Integrated Bioactivity and Exposure Ranking



# Integrated Bioactivity and Exposure Ranking: A Computational Approach for the Prioritization and Screening of Chemicals in the Endocrine Disruptor Screening Program

U.S. Environmental Protection Agency Endocrine Disruptor Screening Program

Jointly developed by:

U.S. EPA Office of Chemical Safety and Pollution Prevention (OCSPP) U.S. EPA Office of Research and Development (ORD) U.S. EPA Office of Water (OW)

NIH National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM)



# FIFRA SAP December 2

- 5, 2014Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 2 of 111 

## Preface:

The U.S. Environmental Protection Agency (EPA) is continuing a series of scientific peer reviews focused on computational approaches for prioritization and screening of chemicals in the Endocrine Disruptor Screening Program (EDSP). This document describes innovative approaches for determining estrogen and androgen bioactivity based on a series of computational models integrating data from ToxCast high throughput screening (HTS) assays. The underlying science for each method is described, and EPA presents t he scientific confidence in each method using computational models of estrogen and androgen bioactivity for:

1. Prioritizing chemicals for further screening and testing; 2. The utility of using HTS to contribute to weight of evidence based on Tier 1 battery data and other scientifically relevant information on a chemical’s potential endocrine bioactivity; and 3. The scientific support for the potential substitution of these HTS assays for some of the lower throughput assays that are currently part of EDSP screen ing.

This document also describes new science for an integrated bioactivity and exposure ranking (IBER) approach to rank chemicals and prioritize further EDSP consideration. The IBER approach is demonstrated using estrogen and androgen receptor pathway, w ith mention of parallel efforts underway to develop similar models for the androgen receptor pathway.

This document describes a series of generalizable chemical prioritization and screening methodologies based on endocrine bioactivity and exposure which c ould be applied and adapted to address other endocrine pathways and EDSP chemicals. The focus for review by the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) Scientific Advisory Panel (SAP) is on the concepts and methodologies, and not on res ults for specific chemicals that may be prioritized for EDSP screening. It is the intention of the EPA to complete the prioritization methodology and process after receiving recommendations from the SAP on the proposed computational approaches. None of the information on any individual chemical substance in this paper, EPA presentations at the SAP meeting, or the meeting summary are intended to support any conclusions other than to aid in the development of potential prioritization and screening methods. No ne of the examples presented should be taken as final decisions regarding potential bioactivity, exposure, hazards or risks of the substance, or that the EPA has or will make a determination that any use of the substance necessarily will pose a risk. Furth er, the results described herein are not designed to provide a scientific basis, by themselves, to allow the EPA to conclude that these substances have potential for endocrine effects, or are "endocrine disruptors".

This document has been reviewed in acc ordance with EPA policy. Mention of trade names or commercial products do not constitute endorsement.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 3 of 111 

## Contents

Preface:

................................ ................................ ................................ ................................ ..... 2 List of Acronyms and Abbreviations

................................ ................................ .......................... 5
Section 1: Introduction

................................ ................................ ................................ ............... 7 Section 2: Estrogen Pathway

................................ ................................ ................................ ... 13 2.1: Computational Tools and Models

................................ ................................ .................. 13

2.1.1: In vitro data and model: ER

................................ ................................ ..................... 13 2.2: Interpretation and application: ER

................................ ................................ ................. 40 2.2.1: Curated literature review of uterotrophic literature

................................ .................. 41 2.2.2: Comparison of ToxCast ER agonist AUC data and Uterotrophic studies

................ 47 2.2.3: Comparison of ToxCast data and Tier 1 ER assays for List 1 chemicals

................ 56 2.2.4: Demonstration of computational toxicological approach to evaluating ER agonist bioactivity of EDSP universe chemicals

................................ ................................ ............ 58 Section 3: Androgen Pathway

................................ ................................ ................................ . 61 3.1: Computationa l Tools and Models: AR

................................ ................................ ........... 61
3.1.1: In vitro data and model: AR

................................ ................................ ..................... 61 3.2: Interpretation and application: AR

................................ ................................ ................ 71 3.2.1: Curated literature review of Hershberger literature

................................ ................ 71 3.2.2: Comparison of ToxCast data and Hershberger studies

................................ ......... 73
3.2.3: Tier 1 AR Data

................................ ................................ ................................ ....... 73
3.2.4: Demonst ration of computational toxicological approach to evaluating AR antagonist bioactivity of EDSP universe chemicals

................................ ................................ ............ 74 Section 4: High

- Throughput Exposure (HTE)

................................ ................................ ......... 77 4.1: Programmatic Objectives and Challenges for High Throughput Exposure Estimation

. 77 4.2: High Throughput Exposur e Modeling

................................ ................................ ........... 77 Section 5: Toxicokinetics / Dosimetry

................................ ................................ ..................... 87 Section 6: Integrated Bioactivity Exposure Ranking (IBER) for Prioritization

.......................... 92 6.1: Basic Approach for IBER

................................ ................................ ............................... 92
6.2: Mode ling uncertainty and variability

................................ ................................ .............. 93 6.3: Interpretation and application: IBER

................................ ................................ .............. 94
Section 7: Future directions

................................ ................................ ................................ ..... 99 7.1: Computational tools, models and data

................................ ................................ .......... 99 7.2: Data Release and Transparency: The EDSP21 Dashboard. 129

................................ 100
7.3: EDSP Future Directions

................................ ................................ .............................. 101
References

................................ ................................ ................................ ............................ 105 List of Appendices

................................ ................................ ................................ ................. 111Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 4 of 111 Appendix 1: ER AUC plots for reference chemicals

................................ ........................... 111 Appendix 2: ER AUC plots for all chemicals

................................ ................................ ....... 111 Appendix 3: Supplementary Data: ER, AR, and IBER

................................ ........................ 111 Appendix 4: NICEATM_UT_GL_DB_All_Data.xls

................................ .............................. 111 Appendix 5: Uterotrophic study radar plots

................................ ................................ ........ 111 Appendix 6: Examination of ER in vitro

- in vivo discordance

................................ ............... 111 Appendix 7: AR AUC plots for reference chemicals

................................ ........................... 111 Appendix 8: AR AUC plots for all chemicals

................................ ................................ ....... 111Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 5 of 111 

## List of Acronyms and Abbreviations

ACToR: EPA’s Aggregated Computational Toxicology

Repository

AIC: Akaike Information Criterion AOP: Adverse Outcome Pathway AR: Androgen receptor

AUC: Area Under the Curve BASS: EPA’s Bioaccumulation and Aquatic System Simulator

BCF: BioConcentration Factor BW: Body weight

CAS

- RN: Chemical Abstracts Service Registry Number

(chemical identification number)

CCL 3: the third Contaminant Candidate List

CDC: the United States Centers for Disease Control CDR: EPA Chemical Data Reporting (formerly IUR)

CPCat: Chemical

- Product Categorization database

CPCPdb: Consumer Product Chemical Profiles database CPRI: Crop Protection Research Institute

C ss : plasma Concentration after Steady

- State is reached due to repeated exposures CTCP: Clinical Toxicology of Commercial Products

DSSTox: Distributed Structure

- Searchable Toxicity

Database EBC: Expected Biological Concentrations EDSP: the Endocrine Disrupter Screening Program EDSTAC: Endocrine Disruptors Screening and Testing Advisory Committee EEC: Expected Environmental Concentration

E

- FA ST2: Exposure and Fate Assessment Screening Tool v2.0 EPA: the United States Environmental Protection Agency EPI Suite: EPA’s Estimation Program Interface Suite ER: Estrogen receptor ExpoCast: EPA’s Exposure foreCast prioritization

research program

EXAMS: EPA’s Exposure Analysis Modeling System FDA: the United States Food and Drug Administration FFDCA: Federal Food, Drug, and Cosmetic Act FIFRA: Federal Insecticide, Fungicide, and Rodenticide Act

f up : fraction of chemical that is unbound in plasma when in

t he presence of endogenous levels of plasma protein HEDS: EPA’s Human Exposure Data System

HPDB: Household Product Database HPV: High Production Volume HT: High

- throughput HTE: High

- throughput exposure HTS: High

- throughput screening HTTK: High Throughput Toxicokinetics HUC: Hydrologic Unit Code IBER: Integrated (RTK) Bioactivity Exposure Ranking IEMS: Integrated Exposure Modeling System IUR: EPA’s Inventory Update Reporting and Chemical Data Reporting list (now CDR)

IVIVE: in vitro

- in vivo extrapolation

KABAM: EPA’s K ow (based) Aquatic BioAccumulation

Model

K OW : Octanol

- Water partition coefficient (also called lipophilicity or hydrophobicity)

LOD: Limit of detection

MC: Monte Carlo simulation MCMC: Markov Chain Monte Carlo simulation (for

Bayesian analysi s) MSDS: Material Safety Data Sheet MySQL An open

- source relational database management system that can be interfaced via a structure query language or “SQL

NCGC: the NIH National Chemical Genomics Center

NHANES: National Health and Nutrition Examination S urvey
NIH: the United States National Institutes of Health NIOSH: the CDC National Institute for Occupational Safety and Health NLM: the United States National Library of Medicine NPDWR: National Primary Drinking Water Regulation

NRC: National Research Cou ncil NTP: the NIH National Toxicology Program

OPPT: EPA Office of Pollution Prevention and Toxics ORD: EPA Office of Research and Development OSHA: the United States Occupational Safety and Health Administration PBTK: Physiologically

- Based Toxicokinetics PCA: Principal Components Analysis PDP: the USDA Pesticide Data Program

PRZM: EPA’s Pesticide Root Zone Model PWS: Public Water System QSAR: Quantitative Structure Activity Relationship RAIDAR: Risk Assessment IDentification, And Ranking modelIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 6 of 111 RED: Rapid E quilibrium Dialysis

RTK: Reverse Toxicokinetics SAP: Scientific Advisory Panel SDWA: Safe Drinking Water Act SEEM: Systematic Empirical Evaluation of Models framework SMILES: Simplified Molecular Input Line Entry System

TK: Toxicokinetic

ToxCast: EPA’s To xicity foreCast prioritization research

program Tox21: Toxicology in the 21 st Century

– the

NTP/NCGC/EPA/FDA consortium for chemical hazard HTS

TSCA: the Toxic Substance Control Act USDA: the United States Department of Agriculture

USGS: the United States Geological Service USEtox: United Nations Environment Program and Society for Environmental Toxicology and Chemistry toxicity modelIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 7 of 111 

## Section 1 : Introduction

EPA developed the EDSP in response to the statutory mandate in the Federal Food, Drug, and Cosmetic Act (FFDCA) to "develop a screening program...to determine whether certain substances may have an effect in humans that is similar to an effect produced by a naturally occurring estrogen, or such other endocrine effects as the Administrator may designate." As part of the EDSP, the statute also gives EPA the authority to "provide for the testing of any other substance that may have an effect that is cumulative t o an effect of a pesticide chemical if the Administrator determines that a substantial population may be exposed to such a substance." Beyond testing and determining endocrine effects, FFDCA also states that : "In the case of any substance that is found...t o have an endocrine effect...the Administrator shall, as appropriate, take action under such statutory authority as is available to the Administrator...to ensure the protection of public health." In addition to FFDCA, the Safe Drinking Water Act (SDWA) giv es EPA authority to provide for testing "of any other substances that may be found in sources of drinking water if the Administrator determines that a substantial population may be exposed to such substance."

To begin meeting these statutory mandate s , in 1996 the EPA chartered a Federal Advisory Committee

, the Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC ), to address endocrine disruption

. The EPA largely adopted the EDSTAC recommendations, and in an August 1998 Federal Register notice

, established the EDSP

. In its final report, EDSTAC made several key recommendations that EPA’s implementation of its endocrine screening program should :

 Address both potential human and ecological effects from chemical exposures

 Examine effects of these chemicals on estrogen, androgen and thyroid hormone

- related processes

 Include pesticide and non

- pesticide chemicals, contaminants, and (after evaluating single chemicals) mixtures

 Develop a two

- tiered screening and testing strategy, now known as the Endocrine Disruptor Screening Program

The application of HTS tools has been an integral component of the EDSP from its beginning

. In 1998, the EDSTAC recommended that “data resulting from HTPS [HTS] will be combined with exposure

- related information, an d with any other effects

- related information that is available, for each chemical for the purpose of setting priorities for [Tier 1 screening] T1S.” However, in 1999 a joint review by the EPA Science Advisory Board (SAB) and the Federal Insecticide, Fungi cide, and Rodenticide Act (FIFRA) Scientific Advisory Panel (SAP) concluded that “EPA failed to sufficiently demonstrate the utility of their HTPS [HTS] system for the purpose of chemical sorting and priority setting of estrogen, androgen and thyroid activ e chemicals.” The SAB/SAP did acknowledge that this was a work in progress and that, in general, the HTS approach had merit but required further development prior to implementation.

Subsequently, EPA embarked on an alternate course for EDSP priority sett ing

. To identify the first chemicals to undergo screening in the EDSP (known as List 1), public comments and the recommendations from the joint SAP/SAB were considered

. Based on these comments and recommendations, 50 and 100 chemicals were selected , draw n from pesticide active ingredients and pesticide inert ingredients that are also high production volume chemicals (HPV)

. The number of chemicals (50 to 100) was thought to be a reasonable undertaking toIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 8 of 111 allow the nascent EDSP to gain experience with the test systems before proceeding through the larger universe of chemicals

. The first list of chemicals was based solely on exposure potential

. To identify the specific chemicals that would comprise List 1, EPA applied the EDSTAC exposure recommendations, f ocusing on four pathways of exposure to pesticide active ingredients:

1. C onsumption of food containing pesticide residues (food pathway); 2. C onsumption of drinking water containing pesticide residues (water pathway); 3. R esidential use of pesticide products (res idential use pathway); and/or 4. O ccupational contact with pesticide

– treated surfaces (occupational exposure pathway).

For HPV pesticide inert ingredients, EPA focused on:

1. H uman biological samples (human biological monitoring pathway); 2. A nimal tissues that h ave human food uses (ecological biological pathway ; e.g., fish tissues ); 3. D rinking water (drinking water pathway); and/or 4. I ndoor air (indoor air pathway).

Based on these exposure considerations and public review, the EPA issued test orders for a list of 67 pesticide chemicals (58 active ingredients and 9 HPV inert ingredients) for EDSP screening in 2009, referred to as List 1.

In developing the second list of chemicals for EDSP screening (List 2), EPA focused on continuing to address pesticide chemicals an d beginning to address drinking water contaminants

. Pesticide chemicals on List 2 represent pesticides that were scheduled for Registration Review during fiscal years 2007 and 2008

. EPA identified drinking water contaminants for EDSP List 2 from chemical s that were regulated under the national primary drinking water regulation (NPDWR) (40 CFR part 141) under the SDWA or were unregulated contaminants that are listed on the third Contaminant Candidate List (CCL 3 ; http://water.epa.gov/scitech/drinkingwater/dws/ccl/ccl3.cfm ) Based on these considerations, the final EDSP List 2 consists of 107 chemicals (41 pesticide active ingredients and 66 SDWA chemicals) was published , though test orders have not yet been issued for these chemicals

In November 2012, EPA published the document entitled “Endocrine Disruptor Screening Program Universe of Chemicals and General Validation Principles” identifying a universe of approximately 10,000 chemic als for EDSP prioritization and screening (US EPA 2012)

, based on the statutory authorities of the FFDCA and SDWA

. This historical approach to listing chemicals based primarily on exposure considerations has only addressed a small fraction of this univers e for screening ( Figure 1.1 )

. Even for the 174 chemicals on Lists 1 and 2, EPA has been careful to note that the public should not presume EPA currently suspects that such chemicals interfere with the endocrine systems of humans or other species

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 9 of 111 Figure 1. 1 : Of the approximately 10,000 EDSP chemicals , including pesticide chemicals (covered under the Federal Insecticide, Fungicide, and Rode nticide Act; FIFRA) and chemicals found in sources of drinking water (covered under the Safe Drinking Water Act, SDWA)

, only 67 List 1 and 107 List 2 chemicals have been identified for screening and testing to date.

Based on the current pace of the Tier 1 screening assays it could take decades to screen all 10,000 chemicals in the EDSP Universe

. Therefore, EPA’s EDSP is actively pursuing the application of computational toxicology and exposure methods to create a more efficient and robust screening program

. Incorporating innovative computational toxicological tools allow the agency to integrate bioactivity and exposure to prioritize and screen chemicals, and is consistent with the approach for originally recommended by EDSTAC in 1998

. These recent advances in computational toxicology have brought EPA to an “evolutionary turning point” for EDSP prioritization, screening and testing

. In addition to rapidly screening thousands of chemicals and overcoming throughput limitations of traditional chemical toxicity testing, EPA expects computational methods will eventually allow EPA to move forward with replac ing some EDSP assays that rely on animals with non

- animal alternative s

. In order to use high throughput methods to prioritize and screen chemicals, EP A must demonstrate that these methods are scientifically sound and can effectively detect endocrine bioactivity via receptor

- mediated interactions and broader endocrine pathway effects

. In FY 2012, EPA began a multi

- year transition for the EDSP to validat e and incorporate computational toxicology methods and high

- throughput screens

. The current FIFRA SAP review is a continuation of a series of reviews focused on the application of these high thr oughput tools for prioritizing and screening the universe of EDSP chemicals

. Summaries of SAP reviews, including the current review and a prospective future review , are listed in Table 1.1

. EDSP Chemical Universe 10,000 chemicals (FIFRA & SDWA) EDSP List 2 107 Chemicals

EDSP List 1 67 ChemicalsIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 10 of 111 Table 1 .1 : A summary of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) Scientific Advisory Panel Reviews addressing computational tools for use in the Endocrine Disruptor Screening Program (EDSP)

. ER = estrogen receptor, ERES = ER Expert System, QSAR = Quantitative S tructure Activity Relationship, ExpoCast = Exp osure Forecaster, SEEM = Systematic Empirical Evaluation of Models, HTTK = High Throughput ToxicoKinetic, RTK = Reverse ToxicoKinetic, IBER = Integrated Bioactivity Exposure Ranking, AR = Androgen Receptor, E= Estr ogen, A = Androgen, T = Thyroid, CompTox = Computational Toxicology.

FIFRA Scientific Advisory Panel Reviews (External Independent Scientific Peer Review) Topics For Review

1

. Scientific Issues Associated with Prioritizing the Universe of EDSP Chemicals Using Computational Toxicology Tools

– J anuary 2013 8 ER Assays Physical

- Chemical Properties ERES QSAR

2

. Scientific Issues Associated with New High Throughput Methods to Estimate Chemical Exposure

– July 2014 ExpoCast SEEM Framework HTTK/RTK

3

. I ntegrated Endocrine Bioactivity and Exposure

- Based Prioritization and Screening for the Endocrine Disruptor Screening Program

- December 2014 18 ER Assays + QSAR 9 AR Assays + QSAR IBER List 2 & Universe (E&A) Scientific evaluation for p artial Tier 1 battery s ubstitution

FUTURE: Use of Computational Toxicology for predicting endocrine bioactivity in an Adve rse Outcome Pathway framework EAT Pathways Steroidogenesis CompTox predictions of neuroendocrine

- intact in vivo response Refined Exposure Models

In January 2013, the SAP reviewed an EPA white paper describing eight HTS estrogen receptor (ER) agonist assays and the ER Expert System quantitative structure

- activity relationship ( QSAR ) model as potential tools to be used for prioritizing EDSP chemicals

. The SAP found steps in the prioritization scheme organized and clearly described

. Panel members encouraged expanding the use of HTS and highlighted the need to consider exposure in the process

. The SAP also noted that the androgen pathway may be similar to the ER path way but sh ould be focused on antagonism, and endocrine effects in the thyroid pathway involve multiple modes of action that are not receptor based.

In July 2014, EPA brought scientific issues associated with new high throughput methods to estimate chemica l exposure to the SAP for review

. ExpoCast is an EPA Office of Research and Development (ORD) initiative to develop the necessary approaches and tools for rapidlyIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 11 of 111 prioritizing and screening thousands of chemicals based on the potential for human and ecolo gical exposure

. This focus of ExpoCast is distinct from many existing traditional, lower throughput exposure tools that require considerable data to either generate screening level assessments on a per chemical basis or full regulatory risk assessment

. E xpoCast efforts have focused on empirically assessing the uncertainty in high throughput exposure ( HTE ) predictions by evaluation and calibration with available monitoring data ( e.g., biomonitoring or water concentration)

. By using rigorous statistical te chniques to reconcile HTE estimates based on limited available data, the uncertainty in the HTE predictions is quantified

. EPA refers to this framework as the Systematic Empirical E valuation of Models (SEEM).

In addition to predictions of exposure, the July 2014 SAP reviewed high throughput toxicokinetic (HTTK) methods for extrapolating in vitro doses to in vivo concentration ( in vitro

to

- in vivo extrapolation or IVIVE) fo r chemicals that have been run in a bat tery of high

throughput endocrine screening assays ( e.g., ToxCast)

. HTTK provides a bridge between bioactivity measured in the high

- throughput screening assays and exposure by either predicting tissue concentrations from an administered dose ( i.e., what h as been called forward toxicokinetics) or inferring administered doses that would be needed to cause tissue bioactive concentrations in vivo ( i.e., reverse toxicokinetics or RTK)

. The importance of applying HTTK/RTK to high throughput exposure estimates t o better discriminate between chemicals for prioritization is demonstrated ( Figure 1.2 )

.

Figure 1. 2 : Bioactive concentrations ( top figure ) as 50% of the maximal active concentrations (AC 50 ; μM ) for 500 chemicals run in a variety of ToxCast assays converted to oral equivalent doses ( mg/kg/day; bottom figure) using high throughput toxicokinetics (HTTK).Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 12 of 111 The current FIFRA SAP review focuses on additional HTS bioactivity tools for the estrogen and androgen pathways, their use in chemical prioritizatio n, and the initial evaluation of their potential to substitute for specific endpoints in lower throughput EDSP Tier 1 screening assays

. In addition, this SAP will review an approach that utilizes both bioactivity and exposure to prioritize chemicals for f urther consideration

. In the Integrated Bioactivity

Exposure Ranking ( IBER ) , RTK can be used to estimate the daily administered dose (mg/kg BW/day) necessary to produce steady

- state in vivo blood concentrations equivalent to concentrations showing biologi cal activity in the in vitro HTS assays (μM)

. The putative bioactive administered doses can then be directly compared with predicted exposures (mg/kg BW/day) as portrayed in Figure 1.3

.

Figure 1. 3 : A graphical representation of the Integrated Bioactivity

- Exposure Ranking (IBER) approach indicating no priority chemicals (those that either have no potential bioactivity or no potential exposure), low (chemicals for which bioactive concentrations are considerably higher than potential ex posures), medium, and high priority (chemicals for which bioactivity and exposure concentrations are close or overlapping).Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 13 of 111 

## Section 2: Estrogen Pathway

2.1: Computational Tools and Models

In this section

, we describe an approach to combine data from multiple in vitro assays measuring estrogen receptor activity to make a weight of evidence prediction concerning “ true ” activity

. Note that “true” ER activity is determined in this analysis by consistent findi ngs of activity across multiple lab oratories and assays, and is exemplified by reference chemicals

. Therefore, a key aspect of the analysis given below is the use of a reasonably large set of well

studied reference chemicals

. Th e need for this approach, using multiple in vitro assays and a mathematical model, arose from our study of a large data set of in vitro assays and chemicals [ i.e., the ToxCast program ( Judson et al. 2010 ) ]

. An important outcome of that analysis was the pervasive nature of technology

- specific false posi tive results (relative to the expected target of the assay, as determined using well

- studied reference chemicals) f or most or all assays , that was largely associated with cytotoxicity and a variety of chemical and cell

- type specific modes of cell stress

. By combining data from multiple assays, and using a model that detects these technology

- specific false positives, we can arrive at a more confident assessment of the in vitro estrogenicity of a chemical.

2 .1.1: In vitro data and model: E R

In order to mea sure the effect of chemical substances on signaling pathways, a variety of in vitro assays have been widely used in drug development and toxicity testing programs

. These assays range from biochemical assays using purified protein to more complex cellular assays that can respond to chemical perturbations in multiple ways

. This observation is fundamental to the concept of “assay interference” ( Auld et al. 2008

, Baell and Holloway 2010

, Thorne et al. 2010

, Bruns and Watson 2012 ) , whereby assays designed to measure binding to a protein or perturbation s of a given pathway may produce false signals when the target protein is denatured or when other pathways in the system are altered

. The standard approach to deal with assay interference issues is to deploy “orthogonal” assays ( Miller et al. 2010

, Thorne et al. 2010 ) that help distinguish activity towards the intended target or pathway from non

- specific activities

. In addition to assay interference issues , every assay has inherent limitations such as dynamic range or levels of background noise

. Using a suite of assays to detect pathway perturbations overcomes limitations of a single assay and improves the ability to identify potential non

- specific effects

. We have investigated several questions concerning pathway activity and assay interference using data from a collection of 18 in vitro assays that probe the ER pathway in a mammalian system

. These assays are a combination of biochemical and cell

- based in vitro assays that probe mechanistic perturbations of the ER pathway at multiple sites: receptor binding, receptor dimerization, chromatin binding of the mature transcription factor, gene transcription and changes in ER

- induced cell growth kinetics

. Thi s battery of in vitro assays was used to screen a library of 1812 chemicals

. Included in the chemical library were reference ER agonists and antagonists, as well as a large number of commercial chemicals with known estrogen

- like activity (the reference ch emicals )

. The library of 1812 chemicals also has a significant overlap with the EDSP Universe of chemicals

. A subset of the chemicals would be expected to elicit activity in some, but not all assays, given the diversity of cell types and technologies inc luded in the in vitro battery

. The expected patterns of assay activity include: no assays activated, allIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 14 of 111 agonist assays activated, all antagonist assays activated, specific subsets of assays across technologies activated, technology

- specific assay activat ion

. To navigate this complexity, we developed mathematical and statistical models to infer whether chemicals that activate specific patterns of the in vitro assays were more likely to be ER agonists or antagonists, or were more likely to be causing assay activity through specific types of assay interference

. Additionally, we combined the in vitro assay data with information derived from chemical structure and, where available, compared the results to data f rom publicly available sources.

Overall approac h : The input data for the model includes chemical structures and concentration

- response data for 18 ER

- related in vitro assays, plus data for many, non

- ER in vitro assay endpoints (ranging from 186

- 821 assays, depending on the chemical)

. The model produce s scores for agonist activity, antagonist activity and for “false” activity in each assay technology

. These scores can then be compared with reference chemicals, structure classes, and other properties.

Assays and Concentration

- Response Analysis : The dat a used were generated by the EPA ToxCast program ( Judson, Houck et al. 2010 )

. The data set comprises concentration

response data on 1812 chemicals, with each chemical tested in 18 ER pathway assays

. The ER pathway assays include 3 cell

- free biochemical radioligand ER binding assays [ Novasc reen ( Knudsen et al. 2011

, Sipes et al. 2013 ) ]; a set of 3 protein complementation assays that measure formation of ER dimers and test for activity against ER

- alpha and ER

beta, (each measured at 2 separate times for a total of 6 assay readouts) [ Odyssey Thera ]; 2 assays measuring interactio n of the mature transcription factor with DNA [ Odyssey Thera ]; 2 reporter gene assays measuring RNA transcript levels [Attagene: ( Martin et al. 2010 ) ]; 2 assays measuring reporter protein level readouts [Tox21 : ( Huang et al. 2014) ]; an ER

- sensitive cell proliferation assay [ACEA: ( Rotroff et al. 2013 ) ]; and 2 transactivation antagonist assays [Tox21 : ( Huang et al. 2014) ]

. The 1812 chemicals used in this model were selected because they had data for the full set of 18 ER assays

. The assay sources refer to the company or laboratory where the assays were performed

. The 18 assays used are listed in Table 2.1

. The assay IDs correspond to Figure 2.1

.

The chemicals were run in concentration

- response format in all assays except for the cell

- free binding assays (NVS)

. These assays were initially run at a single concentration (25 μ M), and if significant activity (3 mean absolute deviation ( MAD abov e) abov e the median or 30% activity) was seen, the chemical was then run in concentration

- response mode

. All of the concentration

- response data were analyzed using the ToxCast data analysis pipeline, which automates the processes of baseline correction, normalization, curve

- fitting, hit

- calling and detection of a variety of potential confounders, which are annotated as “caution flags”

. This pipeline, along with all of the raw and processed data, and annotations is publicly available [ http://epa.gov/ncct/toxcast/data.html ].

All in vitro assays except the Attagene assays were normalized to 17 

- estradiol in the agonist group and 4

- hydroxy

- tamoxifen in the antagonist group

. Attagene data were normalized as a fold

- change over the solvent (DMSO) control

. The concentration

- response data for each in vitro assay were fit to 3 models: a constant model (no activity), a Hill model, and a “Gain

- Loss” model, which is a combination of two Hill models, one increasing in signal, and the other decrea sing at higher concentration

. The Gain

- Loss model helps account for observed loss of activity (likely due to cytotoxicity) at high concentrations for certain chemical

- assay pairs

. Loss of signal assays ( e.g., antagonist assays) were converted to gain of signal to make all assayIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 15 of 111 processing consistent

. From the Hill model, we derived an AC 50 , Hill

- slope and maximum activity (T or Top value)

. For the Gain

- Loss model, there are two AC 50 values and two Hill slopes (for the gain and loss sections

, respectivel y), but a single Top value

. In addition to the AC 50 values, the model returns the AC 10 (concentration at which the activity is 10% of maximum) and the ACC (concentration at which the activity reaches the significance cutoff threshold)

. To call an assay

- c hemical pair a “hit”, at least one concentration has to reach this significance threshold, which is typically 10 MAD around the baseline

. The per

- assay MAD is calculated using the distribution of the lowest 2 concentration points for all chemicals run in the assay, around the median of those points

. The assay baseline is set as the median

. To allow computational synthesis of the different in vitro assays, a set of synthetic concentration

response activities was generated for each chemical

- assay pair at s tandardized concentrations using the experimentally

- derived AC 50 , Hill

- slope for the gain of signal (but not loss) and Top parameters

. All AC 50 values were in μ M, and the synthetic concentrations were a 2

- fold dilution series of 14 concentrations from 0.01

- 100 μ M

. For curves fit with a Gain

- Loss model, only the gain portion of the curve was used to generate the synthetic concentration

- response activities

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 16 of 111 Table 2.1: Summary of the in vitro assays used with their ID mapping to the model in Figure 2.1

. NVS=Novascreen; OT=Odyssey Thera; ATG=Attagene; Tox21=assays run by the NIH Chemical Genomics Center as part of the Federal Tox21 program.

Assay ID Assay Name Source Gene Target Normalized Data Type Assay Design Type Biological Process Target Detection Technology Detection Technology Subtype Measurement Time point Organism Tissue Cell Format Cell Line Name

A1 NVS_NR_bER NVS ESR1 percent activity radioligand

binding receptor

binding Lysate

- based

radioligand detection Scintillation

counting 18 bovine uterus tissue

based cell

free NA

A2 NVS_NR_hER NVS ESR1 percent activity radioligand

binding receptor

binding Lysate

- based

radioligand detection Scintillation

counting 18 human NA cell

- free NA

A3 NVS_NR_mERa NVS Esr1 percent activity radioligand

binding receptor

binding Lysate

- based

radioligand detection Scintillation

counting 18 mouse NA cell

- free NA

A4 OT_ER_ERaERa_0480 OT ESR1 percent activity protein fragment

complementation assay protein

stabilization Protein

- fragment

Complementation Fluorescence

intensity 8 human kidney cell line HEK293T

A5 OT_ER_ERaERa_1440 OT ESR1 percent activity protein fragment

complementation assay protein

stabilization Protein

- fragment

Complementation Fluorescence

intensity 24 human kidney cell line HEK293T

A6 OT_ER_ERaERb_0480 OT ESR1 percent activity protein fragment

complementation assay protein

stabilization Protein

- fragment

Complementation Fluorescence

intensity 8 human kidney cell line HEK293T

A7 OT_ER_ERaERb_1440 OT ESR1 ESR2 percent

activity protein fragment

complementation assay protein

stabilization Protein

- fragment

Complementation Fluorescence

intensity 24 human kidney cell line HEK293T

A8 OT_ER_ERbERb_0480 OT ESR2 percent activity protein fragment

complementation assay protein

stabilization Protein

- fragment

Complementation Fluorescence

intensity 8 human kidney cell line HEK293TIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 17 of 111 Assay ID Assay Name Source Gene Target Normalized Data Type Assay Design Type Biological Process Target Detection Technology Detection Technology Subtype Measurement Time point Organism Tissue Cell Format Cell Line Name

A9 OT_ER_ERbERb_1440 OT ESR2 percent activity protein fragment

complementation assay protein

stabilization Protein

- fragment

Complementation Fluorescence

intensity 24 human kidney cell line HEK293T

A10 OT_ERa_EREGFP_0120 OT ESR1 percent activity fluorescent

protein induction regulation of

gene expression Microscopy Optical microscopy: Fluorescence microscopy 2 human cervix cell line HeLa

A11 OT_ERa_EREGFP_0480 OT ESR1 percent activity fluorescent

protein induction regulation of

gene expression Microscopy Optical microscopy: Fluorescence microscopy 8 human cervix cell line HeLa

A12 ATG_ERa_TRANS_up ATG ESR1 log2 fold induction mRNA induction regulation of transcription factor activity RT

- PCR and

Capillary electrophoresis Fluorescence

intensity 24 human liver cell line HepG2

A13 ATG_ERE_CIS_up ATG ESR1 log2 fold induction mRNA induction regulation of transcription factor activity RT

- PCR and

Capillary electrophoresis Fluorescence

intensity 24 human liver cell line HepG2

A14 Tox21_ERa_BLA_Agonist_ratio Tox21 ESR1 percent activity beta lactamase

induction regulation of

gene expression GAL4 b

- lactamase

reporter gene Fluorescence

intensity 24 human kidney cell line HEK293T

A15 Tox21_ERa_LUC_BG1_Agonist Tox21 ESR1 percent activity luciferase

induction regulation of

gene expression Luciferase

- coupled

ATP quantitation Bioluminescence 48 human ovary cell line BG1

A16 ACEA_T47D_80hr_Positive ACEA ESR1 percent activity real

- time cell

growth kinetics cell proliferation RT

- CES Electrical Sensor: Impedance 80 human breast cell line T47D

A17 Tox21_ERa_BLA_Antagonist_ratio Tox21 ESR1 percent activity beta lactamase

induction regulation of

gene expression GAL4 b

- lactamase

reporter gene Fluorescence

intensity 24 human kidney cell line HEK293T

A18 Tox21_ERa_LUC_BG1_Antagonist Tox21 ESR1 percent activity luciferase

induction regulation of

gene expression Luciferase

- coupled

ATP quantitation Bioluminescence 48 human ovary cell line BG1Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 18 of 111 Accounting for cell

- stress and cytotoxicity

- related assay interference : For many chemicals, we observe that there are a large number of hits (positive assay responses) for ER and non

- ER assays in the concentration range approaching where cytotoxicity is observed

. (Cyto toxicity is measured using a collection of 35 assays in the ToxCast battery that detect cytotoxicity or other measures of cell loss

. These are run in several cell lines and primary cell types.) We call this set of cell

- stress or cytotoxicity

- related hits the “burst”

. The following scheme is used to filter out these non

- selective, cell

- stress or cytotoxicity

- related assay hits

. For chemicals with two or more positive responses in cytotoxicity assays, we calculate the mean logAC 50 (cytotox icity ) and the me dian absolute deviation (MAD) of the logAC 50 (cytotox icity ) hits

. Next, we calculate the median of the MAD of the logAC 50 (cytotox icity ) distributions across all chemicals to define the global cytotoxicity MAD

. A new value (the Z

- score) is then assigned to each assay hit:

Equation (2.1)

If fewer than 2 cytotoxicity assays are hit, the median cytotoxicity concentration is arbitrarily set to 1000 μ M , which simply sets all Z

- values for assay hits to a value >3

. A hit with a large valu e of Z occurs at concentrations significantly below where cytotoxicity is occurring

. This hit is both unlikely to be caused by cell

- stress or cytotoxicity

- related processes, and is more likely to be able to cause toxicity through a target

- selective mechan ism

. The global cytotoxicity MAD from the dataset used in this analysis is 0.26 log units.

Reference chemicals : A set of 45 positive and negative reference chemicals were used to evaluate the performance of the model

. These chemicals have been used pre viously to validate ER in vitro assays and were taken from the OECD Test Guideline 457 (TG457) BG1 Luc Estrogen Receptor Transactivation test method for identifying estrogen receptor agonists and antagonists ( OECD 2012 )

. The reference chemicals, their expected potencies, and their predicted estrogen agonist and antagonist probabilities (defined below) are given in Table 2.2

. Note that some chemicals serve as references for both agonist and antagonist modes.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 19 of 111 Table 2.2: R eference Chemicals

. [Strong = AC 50 <0.0001 μM, moderate = AC 50 < 0.1 μM, weak = AC 50 < 1 μM, very weak = all other activities, inactive = no detected activity].

CASRN Chemical Name Agonist Potency Antagonist Potency

57

- 63

- 6 17alpha

- Ethinyl estradiol Strong Inactive 50

- 28

- 2 17beta

- Estradiol Strong NA
56

- 53

- 1 Diethylstilbestrol (DES) Strong Inactive 84

- 16

- 2 meso

- Hexestrol Strong NA
57

- 91

- 0 17alpha

- Estradiol Moderate NA 140

- 66

- 9 4

- tert

- Octylphenol Moderate NA 53

- 16

- 7 Estrone Moderate NA 599

- 64

- 4 4

- Cumylphenol Weak NA 521

- 18

- 6 5alpha

- Dihydrotestosterone Weak Inactive 80

- 05

- 7 Bisphenol A Weak Inactive
77

- 40

- 7 Bisphenol B Weak NA
486

- 66

- 8 Daidzein Weak NA 446

- 72

- 0 Genistein Weak Inactive 143

- 50

- 0 Kepone Weak Inactive
789

- 02

- 6 o,p'

- DDT Weak NA 58

- 18

- 4 17alpha

- Methyltestosterone Very Weak NA 520

- 36

- 5 Apigenin Very Weak Inactive 85

- 68

- 7 Butylbenzyl phthalate Very Weak Inactive 480

- 40

- 0 Chrysin Very Weak Inactive 115

- 32

- 2 Dicofol Very Weak Inactive
117

- 81

- 7 Diethylhexyl phthalate Very Weak Inactive 84

- 74

- 2 Di

- n

- butyl phthalate Very Weak Inactive 120

- 47

- 8 Ethylparaben Very Weak NA 60168

- 88

- 9 Fenarimol Very Weak NA 520

- 18

- 3 Kaempferol Very Weak NA
72

- 43

- 5 Methoxychlor Very Weak NA
72

- 55

- 9 p,p’

- DDE Very Weak Inactive 104

- 40

- 5 p

- n

- Nonylphenol Very Weak NA 1912

- 24

- 9 Atrazine Inactive NA 50

- 22

- 6 Corticosterone Inactive NA 66

- 81

- 9 Cycloheximide Inactive NA 13311

- 84

- 7 Flutamide Inactive NA 52

- 86

- 8 Haloperidol Inactive NA 52806

- 53

- 8 Hydroxyflutamide Inactive NA 65277

- 42

- 1 Ketoconazole Inactive NA 330

- 55

- 2 Linuron Inactive NA
57

- 30

- 7 Phenobarbital Sodium Inactive NA 32809

- 16

- 8 Procymidone Inactive NA
50

- 55

- 5 Reserpine Inactive NA 52

- 01

- 7 Spironolactone Inactive NA 68392

- 35

- 8 4

- Hydroxytamoxifen (E/Z) NA Active 82640

- 04

- 8 Raloxifene NA Active 10540

- 29

- 1 Tamoxifen NA Active 54965

- 24

- 1 Tamoxifen citrate NA Active
57

- 83

- 0 Progesterone NA InactiveIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 20 of 111 Structure of the Network : Figure 2.1 is a graphical representation of the network used to evaluate the integrated in vitro assay responses

. The model was based on the series of molecular events that typically occur in a nuclear receptor

- mediated response

. The process starts with the interaction of a chemical with a nuclear ER (Receptor node R1)

. For example, an ER agonist will cause the receptors to dimerize (node N1), translocate to the nucleus and recruit co

- factors to form the complete, active transcription factor complex (TF) (node N2)

. This TF then binds to the chromatin DNA (node N3), initiates transcription of mRNA (node N4) and subsequent translation to protein (node N5)

. For ER agonist activity, one downstream effect can be cell proliferation (node N6)

. Each of these processes (with the exception of co

factor recruitment) was measured in the current colle ction of 18 in vitro assays represented as white stars

. Table 2.1 provides the assay ID (A1

- A18) to match the assay on Figure 2.1 and a brief assay description

. The ER pathway is shown in two modes

, agonist (blue) and antagonist (red)

. The model assumes that a chemical that interacts with the ER will bind in one or both of the agonist or antagonist conformations and that this will trigger activity in the appropriate pathway

. Note that the model will allow for the prediction of mixed agonist/antagonist a ctivity

. In addition to ER

- mediated effects, every in vitro assay is subject to processes that can lead to

non

- specific activity, independent of the ER pathway node that it is supposed to measure

. The assay interference pathways were modeled as alternat e “pseudo

- receptors” (gray arrow nodes)

. The details of the process connecting the pseudo

- receptors to the assays were simplified to a single connection because, in general, we do not know the intermediate details of these processes or even the identity o f the pseudo

- receptors

. Note that the pseudo

receptors may not be what a biologist would define as a receptor, but are surrogates for generic processes such as cell

- stress or cytotoxicity that can lead to non

- ER mediated assay activity

. It is possible to describe many possible alternative pathways, but in general

, the data are not sufficient to distinguish between alternate models

. Pseudo

- receptors are assigned to each group of assays (technology group) and to each assay individually

. Only a single

exam ple of an assay

- specific pseudo

- receptor is shown in Figure 2. 1 , but all assays (with the exception of A16/R8 where the assay and the receptor are identical) have their corresponding pseudo

- receptor.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 21 of 111 Figure 2.1: Graphical representation of the network used in the in vitro analysis of the ER pathway across assays and technology platforms

. Colored arrow nodes represent “receptors” with which a chemical can directly interact

. Colored circles represent intermediate biological processes that are not directly observable

. White stars represent the assays that measure activity at the biological nodes

. Arrows represent transfer of information

. G ray arrow nodes are the pseudo

- receptors

. E ach assay (with the exception o f A16) has an assay

- specific pseudo

- receptor , but o nly a single example is explicitly shown, for assay A1

.

Mathematical Model: The computational model assumes that the value (the efficacy, A ) returned by an assay at a given concentration is a linear sum of the contributions from the receptors that it measures, i.e., it is a simple linear additive model:

A i = ∑ F

ij R j j Equation (2.2)

where the elements of the F matrix are 1 if there is a connection between a receptor and an assay, and zero otherwise

. This holds for direct connections, where a receptor is directly linked to an assay in Figure 2.1 , and for indirect connections, where a receptor is linked through one or more internal nodes, designated by the circles in Figure 2.1

. Therefore, the model assumes lossless transmission of signals from the receptor through the internal nodesIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 22 of 111 to the assays

. The goal is then to find a set of R j values that minimize the difference between the predicted assay values ( A i pred ) and the measured ones ( A i meas ) for each chemical and concentration

. For each chemical

- concentration pair, a constrained least

- squares minimization approach is used where the function being minimized is:

ε 2 = ∑ ( A i

pred

− A i

meas ) 2

i + penalty ( R )

E quation (2.3)

where A i

pred must satisfy the constraints:

A i pred ∈ [ 0

, 1 ] Equation (2.4)

It is possible to assign weights to the assays in the sum of Equation 2.3, but in practice, this did not change the results in any qualitative way, and introduced a number of extra free parameters into the model

. The term

, penalty(R)

, penalizes solutions that predict that many receptors are being simultaneously activated by the chemical

. It is given by

penalty ( R ) = α ( SR 2 )

( SR 2 + SR 0 2 ) Equation (2.5 )

where SR is the sum of R values at that concentration, SR 0 is a threshold value and α is a small number and was varied between 0 and 1

. This penalty term helps stabilize the solutions and enforces a reasonable physical assumption about chemical prom iscuity, i.e., that it is unlikely that most chemicals will strongly and specifically interact with many dissimilar molecular targets

. Values used here are α =0.05 and SR 0 =0.5

. Because there may not be a unique solution that minimizes the error term in Equation 2.3, the solution may depend on the initial conditions selected

. In this analysis, we start from the low concentration end where the expected activity is zero, which is then used as the initial condition for all receptors in the model

. For subs equent concentrations, we use the final values for the previous conce ntration as the initial values.

The model results in a response value (between 0 and 1) for each receptor at each concentration

. These results are summarized as area under the curve (AUC)

, which is the integral across the concentration range:

AUC j = 1

N conc ∑ sign ( slope ) × R j ( conc i ) N conc

i = 1 Equation (2.6)

The factor sign(slope) is included to handle cases where one of the assays or sets of assays is active at sign ificantly lower concentrations than the remaining assays

. The corresponding receptor curve will then rise and subsequently fall, and this AUC needs to be discounted

. Finally, AUC values are scaled so that AUC(R1)=1 for 17  estradiol

. The values of AUC c anIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 23 of 111 be related to the potency (AC 50 ), and are useful in interpreting different magnitudes

. Throughout the analysis, we will use the following terminology for AUC values: AUC(R1) and AUC(R2) or AUC(agonist)/AUC(antagonist) will be the activity in the agonis t and antagonist modes, respectively

. AUC(Ri;i>2) will be activity in one of the other pseudo

- receptors 3

- 9

. AUC(Ai) will be the AUC value for one of the single

- assay pseudo

- receptors.

In summary, the network model starts with a vector of 26 values for each chemical, representing the activity of the chemical against each of the 9 receptors and technology

- wise pseudo

- receptors and 17 assay

- specific pseudo

- receptors

. The model propagates this information through the network to create a predicted activity value for each of the assays

. These predicted values can be compared with the measured values and the receptor vector altered to minimize the squared error between the predicted and measured assay activity

. This procedure is then carried out for each of the 14 synthetic concentrations (0.01

- 100 μM) to provide a concentration

- response profile for the receptor and assay

- noise node responses.

Evidence for assay

- technology

- specific “false” activity

. Two

- dimensional hierarchical clustering was performed on t he relative potencies of the 18 ER

- related in vitro assays (Figure 2.2 )

. Strong clustering by technology / pseudo

- receptor was observed

. The activity for specific subsets of chemicals was technology

- specific suggesting potential narrow assay interference

. Other chemicals showed activity across a br oad range of ER

- related assays.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 24 of 111 Figure 2.2 : Two

- way hierarchical clustering of chemical activity across the 18 in vitro assays used to identify ER activity

. The heatmap shows

– log10(AC 50 ) values for all a ssays and all chemicals with at least one assay hit

. Darker red indicates more potent activity (lower AC 50 ), while white represents inactive chemical

- assay pairs

. Note that the assays cluster by technology / pseudo

- receptor

. The “A” and “R” values refer to the assay and receptors/pseudo

- receptors from Figure 2.1 and Table 2.1

. AUC plots for all reference chemicals are illustrated in Appendix 1

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 25 of 111 Interpreting the Results of the Model: One issue that arises with this approach is how to interpret these receptor AUC values

. For a pure ER agonist (no activity in any of the other side pathways), the R1 vs

. concentration curve closely resembles the concentration

- response profile for any of the assays, with activity going from 0 to 1 with an AC 50 - like value close to that observed in the assays

. For mixed cases, the concentration

- response curves may be more complex than a Hill curve and will have a maximum less than 1

. Qualitatively, we interpret these R values as concentration

- specific probabilities that t he chemical is interacting with the corresponding (pseudo) receptor.

The network model displayed four common types of activity

, which can be represented by model chemicals ( Figure 2.3 )

. Bisphenol A (BPA) shows a clear concentration

- response in agonist activity (right

- hand panel, blue curve)

. However, there is activity in the R3 and R6 pseudo

- receptors (binding and transactivation assays) that rise at low concentrations and then drop

. The corresponding in vitro assays show activity at lower concentrations than the others, but at high concentrations, substantial evidence points toward agonist activity

. Note that BPA has activity in one of the antagonist assays (A17, gold), c onsistent with Selective Estrogen Response Modulator ( SERM ) activity at high concentrations

. However, the results of the network model do not reflect this weak antagonist behavior

. Dimethyl hydrogen phosphite shows activity in several of the Odyssey Ther a (OT) assays (A4

- A11), both for receptor dimerization and DNA binding

. The resulting model prediction is mixed, with potential activity in the agonist and antagonist modes

. Alpha

- cyclodextrin shows strong activity only in the binding assays resulting in a strong assay interference signal in the R3 pseudo

- receptor

. This is almost certainly because this chemical interferes with the radioligand assay, potentially due to binding of the radioligand

. This molecule is known to bind hydrophobic molecules such a s fatty acids and cholesterol (Christian et al. 1997)

. Finally, raloxifene hydrochloride shows unambiguous antagonist activity

. The most frequent case (not shown here) is one in which there is no activity in any assay

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 26 of 111Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 27 of 111Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 28 of 111 Figure 2.3 : Results of the model for four example chemicals

. For each chemical, the left

- hand panel shows the synthetic concentration

- response data for the 18 assays, colored by assay group as defined in the legend

. The right

- hand panel shows the corresponding magnitude of the receptor responses

. The agonist receptor (R1) is designated by blue, the antagonist receptor (R2) by red and the other pseudo

- receptors are colored as indicated in the l egend

. Concentrations are in μM

. For chemicals with the “burst” defined (2 or more cytotoxicity hits), the burst center is indicated by a vertical red line, and the burst region (starting 3 burst MAD below the burst center) is indicated by the gray shade d region

. If an assay curve has a “caution flag”, the curve is overlaid with *s (e.g

. Raloxifene)

. ER AUC plost for all chemicals are provided in Appendix 2

.

Model Results for Reference Chemicals : The AUC values for the ER reference chemicals

are s hown in Figure 2.4

. For the positive agonist chemicals, all but two have non

- zero AUC values, these being diethylhexyl phthalate (DEHP) and dicofol

. DEHP is inactive in all assays, but dicofol is active in 3 of the 6 dimerization assays near the top test ed concentration, which results in a positive AUC (R4)

. These are very weak agonists , so are potentially only active at concentrations above where the chemicals have been tested for most or all assays

. The AUC ( R1 ) values for the other positive chemicals a re ordered approximately with the expected potency category

. None of the negative agonist reference chemicals showed non

- zero AUC(R1) values

. However, three negative agonist reference chemicals had a significant pseudo

- receptor scores: spironolactone in R8, ketoconazole in R9 and cycloheximide in R3

. Spironolactone targets the glucocorticoid receptor

. It is known that the T47D cell line used in the proliferation assay (A16, R8) expresses the glucocorticoid and progesterone receptors, and that glucocorti coids and progestins can drive proliferation, just like estrogens (Cappelletti et al. 1995; Wan and Nordeen 2002)

. The activity in ketoconazole is in the two antagonist assays, in the cytotoxicity region

. It is well

- known that these loss

- of

- signal assays can be confounded by cytotoxicity, so this is the likely explanation for this false

- positive call

. Cycloheximide, a DNA cross

- linking agent, activates two of the three cell

- free binding assay s , so there may b e some type of chemical reactivity driving protein denaturation that is leading to this false activity

. All four of the positive antagonist reference chemicals are positive and strong

. With the exception of Di

- n

- butyl phthalate (DNBP), all of the antag onist negative reference chemicals are negative

. Most of the negative antagonist reference chemicals are positive references for the agonist mode, and they appropriately show up with a high AUC( R1 )

. Di

- n

- butyl phthalate is listed as a very weak positive agonist and a negative antagonist, while we see it called as very weak in both modes, but with this activity showing only in the cytotoxicity region (see the inset in the figure)

. Therefore, the activity in the antagonist assays which drive the antagonist AUC may be driven by false

- positive cytotoxicity loss of signal

. In general, we see that the positive reference chemicals, with the exception of some that are very weak, are identified as having the appropriate activity (agonist, antagonist)

. Negative r eference chemicals either have no activity in any assays or are active in one of the pseudo

- receptor channels with scores higher than for the agonist or antagonist receptors . T he AUC agonist (R1 ) and AUC antagonist ( R2 ) values for the reference chemicals and plots of the assay and receptor concentration

- response profiles, are provided as Appendices 3 and 2 , respectively)

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 29 of 111Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 30 of 111 Figure 2.4 : Summary plots of agonist and antagonist activity of reference chemicals and breakdown of agonist score across potency category

. The top panel displays p lots of agonist (top) and antagonist (bottom) activity for reference chemicals

. Chemicals that are intended to be positive are indicated by a green circle, while those intended to be inactive are red

. For the agonist, the expected potency range is also indicated

. For AUC values ≤0.01, the value was set to 0.01

. For chemicals with one or more pseudo

- receptor AUC values greater than the expected one, the value is indicated by an X, and the receptor name is indicated

. T he receptor plot for Di

- n

- butyl phthalate is shown in the inset and the corresponding agonist and antagonist AUC points are boxed

. The bottom panel shows a box and whiskers plot of the AUC( R1/ agonist) values for chemicals in the reference activity ranges.

Activity Classifications of the Commercial and Environmental Chemicals : Our ultimate goal is to determine whether a chemical can interact with ER based on data which is subject to noise and a variety of assay interference types

. Figure 2.5 shows the act ivity against each of the receptors and pseudo

- receptors, including those accounting for a single assay

. From this, one can see that there are several pseudo

- receptors (e.g

. R6, R9 ) for which there are many chemicals with an AUC value≥0.1

. In total, 621 out of 1812 chemicals (36%) had at least one AUC≥0.1

. Only a small number of chemicals (27) have more than one AUC≥0.1

. As mentioned above , an AUC of 0.1 corresponds approximately to an AC 50 of 100  M, which is the upper limit of testing for most of the assays.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 31 of 111 Figure 2. 5: Heatmap of the activity of the 621 chemicals with an AUC value ≥0.1 in at least one receptor

. The AUC matrix was fil tered to show only values ≥0.1.

We explore several potential causes of pseudo

- receptor activity

. The first potential cause has to do with assay noise

. All assays have a background level of noise, and one must define a threshold to decide between a hit and a non

- hit

. To be called a hit in the data analysis pipeline , the maximum efficacy must be above a defined statistical criteria, which is typically 10 MAD above baseline

. Given this relatively high threshold (10 MAD, essentially 10 standard deviations), one would expect few noise

- related false positives if the noise was approximately normally distributed

. In particular, for full agonists in an assay with only normally distributed noise, one should see T values of ~100% for the actives, and T values for inactive chemicals should be small (<<10 MAD)

. We compared the T distributions between the strong agonists and the chemicals in each of the pseudo

- receptor groups using a t

- test

. Table 2. 3 lists the mean T for chemicals active in the pseudo

- receptors and in the agonists, and a t

- test p

- value

. (Note that for the antagonist assays and R9, the comparator group w as the antagonist chemicals, AUC(R2) ≥0.1

. We see that the actives (AUC≥0.1) in R6, A9, A12, A13, A15 and A17 have significantly lower T values than the comparator groups, but are still above the statistical threshold, implying that at least some of this pseudo

- receptor activity is associated with non

- normally

- distributed assay noise

. We also tested to see if the pseudo

- receptors had AC 50 values above that of the comparator (i.e

. were they significantly less potent on average)

. In all cases, the mean AC 50 for the pseudo

- receptor active chemicals was shifted to higher values, but the shift was not statistically significant

. This implies that the low T values for these chemicals are not mostly due to curves with upper asymptotes not resolved because their AC 50Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 32 of 111 values were close to the upper concentration tested

. As part of the assay analysis pipeline, we apply a series of “caution flags” to assay

- chemical data sets with specified properties, for instance being active with T just above the cutoff, or being inactive with T just below this cutoff

. To see if some of the pseudo

- receptor activity was associated with chemical

- assay pairs with

more

- than average warnings, a test was performed for each assay, comparing the number of flags with chem icals called as a ssay

- specific pseudo

- receptor

- active vs

. those called as agonists

. These distributions were compared with a Kolmogorov

- Smirnov (KS) test

. Assay

specific chemicals for assays A13 and A15 had significantly more caution flags than did those for chemicals c alled as agonists.

A second potential cause of off

- target assay activity is cell

- stress or cytotoxicity related activity

. With in vitro assays, one often observes false activity at concentrations near cytotoxicity

. We term this the cytotoxicity “burst”, although it has also been referred to as the “stress explosion”

. The Z

- score (see methods) is used to quantify how close an assay AC 50 is to the cytotoxicity region

. Qualitatively, Z<3 can be associated with cytotoxicity, and Z≥3 not

. For most assays, we observe a bimodal distribution of Z

- scores with a minimum at ~3, an example of which is shown in Figure 2. 6 for ATG_ERa_TRANS_up (assay A12)

. We then compare the distribution of Z

- scores for each pseudo

- receptor with the same distribut ion for the chemicals classified as agonists or antagonists, using a one

- sided t

- test

. From Table 2. 3 , one can see that many of the off

- target activities are more likely to be in the burst region, namely R6, R9, A12, A13, A15 and A17

. However, the mean f or these chemical sets are not necessarily <3

. One specific set of pseudo

- receptors are A17, A18 and R9, which are the antagonist assays, separately or together

. These have the lowest mean Z

- scores for the off

- target chemicals, meaning that hit AC 50 s are concentrated in the cytotoxicity region

. This is expected because these are loss

- of

signal antagonist assays which are often confounded by cytotoxicity

. Note that for A18, the shift in Z

- score is not statistically significant, but this is because the co mparators (positive antagonists) also have low Z

- scores on average.

Table 2. 3 : Statistics for T/top and Z for the pseudo

- receptor active chemicals for pseudo

receptors with 10 or more chemicals

. The reference or comparator chemicals for A17, A8 and R9 ar e the chemicals with AUC(R2)≥0.1, while all others are the chemicals with AUC(R1)≥0.1

. NS=not significant, p>0.05.

Pseudo

Receptor N |T| |T| (Agonist) p(T) |Z| |Z| (Agonist) p(Z)

R3 10 94 86 NS 9 8.8 NS R6 96 60 76 2.00E

- 09 3.8 7.3 2.10E

- 14 R8 33 101 110 NS 7.7 7.9 NS R9 54 98 97 NS

- 0.57 2.8 2.80E

- 05 A2 13 91 88 NS 7.5 9.4 NS A3 23 81 80 NS 6.7 7.9 NS A9 10 47 101 1.40E

- 06 6.1 5.4 NS A10 13 72 85 NS 6.8 6.3 NS A12 37 53 81 2.10E

- 09 3.3 7.3 1.50E

- 06 A13 66 33 71 9.10E

- 23 5.8 7.3 0.021 A15 71 65 116 2.80E

- 08 4.5 6.2 0.022 A17 64 75 86 0.037 1.4 3.6 0.019 A18 28 95 101 NS 2.6 1.9 NSIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 33 of 111 Figure 2. 6 : Z

- score plot for the assay ATG_ERa_TRANS_up showing a histogram of Z values (the distance between the median cytotoxicity concentration and a chemical’s AC 50 in this assay, in units of global cytotoxicity MAD) for all chemicals active in this assay

. One can see a bimodal distribution with one peak at zero (marked with a heavy red line) and another with a peak near 6

. The light red lines are us ed to distinguish the well

- separated low

- Z from high

- Z regions used for certain calculations

. We hypothesize that chemicals active in the low

- Z region are less likely to be estrogenic than those active in the high

- Z region

.
Next, chemicals were removed that had pseudo

- receptor activity confounded by either low efficacy (low T) or activity in the cytotoxicity region (low Z)

. For each chemical, the median T and Z were calculated for the corresponding assays, and a “specificity score” was produced using a smooth sigmoid function centered at T=50% and Z=3, with width(T)=10% and width(Z)=0.2

. Chemicals were excluded at this step if specificity(T) or specificity(Z) was <0.5

. Figure 2. 7 depicts the down

- selection of chemicals with the set of filters just desc ribed

. This is because we see a number of model agonists, including some of the very weak reference chemicals, where most or all of the activity is in the cytotoxicity region

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 34 of 111 Figure 2. 7 : Down

- selection of chemicals through the steps described in t he text

. “Active” are chemicals with at least one AUC value≥0.1; “High

- T” are those with T

- specificity score≥0.5; “High Z” are those with Z

- specificity score ≥0.5

. The diagnostic tests listed are described below, and are used to explore additional causes of pseudo

- receptor activity.

Two other potential cause of false

- positive signals in an assay are crosstalk and cross

reactivity

. Crosstalk is defined here to mean that one readout in a multiplexed assay will be activated inappropriately if another targe t in the assay is activated

. Cross

- reactivity occurs when a chemical that is potent in one receptor (e.g

. AR) is weakly potent in others

. The Attagene assay is highly multiplexed, with 52 readouts in a single well in the CIS assay and 30 readouts in the TRANS assay

. Here we tested to see if there were more actives in

non

- ER assay endpoints for chemicals classified as Attagene pseudo

- receptor actives (A12, A13, R6) than for chemicals classified as ER inactive

. A KS test was performed comparing these dist ributions, separately for the multiplexed CIS and TRANS assay

- readout sets

. In addition to testing the Attagene pseudo

- receptor, all other pseudo

- receptors were also tested

. The statistically significant comparisons are show n in Table 2. 4

. A12 and R6 ps eudo

- receptor chemicals were significantly more active in non

- ER TRANS readouts, indicating direct crosstalk

. However, we also see that A12 chemicals are significantly more active in the non

ER CIS readouts, while A13 chemicals are significantly more acti ve in the non

- ER TRANS readouts (A12 is the TRANS ER readout, while A13 is the CIS ER readout)

. This is evidence of general cross

- reactivity

. We also see that A18 (one of the cytotoxicity

- sensitive antagonist assays) is significantly more active in non

- E R readouts for both CIS and TRANS

.

Crosstalk / cross

- reactivity in the Novascreen cell

- free radioligand nuclear receptor binding assays was tested in the same manner

. These are not multiplexed assays, but all use the same technology

. We would expect pan

- assay activity from any chemicals that, for instance, caused protein denaturation

. Pseudo

- receptor active A2 chemicals had a significantly higher number of other Novascreen NR actives than did ER inactive chemicals ( Table 2. 4 )

. Both model Agonist and An tagonist (R1 and R2) chemicals also had significantly more Novascreen NR actives than did ER

- inactive chemicals, indicating possible cross

- reactivity (defined as where a chemical active in one NR is also active in other related NRs, but at differentIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 35 of 111 concen trations)

. Other non

- Novascreen pseudo

- receptors did not show any significant difference

. Tox21 assays use two different formats (beta

- lactamase or BLA and luciferase or LUC), and there are multiple NR assays run in each technology, in both agonist and a ntagonist modes

. The test for increased activity in the Tox21 non

- ER assays showed the following

. Chemicals classified as A17 (BLA antagonist), had significantly more non

- ER BLA antagonist hits than did ER

- inactive chemicals

. This is most likely a refle ction of cell stress or cytotoxicity that was not already filtered out with the Z filter

. Likewise, the A18 (LUC antagonist) chemicals were also more active in non

- ER LUC antagonist assays than were the ER

- inactive chemicals, again likely reflecting so

- fa r unaccounted for cytotoxicity

. A17 and model

- agonist chemicals were also significantly over

- represented in non

- ER LUC antagonist actives

. Finally, A12 chemicals had more actives in non

- ER LUC agonist assays than did the ER

- inactive chemicals

.

Table 2. 4 : Statistics for crosstalk / cross

- reactivity comparisons

. The “N on

- ER assay S et ” is the collection of assays for which non

- ER assays were selected for comparison

. The “Expected Pseudo

- Receptors” would be expected to be over

- represented in actives in th e non

ER assay set if there was in

- technology crosstalk

. The “C orrelated P seudo

- R eceptors ” are those for which there was statistically significant association in the KS test

. The “ Reference Mean ” is the mean of the number of actives in the non

- ER assay s et for chemicals classified as ER

- inactive

. The “ Pseudo

- R eceptor M ean ” is the mean number of actives in the non

- ER assays set for chemical classified as the “ Correlated P seudo

- R eceptor”

. The p

- value is the

one

- sided KS

- test p

- value

. Only associations wi th p<0.01 are reported

. Rows where the “expected” and “correlated” pseudo

- receptors match are bolded

.

Non

- ER Assay Set Expected Pseudo

Receptors Correlated

Pseudo

Receptor Reference

Mean Pseudo

Receptor

mean p - value

Attagene CIS A13, R6 A12 3.8 15 0.0023 Attagene CIS A13, R6 A18 3.8 18 0.0013 Attagene TRANS A12, R6 A12 1.5 6.7 0.00015 Attagene TRANS A12, R6 A13 1.5 3.4 0.00069 Attagene TRANS A12, R6 A18 1.5 5.9 0.0066 Attagene TRANS A12, R6 R6 1.5 2.7 0.00019 Novascreen NR A1, A2, A3 A2 0.64 2.1 0.01 Novascreen NR A1, A2, A3 Agonist 0.64 1.8 0.00024 Novascreen NR A1, A2, A3 Antagonist 0.64 3.3 0.0015 Tox21 BLA Antagonist A17, R9 A17 0.26 2.2 1.20E

- 07 Tox21 LUC Agonist A15, R7 A12 0.54 1.1 0.0078 Tox21 LUC Antagonist A18, R9 A17 0.14 1.1 4.30E

- 06 Tox21 LUC Antagonist A18, R9 A18 0.14 1.7 1.70E

- 05 Tox21 LUC Antagonist A18, R9 Agonist 0.14 0.41 0.0035

Another potential explanation of the pseudo

- receptor actives is that they are so

- called PAINS (Pan

- assay interference compounds (Baell and Holloway 2010) )

. To test this, we compared the activity of the compounds in each of the receptor and pseudo

- receptor groups with the ER

-Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 36 of 111 inactive set, looking across the large set of ToxCast assays in which these compounds were tested

. This number of assays ranged from 187 to 821, after the 18 ER assays were excluded

. The distribution of per

- chemical hits with Z≥3 for recepto r or pseudo

- receptor group was tested against the distribution for the inactive group using a one

- sided KS

- test (High

- Z in Figure 2. 7 )

. We also performed the same test for chemicals in the Low

- Z group from Figure 2. 7

. We see significant activity in the b road group of non

- ER assays in several pseudo

- receptor chemical groups among those chemicals showing activity only at low Z (i.e

. in the cytotoxicity region)

. These sets are Agonist, Antagonist, R3, R6, A3, A12, A13, A15, A17 and A18

. This is a reflectio n that, in the cytotoxi ci ty region, many assays are active, likely due to non

- receptor

mediated processes

. At high Z, only the Agonist and Antagonist chemicals are more active than ER

- inactive chemicals in the broader set of assays

. This reflect s the fac t that many of the true ER actives (especially drugs and hormones) appear to show significant cross

- reactivity with other receptors, although at higher concentrations than where ER is activated (Agonist reference and receptor means: 0.026 and 0.072, p=1.8E

- 8; Antagonist reference and receptor means: 0.026 and 0.18, p=6.1E

- 5).

All chemicals in the tested library were assigned a structure class, based on structure

- similarity clustering

. We tested for a significant association between being in a given struct ure class and a chemical being assigned to one of the receptors or pseudo

- receptors after Z and T filtering

. This analysis used a chi

- squared test

. The only significant associations were with model

agonist chemicals, AUC(R1)≥0.1 and structure classes gen istein

- like, paraben, alkyl

- phenol, steroids targeting the estrogen receptor and steroids targeting the androgen receptor

. The only structure class significantly associated with model

- antagonists was tamoxifen analogs

. We further examined the broad stero id class of compounds for cross

- reactivity

. Within the 238 agonist, antagonist and pseudo

- receptor actives of Figure 2. 7 , there are 10 steroids that target other nuclear receptors: AR / androgen receptor (Dehydroepiandrosterone, Bicalutamide, 5alpha

- Dihyd rotestosterone, Cyproterone acetate, Testosterone propionate, 4

- Androstene

3,17

- dione), NR3C1/ glucocorticoid receptor (Melengestrol acetate, Triamcinolone, Prednisone, Mifepristone, Pregnenolone carbonitrile) and PGR / progesterone receptor (17alpha

- Hydro xyprogesterone, Norgestrel)

. Assay A16, using an ER

- responsive proliferation readout in T47D cells, is also responsive to chemicals targeting the progesterone receptor

. All four PGR ligands in the chemical set (Hydroxyprogesterone caproate, 17alpha

Hydro xyprogesterone, Norgestrel, Norethindrone) activate this assay with sub

- micro

- molar AC 50 s.

We next looked for significant differences in physico

- chemical properties between the ER

inactive chemicals and chemicals assigned to the pseudo

- receptor groups aft er Z and T filtering

. A t

- test was used

. Calculated properties were taken from QikProp ( http://www.schrodinger.co m/QikProp/ ) , ACD Labs ( http://ww w.acdlabs.com/ ) and MOE ( http://www.chemcomp.com/MOE

- Molecular_Operating_Environment.htm )

. Electronic properties that showed statisticall y significant differences between pseudo

- receptor and inactive chemical sets include: charge state at physiological pH (agonist and A12 chemicals are significantly more likely to be charged); diploe moment (agonist and A10 chemical have lower dipole momen ts on average); electron affinity (A17 and A18 chemicals have higher values on average); ionization potential (A12, agonist and antagonist chemicals have lower values on average); polarizability (A18, agonist and antagonists are more polarizable on average ); and acid pK a (agonist and antagonist chemicals have larger pK a values on average)

. Size

- related properties are molecular weight (A18, agonist and antagonist chemicals are larger on average); solvent

- accessible surface area (SASA) (A17, A18, agonist and antagonistIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 37 of 111 chemicals have larger SASA on average); and molecular volume (A17, A18, agonist and antagonist chemicals have larger volume on average)

. Partition coefficients include octonal

water partition coefficient, logP (A18, agonist and antagonist chem icals have larger logP values on average, so are more lipophilic); charge

- corrected logP (logD) (agonist and antagonist chemicals have larger values on average); and water solubility (logS) (A18, agonist and antagonist chemicals have smaller values on aver age, so are less water soluble and more lipophilic as above)

. Although we see statistical trends between agonists, antagonists and the pseudo

- receptor actives, in general these effects are not large enough to explain the assay

interference effects in a ch emical

- by

- chemical basis

. We have made a comparison of the results of our model with ER in vitro data from the literature

. Literature data was mostly taken from the Endocrine Disruptor Knowledgebase (EDKB) (Ding et al. 2010; Shen et al. 2013) and ChEMBL (Gaulton et al. 2012)

. In addition, reference sets from published QSAR models were included

. The compilation of this curated ER literature database is in preparation

. (See the ER QSAR section below)

. We selected a subset of chemicals from the literature database that had at least 4 reports in the literature, with a preponderance reporting the same call (active or inactive; 80 % or more must be one class)

. Separate classifications were made for binding assays, agonist assays and antagonist assays

. The reason for requiring consistency across multiple reports is that the literature database has the same types of disagreement bet ween different laboratories and assays as we see in the current data set

. Most of the chemicals in our data set have few or no associated reports in the literature

. Table 2. 5 summarizes the results of the comparison for receptors / pseudo

- receptors with at least 5 chemicals with at least 4 reports each

. Comparisons are made separately for binding, agonism and antagonism

. For instance, for chemicals classified as agonists (AUC(R1) ≥ 0.1) before Z and T filtering, 53 were reported in the literature to be po sitive for binding, 23 to be positive for agonist activity and 1 to be positive for antagonist activity

. None of these chemicals was reported to be inactive in any of these modes

. Note that chemicals that are true agonists or antagonists should be active in binding assays

. Other rows in the table can be interpreted in the same way

. Therefore, we see that there is good agreement with agonists and antagonists both before (Initial) and after (Final) Z and T filtering

. However, there are a number of chemic als, prior to Z and T filtering that we have classified as inactive (None) or pseudo

- receptor active in R6

. The 4 chemicals with consistent literature positive calls classified as R6 are Endosulfan, Dieldrin, Endrin and Triamcinolone

. The first three are polychloro

- bicyclic compounds

. These are chemically similar to Kepone, which is one of the positive agonist reference compounds, and which is correctly classified as an agonist

. So for these related compounds, we see some, but limited ER activity

. Tria mcinolone is a steroid drug targeting NR3C1 (glucocorticoid receptor)

. There are 11 compounds that we classify as negative for which there are multiple literature reports of being positive binders

. These include Endosulfan 1, which just missed the cutoff for being called an agonist (AUC(R1)=0.09); Dipentyl phthalate, a weak antagonist (AUC(R2)=0.06); another 7 compounds very weakly active (>10  M) in one or more assays (Sodium 2

- mercaptobenzothiolate, 4,4'

- Dithiodimorpholine, Benz(a)anthracene, 2,4

- Di

- tert

butylphenol, Dicyclohexyl phthalate, 2

- Mercaptobenzothiazole, 5

- Fluorouracil), but with activity too weak to have any pseudo

- receptor value above the 0.1 threshold

. The final two chemicals are maneb and mancozeb

. These were not active in any of the ER assays, but are among the most promiscuous chemicals in the ToxCast battery

. Overall, this set of 11 chemicals is relatively promiscuous, especially in the low

- Z region, indica ting that some of this reported activity may be due to assay interference.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 38 of 111 Table 2. 5 : Results of comparison of the current model results with activity calls from the literature

. A comparison for a chemical was made if at least 4 literature report for th at chemical in one of the 3 modes (Binding, Agonist, Antagonist) were found, and if the calls were at least 80% concordant ( positive or negative )

. The numbers in the cells of the table are counts of chemicals

. Initial and Final chemical sets refer to bef ore and after T and Z filtering

. Chemical Set Receptor N (Binding Positive) N (Binding Negative) N (Agonist Positive) N (Agonist Negative) N (Antagonist Positive) N (Antagonist Negative)

Initial Agonist 55 0 25 0 1 0
Initial Antagonist 5 0 0 0 3 0
Initial None 11 3 2 0 2 0
Initial R6 4 1 3 0 0 0
Final Agonist 41 0 19 0 1 0
Final Antagonist 5 0 0 0 3 0

ER Model Summary : This section has demonstrated a method to combine data from multiple in vitro assays in the ER pathway in order to generate a consensus call of ER activity

. The need for this approach is driven by the presence of false positive and false negative calls in all assays

. An ongoing topic of research is trying to use chemical structur e and properties to understand exactly why specific chemicals cause this (mainly) false positive activity

.
ER QSAR Overview

: For technical and cost

- related reasons, it will be difficult to procure all EDSP Universe chemicals and process them through all of the relevant in vitro assays

. Because of this, we are developing structure

- based models (QSARs and molecular docking) to extrapolate from the current in vitro data set, and to guide the acquisition of further in vitro data in a targeted fashion

. The results of the ER model described above provide a large , high

quality training set to use for developing and testing QSAR models

. We anticipate that we will use an iterative approach to evaluating the EDSP Universe for ER activity consisting of in vitro screening on the ToxCast chemicals

, run ning the model to determine actives in that set

, us ing the entire data set to build QSAR models

, tak ing the predicted ac tives from the QSAR models and test ing them in in vitro assays , refin ing the model with new data

, and iterat ing

. This section is included to briefly summarize our activities and to demonstrate progress in an area deemed important by the January 2013 SAP

. All methods and results of this effort are being documented in several manuscripts that should be published in 2015.

Just as every in vitro assay has false positives and false negatives, so will every structure

based model

. So, just as in the case of th e ER in vitro assays, we have compiled predictions from a large suite of structure

- based models, in an effort called CERAPP (CollaborativeIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 39 of 111 Estrogen Receptor Activity Project)

. The CERAPP combines multiple structure

- based computational models for predictio n of ER activity, and uses the predicted results of these models in prioritization of chemicals for further testing

. The CERAPP approach will (at minimum) address the following questions:

1. What chemicals are predicted to be ER

- active in all or the majorit y of models? These will be prioritized for further study. 2. What chemicals are predicted to be ER

- inactive in all or a majority of the models? These will be set aside relative to any follow

- up. 3. What chemicals are outside of the domain of applicability of all or most models? These chemicals are candidates for generating additional in vitro data to fill in these uncharacterized regions of chemical space.

For CERAPP, the EPA provided the results of the ER model discussed in the previous section for 1677 chemicals with well

- defined structure (a subset of the 1812 chemicals described above are mixtures), and collection of 32,464 chemical structures for which ER activity was to be predicted

. Each group then either used the EPA data to train a new model, or used an existing model that had been trained with other data

. A subset of the CERAPP partners then compiled data from the literature on approximately ~7,500 additional chemicals with limited ER data

. This literature data set was then used to evaluate the predictive accuracy of the individual models

. Finally, the models were combined to give a consensus prediction score across the large 32K chemical structure library.

A total of 17 research groups in the U.S

. and Europe submitted models based on this tra ining set or training data they had from other sources

. Some groups submitted multiple models

. These models used a variety of machine learning approaches, as well as molecular docking

. Models were separately built for binding, agonist and antagonist act ivity

.

The CERAPP goals are being carried out in multiple steps that can be categorized into 5 parts including chemical structure curation

, QSAR/docking modeling, experimental data preparation

, model evaluation and consensus modeling

. Each step was assig ned to a workgroup comprised of representatives from a subset of the participating research groups

. A common theme is that these workgroups would define and document best practices.

In order to evaluate models, we obtained a set of ~7000 compounds with E R activity from the literature, and built consensus models from that

. One finding of this effort is that data from the literature for which the same chemical was tested in multiple labs with different assays, had a similar rate of discordance to what we s ee in the ToxCast ER assays

. We evaluated the performance of each model and the model consensus against increasingly refined subsets of the literature data

. By more refined, we mean subsets in which the chemicals were tested in increasing number of indep endent labs, and had consistent (positive or negative) results across the labs

. The final result of this effort is consensus scores for binding, agonism and antagonism, combining predictions from all of the relevant models

. The combining method weighted models based on their prediction accuracy against the training and external test sets

. One important observation about the individual models is that their overall balanced accuracy values (mean of sensitivity and specificity) do not vary greatly, but some models overemphasize sensitivity over specificity, and others go the opposite direction

. TheIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 40 of 111 consensus model is more balanced

. Overall, the consensus predicts about 10% of the evaluated chemicals could interact with the estrogen receptor

. This consensu s model achieves ~90% balanced accuracy when predicting the most refined and consistent set of experimental data

. A subset of these chemicals will be selected for inclusion in later rounds of in vitro testing, and will then be evaluated with the model des cribed above

. The performance of the final consensus model is illustrated in Figure 2.8

.

Figure 2.8 : Summary of ER QSAR predictivity against literature calls

. Each curve shows the balanced accuracy of the consensus QSAR model against a subset of the literature having at least N consistent calls (sources) for each chemical (active vs

. inactive)

. The x

- axis is the fraction of QSAR models that call a chemical positive used to define “consensus

- positive”

. 

### 2.2: Interpretation and application: ER

In this section, we present various analyses comparing ToxCast models of in vitro ER bioactivity with in vivo activity and evalu ate the scientific confidence in using ToxCast data to :

1. P rioritize chemicals in the EDSP universe based on bioactivity ; 2. P rovide data that may contribute to future weight of evidence evaluations of a chemical’s potential to interact with the estrogen receptor; and 3. S ubstitute for specific assays and/or endpoints in the EDSP Tier 1 battery

. The EDSP is tasked with rapidly screening approx imately 10,000 chemicals in the chemical universe defined by the FIFRA and SDWA statutes, and a s previously mentioned, the chemicals identified for EDSP screening on List 1 and List 2 were selected based on potential exposure or pesticide registration revi ew schedules, rather than indication s of potential bi oactivity

. Conducting the EDSP Tier 1 screening battery require s a great deal of financial,Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 41 of 111 temporal, animal, and human resources

. Since the first tes t orders were issued in 2009, 52 chemicals have been screened in the battery and List 1 weight of evidence determinations are being finalized by EPA

. Incorporating computation al tools in the existing EDSP framework can literally transform a decades to cen turies

- long undertaking into a project that can likely be completed in years , while simultaneously increasing the likelihood that the most bioactive chemicals are prioritized

.

This section illustrates the proposed prioritization approach for estrogen rece ptor agonist activity , as most environmental chemicals that interact with the estrogen receptor are expected to act as agonists (Safe et al. 2001 )

. The prioritization approach uses EPA’s ToxCast high throughput in vitro screening assays, computational models, and semi

- automated curation of peer

- reviewed endocrine toxicology literature

, and discusses scientific confidence in the results

. This strategy would represent a pivotal paradigm shift in the EDSP approach consistent with recommendation of EPA’s s trategic plan for evaluating the toxicity of chemicals

a nd the 2007 NAS report, specifically to: 1) provide broad coverage of chemicals examined ; 2) reduce the cost and time of toxicity testing ; 3) reduce animal use ; and 4) develop a robust scientific basi s for assessing health effects of environmental agents.

2.2.1: Curated literature review of uterotrophic literature

Curation process : The uterotrophic bioassay is a short

- term in vivo screen for estrogenicity and is one of the 11 EDSP Tier 1 screening as says

. The EPA EDSP u terotrophic assay

, OCSPP 890.1600

, is performed on immature female rats or ovariectomized (OVX) adult fem ale mice or rats ( U.S. EPA, 2011 ), and is a measure of in vivo estrogen receptor agonism in female mammal s without an intact hypothalamo

- pituitary gonadal axis

. To build confidence in the ability of computational toxicology methods to prioritize and screen chemicals for potential estrogenic activity, concordance between results from ToxCast in vitro ER assays and the rodent uterotrophic bioassay was examined

.

T he N ational T oxicology P rogram Interagency Center for the Evaluation of Al ternative Toxicological Methods (NICEATM) conducted a comprehensive literature search to identify uterotrophic studies for all 1 812 chemicals run in the 18 high throughput ToxCast assays that are relevant to the ER pathway

. Semi

- automated literature searches were performed with the NCBI PubMatrix tool ( http://pubmatrix.grc.nia.nih. gov/ ), using both chemical name and CASRN as the search terms

. The modifier terms “uterotrophic assay”, “uterotrophic”, “uterotropic” (a common alternative) and “uterine weight” were used to identify articles of interest

. Relevant articles were then down loaded for manual data curation

. The search yielded more than 1000 papers, of which 670 were deemed potentially relevant to the ToxCast chemical inventory

. These manuscripts were downloaded and detailed study information (sp eci es, strain, study type, ass ay type, assay target, dose administration route, age at first dose, OVX status, age at OVX, dosing length/frequency, number of doses, number of animals , positive/negative controls, diet, time of necropsy, lowest effect level, response observed, maximum do se tested) were recorded for each chemical/study/protocol combination along with the reported bioactivity for the dose range tested; “active” with a corresponding lowest effective level (LEL) or “inactive” with a corresponding highest dose tested (HDT)

. Two NICEATM scientists independently reviewed and extracted data from each report.

Compliance with study design requirements set forth in EPA OCSPP 890.1600 and OECD 440 Test Guidelines was evaluated based on the information extracted from each report

. A studyIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 42 of 111 was considered to be “guideline

- like” (GL) if all six of the following minimum criteria (MC) were met:

1. Animal model (any of the following)

a. Immature rats, ovariectomized (OVX) adult rats, or OVX adult mice are acceptable (immature mice are not acc eptable). b. OVX animals: OVX should be performed between six and eight weeks of age (allowing at least 14 days post

- surgery before dosing for rats and seven days post

- surgery for mice)

c. Immature rats: dosing should begin between post

- natal day (PND) 18 and PN D 21, and be completed by PND 25 2. Group size

a. Each control group should have a minimum of three animals and each test group should have a minimum of five animals 3. Route of administration

a. Acceptable routes of administration: oral gavage (p.o.), subcutaneous (s.c.) injection, or intraperitoneal (i.p.) injection 4. Number of dose groups

a. Minimum of two dose levels plus positive control and vehicle control 5. Dosing interval

a. Dosing for a minimum of three consecutive days 6. Necropsy timing

a. Animal necropsy should be carrie d out 18

- 36 hours after the last dose

Data for an additional criterion (MC7) was recorded to indicate whether or not levels of phytoestrogen in the diet were reported, but this criterion was not used to exclude data due to the small number of studies repo rting this information (<5% of the 670 papers reviewed)

. A score of 0 (no) or 1 (yes) was recorded for each of the minimum criteria (MC 1

- 6) based on whether the study/protocol fulfilled that particular requirement

. These scores were added to yield a to tal score for each study , and o nly those studies meeting all six criteria were considered GL

. It should be noted that compliance with the minimum criteria identified above is not equivalent to a thorough assessment of overall study quality ( e.g., comprehe nsive systematic review).

The resulting database of uterotrophic outcomes (UTDB) currently contains data for 98 chemicals in the ToxCast inventory, with information from 442 GL studies extracted from 92 publications

. This compiled information can serve a s a valuable anchoring point for assessing the concordance of uterotrophic outcomes and in vitro /computational approaches

. The “NICEATM_UTDB_GL.xlsx” provides a description of each study chemical combination ( Appendix 4)

. T h is file represent s ongoing wor k and new information will be added as it becomes available.

Impact of study design on uterotrophic outcome : There are six basic study designs that can meet GL criteria, depending on species (rat or mouse), route of administration (oral or injection 1 ), and use of OVX (rat or mouse) or immature (rat only) animals

. A breakdown of

1 While both subcutaneous and intraperitoneal routes of injection are acceptable as GL, 99% of the studies in the database used the subcutaneous route. Therefore, for this analysis, “injection” includes both subcutaneous and intraperitoneal studies

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 43 of 111 results by study design is provided in Table 2. 6

. Data from chemicals commonly used as positive controls ( i.e., estradiol s ) were removed from this analysis due to the large num ber of results and inherent bias associated with their inclusion ( i.e., negative results would indicate a failed “positive” control and would therefore not typically be reported) , leaving 357 GL uterotrophic entries

. The im mature rat model was used for 73 % ( 268/357 ) of the studies in the database, with 73% (196/267 ) of these using injection as the route of administration

. The distribution of active and inactive outcomes was similar across rat models, with active outcomes being more prevalent overall (74% [228/310] of all rat outcomes were active)

. This is in contrast to uterotrophic results reported in mouse models, in which 64% ( 30/47 ) of all outcomes were inactive, with the OVX _mouse_ oral design producing inactive outcomes in 73% (16/22) of the studies

. It should be kept in mind that the selection of chemicals tested in these studies is neither random nor uniformly distributed with respect to uterotrophic bioactivity, and the performance of a particular study design, especially those with a small numbe r of studies ( e.g., OVX_rat_injection), could be heavily influenced by a single publication from one laboratory testing multiple chemicals in that particular study design.

Table 2

. 6 : Distribution of uterotrophic outcomes by study design

.

Im_Rat Inj Im_Rat

Oral OVX_Rat

Inj OVX_Rat

Oral OVX_Mouse

Inj OVX_Mouse

Oral
# Active 142 52 29 5 11 6

# Inactive 55 19 3 5 14 16

%Active 0.72 0.73 0.91 0.50 0.44 0.27

%Inactive 0.28 0.27 0.09 0.50 0.56 0.73

%Total 0.55 0.20 0.09 0.03 0.07 0.06 Im

- immature, O VX

- ovariectomized

, # Active

- the number of experiments reporting substances as active, # Inactive

- the number of experiments reporting substances as inactive

. Data for positive controls is not included in this table.

Reproducibility of uterotrophic out comes : The UTDB provides an opportunity to assess both the qualitative and quantitative reproducibility of the uterotrophic assay across many chemicals tested at many different laboratories

. Of the 7 0 chemicals 2 in the database with at least two reported uterotrophic studies, 1 8 (2 6 %) had at least one study with a discordant outcome

, resulting in a chemical being classified as both “active” and “inactive” for uterotrophic bioactivity, shown in Figure 2.9

. A list of chemicals with discordant results is provided in Table 2. 7 , and a radar chart of each chemical in the table, reflecting study design and outcome

, is provided in Appendix 5

. Discordant outcomes could result from differences in overall study design and/ or the range of doses tested in each study ( e.g., the highest dose tested in an “inactive” study may have been below the dose that would produce a tissue concentration required for bioactivity)

.

Of the 18 chemicals listed in Table 2. 7 , 10 ( 56%, shaded ) of these had discordant uterotrophic outcomes that may be attributable to differences in study design

. Results from the testing of butylparaben provide a n example of how study design can impact uterotrophic outcomes , as shown in Figure 2.10

. In the case of butylparaben , all active outcomes were reported in the

2 Chemi cals routinely used as positive controls, such as estradiols, were uniformly positive and excluded from this analysis.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 44 of 111 three study designs using s.c

. injection as the route of administration (immatur e rat, OVX rat, OVX mouse), whereas inactive results were reported for both study designs that used oral dosing (immatur e rat, OVX mouse)

.

The eight non

- shaded chemicals in Table 2. 7 had discordant outcomes reported for studies using the same basic study design

. Uterotrophic outcomes were compared to determine if the HDT for “ inactive ” outcomes was below the LEL reporte d for “active” outcomes, in which case the results would actually support one another

. LEL and HDT values from the study design producing the mo st discordant results (immature_ rat

_ inj ection ) are provided in Figure 2.11

. For chemicals which had discordant outcomes reported in the same study design, it was common for HDT to be above LEL doses reported in other studies, although the difference in these values was normally less than one order of magnitude

. Most studies in the UTDB typically used log

- spaced d osing and four or fewer doses, resulting in poor resolution of lowest effect levels (generally defined as >10% increase in wet uterine weight, p<0.05), which could explain LELs and HDT reported at similar doses

. However, reports of “ inactive ” results obta ined at doses well above all reported LELs are difficult to reconcile

. For example, see Figure 2.11 , CASRN 80

- 05

- 7 (Bisphenol A), “active”by s.c

. injection at 2 mg/kg/day in one study using the immature rat model, and “inactive” by s.c

. injection at 1000 mg/kg/day in another study using the same model

.

Figure 2.9 : Results from uterotrophic studies for chemicals that have more than one GL study

. Blue bars represent the number of “active” reports; red bars represent the number of inactive reports.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 45 of 111 Table 2. 7 : List of chemicals with discordant uterotrophic results in GL studies

. Shaded chemicals had discordant uterotrophic outcomes (active / inactive) in study designs that differed significantly from one another

. The remaining chemicals (non

- shaded ) had discordant results reported in assays with the same basic study design.

CASRN Name # Active # Inactive 80

- 05

- 7 Bisphenol A 37 6 446

- 72

- 0 Genistein 27 1 72

- 43

- 5 Methoxychlor 18 1 789

- 02

- 6 o,p'

- DDT 15 1 94

- 26

- 8 Butylparaben 8 2 56

- 53

- 1 Diethylstilbestrol 8 1 104

- 40

- 5 4

- n

- Nonylphenol (linear, para ) 5 4 140

- 66

- 9 4

- (1,1,3,3

- Tetramethylbutyl)phenol 3 1 120

- 47

- 8 Ethylparaben 1 3 119

- 61

- 9 Benzophenone 1 2 99

- 76

- 3 Methylparaben 1 2 56

- 55

- 3 Benz(a)anthracene 1 2 1806

- 26

- 4 4

- Octylphenol 1 2 94

- 13

- 3 Propylparaben 1 2 52645

- 53

- 1 Permethrin 1 1 50

- 55

- 5 Reserpine 1 1 520

- 36

- 5 Apigenin 1 1 486

- 66

- 8 Daidzein 1 1Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 46 of 111 Figure 2.10 : Example of differences in study design that may be associated with discordant uterotrophic outcomes

. The number of active (black lines) and inactive (red lines) results is shown for butylparaben as a function of study design.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 47 of 111 Figure 2.11 : LEL / HDT for chemicals with discordant results in the I mmature _Rat_ Injection study design

. Markers reflect Lowest Effect Levels (LEL) for chemicals classified as “active” in the uterotrophic bioassay (blue markers), and highest dose tested (HDT) for those with “inactive” uterotrophic outcome s (red markers).

2.2.2: Comparison of ToxCast ER agonist AUC data and Uterotrophic studies

Chemicals used to assess performance of ER agonist model : OECD Guidance Document (Number 34 ) on the validation and international acceptance of new or updated test methods for hazard assessment indicates that the relevance of a new te st method should be characterized against data generated with a list of reference chemical s in the original test method (OECD, 2005 )

. These reference data should cover the expected range of responses and the range of chemicals to which the assay will be a pplied (the domain of applicability)

. Reference chemical lists previously developed by ICCVAM and OECD to assess the performance of ER agonist assays were based on the reported activity of these chemicals in in vitro assays , and therefore only provide a r eference for expected in vitro activity

. A small number of chemicals (n=7) were used for the OECD validation study of the uterotrophic assay, but a larger consensus list of reference chemicals has not been developed to assess the performance of alternativ es to the uterotrophic assay

. Consequently, a list of reference chemicals was developed de novo using studies from the uterotrophic database (UTDB) described in Section 2.2.1

. Because the focus of th e current evaluation is specific to ER agonist activity , chemicals with strong ER antagonist activity (tamoxifen, clomiphene citrate, and raloxifene hydrochloride) were excluded from consideration as reference chemicals

. TheIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 48 of 111 following criteria

, which characterize the overall potential of chemicals in the UTDB to show bioactivity in the uterotrophic assay, were used to develop a list of reference chemicals:

Active

– a chemical was classified as active if two or more independent GL studies classified the chemical as active in the uterotrophic bioassay, regardle ss of the number of inactive studies.

Inactive

– a chemical was classified as inactive if two or more independent GL studies classified the chemical as inactive in the uterotrophic bioassay, and there were no other GL studies reporting the chemical as bei ng active in the uterotrophic bioassay.

The resulting list of 42 reference chemicals for uterotrophic bioactivity (29 active, 13 inactive) is provided in Table 2. 8

. It should be noted that this list is dynamic and will be updated as more information is a dded to the database

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 49 of 111 Table 2. 8 : 42 Reference Chemicals used for Uterotrophic Bioactivity

.

CASRN Name GL _Active GL_Inact

ive Bioactivity L_LEL (mg/kg/day) R1

AUC 50

- 28

- 2 Estradiol 30 0 Active 0.00001 1.01 57

- 63

- 6 Ethinyl Estradiol 61 0 Active 0.0001 1.01 56

- 53

- 1 Diethylstilbestrol 10 1 Active 0.00025 1.01 72

- 33

- 3 Mestranol 3 0 Active 0.027 0.91 57

- 91

- 0 Alfatradiol 2 0 Active 0.4 1.01 50

- 27

- 1 Estriol 4 0 Active 0.67 0.96 789

- 02

- 6 o,p'

- DDT 15 1 Active 1 0.49 446

- 72

- 0 Genistein 27 1 Active 1 0.77 80

- 05

- 7 Bisphenol A 37 6 Active 2 0.66 68

- 22

- 4 Norethindrone 2 0 Active 2 0.67 53

- 16

- 7 Estrone 8 0 Active 2 0.99 474

- 86

- 2 Equilin 2 0 Active 2 0.98 17924

- 92

- 4 Zearalenone 5 0 Active 2 0.89 1478

- 61

- 1 Bisphenol AF 4 0 Active 4 0.82 58

- 18

- 4 Methyltestosterone 3 0 Active 10 0.44 80

- 09

- 1 Bisphenol S 2 0 Active 20 0.32 77

- 40

- 7 Bisphenol B 2 0 Active 20 0.75 72

- 43

- 5 Methoxychlor 21 1 Active 20 0.38 599

- 64

- 4 p

- Cumylphenol 2 0 Active 20 0.54 521

- 18

- 6 Dihydrotestosterone 3 0 Active 20 0.50 104

- 43

- 8 4

- Dodecylphenol 3 0 Active 40 0.56

140

- 66

- 9 4

- (1,1,3,3

-

Tetramethylbutyl)phenol 3 1 Active 56 0.56

94

- 26

- 8 Butylparaben 8 2 Active 59 0.38 98

- 54

- 4 p

- tert

- Butylphenol 2 0 Active 100 0.22 131

- 56

- 6 2,4

- Dihydroxybenzophenone 3 0 Active 100 0.40 80

- 46

- 6 4

- (1,1

- Dimethylpropyl)phenol 4 0 Active 200 0.40

5153

- 25

- 3 Benzoic acid, 4

- hydroxy

- , 2

-

ethylhexyl ester 2 0 Active 200 0.51

131

- 55

- 5 Benzophenone

- 2 6 0 Active 200 0.56 556

- 67

- 2 Octamethylcyclotetrasiloxane 3 0 Active 250 0.00 99

- 96

- 7 4

- Hydroxybenzoic acid 0 2 Inactive NA 0.00 87

- 86

- 5 Pentachlorophenol 0 2 Inactive NA 0.00 84

- 75

- 3 Dihexyl phthalate 0 2 Inactive NA 0.02 84

- 74

- 2 Dibutyl phthalate 0 2 Inactive NA 0.04 84

- 66

- 2 Diethyl phthalate 0 2 Inactive NA 0.00 84

- 61

- 7 Dicyclohexyl phthalate 0 2 Inactive NA 0.05 61

- 82

- 5 Amitrole 0 2 Inactive NA 0.00 520

- 18

- 3 Kaempferol 0 3 Inactive NA 0.34 51630

- 58

- 1 Fenvalerate 0 2 Inactive NA 0.00 1912

- 24

- 9 Atrazine 0 2 Inactive NA 0.00 1461

- 22

- 9 Tributylchlorostannane 0 2 Inactive NA 0.00 117

- 81

- 7 Bis(2

- ethylhexyl)phthalate 0 2 Inactive NA 0.00 103

- 23

- 1 Bis(2

- ethylhexyl)hexanedioate 0 2 Inactive NA 0.00 GL_Active = number of guideline

- like (GL) studies reporting activi ty, GL_Inactive = number of GL studies reporting no activity a t the highest dose tested, L_LEL = lowest of the lowest effect levels reported for the chemical

. The last column is the ER agonist (R1) AUC value ( see Section 2.1.1 ), with values for the two chemicals that show discordance between in vitro activ ity and in vivo uterotrophic outcomes highlighted in red.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 50 of 111 The 42 chemicals listed in Table 2. 8 (29 active, 13 inactive) were used to assess the performance of the of the ER Pathway model for predicting uterotrophic bioactivity

. A clear separation of reference chemical bioactivity (active/inactive) is apparent when viewed as a function of model score , as shown in Figure 2.12

. When using an ER agonist AUC value of >=0.1 as the lower limit for bioactiv ity , the model scores served as a good surrogate for the qualitative assessment of uterotrophic bioactivity, with a balanced accuracy of 95% ( Table 2. 9 )

.

Figure 2.12 : ER Agonist (R1 ) AUC and uterotrophic outcomes

. Uterotrophic reference chemicals are plotted according to their ER model score (ER agonist AUC) and color coded based on their uterotrophic bioactivity; active (blue), inactive (red).

Table 2. 9 : ER (Agonist) AUC Performance using uterotrophic Reference Chemicals

.

# True Pos 28 # True Neg 12 # False Pos 1 # False Neg 1 PPV 0.97 NPV 0.92 BA 0.95 Sensitivity 0.97 Specificity 0.92Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 51 of 111 Performance of ER Agonist AUC model scores (>=0.1 as active) when used to assess the potential uterotrophic bioactivity of reference chemicals

. PPV

- positive predictive value, NPV

- negative predictive value, BA

- balanced accuracy.

The single false positive chemical was kampferol ( ER agonist R1 AUC=0.33

, inactive in uterotrophic assay ,)

. Kaempferol was classified as inact ive in three uterotrophic assays ; 1) an Ovx mouse study using either oral or s.c

. dosing for seven days at concentrations up to 1000 mg/kg/day, and 2) an immature rat study using oral dosing for four days at concentrations up to 200 mg/kg/day

. Kaempferol is weakly active in the ER Agonist (R1) Model (AUC=0.33)

. W eak acti vity of kaempferol has previously been reported in other in vitro systems (Zava, 1 997), supporting the ER agonist AUC R1 characterization of this chemical as a “ weak ER agonist” in vitro

. On e explanation for chemicals showing activity in vitro but not in vivo could be the lack of bioavailability in the animal model due to a number of factors, including metabolic transformation and/or high clearance

. Extensive metabolism of kaempferol by UDP

glucuronosyltransferase has been reported in both humans and rats ( Oliveira and Watson 2000 ) and may account for the discrepancy between the in vitro and in vivo activities

. In addition, the design of the uterotrophic studies with kaempferol may not have been optimal to detect weak ER bioactivity, as testing in mice is more likely to produce an inactive result (see Section 2.2.1 ), and oral dosing in rats would presumably resul t in lower systemic exposure levels (relative to s.c

. injection) and higher clearance by the gut/intestinal UDP

glucuronosyltransferases cited above.

The single false negative chemical was octamethylcyclotetrasiloxane ( ER agonist R1 A UC=0.00, active in ut erotrophic assay )

. Octamethylcyclotetrasiloxane (D4 ; Figure 2.13 ) is used in the formulation of various hair care, skin care, and deodorant products, as well as topical pharmaceuticals and as a component in breast implants

. Depending on the product type, the concentration of D4 in formulations can be up to 54%

. D4 was classified as active in three uterotrophic assay protocols; 1) a study using two strains of immature rats (SD and F

344) with oral dosing for four days reporting bioactivity at 250 mg/kg/da y, and 2) an OVX mouse study using oral dosing for three days reporting bioactivity with a LEL of 2 5 0 mg/kg/day ( the highest LEL of any reference chemical )

. A report from the EU Scientific Committee on Consumer Safety ( SCCS, 2010 ) cited additional uterotrophic studies with D4 which demonstrated that the uterotrophic effects of D4 were ablated by pre

- treatment with ICI 182,780, an estrogenic receptor (ER) antagonist

. Another cited study demonstrated that ovariectomized αER KO mice showed no increas es in uterine weights when treated with D4, adding further evidence that D4 (or a metabolite) has weak estrogenic activity mediated through estrogen receptor alpha

. The l ack of in vitro activity by D4 may be due to a number of factors, including: i) in vi vo metabolic conversion to an active compound, which cannot be replicated by the in vitro systems, ii) uterotrophic bioactivity mediated via a non

- ER mechanism, iii) weak in vivo activity (high LEL) that would require serum concentrations that are too high to be replicated by in vitro systems, or iv) inaccurate assumptions regarding the quantities of test material used during the screening process (quantity / purity)

. This last possibility is raised due to the fact that D4 is highly volati le (vapor p ressure 132 Pa at 25°C), and it s identify and purity in the HTS stock solution have not yet been confirmed due to the structure’s incompatibility with the separation/detection systems currently being employed in the ToxCast chemical QC program

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 52 of 111 Figure 2 .13 : Structure of Octamethylcyclotetrasiloxane (D4), CASRN 556

- 67

- 2

A further quantitative evaluation was conducted to examine the relationship between ER

a gonist (R1) AUC and the LELs reported for the uterotrophic reference chemicals, Figure 2.14

. Based on these data, there appears to be a mathematical relationship between in vitro AUC values and the level of in vivo potency as determined using the LEL, raising the possibility of both qualitative and quantitative utility for the ER ag onist (R1 ) pathway model

.

Figure 2.14 : Relations hip between AUC and LEL for uterotrophic reference c hemicals

. LELs for each bioactive uterotrophic reference chemical ( y

- axis, log10 scale) are shown as a function of ER agonist (R1) AUC (y

- axis, linear scale )

. The markers are colored by chemical, with all chemicals having a minimum of two reported LELs from independent studies.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 53 of 111 Analysis of additional (non

- reference) chemicals in the UTDB : Chemicals in the UTDB without independent confirmation of their bioactivity in a GL study ( i.e., those that were active or inactive in single study, or inactive in multiple studies with a single active report) were examined with respect to their uterotrophic bioactivity and ER agonist (R1 ) model score, Figure 2.15

.

Figure 2.15 : ER agonist (R1) AUC and uterotrophic bioactivity of non

- reference chemicals

. Non

- reference chemicals are plotted according to their ER model score (ER agonist AUC) and color

- coded based on their uterotrophic bioactivity

. Chemical s with one report of uterotrophic bioactivity are indicated as “active” (blue) and those with one “active” study and one or more inactive uterotrophic outcomes are labeled “active/inactive” (green)

. Chemicals indicated as “inactive” (red) were reported to be inactive in a single uterotrophic study.

Of the 58 non

- reference chemicals having ER agonist (R1 ) AUC values < 0.01 (“ in vitro inactive”), 9% (5/58 ) had a single report of bioactivity in a GL uterotrophic assay

. Chemicals with potential “false negative” in vitro results are listed in Table 2.1 0 , along with their AUC, LEL, and # of active/inactive citations

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 54 of 111 Table 2.1 0 : Chemicals with ER (agonist) AUC values <0.1 and one report of uterotrophic bioactivity (potential false negatives )

CASRN Name AUC # Active # Inactive LEL (mg/kg/day) 77

- 06

- 5 Gibberellic acid 0.00 1 0 ~0.0007 50

- 55

- 5 Reserpine 0.00 1 1 3 3380

- 34

- 5 Triclosan 0.00 1 0 8 99

- 76

- 3 Methylparaben 0.008 1 1 55 52645

- 53

- 1 Permethrin 0.00 1 1 800 AUC = ER agonist (R1) AUC value , #Active = number of GL studies reporting uterotrophic activi ty, #Inactive = number of GL studies reporting no uterotrophic activity a t the highest dose tested, LEL = lowest effect level reported for the chemical.

The trans lational relevance of in vitro assays is a function of their ability to measure the effects of chemicals, or their metabolites, at blood / tissue concentrations required for bioactivity in vivo

. As mentioned previously , some of the possible explanations f or discrepancies between chemicals bein g bioactive in the uterotrophic assay while demonstrating no activity in the ToxCast ER agonist assays are :

i. M etabolic conversion, which cannot be replicated by the in vitro systems, is required for bioactivity ii. W eak in vivo activity may require serum concentrations outside the range assessable by in vitro systems, iii. T he uterotrophic response is mediated via a non

- ER mechanism, iv. I nsufficient quantities/purity of test material used during the screening process and

v. F or papers without external confirmation of the results, the findings may simply not be reproducible (the independent and orthogonal nature of the 16 ToxCast assays used to

assess ER agonist (R1 ) activity greatly enhances the level of confidence in the rep roducibility of the in vitro results).

Methylparaben ( Routledgea

, 1998) and permethrin (Go, 1999) have been reported as possessing weak in vitro ER activity at concentrations near or above 100 uM, the maximum concentration tested in all but one ToxCast as says, raising the possibility that the minimum concentration required for bioactivity is outside the testing range of the ER (agonist) AUC model

. It has also been reported that metabolites of permethrin are more ER

- active than the parent compounds (Tange, 2014), which may further explain the observed difference in ER (agonist) AUC and in vivo bioactivity for this chemical

.

The literature contains mixed information regarding the in vitro bioactivity of Triclosan, ranging from weak ER agonist activity (Gee, 2008) to a lack of ER agonist activity, but active as an ER antagonist (Ahn, 2008)

. These differences may be attributable to the metabolic capacity of the cell lines used, as metabolites of Triclosan ( e.g., dichlorodibenzo

‐ p

‐ dioxin) may be more bioactive than the parent compound (Dann and Hontella 2011)

. The lack of bioactivity seen with Triclosan in the ToxCast assays is consistent with reports in the literature and may reflect the lack of metabolic capacity of the cell lines used

. Rerserpine may also require metabolic transformation for bioa ctivity , with trimethoxybenzoic acid being one of the primary metabolites (Stitzel, 1976 ) , and many phenolic acids are known to be weak ER

- agonist

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 55 of 111 Both Triclosan and permethrin were found to be cytotoxic in the ToxCast assays at concentrations of ~10 μ M , limiting the ability of the ER agonist (R1) model to assess the in vitro activity of these chemicals at concentrations that may be physiologically relevant

. Another possibility is that the lack of in vitro acti vity at concentrations up to the limit of cytotoxicity may indicate that a less toxic, more ER

- bioactive, metabolite is formed in vivo ; permitting systemic exposure at concentrations that are bioactive without causing overt toxicity.

One study reported gibberellic acid (GA) as being highly potent in the uterotrophic assay, with activity similar to 17β

- estradiol ( Gawienowski, 1980 )

. The results of this study are inconsistent with previous reports from the same author using an immature mouse model (therefore not considered GL) that demonstrated a lack of bioactivity at doses up to 100

- fold higher and over a longer time frame ( Gawienowski, 1977 )

. In attempting to repeat the 1980 study, Bejar (1988) found that GA actually caused a decreas e in uterine weight in an OVX rat model.

A more detailed description of the potential false negative chemicals is provided in Appendix 6

.

Of the 16 non

- reference chemicals with ER agonist (R1) AUC values >=0.1, 75% (12/16 ) were identified as “active” in one uterotrophic study , while five chemicals were reported to be “inactive” for bioactivity in a single uterotrophic study report

. The five chemicals associated with potential false positive ToxCast results are listed in Table 2.1 1

.

Table 2.1 1 : Chemic als with ER (agonist) AUC values >= 0.1 and one report of being inactive for uterotrophic bioactivity (potential false positives )

.

CASRN Name AU C # Active #

Inactive HDT

(mg/kg/day) Model Route

1085

- 12

- 7 Heptylparaben 0.384 0 1 1000 OVX_Mouse Oral/Inj
77

- 09

- 8 Phenolphthalein 0.363 0 1 1000 OVX_Mouse Oral/Inj

500

- 38

- 9 Nordihydroguaiaretic

acid 0.289 0 1 1000 OVX_Mouse Oral/Inj

85

- 68

- 7 Butyl benzyl

phthalate 0.249 0 1 1000 Im_Rat Inj

135

- 19

- 3 Betanaphthol 0.122 0 1 1000 Im_Rat Inj AUC = ER agonist (R1) AUC value , #Active = number of GL studies reporting uterotrophic activi ty, #Inactive = number of GL studies reporting no uterotrophic activity a t the highest dose tested, HDT = highest dose tested.

The lack of concordance between the in vitro ER (agonist) AUC and uterotrophic outcomes for the chemicals listed in Table 2.1 1 is most likely related to the lack of bioavailability in vivo

, possibly due to extensive metabolism/clearance of these compounds

. The lack of in vivo activity due to insufficient dose levels being used in the uterotrophic studies is unlikely, as the highest dose tested for each (1000 mg/kg/day) is the maximum recommended by both OECD and EPA.

Conclusions : A database of results from “guideline

- like” (GL) uterotrophic studies was compiled to assist with the evaluation of alternative approaches to the in vivo screen for ER

mediated bioactivity

. Using this database, 43 chemicals having concordant outcomes in a t least two independent studies were chosen as “reference” chemicals to assess theIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 56 of 111 performance of the ToxCast ER agonist (R1) AUC model

. When using this list of reference chemicals (30 Active, 13 inactive), the ER agonist (R1) AUC model demonstrated a bal anced accuracy of 9 5 %, active predictive value of 97%, and inactive predictive value of 92%

. Discordant results between the ER agonist (R1) AUC and uterotrophic outcomes were most likely due to lack of bioavailability of the chemical in vivo (“false posi tives”) or insufficient concentrations of the chemical, or its metabolite, in the ToxCast assays necessary to approximate blood/tissue concentrations that result in bi oactivity (“false negatives”). T here appears to be a strong relationship between in vitro AUC values and doses causing in vivo bioactivity, raising the possibility of both qualitative and quantitative utility for the ER (agonist) pathway model

. These results indicate that the ToxCast ER AUC model may provide a rapid and effective approach for prioritizing and screening chemicals that may have bioactivity in the ER signaling pathway.

2.2.3: Comparison of ToxCast data and Tier 1 ER assays for List 1 chemicals

ToxCast data were compared to results of the EDSP Tier 1 battery assays for List 1 chemicals for which both types of data were available

. The ToxCast R1 (agonist) and R2 (antagonist) scores were compared with results of the two in vitro and one in vivo Tier 1 assays that indicate the potential of a test chemical to interact with the est rogen receptor

. It is worth acknowledging that the EDSP Tier 1 battery was designed to be more sensitive to ER agonist activity, as most environmental chemicals that interact with the estrogen receptor are expected to act as agonists

. The EDSP Tier 1 gui deline in vitro ER binding assay (OCSPP 890.1250) uses primarily ERα obtained from rat uterine cytosol and does not distinguish between estrogen agonist and antagonists activities

. The guideline measures test chemical displacement of radioligand ([ 3 H]17β

- estradiol) from the ER across a range of concentrations in three independent runs of the assay Results of the assay are “positive” if test chemical displaces >50% of radioligand (and Log(IC 50 ) is calculated), “equivocal” if test chemical displaces <50% bu t >25% of radioligand, and “negative” if test chemical displaces <25% of radioligand

. The Tier 1 in vitro Estrogen Receptor Transcriptional Activation Assay (ERTA; OCSPP 890.1300) measures chemiluminescence in response to ER α

- mediated increase ( i.e., agon ist activity) in luciferase gene expression

. A test chemical is “ positive ” if the maximum response induced by the test chemical > 10% of the maximal positive control 17β

- estradiol in at least two of three assay runs ( i.e., RPC max ≥10)

. If the test chemic al fails to achieve at least 10% of the response of the positive control, a negative response is recorded for the test chemical

. The Tier 1 Uterotrophic Assay (OCSPP 890.1600; OECD No.440) is a short

- term, in vivo assay designed to detect estrogen agonist activity indicated by an increase in uterine weight

. Methodological details of the Tier 1 guideline assay are summarized in Section 2.2.1

. Review of the List 1/ Tier 1 assay data described above indicated none of the 52 chemicals had consistent estrog en agonist activity in the ER binding, ERTA, or u terotrophic assays ( Table 2. 12 )

. None of the 52 List 1 chemicals were positive for either ER binding or

u terotrophic assays, and only 4 of 52 chemicals increased ER

- mediated gene expression more than the 1/ 10 that of the positive control, indicating these chemicals have very low or no potential for interacting with the estrogen receptor ( Table 2. 12 )

.

ToxCast result were 100% concordant with the Tier 1 in vitro ER binding

, ERTA

, and in vivo

u terotrophic assa ys, indicating List 1 chemicals had little to no estrogen receptor bioactivity

. ToxCast estrogen agonist (R1) and antagonist (R2) AUC model scores available for 49 of theIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 57 of 111 52 EDSP List 1 chemicals were below 0.1 for both ER agonist and an tagonist activity ( Table 2. 12 )

. Model scores for 11 chemicals were >0 and <0.1 for estrogen agonist (R1) and two chemicals had values >0 and <0.1 for estrogen antagonist (R2) activities

. A ll of the activity for these weak compounds was detected at concentrations similar to those that resulted in cytotoxicity and may be explained by cell

- stress or cytotoxicity

- related false positive activity

. Estrogen (R1) agonist bioactivity of v ery weak or negative reference chemicals were in a sim ilar range ( Table 2. 12 , Figure 2. 1 6 )

, though additional testing is required to confidently resolve “true negatives”

.

Table 2. 12 : Estrogen activity of List 1 chemicals determined from ToxCast ER AUC model scores and EDSP Tier 1 estrogen receptor (ER) bindi ng, estrogen receptor transactivation (ERTA), and u terotrophic (UT) assay results are indicated below

. None of the 49 chemicals run in ToxCast had R1 (agonist) or R2 (antagonist) model scores greater than 0.1, considered the criterion for “positive” activ ity

. Similarly, none of the chemicals had clear positive agonist activity in the Tier 1 ER in vitro assays (ER binding and ERTA) or in vivo (uterotrophic) assays

. ToxCast results for three List 1 chemicals were not available (NA)

. Note: List 1 chemicals in the table below are coded because Tier 1 weight of evidence determinations for List 1 chemicals are not final.

CODE ER AGONIST

(R1) ER ANTAGONIST

(R2) T1 ER

BINDING T1 ERTA T1 UT

17 0.0438 0 Equiv Neg Neg 23 0.0413 0 Neg 19.10% Neg 28 0.0324 0 Neg 18.40% Neg 22 0.027 0 Neg 15.80% Neg 50 0.0221 0 Neg OSRI (P) Neg 8 0.0203 0 Equiv Neg Neg 9 0.0152 0 Equiv OSRI (P) Neg 40 0.00985 0 OSRI (N) Neg Neg 38 0.0095 0 Neg 24.70% OSRI (N) 11 0.00646 0 Neg Incl Neg 5 0.00357 0 OSRI (N) OSRI (N) OSRI (N) 51 0 0 OSRI (N) Neg Neg 12 0 0 Neg Incl Neg 20 0 0 Neg Neg Neg 10 0 0 OSRI (N) Neg Neg 13 0 0 OSRI (N) Incl OSRI (N) 29 0 0 Neg Neg Neg 15 0 0 Neg Incl Neg 14 0 0 Neg Incl Neg 52 0 0 Neg Neg Neg 21 0 0 Neg Incl Neg 30 0 0 Neg Incl Neg 24 0 0 Equiv Incl Neg 25 0 0 Neg Incl Neg 26 0 0 OSRI (N) OSRI (N) OSRI (N) 31 0 0 Neg Neg NegIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 58 of 111 CODE ER AGONIST

(R1) ER ANTAGONIST

(R2) T1 ER

BINDING T1 ERTA T1 UT

32 0 0.000077 Neg Incl Neg 27 0 0.000133 Neg Neg Neg 33 0 0 Neg Incl Neg 34 0 0 Neg Neg Neg 35 0 0 Neg Incl Neg 36 0 0 Neg Neg Neg 16 0 0 Neg OSRI (N) Neg 37 0 0 Neg Neg Neg 39 0 0 Neg Neg Neg 41 0 0 OSRI (N) OSRI (P/N) OSRI (N) 42 0 0 Equiv Neg Neg 43 0 0 Neg OSRI (P/N) Neg 4 0 0 OSRI (N) Neg OSRI (N) 44 0 0 OSRI (N) Neg OSRI (N) 45 0 0 Neg OSRI (N) Neg 46 0 0 Neg Neg Neg 47 0 0 Neg Neg Neg 19 0 0 Incl Neg Neg 17 0 0 Neg Neg Neg 7 0 0 Neg Neg Neg 48 0 0 Neg Incl Neg 49 0 0 Neg Neg Neg 6 0 0 Neg Neg OSRI (N) 3 NA NA Neg Incl Neg 2 NA NA Neg Neg Neg 1 NA NA Neg Neg Neg

2.2.4 : Demonstration of computational toxicological approach to evaluating ER agonist bioactivity of EDSP universe chemicals

Before new computational toxicological tools are used in real

- world applications, the application of new methods should be clearly defined and the utility should be adequately deter mined

. For many test systems , this is often achieved by comparing the performance of the new method for selected chemicals with established activities ( i.e., referenc e chemicals)

, and comparing results of new methods with existing methods in current pract ice

. A variety of pesticides and environmental chemicals act as estrogen agonists and/or androgen antagonists (Kojima et al. 2004, Safe et al. 2001)

. For this reason, ToxCast R1 (estrogen agonist) AUC scores are examined to demonstrate the proposed approach for integrating computational tools into the existing EDSP screening and testing framework

. ER agonist ( R1 )

scores indicate none of the 49 (of 52) List 1 chemicals had values > 0.1, considered the threshold for positive bioactivity based on model scores for reference chemicals with recognized estrogen bioactivity ( see Sections 2.2.1 and 2.2.2 for discussion; Figure 2.8 )

. ER agonist scores for 56 (of 107) List 2 chemicals were in a similar range, indicating low to no ERIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 59 of 111 a gonist activity ( Figure 2.8 )

. However, when R1 AUC scores are examined for the remainder of the ~1800 chemicals from the EDSP universe there are chemicals with estrogen agonist bioactivity (R1 > 0.1 ), many with scores simila r to potent reference chemicals ( Figure 2.8 )

. Those chemicals with potential estrogen agonist bioactivity, considered in concert with estimated exposure levels (Section 8), are high er priority candidates for additional endocrine screening and testing

.

Figure 2. 1 6 : Relative estrogen bioactivity of the List 1, List 2, and EDSP Universe chemicals

. Estrogen receptor agonist ( R1 ) bioactivity is plotted for the EDSP List 1 ( blue ), List 2 ( orange ), and 73 ( 4.4%) EDSP Universe chemicals ( grey ) with R1 scores > 0.1

. The grey solid line depicts the agonist (R1) AUC cut

- off used to identify chemicals for positive estrogen bioactivity

. Positive ( green ) and negative (black) reference chemicals are included for context

. Note: None of the List 1 or List 2 chemicals and only about 5% of the remaining chemicals tested in ToxCast have agonist (R1) AUC scores > 0.1. ER AUC agonist (R1) scores for Reference Chemicals, List 1, List 2, and other chemicals in the EDSP Universe are included in Appendix 3

. Analyses presented in Section 2 are me ant to provide scientific support for the utility of ToxCast models for predicting estrogen receptor bioactivity

. ToxCast high throughput assays measure multiple endpoints in the ER signaling pathway using different tec hnologies and the network models int egrate assay results into an estimate of ER agonist or antagonist bioactivity strength

. ER agonist ( R1 ) and antagonist ( R2 ) AUC scores compare very well with reported bioactivity of reference chemical s across a range of structures and potencies (Section 2.1.1)

. The agonist ( R1 ) AUC model also accurately predicted in vivo ER agonist activity for aIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 60 of 111 relatively large set (~150) of chemicals (Section 2.2.2)

. Together, these analyses provide a high degree of scienti fic confidence in the ability of ToxCast ER models to predict bioactivity and demonstrate the utility of using these computational tools to prioritize chemicals in the EDSP Universe

. In addition to a high degree of concordance with in vitro reference chem icals and in vivo peer

reviewed u terotrophic studies, the agonist (R1) and antagonist (R2) AUC models agreed with ER

- relevant Tier 1 battery endpoints ( i.e., ER binding, ERTA, and u terotrophic results; Section 2.2.3)

. In all cases, Tier 1 results and agon ist (R1) and antagonist (R2) AUC values suggested very low or no endocrine receptor interactions of the 49 chemicals examined ( Table 2. 12 )

. Given the redundancy of coverage in the 18 ToxCast ER assay, it is unlikely that running a single guideline ER bind ing and/or ERTA assay would provide additional or contradictory data

. Additionally, the EDSP guideline u terotrophic study is a short

- term in vivo assay with a single endpoint and may actually be less sensitive than in vitro assays ( Akahori et al. , 2008 ), p articularly given the redundancy in the ToxCast ER assay suite (Section 2.1)

. The performance of the ToxCast models, concordance with other data streams, and agreement with Tier 1 assay results suggests ToxCast data may be used to substitute for the ER binding, ERTA, and u terotrophic endpoints in the Tier 1 battery

. Analyses in thi s section provide a high degree of scientific confidence in the computational tools presented

. As previously stated, most ER bioactive chemicals are expected to have largely agonist, rather than antagonist, activities, and for this reason, far more data f rom literature studies are available for the potential ER in vitro and in vivo agonist response

. Results of the ER agonist (R1) model indicate o nly about 5% of the 1812 chemicals run have potential ER agonist bioactivity

. T he ToxCast model is also capabl e of detect ing estrogen antagonist reference chemicals with a high degree of accuracy ( see Section 2.2.1 ), suggesting the ToxCast ER antagonist (R2) AUC model also performs very well

. Only about 1% of the chemicals tested had ER antagonist bio activity and i t is worth noting that none of the List 1 or List 2 chemicals have threshold level ER bioactivity of either type

. Using computational tools to screen the EDSP universe would allow for rapid identification of chemicals with the greatest potential for end ocrine bioactivity and would allow EPA to move away from lists of a few chemicals with relatively low or no potential endocrine activity

. The use of computational methods to screen for estrogen receptor activity takes advantage of both existing and innovative, emerging technologies to implement a scientifically robust and comprehensive chemical prioritization process for EDSP

. The prioritization of chemicals in the EDSP universe would greatly reduce the time, co st, and animal use required to identify chemicals with the greatest associated endocrine activity and would be consistent with EPA’s strategic plan for evaluating chemical toxicity ( US EPA, 2009 )

. EPA aims to use these tools for 1) prioritizing chemicals in the EDSP universe, 2 ) potential ly contributing to future weight of evidence determinations of a chemical’s potential to interact with the estrogen receptor, and 3) substituting for specific endpoints in the EDSP Tier 1 screening battery.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 61 of 1 11 

## Section 3 : An drogen P athway

3.1: Computational Tools and Models: AR

In this section, we describe an approach to combine data from multiple in vitro assays measuring androgen receptor activity to make a prediction concerning “ true ” activity, in much the same manner de monstrated for the estrogen receptor in the previous section

. Again, a key aspect of the analysis is the use of well

- studied reference chemicals

. We probe the mammalian androgen receptor ( AR ) signaling pathway activity using a collection of nine biochemical and cell

- based in vitro assays that measure mechanistic perturbations of the AR pathway at multiple sites: receptor binding, receptor dimerization, chromatin binding of the mature transcrip tion factor, and gene transcription

. This battery of in vitro assays was used to screen a library of 18 55 chemicals

. Included in the chemical library were reference AR agonists and antagonists, as well as selective androgen receptor modulators (SARMs)

. For the AR , we followed a process parallel to that for the ER

. C hemicals were run in ToxCast f AR in vitro assays, and a model was built to generate agonist and antagonist scores

. Similar to the ER pathway analysis, expected patterns of assay activity in clude: no assays activated, all agonist assays activated, all antagonist assays activated, specific subsets of assays across technologies activated, technology

- specific assay activation

. To navigate this complexity, we developed mathematical and statistic al models to infer whether chemicals that activate specific patterns of the in vitro assays were more likely to be AR agonists or antagonists, or signals were more likely to be due to specific types of assay interference

. By combining data from multiple a ssays, and using a model that detects technology

- specific false positives, we arrived at an assessment of the in vitro androgenicity of a chemical

. Just as with ER, QSAR models are being constructed using the AR model resu lts

.

3.1.1: In vitro data and model: AR

Assays and Chemicals: The data used in model construction were generated by the EPA ToxCast program ( Judson et al. 2010 )

. Th e data set comprises concentration

- response data

on 1855 chemicals, with each chemical tested in up to nine AR pathway assays

. (This is larger than the 1812 for the ER model because more chemicals had data from the complete set of AR assays.) These includ e 2 cell

- free biochemical radioligand AR binding assays [ Novascreen: ( Knudsen et al. 2011

, Sipes et al. 2013 ) ]; a set of 2 cofactor recruitment assays that measure protein complementation between AR and SRC1 [ Odyssey Thera ]; 1 transactivation assay measuring RNA transcript levels [Attagene: ( Martin et al. 2010 ) ]; 2 transactivation assays measuring reporte r protein level readouts [Tox21 ]; and 2 transactivation ant agonist assays [Tox21 ]

. The 1855 chemicals used in this model were selected because they had data for the full set of nine AR assays

. The assay sources refer to the company or laboratory where the assays were performed

. The nine assays used are listed in Table 3.1

. The assay IDs correspond to Figure 3.1 below

. Chemicals were run in concentration response format and analyze d via the ToxCast data analysis pipeline in the same way as the ER pathway assays (See Section 2.1.1 and http://epa.gov/ncct/toxcast/data.html ).Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 62 of 111 Table 3.1: Summary of the in vitro AR assays used with their ID mapping to the model in Figure 3.1

. NVS=Novascreen; OT=Odyssey Thera; ATG=Attagene; Tox21=assays run by the NIH Chemical Genomics Center as part of the Federal Tox21 program.

Assay ID Assay Name Source GeneTarget Norma lized Data Type Assay Design Type Biological Process
Target Detection Technology Detection Technology Subtype Measurement Timepoint Organism Tissue Cell Format Cell Line Name Assay Footprint

A1 NVS_NR_hAR NVS AR percent activity radioligand

binding receptor

binding Lysate

- based

radiodetection Scintillation

counting 20 human NA cell

- free NA 96

- well

A2 NVS_NR_rAR NVS AR percent activity radioligand

binding receptor

binding Filter

- based

radiodetection Scintillation

counting 18 rat prostat

e tissue

based cell

- free NA 96

- well

A3 OT_AR_ARSRC1_0480 OT AR percent activity protein

fragment complementati on assay protein

stabilization Protein

fragment Complementati

on Fluorescence

intensity 8 human kidney cell line HEK2 93T 384

-

well

A4 OT_AR_ARSRC1_0960 OT AR percent activity protein

fragment complementati on assay protein

stabilization Protein

fragment Complementati

on Fluorescence

intensity 16 human kidney cell line HEK2 93T 384

-

well

A5 ATG_AR_TRANS_up ATG AR log2 fold induction mRNA

induction regulation of

transcription factor activity RT

- PCR and

Capillary electrophoresis Fluorescence

intensity 24 human liver cell line HepG 2 24

-

well

A6 Tox21_AR_BLA_Agonist_ratio Tox21 AR percent activity beta lactamase

induction regulation of

gene expression GAL4 b

lactamase reporter gene Fluorescence

intensity 24 human kidney cell line HEK2 93T 1536

-

well

A7 Tox21_AR_LUC_MDAKB2_Agonist Tox21 AR percent activity luciferase

induction regulation of

gene expression Luciferase

coupled ATP quantitation Bioluminesce

nce 24 human breast cell line MDA

kb2 1536

-

well

A8 Tox21_AR_BLA_Antagonist_ratio Tox21 AR percent activity beta lactamase

induction regulation of

gene expression GAL4 b

lactamase reporter gene Fluorescence

intensity 24 human kidney cell line HEK2 93T 1536

-

well

A9 Tox21_AR_LUC_MDAKB2_Antagonist Tox21 AR percent activity luciferase

induction regulation of

gene expression Luciferase

coupled ATP quantitation Bioluminesce

nce 24 human breast cell line MDA

kb2 1536

-

wellIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 63 of 111 Reference chemicals : A set of 23 positive and negative reference chemicals w as used to evaluate the performance of the model

. These reference chemicals were identified using the ICCVAM f inal r eport on the e valuation of in vitro t est m ethods for d etecting p otential e ndocrine d isruptors (ICCVAM 2003) and the OECD d raft validation r eport on the AR

- m ediated Stably Transfected Transcriptional Activation (AR

- STTA) Assay (OECD 2010)

. The following list of referenc e chemicals were those that had consistent in vitro results across both reports , and were also in the ToxCast library

. The reference chemicals and their predicted AR agonist and antagonist activities are given in Table 3.2

. Chemicals designated as SARM ( Selective Androgen Receptor Modulator) are designated to have both agonist and antagonist activity.

Table 3.2 : Androgen Receptor Reference Chemicals

. SARM= Selective Androgen Receptor Modulator

.
CASRN Chemical Name AR Activity
2392

- 39

- 4 Dexamethasone Agonist

63

- 05

- 8 4

- Androstenedione Agonist

521

- 18

- 6 5a

- Dihydrotestosterone Agonist

58

- 18

- 4 Methyl testosterone Agonist

57

- 85

- 2 Testosterone propionate Agonist

13311

- 84

- 7 Flutamide Antagonist

140

- 66

- 9 4

- tert

- Octylphenol Antagonist

32809

- 16

- 8 Procymidone Antagonist

80

- 05

- 7 Bisphenol A Antagonist

50471

- 44

- 8 Vinclozolin Antagonist

72

- 55

- 9 p,p'

- DDE Antagonist

52806

- 53

- 8 Hydroxyflutamide Antagonist

56

- 53

- 1 Diethylstilbestrol Antagonist

84

- 74

- 2 Di

- n

- butyl

- phthalate Inactive

117

- 81

- 7 Diethylhexyl phthalate Inactive

1912

- 24

- 9 Atrazine Inactive

427

- 51

- 0 Cyproterone acetate SARM

50

- 28

- 2 17

- β

- Estradiol SARM

53

- 16

- 7 Estrone SARM

330

- 55

- 2 Linuron SARM

52

- 01

- 7 Spironolactone SARM

57

- 83

- 0 Progesterone SARM

206

- 44

- 0 Fluoranthene SARMIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 64 of 111 Structure of the Network : Figure 3.1 is a graphical representation of the network used to evaluate the integrated in vitro assay responses

. The model was based on the series of molecular events that typically occur in a receptor

- mediated response

. The p rocess starts with the interaction of a chemical with a nuclear AR (Receptor node R1)

. For example, an AR agonist will cause the receptors to dimerize (node N1), translocate to the nucleus and recruit

co

- factors to form the complete active transcription f actor complex (TF) (node N2)

. This TF then binds to the chromatin DNA (node N3), initiates transcription of mRNA (node N4) and subsequent translation to protein (node N5)

. Each of these processes (with the exception of dimerization and DNA binding) was m easured in the current collection of 9 in vitro assays represented as white stars

. Table 3.1 provides the assay ID (A1

- A9) to match the assay on Figure 3.1 and a brief assay description

. The AR pathway is shown in two modes

– agonist (blue) and antagonis t (red)

. The model assumes that a chemical that interacts with the AR will bind in one or both of the agonist or antagonist conformations and that this will trigger activity in the appropriate pathway

. Note that the model will allow for the prediction of mixed agonist/antagonist activity, or identification of SARMs (Selective Androgen Receptor Modulators)

. As was explained in the case of the ER pathway model ( Section 2.1.1 ), every in vitro assay is subject to processes that can lead to non

- specific activ ity, independent of the AR pathway node that it is supposed to measure

. The assay interference pathways were modeled as alternate “pseudo

- receptors” (gray arrow nodes)

. Pseudo

- receptors are assigned to each group of assays (technology group) and to each assay individually

. Only a single example of an assay

- specific pseudo

- receptor is shown in Figure 3. 1 , but all assays (with the exception of A5/R5 where the assay and the pseudo

- receptor are identical) have their corresponding pseudo

- receptor

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 65 of 111 Figure 3.1: Graphical representation of the network used in the in vitro analysis of the AR pathway across assays and technology platforms

. Colored arrow nodes represent “receptors” with which a chemical can directly interact

. Colored circles represent intermediate biological processes that are not directly observable

. White stars represent the assays that measure activity at the biological nodes

. Arrows represent transfer of information

. G ray arrow nodes are the pseudo

- receptors

. E ach assay (with th e exception of A 5 ) has an assay

- specific pseudo

- receptor , but o nly a single example is explicitly shown, for assay A1

.

Model Results for Reference Chemicals : The results for the AR reference chemicals in the agonist and antagonist modes are shown in Figur e 3.2

. The 5 agonist reference chemicals are active as expected, as are all 8 antagonist reference chemicals

. There were two weak false

- positives in the agonist mode [ p,p’

- DDE, AR agonist (R1) AUC =0.04 and 4−(1,1,3,3− t etramethylbutyl)phenol, AR agonist (R1) AUC =0.02 ]

. The SARM f luoranthene had a zero AUC in both AR agonist (R1) and AR antagonist (R2) mode, and was active in the two Odyssey Thera co

- factor recruitment assays

. Linuron, another SARM

, wa s active in AR antagonist (R2) mode (AUC= 0.04) , but not in AR agonist (R1) mode

. Linuron was active in the two cell

- free binding assays and in one of the antagonist transactivation assays

. There were no false

- positives in the antagonist mode

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 66 of 111 Note that the model was not specifically designed to detect mixed, SARM

- like activity

. In particular, the AUC calculation tends to down

- weight one receptor if it is overwhelmed by another at higher concentrations, which is what we see for most of these chemicals (See Figure 3. 3 )

. However, one can see cl ear mixed activity predicted for all of the SARMs except for linuron and fluoranthene

.

Figure 3.2 : Summary plots of agonist (top) and antagonist (bottom) activity of reference chemicals

. Chemicals that are intended to be positive are indicated by a green circle, while those intended to be inactive are red

. For AUC values ≤0.01, the value was set to 0.01

. For chemicals with one or more pseudo

- receptor AUC values greater than the expected one, the value is indicated by an X, and the receptor name is indicated

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 67 of 111 Figure 3.3 : Model plots for SARM reference chemicals

. The AR agonist receptor (R1) is designated by blue, the AR antagonist receptor (R2) by red and the other pseudo

- receptors are colored as indicated in the legend

. Concentrations are in μM

. For chemicals with the

“ cytotoxicity burst” defined (2 or more cytotoxicity hits), the burst center is indicated by a vertical red line, and the burst region (starting 3 burst MAD below the burst center) is indicated by the gray shaded region

. The ve rtical green line designates the predicted AC 50 corresponding to the AR agonist (R1) or AR antagonist (R2) model with the highest AUC

score

. Mixed agonist/antagonist activity is predicted for all SARM reference compounds except linuron and fluoranthene.

Summary of Data for Overall Chemical Set : Of the 1855 chemicals te sted in the AR pathway assays, 1378 were essentially inactive in all assays

. As with ER, the chemical hits tend to cluster with the receptors / assay technologies, indicating that some frac tion of the assay activity is false positives due to technology

- specific assay interference

. There were 20 chemicals with agonist (R1) AUC≥0.1, and 93 chemicals with antagonist (R2) AUC≥0.1

. AIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 68 of 111 total of 477 chemicals had at least one AUC value greater than 0.1, illustrated in Figure 3.4

. Note that there are a large number of chemicals potent against A8 and R7

. This is largely driven by the cytotoxicity

- driven false positives in the antagonist l oss

- of

- signal assays, which is more prevalent in assay A8.

Figure 3.4 : Two

- way hierarchical clustering of chemical activity across the 9 in vitro assays used to identify A R activity

. The heatmap shows

– log10(AC 50 ) values for all assays and all chemica ls with at least one assay hit

. A total of 477 out of 1855 chemicals was active in one or more of the assays

. Darker red indicates more potent activity (lower AC 50 ), while white represents inactive chemical

- assay pairs

. Note that the assays cluster by t echnology / pseudo

- receptor

. The “A” and “R” values refer to the assay and receptors/pseudo

- receptors from Figure 3.1 and Table 3.1

. AR AUC plots for reference chemicals are included at Appendix 7

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 69 of 111 Overall, there were 20/1855 chemicals with AR agonist (R 1) AUC≥0.1 and 93 chemicals with AR antagonist (R2) AUC≥0.1

. Table 3.3 lists the 65 chemicals that are specific for one of the receptors, and have high Z and high Emax values

. This list excludes the majority of the high

AUC AR antagonists (R2), primarily because the activity is confined to the cell

- stress / cytotoxicity region

. There are a large number of these receptor

- specific chemicals that only hit one or the other of the cell

- free binding assays

. One hypothesis that we are pursuing is that these chemicals react with the proteins or otherwise cause denaturation, which will cause displacement of the radioligand, leading to a binding

- like signal

. AR AUC agonist (R1) and antagonist (R2) scores are included, along with AUC plots ( Appendices 3 and 8

, r espectively).

Table 3.3 : AR chemicals that are specific for one of the receptors (AUC≥0.1) and have high Z and Emax specificity, so are therefore less likely to be confounded by cytotoxicity

. However the chemicals specific for A8, A9 and R7 (green highl ighting in the “Specific Receptor” column) typically only have activity in the loss

- of signal antagonist assays. CASRN Name AUC Agonist (R1) AUC Antagonist (R2) Specific Receptor

10161

- 33

- 8 17beta

- Trenbolone 1.09 0 Agonist 58

- 18

- 4 17

- Methyltestosterone 1.08 0 Agonist 521

- 18

- 6 5alpha

- Dihydrotestosterone 1.06 0 Agonist 68

- 22

- 4 Norethindrone 1.02 0 Agonist 57

- 85

- 2 Testosterone propionate 1.01 0 Agonist 797

- 63

- 7 Norgestrel 0.97 0 Agonist 63

- 05

- 8 4

- Androstene

- 3,17

- dione 0.83 0 Agonist 50

- 28

- 2 17beta

- Estradiol 0.7 9 0.06 Agonist 84371

- 65

- 3 Mifepristone 0.0 4 0.75 Antagonist 68

- 96

- 2 17alpha

- Hydroxyprogesterone 0.6 9 0 Agonist 57

- 91

- 0 17alpha

- Estradiol 0.6 1 0.05 Agonist 864283

- 48

- 7 GSK232420A 0.5 7 0.05 Agonist 57

- 83

- 0 Progesterone 0.56 0.0 8 Agonist 52806

- 53

- 8 Hydroxyflutamide 0 0.5 5 Antagonist 90357

- 06

- 5 Bicalutamide 0 0.35 Antagonist 52

- 01

- 7 Spironolactone 0.05 0.29 Antagonist 474

- 86

- 2 Equilin 0.02 0.28 Antagonist 53

- 03

- 2 Prednisone 0.2 7 0 Agonist 427

- 51

- 0 Cyproterone acetate 0.2 6 0.0 7 Agonist 6459

- 94

- 5 C.I

. Acid Red 114 0 0.06 A2 196808

- 45

- 4 Farglitazar 0 0.06 A1 131

- 56

- 6 2,4

- Dihydroxybenzophenone 0 0.0 6 A2 87

- 01

- 4 7

- (Dimethylamino)

- 4

- methylcoumarin 0.0 6 0 A2 126833

- 17

- 8 Fenhexamid 0 0.05 A1 137

- 26

- 8 Thiram 0 0.0 5 R7Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 70 of 111 CASRN Name AUC Agonist (R1) AUC Antagonist (R2) Specific Receptor

10540

- 29

- 1 Tamoxifen 0.0 2 0.0 5 A1 96489

- 71

- 3 Pyridaben 0 0.0 5 R7 126

- 72

- 7 Tris(2,3

- dibromopropyl) phosphate 0 0.02 A8 119168

- 77

- 3 Tebufenpyrad 0 0 R7 83

- 79

- 4 Rotenone 0 0 R7 989

- 38

- 8 Rhodamine 6G 0 0 R7 175013

- 18

- 0 Pyraclostrobin 0 0 R7 122453

- 73

- 0 Chlorfenapyr 0 0 R7 3811

- 73

- 2 Sodium (2

- pyridylthio)

- N

- oxide 0 0 R7 56

- 38

- 2 Parathion 0 0 A1 599

- 79

- 1 Sulfasalazine 0 0 A1 298

- 00

- 0 Methyl parathion 0 0 A1 78491

- 02

- 8 Diazolidinyl urea 0 0 A4 131

- 17

- 9 Diallyl phthalate 0 0 A2 81777

- 89

- 1 Clomazone 0 0 A1 10016

- 20

- 3 alpha

- Cyclodextrin 0 0 A1 2433

- 14

- 9 4

- Cyclohexylcyclohexanol 0 0 A1 149062

- 75

- 9 PharmaGSID_47259 0 0 A1 2870

- 32

- 8 C.I

. Direct Yellow 12 0 0 A2 6153

- 64

- 6 Oxytetracycline dihydrate 0 0 A2 654055

- 01

- 3 Morin hydrate 0 0 A2 106

- 50

- 3 1,4

- Benzenediamine 0 0 A2 28631

- 66

- 5 C.I

. Acid Blue 22 0 0 A2 2610

- 11

- 9 C.I

. Direct Red 81 disodium salt 0 0 A2 2467

- 02

- 9 Bisphenol F 0 0 A2 90

- 43

- 7 2

- Phenylphenol 0 0 A2 68555

- 86

- 2 Acid Orange 156 0 0 A2 1118

- 46

- 3 Butyltin trichloride 0 0 A2 112809

- 51

- 5 Letrozole 0 0 A2 75

- 12

- 7 Formamide 0 0 A3 119

- 53

- 9 Benzoin 0 0 A4 32388

- 55

- 9 1

- Cedr

- 8

- en

- 9

- ylethanone 0 0 A4 98730

- 04

- 2 Benoxacor 0 0 A8 13463

- 41

- 7 Zinc pyrithione 0 0 R7 143390

- 89

- 0 Kresoxim

- methyl 0 0 R7 101463

- 69

- 8 Flufenoxuron 0 0 R7 51

- 28

- 5 2,4

- Dinitrophenol 0 0 R7 220860

- 50

- 4 CP

- 457920 0 0 R7 81

- 88

- 9 Rhodamine B 0 0 R7 80

- 07

- 9 4,4'

- Dichlorodiphenyl sulfone 0 0 R7Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 71 of 111 

### 3.2: Interpretation and application: AR

In this section, we present various analyses and approaches for comparing ToxCast models of in vitro AR bioactivity with in vivo activity and evalu ate the scientific confidence in using ToxCast data to : 1) prioritize chemicals based on bio activity in the EDSP universe ; 2) contribute to a weight of evidence evaluation of a chemical’s potential to interact with the androgen receptor; and 3) substitute for specific endpoints in the EDSP Tier 1 battery

. As discussed in earlier sections, the pr imary objective of EPA's EDSP is to rapidly screen chemicals in the EDSP universe with increased efficiency in order to protect human and environmental health

. EPA is developing a science

- based, strategic approach for EDSP chemical prioritization similar to the method described for the estrogen pathway

. We present EPA’s proposed approach for implementation and application of computational methods in the existing EDSP screening a nd testing framework to rapidly prioritize chemicals for potential androgen receptor

- mediated bioactivity

. The results of AR agonist (R1) and antagonist (R2) model score s for in vitro reference chemicals were presented in the previous section to determine model performance (Section 3.2)

. Here w e describe an effort currently underway to compare computational model scores with results of the Hershberger assay to determine model performance for predicting in vivo bioactivity

. Though not as mature as analyse s for the ER pathway, this section outlines the proposed approach for using computational tools to predict AR pathway bioactivity, describes the status of the current effort, and presents the current scientific confidence associated with incorporating comp utational tools for each of the three steps above.

3.2. 1 : Cu rated literature review of Hers hberger literature

To build confidence in the ability of computational toxicology methods to prioritize and screen chemicals for potential androgen activity, agre ement between ToxCast in vitro results and rodent Hershberger bioassay is being examined

. The traditional short

- term, in vivo screen for androgen activity, the Hershberger assay, was established in the 1930s and has been a standardized and widely used ass ay since the 1970s ( Dorfman 1969 )

. The Hershberger assay uses male rats castrated during the peripubertal stage , removing potential neuroendocrine influences, and determines the effect of exposures to potential androgen agonists and antagonists on five ac cessory sex organ weights : the ventral prostate, seminal vesicle (plus fluids and coagulating glands), levator and ani

- bulbocavernosus ( LABC ) muscle , paired Cowper’s glands, and the glans penis

. Run in the agonist mode, weights of accessory sex organs of animals exposed to two or more concentrations of test chemical are compared to castrated controls

. Run in the antagonist mode, accessory sex organ weights of exposed to test chemical coadmistered with testosterone propionate, a potent androgen agonist, ar e compared to controls administered only testosterone proprionate

. The Hershberger assay was validated by the Organization for Economic Cooperation and Development and the OECD Test Guideline 441 was used to develop the harmonized EPA EDSP Tier 1 Hershber ger assay (OCSPP Guideline 890.1400)

. To leverage information available from existing Hershberger assays published in peer

reviewed scientific literature, EPA contracted a systematic, curated literature review

. A semi

automated literature search for Hers hberger studies was performed for ~1800 chemicals run in ToxCast androgen assays using chemical name and CASRN as search terms, with “Hershberger assay”, “Hershberger”, and “androgen dependent accessory sex tissue weight” as modifiers in the PubMed databas e (http://www.ncbi.nlm.nih.gov/pubmed)

. Relev ant articlesIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 72 of 111 were compiled and methodological details of each study were manually transcribed for each chemical/study/protocol combination

. Studies were reviewed to determine methodological consistency with EP A OCSPP 890.1400 and OECD 441 Hershberger test guidelines

. Each publication was independently reviewed by at least two scientists and evaluated for consistency with the following seven minimum criteria:

1. Animal model

a. Studi es must use male rats castrated b etween PND 42 and PND 53, with > 7 days allowed for post

- surgery recovery before the onset of chemical exposure

. Immature rats are not acceptable due to the lack of demonstrated sensitivity and rats should be Sprague

- Dawley or Wistar strains, due to the la ter development of Fisher 344 animals. 2. Sample size

a. Each test group and all control groups should have a minimum of 4 animals for positive results and 6 animals for negative results. 3. Test chemical administration

a. Test chemicals must be administered by oral g avage or subcutaneous injection. 4. Dose levels

a. The agonist portion of the experiment must include > 2 test chemical dose levels, a positive control group administered 0.2 to 0.4 mg/kg/d of testosterone priopionate (TP) by subcutaneous injection, and a negati ve (vehicle) control

. b. The antagonist portion of the study must include > 3 doses of test chemical + TP, a positive control coadministered TP and 3 mg/kg/d of the androgen receptor antagonist flutamide, and negative (TP only) control

. In the agonist port ion of the study, all 5 accessory sex tissues ( AST ) weights should be increased in the TP (positive) control relative to the vehicle (negative) control

. Similarly, in the antagonist portion, all 5 AST weights in the TP + flutamide (positive) control shoul d be less than the TP (negative) control animals

. 5. Duration of exposure

a. Dosing should begin between PND 49 and PND 60 and extend for a minimum of 10 days. 6. Tissues examined

a. Absolute weights of 5 (AST) [ventral prostate (VP), seminal vesicles (SV, plus fluids and coagulating glands), levator ani

- bulbocavernosus (LABC) muscle, paired Cowper’s glands, and the glans penis] are examined in all animals

. b. Tissue weights are compared to the appropriate negative control, the vehicle controls (agonist) and TP only control (antagonist)

. 7. Sample collection

a. Necropsy of study animals must be conducted within 18 to 36 hours after the last dose

Minimum criteria of agonist and antagonist portions of assay were considered independently

. A score of 0 or 1 was recorded for each of the minimum criteria (MC 1

- 7), based on whether the study/protocol fulfilled the particular requirement

. Each study was reviewed independently by two scientists, and the relevant information recorded and scored for the seven MC

. Any papers that h ad differing MC evaluations between the two reviewers were flagged, and discussed among reviewers and with EPA until a consensus was reached

. Parsing the study quality requirements among the different minimum criteria and scoring them independently afford s the opportunity to relax or tighten the acceptance threshold

. For example, manyIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 73 of 111 studies included chemicals run at a single dose and to include (particularly positive response) data in the analyses, MC4 could be discounted

. The expectation was chemicals tested in Hershberger assays with scores of 7 for both agonist and antagonist portions of the test were “guideline like” studies and would yield the same results for a test chemical run in the EPA EDSP Tier 1 guideline study

.

3.2.2: Comparison of ToxCas t data and Hershberger studies

The curation of data from studies identified in the s ystematic literature search is currently underway

. Initial summaries supplied by contractor indicate about 40 chemicals with Hershberger assays meeting at least six of se ven minimum criteria

. These studies are being secondarily reviewed by EPA scientists to assure agency agreement, data quality, and methodological consistency with guideline studies

. Though the number of Hershberger studies are likely to be more limited t han high quality u terotrophic assays, results will be used to: 1) provide an in vivo comparison for AR bioactivity predicted for EDSP by ToxCast models ; 2) determine the degree of scientific confidence in using ToxCast AR model predictions to contribute to the weight of evidence process; and 3) p otentially substitute for specific endpoi nts in the EDSP Tier 1 battery, similar to the approach described for the ER pathway in Section 2.2.2

3.2.3: Tier 1 AR Data

ToxCast data will be compared to results of the EDSP Tier 1 battery assays for List 1 chemicals for which both types of data are available

. The agonist (R1 ) and antagonist (R2 )

scores will be compared with results of the in vitro AR binding assay and in vivo Hershberger Tier 1 assays that indicate pot ential test chemical androgen receptor bioactivity

. The EDSP Tier 1 guideline in vitro AR binding assay (OCSPP 890.1150) uses AR obtained from rat prostate cytosol and does not distinguish between androgen agonist and antagonists activities

. The assay me asures test chemical displacement of radioligand ([ 3 H]R1881) from the AR across a range of concentrations in three independent runs

. Results are reported as “positive” if test chemical displaces >50% of radioligand (and Log(IC 50 ) is calculated), “equivoca l” if test chemical displaces <50% but >25% of radioligand, and “negative” if test chemical displaces <25% of radioligand

. Tier 1 in vivo Hershberger As say (OCSPP 890.14 00; OECD No.441) is designed to detect androgen agonist and antagonist activities indicated by any change in five accessory sex tissue weight s

. Tier 1 Hershberger assay methodology is summarized in S ection 3.2.1

. Most environmental chemicals that interact with the androgen receptor are expected to act as antagonists or SARMs (Kojima et al. 2004, Safe et al. 2001) , however, in vitro antagonist bioactivity ( i.e., reduction in signal) may be difficult to discriminate from cell

- stress or cytotoxicity

. To be confident in the scientific performance of the AR computational models of bioacti vity, s everal additional steps must be completed before AR model outputs can be compared with Tier 1 AR endpoints to determine if computational models of AR bioactivity are fit for the purpose of contributing to a weight of evidence evaluation and/ or subst ituting for specific Tier 1 AR endpoints

. First, reference chemical potency must be established to ensure a range of chemicals are included and the AR model s reliably detect s weak positive bioactivities

. In addition, AR model scores must be compared with H e r shberger assay results to determine the degree of concordance between high throughput in vitro bioactivity and in vivo response of chemicals for which both types of data are available

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 74 of 111 3.2.4 : Demonstration of computationa l toxicological approach to evaluating AR antagonist bioactivity of EDSP universe chemicals

Analyses presented in Section 3 are intended to provide scientific support for using computational tools to predict androgen receptor bioactivity

. The ToxCast net work model is built from results of nine high throughput assays measuring multiple endpoints in the AR signaling pathway using different technologies

. AR agonist ( R1) and antagonist ( R2) AUC scores compare very well with reported bioactivity of reference chemicals across a range of structures ( 85% concordance; Section 3.1.1), providing a high degree of scientific confidence in the ability of ToxCast AR models

. Though analyses are not as developed for the AR pathway as those described for the ER pathway in Section 2, we have demonstrated robust science supporting the use of computational tools for prioritizing chemicals for potential AR bioactivity

. Potency has not yet been defined for the reference chemicals used in this analysis,

m any of which are pharmaceuticals designed to target the androgen receptor and are therefore expected to be strongly bioactive

. To assure AR models detect weakly bioactive environmental compounds as well as strong pharmaceutical chemicals

, the reference set must perfo rm consistently in in vitro AR assay s and include a wide range of potencies

. Though additional reference chemicals may be added to address the lower limit of bioactivity detected in the AR models

, t he ability of the AR AUC models to detect the current set of AR reference chemicals with a high degree of accuracy demonstrates the utility of computational tools for prioritization

. Chemicals in the EDSP universe with AR agonist (R1) or antagonist (R2) scores > 0.05 are high er priority candidates for additiona l endocrine screening and testing

. AR antagonist ( R2 ) scores indicate eight of 49 List 1 chemicals (including one reference SARM) had values AR antagonist ( R2 ) AUC scores > 0.05 , considered the threshold for positive antagonist bioactivity ( Fig ure 3. 5 )

. AR antagonist ( R2 ) AUC scores for six of 56 List 2 chemicals w ith ToxCast data were positive and included one reference antagonist ( Figure 3. 5 )

. It should be noted that only about 7.8% of chemical (114) of the ~ 1450 chemicals in the EDSP universe for which there are HTS data were active as androgen antagonist s (R 2 AUC > 0.05 ), though some of these has AR antagonist scores similar to or exceeding the most potent reference antagonist actives ( Figure 3.5 )

. Those chemicals with potential androgen antagoni st bioactivity, considered in concert with estimated exposure levels (Section 6 ), are high er priority candidates for additional endocrine screening and testing.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 75 of 111 Figure 3. 5 : AR agonist ( R1 ) AUC scores for EDSP List 1 ( blue ) and List 2 ( orange ) chemicals are shown in the figure above

. Chemicals with androgen antagonist bioactivity ( i.e., R2 AUC scores > 0.0.5 ) in the EDSP Universe are shown above ( grey )

. Reference antagonists ( red ) and selective androgen receptor modulators (SARMs; yellow ) are included

. Note: About 88 % of chemicals had no detectable AR antagonist bioactivity (R2 = 0)

. AR AUC antagonist (R2) scores are included for reference chemicals, List 1, List 2, and other chemicals in the EDSP Universe ( Appendix 3 ).

EPA is proposing use o f computational tools for: 1) prioritizing chemical s for further screening and testing; 2) contribut ing to weight of evidence based on Tier 1 battery data and other scientifically relevant information on a chemical’s potential endocrine bioactivity; and 3) potential substitution of these HTS assays for some of the lower throughput assays that are

cu rrently part of EDSP screening

. Prior to use, scientific confidence for each step must be demonstrated in a fit

- for

- purpose manner

. The AR pathway analyses are currently underway and at this time, we propose that adequate confidence has been demonstrated only for the first goal (prioritization)

. Additional resolution of performance of the AR models and potential utility for contributing to the weight of evidenc e determination of AR bioactivity will likely be improved by adding additional reference chemicals

. In addition, ongoing comparisons of models scores with Tier 1 assay endpoints and in vivo Hershberger results gathered from literature studies will determi ne the scientific confidence in substituting high throughput bioactivity for Tier 1 AR binding and Hershberger assays

. AR antagonist bioactivity for List 1 and List 2 chemicals appears to be relatively limited, thought there are over 100 chemicals in the EDSP universeIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 76 of 111 with antagonist (R2) AUC scores greater than the 0.05 threshold value for potential antagonist bioactivity

. Though using computation al estimates of AR bioactivity to contribute to weight of evidence determination s or su bstitute for specific T ier 1 battery endpoints is premature at this time, g iven the AR AUC model performance for reference chemicals, AR bioactivity predicted by the ToxCast may support prioritizing chemicals with positive model scores for additional screening and testing

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 77 of 111 

## Section 4: High

- Throughput Exposure (HTE)

4.1: Programmatic Objectives and Challenges for High Throughput Exposure Estimation

The challenge to implementing recommendations to the EDSP to incorporate exposure information for screening and priority setti ng (below) has been : 1) the lack of HTE models to handle the thousands of chemicals in the EDSP Universe ; and 2) the lack of exposure/use data readily available for most of the EDSP Universe of chemicals.

 Recommendation to the EDSP Program by the E DSTAC i n August 1998 “Data resulting from HTPS [ HTS ] will be combined with exposure

- related information... for the purpose of setting priorities for T1S [Tier 1 Screening]” and

 January 2013 FIFRA SAP “A number of approaches were identified by the Panel to potentia lly improve comparisons and prioritization efforts

. HTP [HTS] assays could effectively be used to categorize chemicals in a tiered manner...Exposure potential could be incorporated into the ranking and prioritize chemicals for further testing...”

Tradition al low

- throughput exposure models are designed to do one or a few chemicals at a time, usually for a single scenario ( e.g., indoor residential exposure), and require an extensive set of input data

. Using this traditional approach is not feasible for EDSP since it would take decades to collect the necessary data and model exposures for the 10,000+ chemicals in the EDSP universe

. While pesticides may have sufficient exposure/use data to use traditional exposure estimation methods, they are only small fracti on of the EDSP Universe

. The vast majority of EDSP chemicals have a sparse amount exposure/use data

. So

, one of the challenges to the EDSP is to design the best modeling tools to make useful exposure estimations using the available data that can be collected from Web

- based databases and other data mining techniques. The EDSP believes it can produce useful HT E estimations based on the available, albeit in many cases sparse, exposure/use data

. In keeping with ‘fit for purpose,’ the EDSP exposure estimations need not be as precise as traditional low

- throughput exposure models, but need only be sufficiently prec ise to screen and prioritize EDSP chemicals

. This material was covered in detail in the July 2014 FIFRA SAP review on exposure models

. The complete white paper and all meeting materials can be found on the FIFRA SAP website ( http://www.epa.gov/scipoly/sa p/m eetings/2014/072914meeting.html ).



### 4.2: High Throughput Exposure Modeling

In order to provide the necessary exposure science for the thousands of chemicals in the EDSP Universe , the EPA and National Academies have developed a long

- range vision for

ex posure science in the 21st century, and a strategy for implementing this vision over the next twenty years ( National Research Council 2007

, National Research Council 2009

, National Research Council 2011

, National Research Council 2012 )

. Within this vision , efficient exposure screening methods should be developed for rapid implementation, be based on basic chemical properties, and provide quantitative estimates for hundred s to thousands of chemicals ( Mitchell et al. 2013

, Wambaugh et al. 2013 )

. The EPA’s ExpoCast initiative ai ms to provide these capabilities ( Cohen

- Hubal et al. 2010 )

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 78 of 111 T he ExpoCast project has de veloped a method for generating rapid, exposure estimates using minimal information and estimates uncertainty using monitoring data

. The methods described in Wambaugh et al. ( Wa mbaugh et al. 2013 ) make use of the available models that are capable of making potential exposure predictions for thousands of chemicals in minutes

. In order to achieve these efficiencies, the various available HTE models ( Mitchell et al. 2013 )

, make assumptions, particularly with respect to the granularity of space and time ( Arnot et al. 2006

, Rosenbaum et al. 2008 )

. We attempt to compare exposure predictions to exposures inferred from monitoring data, and we do so systematically across as many chemicals (and chemistries) as possible to quantify the impact of these assumptions.

A key development within ExpoCast is the SEEM ( Systematic Empirical Evaluation of Models ) framework, which we have applied to exposure p rioritization

. In order to prioritize thousands of chemicals for the EDSP, we must use limited data and computational models based upon broad assumptions while also carefully characterizing the uncertainty in our approach

. These important components are provided by the SEEM framework illustrated in Figure 4. 1

. SEEM is designed to integrate the predictions of multiple models and empirically evaluate model performance systematically, over as many chemicals as possible ( Oreskes et al. 1994

, Oreskes 1998 )

. SEEM makes predictions of human exposure and weights contributing HTE model components appropriately, all with an appropriate characterization of uncertainty

. The SEEM framework is intended to be sufficiently flexible to make use of all extant models capable of high

- throughput exposure prediction ( Mitchell et al. 2013 ) , and incorporate new models as they become available

. Within this framework, new and revised models c an be evaluated for improved predictive ability, and ability to decrease uncertainty, while new data can be incorporated to better characterize model performance

. Ideally, “actual exposure” values would be used to evaluate the accuracy of the HTE predicti ons

. Unfortunately, actual exposure events are both difficult to monitor and confounded by the inherent complexities of human behavior

. However, as illustrated in Figure 4.2 , we can obtain data that could be used to characterize potential sources ( i.e., upstream) of exposure such as composition of consumer products, characterization of environmental releases, or monitoring data on media concentrations

. We can also obtain exposure indicator data ( i.e., downstream), particularly biomarkers of exposure ( e. g., chemical concentrations in urine or plasma)

. Since the exposure event itself is difficult to observe, we develop models to either estimate exposure from upstream sources (forward modeling), or infer exposure from downstream sources (reverse modeling)

. Both forward and reverse modeling have drawbacks, and recent advances have been made to compare the results of both approaches to establish model effectiveness

– that is, predictive ability of models for chemicals covered by biomonitoring data ( Wambaugh et al. 2013 )

. One program that contributes to monitoring of environmental chemicals is the National Health and Nutrition Examination Survey (N HANES)

. NHANES is designed to assess the health and nutritional status of adults and children in the U.S

. (http://www.cdc.goc/nchs/nhanes.htm)

. As part of NHANES, the CDC conducts ongoing assessments of collected human samples ( e.g., blood, urine) in ord er to quantify the level of chemical compounds present in the average U.S. resident, and regularly presents findings from these studies in The National Report on Human Exposure to Environmental Chemicals ( CDC 2011 )

. NHANES cov ers a few hundred of the thousands of environmental chemicals and potential metabolites for which data are needed ( Egeghy et al. 2012 )

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 79 of 111 Figure 4.1: Illustration of the SEEM framework applied to high

- throughput exposure modeling

. Exposures inferred from biomonitoring data ( y

- axis) are jointly regressed on predicted exposures for those chemicals using multiple HTE models ( x

- axis) to evaluate the predictive ability of the models

. The regression weights allow evaluation of each exposure model (a weight of zero would indicate no predictive ability)

. The regression also serves as a calibration and estimation of uncertainty for ex trapola ting to other chemicals

.

Given that human biomonitoring data are indirect estimates of “actual exposure”, the y

- axis of the SEEM analysis in Figure 4. 1 must be estimated using TK data associated with the chemicals of interest

. The exposure inference was conducted under the assumption of steady

- state conditions ( Mage et al. 2004 )

. This is a significant assumption when trying to relate human tissue and fluid sample chemical composition metrics to real world environmental exposures across various media ( e.g., air, water, soil), which may be sporadic and/or seasonal, over the course of a lifetime

. At this time, however, these assumptions are necessary for chemicals where there are no data to do otherwise ( Egeghy et al. 2012

, Mitchell et al. 2013 ) and these assumptions are a key contributor to the overall uncertainty estimated in Wambaugh et al. ( Wambaugh et al. 2013 )

. The benefit of these assumptions is that the evaluation and calibration of exposure model predictions for thousands of environmental chemicals takes a few days at most

. As HTTK data becomes available (Section 5), it may eventually be possible to characterize the confidence in these inferences with respect to the drivers of variability ( e.g., exposure frequency, duration, and chemical metabolism/excretion half

- life )

. As finer grained chemical use information becomes available, it might be used jointly with basic TK data to eliminate the need for the steady

- state assumption altogether

. At this time, the steady

- state assumption appears to be the best available for H TE methods, and is a contributor to the overall uncertainty estimated in Wambaugh et al. ( Wambaugh et al. 2013 )

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 80 of 111 The NHANES data reflect aggregate human exposures across all the pathways in Figure 4.2 and any unknown pathways

. Thus, while the forward prediction models may only partially cover the exposure pathways in play, the estimated uncertainty will reflect all exposures that resulted in the chemical being present in a biomonitoring sample

. The HTE model calibration performed with SEEM framework is designed to account for pathways not currently covered by the HTE models

. However, the impact of exposure routes not reflected by NHANES

m onitoring, including occupational exposure, cannot be assessed in this manner

.

A simple ( i.e., yes/no) near field indicator variable had greater predictive power than more complicated HTE environmental exposure models ( Wambaugh et al. 2013 )

. G enerally, chemicals above the limit of detection in NHANES had indoor/consumer product use

. The ability of a simple heuristic

– whether or not chemicals were present in the home

– to forecast exposure in the prior study raises the question of what other simple heuristics might be used to categorize chemicals

. Given a variety of rapidly obtained data (putative chemicals use categories and physico

- chemical properties largely obtained from QSAR), which factors best explain exposures inferred from the available NHANES biomonitoring data? Further, if near field sources of exposure tend to be the predominate source of chemical exposures indicated by biomonitorin g, what data and models exist to address near field exposure for the thousands of chemicals with limited exposure data?Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 81 of 111 Figure 4.2 : A graphical representation of human exposure space in relation to exposure pathways, receptors, and monitoring data.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 82 of 111 In the current (second generation) SEEM analysis, a total of 19 potential predictors were considered, comprised of production volume, 13 chemical use categories, 3 physico

- chemical properties, and two randomly generated descriptors (Wambaugh et al. 2014)

. For each subset size and sample, best subset selection was performed using complete enum eration ( Morgan and Tatar 1972 )

. The average Akaike Information Criteria ( AIC ) ( Akaike 1974 ) across all samples was used to indicate the optimal number of factor s

. The frequency of occurrence of the factors among t he best subset size was used to determine the optimal model

– a five

- factor model is suggested by AIC

. The five factors are “Production volume” and four modified use categories from ACToR: “Consumer & Industrial” use, “Pesticide Active” use, “Pesticide In ert” use, and “Industrial no Consumer” use, where “Industrial” indicates use in chemical/industrial processes

. The minimal predicti ve subset of factors we identified in this manner were consistent across all demographic groups and are describe d as the heu ristics of environmental chemical exposure ( Table 4. 1 )

. Using the factors identified by best subsets, a Bayesian regression was performed to jointly i nfer the regression coefficients, stoichiometric relationships, and the parent exposures from the NHANES subject

- specific urine samples for the total NHANES sample and each demographic subset

. For this analysis, Hamiltonian Monte Carlo was performed via S tan ( http://mc

- stan.org )

. The calibrated model , shown for the total population in Figure 4.3

. has an R 2 of approximately 0.5.

Figure 4.3 : Separate linear regressions were made of the inferred exposures on the five optimal descriptors for different demographic groups monitored by the CDC NHANES studies

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 83 of 111 Here we show the correlation of inferred exposures and the calibrated model for the total population

. The solid line indicates the 1:1 line (perfect predictor)

. Ba yesian analysis was used to distribute urine products using mass

- balance, giving the 95% credible intervals (light lines) and medians (solid circles ).

With the Bayesian regression methods and the bio monitoring data

, we obtained a calibrated model that can predict exposure for unknown chemicals, and just as importantly, we also get an empirical estimate of our uncertainty

. Any approximations, omissions, or mistakes should increase the estimated uncertainty when evaluated systematically across chemicals ove r time

. This uncertainty reflects contributions from at least the following four factors: (1) incomplete/inappropriate chemical descriptors; (2) imperfect model assumptions; (3) measurement uncertainty and other sampling issues for the monitoring data; an d (4) imperfect infere nces from the monitoring data

. The calibrated model was then used to predict exposure for 7968 chemicals

. Figure 4.4 shows the median and the upper limit of the 95% credible interval for total population geometric mean predicted exp osures (mg/kg BW/day)

. The highest overall upper 95% limit for the general populations was for diethyl phthalate, a chemical monitored by the NHANES program

. Also plotted in Figure 4.4 are the median and upper 95% predicted exposures for children aged 6

- 11 (the youngest group with NHANES urine biomonitoring data)

. The exposures for children are predicted to be systematically slightly higher than for the total population.

Figure 4. 4 : Ranking of 7968 chemicals with respect to the upper 95% confidence limit on the predicted geometric mean exposure (mg/kg/day) for the total U.S

. population (predictions for children aged 6

- 11 also included)

. For each chemical the lower circle indicates the media n and the upper circle indicates the upper limit of the 95% credible interval for predicted exposures (mg/kg BW/day) derived from the heuristics in Table 4. 1

. The dotted horizontal lines indicate the 25%, median, and 75% quantiles for the distribution of limits of detection in the NHANES samples

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 84 of 111 Table 4. 1 : High

- throughput exposure heuristics

Heuristic Description Number of Chemicals Inferred NHANES Chemical Full Chemical Library

ACToR “Consumer use & Chemical / Industrial Process use” Chemical substances in consumer products ( e.g.,

toys, personal care products, clothes, furniture, and home

- care products) that are also used in industrial manufacturing processes

. Does not include food or pharmaceuticals. 37 683

ACToR “Chemical / Industrial Process use with no Consumer use” Chemical substances and products in industrial

manufacturing processes that are not used in consumer products

. Does not include food or pharmaceuticals 14 282

ACToR UseDB “Pesticide Inert use” Secondary ( i.e., non

- active) ingredients in a

pesticide which serve a purpose other than repelling pests

. Pesticide use of these ingredients is known due to more stringent reporting standards for pesticide ingredients, but many of these chemicals appear to be also used in consumer prod ucts. 16 816

ACToR “Pesticide Active

u se” Active ingredients in products designed to prevent,

destroy, repel, or reduce pests ( e.g., insect repellants, weed killers, and disinfectants). 76 877

TSCA IUR 2006 Total Production Volume Sum total (kg/year) of production of the chemical

from all sites that produced the chemical in quan

tities of 25,000 pounds or more per year

. If information for a chemical is not available, it is assumed to be produced at <25,000 pounds per year. 106 7785

Two complementary statistical approaches (best subsets a nd Bayesian regression using horseshoe priors) were used to determine the optimal factors, with both methods agreeing on the importance of “Pesticide Inert”, “Consumer & Industrial” use, and “Production volume”, but with the best subsets method being less certain about the “Pesticide Active” and “Chemical/Industrial Process with no Consumer” use categories

. Thus, although the heuristics in Table 4.1 seem plausible in retrospect, we should not be surprised if additional data diminishes or eliminates the sig nificance of some factors

. Similarly, plausible heuristics not supported by this analysis ( e.g., vapor pressure) may be significant for chemicals not included in the current set of monitoring data

. These quantitative predictions are useful for prioritiza tion,Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 85 of 111 but the statistical analysis here may be even more useful as a means to organize and prioritize the types of data needed for predicting human exposure to the thousands of chemicals in our environment ( Oreskes et al. 1994

, Oreskes 1998 )

. For chemicals that are similar to those known to have high measured concentrations in the urine of the U.S

. population, we need additional exposure information, such as biomonitoring and environmental sampling

. New biomonitoring data will also be needed for the chemicals whose properties are m ost different from those currently being studied in the NHANES and by the U.S. EPA

. In this manner, factors such as vapor pressure and other environmental factors may be shown to be important exposure heuristics

. Chemicals without monitoring data, but th at are similar heuristically to those chemicals with low exposure indicated by monitoring data, become lower priorities for further evaluation with respect to exposure.

Note that two of the predictive factors are modified ACToR categories “Consumer use an d Chemical/Industrial Process use” (defined as the intersection of the two ACToR categories) and “Chemical/Industrial Process use, no Consumer use.” The final two columns of Table 4. 1 illustrate the difference between the NHANES chemicals and those chemicals that make up the Tox21 library

. Although there are a significant number of chemicals in Tox21 that have similar uses to the NHANES chemicals, the l argest fraction of the total (4182 out of 7968) chemicals have no use identified by ACToR.

With respect to the five predictive factors in the optimal model, the ten demographic groups do not appear to vary substantially ( Figure 4.5 ). When the optimal model with five predictive factors determined for the general population is run using data from ten demographic groups, resultant regression coefficient s are very similar across demographic groups

. The distribution of values fo r the regression coefficient in Figure 4.5 i ndicates the trend corresponding to each heuristic

. For all demographic groups, exposures are estimated to be higher if: (1) both consumer use and chemical/industrial process use is indicated; (2) pesticidal inert use is indicated; and (3) production vo lume is high

. For all demographic groups, exposures are estimated to be lower if chemical/industrial process use without consumer use, or pesticidal active use, is indicated

. The difference between pesticidal inerts and actives is interesting

. Most pest icide active ingredients monitored by NHANES are found to be below the limit of detection in the urine of most Americans, while the chemicals indicated as pesticidal inerts ( e.g., bisphenol a, methyl paraben) are known to be inert ingredients in other clas ses of products that do not conform to the strict ingredient reporting standards of pesticides.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 86 of 111 Figure 4. 5 : Regression coefficients for the model from the second generation SEEM analysis across different demographic groups monitored by NHANES studies (abbreviations include Reproductive Age Females, children aged 6

- 11, children aged 12

- 19, adults aged 20

- 65, adults 66 and older, and people with body mass indices less or greater than 30)

. Separate linear regressions were made of the inferred exposures o n the five optimal descriptors for different demographic groups monitored by the CDC NHANES studies

. The posterior distribution of standardized regression coefficients for the top five factors is shown

. Large positive coefficients indicate correlation wi th increased exposures, while large negative coefficients correspond to decreased exposures

. Among the demographic groups considered there is little variation in the significance of those factors for the chemicals we have examined.

In the absence of any other exposure data, the heuristics of exposure in Table 4. 1 provide a reasonable basis for initial prioritization of those chemicals most in need of additional study

. These heuristics meet the need identified by the NRC for predictive tools that will enable development of exposure information on thousands of chemicals that are now in widespread use, and enable informed prioritization of those chemicals ( Nat ional Research Council 2012 )

. Although the state of knowledge on human exposure is not ideal, some easily obtained information correlates with the exposure as inferred from biomonitoring data for 106 environmental chemicals

. For the total U.S

. populat ion, as well as various demographic groups, including children aged 6

- 11 and women of child bearing age, our high

- throughput heuristic model is able to explain roughly half of the variance in the inferred exposures

. Out of the thousands of other chemicals without publicly accessible biomonitoring data, those with properties and uses that are most like the chemicals to which people are known to be highly exposed are logical targets for further investigation by more traditional exposure science methods.

For the thousands of chemicals with no other data on human exposure, the SEEM framework can determine a calibrated and empirical uncertainty estimate for predicting human exposure and provide a means to prioritize chemicals within the EDSP universe based on e xposure.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 87 of 111 

## Section 5: Toxicokinetics / Dosimetry

TK provides a critical bridge between the concentrations that show bioactivity in the in vitro assays and the administered doses needed to produce those concentrations at the tissue level

. Unfortunately, for many of the compounds under consideration by the EDSP, the in vivo TK data necessary to translate from the administered concentration in vitro ( i.e., μM) to real world exposures ( i.e., mg/kg BW/day) is not available, as is shown in Figure 5.1

. Out of 239 chemicals with in vitro bioactivity examined in the Wetmore et al. (2012) study, only 11 chemicals had in vivo TK data

. Thus, alternative, higher throughput methods are needed as an alternative to traditional TK model development with in vivo data

. This section summarizes work on HTTK , which can be combined with HTS data

. This material was covered in detail in the July 2014 FIFRA SAP review on exposure models

. The complete white paper and all meeting materials can be found on the SAP website ( http: //www.epa.gov/scipoly/sap/m eetings/2014/072914meeting.html ).

Figure 5.1 : Availability of in vitro bioactivity data, in vivo TK, and traditional exposure predictions

. As in Egeghy et al. ( Egeghy, Judson et al. 2012 ) , there is a paucity of data for providing context to HTS bioactivity data.

For pharmaceutical compounds, successful methods have been developed for determining some aspects of TK ( e.g., hepatic metabolic clearance and steady

- state behavior) from limited in vitro methods and chemical structure

- derived property predictions ( Jamei et al. 2009

, Lukacova et al. 2009

, Jame i et al. 2013 )

. In drug developmen t HTTK methods allow an initial determination of the therapeutic doses for clinical studies

. This approach is considered effective for pharmaceutical compounds and it is claimed that the predicted concentrations are typically on the order of the actual, i n vivo concentrations ( Wang 2010 )

. Without the time and resources to generate in vivo TK data for the thousands of chemic als that require TK data, these HTTK methods for pharmaceuticals may be useful for environmental chemicals ( Wetmore et al. 2012

, Thomas et al. 2013 )

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 88 of 111 HTTK methods can estimate the dose (mg/kg BW/day) needed to produce steady

- state plasma concentrations (C ss , in units of μM) equivalent to the activation concentrations observed in vitro (this is RTK) ( Rotroff et al. 2010 ; Wetmore et al. 2012 ; Wetmore et al. 2013 )

. As is shown in Figure 1.2 , considering the dose needed to produce a putative bioactive plasma concentration results in a much greater discrimination between the potencies of various compounds

. In Figure 1.2 , 2 39 chemicals investigated by Wetmore et al. ( Wetmore et al. 2012 ) are compared both in terms of 50% activation concentration (AC 50 , in units of μM) for a bioactive assay ( Figure 1.2A ) and in terms of the oral equivalent dose (mg/kg BW/day) predicted to be necessary to cause that co ncentration in an adult human

. Each plot point indicates an in vitro assay for which a systematic response was observed; some, more promiscuous chemicals that had activity in numerous assays may have many points, while other chemicals may have very few points plotted.

The HTTK model used for the EDSP chemicals, is relativ ely simple, making three key assumptions: (1) that the whole body is at the same concentration ( i.e., plasma); (2) that the environmental exposure is constant and uniform ( i.e., constant infusion), and (3) that enough time has passed that the plasma concen tration is at steady

- state with respect to the environment ( Wetmore et al. 2012 )

. We use a “three

- compartment” model ( Figure 5.2 ) t o predict plasma concentrations as the result of a constant environmental exposure

. We did not determine tissue partitioning, since at steady state we assume that the free concentration i s in all tissues

. This simple model featured elimination due to metabolism of the parent compound in the liver and renal elimination by passive glomerular filtration, adjusted for plasma protein binding ( i.e., the product of glomerular filtration rate and f up )

. We modeled hepatic metabolism using the “well

- stirred” approximation ( Wilkinson and Shand 1975

, Houston and Carlile 1997 ) from the flow into the liver and the in vitro measured values for fraction unbound intrinsic hepatic clearance and the density of hepatocytes in liver (1.1 x 10 8 hepatocytes per gram liver and a l iver density of 1.05 g/mL)

.

Figure 5. 2 : The general, three compartment TK model, used by SimCYP for RTK ( Wetmore, Wambaugh et al. 2012 )

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 89 of 111 An analytic solution for plasma concentration (Equation 7.1) was derived from the differential equations describing the three

- compartment model by assuming constant infusion dosing

. This analytic solution is the equation used for steady state plasma concentrations (C ss ) in previ ous work ( Wilkinson and Shand 1975

, Rotroff et al. 2010

, Wetmore et al. 2012

, Wetmore et al. 2013 )

.

Equation (7.1 )

Two types of in vitro data were used to parameterize the HTTK models: (1) CL int , the intrinsic metabolic clearance of the parent compound by primary hepatocytes (substrate depletion approach) in wells of a multi

- compound plate ( Shibata et al. 2002 ) ; and (2) f up , the fraction of chemical unboun d in the presence of plasma protein as assessed using rapid equilibrium dialysis (RED) in which two wells are separated by a membrane that is permeable by smaller molecules, but prevents the plasma protein added to one well from migrating to the other well (the relative chemical concentration in the two linked wells gives the free fraction of chemical) ( Waters, Jones et al. 2008 )

. For environmental chemicals, data on these two parameters were obtained primarily from Wetmore et al. ( 2012 ) and Tonnelier et al. ( 2012 )

. Once a chemical specific C ss is predicted for a steady

- state 1 mg/kg BW/day dose, we can use this C ss value to perform RTK

. We rely on the linearity of the simple TK model we are using

– a dose X times greater than 1 mg/kg BW/day will produce a steady

- state concentration of X C ss , as in Figure 5.3A

. Specifying the dose and predicting the C ss is “forward” TK

. RT K involves flipping the axes of Figure 5.3A to get Figure 5.3B . We take the AC 50 from an in vitro assay and assume that the steady

- state plasma concentration must be equivalent to the AC 50 in order to cause activity in vivo

. We then use the linear C ss prediction to infer a dose that would cause that concentration to occur ( Figure 5.3B ).

Monte Carlo (MC) was performed to simulate human variability in key physiological and TK p arameters, as is illustrated in Figure 5.2

. L og

- normal distributions, were used with mean values drawn from the references above and a coefficient of variation of 0.3

. Body weight, liver volume, blood flow to the liver, and CL int were all varied in this manner

. The f up was drawn from a censored distribution with identical properties to the other distributions if the mean was above the limit of detection (assumed to be 1% unbound) and a uniform distribution between 0% an d 1% unbound otherwise

. For each chemical, 1000 different combinations of parameters were used to determine C ss , allowing estimation of the 5%, median, and 95% quantiles.

The 5% quantile from the MC simulation represents adult humans who would get lower plasma concentrations from the same fixed dose; for example, this could be due to higher than average expression of a specific metabolizing enzyme resulting in rapid clearance from the body

. Conversely, the 95% quantile from the MC simulation represents a dult humans who would get higher plasma concentrations

– these are the more sensitive individuals

. As is shown in Figure 5.3B

, i f the predicted C ss is higher, then the daily dose predicted as necessary to achieve that concentration in plasma equal to the lowest bioactive concentration is predicted to be lower

. Al though MC simulation is used to characterize some aspects ofIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 90 of 111 human variability ( e.g., body weight), the assumptions we made of how to model these variations can influence the results of the predictions, especially for the extremes ( e.g., the highest 95 th p ercentile) ( McLanahan et al. 2012 )

. Further, any key determinants of variability that are not included in our simulation cannot be assessed for impact

.

Figure 5. 3 : The Steady

- State TK model used for Reverse Toxicokinetics (RTK) is linear

. (A) We can calculate the predicted steady

- state concentration (Css) for a 1 mg/kg/day dose and multiply to get concentrations for other doses

. The “reverse” part of RTK is shown in (B) in which we swap the axes from (A) so that we can divide bioactive concentra tions by the Css for a 1 mg/kg/day dose to get oral equivalent dose in mg/kg BW/day

. We use Monte Carlo (MC)Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 91 of 111 methods to simulate human variability to determine the range of Css produced for the same exposure across the simulated population

. The higher th e predicted Css, the lower the oral equivalent dose, so the upper 95% predicted Css from the MC has a lower oral equivalent dose.

Figure 5. 4 : Monte Carlo (MC) Approach to Variability

We recognize that the HTTK methods we are using do not include prediction of important aspects of TK such as bioavailability and absorption rate, but we can make conservative assumptions for these values and test these assumptions against in vivo data

. Th ere are many ways in which HTTK models in general might omit other key drivers of TK, including: extra hepatic metabolism, secretion in bile/enterohepatic recirculation, unusual binding affinity for one tissue, substrates for transporters in renal, hepatic , and other tissues, and poor absorption ( Rotroff et al. 2010 )

. Until in vitro assays are developed for these processes, QSAR provides the best way forward

. Unfortunately, QSAR is not yet capable of providing rate constants; rather it allows estimation of how likely or unlikely a process is to occur.

We have shown that provisional HTTK models can be developed from in vitro data for many environmental chemical s to allow comparison of HTS bioactivity data and HTS exposure predictions

. While we expect that these methods will be refined and replaced as greater knowledge becomes available, we have provide d the most informed methodology given the data available tod ay.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 92 of 111 

## Section 6: Integrated Bioactivity Exposure Ranking (IBER) for Prioritization

Previous sections have described methods to quantitatively predict concentrations at which endocrine

- pathway activity can occur; how to convert these concentrations to or al equivalent doses using HTTK methods; and how to estimate exposures for chemicals

. In this section we describe how to put these pieces together to determine if a chemical has the potential to activate an endocrine

- related pathway at exposures / doses th at are likely to occur

. We term this the Integrated Bioactivity

- Exposure Ranking (IBER)

. We operate under the assumption that it is more important to perform follow

- up testing on a chemical that has an overlap between expected exposure and bioactivity th an one that does not

. This is the basis of using all of these approaches for prioritization of chemicals for EDSP Tier 1 testing

. This basic approach is illustrated in Figure 6.1

. For each chemical of interest, we evaluate pathway activity in vitro , con vert in vitro concentrations to in vivo doses using HTTK and then compare with exposure

. This figure highlights another key aspect of this comparison, namely that each of these quantities (quantitative pathway activity, pharmacokinetic parameters and expo sure estimates) are subject to uncertainty and variability, which leads to distributions in all of the relevant quantities

.

Figure 6.1: Chemical prioritization can be conducted by using HTTK methods to translate high

- throughput bioactivity data into putative human doses of concern (mg/kg BW/day) and then comparing those doses to HTE predictions.



### 6.1: Basic Approach for IBER

The basic approach for calculating the pathway

- activity dose distribution was developed by Judson et al. ( Judson et al. 2011 )

. One first estimate a pathway

- wise estimate of concentration at which the pathway is activated, uses HTTK to convert from concentration to dose, and then compare this dose against exposure

. Each of the quantities activity concentration, concentration

- to

- dose conversion and exposure estimate, is subject to uncertainty and population variability

. After combining point estimates and uncertainty andIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 93 of 111 variability ranges, the IBER ( Integrated Bioactivity

- Exposure Ranking ) is the ratio between the lowest X

- perc entile (e.g

. 5%) of the bioactivity dose range and the upper Y

- percentile (e.g

. 95%) of the expected exposure range

. The IBER is one proposed method for prioritization of limited testing resources for chemicals

. We will illustrate this approach with data from 182 ToxCast chemicals with in vitro bioactivity data, HTTK data, and exposure estimates

. Of these, 40% have an IBER >100, and 25% have an IBER > 1000

. We next provide somewhat more detail of the IBER approa ch

.

1. The approach is built around biological pathways whose structure is derived from a large body of in vitro and in vivo studies

. For EDSP, we focus on the e strogen and androgen receptor pathways already described

. 2. For each pathway, we select a represen tative set of targets to probe, and develop in vitro assays to measure effects related to those targets

. For human risk assessment

, we would primarily focus on human targets and cells, although assays derived from other species that share significant sequ ence homology at the specific target gene can also be used

. Assays can be as simple as binding to a single protein, or as complex as whole genome microarrays or other genome

- scale measurements

. Sections 2.1 and 3.1 demonstrated the use of in vitro assays for this purpose

. 3. All assays must be run in concentration

- response format in order to yield values for the Biological Pathway Altering Concentration (BPAC ). 4. Usually, it will be necessary to run several assays associated with a pathway and use a systems

- level model to integrate the resulting pharmacodynamic (PD) data for estimating BPAC

. The ER and AR models described above are specific examples of integrating data from multiple assays across a pathway

. 5. We need to estimate PD

- related uncertaintie s and population variability and must characterize the population distribution of the BPAC and its uncertainty using probability distributions

. We have proposed a default approach to estimate uncertainty and variability which is similar to that used in in vivo studies

. Developing better estimates of these quantities is a pressing research need

. 6. HTTK modeling is then used to estimate the external dose that would lead to the internal BPAC (dose

- to

- concentration scaling function)

. As described above, the HT TK methods we use incorporate estimates of population variability

. This dose is termed the Biological Pathway Altering Dose (BPAD)

. Note that the derived BPAD is a distribution and not a single value

. 7. The BPAD and the exposure distributions are then compared as in Figure 6.1

. The mechanics of this process are straightforward, but appropriate treatment of uncertainty and variability is a challenge and a topic of ongoing research

.



### 6. 2 : Modeling uncertai nty and variability

For each of the components of IBER (PD, PK, Exposure), there are uncertainty and variability components

. Table 6.1 summarizes the approaches we are currently taking for these

. However, this is an area requiring significant new develo pment

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 94 of 111 Table 6.1 : Summary of approaches to uncertainty and variability in IBER

Uncertainty Variability Pharmacodynamics (BPAC) Default

. ( Judson et al. 2011 ) Default

. ( Judson et al. 2011 )

Pharmacokinetics Future Research Direction Ch emical

- specific, using Monte Carlo sampling of modeled subjects with different metabolic profiles (Section 5)

Exposure Use of uncertainty in biomonitoring data; in production volume and other parameters

. Future Research Direction



### 6

. 3 : Interpretation and application : IBER

Over the past several years, EPA has initiated a step

- wise scientific review of the status of high throughput tools and defined several potential uses for computational models in the EDSP program

. A subset of in vitro assay and in silico methods were reviewed with the intended purpose of prioritizing chemicals based on ER bioactivity (Jan 2013 SAP)

. More recently, t he agency requested scientific input on new high throughput exposure models and described the potential utility for es timating near

- field exposures for thousands of chemicals (July 2014 SAP)

. In addition to these SAP reviews, numerous articles on high throughput in vitro assays ( e.g., Reif et al. 2010 , Kavlock et al. 2012, Rotroff et al. 2013, Judson et al. 2014 ), toxicokinetics (Rotroff et al. 2010, Wetmore et al. 2013, Wetmore et al. 2014 ), and exposure modeling (Wetmore et al. 2011, Wambaugh et al. 2013, Wambaugh et al. 2014 ) have appeared in peer

reviewed publications , broadly engaging the scientific communit y on the state of the science and potential use by EPA

.

Rather than summarizing or reconsidering the research that has been presented previously

, the aim of this document and subsequent SAP meeting is to request scientific input on EPA’s proposed method f or applying computational tools to :

1. P riorit ize the EDSP chemical universe ; 2. C ontribute to the weight of evidence determination on a chemical’s potential endocrine bioactivity , and 3. S ubstitute for specific endpoints in the EDSP Tier 1 screening battery

.

We describe the approach for integrating c omputational tools ( Figure 6.2 ) to rank high priority chemicals for additional screening and testing, and demonstrate the scientific confidence and robustness of the various models used in EPA’s proposed approach usin g various data sets with the expectation that the three goals described for integrating computational tools into the EDSP framework require differing degrees of scientific confidence ( i.e., “fit for purpose”).Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 95 of 111 Figure 6. 2 : The flow

- chart illustrating EPA’s proposed Integrated Bioactivity

- Exposure Ranking (IBER) approach to integrating computational tools in the existing EDSP framework

. Bioactivity HTTK = high throughput toxicokinetics.

Both the technology and the interpretation of high throughput me thods have advanced greatly and EPA is now preparing to use these innovative methods to pr ioritize chemicals

. The IBER is informed by computational models described in previous sections of this document

. For chemical s in the EDSP universe, high throughpu t bioactivity estimates from the ToxCast models (Sections 2 and 3 ; Appendix 3 ) are used to calculate pseudo AC 50 values and high throughput toxicokinetic models are then used to extrapolate in vivo estimates of bioactive concentrations from in vitro assay data (Section 5 ; Appendix 3 )

. Human oral equivalent doses are then paired with exposure estimated from the SEEM framework (Section 4 ; Appendix 3 )

. Plotted together, these data indicate chemicals for which activity and exposure may overlap or be close enough to prioritize for closer study ( Figures 6. 3 and 6. 4 )

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 96 of 111 Figure 6. 3 : Plots of high throughput predictions of estrogen receptor (ER) bioactivi ty, converted to oral dose equivalents ( Section 5 ) and high throughput predictions of environmental exposures ( Section 4 ) for chemicals for which all data are available

. Note: Graphs are intended to illustrate the approach for Integrated Bioactivity

- Expos ure Ranking and not all chemicals shown above are in the EDSP chemical universe

.

Figure 6. 3 : Plots of high throughput predictions of androgen receptor (AR) bioactivity, converted to oral dose equivalents ( Section 5 ) and high throughput predictions of environmental exposures ( Section 4 ) for chemicals for which all data are available

. Note: Graphs are intended to illustrate the approach for Integrated Bioactivity

- Exposure Ranking and not all chemicals shown above are in the EDSP chemical universe

.

T he extrapolated in vivo bioactive dose and the exposure estim ate are then used rank chemicals ( Figure 6. 5 ; Appendix 3 )

. The lowest IBER values are chemicals for which

, t he upper 95% confidence intervals of exposure estimates come close st to or overlap wit h ( e.g., values < 1) the lower 95% confidence limits for doses estimated to be bioactive and these chemicals warrant highest priority for additional EDSP screening and testing

. The proposed approach is illustrated for both estrogen receptor (ER IBER) and androgen receptor bioactivities (AR IBE R)

. For each endocrine pathway , the greater of the two agonist (R1) or antagonist (R2) bioactivity score is used in rankings

. Examining both bioactivity and exposure data illustrates that cons idering prioritizing en vironmental chemicals with the highest potential bioactivities may not be sufficiently protective for chemical which may be less potent but also have high estimated exposures

. For example, two List 1 chemicals two List 1 chemicals have ER IBER rankings < 1000, despite having very low ER bioactivities ( Figure 6.5 )

. Highest priority would be the six chemicals in the EDSP universe with ER IBER values and three ARIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 97 of 111 IBER values below 1, indicating estimated exposures exceed potential bioactive concentrations ( F igure 6.5 ).

Figure 6. 5 : Integrated Bioactivity

- Exposure R ankings (IBER) f or estimated high throughput estrogen receptor (ER) and androgen receptor (AR) bioactivity of List 1, List 2, and the remaining chemicals for which all types of high throughput data are available

. A ranking of 1 indicates estimated bioactivity and exposure occur at the same concentrations (see Appendix 3 for data)

. Though there are recognized sources of uncertainty around bioactivity, toxicokinetic, and exposure model estimates , these data may still be use ful to prioritize chemicals for additional endocrine screening and testing

. For the thousands of industrial chemicals for which other sources of bioactivity and exposure information do not exist , this approach represents a met hod to begin to address potential endocrine effects

. If the ER and AR AUC models are scientifically robust for detecting the endocrine bioactivity, as illustrated by sensitivity and specificity of model predictions when compared to in vitro reference chem icals , computational data also may be used , along with available Tier 1 (or “Tier 1

- like”) data and other scientifically relevant information, to contribute to EDSP weight of evidence determination for chemicals

. For some endocrine pathway activities, mod el performance may be sufficiently strong to consider using model scores as substitutes for specific Tier 1 endpoints

. For example, agreement of ER agonist (R1) AUC model predictions with both Tier 1 in vitro assays and in vivo uterotrophic response descr ibed in Section 2 may be sufficiently robust that no additionalIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 98 of 111 information would be gained by requiring Tier 1 ER binding, ERTA, or uterotrophic guideline studies for some chemicals

. Further still, positive predictions from computational models when coupled with indications of in vivo positive endocrine response from other data sources ( e.g., Part 158 studies, high

- quality research in peer

- reviewed literature) may identify a need for f urther testing.

To date, reviewing EDSP chemicals for potential bioactivity has proven to be a complex, costly, and time consuming endeavor

. These limitations are compounded by the chemicals on EDSP List 1 and List 2 , which are probably not the most endo crine bio active chemicals in the EDSP universe

. As an alternative, we have described an approach using computational tools to prioritize and screen EDSP chemicals and rapidly identify those with the greatest potential endocrine bioactivity

, and mov ing awa y from lists of chemicals with relatively low or no potential endocrine activity

. The IBER approach for prioritization considers both the biological activity of compounds and the potential environmental exposure

. This approach takes advantage of both exi sting and innovative, emerging technologies to implement a scientifically robust and comprehensive chemical prioritization process for EDSP

. The prioritization of chemicals in the EDSP universe would greatly reduce the time, cost, and animal use required to identify chemicals with the greatest associated endocrine activity and would be consistent with EPA’s strategic plan for evaluating chemical toxicity ( http://www.epa .gov/spc/toxicitytesting/docs/toxtest_strategy_032309.pdf ).Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 99 of 111 

## Section 7: Future directions

7.1: Computational tools , models and data The computational tools described in earlier sections of this document focus on estrogen and androgen receptor

- mediated bioactivity and rely on simple pharmacokinetics and second generation high throughput exposure models in a proposed approach for prioritizing chemicals for screening and testing . Significant ongoing research is underway to include non

- receptor mediated end ocrine effects, to include thyroid bioactivity , and to improve the accuracy of high throughput bioactivity , pharmacokinetics, and exposure estimates

. The outputs of the computational models may change as the models continue to be refined and ongoing will be presented at future SAP meetings. Current and planned efforts include:

 Modeling the thyroid pathway : EPA/ORD has a large effort in generating HTS data around targets in the thyroid pathway, and building models to understand the consequences of perturbi ng these targets. Particular targets are: thyroid hormone receptor; thyroid releasing hormone; sodium

- iodide symporter; multiple dioidinases; and thyroid peroxidase. Results of these models will help parameterize biologically

- based dose

- response models o f the pregnant female. Models are being parameterized for both human and rat. Cell

- level models of the developing brain are being incorporated into the BBDR model.

 Modeling the steroidogenesis pathway : We are generating high

- throughput data using a modif ied variant of the EDSP Tier 1 T47D guideline assay that generates concentration

response profiles on almost all of the intermediate and final metabolites in this pathway. Raw data for the first 1000 chemicals is in house. A whole

- pathway model is being developed to better predict where in this pathway a chemical is causing disruption.

 Metabolically

- competent assays : One criticism of much in vitro toxicology is that the assays are typically not metabolically competent, so one can only measure the effect of the parent compound and not the complete set of metabolites. We are in discussion with a number of groups that have metabolically

- competent cell systems that could be used in endocrine

- related assays. We performed preliminary studies on the ToxCast ch emicals using pretreatment with an S9 fraction followed by a protein complementation assays used in the current ER model. The results were promising in that many compounds showed decreased activity, while the 2 that showed increased potency were known to require metabolic activation (methoxychlor and benzo(a)pyrene). However, the assay was relatively insensitive in that only activity from very potent compounds could be seen. Further experiments of this type are being planned.

 Testing further chemicals : W e are currently running more chemicals through the ER and AR assays already described. Most of these are in the EDSP universe. We are also running more chemicals through the HTTK assays (plasma protein binding and intrinsic clearance) to allow for more qu antitative comparison with exposure estimates. These are being selected based on both inclusion in the EDSP universe and activity against ER and AR.

 Modeling of stress pathways : As we have shown, cell stress and cytotoxicity can cause

non

- receptor mediated activity in all of the HTS assays used, and can confound our ability to tell true from false positives. We are building structure

- based models to predict the type of cell

- stres s process that chemicals are likely causing at high concentrations, with the aim of better understanding this cause of assay interference.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 100 of 111  QSAR models : We have briefly described a crowd

- source d ER activity modeling project (CERAPP) . We are planning to car ry out a parallel study for AR and other endocrine

related targets as the HTS data becomes available.

 HTTK models : Improvements in the models, and QSAR and measurement of parameters are ongoing.

 Exposure models : T he SEEM framework will continue be refine d and calibrated for predicting human exposures. Models providing more detail on sources of human exposure ( e.g., ground water and drinking water), as well as exposure models for ecological species ( e.g., fish) are also being developed.



### 7.2: Data Release and Transparency: The EDSP21 Dashboard. 129

The EPA is committed to make public all of the data and models being used in the EDSP21 prioritization effort described in this document

. A primary route for making the HTS, exposure and dosimetry data public is through the EDSP21 dashboard. This web portal allows users to browse results by chemical or by output type. The dashboard will be available at URL : http://actor.epa.gov/dashboard/edsp21

. Figure 7. 1 shows a n example of assay concentration

response data for Bisphenol A. All data will be available for browsing through the dashboard and will be released as a database export and as a series of flat files ( e.g. Excel worksheet ). In addition, all software used in these analyses are publically available. This public release policy applies to all data and models generated under EPA’s ToxCast and ExpoCast projects, and is not limited to the EDSP chemical universe

.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 101 of 111 Figure 7.1 : Screenshot of the EDSP21 dashboard showing sample concentration

- response data for Bisphenol A.



### 7.3: EDSP Future Directions

In this document, we have presented a proposed Integrated Bioactivity Exposure Ranking to identify EDSP chemicals. We have demonstrated that the ToxCast models of estrogen and androgen receptor bioactivity are capable of detecting in vitro (E and A) and in vivo (E, A in progress) reference chemical with a high degree of sensitivity and spec ificity. Coupled with high throughput exposure predictions, these computational tools help to identify chemicals with the least separation between bioactive concentration and potential environmental exposures. With appropriate scientific confidence estab lished at each level, these data may be useful for:

1. Prioritization of chemicals for additional EDSP screening and testing, 2. Contributing, along with other data, to the weight of evidence determination of a chemical’s potential for endocrine effects, and 3. Substituting for specific EDSP Tier 1 battery endpoints.

Together, these computational tools allow EPA to move away from resource

- intensive examinations of relatively low activity chemicals and increase efficiency of endocrine screening and testing.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 102 of 111 For t he current FIFRA SAP review, EPA has evaluated the performance of AUC models of ER bioactivity based on results for 18 HTS assays for the estrogen pathway, models of AR activity based on results of 9 HTS assays for the A pathway, and proposed an approach f or integrating bioactivity with high throughput exposure predictions using HTTK/RTK conversion of in vitro active concentration to human oral equivalent doses ( i.e. Figure 6.2 ). Analyses presented in previous sections of this document demonstrate the scie ntific foundation of the computational tools used in the IBER approach. In addition, EPA believes the performance of the ER AUC models when compared with reference chemicals (Section 2.1), Tier 1 in vitro data (Section 2.2.3), and in vivo published and Tie r 1 uterotrophic studies (Sections 2.2.2 and 2.2.3) is sufficiently concordant to:

1. P rioritize chemicals for additional screening and testing based on potential ER bioactivity; 2. C ontribute to weight of evidence decisions on the potential of a chemical to in teract with the estrogen receptor; and 3. S uitably substitute for EDSP Tier 1 ER binding, ERTA, and uterotrophic endpoints

. The development of computational tools designed to predict ER interactions benefited greatly from the January 2013 SAP on the topic, a nd as such, the computational models for ER interactions are more mature than the parallel effort for androgen receptor

- mediated bioactivity. None the less, the AUC AR models performed very well when compared with in vitro reference chemicals (Section 3.3 .1). To further develop the AR model, a measure of in vitro potency must be established for the AR reference chemicals and weak positive chemicals must be included in the reference set to be sure the high throughput AR model detects weakly bioactive compo unds. None the less, model performance of the current reference chemical set, which is far more extensive than those typically used to validate lower throughput in vitro assays, provides strong confidence that predicted AR bioactivity is “true”. Based on analyses presented in Section 3, data support the assertion that chemicals with AR model scores > 0.05 are potentially bioactive and the model can be used to prioritize chemicals for potential androgen bioactivity

. Computational tools for detecting AR in teractions will continue to be developed and shaped by feedback from this SAP review. As reference chemicals spanning a wide range of potencies are run in the model and detected with a high degree of sensitivity and specificity, AR agonist and antagonist model scores can contribute to future weight of evidence determinations of androgen pathway bioactivity. Comparison of AR model predictions with in vivo Hershberger response is currently underway, but until analyses are completed and thoroughly reviewed, AR model scores will not proposed as substitutes for EDSP Tier 1 battery endp oints in the androgen pathway.

It is important to acknowledge that the focus of this white paper is restricted to estrogen and androgen receptor

- mediated interactions. The compu tational models of ER bioactivity compare very well with lower throughput in vitro and in vivo responses (Section 2); however, in vivo analyses presented in this paper are restricted to ER and AR effects in animals interrupted hypothalamic pituitary gonada l axes ( i.e., uterotrophic and Hershberger assays) and exclude the potentially confounding influence of a functional neuroendocrine system. We have not presented similar analyses of thyroid bioactivity, though examination of thyroid pathway interactions a re also in the purview of the EDSP. Similar analyses of thyroid receptor (TR) bioactivity, estimated from the four HTS assays included in ToxCast, are underway,Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 103 of 111 though environmental chemicals are expected to affect thyroid homeostasis primarily by mechani sms other than receptor binding. As such, computational models designed to detect thyroid

- active chemicals will include non

- receptor mediated effects.

In addition t o expanding analyses to examine non

- receptor mediated effects , EPA is continually developi ng and improving HTTK and HTE models for the EDSP IBER application. In this paper, HTTK in vitro

- to

- in vivo extrapolation was limited to human oral equivalent doses, however, current models exist for in vitro extrapolation to rodent and fish. HTTK models for all taxa will continue to be refined

. Future high throughput exposure models will simulate multiple exposure pathways for humans and the existing framework will be expanded to model ecological exposures, initially using water concentration data as a p roof of concept.

The EDSP ’s future directions for computational tool development is in a direct effort to meet goals outlined in the September 2011 EDSP21 Work Plan, specifically:

1. R anking chemicals (setting priorities) for further evaluation, 2. S upporting screening determinations by substituting for a portion of the current Tier 1 battery of assays, and 3. S upporting screening determinations by substituting for the full Tier 1 battery of assays.

We have made a case for using innovative computational toxicologi cal methods to prioritize chemicals and to substitute for specific Tier 1 battery assays. To substitute for a larger portion of Tier 1 assays, and ultimately, the full Tier 1 battery, computational models for predicting the full suite of Tier 1 endpoints m ust be evaluated. Computational tools must be applied to key events along the adverse outcome pathway (AOP), the conceptual framework EPA has adopted for evaluating potential bioactivity of a chemical in the estrogen, androgen, and thyroid pathways. The AOP concept begins with the interaction of an agent with the molecular target ( e.g., receptor binding) and through a number of increasingly complex events, results in an adverse effect. The use of the AOP conceptual framework for evaluating the EDSP weight of evidence was demonstrated in case studies presented to a July, 2013 SAP ( http://www.regulations.gov/#!documentDetail;D=EPA

- HQ

- OPP

- 2013

- 0230

- 0004 ).

Progression from using computational tools for priority setting to substituting for th e full Tier 1 battery of assays requires confidence in computational tools to predict interactions along the endocrine AOPs. This “fit

- for

- purpose” approach to new methods for pred icting increasingly complex endocrine effects will be accomplished in a step

- wise evaluation of high throughput models of bioactivity or other appropriate computational tools. At each step, EPA will clearly define the intended use of the computational dat a and demonstrate the scientific confidence supporting the proposed use.

To address the goals of the 2011 EDSP21 Work Plan and expand the application of computational tools, EPA will leverage data available from peer

- reviewed literature and List 1/Tier 1 studies in an approach similar to that described for uterotrophic data (Section 2.2.1) and proposed for Hershberger assays ( Section 3.2.1). EPA will initiate systematic reviews and curation efforts of high

- quality studies methodologically similar to the T ier 1 Pubertal Development and Thyroid Function in Intact Juvenile/Peripubertal Male Rats (OSCPP 890.1500), Pubertal Development and Thyroid Function in Intact Juvenile/Peripubertal Female Rats (OCSPP 890.1450), Fish short

- Term Reproduction Assay, (OCSPP 8 90.1350), and Amphibian Metamorphosis Assay (OCSPP 890.1100). Data extracted from these studies ofIntegrated Bioactivity and Exposure Ranking

November 3, 2014 Page 104 of 111 neuroendocrine

- intact animals will be compared to computational predictions of bioactivity. HTS assays measuring non

- receptor mediated endocrine effects, s uch alterations of steroidogenesis, will be compared to low throughput in vitro and in vivo Tier 1 (and “Tier 1

- like”) results. These analyses will evaluate the scientific confidence in substituting computational predictions of bioactivity for additional Tier 1 battery endpoints across the AOPs, and move closer to full replacement of the Tier 1 screening battery. EPA intends to present these analyses for scientific peer

- review at upcoming FIFRA SAP meetings.Integrated Bioactivity and Exposure Ranking

November 3, 2014 Page 105 of 111 

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## List of Appendices Appendix 1: ER AUC plots for reference chemicals Appendix 2: ER AUC plots for all chemicals Appendix 3: Supplementary Data: ER, AR, and IBER Appendix 4: NICEATM_UT_GL_DB_All_Data.xls Appendix 5: Uterotrophic study radar plots Appendix 6: Examination of ER in vitro

- in vivo discordanc e Appendix 7: AR AUC plots for reference chemicals Appendix 8: AR AUC plots for all chemicals