### 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