at higher concentra tions in selected assays. All

### Prioritization of the EDSP Universe of Chemicals

December 2012 74 | Page Issue a Description Potential solutions Assays affected Solubility Compounds are not appreciably soluble in DMSO Develop one or more assays that can use another solvent. This is technically feasible, but virtually all high throughput assays are optimized to use samples in

DMSO. All

Volatility Compounds that are volatile Methods have been developed to run in vitro assays for volatile compounds, but these are low throughput and still largely experimental. All

Metabolic capacity Compounds require metabolic activation to be active or are metabolically deactivated in vivo

. Run assays both with and without metabolic capacity. The current assay set contains both classes, although none of the assays have in vivo levels of metabolic activity. High throughput ER assays using +/ - S9 fracti on are being evaluated. All

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

technical component (response signal, well capacity of plates, etc

. , or characteristic of chemical tested ( e.g , autofluorescence) ).

1160

### Prioritization of the EDSP Universe of Chemicals

December 2012 75 | Page Section 6 Performance Evaluation of the HTP ER Binding and Transcriptional Activation Assays 1161



### 6.1 Selection of Reference Chemicals 1162



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

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

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

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

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

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

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

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



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

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

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

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

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

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

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

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

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

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

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

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

### Performance- Based Test Gu ideline for Transfected Transactivation In vitro Assays to Detect Estrogen 1183 

### Agonists

, 30 

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

### Assays to Detect Estrogen Agonists for TG 455. Series on Testing and Assessment, No. 173. 31 

### Overall, 1185 

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

### exhibit an EC 50 

### >1x 10 - 11 

### M; moderate 1x10 -9 

### to 1x10 -8 

### M; weak 1x10 -7 

### to 1x10 -6 

### M, and very weak > 1187 

### 1x10 -5 

### M). The current reference list includes 11% strong, 11% moderate, 46% weak, and 9% very weak 1188 

### actives, as well as 23% inactive chemicals. 1189

30 http://iccvam.niehs. nih.gov/SuppDocs/FedDocs/OECD/OECD

- TG455

- 2012

- 508.pdf

31 http://search.oecd.org/officialdocuments/displaydocumentpdf/?cote=env/jm/mono(2012)1 8&doclanguage=en

### Prioritization of the EDSP Universe of Chemicals

December 2012 76 | Page Table 6.1.1: Estrogen Receptor (ER) Reference Chemicals for ER Agonist Activi ty (28 Actives, 9 Inactives) 1190

# CASRN Chemical Name Expected Potency a Chemical Class d (MeSH) Product Class e

1 50

- 28 -2 17beta

- Estradiol b,c Strong Steroid Pharmaceutical, Veterinary Agent

2 56

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

3 57

- 63 -6 17alpha

- Ethinyl estradiol b,c Strong Steroid Pharmaceutical, Veterinary Agent

4 84

- 16 -2 meso

- Hexestrol b,c Strong

- Moderate Hydrocarbon (Cyclic) Pharmaceutical, Veterinary Agent

5 53

- 16

- 7 Estrone b,c Moderate Steroid Pharmaceutical, Veterinary Agent

6 57

- 91 -0 17alpha

- Estradiol b,c Moderate Steroid Pharmaceutical, Veterinary Agent

7 68

- 23

- 5 or

68

- 22 -4 Norethynodrel b ,c or 19

- Norethindrone Moderate Steroid Pharmaceutical

8 131

- 55 -5 2,2',4,4'

- Tetrahydroxybenzophenone c Moderate Benzophenone Cosmetics, Ultraviolet Absorbent

9 140

- 66 -9 4- (1,1,3,3

- Tetramethylbutyl)phenol b,c
(4

- tert

- Octylphenol) Weak Phenol Chemical Intermediate

10 84852

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

11 80

- 05 -7 Bisphenol A b,c Weak Phenol Chemical Intermediate

12 1478

- 61 -1 Bisphenol AF c Weak Phenol

13 77

- 40 -7 Bisphenol B b,c Weak Phenol Chemical Intermediate

14 599

- 64 -4 4- Cumylphenol b,c Weak Phenol Chemical Intermediate

15 143

- 50 -0 Kepone b Weak Hydrocarbon (Halogenated) Pesticide

16 434

- 22 -0 19

- Nortestosterone b Weak Steroid Pharmaceutical, Veterinary Agent

17 58

- 18 -4 17alpha

- Methyltestosterone b Weak Steroid Pharmaceutical, Veterinary Agent

18 446

- 72 -0 Genistein b,c Weak Flavonoid, Heterocylic compound Natural Product, Pharmaceutical

19 479

- 13 -0 Coumestrol b,c Weak Flavonoid, Heterocyclic Compound Natural Product

20 520

- 18 -3 Kaempferol b,c Weak Flavonoid, Heterocylic compound Natural Product

21 520

- 36 -5 Apigenin b Weak Flavonoid, Heterocyclic Compound Dye, Natural Product, Pharmaceutical

Intermediate

22 486

- 66 -8 Daidzein b,c Weak Flavonoid, Heterocylic compound Natural Product

23 789

- 02 -6 o,p'

- DDTc (1,1,1 trichloro

2,2

- Bis(p

- chlorophenyl) ethane) Weak Organochlorine Pesticide

24 94

- 26 -8 Butylparabenc (n

- Butyl 4 Hydroxybenzoate) Weak Paraben

25 120

- 47 -8 Ethylparaben b,c Very Weak Carboxylic Acid, Phenol,

Paraben Pharmaceutical, Preservative

26 85

- 68 -7 Butylbenzyl phthalate b Very weak Carboxylic Acid, Ester,
Phthalic Acid Plasticizer, Industrial Chemical

### Prioritization of the EDSP Universe of Chemicals

December 2012 77 | Page # CASRN Chemical Name Expected Potency a Chemical Class d (MeSH) Product Class e

27 72

- 43 -5 Methoxychlor b,c Very Weak Hydrocarbon (Halogenated) Pesticide, Veterinary Agent

28 1912

- 24 -9 Atrazine b Inactive Heterocyclic Compound Herbicide

29 330

- 55 -2 Linuron b Inactive Phenylurea Herbicide

30 50

- 06

- 6 or

57

- 30 -7 Phenobarbital b o r PhenobarbitalSodium Inactive Heterocyclic Compound, Pyrimidine Pharmaceutical

31 52

- 01 -7 Spironolactone b Inactive Lactone, Steroid Pharmaceutical

32 50

- 22 -6 Corticosterone b,c Inactive Steroid Pharmaceutical

33 57

- 83 -0 Progesterone c Inactive Steroid Pharmaceutical

34 52

- 86 -8 Haloperidol b Inactive Butyrophenone Pharmaceutical

35 65277

- 42 -1 Ketoconazole b Inactive Heterocyclic Compound Pharmaceutical Abbreviations: CASRN= CAS Registry Number (American Chemical Society); MeSH=Medical Subject Headings (U.S. National Library of Medicine)

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

Review Documents (BRDs) for ER Binding and TA test methods (ICCVAM 2002a,b, 2003, 2011), as well as concordance among in vitro ER binding and transactivation assays, and the in vivo uterotrophic assay (Kanno 2001, 2003a,b; Takeyoski 2006, Freyberger 2010, Yamasaki 2004, Hartman 1983, Ohta 2012). Chemicals cover a range of chemical classes and potencies ( e.g. , strong actives typically exhibit an EC50 >1x10 - 11 M; moderate 1x10 -9 to 1x10 - 8 M; weak 1x10 -7 to

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

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

Testing and Assessment, No. 173. Test Guidelines Programme, OCED, Paris, France. 32 See Annex 2, Table 1, Comparison of results from STTA and BG1Luc ERTA Assays for 34 chemicals tested in both assays and classified as positive or negative ER agonists

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

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

recognized standardized classification scheme. 33

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



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



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

32 http://search.oecd.org/officialdocuments/displaydocumentpdf/?cote=env/jm/mono(2012)18&doclanguage=en

33 http://www.nlm.nih.gov/mesh/

34 http://toxnet.nlm.nih.gov/cgi

- bin/sis/htmlgen?HSDB

### Prioritization of the EDSP Universe of Chemicals

December 2012 78 | Page Table 6.2.1: Summary of results for the reference chemicals when tested in the Eight ER (agonists) HTP assays 1193

CASRN Name Expected Potency aTox21 BG1LUCTox21 BLAATG ERaTRANSATG ERECISOT LUCNVS_NR_hERNVS_NR_bERNVS_NR_mERa

50

- 28 -2 17beta

- estradiol Strong A A A A A A A A

56

- 53 -1 Diethylstilbestrol (DES) Strong A A A b A b A b A b A b A b

57

- 63 -6 17alpha

- ethinyl estradiol Strong A A A A A A A A

84

- 16 -2 meso

- Hexestrol Strong

- Moderate A A A A A A A A

53

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

57

- 91

- 0 17alpha

- Estradiol Moderate A A A A A A A A

68

- 23 -5 Norethynodrel Moderate A A

68

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

131

- 55 -5 2,2',4,4'

- Tetrahydroxy

- benzophenone Moderate A A A A A A A A

140

- 66 -9 4- tert

- Octylphenol Weak A A A A A A A A

84852

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

80

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

1478

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

77

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

599

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

143

- 50 -0 Kepone Weak A A A A I i A I A

434

- 22 -0 19

- Nortestosterone Weak A A

58

- 18 -4 17alpha

- Methyltestosterone Weak A A A A I i A I I

446

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

479

- 13 -0 Coumestrol Weak A A c

520

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

520

- 36 -5 Apigenin Weak A A A A A I I

486

- 66

- 8 Daidzein Weak A A A A A A A A

789

- 02 -6 o,p'

- DDT Weak A A A A I j I I I

94

- 26 -8 Butylparaben Weak A A A A A A A A

120

- 47 -8 Ethylparaben Very Weak A A d A A I i A I I

85

- 68 -7 Butylbenzyl phthalate Very weak A A A I i I I I

72

- 43 -5 Methoxychlor Very Weak A e I A A I i A h I I

1912

- 24 -9 Atrazine Inactive I I I I I I I I

330

- 55 -2 Linuron Inactive I I I I I I I I

50

- 06 -6 Phenobarbital Inactive I I

57

- 30 -7 Phenobarbital Sodium Inactive I I I I I I I I

52

- 01 -7 Spironolactone Inactive I I I I I I I I

50

- 22 -6 Corticosterone Inactive I I A k,g A g I I I I

### Prioritization of the EDSP Universe of Chemicals

December 2012 79 | Page

CASRN Name Expected Potency aTox21 BG1LUCTox21 BLAATG ERaTRANSATG ERECISOT LUCNVS_NR_hERNVS_NR_bERNVS_NR_mERa

57

- 83 -0 Progesterone Inactive I I Ag A g I A A I

52

- 86 -8 Haloperidol Inactive I I I A I I I I

65277

- 42 -1 Ketoconazole Inactive I I I I I I I I A=Active; I=Inactive; Grey shaded areas indicates chemicals not tested in assay; Red shaded areas indicate test results that were inconsistent with the expected activity call for the reference chemical.

a Classification as 'actives' or 'inactives' based upon documentation as an ER agonists or negative (inactive) as tabulated in the ICCVAM Background Review

Documents (BRDs) for ER Binding and TA test methods (ICCVAM 2002a,b, 2003, 2011) as well as information obtained from publica tions published and reviewed
after the completion of the ICCVAM BRDs (Kanno 2001, 2003a,b; Takeyoski 2006, Freyberger 2010, Yamasaki 2004, Hartman 1983, Ohta 2012).

b Clear activity but chemical purity in Class B (70

- 90%).

c Clear dose

- response activity but blue

- channel autofluorescence assay is active below AC 50 . d Two samples tested. Classification as 'active' based upon plot with highest concentration tested for this chemical.

e Clear activity but flare is observed and T<flare threshold.

f Some activity but data are noisy.

g Assay is designed to assess the activities of multiple transcription factors in a single experiment (including ER and Glucocorticoid receptor), and results may

indicate possible cross

- talk between two pathways when testing steroids at higher concentrations.

h Triplicate sample from current experiment shows clear activity. Single sample from legacy data shows no activity.

i Maximum dose tested: 1e - 4 M j Maximum concentration tested: 9.5e - 5 M
k Maximum concentration tested: 2e - 4 M

### Prioritization of the EDSP Universe of Chemicals

December 2012 80 | Page Summary of the performance (accuracy) of the eight assay s when testing the set of Reference 1194 

### Chemicals for ER Agonists : The following tables quantify the agreement between the expected 1195 

### activity of the 37 reference chemicals (Table 6.2.1) and what was obser ved in each of the eight assay s 1196 

### for ER agonist activity (Table 6.2.2). 1197



### Table 6.2.2: Results for each assay when testing reference chemicals 1198 

### Assay: Tox21 BG1 LUC Chemicals evaluated: 28 actives, 9 inactives Assay Activity

Active Inactive Total

Expected Activity Active 28 0 28

Inactive 0 9 9
Total 28 9 37 Assay: Tox21 BLA Chemicals evaluated: 27 actives, 9 inactives Assay Activity

Active Inactive Total

Expected Activity Active 25 2 27

Inactive 0 9 9
Total 25 11 36 Assay: ATG ER α TRANS Chemicals evaluated: 23 actives, 8 inactives Assay Activity

Active Inactive Total

Expected Activity Active 23 0 23

Inactive 2 6 8

Total 25 6 31 Assay: ATG ERE CIS Chemicals evaluated: 24 active, 8 inactives Assay Activity

Active Inactive Total

Expected Activity Active 24 0 24

Inactive 3 5 8

Total 27 5 32 Assay: OT LUC Chemicals evaluated: 25 active, 8 inactives Assay Activity

Active Inactive Total

Expected Activity Active 18 7 25

Inactive 0 8 8
Total 18 15 31 Assay: NVS_NR_hER Chemicals evaluated: 25 active, 8 inactives Assay Activity

Active Inactive Total

Expected Activity Active 23 2 25

Inactive 1 7 8
Total 24 9 33 Assay: NVS_NR_bER Chemicals evaluated: 25 active, 8 inactives Assay Activity

Active Inactive Total

Expected Activity Active 17 8 25

Inactive 1 7 8

Total 18 15 33 Assay: NVS_NR_mERa Chemicals evaluated: 24 active, 8 inactives Assay Activity

Active Inactive Total

Expected Activity Active 17 7 24

Inactive 0 8 8

Total 17 15 32

Table 6.2.3 provides the summary statistics for each of the assays using standard metrics of assay 1199 

### performance to include the overall accuracy ( i.e. , concordance, the proportion of all reference chemicals 1200 

### that were correctly classified), sensitivity ( i.e. , the proportion of all active (positive) reference chemicals 1201 

### that were correctly classified), and specificity ( i.e. , the proportion of all inactive (negative) reference 1202 

### chemicals correctly classified). The two Tox21 (BG1Luc and BLA) and the two Attagene TA assays 1203 

### displayed a high level of overall accuracy, correctly predicting 91 to 100% of the active and inactive 1204 

### reference chemicals. The Odyssey Thera TA assay was less sensitive (overall accuracy, 79%), and 1205 

### curves tended to be right- shift ed as compared with other TA assays. Of the three ER binding assays, the 1206 

### NVS_NR_hER performed the best with an overall accuracy of 91%, while the bovine and mouse ER 1207

### Prioritization of the EDSP Universe of Chemicals

December 2012 81 | Page binding assays showed a lower overall accuracy (73-78%) and sensitivity (68-71%), in parti cular 1208 

### missing the weak to very weak reference chemicals. 1209



### Table 6.2.3: Summary statistics for the predictive capacity of each assay when testing the 1210 

### reference chemicals (ER agonist activity) 1211Tox21 BG1LUCTox21 BLAATG ERaTRANSATG ERE CISOT LUCNVS_NR_hERNVS_NR_bERNVS_NR_mERa

Reference Chemicals Tested (Actives/Inactives) 28/9 27/9 23/8 24/8 25/8 24/8 25/8 25/8

Concordance 1,4 1 0.94 0.94 0.91 0.79 0.91 0.73 0.78 Sensitivity 2 1 0.93 1 1 0.72 0.92 0.68 0.71 Specificity 3 1 1 0.75 0.63 1 0.88 0.88 1

1 Overall accuracy ( e.g ., the proportion of correct outcomes) between the test method results and the expected results for

reference chemicals.

2 The proportion of all active (positive) chemicals that were correctly classified by the assay.

3 The proportion of all inactive (negative) chemicals that were correctly classified by the assay.

4 T emplate for Accuracy Analysis:
Assay Activity

Active Inactive Total

Expected Activity Active a c a + c
Inactive b d b + d

Total a + b c + d a + b + c + d a = positive in both new assay and by reference test classification b = positive in new assay and negative by reference test classification c = negative in new assay and positive by reference test classification d = negative in both new assay and by reference test classification Concordance = ([a+d]/[a+b+c+d]) Sensitivity = (a/[a+c]) Specificity = (d/[b+d])



### To evaluate the intra -laboratory reproducibility of the assays, standard deviations (SDs) were calculated 1212 

### from the assay response (normalized to 100% E2 response) for groups of replicates at each 1213 

### concentration tested for a given chemical. The mean of those SDs across the full concentration response 1214 

### for a particular chemical is reported for each assay (Table 6.2.4). Overall the mean S Ds ranged from 1.2 1215 

### to 23.7% relative to the maximal response of 100%, with the highest SDs observed among replicates in 1216 

### the ATG ER α Trans TA assay. 1217

### Prioritization of the EDSP Universe of Chemicals

December 2012 82 | Page Table 6.2.4: Mean Standard Deviatio ns a 

### across the technical replicates for the reference chemicals in the eight ER HTP assays. 1218

CASRN Name Expected Potency bTox21 BG1LUCTox21 BLAATG ERaTRANSATG ERECISOT LUCNVS_NR_hERNVS_NR_bERNVS_NR_mERa

50

- 28 -2 17beta

- estradiol Strong 8.1a 4.4 7.8 6.0 9.6 1.2 2.7

- c

56

- 53 -1 Diethylstilbestrol (DES) Strong 10.1 7.9 19.3 10.0 11.7 - 2.5 1.2

57

- 63 -6 17alpha

- ethinyl estradiol Strong 8.5 4.9 23.7 3.8 12.1 - 2.1 1.6

84

- 16 -2 meso

- Hexestrol Strong

- Moderate 8.4 4.6 8.2 6.4 12.9 1.1 1.9 0.7

53

- 16 -7 Estrone Moderate 9.9 4.5 16.4 5.6 12.7 1.2 3.8 4.6

57

- 91

- 0 17alpha

- Estradiol Moderate 9.0 5.8 8.5 6.7 12.8 0.8 2.8 0.8

68

- 23 -5 Norethynodrel Moderate 13.9 3.5 - - - - - - 68

- 22 -4 Norethindrone Moderate 7.8 2.8 11.6 6.4 7.1 16.2 6.2 8.4

131

- 55 -5 2,2',4,4'

- Tetrahydroxy

- benzophenone Moderate 9.3 2.4 9.1 5.5 9.8 1.7 7.0 11.6

140

- 66 -9 4- tert

- Octylphenol Weak 6.5 3.1 8.8 3.3 5.2 5.2 7.2 6.9

84852

- 15 -3 Nonylphenol (branched) Weak 5.4 3.2 7.9 3.8 4.0 11.8 7.7 12.1

80

- 05 -7 Bisphenol A Weak 6.2 2.4 4.8 2.1 4.0 8.8 6.8 6.5

1478

- 61 -1 Bisphenol AF Weak 9.5 5.2 7.5 6.7 4.0 17.9 6.8 5.8

77

- 40 -7 Bisphenol B Weak 9.8 3.7 14 9.7 1.8 8.8 12.4 3.2

599

- 64 -4 4- Cumylphenol Weak 5.9 3.2 13.3 10.6 3.0 10.1 8.3 10.7

143

- 50 -0 Kepone Weak 5.9 2.8 7.3 5.0 1.6 7.3 8.5 13.7

434

- 22 -0 19

- Nortestosterone Weak 8.0 3.6 - - - - - -

58

- 18 -4 17alpha

- Methyltestosterone Weak 6.6 2.6 7.6 7.2 1.7 9.7 6.8 6.0

446

- 72 -0 Genistein Weak 10.1 2.6 13.7 1.9 6.2 4.8 14 17.5

479

- 13 -0 Coumestrol Weak 8.1 3.8 - - - - - - 520

- 18 -3 Kaempferol Weak 6.8 1.6 - - 2.9 - - - 520

- 36 -5 Apigenin Weak 5.4 4.7 - - 1.5 - - - 486

- 66

- 8 Daidzein Weak 13 3.6 18.6 7.3 7.8 15.6 8.8 6.6

789

- 02 -6 o,p'

- DDT Weak 8.4 4.8 14.1 4.9 1.7 6.6 8.0 9.2

94

- 26 -8 Butylparaben Weak 6.7 2.0 14.7 3.7 3.7 6.0 4.6 11.1

120

- 47 -8 Ethylparaben Very Weak 4.1 1.8 14.0 6.7 2.4 8.5 6.1 10.3

85

- 68 -7 Butylbenzyl phthalate Very weak 5.7 1.9 3.0 5.4

72

- 43 -5 Methoxychlor Very Weak 6.1 2.1 3.3 6.1 1.2 6.3 5.7 18.1

1912

- 24 -9 Atrazine Inactive 3.3 2.3 11 1.9 1.3 4.6 5.5 8.2

330

- 55 -2 Linuron Inactive 5.3 2.1 11.3 2.4 1.5 5.2 4.0 11.2

50

- 06 -6 Phenobarbital Inactive 3.8 1.2 - - - - - - 57

- 30 -7 Phenobarbital Sodium Inactive 4.2 1.6 - - 1.5 - - - 52

- 01 -7 Spironolactone Inactive 5.3 1.5 5.0 5.0 2.2 7.1 7.5 19.6

50

- 22 -6 Corticosterone Inactive 3.6 1.8 6.2 8.0 1.3 5.6 7.5 8.8

### Prioritization of the EDSP Universe of Chemicals

December 2012 83 | Page CASRN Name Expected Potency bTox21 BG1LUCTox21 BLAATG ERaTRANSATG ERECISOT LUCNVS_NR_hERNVS_NR_bERNVS_NR_mERa

57

- 83 -0 Progesterone Inactive 4.4 2.1 12 5.9 2.2 7.6 11.8 18.4

520

- 86 -8 Haloperidol Inactive 4.4 1.1 - - 1.6 - - - 65277

- 42 -1 Ketoconazole Inactive 3.6 1.3 - - 2.8 - - -

a Standard deviations were calculated from the assay response (normalized to 100%) for groups of replicates at each concentrati on tested for a given chemical. The

mean of those SDs across the concentration dose response for a chemical is reported for each a ssay. b Classification as 'actives' or 'inactives' based upon documentation as an ER agonists or negative (inactive) as tabulated i n the ICCVAM Background Review

Documents (BRDs) for ER Binding and TA test methods (ICCVAM 2002a,b, 2003, 2011) as well as information obtained from publications published and reviewed after the completion of the ICCVAM BRDs (Kanno 2001, 2003a,b; Takeyoski 2006, Freyberger 2010, Yamasaki 2004, Hartman 1983, Ohta 2012).

c - No technical replicate for chemical.

### Prioritization of the EDSP Universe of Chemicals

December 2012 84 | Page 6.3 Additional assay performance metrics for the HTP ER assays 1219



### In traditional assay validation, a number of standard performance metrics are used. These typically come 1220 

### from a formal validation process that includes establishing quantitative values for the reproducibility and 1221 

### accuracy of an assay both within and across laboratories. Using the traditional validation process as a 1222 

### guide, the performance of the eight HTP assays described within this document were evaluated and 1223 

### include both traditional performance metrics ( e.g. , accuracy as quantified using a set of reference 1224 

### chemicals, and intra

- laboratory replication), as well as new metrics that are being developed exclusively 1225 

### for HTP technology. As part of the evaluation of assay performance, a number of QC metrics are being 1226 

### carefully monitored as described in Table 6.3.1, and may ultimately be used as performance standards 1227 

### for various assays or groups of assays. In addition, an advantage of HTP tec hnology is that by testing 1228 

### large numbers of compounds in a variety of assays, additional performance information can be provided 1229 

### that is not typically available in traditional validation processes. For example, assessments of the intra1230 

### laboratory replicati on of independently procured samples of the same chemical, and the plate- to

- plate 1231 

### replication using large sets of standard test chemicals are provided for some of the eight assays. Finally, 1232 

### to facilitate sharing of HTP data among collaborators and the research community at large, the 1233 

### transferability of the assay data in a unified format as well as full documentation of any data analysis are 1234 

### necessary. Thus, assessment of the process for transferring HTP data is an essential metric that is 1235 

### currently being de veloped for the ER assays. 1236



### Table 6.3.1: List of additional assay performance metrics. 1237 Potential QC Metrics Available Where Described or Note of Not

Plate

- wise Z' factor Yes Section 6.6 Reagent stability issues Underway Section 4 and Appendix F Signal interference issues Yes Section 5.2 Acceptance criteria (signal window, variability, plate spatial uniformity, replication, and inter

- plate variance) Partial per

assay Acceptance criteria are defined under each assay description in Appendix F.

Plate uniformity with regard to signal and location of chemical placement on plate ( i.e. , spatial uniformity) Partial per

assay Section 6.3

Consistency of assay performance over time No Assays are typically conducted during a single, relatively short, time period. However, evaluations of positive controls and sets of QC chemicals could be routinely tested and metrics developed over longer periods of time. Historical positive control data Available for E2, NVS_ER Binding assays Appendix F

Metrics for Predictive Capacity, Reproducibility, and Transferability of Data

Reference Chemicals Yes Section 6.2

Intra

- laboratory replication for technical replicates
for all chemicals tested Yes Section 6.3

Intra

- laboratory replication for independently procured samples of same chemical Yes Section 6.3

### Prioritization of the EDSP Universe of Chemicals

December 2012 85 | Page Potential QC Metrics Available Where Described or Note of Not Transfer of data and consistency in classification of chemicals when multiple groups evaluate data using same or similar data analysis procedure and
classification criteria. Pilot Study Ongoing Data from the Tox21 BG1 LUC and Tox21 BLA TA assays are currently being independently evaluated and analyzed by Federal Agencies
within the T ox 21 consortium. Results from this pilot study are expected to be published mid

2013.



### 6.3.1 Replicate Analysis 1: Concentration- wise response for technical replicates. 1238



### For each of the eight HTP assays, the assay response (normalized to 100% Emax) for groups of 1239 

### technical replicates for each sample ( e.g. , same chemical vendor, lot and batch) at each concentration 1240 

### evaluated have been compared. This analysis included the complete set of chemicals tested. The assay 1241 

### response for pairs of technical replicates, concentration

- by -concentration, were plotted and a R2 value 1242 

### was calculated across all samples for the assay. Additionally, the distribution of the differences between 1243 

### samples was calculated and the standard deviation was calculated. An example plot is shown in Figure 1244 

### 6.3.1. Table 6.3.1 gives the R2 and standard deviation values for the eight assays. 1245



### Table 6.3.1: Statistical parameters for the technical replicates concentration response correlation. 1246 

### Assay R2 SD

Tox21 BLA 0.83 0.07

Tox21 BG1 LUC 0.70 0.11

ATG ERa TRANS 0.77 0.19

ATG ERE CIS 0.84 0.15

OT LUC 0.82 0.14 NVS_NR_hER 0.81 0.16 NVS_NR_bER 0.88 0.14 NVS_NR_mERa 0.65 0.25

1247

### Prioritization of the EDSP Universe of Chemicals

December 2012 86 | Page Figure 6.3.1: Example of a plot showing reproducibility of response value for an assay

This example is from the ATG ERE CIS assay. Each point corresponds to a single sample and 1248 

### concentration. The x and y values are the response values for two technical replicates for the same 1249 

### sample, in units of %E2 response. The R2 value is derived from using a linear model fitting y=x. The 1250 

### standard deviation is calculated from the vector of differences between the responses from the pairs of 1251 

### technical replicates. 1252



### 6.3.2 Replicate Analysis 2: Plate- to

- plate replication using large sets of standard technical 1253 

### replicates 1254



### As part of the QC process for all of the NCGC Tox21 assays, a set of 88 test chemicals (included six 1255 

### known ER agonists) was selected to be tested on all master 1,408- sample plates. This resulted in a 1256 

### maximum of 39 technical replicates which allows examination of plate

- to

- plate variation among the 1257 

### array of NCGC Tox 21 assays. NCGC runs assays for many targets and this set of 88 chemicals was 1258 

### selected becaus e they are known to be active against many of the targets (not just ER) associated with 1259 

### those assays. An analysis is ongoing to evaluate the entire group of 88 test chemicals. However, a 1260 

### preliminary evaluation of the subset of chemicals with ER agonist act ivity, has demonstrated that the 1261 

### plate to plate variation for the 39 technical replicates is quite low. Figure 6.3.2 shows examples of 1262 

### chemicals that are active or inactive for the two Tox21 ER TA assays. 1263

### Prioritization of the EDSP Universe of Chemicals

December 2012 87 | Page

### Prioritization of the EDSP Universe of Chemicals

December 2012 88 | Page Figure 6.3.2 Examples of the replicatio n of set of 88 test chemicals on every plate used for the two (BLA and Luc BG1) Tox21 TA assays.

6.3.3 Replicate Analysis 3: Hit -calling and AC 50 

### replication for multiple, independently 1264 

### procured, chemicals 1265



### For selected assays, multiple, independently pr ocured, samples of the same chemical were tested. Each 1266 

### of these generated a set of concentration -response values, a curve fit, and a hit call. AC 50 

### values and the 1267 

### hit calls were compared for each matched pair of samples for the same chemical. Table 6.3.3 c ompares 1268 

### the number of samples that agree (active or inactive) or disagree for each of the assays. Samples that 1269 

### were placed in the SETASIDE or DISCARD bins are not included. Figure 6.3.3 compares the AC 50 1270 

### values, and annotates whether the samples were reference chemicals. As can be seen from the figures, 1271 

### most of the replicate pairs that did not give the same hit call (one active, one inactive) had AC 50 

### values 1272 

### close to the upper concentration tested. It is expected that some of this sample- to

- sample discrepan cy 1273 

### will be explained by samples that fail the analytical QC tests, which are only about one third complete at 1274 

### this time. 1275



### Table 6.3.3: Comparison of results from independently procured samples after Level 6 hit calling 1276 

### and Level 7 filtering 1277 

### Assay Both Active Sample 1 Active Sample 2 Active Neither Active Concordance

Tox21 BLA 86 30 57 1949 0.96

Tox21 BG1 LUC 104 50 69 1823 0.94

ATG ERa TRANS 7 2 1 24 0.91

ATG ERE CIS 3 3 1 27 0.88 NVS_NR_hER 3 2 0 19 0.92 NVS_NR_bER 4 0 0 17 1 NVS_NR_mERa 2 0 0 3 1

1278

### Prioritization of the EDSP Universe of Chemicals

December 2012 89 | Page Figure 6.3.3: Plot showing the correlation between the AC 50 

### s and hit calls between independent samples of the same chemical

Plotted are the log( AC 50 

### ) values for the two samples (x and y). For samples that were inactive, the AC 50 1279 

### was arbitrarily set to 0.01 M, and a small random increment was added to keep points from lying on top 1280 

### of one another. For reference chemicals, we indicate the activity class by colors. The values are the 1281 

### number of chemicals for which the replicates are (both active), (replicate 1 active, replicate 2 inactive), 1282

### Prioritization of the EDSP Universe of Chemicals

December 2012 90 | Page (replicate 2 active, replicate 1 inactive), (neither active). AC 50 

### values with an AC 50 

### modifier of ">=" 1283 

### were set to 200 μM for graphical purposes. 1284



### 6.3.4 Replicate Analysis 4: Comparing pairs of assays 1285



### For both the Attagene (TRANS and CIS) and NCGC/Tox21 (BLA and BG1 LUC) assay pairs, AC 50 1286 

### calls were compared between assays. Each plot below shows the correlation between the AC 50 

### values 1287 

### for each pair of assays. This figure (Figure 6.3.4) show discrepa nt calls and the linearity of the potency 1288 

### scale between assays. 1289



### For the Attagene assays, the TRANS assay seems to produce more positive calls not replicated in the 1290 

### CIS than vice versa (comparison of clusters of points both to the left and below of the lar ge cluster of 1291 

### negative chemical sample points in both assays in the upper right hand corner of Figure 6.3.4a). There 1292 

### are only seven chemical samples where the TRANS assay shows activity below 1 μM and the CIS assay 1293 

### shows no activity. The quantitative relationship between AC 50 

### values where the curve is fully resolved 1294 

### is very linear and close to 1:1. 1295



### For the NCGC/Tox21 assays, the BG1 LUC assay shows a significant number of chemical samples 1296 

### being positive when the BLA assay shows no activity. There are several dozen chemicals (only 0.4% of 1297 

### total) where the BG1 LUC AC 50 

### is less than 1 μM and the BLA assay is inactive (Figure 6.3.4b). There 1298 

### are only 9 chemical samples where the BLA AC 50 

### is less than 1 μM and the BG1 LUC assay is inactive. 1299 

### AC 50 

### values for the same chemical sample are approximately 10 -fold lower in the Tox21 BG1 LUC 1300 

### assay than in the Tox21 BLA assay. This increased sensitivity in the Tox21 BG1 LUC assay may 1301 

### explain some of the extra "Active" sample calls over the Tox21 BLA results. 1302

### Prioritization of the EDSP Universe of Chemicals

December 2012 91 | Page Figure 6.3.4 Plot showing the sample by sample correspondence between the AC 50 

### hit calls for the two a) A ttagene (CIS and TRANS), and b) Tox21 (BG1 LUC and BLA) ER assays

Plotted are the log( AC 50 

### ) values for the two assays (x and y). For samples that were inacti ve, the AC 50 1303 

### was arbitrarily set to 0.01 M, and a small random increment was added to keep points from lying on top 1304 

### of one another. Samples with borderline activity ( AC 50 

### modifier of ">=") have their AC 50 

### value set to 1305 

### 200 μM for visualization purposes. These samples then form the bands between the inactive and fully 1306 

### active groups. For reference chemicals, we indicate the activity class by colors. 1307

### Prioritization of the EDSP Universe of Chemicals

December 2012 92 | Page Section 7 Analysis of ES TrSet and HTP Assay Data 1308



### This section provides a preliminary assessment of the potenti al for HTP assay data to expand the ES. 1309 

### This analysis compares a subset of the HTP ER assays and data used to train the ES. The assays that 1310 

### underlie the ES have been optimized and the data thoroughly analyzed, and provide a high- quality data 1311 

### set against which new assays can be compared. The HTP data have been generated using available 1312 

### technologies that have not undergone the type of rigorous optimization that the ES assays have to 1313 

### capture low affinity chemicals, but the HTP assays are providing empirical data on 1,000's of chemicals. 1314 

### Additionally, the analysis of the HTP assays is still ongoing, for instance on understanding how to deal 1315 

### with technology-specific false positives and negatives. The goal of this section is to demonstrate an 1316 

### approach for performing this comparison. However, both because only a subset of the HTP assays are 1317 

### included in this analysis, and because of time pressures, any conclusions of this analysis should be 1318 

### treated as preliminary. 1319



### 7.0 Assays utilized in developing the ER Expert Syst em 1320



### The development of the ES was based on the information derived from two in vitro assays optimized to 1321 

### detect low affinity binders that were the only types of ER Binders likely to be found on the FI and AM 1322 

### chemical lists as presented in the 2009 SAP paper (SAP, 2009a). The first assay is a classic competitive 1323 

### receptor binding assay that utilizes a rainbow trout liver cytosol as the source of ERs. This assay 1324 

### establishes whether a chemical can displace the endogenous hormone from the ER, and the affinity for 1325 

### this interaction is determined relative to the binding affinity of 17

- beta -estradiol (E2). The second assay 1326 

### detects the ability of a chemical to activate an estrogen

- responsive gene at the tissue level along the ER

1327 

### mediated reproductive impairment AOP (se ction III). For the ES, the gene activation (GA) assay 1328 

### measures the induction of the mRNA for vitellogenin, which is an egg yolk precursor protein. The assay 1329 

### utilizes precision cut liver slices from immature rainbow trout which have very low or no vitellog enin 1330 

### expressed; but when exposed to E2 or other chemicals that activates the ER, vitellogenin mRNA is 1331 

### significantly induced. The low background activity of this gene, the large dynamic range of the 1332 

### response, and the sensitivity of quantitative real- time PC R as the endpoint makes this a very sensitive 1333 

### assay for detecting ER activation, thus giving confidence in detecting very low affinity ER binders. The 1334 

### principle assay components of the rtER binding and liver slice vitellogenin assay are provided in 1335



### summary tables in Appendix H (Table H-1 and Table H- 2). 1336



### The results of the response to a chemical in these two assays are taken together and used to interpret 1337 

### whether a chemical has the potential to be active or inactive for interacting with an ER

- mediated 1338 

### pathwa y. An initial assessment is done in the rtER binding assay to assess whether the chemical can 1339 

### displace E2 from the receptor. The results of the binding data, in combination with any previous 1340 

### information derived for other chemicals in the ES TrSet in the sa me chemical group, determines whether 1341 

### the chemical is tested further in the trout liver slice assay for verification of activity. Generally, a trout 1342 

### slice assay is run as a follow -up to the binding assay, except when there already is compelling evidence 1343 

### in the ES TrSet from previous testing that provides adequate mechanistic understanding and chemical 1344 

### group-based evidence to ensure that the activity call can be based on the binding data alone when adding 1345 

### this new chemical to the training set. Supporting inf ormation is also collected to determine if parameters 1346 

### specific to each chemical could be impacting the apparent assay results. In the binding assays this 1347 

### includes assessment of the formation of precipitates, and/or changes in media pH. In the slice assay, 1348 

### cytotoxicity (usually determined by LDH leakage from the slices, but occasionally other cytotoxicity 1349 

### assays), chemical insolubility (from visual observation of the formation of precipitates or other 1350 

### indications such as light scattering by nephelometery), m easured changes in media pH, calculated pK
a 

### , 1351

### Prioritization of the EDSP Universe of Chemicals

December 2012 93 | Page among others are used to evaluate the performance of the assay. As knowledge is gained on activity of 1352 

### individual chemicals, and of the activity of chemicals in the same ES group, the classification of a 1353 

### chemical as potentially active or inactive (Binder or Nonbinder) is made with greater confidence. Note 1354 

### that this complete analysis, e.g. comparing assays against one another, looking at chemicals class 1355 

### information, etc. has not yet been completed for the HTP assays. 1356



### Decision diagrams and accompanying text showing the process for data analysis and interpretation of 1357 

### activity of ES TrSet chemicals in the rtER binding and trout liver slice Vtg mRNA induction assays are 1358 

### included in Appendix G: ES TrSet Decision Criteria for Binder, Nonbinder and Data Processing 1359 

### Details. 1360



### 7.1 ER Expert System TrSet and HTP In vitro Assays 1361



### Analysis of the concordance in responses between the assays used in developing the ES and a subset of 1362 

### five of the HTP assays was conducted. The five HT P assays were selected because they utilized in vitro 1363 

### assay formats which correspond to the technologies used in the lower-throughput TrSet assays f. The 1364 

### remaining three assays were not included in this current analysis primarily due to lack of time to 1365 

### perform a complete analysis. The three NVS ER binding assays all utilize the measurement of 1366 

### displacement of [ 3 

### H] -E2 from the receptor, which is the same detection method utilized in the trout liver 1367 

### cytosol assay. The NVS assays are conducted up to a maximum c hemical concentration limit of 50 μM 1368 

### if a prescreen up to 25 μM was positive, whereas the rtER cytosolic binding assay is conducted up to 1369 

### chemical solubility. The two reporter gene assays, the Tox21 BG1-Luc and Tox21 BLA utilize standard 1370 

### ER responsive repo rter gene assays with luciferase or fluorescence, and again are well reported when 1371 

### run in a lower-throughput mode. They were conducted up to 100 μM maximum test concentration. The 1372 

### trout GA assay was conducted up to chemical solubility or cytotoxicity (whichever was noted first) with 1373 

### initial test concentrations selected based upon rtER binding activity results. 1374



### The analysis that follows was done with a set of 296 chemicals that were selected in June 2011 to be run 1375 

### as part of the chemical selection process fo r HTP testing in endocrine related assays to address the 1376 

### question of if and how HTP data can be used to expand the ES (section 3.1.1, goal 5). This set of 1377 

### chemicals was selected to include the ES chemical groups covered by the TrSet to enable comparison of 1378 

### the assay results or 'hit calls' of the ES TrSet and the HTP assays (Appendix H, Tables H-3 to H-14). 1379 

### Activity could be compared directly between the trout based assays and the HTP assays for these 296 ES 1380 

### TrSet chemicals. 1381



### 7.2 Binding Assays: Trout Liver Cytosolic ER and HTP ER Binding Assays 1382



### A comparison of the activity determinations for the 296 chemicals in the ES TrSet binding assay and the 1383 

### three HTP binding assays follow. It is important to note a key design criterion in generating the HTP 1384 

### binding ass ay data set used here. These assays were run as part of a broad survey of chemicals against 1385 

### potential targets using a collection of cell

- free assays. The goal was to finding relatively potent 1386 

### chemicals against close to 300 targets. In order to do this anal ysis in a cost

- effective manner, chemicals 1387 

### were prescreened against each target at a single screening concentration of 25 μM. Only those chemicals 1388 

### showing appreciable activity in the single screen run were reevaluated in concentration -response mode, 1389 

### and then only up to 50 μM. Therefore the HTP ER binding assay data set being compared here was not 1390 

### run up to the limit of solubility in a way that allows a one

- to -one comparison with the low-through TrSet 1391 

### assays, so no final conclusions about absolute assay sensitivity can be made. 1392

### Prioritization of the EDSP Universe of Chemicals

December 2012 94 | Page For those chemicals that are Nonbinders (Inactive) in the trout binding assays there is generally good 1393 

### agreement in the HTP binding assays (Table 7.2.1). This is shown by the 88% of the 172 Non-binders 1394 

### from the ES TrSet also being cal led Inactive in all three of the HTP binding assays. One aspect to take 1395 

### into account in this comparison of the agreement between the Inactive chemical calls, however, is that a 1396 

### chemical concentration maximum of 25 μM was used as a single concentration prescreen in the HTP 1397 

### binding assays and only if that was positive was the chemical further tested in a full dose-response up to 1398 

### 50 μM concentration maximum. This dose selection in itself will produce a higher number of negatives 1399 

### than the rtER binding assay that was run at higher concentrations and often up to chemical solubility if 1400 

### no effect was seen at lower concentrations. As the maximal concentration tested in any assay is limited 1401 

### to lower concentrations, there will be fewer chemicals, particularly lower aff inity chemicals, identified 1402 

### as active. Thus, when making a direct comparison of Inactive determinations among assays, it is 1403 

### important to interpret the results within this context. 1404



### Table 7.2.1: Comparison of ES TrSet with HTP hER, bER and mERa binding assay calls. 1405



### Comparison of Inactives hER Inactive bER Inactive mERa Inactive All three NVS ER binding Inactive

172 chemicals in TrSet with RBA=0 (Inactive) 158 (92%) 164 (96%) 161 (94%) 152 (88%)

Comparison of Actives: hER Active bER Active mERa Active Any HTP ER

Active

124 chemicals in TrSet with RBA > 0 (Active) 42 (34%) 31 (25%) 29 (23%) 44 (35%)

Most of the discordance between the ES TrSet benchtop assay results and HTP binding assays occurred 1406 

### when the chemical was active in the rtER binding assay but was inactive in the HTP binding assay. For 1407 

### those chemicals active in the rtER binding assay, there were 35% that were active in at least one of the 1408 

### three HTP binding assays. As expected, given the screening approach described above, all o f these 1409 

### mismatches appear to be due to the 50 μM maximum test concentration cutoff used in the HTP assays. 1410 

### The four chemicals below in the 2 -hydroxybenzophenone chemical group (Figure 7.2.1), that were 1411 

### tested in the rtER and HTP binding assays are compared to illustrate the point (Figure 7.2.2 and 7.2.3). 1412

1413

### Prioritization of the EDSP Universe of Chemicals

December 2012 95 | Page Figure 7.2.1: 2

- Hydroxybenzophenones

The two bottom chemicals in Figure 7.2.1 (2-hydroxybenzophenone and 2-hydroxy1414 

### 4methoxybenzophenone) did not produce significant displacement from the hER, bER, or mERa in the 1415 

### HTP 25 μM pre-screen. Thus, they were not tested in the full dose- response mode and were called 1416 

### inactive in the HTP assays. The other two chemicals were active in at least two of the three HTP binding 1417 

### assays (Figure 7.2.2). 1418

### Prioritization of the EDSP Universe of Chemicals

December 2012 96 | Page Figure 7.2.2: Binding curves for two hydroxybenzophenones in HTP assays.The NVS binding assays for each receptor are shown side by side: hER (A,B), NVS bER (C,D), and NVS mERalpha (E,F). The left panels (A, C, E) of three sets of curves show 2,2',4,4'-tetrahydroxybenzophenone [131- 50 -5] and the right panels (B,D,F) are 2,4- dihydroxybenzophenone [131 -56-6]. The red symbols and line are the E2 positive controls run in the assay, the black symbols are the test chemical. 

# A B

C D

E F

### Prioritization of the EDSP Universe of Chemicals

December 2012 97 | Page From Figure 7.2.3 one can also see that if the trout cytosolic binding assay were conducted with the 1419 

### same maximal cutoff concentration (red dashed line in figure is at 50 μM) for this class of chemicals as 1420 

### in the HTP assays, then two of the four hydroxybenzophenones (C and D) would have been called 1421 

### inactive, matching what was found with the HTP assays. Similar relationships between differences in 1422 

### calls in activity and this cutoff concentration were seen within other chemical classes analyzed including 1423 

### the alkylphenols, alkylanilines, and other chemical groups active in the trout cytosolic binding assay. 1424



### Figure 7.2.3. Results of binding assays of four hydroxybenzophenones in rtER binding assay. The positive control curves for E2 are the left most curves, and two to three replicate curves for each hydro xybenzophenone are the right most curves in these graphs. The dotted red line at -4.3logM is the 50 μM maximal concentration used in the HTP binding assays for a point of reference.

In general, the HTP competitive binding assays as conducted tend to call the low affinity chemicals in 1425 

### the ES TrSet as Inactive due to the concentration cut- off, but they do well identifying the chemicals with 1426 

### greater affinity for the receptor. This is illustrated in Table 7.2.2 where the ES TrSet Binders are binned 1427 

### into groups of decreasing RBA (relative to E2 as 100%), and the number of chemicals identified as 1428 

## A B

C D

0 10 20 30 40 50 60 70 80 90 100

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CTRL 2,2',4,4'-Tetrahydroxybenzophenone

E2

Log Concentration (M)[3H]-E2Binding (%) 131

- 55 -5

0 10 20 30 40 50 60 70 80 90 100

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CTRL 2,4-Hydroxybenzophenone

E2

Log Concentration (M)[3H]-E2Binding (%) 131

- 56 -6

0 10 20 30 40 50 60 70 80 90 100 110

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CTRL E2 2-HydroxyBenzophenone

Log Concentration (M)[3H]-E2Binding (%) 117

- 99 -7

0 10 20 30 40 50 60 70 80 90 100 110

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CTRL E2 2-Hydroxy-4-methoxybenzophenone

Log Concentration (M)[3H]-E2Binding (%) 131

- 57 -7

### Prioritization of the EDSP Universe of Chemicals

December 2012 98 | Page Active in the HTP ER binding assays are also shown. As binding affinity increases, the number of 1429 

### reported Active chemicals in HTP assays increase in each group. In the r ange of RBA from 0.01 to 0.1% 1430 

### the hER HTP assay identified 10 of 18 ES TrSet Binders; in the 0.0001 to 0.01% range 10 of 33 were 1431 

### found; in the next lower range there were 6 of 36. In the lowest RBA category for the TrSet Binders 1432 

### there were no Actives in the hER, bER, or mERa assays. Separate tables for each RBA bin have been 1433 

### included in Appendix H listing the individual chemicals and their corresponding activity in the assays 1434 

### (Tables H15 to H19). 1435



### Table 7.2.2: Comparison of activity of binding assays based upon HTP positive matches to ES 1436 

### TrSet positives grouped by relative binding affinity (RBA) in the rtER assay. 1437



### ES TrSet RBA (%) Number of TrSet

Actives Number of Chemicals Matching TrSet

NVS

- hER NVS

- bER NVS

- mERa

RBA > 1 * 

### 7 6 6 7

0.1 ≤ RBA < 1 7 7 7 6

0.01 ≤ RBA < 0.1 18 10 6 7

0.001 ≤ RBA < 0.01 33 10 7 6

0.0001 ≤ RBA < 0.001 36 6 3 2

0.00001 ≤ RBA < 0.0001 22 0 0 0

* 

### Group excludes E2

A noted exception to the trend is the one chemical in the high affinity range, RBA>1%, that was not 1438 

### active in the NVS hER or bER, assays- triphenylethylene (3PE). In rtER binding 3PE exhibits relatively 1439 

### strong affinity for the receptor (RBA = 3.0%), however it did not show any indication of competitive 1440 

### binding in hER or bER HTP assays (Figure 7.2.4). It did appear to bind in the NVS mERa assay, which 1441 

### utilizes the ligand binding domain of the mouse ER- alpha. It is unclear why this chemical with high 1442 

### affinity in the rtER binding assay, and known mammalian ER affinity (Blair et al . 2000) would not 1443 

### exhibit a competitive binding curve in the HTP hER and bER assays. 1444

Triphenylethylene cyto rtER binding

0 10 20 30 40 50 60 70 80 90 100

-10 -9 -8 -7 -6 -5 -4 -3 -2 E2

3PE

CTRL[3H]-E2 Binding (%)

Log Concentration (M) 

### Figure 7.2.4: Binding displacement of triphenylethylene (3PE) in rtER and mERa. The dotted red line at -4.3logM in the cyto rtER binding assay (figure middle) is the 50 μM maximal concentration used in the HTP binding assays for a point of reference. The HTP NVS mERa binding result is shown on the right.

In the group of ES TrSet chemicals in the RBA range from 0.1 – 1.0% one chemical was called inactive 1445 

### in just one of the HTP assays. This was HPTE, which is the estrogenic metabolite of methoxychlor, and 1446 

### it was called Inactive in the mERa binding assay. This was not active in the 25 μM single concentration 1447 

### Triphenylethylene

### Prioritization of the EDSP Universe of Chemicals

December 2012 99 | Page pre -screen so was not carried forward to a full dose-response analysis. HPTE was active in both the hER 1448 

### and bER binding assays. 1449



### Among the 18 ES TrSet chemicals in the RBA range from 0.01 – 0.1% there were four alkylphenols and 1450 

### these had the highest affinity of all of the alkylphenols in the ES TrSet. All four were tested in full dose 1451 

### response mode in the HTP binding assays. Both p-n-nonylphenol and p-n-heptylphenol were Inactive in 1452 

### these HTP binding assays. The NVS hER, NVS bER, and NVS mERa identified the branched p1453 

### nonylphenol as an Active binder, and p-dodecylphenol, (a mixture of branched isomers) was identified 1454 

### as active in the NVS hER assay. There were five DDT

- related chemicals in this RBA range that were all 1455 

### active in the ES TrSet where their binding displacement curves were from 50% to 80%. In th e HTP 1456 

### binding assays, the NVS mERa assay produced a binding curve for the o,p'- DDD. The other DDT

1457 

### related chemicals were Inactive in the HTP binding assays. And finally of note in this RBA range from 1458 

### 0.01 – 0.1% were five Mixed Phenols, of which there were three on the reference chemical list 1459 

### (bisphenol A, bisphenol B, and the flavonoids daidzein; section 6) and phenolphthalien which were all 1460 

### active in all three HTP binding assays (Figure 7.2.5). 3,3',5,5'-Tetrabromobisphenol A is an Active ES 1461 

### TrSet Mixed P henol in this RBA range that was Inactive in the NVS hER, NVS bER, and NVS mERa 1462 

### binding assays when tested in a single replicate (Figure 7.2.6). This chemical was run in four replicates 1463 

### in the rtER binding assay using different fish liver cytosol ER source s for each replicate. Three 1464 

### replicates agreed very closely while a fourth gives considerable variability beyond what is normally seen 1465 

### with this assay and considered an outlier, although still yielded a positive response. 1466

### Prioritization of the EDSP Universe of Chemicals

December 2012 100 | Page Figure 7.2.5: Four Mixed Phenols in the ES TrSet with RBA from 0.01 – 0.1% that were active in all three HTP binding assays and the rtER binding assay.
Structures are shown in A- D. Below each is first the cytosolic rtER binding curve result (E -H) and below that is the corresponding chemical in the HTP hER binding assay. The bER and mERa HTP binding assays are not shown.

1467 OH OH CH 3 CH 3

OH CH 3

CH 3

OH OH O

OH O OH OH O

O 

### A B C D

E F G H

I J K L

### Prioritization of the EDSP Universe of Chemicals

December 2012 101 | Page Figure 7.2.6: Tetrabromobisphenol A (TBBA) was Active in the rtER binding assay (A) but Inactive in all three HTP Assays (B: hER; C: bER; D: mERa). Graph of TBBA in rtER (panel A) shows 4 replicate runs for this chemical in three different cytosol preparations. Three were nearly identical, and one was quite different. This 'outlier' curve was not used
to calculate the RBA for TBBA.

There were five ES TrSet ch emicals in the next lower affinity RBA range (0.001 to 0.01%) that were 1468 

### also Active in all three HTP binding assays. This included two Alkylphenols (4-t-octylphenol and 4- 1469 

### cumylphenol), one Mixed Phenol (2- Benzyl -4-chlorophenol, and two Parabens (butyl-4- 1470 

### hy droxybenzoate and Benzyl 4- hydroxy

- benzoate). However, there were three salicylates that were ES 1471 

### TrSet Actives that were Inactive in the 25 μM pre -screen for all three HTP binding assays so were not 1472 

### tested in full concentration response mode. These were be nzylsalicylate, hexylsalicylate, and Benzoic 1473 

### acid, 2,4-dihydroxy-3,6- dimethyl -, methyl ester. Also Inactive in the three HTP binding assays but 1474 

### active in the rtER binding assay were two alkylanilines: 4

- hexylaniline and 4 -pentylaniline. The 1475 

### demonstration of the likely miss of these two chemicals and a whole series of alkylanilines Active in the 1476 

### rtER binding assays due to the use of the 50 μM cutoff is shown in Figure 7.2.7. This left graph shows 1477 

### that if the 50 μM maximal test concentration were applied to the rtER binding assay, neither the pentyl1478 

### or hexylaniline would be Binders. The right graph shows that the entire series of these 4- alkylanilines in 1479 

### the ES TrSet would also appear to have flatline non- binding curves. This chemical series is the lead 1480 

### seri es for B -Type Binders in the ERES. 1481 

## A B

C D Br

OH Br CH 3 CH 3

Br OH Br

### Prioritization of the EDSP Universe of Chemicals

December 2012 102 | Page 0 10 20 30 40 50 60 70 80 90 100 110 120

-10 -9 -8 -7 -6 -5 -4 -3 -2 Hexylaniline Pentyaniline

CTRL Pentyaniline and Hexylaniline rtER Binding

E2 A

Log Concentration (M)[3H]-E2Binding (%)



### Figure 7.2.7: The binding curves in the rtER binding assay for 4

- pentyl - and 4 -hexyl- aniline (panel A) and the full series of 4

- alkylanilines tested in the ES TrSet (Panel B) are shown with a red dashed line at the 50 μM cutoff concentration used in the HTP binding assays. The binding curves in panel BA include the following 4- alkylanilines: aniline (ANL); methylaniline (PT); ethylaniline (EA), n -propylaniline (PA); n-butylaniline (BA), t-bu tylaniline (TBA); n

pentylaniline (AAN); n

- hexylaniline (HAL) and n -octylanilne (OA). for the anilines shown in panel B are pentyl aniline (AAN; yellow diamonds) and the hexylaniline (HAL; red diamond symbol) are also on the full ES TrSet graph.

The secon d to lowest ER affinity group (with RBAs from 0.0001 to 0.001%) contained chemicals across 1482 

### a wide range of chemical groups. Many of those were called Inactive by the HTP binding assays. There 1483 

### were two of 36 ES TrSet Actives that were Active in all three HT P binding assays. These were phenol 1484 

### red and fluorescein. Fluorescein had an RBA of 0.00065% and phenol red was 0.00010%. Both the hER 1485 

### and bER called 4

- tert

- pentylcyclohexanone as an Active chemical, but this was not detected as active in 1486 

### the mERa binding assay (Figure 7.2.8). The hER HTP binding assay alone identified ethyl-4- 1487 

### hydroxybenzoate, morin, and diphenylphosphite as Active within this RBA range. Other chemicals of 1488 

### note in this TrSet RBA range that were Inactive in the HTP binding assays were five al kylphenols with 1489 

### LogK ow 

### values ranging from 3.0 to 3.5 and perfluorooctanoic acid (PFOA). 1490



### Finally, none of the TrSet chemicals in the lowest RBA range of ES TrSet Actives were identified as 1491 

### Actives in the HTP binding assays. This group included two benzoates, two alkoxyanilines, two 1492 

### alkylphenols, two alkoxyphenols, two phenones, two phthalates and two salicylates, and several 1493 

### chemicals in the Mixed Organics category. 1494



### One chemical group for which there was noticeable agreement between the rtER activity and th e HTP 1495 

### assays was for the ES 'Parabens (non

- benzoate)' group (Appendix I, Figure I -1). The non- benzoate 1496 

### designation in paraben and salicylates in the ES indicates there is no benzene bound directly to the ether 1497 

### oxygen of the paraben or salicylate. For example, replacing the methyl group of the methylparaben 1498 

### shown in Appendix I, Figure I- 1 with a benzene ring would change the chemical classification from 1499 

### Paraben (non-benzoate) to Paraben (benzoate). The hER HTP binding assay calls were consistent with 1500 

### the ES TrSet activity of these 'Parabens (non

- benzoate)' calling six of the seven as Active. The one HTP 1501 

### Inactive was methyl -4-hydroxybenzoate, which was the lowest affinity in this TrSet group (RBA = 1502 

### 0.00015%). The Active chemicals called by the hER HTP assay r anged from n -heptyl-4- 1503 0 10 20 30 40 50 60 70 80 90 100 110 120

-10 -9 -8 -7 -6 -5 -4 -3 -2 BA

E 2 TBA

HAL AAN

OA ANL

EA PA PT

CTRL B

Log Concentration (M)[3H]-E2Binding (%)

### Prioritization of the EDSP Universe of Chemicals

December 2012 103 | Page hydroxybenzoate with rtER RBA = 0.015% down to the ethyl-4-hydroxybenzoate with rtER RBA = 1504 

### 0.00080%. The bER and the mERa called 2 and 3 'Parabens (non- benzoate)' as Active, respectively. 1505



### Figure 7.2.8: Examples of chemicals detected as active from the ES TrSet chemicals with RBAs
from 0.0001 to 0.001%

In contrast to the 'Parabens (non- benzoate)' where the HTP assays largely agreed with the TrSet results, 1506 

### the HTP assays did not identify the 'Salicylates (non

- benzoate)' to be Active. The salicylates are a 1507 

### similar structural group to the parabens (salicyla tes having ortho- instead of para-OH substitution). Yet 1508 

### all of the TrSet Active 'Salicylates (non -benzoate)' were Inactive in the HTP binding assays (Appendix I, 1509 

### Figure I

- 1). Again, there is an apparent assay difference as some of these chemicals in the 'S alicylates 1510 

### (non-benzoate)' group have been shown active with the hER in the literature (for example, see Miller et 1511 

### al ., 2001; Schreurs et al ., 2002). 1512



### The 25 μM single concentration pre- screen 1513



### The use of the 25 μM single concentration cutoff as a pre-screen tool in the NVS binding assays appears 1514 

### to play a role in the discordance between HTP and ES TrSet binding assays. Some of the chemicals that 1515 

### were carried forward to test in full dose-response mode, i.e. were positive at 25 μM in the pre- screen, 1516 

### gave flat line responses when run in dose-response mode. Out of the 296 chemicals tested in these 1517 

### assays, 110, 115, and 96 of these chemicals gave an active signal in the 25 μM pretest with the NVS 1518 

### bER, NVS hER, and NVS mERa, respectively, but were then negative in the full dose-response run. 1519 

### Thus, the initial pre- screen may not be accurately assessing the activity of the chemicals. It is the 1520 

### opposite scenario that is particularly important, where the ch emical was negative in the 25 μM single 1521 

### concentration pre-screen but might have been positive if tested in full dose-response mode. 1522 CH 3

CH

3 O

CH

3 OH O

O

CH 3 O PH

O O 4-t- cyclohexanone ethyl -4- hydroxybenzoate morin diphenylphosphite

4-t-cyclohexanone [16587-71-6] cyto rtER binding

0 10 20 30 40 50 60 70 80 90 100

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 E2

TPCH

CTRL
Log Concentration (M)[3H]-E2Binding (%) OH

OH O OH OH

OH OHTPhERbindingrtERbinding

### Prioritization of the EDSP Universe of Chemicals

December 2012 104 | Page To assess this issue, the number of chemicals that were inactive in the 25 μM prescreen for the NVS 1523 

### binding assays but were active in the ES TrSet was determined. For the NVS mERa, there were 50 1524 

### chemicals Inactive due to the pre -screen but ES TrSetActive. The most potent of those missed was 1525 

### HPTE, the hydroxylated metabolite of methoxychlor, which in the trout assays has an RBA of 0.52%. 1526 

### Similarly, there were 31 TrSet chemicals with RBAs from 0.00003% to 0.023% that were not tested in 1527 

### dose-response mode in NVS hER, and 44 TrSet chemicals missed in the NVS bER assay. Overall, these 1528 

### negatives due to the 25 μM prescreen were not specific to only one ES chemical group but were found 1529 

### across many chemical classes. For example, chemicals that were inactive in the NVS mERa assay due to 1530 

### the pre-screen were from: alkylphenols, benzoates, hydroxybenzophenones mentioned earlier, 1531 

### phthalates, salicylates, phenones and many from Mixed Organics and Mixed Phenol groups. 1532



### 7.3. Gene Activation Assays: Trout Vtg mRNA and HTP Transactivation Assays 1533



### A comparison was conducted of the 296 ES TrSet chemicals with two of the HTP TA assays (the Tox21 1534



### assays ). The resulting agreement and discordant calls are shown in Table 7.3.1. This table shows the 1535 

### majority of the chemicals were in agreement with Active and Inactive calls. The discordance mostly 1536 

### occurred where the TrSet chemicals are categorized as Binders and the HTP assay hit call was Inactive, 1537 

### as was the case in the comparison of the binding data. A large part of this difference can be attributed to 1538 

### the lower concentrations and cutoffs used in the HTP TA assays compared to testing in the rtER binding 1539 

### and liver slice Vtg mRNA expression assays which go up to limits of chemical solubility or cytotoxicity, 1540 

### whichever came first. Another reason that different assays may produce discordant results is due to 1541 

### differences in sensitivity. The 4

- alkylanilines can be used to demonstrate this phenomenon. 1542



### Table 7.3.1: Concordance of activity between TrSet and HTP assays. Comparison of the 296 1543 

### TrSet Chemicals with the HTP TA Assay Hit Calls 1544


### HTP Assayed Agree

Active Agree

Inactive Disagree TrSet (+) & HTP ( -) Disagree TrSet (

- ) & HTP (+) BLA_ERa_fluor ratio 296 46 158 76 16 BG1_ERa_Luc 296 46 158 77 15

7.3.1 Effects of Assay Sensitivity and Test Concentration Limits 1545



### As seen with binding in Figure 7.2.7, discrepancies between the 4- alkylanilines in the rtER GA assay 1546 

### versus the Tox21 TA assays might be in part due to assay sensitivity differences. The 4- alkylanilines are 1547 

### Inactive in BG1 and BLA assays but active in the ES TrSet. As mentioned earlier in section 7 the 1548 

### determination of the Vtg mRNA expression by real time quantitative PCR is a very sensitive method for 1549 

### detecting activity. Significant changes in Vtg mRNA expression can be detected for weakly active 1550 

### chemicals. 1551



### Figure 7.3.1 shows an example of one of the experimental runs for 4- pentylaniline in the trout Vtg assay 1552 

### with the red dashed line showing the concentration maximum of 9.2x10 -5 

### M ( -4.0logM) used in the HTP 1553 

### assays. This shows that the maximum concentration used in the BLA or BG1 assays may have been 1554 

### sufficient for these Tox21 TA assays to detect this anili ne as Active with one significant data point 1555 

### indicating TA, assuming all other factors are equal. Testing the chemical to higher concentrations or to 1556 

### its solubility limit in the assay could reduce uncertainties in interpreting the assay results. If there a re 1557 

### differences in sensitivity between the assays it would be expected to take greater concentrations of test 1558 

### chemical to detect an active chemical. Assay sensitivity might be influenced by factors such as the free 1559

### Prioritization of the EDSP Universe of Chemicals

December 2012 105 | Page fraction of chemical available to the cell s or differences in metabolism. So understanding how assay 1560 

### parameters can affect interpretation of the resultant data is critical. 1561



### Figure 7.3.1: Examples of activity of 4- pentylaniline in responses in trout Vtg mRNA assay tested to

- 2.3logM, BLA assay tested to

- 4.1logM (7.7x10 -5 

### M), and BG1 assay tested to

- 4.0logM (9.2x10 -5 

### M)

Discordance in calls in the other direction, i.e. , TrSet Inactive and HTP TA assay active, are due to other 1562 

### reasons than differences in maximum concentration used or potential sensitivity differences between 1563 

### assays. Of the chemicals in the "TrSet Inactive/HTP Active" category, there are 27 chemicals, of which 1564 

### 16 and 15 were active for the BLA and BG1 assays respectively, as shown in Table 7.3.1 above. There 1565 

### were four chemicals tha t were Active in both assays indicating that the two assays were not equally 1566 

### responsive across the chemical structure space. These four shared chemicals were two ES TrSet 'Mixed 1567 

### Organics' (1,2-benzendicarboxaldehyde and methyltestosterone), 4-hydroxybenzoi c acid phenyl ester 1568 

### which is a 'paraben (benzoate)', and the 'triazine', terbuthylazine. 1569



### 7.3.2 Interpreting Responses Where Replicates Disagree 1570



### For those chemicals that were active in either the BG1 or the BLA assay but not both, these remaining 1571 

### 23 chemica ls were spread across 18 chemical groups (Appendix H, Table H- 20). There were three ES 1572 

### TrSet Inactives in the LogK
ow 

### Cut-off group that were active in the BLA assay but inactive in the BG1 1573 

### assay; niacinamide, m -Xylenediamine, and hydroquinone. For each of these chemicals there was one of 1574 

### the three replicate runs in which it was called Active (Figure 7.3.2). The response for niacinamide and 1575 

### 1,3-benzenedimethanmide could be questioned due to the high variability around the single positive 1576 

### point. However, the result of the hydroquinone curve is more indicative of an Active response. For 1577 

### hydroquinone the analytical QC on one of the Inactive replicates and on the single Active replicate 1578 

### indicated the chemical purity of >90% was confirmed. For the second Inactive replicate the analytical 1579 

### chemistry provided no information on purity or identity. So the analytical chemistry information does 1580 

### not suggest a reason to disregard the Active or Inactive replicate response. Information on other possible 1581 

### interferences should be examined ( e.g. , "flare" effects, fluorescent interferences, metabolism). 1582 1.0 × 10 5 1.0 ×

10 6 1.0 ×

10 7 1.0 ×

10 8

-10 -9 -8 -7 -6 -5 -4 -3 -2 AAN

E 2
CONTROL

CTRL n-Amylaniline (pentylaniline) trout liver slice Vtg mRNA

Log Concentration (M)VTG mRNA copies/400 ng total RNA 

## A B C

### Prioritization of the EDSP Universe of Chemicals

December 2012 106 | Page Figure 7.3.2: Three chemicals that were ES TrSet Inactives, BG1 Inactives, but BLA Actives. Panels A, B, and C show the replicates called Active for niacinamide, 1,3-benzendimethanimine, and hydroquinone, respectively. The two other replicate runs for these three chemicals were all flat line Inactive curves (not shown).

The noted potential variability between replicates does raise concern over the interpretation of an Active 1583



### or Inactive call when chemicals are tested in one replicate. In those cases there is a need to be certain the 1584 

### chemical quality is acceptable, the correct chemical was tested, and there were no other potential 1585 

### interferences that may affect the response, espec ially to have high confidence in negative results. Note 1586 

### however that there is always a trade

- off between being absolutely certain of the results and testing a 1587 

### large number of chemicals in a rapid and economical manner to carry out a prioritization process. 1588



### The discordant calls that were ES TrSet inactive and HTP TA active (27 of the 296) can be divided into 1589 

### three categories. Some of these may be differences in metabolic capacity of the assays. For some the TA 1590 

### activity calls is inconsistent with knowledge o f activity of other chemicals in that group. For instance, 1591 

### the acyclic alcohol, 2

- ethyl -1-hexanol gave apparent rtER binding but was inactive in slices, but in the 1592 

### BLA assay was weakly active. This chemical warrants further examination and confirmation usi ng other 1593 

### lines of evidence. Chemical groups that may give false negatives are described in more detail in section 1594 

### 7.4.1. And finally, there are a few discordant results between the ES TrSet chemicals and the HTP 1595 

### assays for which no explanation could be off ered at the time of this assessment with the information that 1596 

### was available. It is especially important to follow -up on these Tox21(+)/TrSet(- ) chemicals because they 1597 

### indicate potential false negatives in the ES applicability domain. It also shows the potential value of 1598 

### including the HTP data throughout the EDSP21 chemicals universe. 1599



### 7.4 Interpretation of Combined ER Binding and Gene Activation Assay Results 1600



### Finally an initial analysis of the ES TrSet assays and the activity calls of the combined HTP assay data 1601 

### was conducted. Note that any conclusions from this analysis need to be treated as preliminary because 1602 

### data from all of the assays have not been included. Two different ways of categorizing Actives and 1603 

### Inactives in the HTP assays were used. In the first case, a chemical was considered Active in the HTP 1604 

### assays if automated scoring indicated 'Active' in at least one of the 5 HTP assays. In the second case, a 1605 

### chemical was considered Active if it was scored Active in at least one binding HTP assay and one 1606 

### transactivation HTP assay in any combination. Across the list of 296 ES TrSet chemicals in this 1607 

### comparison, 124 of the 296 chemicals had measureable ER binding (RBA > 0.00001%), and 172 of this 1608 

### set were Nonbinders (RBA <0.00001%). For the HTP assays, there were a total of 191 of the 296 that 1609 

### were Inactive in all five assays. There were 43 chemicals that were Active in at least one HTP binding 1610 

# A B C

### Prioritization of the EDSP Universe of Chemicals

December 2012 107 | Page assay and active in the BG1 or BLA HTP assays. There were 62 chemicals that were Active in either an 1611 

### HTP binding or an HTP transactivation assay (Figure 7.4.1). 1612



### Figure 7.4.1: Comparison of Active and Inactive Calls within the 296 chemicals in the ES TrSet.
Overall resu lts of the ES TrSET assays are shown on the left, and HTP assay results are shown on the right (three competitive binding and two reporter gene transactivation (TA) assays out of 5 TA assays from section 5).

The overall proportions of Active and Inactive calls in this set of 296 chemicals across these two 1613 

### classification schemes are similar. However, there are larger differences in distribution of the specific 1614 

### chemicals beyond what the difference of 19 more Inactives in the HTP assays indicated by these pie 1615 

### charts would suggest. This further breakout is shown in the following Table 7.4.1. 1616



### Table 7.4.1: Breakout of activity calls between HTP* and ES TrSet data 1617 

### HTP Call ES TrSet Active ES TrSet Inactive INACTIVE (All 5 HTP Assays) 191 57 134 ACTIVE (1 Binding and 1 TA) 43 34 9 ACTIVE (1 Binding or 1 TA) 62 33 29

Sum 296 124 172 * Five HTP assays: NVS bER, NVS hER, NVS mERa, Tox21 BLA, Tox21 BG1_Luc

Of the 191 chemicals that were Inactive in all five HTP assays, 134 of those were also called Inactive in 1618 

### the ES assay. This was a 70% agreement rate on Inactive calls in the HTP assays with the ES TrSet data 1619 

### calls within this subset of chemicals. Of those 57 Inactive chemicals in the HTP assays that were Active 1620 

### in the ES TrSet assays, they represe nt 21 different chemical groups and had RBAs that ranged from 1621 

### 0.00001 to 0.02 (Table 7.4.2). For the most part, those chemicals that were Inactive in the HTP assays 1622 

### but were Active in the ES TrSet assays had RBAs less than 0.001%, indicating that the ES Tr Set assays 1623 

### were identifying more low activity chemicals than the HTP assays. In this set of chemicals, the DDT

1624 

### like group was the group with the highest RBAs (0.016 to 0.023%) that were called Inactive in the HTP 1625



### assays. 1626



### There were no apparent overall tren ds in LogK
ow 

### of these 61 chemicals that were Active in the ES TrSet 1627 

### assays but Inactive in the five HTP assays. The LogK
ow 

### of this subset ranged from 1.51 for phenol to 1628 

### 6.79 for pp- DDT. The alkylphenols represent the chemical category with the largest numb er of 1629 

### discordant calls in this subset with 11 chemicals. They ranged in LogK
ow 

### from near the lowest LogK
ow 1630 

### for known Active chemicals (LogK
ow 

### = 1.51 for phenol) to 5.33 for 2,4-di- tert -butylphenol, and they 1631 

### had RBA values determined in the trout binding as say across a 100 -fold range from 0.000025 to 1632 

### 0.0035%. 1633

### Prioritization of the EDSP Universe of Chemicals

December 2012 108 | Page Table 7.4.2. Chemicals Classified as Inactive in All Five HTP Assays but Active in ES TrSet 1634 

### Assays 1635 

### Chemical Class No. of chemicals RBA (%) Range LogK ow 

### range
2- ;4

- ; or 2,4,6

- Benzoates 2 0.00005 – 0.0001 2.87

2- Hydroxy Benzophenones 1 0.00097 3.44

4- Alkoxyaniline 1 0.000064 2.63

4- Alkylaniline 3 0.00007 – 0.0015 1.62 – 4.08 Acyclic_Perfluoro 1 0.00052 6.30 Alkoxyphenols 2 0.000025 – 0.00079 1.59 – 3.50 Alkylchlorobenzenes 2 0.00016 – 0.00018 4.66 – 5.15 Alkylcyclohexanols 2 0.00024 – 0.00030 3.53 – 4.32 Alkylphenols 11 0.000026 – 0.0035 1.51 – 5.33 Cyclohexanones (#cycles=1) 1 0.00011 2.53

DDT

- like Exact Match 3 0.016 – 0.023 5.87 – 6.79 Mixed Organics - Exact Match 11 0.000029 – 0.0026 2.05 – 5.02
Mixed Phenol

- Exact Match 2 0.00019

– 0.00079 1.91

– 2.94 Parabens (non

- benzoate) 1 0.00015 2.00 Phenones (Branched) 2 0.00007 – 0.00009 2.58 – 3.04 Phenones cyclo

- substituted 1 0.00013 2.96 Phosphoric Acid Esters 2 0.0012 – 0.0019 4.70 – 6.30 Phthalates 1.6<LogK ow 

### <6.6 4 0.000066 – 0.00046 2.65 – 6.57 Salicylates (non

- Benzoate) 4 0.000062 – 0.0017 2.60 – 6.16 Thiophosphate Esters 1 0.00067 3.44 The most restrictive of the Active call categories utilized in Table 7.4.1 was the case in which there 1636 

### needed to be at least one HTP binding assay and one of the two HTP TA assays scoring as Active. There 1637 

### were 43 chemicals of the 296 chemical set that met these criteria. Of these 43 chemicals, 34 also fall 1638 

### within the Active classification of the ES TrSet assays. These included an alkoxyphenol, three 1639 

### alkylphenols, a DDT-like chemical, two mixed organics, 16 mixed phenols which included estradiol and 1640 

### related steroids, a multicyclic hydrocarbon, six parabens, and two phenylphenols. The other nine of 1641 

### these 43 chemicals that were Active in both an HTP binding and TA assay were Nonbinders in the ES 1642 

### TrSet. These were also a mix of diverse chemicals across a range o f chemical groups. A discussion of 1643 

### the discordance in the activity calls for some of these nine chemicals in the context of their chemical 1644 

### group and some potential causes for this disparity are addressed in section 7.4.1. 1645



### 7.4.1 Interpreting results in the context of structural similarity and chemical group activity 1646



### When using assays in a chemical category

- based approach for prioritizing chemicals it is important to 1647 

### interpret the results in the context of what is known about the activity of similar chemicals . This 1648 

### provides a sound basis for defining the chemical category and understanding the structure- activity 1649 

### relationships for the activity of the chemicals in that category. The following example with the 1650 

### alkylbenthiol group illustrates this point. 1651



### The alkyl benzthiol group is a negative group in the ES TrSet. The two chemicals in this group are 1652 

### p-toluene thiol and 4-t-butylthiophenol (4TB) (Figure 7.4.2 below). p-Toluene thiol is structurally 1653 

### similar to 4 -t-butlylthiophenol (4TB) but has a methyl group in place of the t-butyl. The p-Toluene thiol 1654 

### produced a "ski jump" shaped curve in the rtER binding assay nearly identical to the binding curve of 1655

### Prioritization of the EDSP Universe of Chemicals

December 2012 109 | Page 4TB shown below in Figure 7.4.3. This chemical was also scored as Inactive across all five of the HTP 1656 

### assays. Thus, this was a consensus Inactive chemical between the ES TrSet data and HTP assays 1657 

### considered here. 1658



### The chemical 4TB gave a "ski -jump" shaped binding curve in the 1659 

### trout ER binding assay with precipitates reported at -3logM (Figure 1660 

### 7.4.3). The detection of precipitates is often associated with this 1661 

### upward shape of the binding curve. In all three of the HTP binding 1662 

### assays, this chemical produced binding curves of approximately 1663 

### 75% dis placement of [ 3 

### H] -E2 (only the bER and hER binding 1664 

### curves are shown). This chemical was not run in the trout liver 1665 

### slice assays to determine Vtg mRNA expression because it was not 1666 

### active in the ER binding assay and the inactivity of the structurally 1667 

### simila r p -toluene thiol in this chemical group provided strong 1668 

### evidence that this was an inactive chemical. In the HTP BLA assay 1669 

### 4TB was Inactive, but in the BG1 assay it produced a response 1670 

### greater than 50% of the E2 max (Figure 7.4.4). 1671



### The discrepancy in the responses between these two structurally similar chemicals in the HTP assays 1672 

### was unexpected, especially when 4TB was active in both the HTP binding and BG1 assay. Because of 1673 

### the high structural similarity between the p -toluene thiol and MTB, it would be ex pected that their 1674 

### responses should be consistent. This suggests that before final activity calls on these chemicals are made 1675 

### that it would be appropriate to examine whether analytical purity or other factors might be producing 1676 

### this discordance in activity. 1677



### Figure 7.4.3: Binding activity for 4- tert

- butylbenzenthiol in the rtER assay (left panel), the HTP bER binding assay (middle panel) and hER binding assay (right panel).
The vertical red dashed line in the left panel indicates where precipitates were detected in the assay.

1678 CH 3 CH 3

CH 3 SH

SH CH 3

p-toluene thiol 4-t-butylbenzenethiol



### Figure 7.4.2: Alkylbenthiols in ES TrSet

4-t-Butylthiophenol

rtER Binding

0 50 100 150

-10 -9 -8 -7 -6 -5 -4 -3 200 225 250

Log Concentration (M) E 2 4-t-butylthiophenol

CRTL[3H]-E2Binding (%)

### Prioritization of the EDSP Universe of Chemicals

December 2012 110 | Page Figure 7.4.4: HTP BG1 agonist assay showing Active response of the assay to 4- tert

butylbenzenethiol.

7.4.2 Understanding chemical parameters that can potentially lead to false activity 1679



### As described in the decision pathway for the collection and interpretation of assay data used in 1680 

### development of the ES ( Appendix G, Figures G-1 to G-8 ) there are many factors inherent in the assays 1681 

### that can lead to false or misleadi ng activity. The following two examples help to illustrate how 1682 

### assessment of multiple assay parameters can provide alerts to when apparent activity may not be true. 1683



### Example 1. Alkylaromatic Sulfonic Acids . The Alkylaromatic Sulfonic Acid dodecylbenezenesul fonic 1684 

### acid (DBS) was a Nonbinder in the ES TrSet but was an apparent active in the HTP assays. Note that the 1685 

### TrSet "Nonbinder" call was arrived at after follow -up analyses, but that the initial concentration1686 

### response profile of the Trout binding assay (shown below) shows activity, just as is seen in the HTP 1687 

### assays. A discussion of the potential for Alkylaromatic Sulfonic Acid groups to give false positive 1688 

### signals was discussed in the 2009 SAP (SAP, 2009b). Chemicals in this group can yield apparent 1689 

### binding curves due to low pH and protein denaturation, but produce no gene activation. Verification of 1690 

### whether binding is truly competitive binding can be determined by Ki determinations and the pK
a 

### can 1691 

### provide further information on whether these chemicals are charged species at test solution pH. This 1692 

### additional mechanistic information obtained for the octylbenzenesulfonic acid presented in the 2009 1693 

### SAP (SAP, 2009a,b) supported the handling of charged species in this chemical group as Non-Binders. 1694 

### The octylbenzens ulfonic acid figures are included in Appendix I (Figure I -2 to I- 4). 1695



### Dodecylbenzenesulfonic acid which has an apparent binding curve with the rtER was an apparent active 1696 

### chemical in the three HTP binding assays (Figure 7.4.5). This chemical was inactive in the BG1 assay. 1697 

### The single high concentration where it was active in the BLA was also at concentrations that correspond 1698 

### to evidence of increasing cytotoxicity 1699

### Prioritization of the EDSP Universe of Chemicals

December 2012 111 | Page 0 10 20 30 40 50 60 70 80 90 100

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL Dodecylbenzenesulfonic acid rtER Binding

E2

Log Concentration (M)[3H]-E2Binding (%)



### Figure 7.4.5: Activity of Dodecylbenzenesulfonic acid in rtER b inding assay (top left panel), HTP bER binding assay (top middle panel), HTP hER binding assay (top right panel), HTP mERa binding assay (lower left panel), and HTP BLA assay (lower right panel). In each panel the curves to the right with the squares are estradiol standards for comparison.

In the trout liver slice assay there was no indication of agonism or antagonism (Figure 7.4.6). 1700 

### Cytotoxicity as determined by LDH leakage was evident at -3.3 and -3.0logM indicating the drop in the 1701 

### Vtg mRNA induction in the antagonism test was due to toxicity not antagonism. 1702

Dodecylbenzenesulfonic acid trout Vtg Gene Expression PCR_8.28.6

1.0×10 3 1.0×10 4 1.0×10 5 1.0×10 6 1.0×10 7

-10 -9 -8 -7 -6 -5 -4 -3 -2 DBS

E 2

CTRL

CTRL

Log Concentration (M)VTG mRNA copies/400 ng total RNA Dodecylbenzensulfonic Acid rtER Gene Expression:Antagonist Mode

1.0×10 4 1.0×10 5 1.0×10 6

-10 -9 -8 -7 -6 -5 -4 -3 -2 DBS + E2 at -8 log M

E2

Control

CTRL LDH

Log Concentration (M)VTG mRNA copies/400 ng total RNA LDH Leakage LDH 8.28.6

0 25 50 75 100 125 150 175 200

-10 -9 -8 -7 -6 -5 -4 -3 -2 DBS 48h

E 2 48h

Ctrl 48h

CTRL

Log Concentration (M)Leakage % of Total LDH



### Figure 7.4.6: Response of trout liver slice Vtg assay to dodecylbenzenesulfonic acid. Left panel shows assay run in agonist mode showing no significant Vtg activation, middle panel is assay run in antagonist mode showing drop off in signal occurring at same concentration as toxicity is detected by LDH (right panel and shown as vertical red dashed line in middle panel ).

The activity of the dodecylbenzene sulfonic acid is very similar between the HTP and ES TrSet binding 1703 

### and TA assays. Based upon what is known about this chemical group, these assays indicate a false 1704 

### positive binding and no gene induction and potential cytotoxicity at higher chemical concentrations. 1705 

### This demonstrates the need to interpret these curves in the context of what is known about the chemical 1706 

### group such as charge effects and pK
a 

### , and to be able to utilize accessory assays such as cytotoxity and 1707 

### Ki determinations to help understand the mechanism behind the discordant responses. 1708

### Prioritization of the EDSP Universe of Chemicals

December 2012 112 | Page Example 2. Chemical affects on media pH . This example uses the results for 8-hydroxyquinoline sulfate 1709 

### (8HQ) which was also classified as a Mixed Phenol. In the HTP assays i t was active in the hER binding 1710 

### assay with a single concentration showing displacement and was active in the BLA assay (Figure 7.4.7). 1711 

### It exhibited no indication of binding in the other two assays or activation in the BG1 assay. 1712



### Figure 7.4.7: HTP binding assay with the hER (left panel) and HTP BLA agonist assay (right panel) showing significant Activity at the highest test concentration.

Although in the rtER binding assay 8HQ exhibited a very steep, full displacement binding curve this 1713 

### chemicals was classified as inactive in the ES TrSet because this was determined to be an artifact of a 1714 

### drop in assay pH at these concentrations (right panel Figure 7.4.8). 1715

0 20 40 60 80 100

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 CTRL 8-Hydroxyquinoline Sulfate rtER Binding

E2 8HQ

Log Concentration (M)[3H]-E2Binding (%) pH Data 8HQ in cytosol

-11 4 5 6 7 8 9

-4 -3 -2 -1 Time: 0h

Time: 20h

Log Concentration (M)pH



### Figure 7.4.8: Binding activity for 8- hydroxyquinoline sulfate in rtER binding assay (left panel) and pH of the binding assay cytosol with graded concentrations of the test chemical (right panel).

Further evidence that the apparent binding was not true competitive binding was produced in th e trout 1716 

### liver slices. 8HQ did not exhibit any agonist activity and in antagonist mode it appeared to be toxic near 1717 

### -4logM. This interpretation of toxicity comes from a comparison of the two antagonism curves that use 1718 

### E2 at -6 or -9logM with graded concentrations of 8HQ. These two curves show a drop in activity at the 1719 

### same concentrations of 8HQ (Figure 7.4.9). If it was an antagonism effect the drop in activity should be 1720 

### shifted to the right with the higher concentration of E2. This chemical also produced a pH drop in the 1721 

### slice media that would be predicted to impair Vtg mRNA expression (below right). 1722

### Prioritization of the EDSP Universe of Chemicals

December 2012 113 | Page 8-Hydroxyquinoline Sulfate liver slice Vtg mRNA_agonist PCR 8HQ

1.0×10 3 1.0×10 4 1.0×10 5 1.0×10 6 1.0×10 7 1.0×10 8

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL 8HQ

E2

control

Log Concentration (M)VTG mRNA copies/400 ng total RNA 8-Hydroxyquinoline Sulfate Vtg mRNA _ antagonist PCR 8HQ

1.0×10 3 1.0×10 4 1.0×10 5 1.0×10 6 1.0×10 7

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL 8HQ+E2-9
8HQ+E2-6

E 2 control

Log Concentration (M)VTG mRNA copies/400 ng total RNA pH of Slice Media 8-Hydroxyquinoline Sulphate

6.6 6.8 7.0 7.2 7.4 7.6 7.8 8.0 8.2

-5 -4 -3 -2 Expt. 0524 Expt. 0907 Expt. 0927

CTRL Concentration (log M)pH of Slice Media



### Figure 7.4.9: Effect of 8-hydroxyquinoline in trout liver slice Vtg assay. The left panel shows the assay run in agonist mode and no evidence of Vtg mRNA activation is present. In the middle panel the assay was run in antagonist mode with two different concentrations of E2 (- 6 and

- 9logM) with graded concentrations of 8HQ.

7.4.3 Strength of Evidence for Inactive Parent but Active Metabolite 1723



### As described in section III, placing assays within the context of an AOP framework provides greater 1724 

### confidence for making the linkage between the activity of the chemical and the molecular initiating 1725 

### event used to develop the structure- activity relationships. Whether the assay endpoint is measuring at the 1726 

### molecular initiating event (MIE), or further downstream such as a gene activation response downstream 1727 

### from a receptor binding MIE, having confidence that the activity is due to parent chemical and not an 1728 

### active metabolite structure interacting at the receptor is critical for establishing sound predictive 1729 

### chemical categories based on these structure -activity relationships. The following two e xamples 1730 

### illustrate the types of assay responses that can be indicative of metabolism and the level of information 1731 

### that can inform whether a parent chemical is called active. 1732



### Example 1. The Benzotriazole Phenols. This is another group classified as Non-Binders in the ES TrSet. 1733 

### The chemical 2

- (2 ́ -hydroxy- 5 ́ -methylphenyl)benzotriazole produced no sign of binding in the rtER 1734 

### binding assay, no binding in the bovine or human HTP assays, but it gave nearly a 70% displac ement 1735 

### curve in the HTP mouse ER-alpha LBD assay (Figure 7.4.10). 1736



### This chemical was not tested in the trout liver slice vitellogenin mRNA induction assay because of the 1737 

### flatline response in the rtER binding assay and further evidence for this class as inact ive by the flatline 1738 

### rtER binding curve of 2-(2'- hydroxy

- 5' -t- octylphenyl)benzotriazole, a structurally similar benzotriazole 1739 

### phenol in the ES TrSet (Figure 7.4.11b). In addition, this t-octylphenyl substituted benzotriazole was 1740 

### also Inactive in all five of the HTP assays, further supporting this as an Inactive group. 1741

### Prioritization of the EDSP Universe of Chemicals

December 2012 114 | Page Figure 7.4.10: Activity of 2- (2 ́ -Hydroxy-5 ́- methylphenyl)benzotriazole in binding assays (A) rtER, (B) HTP bER, (C) HTP hER, (D) HTP mERa.

Figure 7.4.11: Structure comparison of two Ben zotriazole Phenols in ES TrSet.

The methylphenyl benzotriazole was Inactive in the BLA assay in three replicate experiments, but gave 1742 

### a response equivalent in efficacy to estradiol in the BG1 assay in three replicate experiments (Figure 1743 

### 7.4.12). 1744 2-(2-Hydroxy-5-methylphenyl)benzotriazole rtER Competitive Binding

-11 -10 -9 -8 -7 -6 -5 -4 -3 -2 0 10 20 30 40 50 60 70 80 90 100 110 120

E2

Competitor (M)Specific Binding (%) 

## A B

C D

### Prioritization of the EDSP Universe of Chemicals

December 2012 115 | Page Figur e 7.4.12: Activity of - (2 ́ -Hydroxy-5 ́-methylphenyl)benzotriazole in the BG1 agonist assay showing strong reproducible Activity in three replicate experiments each with chemical from a different source library.

When considering the results across all of the HTP assays being compared, this chemical gives an 1745 

### equivocal signal. The lack of binding data in three of the binding assays, the shallow slope of the 1746 

### binding curve in the mERa binding assay and the strong indication of activity in the BG1 assay might 1747 

### suggest that the activity is due to metabolic activation. The BG1 responses were obtained from three 1748 

### different chemical sources, thus chemical purity is likely not the cause of discrepancy with BLA. The 1749 

### evidence shown above in the binding assays strongly su ggests that the parent chemical is not an ER 1750 

### binder. Metabolic activation should be investigated as a source of discrepancy in activity to verify 1751 

### whether the chemical structure of the parent chemical is responsible for this activity. 1752



### Example 2. Methyltesto sterone . Methyltestosterone was classified as a nonbinder Mixed Phenol in the 1753 

### ES TrSet while its metabolite methylestradiol was a binder. Methyltestosterone was classified as active 1754 

### exhibiting partial displacement of E2 in the hER binding assay using cell homogenates (Figure 7.4.13), 1755 

### but negative in the NVS bER and NVS mERa. 1756



### Figure 7.4.13: HTP binding curves for Methyltestosterone. Binding curve on the left is the hER assay showing nearly 50% displacement. The bER (middle panel) and mERa (right panel)

Methyltestosterone was very active in both the BG1 and BLA assays, giving activation responses near 1757 

### that of the E2 max in the three replicate runs with the BG1 assay and from 40-60% of E2 max in all 1758 

### three replicate runs of the BLA assay (Figure 7.4.14). This chemical is known to be activated via 1759 

### aromatase to the more potent 17

- methylestradiol, thus the activity in the metabolically competent cell

1760 

### based assays is not surprising. 1761



### The panels below show examples of one of three replicates of each of the BG1 assay and BLA assay 1762 

### results for methyltestosterone showing significant activity. 1763

### Prioritization of the EDSP Universe of Chemicals

December 2012 116 | Page Figure 7.4.14: Panels showing examples of one of the three replicate runs of the BG1 assay (left panel) and BLA assay (right panel) for methyltestosterone. Both assays showed significant activity.

Methyltestosterone (MT) was tested in rtER binding assay in four replicate runs and gave minimal 1764 

### displacement of 3 

### H-E2 from the receptor so was classified as inactive (Figure 7.4.15). Although the 1765 

### parent chemical is inactive, this chemical is metabolized in fish to 17

- methylestradiol which has far 1766 

### greater affinity for the receptor than MT (RBA = 68.3% in fathead minnow; Hornung et al . 2004). The 1767 

### formation of this active metabolite in the fish was verified by analytical chemistry to establish the 1768 

### mechanism for this activity. No Vtg slice assays were conducted as it was determined that enough 1769 

### information was available to be confident that slice assays would be positive but Vtg induction would be 1770 

### due to the 17-methylestradiol produced from MT and not to direct ER binding and activation by MT. 1771 

### Methyltestosterone was classified as a Non

- binder in the ES TrSet while its metabolite 1772 

### 17- methylestradiol is classified as a Binder. 1773

Methyltestosterone rtER Binding

-11 -10 -9 -8 -7 -6 -5 -4 -3 -2 0 20 40 60 80 100 120

E2 MT

Competitor (M)SpecificBinding (%)



### Figure 7.4.15: Methyltestosterone in rtER binding assay showing a maximum of 30% displacement of 3 

### H

- E2 from the receptor at the highest test concentration.

7.4.4. Potential Assay Signal Artifacts in the Context of Other Evidence 1774



### In this final example, id entifying potential artifacts based upon the detection endpoint of the assay can 1775 

### influence whether a chemical is determined to be active. The potential for fluorescence based assays to 1776 

### show flare responses has been described earlier in section 4, but wheth er these are detected or not, other 1777 

### indicators may be suggestive that some signal artifact is occurring. Looking at responses of the 1778

### Prioritization of the EDSP Universe of Chemicals

December 2012 117 | Page fluorescence assays in the context of the strength of the information from other assays can also help to 1779 

### determine the confidence in interpreting the results of the assay for a given chemical. 1780



### n- Dodecylgallate was positive in the mER -alpha LBD binding assay and the BLA assay (Figure 7.4.16- 1781 

### 7.4.18). This chemical was conducted in single replicate in the NVS ER assays. The NVS bER and hER 1782 

### assays showed flatline binding curves with no indication of binding, whereas the NVS mERa assays 1783 

### showed about 50% displacement leading to a call of Active in this assay. Due to the limited number of 1784 

### control E2 concentrations in the NVS mERa assay it does not allow for generation of a full dose 1785 

### response curve for E2; thus the shape of the apparent binding curve for the test chemical cannot be 1786 

### compared to that of E2. 1787



### Figure 7.4.16: HTP binding curves for dodecylgallate.
bER is on the left, mERa is shown in the middle panel, and the hER binding curve showing no binding is on the right.

In the HTP TA assays (see Figure 7.4.11), the BLA assay showed a single significant point above the hit 1788 

### call threshold). The BLA assay is a fluorescence based assay so this spike in response at the high test 1789 

### concentration may indicate a fluorescence interference or other signal based artifact, especially as these 1790 

### responses at this high concentration appear nearly identical between the two repli cates. The bottom two 1791 

### panels below show the two replicate runs of dodecylgallate in the BG1 assay showing no response and a 1792 

### reproducible drop-off of background activity above 1e- 6 M which might be indicative of toxicity; 1793 

### although the cytotoxicy data points (yellow symbols) do not indicate toxicity at these concentrations. 1794

### Prioritization of the EDSP Universe of Chemicals

December 2012 118 | Page Figure 7.4.17: HTP TA assays run for dodecyl gallate. Two replicate runs in the BLA assay in the top two panels and two replicate runs in the BG1 assay in the bottom two panels. In the trout binding assay, there is some apparent displacement of E2 from the receptor, but this is 1795 

### confounded by significant precipitation occurring at those concentrations (Figure 7.4.11; top left). 1796 

### Ignoring those concentrations at which precipitation was evident, the displacement of 3 

### H-E2 from the 1797 

### receptor was 10

- 30% at -4logM. In the trout liver slice assay, there was no indication of Vtg mRNA 1798 

### induction in the two replicate runs (Figure 7.4.12; two bottom figures), thus, taken together, this 1799 

### evidence cl early supported calling dodecylgallate as a Non -binder in the ES TrSet. 1800

### Prioritization of the EDSP Universe of Chemicals

December 2012 119 | Page 0 10 20 30 40 50 60 70 80 90 100

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL E2 Dodecylgallate rtERBinding ppt

Log Concentration (M)[3H]-E2Binding (%) Solubility of Dodecylgallate in Cytosol

0 10

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 100 200 300 400

Control-0h EtOH DDG-0h

Control-20h EtOH DDG-20h

H2O blank Ctrl

Log Concentration (M)Solubility (Nephelometry Values)

Dodecylgallate trout Vtg mRNA PCR 3.4.8

1.0×10 3 1.0×10 4 1.0×10 5 1.0×10 6 1.0×10 7 1.0×10 8

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL DDG
E 2 control

Log Concentration (M)VTG mRNA copies/400 ng total RNA Dodecylgallate: trout Vtg mRNA PCR 4.15.8

1.0×10 4 1.0×10 5 1.0×10 6 1.0×10 7 1.0×10 8 1.0×10 9

-10 -9 -8 -7 -6 -5 -4 -3 -2 CTRL DDG
E 2 control

Log Concentration (M)VTG mRNA copies/400 ng total RNA



### Figure 7.4.18: Dodecylgallate responses in ES TrSet assays. Top left shows rtER binding results with vertical dashed line indicating where ppt formation occurred as shown by nephelometry in the upper right panel. The two lower panels show replicate runs in agonist mode showing no significant Vtg mRNA induction.

The co mbined interpretation of the HTP and ES TrSet assays would suggest that this is likely an 1801 

### Inactive chemical. Because the NVS binding assays were conducted in limited replication in these runs 1802 

### it does not provide the strongest evidence that this chemical is an ER active and before calling the spiked 1803 

### response in the BLA assay as an active, it should be further investigated to make sure this is not due to 1804 

### some fluorescent interference or signal artifact. 1805



### 7.5 Findings: ES TrSet and HTP Binding and TA Assays 1806



### In general, all of the data gathered to make a determination of whether to call a chemical active or 1807 

### inactive needs to be done by keeping in mind all of the data obtained about the chemical and the assay, 1808 

### and interpreted with information about the activity of the chemical group that the chemical is in or the 1809 

### activity of similar structural neighbors. When evaluating discordant calls between ES TrSet and HTP 1810 

### assay data it is important to consider all reasons for discordance. Many of the factors associated with 1811 

### discordance in the examples were related to the activity of the chemical in one way or another. These 1812 

### types of factors include charge effects and pH effects, which may give false positive. The potential for 1813 

### the chemical to produce cytotoxicity needs to be taken into account to understand the lack of activity of 1814

### Prioritization of the EDSP Universe of Chemicals

December 2012 120 | Page an apparent binder. Discordance between assay responses can also be due to metabolism, and here it is 1815 

### important to understand if the activity or lack of activity is due to the parent chemical or like ly to be a 1816 

### metabolite. In the context of building a category rule -based approach it is important to understand if the 1817 

### activity is attributed to a metabolite. If so, then additional information to confirm the formation of the 1818 

### metabolite in the system under study is warranted. Interpretation of any disparate results between assays 1819 

### also needs to be done in the context of the concentration of chemical in each of the assays. The HTP 1820 

### assays can raise the potential of positives, but should be followed up with some additional types of tests. 1821 

### Thus, where possible, information should be gathered to understand differences specific to components 1822 

### of each assay, such as those that may affect the free concentration of chemical available to interact with 1823 

### the intended biolog ical target. 1824



### A preliminary analysis of the HTP assays and the ES TrSet assays was conducted to enable an 1825 

### understanding of HTP results for consideration in expanding the ES. 1826



### The major findings relative to Goal 5 (If and how HTP data could expand the ER tr aining set to cover 1827 

### the UnkBP chemicals) are as follows: 1828



### Part 1. The data from the HTP assays as conducted have some strengths but also some limitations for 1829 

### expansion of the ES. 1830



### HTP assays have the ability to test larger numbers of chemicals in a relativel y short amount of time. 1831 

### Utilizing ER assays with different means of signal detection can help indicate when a test chemical 1832 

### might be interfering with the detection endpoint and yielding erroneous results, ( e.g. , BG1 and BLA ER 1833 

### transactivation assays that u tilize luciferase and fluorescence readouts, respectively). For instance, an 1834 

### autofluorescent chemical giving a misread in the BLA assays should not erroneously affect the BG1 1835 

### luciferase activity. Controls to detect chemical interferences at assay readout wavelengths are helpful to 1836 

### detect the potential for misreads. 1837



### In the subset of HTP assays examined here there was a consistent lack of detection of chemicals that 1838 

### have low but significant activity in the ES TrSet assays, across a variety of chemicals class es. This 1839 

### increases uncertainty around the degree of confidence that can be placed in identifying the lowest 1840 

### affinity binders as well as the degree of confidence that an inactive call is an inactive, and not a missed 1841 

### low affinity active. 1842



### Another uncertainty relates to the lack of replication within assays, especially in the situation where only 1843 

### one measurement point at the highest concentration tested indicates activity. Confirmation of the 1844 

### response within an assay through replicate agreement, whether active or inactive, provides greater 1845 

### confidence in the result. Replication is important in resolving discordant results among assays. 1846



### Part 2. An important programmatic goal of the EDSP prioritization is to have confidence that "inactive" 1847 

### is truly negative and t o identify potential active chemicals that trigger the molecular initiating event. The 1848 

### following are areas that deserve attention during data interpretation (discussed also in Sections 5 and 6): 1849



### • Assay Sensitivity: As an initial step for prioritizing chemi cals is to determine whether they have 1850 

### potential to initiate or trigger the AOP , the sensitivity of an assay for weak responses should be 1851 

### optimized to reduce the likelihood that chemicals with any potential for activity will be missed in 1852 

### the analysis. Thus, the assays need to be optimized to detect low affinity or low potency 1853 

### chemicals as was in the case of the ES TrSet assays. 1854

### Prioritization of the EDSP Universe of Chemicals

December 2012 121 | Page • Flare Effects: Flare effects were found with the HTP results and are likely to compromise the 1855 

### detection of the true result of adjacent wells or wells in close proximity. This could potentially be 1856 

### resolved by greater spatial randomization of treatment wells across multiple plates. Alternatively, 1857 

### where serious and obvious flare effects occur on large portions of plates, the data from those 1858 

### plates should be discarded to avoid any potential for carrying undetected flare effects forward in 1859 

### the data analysis. 1860



### • Replication: Replication was an issue with the HTP assays, especially when interpreting the 1861 

### weaker responses. For example, where one data point for the highest concentration measured 1862 

### within the assay could potentially change the overall 'activity call', the lack of any replication 1863 

### causes uncertainty in the interpretation. The uncertainty is magnified where discordant responses 1864



### were found among assays (among HTP assays, or HTP in comparison with ES TrSet assays) or 1865 

### in instances where the apparent 'activity call' was suspect based on measured results for other 1866 

### members of a chemical group. Conducting replication in a completely separate run would help to 1867 

### resolve the uncertainty and increase confidence in each individual assay call. Allowing for data 1868 

### inspection and interpretation between replicate runs provides an opportunity to detect and correct 1869 

### for other potential problems ( e.g. , flares, dose ranges, etc). 1870



### • Single Concentration Pre-screening: Examples provided in the section 7.2 demonstrated the 1871 

### potential for inaccurate assay calls using the single concentration pre-screen of 25 μM in HTP 1872 

### ER binding assays. Based on the number of prescr een hits that were later negative in full dose

1873 

### response mode, and the number of prescreen negatives that would have been binders if run in full 1874 

### dose-response mode would argue that the prescreening procedure be reassessed for 1875 

### improvements or that running in a full dose-response mode would be more appropriate for a 1876 

### prioritization approach. An initial dose-response followed by interpretation and resetting of the 1877 

### dose range for a replicate dose -response run is recommended. 1878



### • Positive Controls. Positive controls, specifically a full E2 standard curve, should be run with 1879 

### each assay. This would give greater confidence in the performance of the assays, especially the 1880 

### binding assays, where the three point estradiol controls limited data interpretation. 1881



### We have shown a preliminary assessment of the potential for HTP assay data to expand the ES using a 1882 

### subset of the HTP and ES TrSet assays across 296 chemicals. Notable is that the non- coherence is 1883 

### observed in both directions – some chemicals are active in the low -throughput assays but in none of the 1884 

### HTP assays, while some chemicals are active in multiple HTP assays, but not in the low -throughput 1885 

### ones. A more comprehensive comparison of these assay sets is already underway that includes all data 1886 

### from the HTP assays. We full y expect that there will remain some chemicals for which the two assay 1887 

### sets disagree, and by following up on those, we can help improve both the HTP assays and the 1888 

### predictivity of the ES. 1889



### Next steps may include the following specific analyses: 1890



### 1. Chemical

- by

- chemical comparisons of all discrepant chemicals across all assays to see if there are 1891 

### chemical

- structure trends that can help explain discrepancies. 1892 

### 2. Experimental follow -up, including reordering chemical samples for some of the discrepant 1893 

### chemicals and ru nning them in either the current assays or variants that can act as confirmatory. For 1894 

### instance, some of the HTP(+)/ES( -) chemicals could be run in a mode that can be used to confirm 1895 

### whether activity is due to binding to the ER binding pocket rather than th rough another mechanism. 1896

### Prioritization of the EDSP Universe of Chemicals

December 2012 122 | Page Section 8 Category- Based Approach and Strategic Testing to Cover Unknowns 1897



### A testing approach incorporating the principles used in the category-based approach for building the 1898 

### original ES to cover the FI and AM inventories, and then used to expand coverage to the larger NF 1899 

### inventory, can be applied again to cover the remaining EDSP Universe unknowns after application of 1900 

### ESv2. The 'strategic testing' strategy presented in this section illustrates an approach that is relevant to 1901



### any in vitro assay data that is applicable for measuring the endpoint under study. This specific example 1902 

### utilizes information on the chemicals class and LogK ow 

### ranges within classes where HTP data are 1903 

### available to demonstrate the concept that a minimal amount of chemical testing (~100 -200 chemicals) if 1904 

### done in a 'strategic testing' chemical category -based approach can cover the remaining 1,700 unknowns 1905 

### avoiding the need to test ~1, 000 remaining chemicals for which no training set data are available. 1906



### 8.1 HTP coverage of the 29% ESv2 UNKs 1907



### There were 1 ,704 inventory chemicals of the 5 ,975 that were outside of the ESv2 expanded domain, and 1908 

### thus, assigned UnkBP . Table 8.1.1 shows the 1,704 chemicals distributed across ESv2 groups, with the 1909 

### chemical group names shown ( column 1), the total number of chemicals in the group (column 2), the % 1910 

### of 1 ,704 unknowns in the group (column 3). The table also shows (column 4) what % of all chemicals in 1911 

### a group (% of column 2 chemicals) that have HTP data available, and whether there is HTP for eight 1912



### assay s (column 5) of just the 2 TA assays (column 6). The remaining columns in Table 8.1.1 (columns 7 1913 

### & 8) show the %, and number, respectively, of chemicals in that group that do not any HTP data 1914 

### available. 1915



### Overall, HTP data was found for 44% or 744 chemicals (sum of columns 5 & 6 in Table 8.1.1) of the 1916 

### total 1 ,704 UnkBP. Further, assessment of the 744 chemicals shows that 509 chemicals (68%) have HTP 1917 

### data for the 2 NCGC ERTA assays, while 235 more UNKs have HTP data for all eight ER assays. As 1918 

### mentioned in a previous section, the vast majority of these UnkBP chem icals are 'Mixed Organics' 1919 

### (1 ,368 chemicals; 80%) and another 133 (8%) are 'Mixed Phenols'. The next largest UNK grouping is 1920 

### 'MultiCyclic Hydrocarbons' with 67 chemicals or 4% of all the UNKs. The remaining chemical UNKs 1921 

### are from 21 ES chemical groups with 1 to 27 chemicals found within a single group (Table 8.1.1). The 1922 

### HTP data coverage of the large MO class is 43%, with 41% coverage of the MP class, and 69% of 67 1923 

### MultiCyclic Hydrocarbons with HTP data available from 2 or eight HTP ER binding and ERTA assa ys. 1924 

### The HTP data coverage for the entire EDSP inventory is 44% overall consistent with the coverage of 1925 

### MO and MP groups. 1926

### Prioritization of the EDSP Universe of Chemicals

December 2012 123 | Page Table 8.1.1: Distribution of 1,704 chemicals of UnkBP (from t he 5 ,975 EDSP inventory prior to final physical- chemical property 1927 

### filtering) across ESv2 chemical groups as defined in the decision tree (Figure3 -3). 1928 

### The total number of chemicals in a class and % of 1 ,704 Total UNKs is listed, as well as number and % of chemicals within a class with 1929 

### either TC&T21 ( eight assay s), 2 HTP assays (T21 only), or no HTP. Mixed Organics represent 80% of UNKs, and Mixed Phenols are 1930 

### 8%. Of the UnkBP chemicals, 744 (44%) have HTP data available, with 235 (32%) of chems with HTP havi ng data from eight assay s (3 1931 

### binding, 5 TA), and 509 (68%) of chems with HTP having data from 2 (T21) ERTA assays. 1932



### Chem Class # chem % of

1704 HTP
% chems TC&T21
# chems T21 only
# chems no HTP

%
chems #
chems I. Acyclic_Perfluoro Acyclic Perfluoro (Unknown Binding Potential) 16 0.9 6 0 1 94 15 III. Belongs to a known Inactive class

Mono

- cyclic Hydrocarbons [LogK ow 

### <2.5,>6.4] 13 0.8 31 2 2 69 9 Triazines [LogK ow 

### >4.1] 9 0.5 22 1 1 78 7 Benzoates (non

- ring Subst) [LogK ow 

### <3.0,>4.3] 6 0.4 67 1 3 33 2 Sorbitans [LogK ow 

### <3.1,>5.9] 4 0.2 25 0 1 75 3 Imidazolidines [LogK ow 

### >0] 3 0.2 67 2 0 33 1 Benzamides [LogK ow 

### <2.1,>2.8] 1 0.1 0 0 0 100 1

Bis

- Anilines [LogK ow 

### <2.1,>6.4] 1 0.1 0 0 0 100 1 Phenones (n

- alkyl) [LogK ow 

### >4.2] 1 0.1 100 0 1 0 0 Pyrrolidiones [LogK ow 

### >3.4] 1 0.1 100 1 0 0 0
III. Known Inactives Subtotal= 39 2.3 38 7 8 62 24 V. Special Rule applies Multi Cyclic Hydrocarbons Unknown Binding Potential 67 3.9 69 21 25 31 21 Thiophosphate Esters Unknown Binding Potential 14 0.8 71 3 7 29 4 Alkylbromobenzene 7 0.4 29 0 2 71 5 V. Special Rules Subtotal= 88 5.2 66 24 34 34 30 VI. Site "A": Contains a Phenol fragment

Mixed Phenol - Unknown Binding Potential 133 7.8 41 19 36 59 78 Alcohols (LogK ow 

### >3.4) 27 1.6 11 2 1 89 24 Alkoxyphenols [LogK ow 

### >4.1] 1 0.1 100 1 0 0 0
VI. Site "A" Subtotal= 161 9.4 37 22 37 63 102

### Prioritization of the EDSP Universe of Chemicals

December 2012 124 | Page Chem Class # chem % of

1704 HTP
% chems TC&T21
# chems T21 only
# chems no HTP

%
chems #
chems VII. Site "B": Contains "Specified" fragment Mixed Organics - Unknown Binding Potential 1368 80.3

2- Alkylanilines 7 0.4 43 175 414 57 779

Mixed Acetanilides (LogK ow 

### >1.3) 7 0.4 86 4 2 14 1 Phenones cyclo

- substituted - Unknown Binding
Potential 6 0.4 29 0 2 71 5

Multi subst. Alkylanilines 5 0.3 67 0 4 33 2 Alkylcyclohexanols LogK ow 

### >4.4 or >2.6 and <3.5 4 0.2 100 1 4 0 0

3- Alkylanilines 2 0.1 50 1 1 50 2

2- ;4

- ; or2,4,6

- Benzoates Unknown Binding Potential 1 0.1 100 1 1 0 0
VII. Site "B" Subtotal= 1400 82.2 100 0 1 0 0 Unknown Binding Potential Total= 1704 44 235 509 56 960

### Prioritization of the EDSP Universe of Chemicals

December 2012 125 | Page An additional assessment beyond just the exact number of UnkBP chemicals for which HTP data are 1933 

### available is warranted. Employing the ES conceptual approach, it is appropriate to assess where HTP 1934 

### test data occurs with respect to th e total number of inventory chemicals within a chemical class, and 1935 

### with respect to the LogK ow 

### range represented. This further analysis also results in a proposal for 1936 

### strategic testing to reduce the number of UnkBP without testing all of them. The approach consisted of 1937 

### two parts: 1938



### Part 1: A systematic approach similar to that used to build the initial ES can be taken for UnkBP 1939 

### chemicals that are classified in ES chemicals groups. 1940



### Part 2: A separate approach is needed for chemicals in the two large mixed functional group classes 1941 

### (MP and MO). 1942



### Testing Proposal: Part 1_Grouped Chemicals . For those chemicals that could be assigned to ESv2 1943 

### chemical groups, an assessment was done of the available HTP data, what type of data, i.e.

, eight assay s 1944 

### from the ToxCast and t he Tox21 research programs described in section 4 or 2 assays from the Tox21 1945 

### only, and where the data o ccurred with respect to the Log K ow 

### range of all chemicals in the class (Table 1946 

### 8.1.2). Table 8.1.2 includes columns noting if further testing is needed, t he LogK ow 

### range not well 1947 

### covered, and the number of chemicals recommended to test to cover the class. This approach uses the 1948 

### mechanistic rationale that RBA is correlated with LogK ow 

### across ES chemical classes. For the purpose 1949 

### of this exercise, it was assum ed that any HTP ER binding or ERTA data available (whether only for the 1950 

### two NCGC ERTA assays (Tox21 only') or for all 8 ToxCast and Tox21' assays were appropriate to 1951 

### assess the ER binding potential of all chemicals regardless of the ES chemical group. Some insight into 1952 

### that assumption is presented in section s 6 and 7 of this document. There are 203 total UnkBP chemicals 1953 

### in Table 8.1.2, with 41 having 'TC & T21' HTP data, and 77 with 'T21 only' data, leaving 126 more 1954 

### needing information. All 126 could be tested, or, based on where the HTP data occurs with respect to the 1955 

### total Log Kow range of a chemical class, using the ES concepts described in previous sections the 1956 

### number of additional chemicals recommended for testing to expand the ES to cover all 203 is 35 1957 

### chemicals. 1958



### Testing Proposal: Part 2_Defining new chemical classes within Mixed Organics and Mixed Phenols. 1959 

### The second approach examined the chemical structures represented in mixed g roups (MO and MPs) 1960 

### representing 88% of all UNKS remaining after application of ESv2 to the EDSP inventory. An 1961 

### inspection of chemicals structures was done to search for common functional groups which will serve as 1962 

### the basis of new chemical classes. Structur al fragments for 16 proposed new classes were found (Figure 1963 

### 8.1.1). The approach is consistent with the iterative approach used to build the ESv1 and the expansion 1964 

### to ESv2 described in section 3.1c. 1965

### Prioritization of the EDSP Universe of Chemicals

December 2012 126 | Page Table 8.1.2. Summary of UNK chemicals within ESv2 chemicals classes, LogK ow 

### ranges, number of chemicals with HTP ToxCast 1966 

### or Tox21 data and LogK ow 

### range covered, and recommendations for additional number LogK ow 

### ranges to test. Recommendations 1967 

### to test are based on assuming all available HTP data is acceptable for the respective chemical class where available. Mixed 1968 

### Organics and Mixed Phenols are excluded. Tox21*= total of ToxCast&Tox21 plus Tox21 only to assess total LogP range covered 1969 

### by Tox21 ERTA assays. 1970

Total Class HTP HTP Testing needed or recommend

ESv2 Chemical Class #
chem LogP

range ToxCast &

Tox21 # chems LogP

range covered Tox21*#

chems LogP range
covered HTP

coverage Adequate? LogP range

to test # chems to

test I. Acyclic_Perfluoro Acyclic Perfluoro UnkBP 16

- 2.48

- 9.98 0 1 6.02 test to
bound

range - 2.48

- 4.99;

>6.02

- 9.98 5 to ?

III. Belongs to a known Inactive class

Mono- cyclic Hydrocarbons [ LogK ow <2.5,>6.4] 13 1.99

- 2.47;

6.83

- 9.42 2 2.25;7.94 2 1.99

- 2.25;7.38

- 7.94 -- 2.47;6.83

- 7.38;

7.94

- 9.42 3 to 4

Triazines [ LogK ow >4.1] 9 5.12

- 36.54 1 5.27 2 5.12

- 5.27

-- >5.27

- 36.54 3 to 4

Benzoates (non

- ring Subst)[ LogK ow <3.0,>4.3] 6 1.83

- 2.74;

4.30

- 5.93 1 2.32 4 1.83

- 2.74;5.84 -- 4.30;5.93 2

Sorbitans [ LogK ow <3.1,>5.9] 4 5.39

- 22.35 0

-- 1 6.10

-- 5.39

- <6.10;>6.10

- 22.35 3

Imidazolidines [ LogK ow >0] 3 0.15

- 2.16 2 0.22

- 2.16 2 0.22

- 2.16

-- 0.15 1 Benzamides [ LogK ow <2.1,>2.8] 1 1.71 0

-- 0

-- -- 1.71 1

Bis

- Anilines [ LogK ow <2.1,>6.4] 1 8.81 0

-- 0

-- -- 8.81 1 Phenones (n

- alkyl)[LogK
ow >4.2] 1 4.68 0

-- 1 4.68 T21
covered -- --

Pyrrolidiones [ LogK ow >3.4] 1 5.30 1 5.30 1 5.30 TC&T21
covered -- --

III. Known Inactives Subtotal= 39 7 13 V. Special Rule applies Multi Cyclic Hydrocarbons UnkBP 67 3.16

- 17.62 21 3.16

- 6.70 25 3.16

- 17.62 T21
covered -- --

Thiophosphate Esters UnkBP 14 0.72

- 6.47 3 4.08

- 5.65 10 1.99

- 5.65

-- 0.72;6.47 2 Alkylbromobenzene 7 2.88

- 7.48 0

-- 2 2.88

- 3.43

-- 3.43

- 7.48 2 to 3 V. Special Rules Subtotal= 88 24 37 VI. Site "A": Contains a Phenol fragment

### Prioritization of the EDSP Universe of Chemicals

December 2012 127 | Page Total Class HTP HTP Testing needed or recommend

ESv2 Chemical Class #
chem LogP

range ToxCast &

Tox21 # chems LogP

range covered Tox21*#

chems LogP range
covered HTP

coverage Adequate? LogP range

to test # chems to

test Alcohols ( LogK ow >3.4) 27 3.42

- 7.62 2 5.63

- 7.62 3 3.46

- 7.62

-- 3.42

- 5.62 2 to 4 Alkoxyphenols [ LogK ow >4.1] 1 1.34 1 1.34 1 1.34 TC&T21
covered -- --

VI. Site "A" Subtotal= 28 3 4
VII. Site "B": Contains "Specified" fragment

2- Alkylanilines 7 1.62

- 3.99 4 1.62

- 3.15 6 1.62

- 3.99 TC&T21
covered -- --

3- Alkylanilines 2 1.62

- 2.17 1 1.62 2 1.62

- 2.17 T21
covered -- --

Multi subst. Alkylanilines 5 2.17 but

multi

sites 1 2.17 dimethyl

only 5 2.17 all

- sites
tested T21
covered -- --

Mixed Acetanilides ( LogK ow >1.3) 7 0.01

- 4.44 0

-- 2 0.89

- 2.05

-- 2.05

- 4.44 1 to 2 Phenones cyclo

- substituted

- UnkBP 6 2.60

- 4.73 0 4 2.60

- 4.53

-- 4.53

- 4.73 1 Alkylcyclohexanols LogK
ow >4.4 or

>2.6 & <3.5 4 2.96

- 3.42;5.0

- 5.4 1 3.42 2 2.96

- 3.42

-- 5.0

- 5.4 2

2- ;4

- ; or2,4,6

- Benzoates UnkBP 1 2.38 0

-- 1 2.38 T21
covered -- --

VII. Site "B" Subtotal= 32 7 22 Unk Binding Potential Total= 203 41 77 Max # chems to test= 35

1971

### Prioritization of the EDSP Universe of Chemicals

December 2012 128 | Page The analogs shown earlier in Figures 3-12A to 3- 12F ( section 3.1c) were chosen to better cover the 2009 1972 

### NF list ( section 3.1c) and will be combined with the proposed new classes resulting from the application 1973 

### of ESv2 to the EDSP inventory (Figure 8.1.1) to provide the basis for future expansion of the ESv2 to 1974 

### ESv3. The expansion to ESv2 to cover the NF 1, 423 list is relevan t to increased coverage of the EDSP 1975 

### inventory because 1 ,205 of the 1,423 NF structures are also part of the EDSP inventory. As described 1976 

### previously in this paper, the EDSP inventory is comprised of Inerts (FI and NF, and Frag), and SDWA 1977 

### chemicals. The anal ogs in the figure from the section 3 (Figure 3 -12A to 3-12F) primarily covered FI 1978 

### and NF Inerts, while the proposed new classes (Figure 8.1.1), cover predominately SDWA chemicals 1979 

### and Fragrances. Thus they should complement each other for a proposed ESv3 expansion to cover a 1980 

### greater portion of the EDSP inventory . A testing strategy to efficiently generate training set data and 1981 

### accomplish this is further described below. 1982



### Figure 8.1.1: Structural fragments common to multiple chemicals classified as Mixed Or ganics or Mixed Phenols when ESv2 was applied to EDSP inventory. These fragments provide the basis of new chemical groupings for development of ER ESv3.

The 16 new chemical groups proposed for ESv3 expansion to cover additional Frag and SDWA 1983 

### chemicals ar e listed in Table 8.1.3. Classes composed of a majority of Frag chemicals include 1984 

### aldehydes, ketones, carboxy esters, cyclohexanols and cyclohexanones, with several in the Pyridines and 1985 

### Quinolines group. Not surprisingly, the most number of UnkBP chemicals are found in the Frag classes 1986 

### given that the ES was not built to cover these classes and only 48 % of Fragrances overall were covered 1987 

### by ESv2. Classes composed entirely or largely of SDWA chemicals include anthraquinones, 1988 

### benzothiazoles, chlorinated cyclic hydrocarbons, halobenzenes, halophenols, isocyanates, 1989 

### nitroaromatics, nitrophenols, siloxanes, and sulfonamides. 1990

### Prioritization of the EDSP Universe of Chemicals

December 2012 129 | Page The search queries defined for the 16 new groups proposed for ESv3 expansion (Figure 8.1.1) were 1991 

### applied to the 5,975 EDSP inventory to assess the number of chemicals that currently have information 1992 

### available in the ES TrSet or would be covered as new testing is done within the 16 groups as 1993 

### recommended in this section . Table 8.1.3 shows how many chemicals are found within a new group 1994 

### (sum of co lumns 2, 3, & 4). It also includes information on where predictions can currently be made, 1995 

### i.e. , number of chemicals predicted to RBA<0.00001% (Nonbinders) and RBA>0.00001% (Binders) 1996 

### within each class based on current TrSet data incorporated into ESv2. For example, for Ketones there 1997 

### are five chemicals falling within this class that were tested; one Non

- binder and four Binders, and 1998 

### another 49 UnkBP. These results would indicate the ketones are a class of concern since there are 1999 

### several Binders found in the group. Strategic testing of a few more representatives structures from 2000 

### within the 49 UnkBP chemicals, should facilitate establishment of additional logic rules for predictionof 2001 

### activity within the class. Other groups of higher priority due to presence of Binders would be aldehydes, 2002 

### cyclohexanols/cyclohexanones, and sulfonamides. A strategic testing plan that builds from where 2003 

### current TrSet data is available within a class can be developed, similar to that used in expanding the 2004 

### TrSet to allow development of ESv2. 2005



### Table 8.1.3: Number of chemicals within the 5, 975 EDSP Universe found in the 16 new groups 2006 

### proposed to expand to ESv3, and the current coverage of ESv2 within each chemical group 2007 

### displayed as the number of chemicals within the group predicted to be a Nonbinder 2008 

### (RBA<0.00001%) or a Binder (RBA>0.00001%). For example, 2 aldehydes are predicted 2009 

### Nonbinders, 1 is a predicted Binder, and 55 UnkBP are out of the model domain and cannot be 2010 

### predicted. 2011 

### Number of Chemicals

RBA<0.00001% RBA>0.00001% UnkBP Aldehydes 2 1 55 Carboxy Esters 12 0 151

Ketones 1 4 49 Cyclohexanols 2 0 32 Cyclohexanones 4 2 19 Pyridines & Quinolines 14 0 35 Anthraquinones 1 0 29
Benzothiazoles 5 0 5 Chlorinated Cyclic Hydrocarbons 0 0 18

Halo Benzenes 0 0 21
Halo Phenols 0 0 25 Isocyanates 3 0 18

Nitro Aromatics 1 0 24

Nitro Phenols 0 0 13
Siloxanes 1 0 7
Sulfonamides 19 1 9

While all five prioritized ketone chemicals were tested in the example given above, this is not to imply 2012 

### that all active or inactive chemicals in the table were tested but rather some test values were combined 2013 

### with other exclusion rules, e.g. , LogK ow 

### <1.3, to make a determination. In fact, for the 12 carboxy esters 2014 

### of RBA<0.00001%, four chemicals were tested negative and the remaining eight were below the 2015 

### LogK ow 

### <1.3 cutoff. The aldehydes, ketones, carboxy esters, cyclohexanols, and cyclohexanones are all 2016

### Prioritization of the EDSP Universe of Chemicals

December 2012 130 | Page important for expanded coverage of the ES as they predominantly make-up the contribution of Frag to 2017 

### the EDSP inventory, and if percentages hold as seen for other Frag among the chemicals within the 2018 

### model domain it might be expected that 10% of these will be Binders (Table 3 -12B). 2019



### Similar to what was done for Part 1 using defined ES groups, an assessment was done for the 16 new 2020 

### groups established on the MO and MPs to develop an approach for use of available HTP assay data 2021 

### found appropriate to each ES group (s ection 7). The number of EDSP inventory chemicals found in each 2022 

### new proposed group from Table 8.1.3 was reported along with the LogK ow 

### range covered by the 2023 

### chemicals (Table 8.1.4). The HTP data availability for chemicals within the group, and the LogK ow 2024



### ra nge of test data relative to the entire chemical group was collated. Again, the number of chemicals 2025 

### having 'ToxCast and Tox21' HTP data ( eight assay s) and the LogK ow 

### ranges covered, and the number 2026 

### with 'T21 only' (2 assays) HTP data and LogK ow 

### coverage is reported in the table. The LogK ow 

### range 2027 

### not well covered, whether testing is needed, and how many additional chemicals would likely need to be 2028 

### tested is included. As before, assuming all currently available HTP data can be used for the new 2029 

### che mical classes, then testing of 10 to 22 additional chemicals to expand the ES to cover these classes 2030 

### should allow predictions for the 506 UnkBP in Table 8.1.4, even though HTP data was available for less 2031 

### than half the chemicals ( i.e. , 69 chemicals with data from eight assays, and 229 chemicals with data 2032 

### from two to eight assays). The discussion here is meant to illustrate the approach to iterative 2033 

### development of new chemical classes and testing within those classes, but is not inclusive of all new 2034 

### classes p ossible. Additional assessments should be done of the remaining MO and MP chemicals 2035 

### looking for new chemical groupings. 2036



### The remaining 995 UnkBP chemicals in MO and MP groups that were not included in the 16 new 2037 

### proposed groups or earlier ESv2 chemical groups 65% are from the SDWA list (with some overlap with 2038 

### NF + Frag), 'Frag only', NF, FI inert classes. More could likely be assigned to classes with additional 2039 

### scrutiny, or a few chemicals may be determined not to need testing such as Vitamin K 2040 

### [CASNR = 12001-79-5] and chlorophyll a [CAS NR = 479-61-8]. Of the 995, 125 chemicals have TC& 2041 

### T21 data, and 294 more have 'T21only' HTP data, leaving 576 or 34% of all UNKs not assigned to new 2042 

### classes and with no HTP data (Table 8.1.5). These 576 UNKs are 65% from the SDWA list (with some 2043 

### overlap to Frag & NF), 27% Frag, 7% NF and <1% Food use INERTS. 2044

### Prioritization of the EDSP Universe of Chemicals

December 2012 131 | Page Table 8.1.4: Proposed new chemical groups for testing to cover 30% of UnkBP Mixed Organics and Mix ed Phenols 2045

Proposed New Chem Group Use/Occurrence

Class Total Class

# chem Total Class LogP

range HTP TC

& T21 LogP range
covered HTP T21 LogP range
covered Testing
needed or
HTP found LogP range

to test #
chems

to test Aldehydes Fragr 55 1.34

- 5.27 3 1.34

- 4.36 17 1.34

- 4.36 need testing 4.36

- 5.27 1 to 3 Carboxy Esters Fragr, some SDWA 151 1.58

- 15.51 11 1.51

- 8.63 57 1.51

- 15.51 TC&T21
covered

Ketones Fragr, few SDWA or

NF 49 1.96

- 6.35 4 2.04

- 6.35 15 1.96

- 6.35 T21 covered

Cyclohexanols (more to add) Fragr, some SDWA or

NF 28 2.85

- 10.44 4 2.85

- 10.44 11 2.85

- 10.44 TC&T21
covered Cyclohexanones (more to add) Fragr, some SDWA 20 1.54

- 5.48 1 2.94 9 2.25

- 3.81 need testing 1.54

- 2.25;

3.81

- 5.48 1 to 3

Pyridines &
Quinolines SDWA, many Fragr 35

- 0.67

- 5.95 5 1.35

- 4.04 14 1.35

- 4.04 need testing 4.09

- 5.95 1 to 3

Anthraquinones all SDWA, w/ 1 NF 29 3.16

- 9.02 1 2.43 11 2.43

- 9.02 T21 covered

Benzothiazoles all SDWA 5 2.56

- 5.95 0 4 2.56

- 5.95 T21 covered Chlorinated Cyclic Hydrocarbons all SDWA 18 3.36

- 11.27 6 4.26

- 7.35 9 4.26

- 7.35 need testing 3.36

- 3.81;

11.27 1 to 3

Halo Benzenes all SDWA 21 3.08

- 7.46 8 3.08

- 4.57 17 3.08

- 7.33 need testing 7.33

- 7.46 1

Halo Phenols all SDWA 25 1.71

- 5.96 7 2.16

- 4.18 15 1.71

- 5.96 T21 covered
Isocyanates all SDWA 18 2.59

- 6.11 2 3.7

- 4.75 12 3.06

- 6.11 need testing 2.59

- 3.7 1

Nitro Aromatics all SDWA 24 1.45

- 5.39 10 1.63

- 2.91 19 1.63

- 5.39 need testing 1.45

- 1.63 1
Nitro Phenols all SDWA 12 1.54

- 4.88 4 1.73

- 3.67 8 1.73

- 4.88 need testing 1.54

- 1.73 1

Siloxanes SDWA, some NF 7 5.71

- 14.82 2 5.71

- 6.51 2 5.71

- 6.51 need testing 6.51

- 14.82 1 to 3

Sulfonamides SDWA, some NF& F 9 1.87

- 4.33 1 2.86 3 1.87

- 2.86 need testing 2.86

- 4.33 1 to 3

Total= 506 69 223 Recommended

# chems to test= 10 to

22

### Prioritization of the EDSP Universe of Chemicals

December 2012 132 | Pa ge Table 8.1.5. Remaining Ungrouped MO and MPs UnkBP chemicals and HTP data availability. 2046 

### 166 Ungrouped MO & MP with Both TC&T21 Mixed Organics - Unknown Binding Potential 117

Mixed Phenol - Unknown Binding Potential 8
TC&T21 Total= 125 353 Ungrouped MO & MP, or ESv2 Grouped UnkBP Chems with 'T21 only' by mixed class Mixed Organics - Unknown Binding Potential 276

Mixed Phenol - Unknown Binding Potential 18 T21 only Total = 294 679 Ungrouped MO & MP or ESv2 Grouped UnkBP with no HTP Mixed Organics - Unknown Binding Potential 518

Mixed Phenol - Unknown Binding Potential 58
No HTP Total= 576

### Prioritization of the EDSP Universe of Chemicals

December 2012 133 | Pa ge Section 9 Summary and Key Goals for EDSP Prioritization 2047



### An EDSP universe of 10,341 unique chemicals (including pesticide active and inert ingredients 2048 

### including fragrances, and drinking water contaminants) was identified by the agency for sorting and 2049 

### priority setting for purposes of EDSP Tier 1 screening ( U.S. E PA , 2012). The Tier 1 battery consists of 2050 

### eleven in vitro and in vivo assays. Based on the experience with test orders issued in 2009 on 67 2051 

### chemicals, the current trajectory with the current suite of Tier 1 assays would require decades to screen 2052 

### the 10, 34 1 chemicals, requiring significant animal use and millions of dollars in testing and data 2053 

### reviews. Thus, it is important that the agency reliably discerns which of the 10,341 chemicals do not 2054 

### need to be subjected to EDSP Tier 1 screening and which ones should be screened. By fully optimizing 2055 

### the use of both new and existing tools, the agency believes it can effectively achieve this goal. 2056



### The agency plans to implement a prioritization scheme that will use both exposure and effect based 2057 

### metrics. This prioriti zation methodology draws on several fundamental concepts critical to the 2058 

### development of computational tools: 2059



### • Use of existing knowledge, including the understanding of inherent chemical properties and 2060 

### chemical structure and bioactivity relationships, 2061



### • Use of category -effects based and read across methods to maximize incorporation of existing 2062 

### knowledge and effectively enable chemical extrapolation to avoid unnecessary testing ( i.e.

, 2063 

### strategic testing regardless of whether it's in vitro or in vivo ), 2064



### • Use of the adverse outcome pathway (AOP) concept to guide the development and application of 2065 

### in vitro testing including lower or higher throughput methods with computer-based simulations, 2066 

### and 2067



### • Use of principles and practices to ensure scientific transparency, structure and rigor. 2068



### In applying these concepts, section 2 demonstrated how effective the use of physiochemical properties 2069 

### are in reducing a large inventory by removing compounds untestable or unlikely to present a systemic 2070 

### effect ( i.e. , ~ 10,000 chemical inventor y reduced to ~ 5 ,000 or less). Section 3 illustrated the use of the 2071 

### AOP construct in targeting the molecular initiating event with additional information gathered at a 2072 

### higher level of biological organization along the pathway. The expert system was develop ed based on 2073 

### measuring and understanding chemical structure - ER binding and ER- mediated gene activation 2074 

### relationships by strategically testing in vitro . Two assays are used: (i) an ER (rainbow trout) 2075 

### competitive binding assay to directly measure chemical

- biological interaction and (ii) a trout liver slice 2076 

### assay in which the consequences of ER activation or inhibition are measurable as a result of chemical

2077 

### tissue uptake and partitioning in the presence of xenobiotic metabolism. Thus, the sequential application 2078 

### of two in vitro assays along an AOP can be used to understand the consequence of different types of 2079 

### chemical -receptor interactions. This understanding was important to the efficient use of chemical 2080 

### extrapolation in addressing a large inventory for purposes of prioritization ( i.e. , greater than 70 percent 2081 

### of approximately 6,000 chemicals were prioritized). Sections 4 and 5 present the high throughput 2082 

### technology intended to expand effectively the knowledge of the chemical -receptor interactions for 2083 

### purposes of covering the remaining 1,700 chemicals that cannot be predicted by the current ER expert 2084 

### system. Sections 6 and 7 provided information on reference and training set chemicals to further the 2085 

### understanding of strengths and limitations around the HTP in vitro data. There will be uncertainties in 2086 

### any method, and it is important to characterize the nature of these uncertainties and objectively 2087 

### determine how to use appropriately the data for the intended purpose, i.e. , priority setting. 2088 

### Understanding the type and degree of uncertainty tolerated in decision

- making will help chart the 2089

### Prioritization of the EDSP Universe of Chemicals

December 2012 134 | Pa ge research and incremental application to decision making. In the case of priority setting for EDSP, it is 2090 

### important to have confidence that negative findings are likely to indicate inactivity for the endpoint 2091 

### measured/predicted. Section 8 provides a strategic testing approach based on understanding of the 2092 

### chemical structures/properties to address the 1 ,700 unknown chemicals in a chemical category-based 2093 

### approach. Rather than t esting all 1 ,700, only a fraction needs to be tested in vitro ( i.e. , ~15%) to 2094 

### understand and predict the chemical

- receptor activity. Thus, the iterative process of in vitro testing and 2095 

### chemical extrapolation lead to a more focused testing strategy. 2096



### By taki ng advantage of both existing and emerging technologies, a scientifically robust and 2097 

### comprehensive chemical prioritization process is being proposed. 2098



### While the agency has not displayed the full prioritized list of chemicals, it has demonstrated the general 2099 

### methodology and concepts in which the chemical prioritization scheme would be applied for the ER 2100 

### pathway, in sequence and chemical coverage. Dependent on the recommendations from the FIFRA SAP 2101 

### expert panel, the agency plans to exercise the following steps in its completion of the prioritization 2102



### process: 2103



### • Apply all physico

- chemical exclusion criteria, exposure filters and engage in expert chemistry 2104 

### review to ensure a "reality" check for scientific soundness and biological plausibility. Finalize 2105 

### the analysis of additional HTP assays and select a subset that are of sufficient quality and 2106 

### accuracy to use in expanding the ES. 2107



### • Expand ER expert system to version 3 to capture the remaining universe of chemicals currently 2108 

### covered by applicable HTP data. 2109



### • For those chemicals not covered by the ES domain of applicability and by HTP data, generate 2110 

### the appropriate training set data to complete coverage. 2111



### • Prior to officially advancing any chemical for EDSP Tier 1 screening, the agency must make the 2112 

### exposure finding as stated in the 1996 SDWA Amendment, section 1457 and excluding 2113 

### chemicals no longer produced or used in the United States. 2114



### As the agency has envisaged and demonstrated a prioritization process for the ER pathway, the agency 2115 

### plans to apply the same general conce pts and decision logic for the remaining androgen receptor and 2116 

### thyroid pathways, acknowledging that the androgen pathway may be more similar to the estrogen 2117 

### receptor pathway than for the thyroid pathways. As the agency develops the additional prioritization 2118 

### tools for the remaining pathways, the androgen and thyroid based prioritization schemes will be applied 2119 

### to those chemicals that pass the physico

- chemical exclusion criteria filters. 2120

### Prioritization of the EDSP Universe of Chemicals

December 2012 135 | Pa ge References 2121



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### of environmental chemicals on key transcription regulators and correlation to toxicity end points 2164 

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### method based on robust nonlinear regression and the false discovery rate. BMC Bioinformatics. 2171 

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### of a precision-cut trout liver tissue slice assay as a screen for vitellogenin induction: comparison 2197 

### of slice incubation techniques. Aquatic Toxicology. 49, 251-268. 2198



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### 2004. Use of trout liver slices to enhance mechanistic interpretation of estrogen receptor binding 2200 

### for cost

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### Schreurs R., Lanser, P., Seinen, W., van der Burg, B. 2002. Estrogenic activity of UV filters determined 2203 

### by an in vitro reporter gene assay and an in vivo transgenic zebrafish assay. Archives 2204 

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### Disruptors. April 1999. http://www.epa.gov/scipoly/sap/meetings/1999/033099_mtg.htm 2207



### U.S. E PA . 2008. Technical Review Document, Endocrine Disruptor Screening Program, Proposed Tier 2208 

### 1 Screening Battery. http://www.epa.gov/scipoly/sap/meetings/2008/march/technica l_review.pdf 2209



### U.S. EPA. 2009a. Final Contaminant Candidate List 3 Chemicals: Identifying the Universe. EPA 815- 2210 

### R-09-006. August 2009. 2211 

### http://water.epa.gov/scitech/drinkingwater/dws/ccl/upload/CCL3_Chemicals_Universe_08-31- 2212 

### 09_508_v3.pdf 2213



### U.S. EPA. 2009b. Final Contaminant Candidate List 3 Chemicals: Screening to a PCCL. EPA 815-R2214 

### 09-007. August 2009. 2215 

### http://water.epa.gov/scitech/drinkingwater/dws/ccl/upload/CCL3Chem_Screening_to_PCCL_08- 2216 

### 31-09_508v2.pdf 2217



### U.S. EPA. 2012. The EDSP Universe of Chemicals and General Validation Principle. November 2012. 2218 

### http://www.epa.gov/endo/pubs/edsp_chemical_universe_and_general_validations_white_paper_ 2219 

### 11_12.pdf 2220



### Takeyoshi, M. (2006), Dr aft Report of Pre -validation and Inter-laboratory Validation For Stably 2221 

### Transfected Transcriptional Activation (TA) Assay to Detect Estrogenic Activity
- The Human 2222 

### Estrogen Receptor Alpha Mediated Reporter Gene Assay Using hER- HeLa -9903 Cell Line, 2223 

### Chemical s Evaluation and Research Institute (CERI): Japan. p. 1-188. 2224



### Yamasaki K et al ., (2004). Comparative study of the uterotrophic potency of 14 chemicals and their 2225 

### receptor binding affinity. Tox Letters 146:111-120. 2226

### Prioritization of the EDSP Universe of Chemicals

December 2012 138 | Pa ge List of Appendices 2227



### Appendix A: EDSP Universe of Chemicals and General Validation Principles 2228



### Appendix B: An Effects -based Expert System to Predict Estrogen Receptor Binding Affinity for Food 2229 

### Use Inert Ingredients and Antimicrobial Pesticides: Application in a Prioritization 2230 

### Scheme for Endocrine Disruptor Screening 2231



### Appendix C: SAP Minutes on "An Effects-based Expert System to Predict Estrogen Receptor Binding 2232 

### Affinity for Food Use Inert Ingredients and Antimicrobial Pesticides: Application in a 2233 

### Prioritization Scheme for Endocrine Disruptor Sc reening" 2234



### Appendix D: Section 4 Supplemental, Details of Chemical Library 2235



### Appendix E: Section 5 Supplemental, HTP Data Processing Pipeline 2236



### Appendix F: Section 6 Supplemental, Descriptions of HTP Assays for the Detection of Estrogen 2237 

### Receptor Agonists 2238



### Appendi x G: Section 7 Supplemental, ES TrSet Decision Criteria for Binder/Non binder and Data 2239 

### Collection and Processing Details 2240



### Appendix H: Section 7 Supplemental, Tables 2241



### Appendix I: Section 7 Supplemental, Figures 2242