1

Appendix C: SAP Minutes on “An Effects-based Expert System to Predict Estrogen 

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

Application in a Prioritization Scheme for Endocrine Disruptor Screening” 

Available here:

http://www.epa.gov/scipoly/sap/meetings/2009/august/082509minutes.pdf


SAP Minutes No. 2009-08

A Set of Scientific Issues Being Considered by the 

Environmental Protection Agency Regarding:

The Use of Structure Activity Relationships of 

Estrogen Binding Affinity to Support Prioritization of 

Pesticide Inert Ingredients and Antimicrobial 

Pesticides for Screening and Testing

August 25-26, 2009

FIFRA Scientific Advisory Panel Meeting

held at 

One Potomac Yard 

Arlington, Virginia
NOTICE

These meeting minutes have been written as part of the activities of the Federal Insecticide, 

Fungicide, and Rodenticide Act (FIFRA), Scientific Advisory Panel (SAP). The meeting 

minutes represent the views and recommendations of the FIFRA SAP, not the United States 

Environmental Protection Agency (Agency). The content of the meeting minutes does not 

represent information approved or disseminated by the Agency. The meeting minutes have not 

been reviewed for approval by the Agency and, hence, the contents of these meeting minutes do 

not necessarily represent the views and policies of the Agency, nor of other agencies in the 

Executive Branch of the Federal government, nor does mention of trade names or commercial 

products constitute a recommendation for use.

The FIFRA SAP is a Federal advisory committee operating in accordance with the Federal 

Advisory Committee Act and established under the provisions of FIFRA as amended by the 

Food Quality Protection Act (FQPA) of 1996. The FIFRA SAP provides advice, information, 

and recommendations to the Agency Administrator on pesticides and pesticide-related issues 

regarding the impact of regulatory actions on health and the environment. The Panel serves as 

the primary scientific peer review mechanism of the Environmental Protection Agency (EPA), 

Office of Pesticide Programs (OPP), and is structured to provide balanced expert assessment of 

pesticide and pesticide-related matters facing the Agency. FQPA Science Review Board 

members serve the FIFRA SAP on an ad hoc basis to assist in reviews conducted by the FIFRA 

SAP. Further information about FIFRA SAP reports and activities can be obtained from its 

website at http://www.epa.gov/scipoly/sap/ or the OPP Docket at (703) 305-5805. Interested 

persons are invited to contact Sharlene R. Matten, Ph.D., SAP Designated Federal Official, via email at matten.sharlene@epa.gov.

In preparing these meeting minutes, the Panel carefully considered all information provided and 

presented by EPA, as well as information presented in public comment. This document 

addresses the information provided and presented by EPA within the structure of the charge.

2
TABLE OF CONTENTS

NOTICE .................................................................................................................................................... 2

PARTICIPANTS ...................................................................................................................................... 5

INTRODUCTION .................................................................................................................................... 8

PUBLIC COMMENTERS..................................................................................................................... 10

SUMMARY OF PANEL DISCUSSION AND RECOMMENDATIONS........................................... 11

DETAILED RESPONSES TO CHARGE QUESTIONS..................................................................... 22

1. EVALUATION OF THE EXPERT SYSTEM IN THE CONTEXT OF THE ORGANIZATION FOR ECONOMIC 

COOPERATION AND DEVELOPMENT (OECD) VALIDATION PRINCIPLES ....................................... 22

2. ACYCLIC COMPOUNDS ..................................................................................................................... 54

3. PRIORITIZATION FOR EDSP TIER 1 SCREENING................................................................................ 57

4. CROSS SPECIES APPLICABILITY........................................................................................................ 60

REFERENCES ....................................................................................................................................... 63

SELECTED ABBREVIATIONS........................................................................................................... 68

FIGURE 1. THE KEY KNOWN RECEPTORS AND P450’S INVOLVED IN THE

STEROIDOGENIC PATHWAY........................................................................................................... 69

FIGURE 2. AN OVERVIEW OF XENOBIOTICS AS MODULATORS OF NUCLEAR 

RECEPTOR FUNCTION...................................................................................................................... 70

3
SAP Minutes No. 2009-08

A Set of Scientific Issues Being Considered by the 

Environmental Protection Agency Regarding:

The Use of Structure Activity Relationships of Estrogen 

Binding Affinity to Support Prioritization of Pesticide Inert 

Ingredients and Antimicrobial Pesticides for Screening and 

Testing

August 25-26, 2009

FIFRA Scientific Advisory Panel Meeting

held at 

One Potomac Yard 

Arlington, Virginia

Janice E. Chambers, Ph.D., DABT, ATS Sharlene R. Matten, Ph.D.

FIFRA SAP Session Chair Designated Federal Official

FIFRA Scientific Advisory Panel FIFRA Scientific Advisory Panel 

Staff

Date: Date: 

4
Federal Insecticide, Fungicide, and Rodenticide Act

Scientific Advisory Panel Meeting

August 25-26, 2009

Scientific Issues Associated with The Use of Structure Activity Relationships 

of Estrogen Binding Affinity to Support Prioritization of Pesticide Inert 

Ingredients and Antimicrobial Pesticides for Screening and Testing

PARTICIPANTS

FIFRA SAP Session Chair

Janice E. Chambers, Ph.D., DABT, ATS 

William L. Giles Distinguished Professor 

Director, Center for Environmental Health Sciences 

College of Veterinary Medicine 

Mississippi State University 

Mississippi State, Mississippi 

Designated Federal Official

Sharlene Matten, Ph.D.

US Environmental Protection Agency

Office of Science Coordination & Policy

FIFRA Scientific Advisory Panel

EPA East Building, MC 7201M

1200 Pennsylvania Avenue, NW 

Washington, DC 20460

Tel: 202-564-8450, Fax: 202-564-8382, matten.sharlene@epa.gov

FIFRA Scientific Advisory Panel Members

John R. Bucher, Ph.D., DABT 

Associate Director 

National Toxicology Program 

National Institute of Environmental Health Sciences 

Research Triangle Park, North Carolina 

Carey N. Pope, Ph.D.

Professor, Head & Sitlington Chair of Toxicology

Department of Physiological Sciences

Oklahoma State University College of Veterinary Medicine

Stillwater, Oklahoma 

5
Kenneth M. Portier, Ph.D.

Program Director, Statistics

American Cancer Society

National Home Office

Atlanta, Georgia 

Daniel Schlenk, Ph.D.

Professor of Aquatic Ecotoxicology & Environmental Toxicology 

Department of Environmental Sciences 

University of California, Riverside 

Riverside, California 

FQPA Science Review Board Members

Mark T. D. Cronin, Ph.D.

Professor of Predictive Toxicology

School of Pharmacy and Biomolecular Sciences

Liverpool John Moores University

Liverpool, UK

Nancy Denslow, Ph.D.

Professor

Department of Physiological Sciences

University of Florida

Gainesville, Florida 

Miriam Jacobs, Ph.D.

Scientific Officer

European Food Safety Authority - EFSA

Assessment Methodology Unit

Parma, ITALY

Bette Meek, M.Sc. 

Associate Director

Chemical Risk Assessment

McLaughlin Centre for Population Health Risk Assessment

University of Ottawa

Ottawa, Ontario, CANADA 

Reynaldo Patiño, Ph.D.

Unit Leader, U.S. Geological Service, Cooperative Fish & Wildlife Research Unit 

& Professor, Texas Tech University 

Lubbock, Texas 

6
Terry Schultz, Ph.D.

Professor Emeritus

Department of Comparative Medicine

College of Veterinary Medicine

University of Tennessee

Knoxville, Tennessee 

Leming Shi, Ph.D.

Computational Chemist – Principal Investigator

National Center for Toxicological Research

U.S. Food and Drug Administration

Jefferson, Arkansas 

7
INTRODUCTION

The Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) Scientific Advisory Panel 

(SAP) has completed its review of the Agency’s analysis of Scientific Issues Associated with 

The Use of Structure Activity Relationships of Estrogen Binding Affinity to Support 

Prioritization of Pesticide Inert Ingredients and Antimicrobial Pesticides for Screening and 

Testing. Advance notice of the SAP meeting was published in the Federal Register on June 3, 

2009. The review was conducted in an open Panel meeting August 25-26, 2009 held at One 

Potomac Yard, Arlington, Virginia. Dr. Janice Chambers chaired the meeting. Dr. Sharlene 

Matten served as the Designated Federal Official. Mr. Stephen Owens, Assistant Administrator, 

Office of Pesticides, Prevention, and Toxic Substances; Ms. Laura Bailey, Executive Secretary, 

FIFRA SAP, Office of Science and Coordination Policy (OSCP); and Dr. Stephen Bradbury, 

Deputy Office Director for Programs, Office of Pesticide Programs (OPP) provided opening 

remarks at the meeting. Presentations of technical background materials were provided by Dr. 

Stephen Bradbury, Deputy Office Director for Programs, OPP; Dr. Patricia Schmieder and Mr. 

Richard Kolanczyk, Mid-Continent Ecology Division, National Health and Environmental 

Effects Research Laboratory (NHEERL)-Duluth, Office of Research and Development; and Mr. 

Gary Timm, OSCP. 

The Office of Pesticide Programs (OPP) has a number of ongoing activities to broaden the suite 

of computer-based methods to better predict potential hazards and improve the effectiveness of 

risk assessment and risk management by focusing the generation of new data on likely risks of 

concern. The use of quantitative structure-activity relationships ((Q)SARs) in early hazard 

identification is one such cost effective prioritization tool that can guide the systematic collection 

of key test data. The U.S. EPA is required by the FQPA of 1996 to screen all pesticide 

chemicals, which includes active ingredients and other (“inert”) ingredients for potential 

endocrine disruption (ED). The Agency must prioritize hundreds of chemicals for ED screening 

and testing in biologically complex and resource intensive assays. This FIFRA SAP review 

focused on a (Q)SAR approach that was developed for pesticide food use inert ingredients and 

antimicrobial pesticides to help prioritize candidate chemicals for the Tier 1 Endocrine Disruptor 

Screening Program (EDSP) specific to an estrogen-mediated toxicity pathway. A particularly 

challenging issue is the development of prioritization techniques for chemicals, such as inert 

ingredients and some antimicrobial active ingredients that have minimal existing toxicological 

information. This SAR/Rule-Based Expert System (expert system) for predicting estrogen 

receptor (ER) binding affinity is based on data derived from two in vitro assays: one optimized to 

measure the potential of chemicals to bind rainbow trout estrogen receptors (rtER), and a second 

that measures gene activation through production of vitellogenin. 

The expert system was guided by the Organization for Economic Cooperation and Development 

(OECD) Principles for the Validation, for Regulatory Purposes, of (Q)SAR Models (OECD, 

2004), found at http://www.oecd.org/dataoecd/33/37/37849783.pdf). The five OECD validation 

principles are: a well defined endpoint; a mechanistic interpretation of the model; a defined 

domain of the model applicability; an unambiguous algorithm and appropriate measures of 

goodness-of-fit, robustness, and predictivity. These principles are intended to guide the (Q)SAR 

validation process undertaken within OECD member countries and to facilitate their regulatory 

8
acceptance by defining the types of information that regulators would find useful when 

considering the acceptability of individual (Q)SARs. These principles are also consistent with, 

and complement, recommendations of the Endocrine Disruptor Screening and Testing Advisory 

Committee (EDSTAC) (USEPA, 1998). Two important characteristics of (Q)SAR validations 

are addressed by OECD to enhance regulatory acceptance for using estimated values for prioritysetting. The first characteristic is transparency of the (Q)SAR estimate, not so much in terms of 

the methods used, but rather in terms of how the estimate can be explained mechanistically and 

how the estimate is reasonable based on data for comparable chemicals. The second major 

characteristic for (Q)SAR acceptance is usefulness of a particular (Q)SAR model for estimating 

endpoints of regulatory relevance for all compounds within specified chemical inventories. A 

prototype of the expert system was the subject of an OECD-convened peer consultation in 

February 2009, at which time the system was evaluated using the (Q)SAR validation principles 

(OECD, 2009). Based on input from this peer consultation, the Agency further refined the expert 

system, particularly as it related to the OECD validation principles. 

The SAP was asked to address specific issues concerning the (Q)SAR validation principles and 

the subject expert system in the context of its use to determine the order in which chemicals, i.e., 

food use inert ingredients and antimicrobial pesticides, will be subject to Tier 1 screening 

specific to an estrogen-mediated toxicity pathway under EPA’s Endocrine Disrupter Screening 

Program (EDSP), i.e., for prioritization. The SAP also discussed the applicability of ER binding 

affinity findings for acyclic compounds in the food use inerts and antimicrobial pesticide 

inventories to other acyclic compounds in other chemical inventories. Finally, the SAP 

commented on the cross species applicability of an Expert System based primarily on trout 

estrogen binding affinity data for predicting relative binding affinity to human estrogen receptors 

to assist in prioritizing food use inert ingredients and antimicrobial pesticides for EDSP Tier 1 

screening. 

9
PUBLIC COMMENTERS

Oral statements were presented by:

1) On behalf of CropLife America: Erik Janus, Director; Human Health Policy

2) On behalf of the American Chemistry Council, Biocides Panel: Elizabeth Brown, Ph.D.,

Senior Technical & Regulatory Analyst, Steptoe & Johnson, LLP.

Written statements were provided by:

On behalf of the American Chemistry Council, Biocides Panel and Regulatory and 

Technical Affairs Department: Elizabeth Brown, Ph.D., Senior Technical & Regulatory 

Analyst, Steptoe & Johnson, LLP.

10
SUMMARY OF PANEL DISCUSSION AND RECOMMENDATIONS

1. Evaluation of the Expert System in the Context of the Organization for Economic 

Cooperation and Development (OECD) Validation Principles

The components to Charge Question 1 address specific issues concerning the (Q)SAR 

validation principles and the subject expert system in the context of its use to determine 

the order in which chemicals (i.e., food use inert ingredients and antimicrobial pesticides) 

will be subject to Tier 1 screening under EPA’s Endocrine Disrupter Screening Program 

(EDSP) (i.e., for prioritization). 

a. Biological Endpoint

Question a.1. Please comment on the methods employed and their adequacy for 

detecting and measuring ER binding affinity for the compounds in the two 

chemical inventories of immediate interest. 

The overall conclusion of the Panel was that the two methods for detecting and 

measuring ER binding affinity of the compounds in the food use inerts (FI) and 

antimicrobial (AM) pesticide inventories were appropriate for helping EPA set 

testing priorities for these chemicals. The two methods provide a logical 

approach to evaluate compounds with a specific mechanism of action (estrogen 

receptor (ER)-binding) and then linking this to a transcriptional activation 

outcome, vitellogenin (Vtg) production. The Panel stated that it was imperative to 

note that ER-binding or Vtg induction should not be equated with higher order 

reproductive effects. They recommended that EPA consider adding more 

discussion in the White Paper to better define the limitations of the assays used to 

generate binding and biological response data, i.e., what can and cannot be 

concluded from these data.

Question a.2. In the context of chemical prioritization for Tier 1 screening, please 

comment on the decision to measure binding affinity up to the maximum 

concentration based on the solution properties of the chemical, rather than using 

ligand concentration ‘cut-off’ values of -4 Log Molar to -3 Log Molar, which 

have typically been used to conclude a compound does not bind to the ER. 

There was general agreement among the panel members that the chemicals of 

concern (FI and AM pesticides) would be of low binding affinity. The Panel 

indicated that the cut-off values of -4 log Molar to -3 log Molar were established 

in the pharmaceutical industry as a screen for strong estrogens and are not 

applicable for the purposes of screening and prioritization. They recommended 

that the FI and AM pesticides should be tested using concentrations up to their 

solubility in the media. In addition, the concentrations tested should be in 

concordance with concentrations that are biologically relevant and cut-off values 

should be set more broadly to be as inclusive of chemicals that might be able to 

11
bind to estrogen receptors. These chemicals would then be tested further using 

more in-depth assays. 

Question a.3. Please also comment on how any in vivo studies that are available 

for compounds with low receptor binding affinity could be used to provide a 

relative binding affinity ‘cut-off’ value either alone or in combination with cut-off 

values based on the maximum solubility of a ligand in the buffer solution. 

Panel members interpreted this charge question in different ways. As a result, the 

Panel provided several in vivo and in vitro methods in response to this question.

1) Distribution of compounds within tissues using dosimetry. Fathead 

minnow adults (male or female) and embryo tests could be used to 

determine whether or not compounds with low receptor binding affinity 

are bioaccumulated in tissues of endocrine importance, for example, liver, 

brain, pituitary, gonad, and kidney.

2) Vitellogenin induction in vivo experiments. Vitellogenin induction, in 

vivo, has been generally accepted as a good biomarker for compounds that 

bind to and activate ERs in fish. 

3) MCF-7 cell proliferation assays. These assays use an ERα human breast 

cancer cell line and monitor cell proliferation following exposure to both 

estrogen and estrogen mimics that bind through estrogen receptors to 

induce activity.

4) 48-hour transcriptomics experiments. Microarrays are now available 

for a large number of fish species that are routinely tested for toxicity 

experiments. Genes that are important for reproduction, growth and 

susceptibility to disease are now known and this assay could be used to 

determine whether any of these are affected by low concentrations of test 

chemicals mixed in the food at levels that would be normally experienced 

by humans.

5) In vivo test for ova-testis. This assay would look for downstream effects 

of exposure to estrogenic compounds. The end point would be 

histological determination of ova-testis.

6) Fish recrudescence assay. Cost effective in vivo studies would include 

fish recrudescence assays (Tier 1, i.e., 21-day fish reproductive assay) that 

partner well with the in vitro approach using the rainbow trout estrogen

receptor. 

7) In vitro assays with other endpoints. In vitro assays could be conducted 

with regard to other biological endpoints, e.g., estrogen sulfotransferase, 

aromatase, and other receptors.

8) Other in vivo test guidelines. Other in vivo test guidelines such as the 

“enhanced” OECD conventional 28-day repeat dose toxicity test (OECD 

TG 407) and the updated draft physiologically-based pharmacokinetic

(PBPK) test guidelines when available would also be useful.

9) PBPK modeling. Dosimetry PBPK studies would allow estimates of 

hepatic concentrations that could be partnered with concentrations 

12
identified from in vitro responses (ER-binding/Vtg mRNA induction and 

Vtg production).

b. Mechanistic Interpretation 

Question b.1. Please comment on the adequacy of this training set for supporting 

the expert system’s rule that acyclic compounds do not bind to the ER. 

The Panel determined that the current acyclic compound training set is adequate 

for the structures within the two inventories, especially for the intended purpose 

of screening, i.e., prioritization. The Panel, however, considered the current 

training set to be inadequate to cover all chemicals. To illustrate this point, the 

Panel noted that the acyclic compounds, perfluorooctane sulfonate (PFOS) and

perfluorooctanoic acid (PFOA), were shown to bind ER and induce Vtg in at least 

two fish species (Ishibashi et al., 2008; Liu et al., 2007). The Panel thought that 

for the acyclic rule to gain more universal acceptance, it would have to undergo 

further verification. 

Question b.2. Please comment on strengths and limitations of this mechanistic 

interpretation for selecting chemicals in the training sets and for interpreting the 

observed binding data.

The Panel concurred with the mechanistic interpretation of ER-binding used by 

the Agency as consistent with the structural characteristics of the ER-binding 

domain discussed in the literature (e.g., Katzenellenbogen et al., 2003 and 

references therein) and informs testable hypotheses of how chemicals bind to the 

ER. 

The Panel provided several strengths of the mechanistic interpretation of the ERbinding. 

1) There is abundant knowledge to support the chemical basis for defining 

the chemical structure space(s).

2) There is a good understanding of the energetic and steric characteristics of 

the ERα binding domain.

3) The mechanistic interpretation focuses on chemistry with specific 

biological relevance.

The Panel noted several limitations of the mechanistic interpretation of the ERbinding.

1) Current knowledge largely addresses ER-binding at the A and B sites and 

not other sites. The expert system may miss some compounds that bind to 

the ER via other binding sites not included in the model.

2) “Mixed phenols” rules need further development and will involve further 

testing. The same criticism was stated for the “mixed organics” rules. 

13
Question b.3. Please comment on the clarity of the White Paper in describing the 

differences in (Q)SAR development when the goal is to predict in vitro ER 

receptor binding from chemical structure vs. when the goal is to predict in vivo 

reproductive/developmental responses from chemical structure. Please indicate if 

additional discussion in the White Paper is needed to establish the relevance of 

ER binding affinity (either measured or predicted) to interpret the potential for in 

vivo outcomes.

The Panel agreed that the ER-binding adverse outcome pathway illustrated in 

Figure 1 of the White Paper is transparent and mechanistically sound. The Panel 

stressed that the White Paper makes it very clear that the goal of the expert system

is to provide for the use of “the molecular initiating event as a basis for 

prioritizing chemicals for further screening with EDSP Tier I assays, which 

incorporate endpoints at higher levels of biological organization” (see page 12 of 

the White Paper). While the prediction of in vivo reproductive/developmental 

responses from in vitro responses is very tantalizing, the Panel concurred that ERbinding will not necessarily translate into predicting these effects in vivo. The 

Panel emphasized that ER-binding is only one of many key events that might lead 

to reproductive impairment. DNA activation events that occur downstream from 

ligand binding, agonist/antagonists or partial agonist/antagonists are also critical

to the manifestation of reproductive effects.

Panel members offered some suggestions to improve the clarity of the White 

Paper with regard to the relevance of ER-binding and in vivo

reproductive/developmental effects.

1) Repeatedly stress that the expert system currently only allows 

prioritization of FI and AM pesticides for EDSP Tier 1 testing.

2) Add a discussion of how the mechanistic/mode of action approach can be 

used in mixture assessments, and how this information can be partnered 

with ongoing research in EPA’s Office of Research and Development 

(ORD), i.e., genomics/dosimetry. 

3) Add some examples of non-receptor mediated estrogenic activity to the 

White Paper as ER-binding activation may not pick up all estrogenic 

compounds, e.g., nonylphenols which have several non-ER mechanisms of 

estrogenic activity. 

c. Model Domain 

Question. Please comment on the adequacy of the approach that was used to 

select chemicals for the training sets in terms of these two inventories. 

The Panel agreed that the iterative approach to select chemicals for the trainings 

sets of these two inventories was adequate and congratulated EPA for conducting 

this important work. This approach involved strategic selection of chemicals 

within chemical groups, assessment of the results in the context of the ER-binding 

14
hypotheses, and testing additional chemicals until sufficient information is 

collected to make a prediction for all the chemicals in the inventory. The Panel 

suggested that the large body of literature data on ER-binding could be used in a 

meta-data analysis format either for developing or validating the decision rules 

implemented in the ER-binding expert system. 

d. Algorithm

Question d.1. Please make suggestions for improvements in presenting the expert 

rules and their underlying rationale, especially with regard to groups with 

multiple functional groups.

The Panel made several suggestions for improvements in presenting the expert 

rules and their underlying rationale, especially with regard to groups with multiple 

functional groups. 

1) Mixed phenols. The Panel recommended developing a systematic 

testing program based on, for example, Simplified Molecular Input Line 

Entry Specification (SMILES) or Smiles Aritary Target Specification 

(SMARTS) strings. A SMILES or SMARTS string could be used as an 

input into something like a classification tree methodology which could 

then be used to determine if certain “structures” could be used to predict 

activity level.

2) Mixed organics. The Panel thought that a small number of mixed 

organic compounds could act as “skeleton keys” to indicate binding to the 

ER.

3) Substructure coding. The Panel indicated that EPA could pick out 

substructure characteristics that would be important by just looking at the 

structure, e.g., bisphenol A-like, dichlorodiphenyltrichloroethane (DDT)-

like, chemicals with a long fatty acid tail, and phthalate-like chemicals.

4) Nature of hydrogen (H) bonds. The Panel suggested that EPA more 

closely examine chlorinated compounds/pesticides and the nature of H 

bonds. 

5) Decision rules. Three suggestions were made by the Panel to clarify the 

presentation of the rules.

a. The first suggestion was to use an automatic approach to derive the 

decision rules. 

b. The second suggestion was to construct the expert system decision 

tree as chemical classes using log Kow modifier rules. 

c. The third suggestion would be to have decision rules that center on 

chemical properties. Properties of the sub-classes beyond log Kow 

would determine relative binding affinity (RBA) activation class. 

Question d.2. Please also comment on the ability of the expert rules to identify 

chemicals outside the model domain.

15
In general, the Panel agreed that the expert system is expected to perform 

reasonably well for certain chemicals outside the model domain. Some panel 

members presented examples of a limited number of chemicals that appear to be 

exceptions to some of the rules, especially the acyclic one. The Panel

commented that the model domain may mean different things for different people 

and that better definitions are needed in the White Paper. EPA has defined the 

model domain by chemical classes and log P limits on the class. As a 

consequence of using the class-wise approach, the model domain is very broad.

The Panel suggested one way to increase the expert system’s utility for untested 

chemicals in other chemical inventories would be to convert the seven specific 

decision or domains in the expert system to seven chemical property rules, which 

would better describe the “model domain” as limits on chemical properties. The 

Panel remarked that the “acyclic rule” is well-described in the Agency’s White 

Paper for the two regulatory inventories of interest; however, it may be difficult to 

extend this rule to other chemical inventories because of the likelihood of 

different structural activity relationships.

The Panel encouraged EPA to further evaluate the expert system’s predictivity 

using large literature data sets of ER binding and/or the generation of new 

relevant data to include a wide variety of chemicals representing all the chemical 

classes or subgroups within the inventory in question, and at the same time,

covering the relevant physico-chemical property ranges of each class or subgroup. 

The Panel indicated that the EPA had implemented these strategies for the two 

specific inventories in question, i.e., FI and AM pesticides. Chemical selection 

might be based on the presence of organic functional groups in particular 

structural environments. This can be accomplished by developing a systematic 

testing program based on, for example, use of SMILES or SMARTS strings (see

above).

e. Goodness-of-fit, Robustness, and Predictivity

Question e.1. Please comment on the adequacy of information presented in the 

White Paper to evaluate the scientific rationale of how a chemical is processed 

through the decision logic; i.e., how a chemical is assigned to a subgroup with an 

associated binding affinity value; the mechanistic rationale for estimates of 

binding affinity data, including data for related chemicals; and how it is 

determined that a chemical is outside of the domain of the expert system.

As a general principle, all panel members agreed that the information in the White 

Paper was adequate to evaluate the scientific rationale of how a chemical is 

processed through the decision logic. That is, the Agency addressed, in detail, all 

of the descriptive elements needed to provide a clear and transparent description 

of the expert system, i.e., how a chemical is assigned to a subgroup with an 

associated binding affinity value; the mechanistic rationale for estimates of 

binding affinity data, including data for related chemicals; and how it is 

determined that a chemical is outside the domain of the expert system. The 

16
Agency’s description of the expert system conforms to the OECD Principles of 

(Q)SAR validation for goodness-of-fit, robustness, and predictivity. The Panel

noted, however, that the Agency’s expert system is not a (Q)SAR model by the 

traditional understanding of regression-based or discriminant analysis model

(Q)SARs. Statistical methods such as those used to assess QSARs based on 

regression models are not applicable to the Agency’s expert system. Therefore,

the description of the expert system in the White Paper reflects this difference and 

focuses on the transparency and validity of the decision logic. 

Question e.2. While to date the Agency is not aware of statistical approaches that 

would provide the means to assess goodness-of-fit or predictivity of expert 

systems such as the one described here, is the SAP aware of any statistical 

approaches or data presentations that could be amenable for such evaluations?

The Panel agreed that the rule-based expert system for predicting the activity of 

chemicals is not a classical regression-based or discriminant analysis regression or 

discriminant analysis (Q)SAR model. Traditional statistical methods used to 

measure goodness-of-fit for these types of models are not applicable in the case of 

a rule-based expert system. The Panel agreed that other types of statistical 

evaluation of the expert system would provide additional confidence to the user. 

They stated that one method of statistical evaluation could be an assessment of 

predictivity for compounds not in the training set. The validation data for 

compounds not in the training set, but within the applicability domain of the 

model, could be obtained from the literature and can be used to assess the 

predictivity of the expert system. The Panel thought that using misclassification

rates, as well as sensitivity and specificity of the validation data, would provide 

further confidence of goodness-of-fit in the predictivity of the expert system. 

f. Transparency and Clarity of the Expert System

Question f.1. Please provide any additional comment on how well the White 

Paper’s summary of the expert system conforms to the OECD validation 

principles and provide suggestions, as appropriate, to enhance the clarity or 

transparency of the expert system’s development and intended use with regard to 

the validation principles.

The Panel concurred that the Agency’s expert system conforms to the five OECD 

Principles of (Q)SAR validation which include demonstration of:

1) a well defined endpoint,

2) mechanistic interpretation of the model,

3) defined domain of model applicability,

4) an unambiguous algorithm, and

5) appropriate measures of goodness-of-fit, robustness, and predictivity.

Overall, the Panel commended EPA on their delineation of the basis for the expert 

system, especially in a context suitable for regulatory risk assessment. The Panel 

recommended that the expert system be modified as new data become available.

17
The Panel thought the clarity of the White Paper could be improved by adding a 

glossary to define terms such as “model domain.” The Panel also suggested that 

each of the seven “models” which make up the expert system should be discussed 

separately in the context of their applicability domain, training set, etc. See also 

suggestions provided by the Panel in response to question 1) f.2.

The Panel thought the transparency of the expert system could be improved by 

linking the training sets, especially their molecular structures, to each of the seven 

model domains. This would be particularly helpful if done in an electronic format 

linked with automation of the expert system. Transparency would be further 

enhanced with some form of validation or verification exercise (see discussion in 

section 1) e. They recommended that this exercise be performed for each of the 

seven models in the expert system as some are better documented than others. 

One panel member recommended that a standard reporting format, such as 

(Q)SAR Modeling Report Format (QMRF), would be considered as a transparent 

method to record information regarding the rule-based expert system, including 

results of any validation. The QMRF information is structured according to the 

OECD (Q)SAR validation principles.

Question f.2. Please provide any suggestions for preparing the system 

documentation that will enhance clarity and understanding for users. 

The Panel complimented the Agency on the level of documentation of the ER 

binding expert system. Panel members recommended some areas for 

improvement, including: 

1) reorganization of the White Paper. This specifically related to the 

information starting with page 30, "A Rule-Based Exert System to Predict 

ER Binding Affinity.” They agreed there are essentially seven rules (i.e.,

I-VII in Figure 9) and there should be a separate discussion for each of the 

seven models in context of their applicability, domain, training set etc. 

2) that an Executive Summary to the White Paper would be very useful to the 

reader. 

3) that a glossary be added to the White Paper to clarify terms such as “model 

domain.” 

4) that the two-sided arrows in Figure 1 of the White Paper be changed to 

one-way arrows to reflect the current understanding of the flow of 

responses in the Agency’s expert system.

5) that EPA amplify the specific criteria by which the number of compounds 

tested in each of the subgroups were selected as these were only briefly 

described in the White Paper as being “a wide variety” selected “across the 

respective log Kow ranges” of each subgroup.

6) the development of illustrative training aids would provide greater clarity 

and understanding of the expert system, e.g., animation of Figure 9 in the 

White Paper. 

18
7) that automation of the expert system, including more detailed description 

of the individual parts, would be critical to increase availability and 

acceptance of the system. 

2. Acyclic Compounds

Question a. Please comment on the extent to which the finding with acyclic 

compounds in the FI and AM inventories may be broadly applicable to other 

acyclic compounds. Suggestions on an approach to empirically and efficiently 

assess a hypothesis that acyclic compounds will not bind to the ER in other 

chemical inventories would be welcomed.

The Panel concluded that the findings for acyclic compounds in the FI and AM 

inventories could be extended to most acyclic compounds in other inventories, but 

not to all. Acyclic compounds may bind to the ER by different mechanisms. 

These compounds may bind to receptors at binding sites other than A and B. In a 

broad sense, the Panel explained, these acyclic compounds may bind directly to 

the ER at sites outside of the main ligand binding domains, affect binding of 17βestradiol (E2) at the receptor site, affect structural changes related to receptor 

activation or co-activator protein interactions, and/or affect other downstream 

actions related to transcriptional activation. 

They indicated that there are several databases that might prove to be useful to 

inform the expert system, e.g., Interagency Coordinating Committee on the 

Validation of Alternative Methods (ICCVAM) database of both positive and 

negative compounds tested in vitro for endocrine receptor (ER) and androgen 

receptor (AR) binding and studies conducted under the Tox21 federal partnership.

Tox21 studies will ultimately test approximately 10,000 chemicals for 

transcriptional activation and antagonism of ERα and ARs), and a dozen other 

nuclear receptor using high throughput assays (Collins et al., 2008). The Panel 

thought that there may be an automated way to test for fragments that might be 

able to bind to the ER receptor, e.g., Analog Identification Method (AIM). Some

panel members suggested one way to quickly address the chemical structural 

requirements for binding to these receptors would be to use combinatorial 

chemistry, as done in the pharmaceutical industry. This is done by formulating 

similar chemical structures with different substructures to explore the binding 

space both singly and as known mixtures. The Panel cautioned that special 

attention should be given to halogenated acyclic compounds.

Question b. Please comment on the extent to which the finding with acyclic 

compounds in the FI and AM inventories can be applied to other nuclear steroid 

receptors in general. Suggestions on an approach to empirically and efficiently 

assess a hypothesis that acyclic compounds will not bind to the androgen receptor 

would be welcomed.

19
The Panel concluded that it would be difficult to extrapolate the finding for 

acyclic compounds in the FI and AM inventories to other nuclear steroid receptors 

because of different chemical structural-binding interactions expected for other 

receptors, e.g., other nuclear receptors, such as thyroid hormone receptors, ARs

and membrane receptors. The Panel commented that thinking about other nuclear 

receptors is a valid extension of the work completed by EPA on the ER. The 

Panel encouraged EPA to study the structure activity relationships for these other 

receptors with the same logic and thoroughness that was used for soluble ERs, 

i.e., defining the optimum structure for receptor binding and activation. For the 

greatest efficiency, acyclic compounds could be tested using these assays as high 

throughput screens for both ER and AR binding. Some panel members suggested 

that test compounds could be synthesized by combinatorial methods to aid study 

of structure binding relationships of other steroid receptors as noted above and

that one possible structural motif of interest would be halogenated long chain 

molecules.

3. Prioritization for EDSP Tier 1 Screening

Question. Based on the characteristics of the (Q)SAR-based expert system 

presented in the White Paper, please comment on the Agency’s view that the 

expert system could be employed to support "sorting and prioritizing" food use 

inert ingredients and antimicrobial pesticides for EDSP Tier 1 screening.

The Panel was in strong agreement that the expert system is likely to be an 

important tool to support "sorting and prioritizing" FI and AM pesticides for 

EDSP Tier 1 screening by the ER α binding mechanism. Considerable and 

careful effort to cover the structural domains of these two regulatory inventories

was invested in development of the expert system. 

As designed, the expert system separates chemicals into two bins defined by the 

limit of detection of the ER binding assay, i.e., 0.0001%. Chemicals with a 

RBA>0.00001%, i.e., “the positives”, would be assigned a higher priority for 

EDSP Tier 1 testing and chemicals whose RBA< 0.00001%, i.e., “the negatives”,

would be assigned a lower priority for testing. The Panel indicated that the 

“negatives” should be characterized as chemicals in which prediction of ERα

binding is not possible. The Panel suggested that sorting and prioritization 

aspects of the expert system might be enhanced if each of the seven model rules 

were weighted separately. Different weights would be given to positive events 

resulting from each rule.

The Panel stressed that the ER binding mechanism is only part of the estrogenic 

mode of action, and the estrogenic mode of action is only one component of the 

endocrine system. Other aspects of steroid metabolism and action pathways could 

also be important for prioritization for endocrine disruptor screening (see Figure 

1). Therefore, the expert system should be seen in the context of the overall 

EDSP, i.e., use a weight of evidence approach for integrating information coming 

20
from many possible sources including the results of various endocrine mechanism 

and mode of action tests, modeling such as the rule-based expert system for ERbinding, as well as other information related to the reactivity and potency of the 

chemical. 

4. Cross Species Applicability

Question. Given what is reported in the literature and similarities between 

human and rainbow trout ER binding affinity observed thus far in the research 

described in the White Paper, please comment on the extent to which use of an 

expert system based primarily on trout ER binding affinity data is a reasonable 

effects component for prioritizing food use inert ingredients and antimicrobial 

pesticides for EDSP Tier I screening. 

The Panel responded to this question in two different ways.

1) Some panel members commented in a broad sense about the extrapolation of 

trout cytosolic ER binding data “across species” and were not confined to 

comparisons between the rainbow trout and mammalian ERs. These 

comments were meant to consider the ecological implications of screening 

tests in addition to the human health implications. Overall, the Panel 

concluded that currently available information suggests that the trout cytosolic 

ER assay can be used to develop reasonable predictions of the RBA of 

xenobiotics for ERα across species. However, what is unclear at the present 

time is whether the trout cytosolic ER assay can be used for estimating RBAs 

of xenobiotics across all ER subtypes and all species. 

2) Other panel members discussed how the trout cytosolic ER assay may fit into 

prioritization schemes for endocrine disruption screening tests primarily from 

the viewpoint of human health. In this latter context, the Panel thought that 

trout cytosolic ER and human ER assays would likely yield comparable 

results. However, in extrapolating a negative result from the trout cytosolic 

ER assay to human systems, there would be some level of uncertainty because 

some differences in performance have been reported between cytosolic and 

recombinant ER assays (EPA’s White Paper, page 38; and associated 

presentations).

21
DETAILED RESPONSES TO CHARGE QUESTIONS

1. Evaluation of the Expert System in the Context of the Organization for Economic 

Cooperation and Development (OECD) Validation Principles

As discussed in the Preface and Introduction of the White Paper, EPA’s development of 

the Quantitative Structure Activity Relationship (QSAR)-based expert system was guided 

by the OECD principles for (Q)SAR validation. The five principles include 

demonstration of:

• a well defined endpoint,

• mechanistic interpretation of the model,

• defined domain of model applicability,

• an unambiguous algorithm, and

• appropriate measures of goodness-of-fit, robustness, and predictivity. 

A prototype of the expert system was the subject of an OECD-convened peer 

consultation in February 2009, at which time the system was evaluated using the (Q)SAR 

validation principles. Based on input from this peer consultation, the Agency further 

refined the expert system, particularly as it related to the OECD validation principles. 

The components to Charge Question 1 address specific issues concerning the (Q)SAR 

validation principles and the subject expert system in the context of its use to determine 

the order in which chemicals, i.e., food use inert ingredients and antimicrobial pesticides,

will be subject to Tier 1 screening under EPA’s EDSP, i.e., for prioritization. 

a. Biological Endpoint

The biological endpoint that is predicted by the expert system is relative binding 

affinity to the cytosolic rainbow trout estrogen receptor. Based on preliminary 

studies it was anticipated that food use inert ingredients and antimicrobial 

pesticides would have low relative binding affinities. In addition, an evaluation of 

the two respective inventories indicated a wide range of structures and associated 

physical-chemical properties (e.g., solubility, Kow, etc.). Consequently, assay 

methods used to measure binding affinity to establish the training set were 

designed to detect low levels of binding affinity (e.g., testing to solubility in 

binding assays and cytotoxicity or solubility, as appropriate, in slice assays). 

Confirmatory binding studies and transactivation assays were employed to 

systematically verify that apparent low affinity binding levels represented 

competitive displacement and translated to ER-mediated transactivation.

Question a.1. Please comment on the methods employed and their adequacy for 

detecting and measuring ER binding affinity for the compounds in the two 

chemical inventories of immediate interest.

22
Panel Response

The Panel was in general agreement that the two assays employed for detecting 

and measuring ER binding affinity for the compounds in the two chemical 

inventories were satisfactory. The methods provide a logical approach to 

evaluating compounds with a specific mechanism of action (ER-binding) and then 

linking this to Vtg transcriptional activation and Vtg production. The Panel stated 

that it was imperative to note that binding or Vtg induction should not be equated 

with higher order effects.

The rainbow trout ER binding assay is based on cytosol preparations from livers 

of individual immature rainbow trout, Oncorhynchus mykiss. In brief, tissues are 

homogenized in a Tris buffer, pH 7.6 (Denny et al., 2005) equal to 20 times the 

weight of the liver tissue, and centrifuged to collect cytosolic fractions. 

Competition assays are based on maximum binding capacity for 5 nM for tritiated 

Tritiated 17β estradiol [[

3

H]-E2] and are used to determine relative binding 

affinity constants for test chemicals. Free [3H]-E2 is separated from bound [3H]-

E2 to calculate how much is displaced by the addition of test chemicals. In 

addition, non-specific binding is subtracted from the total bound to calculate the 

amount of specifically bound [3H]-E2. The Panel agreed that the performance of 

the assay is standard and well documented in the scientific literature including 

researchers working for the Agency (Denny et al., 2005). The Panel concurred 

that the Agency used sufficient replication, appropriate positive and negative 

controls, and the assay results were highly reproducible. 

The basis of the trout liver slice assay is to use liver slices prepared from 

immature male rainbow trout. About 100 slices can be obtained from one liver. 

The slices are incubated in multi-well plates in incubation medium and the idea is 

look for the ability of E2, or test chemicals, to bind and activate ERs resulting in

induction of Vtg mRNA and the expression of Vtg. The Panel thought that the 

use of this assay, in conjunction with the ER-binding assay, was a very good 

approach for detecting and measuring ER-binding and transactivation capacity for 

the compounds in the two inventories of interest. The Panel, therefore, deemed 

this combination of assays was highly satisfactory to validate the (Q)SAR expert 

system with the goal of prioritizing chemicals in the two chemical inventories, 

i.e., FI and AM pesticides, for Tier 1 testing under the EDSP.

The main advantage of the liver slice assay is that it is used to look for activity of 

the estrogen receptors. The tissue slices are composed of intact tissues and are 

therefore closer to the function of liver in vivo. In addition, the Panel indicated 

that it was possible for compounds to be metabolized in these assays, which may 

be important if the active chemical is actually a metabolite of the parent 

compound tested. The Panel stated that the Vtg gene and resulting translated

protein are accepted biomarkers of estrogen activity through soluble ERs. Both 

Vtg mRNA and its translated Vtg protein are well studied. It is known that the 

23
promoter for Vtg mRNA contains estrogen response elements that would respond 

to active ERs, i.e., ERs that have bound with E2 or an E2-mimic. The assay has a 

wide dynamic range with Vtg mRNA expression levels sometimes exceeding a 

thousand-fold that of the control depending on the concentration of estrogen or 

estrogen mimics. 

The Panel enumerated several positive aspects of both assays. 

1) The molecular event, which is based upon physicochemical data for 

receptor binding, is linked to subsequent confirmation of this response 

through transcriptional ER activation. 

2) The cytosolic method simultaneously evaluates the binding of all soluble 

subtypes. The Panel thought it might be necessary to differentiate the 

binding affinities for each soluble receptor subtype particularly for 

interspecies calibration (see Question 4). 

3) The assays are relatively easy to perform in a high throughput manner. 

Many cytosol fractions can be obtained from a few fish for the binding 

assay and about 100 slices can be obtained from one liver for the 

transactivation assay. 

4) Tissue integrity is maintained; thus, the transactivation results should 

mimic in vivo changes more directly.

Nevertheless, the Panel pointed out several limitations of both assays. 

1) The binding assay does not distinguish agonists from antagonists, as both 

would bind to the ERs. However, this limitation is removed by coupling 

the binding assay with the tissue slice assay, which will distinguish these 

activities. 

2) The binding assay may underestimate the true binding affinity of the 

chemicals for ERs because the actual concentration of ER is not known in 

the cytosolic fractions and because other proteins in the fraction may 

interfere with the assay in non-specific ways. Other assays have been 

developed to measure binding affinities that are more closely indicative of 

“true” binding affinities, but these rely on test systems with recombinant 

ERs. 

3) Neither of the assays distinguishes binding or activity of specific ER 

isotypes. Rainbow trout liver contains both ERα and ERβ isotypes and 

there are two forms of each of these isotypes for a total of four ERs 

(Nagler et al., 2007). The Panel stated that it was not clear how many and 

which of the two isoforms of each nuclear ER subtype in rainbow trout is 

present in the cytosolic preparation, and therefore the contribution of each 

ER to the measured binding is unknown. 

The Panel indicated that distinguishing among binding activity of specific 

ER isotopes is not a trivial point. Significant differences have been 

reported for some species in the binding affinity of ERα and ERβ to 

24
xenobiotics and even to natural ligands such as E2. In channel catfish, for 

example, ERβ binds E2 with 20-fold greater affinity than ERα (Xia et al,

2000; Gale et al., 2004). Also, the RBA’s of xenobiotics, such as 4-

nonylphenol, can differ by nearly two orders of magnitude between the 

two catfish receptor subtypes (Gale et al., 2004). Thus, selection of 

chemicals based on cut-off RBA values for further study could be

potentially influenced one way or the other depending on which receptor 

subtype is used for the binding assay. 

The Panel identified additional reasons why a clarification of the 

contribution of different ERs to the ligand binding activity of cytosol 

extracts would be desirable. Information provided by the White Paper

suggested that the binding activity of trout cytosol behaves similarly to the 

rainbow trout ERα. In a study using selective agonists and antagonists of 

ERα and ERβ, Leaños-Castañeda and Van Der Kraak concluded that 

despite the fact that most ligand binding activity present in rainbow trout 

liver cytosol may be due to ERα under certain conditions, ERβ was

responsible for mediating the vitellogenic response of the trout liver to E2 

(Leaños-Castañeda and Van Der Kraak, 2007). The Panel concluded that 

the trout cytosol assay that was used to develop the training set of binding 

data and the trout tissue slice assay that was used to confirm the biological 

relevance of the binding data may be measuring different estrogen 

receptors (ERα and ERβ, respectively). In general, while both subtypes, in 

balance, contribute to overall estrogenic activity, ERα activation is more 

implicated with hazard while ERβ is implicated in protection against 

hyperproliferation and carcinogenesis (reviewed in, e.g., Jacobs, 2005; 

Macpherson et al., 2006; Carruba 2007; Ellem and Risbridger, 2009; 

Wilson and Westberry, 2009; Bian et al., 2001). Most of the Panel 

thought that it might be important to know their relative ratios. However, 

two panel members did not think that this limitation was a problem for 

using the assays as a screening tool. 

4) ER subtype profiles may change throughout the life history and gender of 

the animal. The Panel suggested that identification of the relative 

contributions of each subtype at various life stages would reduce 

uncertainty.

5) The liver based assays may not be representative of action of ERs in other 

endocrine tissues, which may have a different composition of ER subtypes 

(e.g., brain and gonad, where the ratios of the two isoforms differ). 

6) The two assays completely neglect membrane receptor binding (e.g., Gprotein-coupled receptor 30 [GPR30]). 

7) Slice-to-slice variability in the liver slice assay may be high depending on 

the exact location in the liver core for each of the slices, e.g., they may 

vary in hepatocyte content. However, the Panel indicated that this 

limitation can be overcome by performing a higher number of replicates. 

8) Livers of individual fish may vary in their ER concentrations especially 

since E2 (and other estrogenic compounds) appears to be able to up25
regulate ERα This last point, noted by the Panel, would also be a source 

of variance in juvenile fish according to gender. 

9) Neither of the assays measures effects of estrogen-like compounds on 

membrane receptors, which feed into important signaling pathways that 

may be linked to estrogenic effects.

In summary, the overall conclusion of the Panel was that the two assays were 

appropriate for helping to set testing priorities of the FI and AM chemical 

inventories. Having both assays together showed a logical progression in defining 

the toxicity pathway, i.e., combining binding of a chemical to the ER, activation 

of the receptor in a sensitive tissue that can also metabolize chemicals. In terms 

of the current OECD conceptual framework for endocrine disruptor testing, the 

two assays represent both level 1 and level 2 testing. However, given the 

limitations of the assays enumerated above, a negative result of a chemical in the 

selected assays should not be construed as meaning that the chemical was not an 

endocrine disruptor or endocrine active substance. The Panel recommended that 

EPA consider adding more discussion in the White Paper to better define the 

limitations of the assays used to generate binding and biological response data 

(i.e., what can and cannot be concluded from these data).

Question a.2. In the context chemical prioritization for Tier 1 screening, please 

comment on the decision to measure binding affinity up to the maximum 

concentration based on the solution properties of the chemical, rather than using 

ligand concentration ‘cut-off’ values of -4 Log Molar to -3 Log Molar, which 

have typically been used to conclude a compound does not bind to the ER. 

Panel Response

There was general agreement among the panel members that the chemicals of 

concern (FI and AM pesticides) would be of low binding affinity. The Panel 

indicated that these chemicals should be tested using concentrations up to their 

solubility in the media. Thus, the maximum concentration tested per chemical 

would vary depending on their solubility and this solubility would vary with the 

log Kow of the test chemicals. This flexibility in testing means that false 

negatives would be reduced. 

The Panel stated that the cut-off values of -4 log Molar to -3 log Molar were 

established in the pharmaceutical industry as a screen for strong estrogens and 

may not be applicable for screening and prioritization of FI and AM pesticides. 

They commented that attention should be given to possible overt toxicity at high 

concentrations. The Panel recommended that concentrations tested should be in 

line with concentrations that are biologically relevant. As a screening and 

prioritization mechanism, “cutoff” values should be set more broadly to be as 

inclusive of chemicals that might be able to bind to ER’s so that they can be 

further tested using more in depth assays. 

26
Question a.3. Please also comment on how any in vivo studies that are available 

for compounds with low receptor binding affinity could be used to provide a 

relative binding affinity ‘cut-off’ value either alone or in combination with cut-off 

values based on the maximum solubility of a ligand in the buffer solution. 

Panel Response

Panel members interpreted this charge question in different ways. As a result, the 

Panel provided several in vivo and in vitro methods in response to this question.

1) Distribution of compounds within tissues -- dosimetry

Fathead minnow adults (male or female) and embryo tests could be used 

to determine whether or not compounds with low receptor binding affinity 

are bioaccumulated in tissues of endocrine importance, for example, liver, 

brain, pituitary, gonad, and kidney. Such in vivo assays can be performed 

by either adding the test chemicals to the water or mixed in the food. This 

information on tissue distribution could be useful to determine whether 

assaying high concentrations of the test chemicals is essential. 

2) Vitellogenin induction experiments

Vitellogenin induction, in vivo, has been generally accepted as a good 

biomarker for compounds that bind to and activate ERs in fish. The assay 

is very sensitive and is performed by exposing fish to estrogen or estrogen 

mimics in the water for 7 days (Brian et al., 2005), or by mixing the 

compounds with the food. This assay can be used to show general 

concentration cut-offs for a single compound. For mixtures of compounds 

that act through the same mechanism of action, it is possible to show 

additivity of effects even when each individual compound is below its No 

Observed Effective Concentration (NOEC). This assay can also be used 

to measure anti-estrogenic compounds if used in female fish. Thresholds 

of toxicological concern have been discussed and developed for estrogenic 

agonists in aquatic environments (Crane et al., 2009). These thresholds 

could be used to determine cut-off values for test chemicals in aquatic 

environments using regulatory priority lists and existing databases.

3) MCF-7 cell proliferation assays

These assays depend on using a MCF-7, a human breast adenocarcinoma 

cell line, to monitor cell proliferation following exposure to estrogen or to 

estrogen-like substances. The assay is very sensitive to estrogen and 

estrogen-like susbstances, but does not work solely through the ERα as 

other estrogenic pathways are involved.

4) 48-hour transcriptomics experiments

Microarrays are now available for a large number of fish species that are 

routinely tested for toxicity experiments. For example, there are arrays 

now for rainbow trout, zebrafish, medaka, three-spined stickleback and 

27
fathead minnow, largemouth bass among others. Recent research has 

shown that a 48 hour exposure of fish to chemicals is sufficient to induce 

gene transcription in various endocrine tissues including brain, liver, and 

gonad. This assay would not just measure changes based on estrogen 

receptor binding, but would evaluate changes in toxicity pathways of 

concern for all endocrine pathways including other sex hormone receptors 

and pathways that alter the concentration of endogenous hormones (either 

synthesis or metabolism). There are definite thresholds for gene 

transcription. Some genes are more sensitive than others. More genes are 

changed with higher concentrations. Test compounds can be administered 

in the water or mixed in the fish food. Genes that are important for 

reproduction, growth and susceptibility to disease are now known and this 

assay could be used to determine whether any of these are affected by low 

concentrations of test chemicals mixed in the food at levels that would be 

normally experienced by humans. 

Transcriptomics (and proteomics) could also be accomplished on the fish 

liver slices to look for estrogen fingerprints and to determine what other 

pathways are being affected by the exposure. If funds are not a problem, 

the microarray studies can be coupled to fish recrudescence assays (21-day

fish reproductive assays) (see #6 below).

5) In vivo test for ova-testis 

This assay would look for downstream effects of exposure to estrogenic 

compounds. The end point would be histological determination of ovatestis.

6) Fish recrudescence assay

Cost effective in vivo studies would include fish recrudescence assays 

(Tier 1, i.e., 21-day fish reproductive assay) that partner well with the in 

vitro approach using the rainbow trout estrogen receptor. These are 

already approved for use.

7) In vitro assays with other endpoints

In vitro assays could be conducted with regard to other biological 

endpoints, e.g., estrogen sulfotransferase, aromatase, and other receptors.

8) Other in vivo test guidelines. Other in vivo test guidelines such as the 

“enhanced” OECD conventional 28-day repeat dose toxicity test (OECD 

TG 407) and the updated draft of physiologically-based pharmacokinetic 

(PBPK) test guidelines (when available) would also be useful.

9) Physiologically-based pharmacokinetic (PBPK) modeling

Dosimetry PBPK studies would allow estimates of hepatic concentrations 

that could be partnered with concentrations identified from in vitro

responses (ER-binding/Vtg mRNA induction).

28
Given the occurrence of natural ligand-receptor interactions and in vivo 

metabolism, the prediction of in vivo responses from in vitro data currently 

available may be quite difficult or not currently possible. Compounds 

with low receptor binding affinity may cause in vivo endocrine responses 

by non-receptor mechanisms that involve modulation of the natural ligand 

for the receptor. Examples would include interactions with gonadotropin 

release, hormone biosynthesis, or impairment of hormone clearance. 

Extrapolation from in vitro data would need to take this into account 

(reviewed in Jacobs et al., 2008).

b. Mechanistic Interpretation 

Question b.1. Numerous studies have established the alignment of estrogen and 

other high affinity ligands within the ER binding domain and indicate that a 

distance of 10.2 to 11A between the two H-bonding sites and stable (non-flexible) 

ring structure is optimal for binding. These and other studies lead to the 

assumption that acyclic compounds would not bind to the ER, although a 

systematic analysis across a diversity of structures was not available in the 

literature. In the current study 25 acyclic compounds across 10 classes present in 

the inventories were evaluated in the training set and none were found to bind at a 

Relative Binding Affinity (RBA) detection limit of 0.00001%. 

Please comment on the adequacy of this training set for supporting the expert 

system’s rule that acyclic compounds do not bind to the ER. 

Panel Response

General comments:

The intent of OECD (Q)SAR Validation Principle 2 (i.e., “mechanistic 

interpretation of the model”) is to make sure that consideration is given to the 

possibility of a mechanistic association between both the descriptors used in a 

model and the endpoint being predicted, and to make certain that this association 

is documented in the expert system.

To meet the mechanistic principle three questions need to be addressed:

1) In the case of a SAR, is there a description of the molecular events that 

underlie the reactivity of the molecule? In this case is there a description 

of how substructural features could form part or all of a receptor binding 

region?

2) In the case of a (Q)SAR, do the descriptors have a physicochemical 

interpretation that is consistent with a known mechanism (of biological 

action)?

29
3) Are any literature references cited in support of the purported mechanistic 

basis of the (Q)SAR?

For the ER-binding expert system, the Panel answered all three of these questions 

in the affirmative.

Specific comments:

The ER-binding training set for acyclic compounds includes many of the major 

chemical classes (e.g., alcohols, amines, aldehydes, carboxylic esters, and 

ketones) and covers the structural domain of the two inventories (i.e., FI and AM 

pesticides) evaluated in this exercise. The Panel determined that the current 

acyclic compound training set is adequate for the structures within the two 

inventories, especially for the intended purpose of screening (i.e., prioritization). 

The Panel, however, considered the current training set to be inadequate to cover 

all chemicals. To illustrate this point, the Panel noted that the acyclic compounds, 

perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), were 

shown to bind ER and induce Vtg production in at least two fish species 

(Ishibashi et al., 2008; Liu et al., 2007). 

The Panel thought that for the acyclic rule to gain more universal acceptance, it 

would have to undergo further verification. Part of this verification might be 

addressed by examining test data from other species (e.g., human) and other 

protocols (e.g., yeast assays). The Panel noted that these data would eventually 

need a peer reviewed intelligent testing strategy, where adequate coverage of the 

chemical space is balanced with the need to move away from a chemical by 

chemical testing scheme. Computational chemistry and other techniques could 

assist in chemical selection.

The Panel stated that the question of whether all acyclic compounds do not bind 

to the ER (acyclic rule) is part of the general issues concerning the use of 

category-based assessments. Panel members generally perceived that regulators 

are much more likely to accept a positive prediction than a negative one so there 

would be a natural tendency to be cautious about the general rule that all acyclics 

are negative ER-binders. The Panel noted that moving from a chemical by 

chemical evaluation to a category based evaluation will reduce testing, especially 

animal-based testing. As a consequence, the issue of accepting (or not) a

prediction of non-binding for a chemical will be quite important.

Question b.2. Based on studies by Katzenellenbogen et al. (2003 and references

therein) a working hypothesis in developing the training set was that compounds 

in the inventories of interest could bind at one site; i.e., the A site or the B site, 

based on the presence of a hydrogen bond donor or acceptor substituent. The 

development of the training sets and the resultant ER expert system use a 

chemical hierarchy based on different binding mechanisms (i.e., A-B binding 

sites, A binding site only, B binding site only). 

30
Please comment on strengths and limitations of this mechanistic interpretation for 

selecting chemicals in the training sets and for interpreting the observed binding 

data. 

Panel Response

Strengths

The Panel agreed with the prevailing knowledge that chemicals which bind to the 

ER form a group of compounds with an important mechanism that can alter 

processes involved in reproduction. The Panel agreed that the ER-mediated 

adverse outcome pathway provides strength to the mechanistic interpretation. 

This pathway clearly demonstrates how ER binding is linked through a series of 

measurable events at different levels of biological organization to a hazard of 

regulatory concern (alteration in reproduction). The Panel indicated that as

chemicals likely to initiate or hinder the ER-mediated pathway are identified, the 

pathway will be useful for generating testable hypotheses at various levels of 

biological organization along the pathway. 

The Panel reviewed the four OECD (Q)SAR validation criteria for a mechanistic 

pathway:

1) It has to have chemistry which has biological relevance.

2) The critical chemical, cellular, and other in vitro measurements must 

have in vivo relevance, especially the molecular initiating event.

3) It should have identified events along the pathway which provide means 

of eliminating chemical from further testing.

4) In the end, the pathway or at least the critical events along the pathway 

must be simple enough to be manageable, but complex enough to be 

useful.

They concluded that the two biological endpoints from the two in vitro assays

used as the basis for this expert system, clearly meet these pathway criteria. One 

assay was optimized to measure the potential of chemicals to bind rainbow trout 

estrogen receptors and initiate the ER-mediated pathway, and a second assay 

measured gene activation through production of Vtg in rainbow trout liver slices 

to confirm that ER binding translates to an effect further along the pathway.

Based on the literature, the absence of hydrogen bonding group(s), or 

inappropriate molecular geometry and molecular dynamics can explain the failure 

of chemicals, especially aromatics, to bind to ER. The Panel agreed that the

understanding of the energetic and steric characteristics of the ER binding domain 

was important to establish the mechanistic basis for defining a chemical structure 

space(s) associated with ER-binding. The mechanistic interpretation is supported 

by a clear chemical basis for receptor binding, transparency of the process, and 

chemistry with specific biological relevance.

31
The Panel agreed that the mechanistic interpretation of ER binding used by the 

Agency is consistent with the structural characteristics of the ER binding domain

discussed in the literature (e.g., Katzenellenbogen et al., 2003 and references 

there-in) and informs testable hypotheses of how chemicals bind to the ER. 

Theories of chemical interaction in the various “sub-pockets” within the ER have 

been presented by other investigators (e.g., Jacobs et al., 2003, 2004; Vedani et 

al., 2005; Hartman et al., 2009).

Historically, the ideas of chemical interaction(s) with the ER have been based on 

“lock and key” information gained from both the ER and steroidal structures. As 

described in EPA’s White Paper, the “lock” focuses on two hydrophilic pockets 

(i.e., A and B) within a specified distance of each other. The “key” is the 

complement, i.e., a chemical with two hydrogen-bonding groups within the same 

specified distance. The Panel also pointed to widely known information that 

chemicals which contain only one hydrogen-bonding group (e.g., such as 

alkylphenols, alkyloxyphenols, and parabens) that all mimic the A-ring of E2, are 

active in human ERα binding assays as well as transcriptional activation assays 

using bioreporter- and human- ERα. These data describe specific structureactivity relationships in docking models and that potency is related to size and 

shape functionalities as illustrated by conformational models. These findings are 

indicative of the contributions in ER binding from the B-and C-rings of E2. The 

A-site binding results from the rainbow trout estrogen receptor binding assay used 

in the Agency’s expert system are consistent with the literature. This consistency 

further strengthens the stated mechanistic interpretation.

The Panel noted that ER-mediated gene activation for alkylanilines has been 

reported; however, the mechanistic interpretation of binding at the B-site has not 

been specifically described. The structural-activity relationships involving the Bsite binding may be extended to other chemicals such as phenones and 

cyclohexenols and is a further strength of the mechanistic interpretation.

Other strengths noted by panel members include the mechanistic interpretation 

which allows prediction of a biological response (ER-binding and transcriptional 

endpoints) from physicochemical features of chemicals, which are relatively 

simple to obtain. The pathway allows for these in vitro responses to be 

hypothesized to impact higher biological levels of organization (i.e., in vivo

effects), but empirical confirmation is necessary. The tissue slice assay also 

allows metabolism to be included for pro-estrogens.

A final strength is the use of mode/mechanism of action information which will 

allow for the eventual evaluation of mixture scenarios. For example, if multiple 

estrogenic compounds are present in a system, overall estrogenic activity through 

ligand binding equivalents may be used to quantify effect. 

See also discussion in the Panel’s response to charge question 1) b.3.

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Limitations

The Panel stated that the major limitation of the expert system was its inability to 

assess ER binding in other chemical (non-FI and non-AM) inventories. The Panel

noted that the “mixed phenols” rules need further development and will involve 

further testing. The same criticism was stated for the ‘mixed organics” rules. 

Another limitation of the expert system noted by the Panel is that current 

knowledge largely addresses ER-binding at the A- and B-sites and not other sites. 

The Panel stated that they could not rule out anti-estrogenic activity resulting 

from binding of compounds, particularly acyclic ones, at other sites. The Panel 

thought that other chemical inventories may not be encompassed by the existing 

“training set” as the structural binding rules for the expert system were 

specifically designed to predict relative ER binding affinity for the two 

inventories in question, i.e., FI and AM pesticides. They indicated that the 

“training set” used to estimate relative ER-binding affinity had no uncertainty

built into it, in other words, it was perfect. Other chemicals (e.g., PFOS/PFOA) 

would provide uncertainty in predicting relative binding affinities as they might 

not conform to the rules of this expert system. While much is known about ERα, 

for example, the receptor is “dynamic and plastic,” less is known about the 

chemical interactions with other ER subtypes. The Panel agreed that additional 

studies would be necessary to reduce these uncertainties for other ER subtypes 

and other chemical inventories.

Question b.3. While ER binding can be an initial step in a toxicity pathway 

leading to adverse reproductive outcomes (see Figure 1 in the White Paper), the 

ER binding data in the training set, and the associated expert system, were not 

designed to predict in vivo responses. Rather the expert system was designed to 

predict relative ER binding affinity from a chemical’s structure to support the 

prioritization of food use inert ingredients and antimicrobial pesticides for in vitro

and in vivo Tier 1 screening, which is designed to ascertain if a compound has the 

potential to interact with the estrogen system.

Please comment on the clarity of the White Paper in describing the differences in 

(Q)SAR development when the goal is to predict in vitro ER receptor binding from 

chemical structure vs. when the goal is to predict in vivo 

reproductive/developmental responses from chemical structure. Please indicate if 

additional discussion in the White Paper is needed to establish the relevance of 

ER binding affinity (either measured or predicted) to interpret the potential for in 

vivo outcomes.

Panel Response

The Panel concurred that the ER-binding adverse outcome pathway illustrated in 

Figure 1 of the White Paper is transparent and mechanistically sound. Figure 1

provides a good representation of how mode/mechanism of action can be used to 

33
better understand and predict higher order effects (i.e., reproductive effects). The

Panel stressed that the White Paper makes it very clear that the goal of the expert 

system is to provide for the use of “the molecular initiating event as a basis for 

prioritizing chemicals for further screening with EDSP Tier I assays, which 

incorporate endpoints at higher levels of biological organization” (see page 12 of 

the White Paper). The Agency went on to say that “It is important to recognize 

that while the determination of potential adverse reproductive effects in whole 

organisms or populations is an important risk assessment issue, it is not a goal of a 

prioritization application” (page 14 of the White Paper). The Panel had no trouble 

in understanding that the current expert system only allows prioritization for

EDSP Tier I testing. EPA’s presenters did a nice job of pointing this out.

The Panel agreed with statements in the White Paper that ER-binding or Vtg

induction may not indicate in vivo adverse effects. While the prediction of an in 

vivo reproductive/developmental response from chemical structure is very

tantalizing, the Panel concurred that ER-binding does not necessarily translate to

in vivo effects on reproduction. Likewise, lack of ER-binding does not 

necessarily translate to lack of in vivo effects on reproduction. The OECD 

consultation came to this same conclusion. The Panel emphasized that ER-binding

is only one of many key events that might lead to reproductive impairment. DNA 

activation events that occur downstream from ligand binding, agonist/antagonist 

or partial agonist/antagonist, are also critical to the manifestation of reproductive 

effects. In vitro assays that give a greater level of information are those such as 

transactivation assays, and those that have more metabolic competence. Currently

there is a lack of systematic experimental results for model chemicals representing 

the different sub-domains of ER-binding at higher levels of biological 

organization including gonadal histopathology and developmental assays in 

zebrafish embryos. 

The Panel stated that more data would be needed should the Agency wish to 

explore the relevance of in vitro ER-binding to adverse in vivo reproductive

effects. The Panel noted that oviparous vertebrates provide an excellent model to 

allow mechanism/mode of action linkages between ER binding and potential 

reproductive impairment and population level impacts (Kidd et al., 2007). Panel 

members recognized that while ER binding is not the only way that chemicals can 

cause in vivo reproductive effects, they agreed that this was an excellent place to 

start. Concordance analyses between data generated from in vitro ER assays and 

the uterotrophic assays, for example, could provide the basis of a mode of action 

linkage to in vivo effects. Recent and ongoing European Union (EU) projects 

concerning mixtures and estrogenic responses could provide data to support such

a development for fish (e.g., see Brian et al., 2005; Brian et al., 2007; and

CREDO Cluster (the cluster of research into endocrine disruption in Europe): 

http://ec.europa.eu/research/endocrine/projects_clusters_en.html).

As the pathway proceeds from the molecular initiating event (i.e., ER-binding), 

through cell and tissue level gene transcription and translation (as measured via 

34
the liver slice assay), and continues through organ effects to an adverse outcome 

observed in the individual or population, the in vivo effects can be measured. 

Among these effects is the conversion of testicular tissue to ova tissue measured

morphometrically from histological section in whole fish assays (Zha et al., 2007)

and reproductive impairment of fish resulting from feminization of male fish 

(Seki et al., 2003) especially when exposed during early life stages (Van Aerle et 

al., 2002). To date, the majority of the documented in vivo reproductive 

impairment effects are for compounds with high or moderate ER-binding 

affinities. The Panel noted that the USEPA’s Office of Research and 

Development, Mid-Continent Ecology Division, National Health and 

Environmental Effects Research Laboratory in Duluth, Minnesota has ongoing 

studies of in vivo reproductive impairment effects. Results from these efforts are 

not reported in the White Paper and have yet to be peer-reviewed and published. 

However, the Panel believed that these efforts will provide useful information 

regarding the likelihood of in vivo adverse outcomes from exposure to chemicals

(i.e., chemicals are associated with either A- or B-site binding, but are not 

necessarily in the FI or AM pesticide regulatory inventories) with low ER-binding 

affinities.

Panel members offered some suggestions to improve the discussion of 

prioritization based on ER-binding affinity in the White Paper. 

1) Add a discussion of how the mechanistic/mode of action approach can 

be used in mixture assessments, and how this information can be 

partnered with ongoing research at ORD (i.e., genomics/dosimetry). 

2) Add some examples of non-receptor mediated estrogenic activity to the 

White Paper as ER-binding activation may not pick up all estrogenic 

compounds (e.g., nonylphenols which have several non-ER mechanisms 

of estrogenic activity). 

3) Stress that this model currently only allows prioritization of FI and AM 

pesticides for EDSP Tier 1 testing. The presentations did a nice job of 

pointing this out, but the White Paper was not as clear in this respect. 

c. Model Domain 

The domain of the current ER expert system includes rules to support predictions 

of ER binding affinity for chemicals in the food use inert ingredient and 

antimicrobial pesticide inventories. 

Please comment on the adequacy of the approach that was used to select 

chemicals for the training sets in terms of these two inventories. 

Panel Response

The Panel agreed that the iterative approach to select chemicals for the trainings 

sets of these two inventories was adequate and congratulated EPA for conducting 

this important work. The iterative approach involves strategic selection of 

35
chemicals within chemical groups, assessment of the results in the context of the 

ER-binding hypotheses, and testing additional chemicals until sufficient 

information is collected to make a prediction for all the chemicals in the 

inventory. Chemicals in the training set or assay domain should include the 

expert system’s ER binding domain and be representative of the structures in the 

regulatory inventories or the chemical domain. Equally important to defining the 

structural limits of activity is to define the physicochemical limits (i.e., solubility) 

of the activity. The Panel noted that both of these approaches were used in

developing the positive binding applicability domain of the expert system, 

especially for chemicals with low binding affinity. However, the Panel pointed 

out that the level of adequacy depends on specific decision rules, because at a 

specific decision point, the number and representativeness of the training set 

chemicals may be different. The Panel stated that the scientific community would 

be interested in assessing compounds outside of the FI and AM inventories. 

The Panel concurred that the Agency clearly stated in its White Paper that the 

intended regulatory use of the ER-binding expert system was as a priority-setting 

tool. That is, identifying ER-binding chemicals from the FI and AM pesticide

inventories that would be placed at a higher priority for EDSP Tier 1 screening. 

The FI inventory contains 393 discrete chemicals and the AM inventory contains 

211 discrete chemicals. The regulatory applicability of the ER-binding expert 

system to FI and AM inventories is addressed by subdividing the FI and AM 

chemicals into respective subgroups that have specific chemical attributes that are

mechanistically related to the different modes of ER binding. 

Some panel members questioned whether the model domain was adequate to 

identify antagonists of ER-binding both in the binding pocket and outside the 

binding pocket. They indicated that other data need to be included (see Blair et 

al., 2000; and Hartman et al., 2009). As an example, the expert system might be 

inadequate for identifying the pure ER antagonist, ICI 182,780, (an experimental 

compound to study estrogenic effects), or the partial antagonist Tamoxifen® (a 

drug used in treatment of estrogen receptor-positive metastatic breast cancer in 

postmenopausal women). For example, with potent ER antagonists, such as ICI 

182, 780, the tail of the molecule binds outside the main binding pocket (Jacobs et

al., 2003). In response to a question from the Panel, EPA clarified that they used 

ICI 182, 780 in the liver slice assay and hoped to provide that information soon. 

The Panel appreciated the evolving nature of the development of an expert system 

as new data and knowledge become available. However, they emphasized that 

any predictive computational model including the ER-binding expert system 

should be “frozen” at some point during the development process so that its 

performance can be appropriately evaluated with additional chemicals not used in 

the development of the model before regulatory use. The Panel was unclear as to

how many discrete chemicals from the FI and AM inventories had been tested in 

the rainbow trout ER binding assay, and how many of them were used in training 

and validating the expert system. They suggested that the White Paper would be 

36
more readable and informative if these numbers were explicitly stated in the 

nodes of the decision tree shown in Figure 9 and other figures of the White Paper. 

The Panel noted that EPA agreed to incorporate this suggestion in the White 

Paper.

The Panel suggested that the large body of literature data on ER binding could be

used in a meta-data analysis format either for developing or validating the 

decision rules implemented in the ER-binding expert system. The Agency built

the expert system using a single set of data, e.g., the rainbow trout ER-binding 

data to avoid “noise.” Other ER-binding data from different sources and different 

protocols from the literature could be used to test the confidence of the expert 

system. The Panel pointed out that meta-analysis will provide more information 

and confidence in the binding domain of the model. 

d. Algorithm

The ER expert system provides predictions for each chemical, with each 

individual prediction traceable to chemical subgroups, binding mechanism and 

endpoint databases. In developing the expert system several chemical subgroups 

were identified as chemicals that contain multiple functional groups. 

Question d.1. Please make suggestions for improvements in presenting the expert 

rules and their underlying rationale, especially with regard to groups with 

multiple functional groups. The Panel made several suggestions for improvements 

in presenting the expert rules and their underlying rationale, especially with 

regard to groups with multiple functional groups (enumerated below). Chemicals 

with multiple functional groups were termed “mixed phenols,” if there was a ring 

hydroxyl group, or “mixed organics,” if there was no hydroxyl group. 

Mixed phenols. The Panel recommended developing a systematic testing 

program based on, for example, Simplified Molecular Input Line Entry 

Specification (SMILES) or Smiles Aritary Target Specification (SMARTS) 

strings. A SMILES or SMARTS string could be used as an input into something 

like a classification tree methodology which could then be used to determine if 

certain “structures” could be used to predict activity level. Mixed phenols are 

potential A-site binders because of the presence of the phenolic group; however, 

the presence of additional substituents can hinder binding activity. As described 

in the White Paper (page 29), there is insufficient information to establish general 

rules for these types of chemicals. The Panel stated that the conservative 

approach employed by EPA of an exact match is appropriate. Clearly, 

improvement in the expert rules is predicated on additional testing. 

Mixed organics. While there is a mechanistic rationale for additional testing for 

mixed phenols, no such rationale is apparent for mixed organics. The fact that 

some mixed organics were active on the first reading may discourage further 

development of structural alert-based expert systems. The Panel thought that a 

37
few mixed organics would act as “skeleton keys” to bind to the ER. However, 

these chemicals should be weak binders and bind only at concentrations near their 

cytotoxicity level. 

Substructure coding. The Panel indicated that EPA could pick out substructure 

characteristics that would be important by just looking at the structure, e.g.,

bisphenol A-like, DDT-like, chemicals with a long fatty acid tail, and phthalatelike chemicals. Chemicals with multiple functional groups would have further 

coding of the substructures which would be key coders for receptors. 

Nature of hydrogen (H) bonds. The Panel suggested that EPA more closely 

examine chlorinated compounds/pesticides. The nature of the hydrogen bonds 

(e.g., formed by the hydroxyl group), important for binding in the receptor, could 

be investigated to determine the effect of adjacent substituents and the strength of 

hydrogen bonding and distances for hydrogen bonding required for binding. 

Decision rules. An expert system consists of a set of decision rules. The order in 

which they appear in the expert system is important in determining the system’s 

prediction performance and the interpretability of the prediction results of new 

chemicals. The development of the decision rules and the order in which they are 

placed in the current ER-binding expert system is a reasonable manual process, 

mainly based on human experts’ knowledge and interpretation of the experimental 

ER binding data. Three suggestions were made by the Panel to clarify the 

presentation of the rules.

1) The first suggestion was to use an automatic approach to derive the 

decision rules. This would be done through the following steps: (1) 

Describing all training set chemicals in terms of the decision values 

(descriptors) currently used in the expert system, e.g. log Kow and the 

present/absence of a structural element (cycle, hydroxyl group etc.). (2) 

Assign each chemical as an ER binder or a non-binder. (3) Use 

partitioning methodology such as a classification tree (Breiman et al., 

1984) to derive the decision rules automatically. 

The “automatic” approach will allow for more objective assessment of the 

performance of the system and the implicit “coding” for chemicals with 

multiple functional groups. The “manual” and “automatic” versions of the 

expert system should have a lot of similarity (in terms of the types of rules), 

confirming to some degree the validity of the current, manually-built expert 

system. The final implementation of the expert system could be a hybrid of 

the “manual’ and “automatic” versions of the expert system, both in terms of 

the actual rules and the order in which the rules appear in the decision tree. 

2) The second suggestion was to construct the expert system decision tree as 

chemical classes using log Kow modifier rules. One panel member noted 

that there seems to be two kinds of "rules" used in the system. Rules I and 

38
II discuss chemical properties that result in low RBA. Rule III describes

properties that result in high RBA. The rest of the decision tree is a 

mixture of these two types of “rules.”

3) The third suggestion would be to have decision rules that center on 

chemical properties. Properties of the sub-classes beyond log Kow would 

determine RBA activation class. The Panel noted that a model based on 

these decision rules would provide greater insight about whether a 

subclass would be considered “ER-binders” or “non ER-binders” 

Question d.2. Please also comment on the ability of the expert rules to identify 

chemicals outside the model domain.

In general, the Panel agreed that the expert system is expected to perform 

reasonably well for certain chemicals outside the model domain especially in the 

context of A-site and B-site binding. Panel members commented that the model 

domain may mean different things for different people and that better definitions 

are needed in the White Paper. EPA has defined the model domain by chemical 

classes and log P limits on the class. As a consequence of using the class-wise 

approach, the model domain is very broad. The Panel remarked that EPA uses 

only a limited set of characteristics to describe the chemicals. Solubility, as 

measured by log Kow, does not by itself contain sufficient information to predict 

the activity level of all chemicals of interest in the training set. The model may 

perform well to predict the activity of similar chemicals from other inventories, 

but also may require additional characteristics not used in the current model if the 

goal is to accurately predict the activity level of all chemicals from these other 

inventories.

The Panel recommended that a series of definitions be added to the White Paper

to improve readability, e.g., model domain, applicability domain. For example, 

model domain should be clearly defined in the following statement found on p. 13 

of the White Paper, “The chemicals in the training set make up the expert 

system’s model domain.” One suggestion was that the model domain would be 

defined better by the descriptions of the molecules. 

The Panel encouraged EPA to evaluate the expert system’s predictability using 

large literature data sets of ER binding. Panel members noted that in most of the

literature, the chemicals were not tested in concentrations as high as those used in 

the current White Paper. Inconsistency between the prediction of the expert 

system and literature data, therefore, could be due to the different limits of 

detection, and not due to the lack of predictivity of the expert system. The Panel 

stated that the scientific community will use the expert system when it becomes 

publicly available for chemicals outside the model domain and beyond those in 

the two inventories. Therefore, the expert system released to the public should be

consistent with as much available data as possible. 

39
The Panel remarked that the scientific and regulatory communities are more 

reluctant to accept negative predictions (e.g., non ER-binding) than positive 

predictions (e.g., ER-binding). Therefore, they recommended that the assayed 

domain (the chemicals tested) includes proper coverage of the inventory being 

probed. This coverage has to be balanced with the need to move away from a 

chemical by chemical testing scheme. The best balance is reached by testing a 

wide variety of chemicals, which represent all the chemical classes or subgroups 

within the inventory in question and at the same time cover the relevant physicochemical property ranges of each class or subgroup. The Panel indicated that this 

strategy has been implemented for the two specific inventories in question (i.e., FI 

and AM), which forms the basis of EPA’s White Paper. For example, 70 

alkylaromatic sulfonic acids in the two inventories were represented by six tested 

chemicals spanning a log Kow range from -0.62 to 5.67. The Panel noted that the 

Expert System could be used to assign an RBA classification to new chemicals 

without full structural characterization if the user determines it is sufficiently 

similar to a chemical used in the training set. Some on the Panel felt that this use 

has a number of problems, one of which is that it is based on a determination that 

does not require assessment of structural similarity. The utility of this approach 

decreases as the chemical molecule becomes more complex. 

The challenge, as noted by the Panel, is how this approach is extended to all 

chemicals. One way of doing this is to implement chemical selection based on the 

presence of organic functional groups in particular structural environments. This 

can be accomplished by developing a systematic testing program, based on, for 

example, use of SMARTS or SMILES strings (see explanation above in 1) d.1). 

The Panel remarked that the “acyclic rule” is well-described in the Agency’s 

White Paper for the two regulatory inventories of interest; however, it may be 

difficult to extend this rule to other chemical inventories because of the likelihood 

of different structural activity relationships. An integrated testing strategy would 

be needed for the greatest efficacy. See additional discussion in the Panel’s 

response to question 2.

The Panel agreed that the expert system is a knowledge-based system in which 

there are seven specific decisions or domains. Each of these domains requires a 

specific set of rules, each of which should be used separately to judge within or 

outside the “model domain.” Similarly, one panel member remarked that the 

expert system could be viewed as a hierarchically-ordered set of seven models, 

resulting in the model domain space as seven “clusters” in multidimensional 

space. Using this illustration, this panel member suggested the seven chemical 

class rules could be converted to seven chemical property rules, which would 

increase the expert system’s utility for untested chemicals in other chemical 

inventories and better describe the “model domain” as limits on chemical 

properties.

40
e. Goodness-of-Fit, Robustness, and Predictivity

Consistent with suggestions by the EDSTAC (1998), and typical processes for 

(Q)SAR development, the expert system rules were established through an 

iterative process of defining subgroups, gathering empirical data to refine 

subgroup rules, followed by collection of additional empirical data to cover the 

structural domain and/or until a consistent pattern of structural rules and activity 

emerged. The expert rules permit each chemical to be assigned to subgroups and 

an associated estimated binding affinity value, accompanied by an explanation of 

the basis for the estimate as well as how the estimate compares to measured data 

for other members of the same subgroup. The 2009 OECD expert consultation 

report on the expert system recognized that standard statistical methods such as 

those used to assess regression model (Q)SARs are not necessarily applicable to 

expert systems (OECD, 2009). Rather transparency and usefulness as described 

in the White Paper are more appropriate parameters for assessing the validity of 

an expert system. The peer consultation report found the current approach, with 

individual predictions traceable to chemical subgroups, binding mechanism and 

endpoint databases, to be appropriate although the report noted that if additional 

information could be made available it would facilitate future peer-review on this 

issue. 

Question e.1. Please comment on the adequacy of information presented in the 

White Paper to evaluate the scientific rationale of how a chemical is processed 

through the decision logic; i.e., how a chemical is assigned to a subgroup with an 

associated binding affinity value; the mechanistic rationale for estimates of 

binding affinity data, including data for related chemicals; and how it is 

determined that a chemical is outside of the domain of the expert system. 

Panel Response

As a general principle, all panel members agreed that the information in the White 

Paper was adequate to evaluate the scientific rationale of how a chemical is 

processed through the decision logic. That is, the Agency addressed, in detail, all 

of the descriptive elements needed to provide a clear and transparent description 

of the expert system, i.e., how a chemical is assigned to a subgroup with an 

associated binding affinity value; the mechanistic rationale for estimates of 

binding affinity data, including data for related chemicals; and how it is 

determined that a chemical is outside of the domain of the expert system. The 

Agency’s description of the expert system conforms to the OECD Principles of 

(Q)SAR Validation. The Panel noted, however, that the Agency’s expert system 

is not a (Q)SAR model by the traditional understanding of regression or 

discriminant analysis model (Q)SARs. Statistical methods such as those used to 

assess regression model QSARs are not applicable to the Agency’s expert system. 

Therefore, the description of the expert system in the White Paper reflects this 

difference and focuses on the transparency and validity of the decision logic.

41
The Panel indicated that there were a number of very good reasons for providing 

the details of the scientific rationale to the user. Foremost is that the operation of 

the expert system is clear and transparent to the user. The detailed explanation of 

the scientific rationale behind the decision logic, in turn, provides the user with 

confidence in the expert system. 

The Panel addressed the adequacy of the information in the White Paper related 

to: 1) how a chemical is assigned to a subgroup with an associated binding 

affinity value, 2) the mechanistic rationale for estimates of binding affinity data, 

including data for related chemicals, and 3) how it is determined that a chemical is 

outside of the domain of the expert system. 

1) How a chemical is assigned to a subgroup with an associated binding 

affinity value 

The Panel concurred that the considerable amount of information available in 

the White Paper is adequate for the intended purpose. The presentation of the 

information in the White Paper, whilst complex, is comprehensive and easy to 

understand and follow for any person with a reasonable understanding of 

chemical structure. A user should be able to recognize the structural features 

underlying the issue and appreciate how and why the conclusions have been 

made. That is, detailing why a subgroup has been utilized and why a 

compound has been assigned to a subgroup. The Panel agreed that the 

educated user would be able to understand the process, which is described in 

full beginning with page 30 in the White Paper. The use of Figures 9-12 in 

the White Paper was considered to be particularly useful. The Panel 

remarked that the information provided is consistent with the current state of 

the art in this area. One panel member commented that recognized structural 

features could be used in some fashion to automate the expert system to 

rapidly screen inventories, based on, for example, use of SMARTS or 

SMILES strings.

2) The mechanistic rationale for estimates of binding affinity data, including 

data for related chemicals

The Panel concurred that the considerable amount of information available in 

the White Paper is adequate for the reader to understand the mechanistic 

rationale for estimates of binding affinity. The Panel agreed that a complete 

description of the mechanistic rationale is very important to understand the 

expert system. A complete description of the rationale provides the nonexpert user with all necessary information to grasp how and why the expert 

system was developed and how it makes predictions. The user will therefore 

have more confidence in the model. Comfort in the mechanistic rationale for 

the expert system would be useful to screen other chemicals within/outside the 

training set. The Panel agreed that the mechanistic rationale stems from a deep 

understanding of the binding of xenobiotics to the ER. They noted that this 

42
point is discussed in detail in response to question 1) b.2. In particular, they 

noted the detailed information on several aspects of the mechanism including 

the differentiation of ER-binding affinity at A-site, B-site or or C-site and how 

this specifically relates to the concept of chemical grouping and the physicochemical basis of mechanism. 

The Panel recognized that the mechanistic rationale is for ERα and not the 

entire estrogenic mode of action because other pathways are not included. 

There are other receptors that may play a role in endocrine disruption. 

The White Paper provides a good description of the role of ERα, but it was 

noted that this is one of several receptors that are responsible for endocrine 

disruption. The role of receptors and non-receptor mediated responses in 

endocrine mechanisms and modes of action, other than ER, should be stated 

more clearly in the White Paper.

The Panel thought that EPA provided a strong justification for the mechanistic 

relationship between binding affinity and hydrophobicity (as parameterized by 

the logarithm of the octanol-water partition coefficient, log P). Log P is a 

measure of hydrophobic binding interaction as opposed to binding interaction 

at the A-, B-, or C-sites which is “electrostatic.” The Panel recommended that

the context of log P used in the expert system be more explicitly stated as 

relating to hydrophobic binding interactions to prevent confusion with the 

context of log P relating to uptake, distribution, and bioavailability of a 

chemical used in ecotoxicology studies. 

The Panel agreed that the White Paper adequately described ER-binding as the 

molecular initiating event for the ER-binding mediated adverse effects 

pathway. The description in the White Paper was clear that ER-binding 

affinity cannot be translated in to adverse in vivo effects. The Panel stressed 

that this point be made very clear in the White Paper and should be reenforced wherever possible. 

The Panel recommended that the EPA should provide the ER-binding and Vtg 

expression data to support the information behind each of the chemical groups 

when the expert system is implemented. This will allow the user to see the 

potency level of the prediction and the distinction between the chemicals with

RBA greater or less than 0.0001%. The Panel noted that utilization of the 

source data with the chemical grouping concept, i.e., within the categories 

formed by the structural rules, could allow for semi-quantitative estimations of 

binding potency to be made.

3) How it is determined that a chemical is outside of the domain of the 

expert system. 

The Panel concurred that the considerable amount of information available in 

the White Paper is adequate for explaining how a chemical is determined to be 

43
outside of the domain of the expert system. The Panel agreed that the 

identification of whether a chemical is outside of the domain of a model is 

very important for its proper use and application. Before a chemical is 

determined to be in or outside the domain of the expert system, the model 

domain must be clearly defined. Here the domain of the model is defined by 

the single organic chemical structures in the two chemical classes or “bins.” 

Those chemicals outside of the domain are termed “Unknown Binding 

Potential.” The Panel recommended a possible note of caution regarding the 

phrase “Unknown Binding Potential” in case of possible misinterpretation. A 

more explicit phrase, such as “Prediction of the binding potential is not 

possible as the compound is outside of the domain of the model,” whilst not as 

succinct, may be preferable. The Panel commended EPA on the use of 

solubility cut-offs to define the domain of the ER binding assay implicitly

(also see detailed discussion in 1) a.2).

The Panel noted that there are many definitions of domains used in this 

scientific area and in the White Paper. They recommended that the Agency 

provide additional definitions and clarification of terms in the White Paper, in 

particular, with regard to each domain (also see detailed discussion in 1) d.2. 

For example, the domain(s) of (Q)SAR models can be defined on a number of 

levels including: the domain of the assay itself, physico-chemical descriptor 

range, structural fragments, mechanism of action, and metabolic domain. The 

Panel noted that not all of these domain definitions are necessary for every 

model including the expert system. 

Question e.2. While to date the Agency is not aware of statistical approaches that 

would provide the means to assess goodness-of-fit or predictivity of expert 

systems such as the one described here, is the SAP aware of any statistical 

approaches or data presentations that could be amenable for such evaluations?

Panel Response

The Panel noted that there are a large number of diagnostics and descriptive 

statistics available for regression-based (e.g., r2, F, etc.) or discriminant (or 

logistic regression) analysis (e.g., Cooper’s statistics for sensitivity and 

specificity) QSARs. Many, if not all, of these are in the OECD documentation 

[http://appli1.oecd.org/olis/2007doc.nsf/linkto/env-jm-mono(2007)2] and also 

supported by the guidance provided by the European Chemicals Agency 

[http://guidance.echa.europa.eu/docs/guidance_document/information_requireme

nts_r6_en.pdf?vers=20_08_08]. Such types of statistical measures provide 

estimates of “goodness-of-fit”, i.e., how much of the variance in the effect data is 

described by the descriptor data.

The Panel agreed that the rule-based expert system for predicting the activity of 

chemicals is not a classical (Q)SAR. Never before has there been an opportunity 

to develop an expert system for a defined and reasonable number of chemicals

44
with both a strong mechanistic basis to activity and natural groupings of 

chemicals. They indicated that this situation is an ideal opportunity to make an 

expert system inclusive of these defined and restricted inventories (i.e., FI and 

AM pesticides). This is the first example of a regulatory authority developing a 

method for the optimization of an ER binding expert system for public domain 

use. Statistical methods used to measure goodness-of-fit are not applicable in the 

case of a rule-based expert system. The Panel agreed that the unique nature of the 

expert system and its development must be borne in mind and some other kind of 

statistical evaluation is needed. 

External validation is recommended in order to obtain a true estimate of the 

predictivity of a model. This requires predicting the effects for compounds that 

are not in the original training set, but which have similar structural space and 

applicability domain for assessing the accuracy of the predictions. Thus, the Panel 

recognized that typically in model building, one starts with two groups of data, a 

training set and a validation set. The training set is used to build the model and 

the validation set is used to test the model. In the expert system presented, all of 

the training data are used to fit the model and the model has been restructured and 

optimized so that the ER-binding activity of each tested substance is fit perfectly 

by the final model. No data are available to test or validate the model. Therefore, 

the Panel remarked that an assessment of goodness-of-fit or adequacy of model 

predictivity using the training data provides a very optimistic view of the quality 

of the final model. The Panel concluded that the types of statistics associated with 

goodness-of-fit are not applicable to the expert system. 

The Panel agreed that some other kind of statistical evaluation of the expert 

system is needed to provide confidence to the user. They stated that the statistical 

evaluation should be an assessment of predictivity for compounds not in the 

training set. The validation data can be used to truly assess the predictivity of the 

expert system. 

For instance, the Panel thought that using misclassification rates, sensitivity, and 

specificity of the validation data, would provide a good measure of goodness-offit for the predictivity of the expert system. The Panel provided an illustration of 

how this would work. The expert system is essentially a decision tree that takes 

inputs, i.e., chemical class and chemical properties, and ends with a classification 

into two ER-binding activity classes (i.e., RBA <0.00001% or RBA > 0.00001%). 

The expert system is therefore comparable to a multiple logistic regression which 

is based on binary classification, in this case RBA <0.00001% or RBA > 

0.00001%. Therefore, the methods to assess goodness-of-fit of a multiple logistic 

regression model would be applicable. Many statistics for goodness-of-fit are 

derived from the difference between the observed value and the predicted value of

the model, or the residual value. However, in the case of the expert system, the 

equivalent would be the misclassification rate. 

45
The Panel agreed that the expert system should be frozen at a point of time before 

any validation analysis is attempted. This would mean that the expert system is 

first clearly and unambiguously defined, as in the White Paper, and not updated 

with new information. Next, predictions with new data could be made for data by

the expert system without biasing the outcome, i.e., RBA classification, and a true 

assessment of system’s performance could be achieved. 

The Panel recommended that one possibility to investigate the performance of the 

expert system would be to use data from the literature to assess its predictions.

Panel members recognized that there are caveats in using this approach. There are

inherent differences in the test methods used and level of binding specified. 

Despite these concerns, the Panel thought the approach would be illustrative. A 

particular advantage of using external validation data to test the predictability of 

the expert system is the ability to determine clearly when a chemical is out of 

domain. This will prevent studies from being biased by predictions for 

compounds out of domain. 

The Panel agreed that care and effort was required to choose training set data 

(chemicals) to ensure the expert system was capable of spanning the domains of 

interest. Similarly, data for the validation set should also be chosen across all

domains of interest to challenge the model. For example, Figure 7 in the White 

Paper shows all of the sulfonic acid dyes that were in the rainbow trout training 

set and then some representative additional structures in this class. Only a few of 

the training set chemicals were chosen to be tested for ER-binding activity. The 

expert system is then able to "predict" RBA for all chemical structures, tested or 

not. One possibility is a validation data set selected from chemicals in either 

inventory which had not been tested. These chemicals would be assayed for ERbinding activity and then results would be compared to the expert system’s 

predictions. The Panel commented that there would be obvious cost and resource 

implications in this approach if many chemicals were tested as part of the 

validation. 

Panel members indicated that a possible source of active ingredient pesticide 

chemicals for validation purposes might be the European Union list 4 (Link for 

regulatory status of pesticide active substances in the EU:

http://ec.europa.eu/food/plant/protection/evaluation/index_en.htm.) There might 

be chemicals not tested in the FI and AM pesticide inventories for which there are 

existing data that could to be used for validation In addition, the Panel suggested 

comparing the overlay between the FI and AM pesticide inventories and the 

OECD high production volume chemical (HPVC) list. The HPVC list contains 

approximately 7000 chemicals and could provide suggestions for chemicals and 

directions for examining model validation, evaluation of model extrapolation 

potential and directions for further extension of the model to cover the expanded 

interest domain. This would help to reduce the number of situations where the 

model is not able to make a prediction due to the compound being out of the 

domain.

46
The Panel agreed that it would be very difficult to define how many compounds 

would be needed for validation. There are different levels of confidence in each 

of the seven different models/rules in the expert system. Some models may 

perform very well with a limited number of chemicals tested; thus they may not 

need such detailed analysis. Other rules may have lower performance and 

uncertainty; thus, more chemicals would be required to be tested. Therefore, the 

Panel specified that the number of chemicals tested to validate the expert system

would change according to the rules. There may also be regulatory priorities for 

different rules; thus, there may be a need to create prioritized rules with the 

appropriate information regarding uncertainty. 

The Panel stressed that the evaluation process of the model is an assessment of the 

performance of the model, not an extension of the model. As noted above, the 

model should be frozen while the rules are validated. The validation process 

could be used to examine the extent of the boundaries of the rules in the expert 

system. 

The difficult task is determining how much the domain can be expanded without 

losing predictability. For this expert system, different rule domains offer 

different abilities for expanding its domain. For example, phthalates between log 

Kow < 1.3 are classified in the group RBA < 0.00001% and those above log Kow 

>1.9 are classified in the group RBA >0.00001%. Understanding chemical and/or 

biological mechanisms of ER binding might allow for the extension of the model 

utility to cover the 1.3<log Kow <1.9 range for this rule. In this expert system,

category membership is based on substructure and physico-chemical property cutoffs which were developed and verified with trout ER-binding test data acquired 

in an iterative process and which were hypotheses driven. The greater the number 

of chemicals evaluated to form or delineate a category, the greater the confidence 

in the applicability domain of that category, i.e., the more valid the category. The 

category can also be validated with data from other test systems, in particular 

looking for similar relative potency and thresholds. The other test systems 

include not only the liver slice assay used here in conjunction with the trout ERbinding, but also other ER-related assays such the yeast-based ones which are 

necessarily ER-mediated (see Schultz et al., 2002, Hamblen et al., 2003; 

Senseverino et al., 2009) and ER transactivation assays (reviewed in Jacobs et al., 

2008).

f. Transparency and Clarity of the Expert System

Question f.1. In its validation principles, OECD recognized the importance of a 

transparent validation process for the development of (Q)SAR models in order to 

further enhance their regulatory acceptance.

Please provide any additional comment on how well the White Paper’s summary 

of the expert system conforms to the OECD validation principles and provide 

47
suggestions, as appropriate, to enhance the clarity or transparency of the expert 

system’s development and intended use with regard to the validation principles.

Panel Response

General Comments on Clarity and Transparency

Overall, the Panel commended EPA on their delineation of the basis for the expert 

system, especially in a context suitable for regulatory risk assessment. Specifically, this 

includes explicit description of the decision tree’s logic for grouping of chemicals and 

provision of the expert rules on which estimates are based, along with reference to 

empirically measured data for chemicals in the associated training set. This type of 

transparency is critical as a basis for acceptance and application of the expert system. 

which represents a knowledge set from a given point in time. Science marches on and 

new data will be generated. As this occurs, the Panel recommended that the expert 

system be modified.

Generally the text flows well in the White Paper. The writing is clear and important 

information is well-illustrated by relevant figures and tables. The Panel commented on 

the excellence of the separate document of illustrative case studies. 

The Panel thought the clarity of the White Paper could be improved by adding a glossary 

to define terms such as “model domain.” They also suggested that each of the seven 

“models” which make up the expert system should be discussed separately in the context 

of their applicability domain, training set, etc. See also suggestions provided by the Panel 

in response to question 1) f.2.

The Panel thought the transparency could be improved by linking the training sets, 

especially their molecular structures, to each of the seven model domains. This would be 

particularly helpful if done in an electronic format linked with automation of the expert 

system. Transparency would be further enhanced with some form of validation or 

verification exercise. See discussion in section 1)e. The Panel recommended that this 

exercise be performed for each of the seven models in the expert system as some are 

better documented than others. One panel member recommended that standardizing the

reporting format such as (Q)SAR Modeling Report Format (QMRF) be considered as a 

transparent method to record information regarding the rule-based expert system, 

including results of any validation. The QMRF information is structured according to the 

OECD (Q)SAR validation principles (see QMRF at http://ecb.jrc.it/qsar/qsartools/qrf/QPRF_version_1.1.pdf). A separate QMRF would be used for each of the seven 

models.

Background on the OECD Principles of (Q)SAR Validation

One panel member provided background on the origin of the OECD Principles of 

(Q)SAR Validation. A number of principles for assessing the validity of (Q)SARs were 

originally proposed at an international workshop on the “Regulatory Acceptance of 

(Q)SARs for Human Health and Environment Endpoints,” organized by the International 

48
Council of Chemical Associations (ICCA) and the European Chemical Industry Council 

(CEFIC) held in Setubal, Portugal, on 4-6 March, 2002. The outcome of the meeting was 

the so-called “Setubal principles” summarized by Jaworska and colleagues (Jaworska et 

al., 2003). These Setubal principles state that a (Q)SAR should:

1) be associated with a defined endpoint of regulatory importance,

2) take the form of an unambiguous algorithm,

3) ideally, have a mechanistic basis,

4) be accompanied by a definition of domain of applicability,

5) be associated with a measure of goodness-of–fit,

6) be assessed in terms of its predictive power by using data not used in the 

development of the model.

However, the workshop stopped short of producing any guidance on how to interpret and 

apply these principles. The OECD Principles of (Q) SAR Validations were derived from 

the Setubal principles (OECD, 2004).

Agency White Paper and OECD Principles of (Q)SAR Validation

The Panel concurred that the Agency’s expert system conforms to the five OECD 

Principles of (Q)SAR validation which include demonstration of:

1) a well defined endpoint,

2) mechanistic interpretation of the model,

3) defined domain of model applicability,

4) an unambiguous algorithm, and

5) appropriate measures of goodness-of-fit, robustness, and predictivity.

The Panel stated that these principles should be viewed as a means of evaluating the 

expert system and not a formal validation. The regulatory domain of the expert system 

contains the two chemical inventories, FI and AM pesticides. Below is the Panel’s 

explanation of how the EPA’s expert system met each of the five OECD validation 

principles.

1) Defined Endpoint Principle

In the case of software programs and expert systems, as is the case with this White 

Paper, which are based on multiple models, the Panel stressed the importance of 

identifying the smallest component of the expert system that functions independently, 

and to apply the principles to the individual component. The intent of Principle 1, a 

well-defined endpoint, is to ensure clarity in the endpoint being predicted by the 

given model, or in this case expert system. Because a given endpoint could be 

determined by different experimental protocols and under different experimental 

conditions, the Panel noted the importance of identifying the experimental system 

that is being modeled. In the scientific sense, a defined endpoint is referred to as a 

specific effect within a specific tissue/organ under specified conditions.

49
To meet the defined endpoint principle, Principle 1, the Agency must address the 

following four questions:

a. Does the model have a clearly defined scientific purpose? Does it make 

predictions of a clearly defined physicochemical, biological or environmental 

effect?

b. Does the model have the potential to address, or partially address, a clearly 

defined regulatory need? Does it make predictions of a specific endpoint 

associated with a specific test method?

c. Is information given about important experimental conditions that affect the 

measurement and therefore the prediction, e.g., species, exposure period?

d. Are the units of measurement of the endpoint given?

The Panel answered all four of these questions in the affirmative.

2) Molecular Basis Principle.

The intent of this molecular basis principle, Principle 2, is not to reject models that 

have no apparent mechanistic basis, but to ensure that some consideration is given to 

the possibility of a mechanistic association between both the descriptors used in a 

model and the endpoint being predicted, and to ensure that this association is 

documented.

To meet the mechanistic basis principle, the Agency must address the following three 

questions:

a. In the case of a SAR, is there a description of the molecular events that 

underlie the reactivity of the molecule? In this case, is there a description of 

how substructural features could form part or all of a receptor binding region?

b. In the case of a QSAR, do the descriptors have a physicochemical 

interpretation that is consistent with a known mechanism of biological action?

c. Are any literature references cited in support of the purported mechanistic 

basis of the (Q)SAR?

The Panel answered all three of these questions in the affirmative.

3) Defined Applicability Domain Principle

The need to define an applicability domain, Principle 3, expresses the fact that the 

expert system is a series of reductionist models, which are associated with limitations 

in terms of the chemical structures, physicochemical properties, for which the system 

can generate reliable predictions.

To meet the defined applicability domain principle, the Agency must address three 

questions:

a. In the case of a SAR, is the substructure associated with any inclusion and/or 

exclusion rules on its applicability to groups of chemicals?

50
b. In the case of a SAR, is the substructure associated with rules regarding the 

modulatory effects of the substructure’s molecular environment?

c. In the case of a (Q)SAR, are the descriptor and response variables associated 

with inclusion and/or exclusion rules that define the variable ranges for which 

the (Q)SAR is applicable (i.e. makes reliable estimates)?

All three of these questions are answered in the affirmative.

4) An Unambiguous Algorithm Principle

The intent of Principle 4, an unambiguous algorithm, is to ensure transparency in 

each part of the system which generates predictions of an endpoint from information 

on chemical structure and/or physicochemical properties. Without this information, 

the performance of each aspect of the system cannot be independently established and 

this is likely to be a barrier to regulatory acceptance. The issue of reproducibility of 

the predictions is covered by this Principle.

To meet the defined algorithm principle, the Agency must address one of the two 

following questions:

a. In the case of a SAR, is there an explicit description of the substructure, 

including an explicit identification of its substituents?

b. In the case of a (Q)SAR, is the equation explicitly defined, including 

definitions of all descriptors used?

The Panel responded that only question “a.” is applicable and this question was

answered in the affirmative.

5) Appropriate Measures of Goodness-of-fit, Robustness, and Predictivity 

Principle

Principle 5, appropriate measures of goodness-of-fit, robustness, and predictivity,

includes the intent of the original Setubal Principles 5 and 6. The Panel noted that 

this principle simplifies the overall set of principles, but does not lose the distinction 

between the internal performance of the system, as represented by goodness-of-fit 

and robustness, and the external validation of the system, as determined by 

predictivity.

To meet the internal performance of the system principle, principle five, the Agency 

must address the following questions:

a. Are full details of the training set given, including details of chemical names, 

structural formulae, CAS numbers, if available, and data for all descriptor and 

response variables?

b. If the data used to develop the model is based upon the processing of raw data,

e.g., the averaging of replicate values, is there an adequate description of the 

data processing and are the raw data provided?

51
c. Is there a specification of the statistical method(s) used to develop the 

(Q)SAR, including details of any software used?

d. Is the (Q)SAR associated with basic statistics for its goodness-of-fit to the 

training set?

e. Is the (Q)SAR associated with any statistics based on cross-validation or 

resampling? If yes, is the number or samples used indicated?

The Panel determined that only questions a. and b. are applicable to this expert 

system and both are answered in the affirmative.

To meet the external validation of the system, principle five, the Agency must address 

three questions:

a. Does application of the appropriate statistical method(s) to the training set 

result in the same (Q)SAR model?

b. Is there any information to indicate that the (Q)SAR has been validated 

previously, using a test set that is independent of the training set?

c. If an external validation has been performed, is the following information 

available: 1) the number of test structures, 2) the identities of the test 

structures, 3) the approach for selecting the test structures, 4) the statistical 

analysis of the predictive performance of the model, e.g., including sensitivity, 

specificity, and positive and negative predictivities for classification models, 

and 5) a comparison of the predictive performance of the model against 

previously-defined quantitative performance criteria.

Traditionally, “validation” within the OECD principles for (Q)SAR validation is 

thought of in the context of checking a statistical model for predictivity. The Panel 

stated that this type of statistical validation is not appropriate for this expert system 

nor is it attempted in the White Paper. [Note: see section 1) e.2 for a parallel 

discussion.]

Question f.2. The White Paper and associated presentations at the SAP meeting 

form the basis of the documentation of the expert system.

Please provide any suggestions for preparing the system documentation that will 

enhance clarity and understanding for users. 

Panel Response

The Panel complimented the Agency on the level of documentation of the ER binding 

expert system. Panel members recommended some areas for improvement: 

1) Reorganization of the White Paper. This specifically related to the 

information starting on page 30, "A Rule-Based Expert System to Predict ER 

Binding Affinity.” They agreed that there are essentially seven rules (i.e., IVII in Figure 9) and that discussion to assist in their evaluation according to 

52
the OECD Principles for the Validation of QSARs, e.g. making clear 

references to the training data used, mechanistic rationale and domain covered 

etc., should be made for each model domain rather than for the expert system 

in toto. Separate discussion for each of the seven models domain would be 

more transparent.

2) An Executive Summary to the White Paper would be very useful to the reader. 

This could include a bulleted list of the rule specific domains, mechanistic 

rationale and domain to prepare the reader for what follows in the body of the 

paper. 

3) A glossary be added to the White Paper to clarify terms such as “model 

domain.” 

4) That each of the seven “models” which make up the expert system should be 

discussed separately in the context of their applicability domain, training set, 

etc. 

5) The two-sided arrows in Figure 1 of the White Paper be changed to one-way 

arrows to reflect the current understanding of the flow of responses in the 

Agency’s expert system. Currently Figure 1 has two-sided arrows between all 

of the "response" boxes in the path from xenobiotics through ER binding to 

altered protein and eventually to population effects suggesting a reversible 

path. However, the current expert system only describes or uses the one-way 

argument from xenobiotic to population effects to justify the use of ERbinding assays. In other words, it is a one-way arrow flow of responses.

6) EPA could amplify the specific criteria by which the number of compounds 

tested in each of the subgroups were selected as these were only briefly 

described in the White Paper as being “a wide variety” selected “across the 

respective log Kow ranges” of each subgroup.

7) The Panel thought the development of illustrative training aids would provide 

greater clarity and understanding of the expert system, e.g., animation of 

Figure 9 in the White Paper. 

8) The Panel stated that automation of the expert system, including more detailed 

description of the individual parts, would be critical to increase availability 

and acceptance of the system. This would include direct access to the 

databases of supporting training sets and related data. For example, EPA

could generate a computer-assisted module for the expert system as it has 

done with “Oncologic” decision tree which predicts genotoxicity from 

molecular structure (see:

http//www.epa.gov/oppt/newchems/tools/oncologic.htm). 

53
2. Acyclic Compounds

Acyclic compounds comprise ~58% of the FI and AM pesticide inventories. As 

discussed in Question 1c, acyclic compounds were found not to bind to the ER. 

Generally, the absence of hydrogen bonding groups or inappropriate geometry can 

explain the failure of these chemicals to bind to ER (e.g., see Katzenellenbogen et al.,

2003). Prior to the EPA research described in the SAP review, a diverse set of acyclic 

structures had not been evaluated for ER binding affinity.

Question a. Please comment on the extent to which the finding with acyclic 

compounds in the FI and AM inventories may be broadly applicable to other 

acyclic compounds. Suggestions on an approach to empirically and efficiently 

assess a hypothesis that acyclic compounds will not bind to the ER in other 

chemical inventories would be welcomed.

Panel Response

The Panel agreed with the general supposition that acyclic compounds in the FI and AM 

pesticides inventories do not bind to the ER receptor (α and β) and do not activate the 

ER-binding mediated pathway. The fact that they are not agonists is broadly supported 

by the FI and AM pesticides data presented by EPA and by differences with published 

data for other compounds that are considered ER agonists through interaction with the 

ER receptor binding site, e.g., Interagency Coordinating Committee on the Validation of 

Alternative Methods (ICCVAM, 2006).

The Panel concluded that the findings for acyclic compounds in the FI and AM 

inventories could be extended to most acyclic compounds in other inventories, but not to

all. Panel members pointed out the training set used for the FI and AM pesticides 

inventories may not be adequate for all acyclic compounds. Acyclic compounds may 

bind to the ER by different rules. The Panel thought that there may be an automated way 

to test for fragments that might be able to bind to the ER receptor, e.g., Analog 

Identification Method (AIM). Some panel members suggested that test compounds 

could be synthesized by combinatorial methods to aid study of structural binding 

relationships. The Panel cautioned that special attention should be given to halogenated 

acyclic compounds.

They pointed out that acyclic compounds may bind to receptors at binding sites other 

than A and B. Based on the crystal structure of the estrogen receptor ligand binding 

domain crystallized in the presence of ligands and inhibitors, there are three important 

binding sites, A, B, C and there is significant plasticity in the binding domains

(Katzenellenbogen et al., 2003). They stated that binding can also occur outside the 

ligand binding domain or other subdomains. This is particularly important for 

antagonists. Diethylstilbestrol should also be considered. 

They indicated that there are several databases which might prove to be useful to inform 

the expert system. For example, ICCVAM has collated a literature database of both 

54
positive and negative estrogenic and androgenic compounds tested in vitro for estrogen 

(ER) and androgen (AR) receptor binding that could be evaluated using the rules of the 

expert system (ICCVAM, 2006). That is, one could run the chemicals in the ICCVAM 

database through the expert system and then compare the predicted results with the 

experimental results. One panel member pointed out that the European Commission is 

developing an “Endocrine-Active Compounds Database and Web Portal” which is 

intended to become a living publicly accessible literature based database covering a wide 

range of endocrine pathways. 

Another example is the Tox21 program. The Tox21 effort is a federal government 

partnership composed of the National Toxicology Program (NTP), part of the NIH 

National Institute of Environmental Health Sciences (NIEHS); the NIH Chemical 

Genomics Center (NCGC), managed by the National Human Genome Research Institute 

(NHGRI); and the EPA's National Center for Computational Toxicology (NCCT) (see 

http://www.epa.gov/comptox/articles/comptox_mou.html). Tox21 studies will 

ultimately test approximately 10,000 chemicals for transcriptional activation of ERα and 

androgen receptors, and a dozen other nuclear receptors using high throughput assays 

(Collins et al., 2008). These assays have 15-point dose response curves from 5 nM to 

100 μM. So far, approximately 2,500 chemicals have been tested. The data that are

being collected implicate other downstream events in the ER-mediated pathway that are 

also important, e.g., interactions with coactivators. The Panel noted that as the data 

from the Tox21 screening assays become available, there will be more additional data 

with which comparisons can be made. This applies not only to the ER but also to the 

AR, as well as to the dozen or so other nuclear receptors against which the chemical 

libraries are currently being screened. 

The Panel discussed ongoing studies whose results may influence the knowledge basis 

of the expert system. One panelist reported that the nuclear receptors mentioned in the 

Tox21 program are being assayed using cell-based commercially available betalactamase transcription reporter assays. Preliminary findings indicate some acyclic 

compounds, in particular, halogenated compounds, are showing up as positive in initial 

evaluations of the output of the high throughput testing work with the ERα assay. Other 

studies have shown that compounds such as PFOS and PFOA are estrogenic in other 

assay systems. The Panel noted that there are many reports of certain metals which can 

be grouped with acyclic compounds, which show up as estrogenic in certain assay 

systems (Byme et al., 2009; Denier et al., 2009). Based on the results of current studies, 

the Panel concluded that acyclic compounds could conceivably have inherent estrogenic 

activity or perhaps affect the binding of natural ligands for any of the estrogen receptors. 

Aside from the cell-based transcription activation assay results, there are other potential 

effects of acyclic compounds on ER-binding, for example, altering the structure of the 

ERs and thereby limiting transactivation by ER ligands. In a broad sense, the Panel 

explained, these acyclic compounds may bind directly to the ER, affect binding of E2 at 

the ligand binding site, affect structural changes related to receptor activation or 

coactivator protein interactions, and/or affect other downstream actions related to 

transcriptional activation.

55
The Panel commented that if the charge question is strictly restricted to ER-binding and 

activation in a manner similar to the natural ligand, then EPA’s proposal that acyclic 

compounds be excluded seems reasonable. However, binding to the A and B sites in 

the ER is only one of many possible ways of chemical interactions that can result in 

increased or decreased hormonal action. Because of this, the Panel indicated that the 

expert system should be viewed in the context of the entire EDSP in setting priorities for 

Tier 1 screening. 

Question b. Please comment on the extent to which the finding with acyclic 

compounds in the FI and AM inventories can be applied to other nuclear steroid 

receptors in general. Suggestions on an approach to empirically and efficiently 

assess a hypothesis that acyclic compounds will not bind to the androgen receptor 

would be welcomed.

The Panel concluded that it would be difficult (if not impossible) to extrapolate finding 

for acyclic compounds in the FI and AM inventories to other nuclear steroid receptors 

because of different chemical structural-binding interactions expected for other 

receptors, e.g., membrane binding receptors, thyroid hormone receptors, and androgen 

receptors. 

The Panel indicated that there are membrane receptors for androgens and progestins 

(Thomas et al., 2006) that may have possible binding sites for acyclic compounds 

belonging to the regulatory inventories of interest. Other nuclear receptors, such as 

androgen receptors and thyroid hormone receptors, while less studied than estrogen 

receptors, have specific structure limitations in their ligand binding pockets. Crystal 

structures for androgen receptor ligand binding domains have been studied (Bohl et al., 

2007; Pereira de Jésus-Tran et al., 2006) and (Q)SAR models have been built (Lill et al., 

2005). Thyroid hormone receptors have been crystallized as well (Nunez et al., 2004),

and utilized for (Q)SAR modeling (Vedani et al., 2007). The binding of ligands to these 

receptors rely on different binding interaction principles, i.e., H-bonding - donation and 

acceptance capacities. Other receptor binding interactions may rely on Pi-Pi stacking 

interactions, ion bonding, energetics, shape, globularity, and other determinants for 

binding (Jacobs, 2005). For example, understanding the required interactions for 

binding to the AR is harder than it has been for the ER, because there is much less 

information available. For other nuclear receptors, including AR, there are different 

conformational changes and different modes of binding and downstream DNA activation 

or inactiviation that are receptor specific. Even for ERα, some compounds bind to sites 

outside of the core ligand binding domain such as the antagonist, ICI 182,780. 

Phenobarbital-like compounds also bind on the outside of the Constitutive Androstanen 

Receptor (CAR), not in the ligand binding pocket. 

The Panel commented that thinking about other nuclear receptors is a valid extension of 

the work completed by EPA on the ER. They encouraged EPA to study the structural 

activity relationships for these other receptors with the same logic and thoroughness that 

was used for soluble ERs, i.e., defining the optimum structure for receptor binding. The 

56
Panel agreed that it would be reasonable to construct an appropriate expert system for 

other hormone receptors given enough information about structure binding relationships. 

The Panel thought that it might be interesting to use a wide variety of in vitro assays 

including for example, recombinant yeast assays and transactivation assays transfected 

with other relevant receptors, to develop the set of rules for other receptors. These assays 

are available for a variety of receptors including ER agonist/antagonist and AR 

agonist/antagonist activities. One possible structural motif of interest would be 

halogenated long chain molecules. Some panel members suggested that test compounds 

could be synthesized by combinatorial methods to aid the study of structural binding 

relationships with other steroid receptors. 

3. Prioritization for EDSP Tier 1 Screening 

OECD member countries have long recognized the potential of (Q)SAR for initial 

assessments for thousands of untested chemicals and to establish priorities for follow up 

actions. The OECD "Integrated Approaches to Testing and Assessment" framework has 

encouraged the use of existing knowledge including (Q)SAR to effectively assess and 

manage large chemical inventories (http://www.oecd.org/dataoecd/45/52/40705314.pdf). 

In its final report, the EDSTAC (USEPA, 1998) recommended a tiered approach for 

detecting chemicals with endocrine disrupting potential using a resource-efficient 

manner that is similar to OECD's Endocrine Disruptor Testing and Assessment 

Framework 

(http://www.oecd.org/document/58/0,3343,en_2649_34377_2348794_1_1_1_1,00.html). 

Like the OECD approach, the framework proposed by the EDSTAC includes use of 

(Q)SARs and high through put screening assays.

Based on the characteristics of the (Q)SAR-based expert system presented in the 

White Paper, please comment on the Agency’s view that the expert system could 

be employed to support "sorting and prioritizing" food use inert ingredients and 

antimicrobial pesticides for EDSP Tier 1 screening.

Panel Response

The Panel was in strong agreement that the expert system is likely to be an important 

tool to support "sorting and prioritizing" FI and AM pesticides for EDSP Tier 1 

screening by the ERα binding mechanism. They supported the intended use of the expert 

system as a prioritization tool given the likely paucity of data available for food use 

inerts and antimicrobial pesticides. Considerable and careful effort to cover the 

structural domains of these two regulatory inventories was invested in development of 

the expert system. The expert system would therefore constitute a critically important 

source of relevant information for prioritization of the two regulatory inventories of 

interest. 

The Panel recommended that the Agency continue with its efforts to collect additional

data to better understand the relationship of the predicted ERα binding at the cellular and 

tissue level, e.g., medium and high throughput assays and Tier 1 EDSP testing among 

species. Additionally, these data would contribute to defensibility of their use in the 

57
expert system. They also recommended that ER-binding of mixed phenols be further 

studied. 

They stressed that the ER binding mechanism is only part of the estrogenic mode of 

action, and the estrogenic mode of action is one component of the endocrine mode of 

action. Other aspects of steroid metabolism and action pathways could also be important 

for prioritization. As an illustration, Figure 1 highlights several key upstream receptors 

and P450 enzymes such as aromatase in the steroidogenic pathway. Furthermore, steroid 

hormone nuclear receptors are only part of the potential endocrine disruptor xenobiotic 

mechanistic pathway. In addition, there are non-steroidogenic receptors and non-receptor 

mediated pathways to consider as well (Figure 2). For now, the Panel agreed that 

prioritization is only within the context of relative ERα binding. The “positives” will 

help decide which chemicals are high priorities for EDSP Tier 1 screening in the next few 

years.

As one panel member explained, the current expert model orders chemicals into two bins 

defined by the limit of detection of the associated outcome of interest, ERα binding. 

Sorting to a certain extent implies ranking highest to lowest based on a scoring method. 

The “cut point” for declaring priorities versus non-priorities in such a system is arbitrary, 

in this case, the limit of detection of the specific binding assay. Hence, the Panel noted 

that there is no need to order priority of the chemicals that test positive, as these will all 

be a priority for testing, i.e., FI and AM pesticides that will be screened first under the 

EDSP and those that are below the limit of detection will all be considered a lower 

priority for testing ERα.

The Panel suggested that sorting and prioritization aspects of the expert system might be 

enhanced if each of the seven model rules were weighted separately. "Weighting,” in this 

context, means comparing the output of each of the seven model rules to other relevant 

information on hazard as to whether or not the compound in question may be an 

endocrine disruptor. This analysis would include all relevant information not captured by 

the proposed expert system, for example, there is often in vitro data from other assays, 

physical/chemical data, etc., that would inform in this context. 

The Panel commented that substructures might also be further classified into ICI 182780-

like, PFOS-like, fatty acid-like, etc., to be screened for antagonist properties to key 

binding sites not thus far included in the ERα binding sites A and B as well as in other 

related receptor (Q)SAR expert systems. Rules could be developed based on the space 

of these other receptor binding pockets. This would allow binning into more groups and 

get closer to a "highest to lowest" priority sorting system.

One panelist discussed whether the ERα binding assay might serve as a replacement or 

surrogate for the fish short-term reproductive assay and 21-day fish screening assay in the 

hypothalamic/pituitary/gonadal (HPG) activity Tier I screening assessment. The Panel 

referred to the conclusions made by the March 2008 SAP on this question (see meeting 

report; FIFRA SAP, 2008). The March 1998 SAP was very clear that the fish short-term 

reproductive assay was an "essential part of apical analysis" of HPG. Essentially this 

58
assay integrates across the multiple potential pathways that could produce individual and 

population effects. The August 2009 SAP affirmed this conclusion.

The Panel highlighted a couple of specific issues for “priority setting.”

1) What happens to the chemicals for which ERα binding is not possible?

In a priority setting context, the Panel indicated that there should be a clear 

understanding of whether compounds for which binding to the ER is not detected and 

are not considered for further testing or as a low priority for further testing. In the 

latter case, additional information might prompt reconsideration. As designed, the 

expert system separates chemicals into two bins defined by the limit of detection of 

the ER binding assay, i.e., 0.0001%. Sorting is not an issue; therefore, all positives 

are a priority and all negatives are less of a priority. Chemicals with 

RBA>0.00001%, i.e., “the positives”, would be assigned a higher priority for EDSP 

Tier 1 testing and chemicals whose RBA< 0.00001%, i.e., “the negatives”, would be 

assigned a lower priority for testing. The Panel indicated that the “negatives” should 

not be dismissed as those that do not bind to the ER and do not need further testing; 

rather, they should be characterized as chemicals in which prediction of ERα binding 

is not possible. The Panel recommended that the term “prediction of ER binding is 

not possible” be used in the expert system.

2) How will the information from the expert system be integrated with existing 

information, e.g., likely limited available data from toxicity testing, in a weight of 

evidence approach, to address traditional criteria such as consistency? 

The Panel indicated that the expert system used to predict estrogen receptor binding 

affinity for FI and AM pesticides is useful for application in a prioritization scheme 

for endocrine disruptor screening. However, they indicated that the expert system 

should be viewed in the context of the overall EDSP. That is, use a weight of 

evidence approach for integrating information coming from many possible sources,

including the results of endocrine testing, modeling such as the rule-based expert 

system for ER-binding, as well as other information relative to the reactivity of the 

chemical. This would then represent a true screening system, one that is designed to 

resemble a funnel, with a very wide mouth, rather than a narrow funnel screening,

e.g., ER-binding, which is only part of the screening program. Only if substances are 

negative in this comprehensive, integrated screen would they be declared negative for 

endocrine disruption and not considered further. Model components of such a metascreen approach need not be integrated into the model at the outset. The first areas 

screened might be ERα binding, androgen receptor binding, aromatase activation and 

thyroid receptor mediation. Other components could be added as they are built from

high throughput screening programs, e.g., Tox21. Other information would come 

from data produced by EPA, in the literature, or from OECD Test Guideline studies.

Panel members commented that the criteria by which information from various 

sources will be weighted constitutes a critical element of transparency in defensibility 

59
of use of the evolving expert system in a priority setting system. In programs 

mandated to address priority setting for large inventories, this has been a relatively 

critical component and requires consideration in the context of criteria such as 

consistency (see, for example, the principles for combining information and data for 

cancer/genotoxicity in documentation for health components of the Canadian 

Domestic Substances List (DSL) program): [http://www.hc-sc.gc.ca/ewhsemt/contaminants/existsub/categor/approach-approche-eng.php and http://www.hcsc.gc.ca/ewh-semt/contaminants/existsub/categor/publi-comment/index-eng.php]. 

4. Cross Species Applicability

As discussed in the White Paper, when comparable assay systems are used, e.g., 

comparing recombinant receptors to recombinant receptors; comparing cytosolic 

receptors to cytosolic receptors; comparing assays with similar total protein 

concentrations and thus chemical availability, there is general agreement in 

measurements of binding affinity across species (fish, rat, human). Thus, the type of 

assay used appears to explain differences more than species origin of the receptors. To 

evaluate the applicability of the current expert system based on a trout ER training set for 

predicting relative binding affinity to human ERα, binding assays using full-length 

recombinant human ERα and transactivation assays in human T47D cells are in progress 

with food use inert ingredients and antimicrobial active ingredients. Chemicals selected 

for human ER testing are based on predictions from the current expert system and 

designed to cover each chemical group and bracket the log Kow ranges within the group. 

To date, results show good species concordance with chemical groups that have members 

that bind to the trout ER also having members that bind to the human ER, although the 

trend is toward fewer members of a chemical group binding to the human ER than to the 

rainbow trout ER, e.g., a more restrictive log Kow range for binding within a chemical 

group for human ER. Therefore rainbow trout ER appears to bind more low affinity 

chemicals within a group but binds the same type of chemicals as does human ER. 

Given what is reported in the literature and similarities between human and 

rainbow trout ER binding affinity observed thus far in the research described in 

the White Paper, please comment on the extent to which use of an expert system 

based primarily on trout ER binding affinity data is a reasonable effects 

component for prioritizing food use inert ingredients and antimicrobial pesticides 

for EDSP Tier I screening. 

Panel Response

The Panel responded to this question in two different ways.

1) Some commented in a broad sense about the extrapolation of trout cytosolic ER 

binding data “across species” and were not confined to comparisons between the 

rainbow trout and mammalian ERs. These comments were meant to consider the 

ecological implications of screening tests in addition to the human health 

implications. 

60
2) Others discussed how the trout cytosolic ER assay may fit into prioritization 

schemes for endocrine disruption screening tests primarily from the viewpoint of 

human health. In this latter context, the Panel thought that trout cytosolic ER and 

human ER assays would likely yield comparable results. 

The following paragraphs provide further details of the Panel’s discussion. 

Receptor-mediated interference with the estrogen hormone system by xenobiotics can 

occur via several types or subtypes of ER in target tissues, including not only ERα, but 

also ERβ and unrelated estrogen receptors such as the membrane ER and G proteincoupled receptor 30 [GPR30] (Thomas et al., 2005; Revankar et al., 2005). The rainbow 

trout presents a particularly complex situation, in which at least two isoforms of ERα and 

two of ERβ are known to be expressed in the liver (Boyce-Derricott et al., 2009). EPA’s 

White Paper and associated presentations provided evidence suggesting that the trout 

cytosolic ER assay behaves similarly to the recombinant trout ERα assay, and that the 

latter behaves similarly to recombinant ERα assays across species. The Panel discussed 

results of recent unpublished studies by European investigators that also provide support 

to the notion that ERα subtypes have similar patterns of ligand affinities across species. 

Overall, the Panel concluded that currently available information suggests that the trout 

cytosolic ER assay can be used to develop reasonable predictions of the RBA of 

xenobiotics for ERα across species. However, what is unclear at the present time is 

whether the trout cytosolic ER assay can be used for estimating RBAs of xenobiotics 

across all ER types and species. 

The results of a few available studies suggest that the RBA of ligands, including natural 

ligands, can vary between the different subtypes of nuclear ERs across species. Whereas 

ERα and ERβ both appear to bind E2 with similar affinity in mammals (e.g., Kuiper et 

al., 1997), this is not the case for fish. For example, binding affinity of E2 for ERβ

relative to ERα among teleost fishes ranges from similar-or-slightly higher in Atlantic 

croaker (Hawkins and Thomas, 2004), to greater-than-6-fold higher in gilthead sea 

bream (Passos et al. 2009), and to almost 20-fold higher in channel catfish (Xia et al.,

2000; Gale et al., 2004). Conversely, the RBAs of a handful of high and low affinity 

xenoestrogens tended to be higher for ERα than for ERβ, in channel catfish; and in the 

case of 4-nonylphenol, its RBA for ERα was almost 100-fold higher than for ERβ (Gale 

et al., 2004). These observations suggest that the RBAs of natural estrogens and 

xenobiotics for the different subtypes of nuclear ERs (ERα and ERβ) vary widely across 

species. Most of the RBAs are defined for soluble estrogen receptors which are found in 

the soluble cellular extracts. Binding to membrane receptors may be missed by these 

assays, but they function in vivo and would also be captured in the transactivation assay 

using liver slices.

Therefore, models used to predict the RBA of xenobiotics for ERβ based on data 

obtained with trout cytosolic ER or ERα assays may yield unreliable results for the 

purpose of cross-species comparisons. They also noted that membrane and nuclear ERs 

may have different binding affinities. The Panel suggested that additional research 

would be needed to understand the extent of taxonomic similarities or differences in the 

RBAs of xenobiotics for nuclear ERs, particularly ERβ, and for membrane ER. 

61
They specifically discussed whether results from the trout cytosolic ER binding assay 

should or could be used to prioritize food use inerts or antimicrobial pesticides for EDSP 

Tier 1 screening in the context of mammalian ER. The Panel thought that if a given 

chemical yielded a positive result in the trout cytosolic ER binding assay then this 

chemical would also be likely to yield a positive result in mammalian systems because 

the trout cytosolic ER assay seems to be less specific than the human ERα assay. 

However, in extrapolating a negative result from the trout cytosolic ER assay to human 

systems, there would be some level of uncertainty because some differences in 

performance have been reported between cytosolic and recombinant ER assays (EPA’s 

White Paper, page 38; and associated presentations). Differences include the fact that 

the rainbow trout cytosolic ER is less sensitive than the rainbow trout recombinant ERαligand binding domain (LBD) assay (Matthews et al., 2000; 2002). The Panel 

recommended that the Agency pursue studies to calibrate the cytosolic and recombinant 

ER assays, including β isoforms, to better define the degree of uncertainty. 

Panel members were generally in agreement that cross-species extrapolation, e.g., from 

fish to mammals, can be done as there is good concordance in RBA of cytosolic ERα

among species. The Agency is examining the endocrine disrupting potential using a 

resource-efficient manner that is similar to OECD’s Endocrine Disruptor Testing and 

Assessment Framework. The OECD level 1 testing uses existing data or (Q)SAR 

systems. Similarly, EPA is considering use of a (Q)SAR expert system as a mechanism 

to assist in the prioritization of FI and AM pesticides for EDSP Tier 1 screening. They

also considered the fact that other endocrine-disruptor screening tests being pursued by 

the Agency and the OECD include both mammalian and fish systems. They concluded

that the trout cytosolic ER assay was adequate for the purposes of using the expert 

system in prioritization of FI and AM pesticides for EDSP Tier 1 testing. 

The Panel also discussed issues concerned with what subtypes of ERs were present in the 

trout livers and seasonal influences on their tissue expression. They suggested that the 

Agency pursue some simple assays using trout liver cytosol to better define the ER 

composition of the preparations and assist in the interpretation of the binding data. For 

example, one assay would be conducted to determine the relative amounts of ERα and 

ERβ mRNAs as a measure of their relative expression at the mRNA level. Another 

assay would be conducted to measure the RBAs of chemicals compared to each other

with trout recombinant ERα. However, despite gaps in knowledge and the caveats 

discussed during the meeting, the Panel concluded that a positive result from the trout 

cytosolic ER assay would be strong reason to prioritize chemicals (i.e., FI and AM 

pesticides) for further EDSP Tier 1 testing.

62
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SELECTED ABBREVIATIONS

AhR: Aryl hydrocarbon Receptor

AIM: Analog Identification Method 

AR: Androgen Receptor

ARNT: AhR Nuclear Translocator

CAR: Constitutive Androstane Receptor

CYP: Cytochrome P450

DDT: Dichlorodiphenyltrichloroethane

DNA: Deoxyribonucleic acid

E2: 17β-estradiol

EA: Endocrine Activity

ED: Endocrine Disruptor

ER: Estrogen Receptor

FXR: Farnesoid X Receptor

GPR30: G Protein-coupled Receptor 30

GR: Glucocorticoid Receptor

HPG: Hypothalamic/pituitary/gonadal 

HTP: High Throughput

HVC: High Volume Chemical

H: Hydrogen

Kow: Octanol-water partition coefficient

Log P: Log Partition Coefficient, same as Kow when the two phases are octanol and water

LXR: Liver X Receptor

OECD: Organisation for Economic Cooperation and Development

P450: Cytochrome P450

PBPK: Physiologically-based Pharmacokinetic

PFOA: Perfluorooctanoic acid

PFOS: Perfluorooctane sulfonate 

PGP: P-glycoprotein

PPAR: Peroxisome Proliferator Activated Receptors

PR: Progesterone Receptor

PXR: Pregnane X Receptor (also termed SXR: Steroid and Xenobiotic Receptor)

QMRF: (Q)SAR Model Report Format 

(Q)SAR: Quantitative Structure-Activity Relationship

ROS: Reactive Oxygen Species

RXR: Retinoid X Receptor

SMARTS: Smiles Aritary Target Specification

SMILES: Simplified Molecular Input Line Entry Specification

SULT: Sulphotransferase 

TR: Thyroid Receptor

UGT: UDP-dependent glucuronosyl transferase

VDR: Vitamin D Receptor

Vtg: Vitellogenin

68
Figure 1. The key known receptors and P450’s involved in the steroidogenic pathway (Jacobs,

2004) [see p. 68 for a list of Abbreviations]

69
Figure 2. An overview of xenobiotics as modulators of nuclear receptor function (Jacobs, 2004)

[see p. 68 for a list of Abbreviations]

70