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

7/27/2009

U.S. Environmental Protection Agency

Office of Pesticide Programs

Washington DC 20460

Mid-Continent Ecology Division

National Health and Environmental Effects Laboratory

Office of Research and Development

Duluth MN 55804

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PREFACE

The Office of Pesticide Programs has a number of ongoing activities to advance 

computational tools 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 (QSARs) in early hazard 

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

of key test data. The Food Quality Protection Act of 1996 requires EPA to screen all pesticidal 

chemicals, which includes active ingredients and other (pesticidally “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. 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 Federal Insecticide Fungicide and Rodenticide Act Scientific Advisory Panel

(FIFRA SAP) review will focus on a (Q)SAR approach that was developed for pesticide inert 

ingredients and antimicrobial pesticides to help prioritize candidate chemicals for Tier 1 

screening under the Endocrine Disruptor Screening Program. This (Q)SAR rule-based expert 

system for predicting estrogen receptor (ER) binding affinity was developed by the Office of 

Research and Development in collaboration with the Office of Pesticide Programs. 

As part of the Organization for Economic Cooperation and Development (OECD) 

activities to increase the regulatory acceptance of (Q)SAR methods, this (Q)SAR model 

conforms to an “OECD Principles for the Validation, for Regulatory Purposes, of (Q)SAR 

Models” (see http://www.oecd.org/dataoecd/33/37/37849783.pdf) (OECD, 2004a) and was the 

subject of the OECD Expert Consultation to Evaluate an Estrogen Receptor Binding Affinity 
ii

Model for Hazard Identification (OECD, 2009a). The Agency has considered and incorporated 

the OECD comments into the current white paper for the FIFRA SAP review. The comments and 

recommendations agreed to at the OECD Expert Consultation were: 

a. While not all the data [was] reported there is a strong experimental basis for the system 

with even more experimental evidence for the pathway to be presented in the near future.

b. Grouping of chemicals using the proposed expert system looks promising and provides 

the most transparent mechanistic explanation system to date.

c. As an expert system it can be expanded as described in the consultation document for 

broader regulatory domains.

d. The tool was developed to be consistent with Level 1 of the OECD Endocrine 

Disruption Testing and Assessment Conceptual Framework.

e. The tool is useful to build chemical categories.

f. ER-binding will not automatically translate to reproductive toxicity. Therefore, there 

would be greater risk assessment relevance if the system was assessed for its ability to 

use ER-binding affinity predictions to predict effects at higher levels of biological 

organization. 

g. The expert system on ER-binding affinity should be automated.

h. It is recommended that the tool should be implemented in the OECD (Q)SAR 

Application Toolbox. See 

(http://www.oecd.org/document/54/0,3343,en_2649_34379_42923638_1_1_1_1,00.html)

i. Further discussions are necessary on how to implement such an expert system in the 

Toolbox.
iii

Once the Agency receives and considers the FIFRA SAP comments and 

recommendations, it intends to make this model publicly available through the OECD (Q)SAR 

Application Toolbox, an international means of making (Q)SAR technology readily accessible 

and transparent for use by risk assessors, as well as through other means.
iv

DISCLAIMER

None of the discussions surrounding any individual chemical substance in this paper, 

EPA presentations at the SAP meeting, or the meeting summary are intended to support any 

conclusions other than to aid in the development of potential priority-setting methods. None of 

the discussions, conclusions or examples presented should be taken as a conclusion regarding the 

potential risks of the substance or that the Agency has or will make a determination that any use 

of the substance necessarily will pose a significant risk. Further, the research results described 

are not designed to provide a scientific basis, by themselves, to allow EPA to conclude that these 

substances are "endocrine disruptors." Measured or predicted ER binding affinity data for 

substances mentioned in this paper may prove to be useful in developing future prioritization 

scheme(s) for EDSP Tier 1 screening. 

This document has been reviewed in accordance with U.S. Environmental Protection 

Agency policy. Mention of trade names or commercial products do not constitute endorsement or 

recommendation of use. 
v

TABLE OF CONTENTS

 PAGE

CHAPTER I. PRIORITIZING CHEMICALS FOR SCREENING AND ASSESSMENT: AN APPLICATION 

FOR THE ENDOCRINE DISRUPTOR SCREENING PROGRAM...................................................1

Background................................................................................................................1

Prioritizing Testing and Assessment of Pesticide Chemicals....................................3

Prioritizing Chemicals for Endocrine Disruptor Screening.......................................6

Evolution of the Expert System ..............................................................................11

CHAPTER II. MECHANISTIC BASIS OF THE EXPERT SYSTEM TO PREDICT RELATIVE 

ESTROGEN RECEPTOR (ER) BINDING AFFINITY ...............................................................12

Overview of the Approach to Build an Effects-Based Expert System....................12

ER Binding Affinity: An Indicator of Potential Reproductive Effects....................14

The ER Binding Domain .........................................................................................16

The Regulatory Chemical Domain ..........................................................................17

Characterization of Inert and Antimicrobial Inventories............................18

The Chemical Inventory Domains of Interest and Existing Information.....18

The Receptor Binding Assay Domain .....................................................................19

CHAPTER III. REGULATORY INVENTORIES OF INTEREST AND EXPERT SYSTEM MODELING

DOMAIN ...............................................................................................................................21

Overlaying Model Domains and Regulatory Inventories........................................21

Expanding the ER Binding Affinity Knowledge Base ...........................................23

A Rule-Based Expert System to Predict ER Binding Affinity ................................30

CHAPTER IV. ACCESSIBILITY OF THE EXPERT SYSTEM ...................................................36

Expert Systems for ER Binding Affinity.................................................................36

Ongoing Research: Human ER Binding Affinity and Gene Activation..................37

CHAPTER V. CONCLUSIONS ................................................................................................39
vi

References...........................................................................................................................43

Figures & Tables................................................................................................................48
1

Chapter I. Prioritizing Chemicals for Screening and Assessment: an Application for the 

Endocrine Disruptor Screening Program

This chapter briefly reviews national and international efforts to formulate approaches for 

prioritizing large chemical inventories for toxicity screening, testing, and risk assessment. The 

development and application of structure activity relationships (SARs) and quantitative structure 

activity relationships (QSARs) to prioritize inert ingredients used in pesticide formulations that 

require a tolerance or tolerance exemption under the Federal Food, Drug, and Cosmetic Act 

(FFDCA) (referred to as “food use inerts” or “FI”) and active ingredients contained in 

antimicrobial pesticides (referred to as “antimicrobial pesticides” or “AM”) for screening in US

EPA’s Endocrine Disruptor Screening Program (EDSP) is then introduced. 

Background

Hazard assessment of chemicals has historically relied on empirical in vivo data from 

specialized test guidelines that are designed to identify adverse outcomes in different species. 

The pace of conducting hazard assessments is generally limited by the availability of test data for 

many environmental chemicals. There are more new chemicals being produced each year than 

can practically be assessed annually for hazards through the development and review of new in 

vivo test data which is both a time and resource intensive process. This trend is likely to continue 

unless a more strategic approach is adopted to set priorities for testing and assessment so the 

chemicals with greatest potential for impacts in ecosystems and human populations are evaluated 

in a timely manner. Even for the endpoints covered in the Screening Information Data Sets 

(SIDS) established by the Organization of Economic Cooperation and Development (OECD),

which is summary information for commonly used hazard endpoints (www.chem.unep.ch/ 
2

irptc/sids/oecdsids/sidspub.html), there are reasons to lessen the dependence on in vivo testing all 

chemicals for all endpoints. These reasons include the length of time to undertake the studies and 

generate the data, the large numbers of animals sacrificed in the testing, as well as the high cost 

of toxicity testing and review of the resultant data. These concerns are “amplified” given that 

when considering large chemical inventories and the breadth of SIDS endpoints only a small 

percentage of compounds are likely to be identified to pose a significant hazard in any given test 

guideline. Consequently, a strategic approach is needed to set priorities for more focused in vivo

testing. A number of recent reports reflect the growing recognition that more efficient testing 

strategies and prioritization approaches are needed in both ecological and human health risk 

assessments (Bradbury et al., 2004; NRC, 2007; OECD, 2008). 

Broadly, there are two primary, and complementary, approaches to setting priorities for 

initial hazard identification of untested chemicals. One approach is to develop faster high 

throughput in vitro screens. Enormous efforts are underway to develop and apply these methods

(e.g., http://www.epa.gov/ncct/toxcast). The other, complementary, approach is to develop 

SARs and QSARs to estimate relevant endpoint values to form prioritization schemes. The 

OECD member countries have long recognized the potential of (Q)SAR to establish appropriate 

priorities for the conduct of initial hazard assessments for thousands of untested chemicals. The 

OECD countries, with substantial US participation, have developed principles to guide the 

validation of (Q)SAR models for regulatory purposes (OECD, 2004a) and have compiled case 

studies to illustrate how member countries are using, or anticipate using, (Q)SAR methods 

within national regulatory contexts. 
3

Prioritizing Testing and Assessment of Pesticide Chemicals

US EPA’s Office of Pesticide Programs (OPP) is committed to protecting public health 

and the environment through application of the latest scientific tools to increase the reliability 

and effectiveness in assessing and managing potential pesticide risks (will be available at 

http://www.epa.gov/pesticides/science/index.htm). OPP’s critical path focuses on fully utilizing 

an integrated approach to testing and assessment. This vision is consistent with the Toxicology 

Testing in the 21st Century report published by the National Research Council (NRC, 2007). The 

future goal is to move toward a hypothesis-based paradigm where in vivo testing is targeted to 

the most likely hazards of concern. This progressive, tiered-testing approach is envisioned to 

start with hazard-based hypotheses about the plausible toxicological potential of a pesticide or 

group of pesticide chemicals. Existing exposure and toxicity information would then be 

combined with computer modeling and diagnostic in vitro assays to target toxicity testing to the 

specific data needed for human health and ecological risk assessments. These advances will be 

incorporated within a risk assessment framework of problem formulation, hazard, dose response, 

exposure assessment, and risk characterization to support pesticide registration decisions. This 

understanding will allow the program to advance the risk assessment approach from one reliant 

on extensive in vivo animal testing to one that is based on a chemical’s mode of action (i.e., 

understanding how chemicals perturb normal biological function) and a better understanding of 

real world exposures to pesticides that is more directly relevant to humans and wildlife. 

Achieving these objectives will require close collaboration with the scientific community, 

international organizations, and government partners to build the foundation for understanding 

chemically-induced toxicity pathways. 
4

An initial focus of the Program’s efforts to develop and apply an integrated approach to 

testing and assessment is to better determine and prioritize what toxicity data are needed to 

inform risk assessments for chemicals that do not have extensive in vivo toxicity information 

(e.g., inert ingredients and certain antimicrobial pesticides, as well as metabolites and degradates 

of pesticide active ingredients). The transition to this approach includes maximizing use of 

existing data from similar compounds, hazard and exposure modeling, and in vitro data to 

prioritize specific animal toxicity testing that is needed to assess and subsequently manage risks.

The Program is including use of integrated approaches to testing and assessment to prioritize 

information needs either across specific chemical inventories, or endpoints for a given chemical 

by combining different types of existing information on a similar chemical or group of 

structurally similar compounds, using predictive computer modeling (e.g., SARs/QSARs) and in 

vitro and high throughput screening assays. 

The USEPA Office of Pollution Prevention and Toxics (OPPT) has long used 

SAR/QSAR to identify and prioritize information needs and estimate potential hazards of 

industrial chemicals. USEPA’s Pesticide Program, in collaboration with OPPT, has applied 

these methods in situations where there is limited data. In the assessment of inert ingredients, for 

example, SARs have been used in identifying structurally-similar compounds to form categories 

of chemicals. This use of SAR allows for the testing of selected members of a category with the 

results then being applied to the other members of the group. Additionally SAR principles are 

used to identify chemical “analogs” (chemicals that are closely related to the chemical of 

interest) for compounds where there is limited data on the chemical of interest but for which 

more robust data are available on the analog chemical.
5

The applications of SARs/QSARs are being advanced in both ecological and human 

health risk assessments of pesticide inert ingredients. In the area of ecological risk assessment, 

QSAR programs such as ASTER (USEPA, 2009a) and ECOSAR (USEPA, 2009b) are utilized 

to estimate toxicity to fish, invertebrates, and algae. In the area of human health risk assessment, 

SAR estimations are used as part of an overall weight of the evidence approach to better 

characterize potential risk concerns for pesticide metabolites and contribute to decisions 

regarding additional data requirements. For example, SAR models for prediction of 

carcinogenicity, such as Oncologic (USEPA, 2005a), along with toxicity data from studies of 

similar chemicals can help to reduce uncertainties regarding potential carcinogenicity of a 

chemical for which there are no actual cancer studies. Additionally, analog data can facilitate the 

identification of potential toxicological endpoints of concern and thereby target testing needs or 

follow-up actions.

Antimicrobial active pesticide ingredients generally have less data available for risk 

assessment than conventional pesticides. Consistent with the advancement of SAR/QSAR 

approaches for industrial chemicals and pesticide inert ingredients, the Pesticide Program

recently released a White Paper on “Use of Structure-Activity Relationship (SAR) Information 

and Quantitative SAR (QSAR) Modeling for Fulfilling Data Requirements for Antimicrobial 

Pesticide Chemicals and Informing EPA’s Risk Management Process” in support of a proposed 

rule on data requirements for antimicrobial pesticides. (USEPA, 2008a, Docket: 2008-0110; 

USEPA, 2008b, Document 0045; 

http://www.regulations.gov/fdmspublic/ContentViewer?objectId=0900006480665444&dispositi

on=attachment&contentType=pdf)
6

The current white paper describes a (Q)SAR-based expert system to support 

determinations of the order in which chemicals will be subject to Endocrine Disruptor Tier 1

screening. In other words, for prioritization of food use inert ingredients and antimicrobial 

pesticides for screening of endocrine disruptor potential based on their potential to bind to the

estrogen receptor (ER). This expert system provides a new tool for the Pesticide Program to 

advance its integrated approach to testing and assessment.

Prioritizing Chemicals for Endocrine Disruptor Screening

The international community, through the OECD Endocrine Disruption Testing and 

Assessment Advisory Group (EDTA), identified the great importance of and urgent need for 

relatively cheap and quick screens, and tests not requiring animals, to prioritize chemical 

evaluation for potential endocrine disruption. The EDTA subsequently created the Validation 

Management Group-Non-Animal (VMG-NA), whose main objective is to identify or propose 

validated or promising non-animal assays for endocrine testing, and in particular to develop and 

validate tools necessary for Level 1 (sorting and prioritization with existing data and/or (Q)SAR 

systems) and Level 2 (in vitro assays providing mechanistic data) within the EDTA Conceptual 

Framework for the Testing and Assessment of Endocrine Disrupting Chemicals. Levels 3, 4, and

5 of the OECD Framework include in vivo testing and assessment for single endocrine 

mechanisms and effect, multiple endocrine mechanisms and effects, and endocrine plus other 

mechanisms, respectively (OECD, 2004b). 

In the US, the Food Quality Protection Act (FQPA) mandates the USEPA to determine 

whether pesticide chemicals (active and inert ingredients) may have an effect similar to an effect 

produced by naturally occurring estrogen or other endocrine effects. In developing the Endocrine 
7

Disruptor Screening Program (EDSP) the Agency received expert advice from Federal Advisory 

Committees, including the Endocrine Disrupter Screening and Testing Advisory Committee 

(EDSTAC) and USEPA’s Science Advisory Board (SAB) and the FIFRA Scientific Advisory 

Panel (SAP). The Agency, consistent with EDSTAC, SAB and SAP input has been developing 

the EDSP program that initially includes estrogen, androgen and thyroid hormone systems and 

includes an evaluation of potential effects in humans and wildlife (i.e., fish, birds and 

amphibians) (http://www.epa.gov/scipoly/oscpendo/index.htm). Also consistent with 

recommendations from the advisory panels, the EDSP encompasses non-pesticide chemicals, in 

addition to active and inert ingredients in pesticide formulations.

 In its final report, the EDSTAC (http://www.epa.gov/scipoly/oscpendo/pubs/

edspoverview/finalrpt.htm) (USEPA, 1998a) recommended a tiered approach for detecting 

chemicals with endocrine disrupting potential using a resource-efficient manner that is similar to 

that established by the OECD. The framework proposed by the EDSTAC involved: 1) sorting 

and prioritizing chemicals; 2) Tier 1 screening; and 3) Tier 2 testing. Tier 1 screening data were

envisioned to provide information to determine if a chemical had the potential to interact with 

the estrogen, androgen and/or thyroid systems, while Tier 2 test data would be used to support 

hazard and risk assessments. This framework was subsequently adopted in large part by the 

Agency (USEPA, 1998b) and generally supported by a joint committee of the SAB/SAP 

(USEPA, 1999). 

With regard to sorting and prioritization, the EDSTAC (USEPA, 1998a) recommended 

that chemical inventories be sorted into 4 categories: 1) chemicals considered unlikely to interact 

with hormone systems, such as certain polymers and strong mineral acids and bases; 2) 

chemicals without sufficient data to make a determination whether or not to proceed to Tier 2 
8

testing or hazard/risk assessment; 3) chemicals with sufficient existing data to make a 

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

with the Tier 1 screening assays); and 4) chemicals with sufficient existing information to 

support a hazard assessment. Given that the vast majority of chemicals in commerce would fall 

into category 2, the EDSTAC provided detailed recommendations for prioritizing chemicals for 

Tier 1 screening. The priority-setting framework included the use of existing, or predicted, 

exposure and effects information. To the extent possible, prioritization was recommended to 

involve a scheme that combined exposure and effects information. With regard to effects 

information, the EDSTAC encouraged the Agency to evaluate the use of high-throughput 

screening (HTPS) assays for receptor binding and transcriptional activation and (Q)SARs for 

these endpoints to obtain empirical or predicted information on chemicals for which no data were

available to support prioritization.

The Agency subsequently formulated a framework generally consistent with the 

EDSTAC’s recommendations (USEPA, 1998a), which was presented to the SAB/SAP 

(http://yosemite.epa.gov/sab/sabproduct.nsf/C8ABD410E357DBCF85257193004C42C4/$File/e

c13.pdf) (USEPA, 1999). The SAB/SAP supported sorting chemicals into the 4 categories and 

stressed the importance of having exposure and effects information to prioritize chemicals in 

category 2. The SAB/SAP, however, concluded at that point in time HTPS and (Q)SARs were 

not sufficiently developed to be used in priority setting. Consistent with the EDSTAC’s 

conclusions (USEPA, 1998a), the SAB/SAP endorsed continued research to advance HTPS and 

bench-level in vitro assay systems as well as (Q)SARs to provide measured or predicted data 

concerning receptor binding and/or transcriptional activation that could be used in future 

prioritization efforts. The SAB/SAP also recommended that, given the ambitious scope of the 
9

universe of chemicals the EPA envisioned as potentially being included in the EDSP, the Agency 

initiate Tier 1 screening with 50 to 100 chemicals. 

Following review of public comment on an initial prioritization proposal (USEPA,

2002), the Agency decided to select the first 50 to 100 chemicals based on exposure potential 

only (USEPA, 2005b). The Agency further determined that the first 50 to 100 chemicals 

selected for Tier 1 screening would be drawn from pesticide active ingredients as well as

pesticide inert ingredients with relatively large overall production volumes when considering 

both pesticide and non-pesticide uses. In 2007, the Agency published a final exposure-based 

prioritization scheme and a proposed list of 73 pesticide chemicals to be subject to Tier 1 

screening (USEPA, 2007a,b). In 2009, the Agency published the final list of 67 pesticide 

chemicals (58 active ingredients and 9 inert ingredients) that will be subject to Tier 1 screening 

(USEPA, 2009c). 

Consistent with EDSTAC and SAB/SAP recommendations the Agency continued to 

develop in vitro (HTPS and bench-level platforms) and (Q)SAR methods to support future 

prioritization efforts for chemicals in category 2 that contain little or no in vitro or in vivo effects 

data. The Agency’s has on-going efforts to develop and evaluate Medium- and HighThroughput assay methods (e.g., biochemical assays of protein function, cell-based 

transcriptional reporter assays, multi-cell interaction assays, transcriptomics on primary cell 

cultures, and developmental assays in zebrafish embryos). EPA will apply these methods to over

300 food-use active ingredients, including ~50 of the 58 active ingredients in the initial group of 

67 pesticide chemicals that will be subject to Tier 1 screening. Several HTPS assays directly 

relate to estrogen, androgen, thyroid and aromatase activity; results for the pesticide active
10

ingredients will be available at [http://www.epa.gov/comptox/toxcast]. Future HTPS efforts will 

include high production volume industrial chemicals and pesticide inert ingredients. 

 To advance prioritization techniques that include an effects component, to complement 

an exposure-based prioritization method, the Pesticide Program and EPA’s Office of Research 

and Development are also developing (Q)SAR-based approaches to prioritize chemicals with an 

initial emphasis on antimicrobial pesticides and inert ingredients used in food-use pesticide 

formulations. The potential of these chemicals to bind to the ER has been the initial focus of this 

effort. This paper outlines the development of a (Q)SAR-based expert system and how it can be 

used to prioritize food-use pesticide inert ingredients and antimicrobial pesticides for potential 

estrogenicity through gaining an understanding of a chemical’s potential to bind the ER.

The appropriateness of this expert system for prioritizing Tier 1 screening was evaluated 

using the OECD Principles for the Validation of (Q)SARs (OECD, 2004a). These principles are 

intended to guide the (Q)SAR validation process undertaken within member countries and to 

facilitate their regulatory 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 EDSTAC (USEPA, 1998a). The five 

OECD validation principles include: a defined endpoint; a mechanistic interpretation; a defined 

domain of the (Q)SAR model applicability; appropriate measures of goodness of fit, robustness, 

ability to predict; and an unambiguous algorithm. Beyond the importance of validating model 

equations and algorithms for national regulatory needs, two important characteristics of (Q)SAR 

validations are addressed by OECD to enhance regulatory acceptance for using estimated values 

for priority-setting. 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 
11

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. Since the OECD principles of validation for (Q)SAR seek to describe the domain of 

the (Q)SAR model in terms of the chemical structures used to create the model, usefulness can 

be evaluated by comparing the domain of the (Q)SAR methods and the domain of the regulatory 

inventory assessed in a specific regulatory context. Unfortunately, most developers of (Q)SAR 

models do not use a specific regulatory inventory as a target for the model domain. This 

document will discuss a (Q)SAR-based approach for estrogenic activity that maintains 

mechanistic transparency and allows the domain of the knowledge base to be aligned with the 

domains of specific regulatory inventories. 

Evolution of the Expert System

The expert system reflects the culmination of research over the past several years and 

input from scientists and regulators familiar with the OECD (Q)SAR validation principles 

through two OECD forums. As part of a Workshop on Structural Alerts for the OECD (Q)SAR 

Application Toolbox (held on May15-16, 2008 in Utrecht, The Netherlands) an earlier iteration 

of this work, a chemical sub-class approach to form a decision support system for ER binding, 

was presented (OECD, 2009b) [see http://www.olis.oecd.org/olis/2009doc.nsf/linkto/env-jmmono(2009)4]. The findings from this workshop indicated that the prototype system could be 

useful for priority setting and appropriate for inclusion into the OECD (Q)SAR Application 

Toolbox. The “Toolbox” is a stand-alone software system developed by OECD for use by risk 

assessors around the globe to facilitate the use of (Q)SAR systems documented in accordance 
12

with OECD (Q)SAR validation principles (OECD, 2004a). Subsequent to this workshop, an 

OECD Expert Consultation to Evaluate an Estrogen Receptor Binding Affinity Model for Hazard 

Identification (OECD, 2009a) was held February 16, 2009 in Paris, France. This was the first in a 

series of OECD consultations to review expert systems and to expand the number of 

toxicologically-based categories for use by risk assessors. The expert system presented here 

reflects the peer input from the 2009 OECD consultation. 

Chapter II. Mechanistic Basis of the Expert System to Predict Relative Estrogen Receptor 

(ER) Binding Affinity

Overview of the Approach to Build an Effects-Based Expert System 

The conceptual approach providing the foundation for the expert system is the description 

of the chemically-initiated perturbation of the ER system in the form of an adverse outcome 

pathway. The ER-mediated reproductive impairment adverse outcome pathway (Schmieder et 

al., 2004) describes the linkage between the event that initiates the pathway (e.g., a chemical 

binding the ER) and measures made at successively higher and more complex levels of 

biological organization (Figure 1). The pathway progresses from the molecular initiating event, 

through cell and tissue level gene transcription and translation, continuing through organ effects 

to an adverse outcome observed in the individual or a population. With a plausible pathway to an 

adverse outcome described, a rationale is provided for using 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. The specific in vitro assays used to develop 

the expert system are: i) measured chemical binding to the rainbow trout ER to detect the 
13

potential for a chemical to initiate the ER-mediated pathway; and ii) ER-mediated vitellogenin 

induction in rainbow trout liver slices to confirm that ER binding translates to an effect at a point 

further along the ER-mediated adverse outcome pathway (Schmieder et al., 2004). 

The four sections of Chapter II present the key components of the approach to developing 

the expert system. The first section presents ER binding affinity as an indicator of potential 

reproductive effects. The next section reviews what was known of chemical interaction in the ER 

binding domain. The third section described the regulatory chemical domain of interest in this 

study and how the structures in the desired inventories (i.e., food use pesticide inert ingredients 

and antimicrobial active ingredients) were characterized. This section also discusses the chemical 

domain that the expert system was developed to cover and the extent to which existing 

information could be used in developing the system. Finally, how the assays used to develop the 

training set are optimized for measuring chemical-ER binding for compounds in the chemical 

domain to ensure the collection of accurate data is presented. 

Chapter III describes how understanding of the ER binding domain, chemical domain, 

and assay domain in Chapter II is applied to establish an ER binding training set representative 

of the inert and antimicrobial chemical domains of interest. The chemicals in the training set 

make up the expert system’s model domain, and thus they must adequately represent the 

structures in the regulatory inventories. Chapter III also describes how the knowledge gained

from the training set can be “coded” into systematic logic rules that establish a rule-based expert 

system. 

Chapter IV of the report describes how the expert rules are implemented in a decision 

tree to support prioritization of chemicals in the two inventories for Tier 1 screening. Chapter IV 
14

also summarizes on-going research on human ER binding of a strategically chosen subset of food 

use inert ingredients and antimicrobial pesticides. 

ER Binding Affinity: An Indicator of Potential Reproductive Effects

There are multiple protein targets and pathways through which chemicals may interfere 

with normal processes resulting in impaired reproduction. Predicting which chemicals may be 

capable of interfering with any given pathway remains a challenge and many of the research 

efforts over the past two decades in the area of endocrine disruption have focused on increasing 

understanding of how chemicals perturb these endogenous hormone systems. Despite the 

complexity of events that can lead to reproductive impairment, it has long been appreciated that 

chemical binding to the ER is one important conserved mechanism of interfering with processes 

involved in reproduction. The ER-mediated adverse outcome pathway (Figure 1) is a conceptual 

model that is useful to illustrate how ER binding can be linked through a series of measurable 

events to adverse outcomes of regulatory concern. As chemicals likely to initiate or block the 

ER-mediated pathway are identified, the conceptual model is useful for generating testable 

hypotheses at various levels of biological organization along the pathway and provides risk 

assessors and managers a readily articulated and testable rationale for determining the order in 

which chemicals will undergo required screening (i.e., prioritizing).

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. The potency of a chemical for producing a specific adverse outcome 

in vivo cannot be assumed to be the same as that for chemical-ER binding in a cell-free assay due 

to many considerations including chemical kinetics as well as interactions and feedback between 
15

cell and organ systems. While a broad generalization of the quantitative relationship between 

ER binding affinity and in vivo response cannot be drawn at this time, it is important to note that 

there is evidence that chemicals with relatively low ER binding affinity can elicit significant in 

vivo effects in fish, including reduced testicular growth, reduced fecundity, changes in secondary 

sex character and behavior, testis-ova, as well as complete phenotypic sex reversal (e.g., see

Jobling et al., 1996; Gray et al., 1999; Seki et al., 2003a,b; Yokota et al., 2005). However, 

predicting in vivo potency of a chemical that can bind to the ER is a more complicated and 

different problem than predicting relative ER binding affinity to support an effects-based 

prioritization scheme designed to focus in vivo testing that can determine whether or not 

reproductive effects can occur, and if they do occur, at what dose.

While the expert system reported here is designed to predict rainbow trout ER binding 

affinity, based on a training set of binding data, an in vitro rainbow trout tissue slice assay was 

employed to confirm ER-mediated gene activation within a metabolically-competent tissue

(Gilroy et al., 1996). Rainbow trout liver slices have been shown to be ER responsive to parent 

chemicals and their metabolites (Shilling and Williams, 2000; Schmieder et al., 2000; Schmieder 

et al., 2004). The liver slice assay provides confirmation that the ER binding translates to gene 

activation or antagonism at the next, higher level of biological organization along the ERmediated adverse outcome pathway (e.g., tissue/organ level; Figure 1) and increases confidence 

in the linkage between low affinity ER binding (see final section of Chapter II),and gene 

activation. 
16

The ER Binding Domain

The energetic and steric constraints dictated by the ER itself, shapes the domain of the 

chemical structures that can bind to the receptor. Although only a small percentage of pesticide 

and industrial chemical structures may be capable of binding, the ER is sufficiently promiscuous 

to permit binding with a diverse array of chemical structures. An understanding of the energetic 

and steric characteristics of the ER binding domain, therefore, provides the means to establish a 

mechanistic basis for defining a chemical structure space associated with ER ligands.

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

binding domain have been presented by numerous investigators. Theories for chemical 

interaction with the ER were typically based on information gained from steroidal structures that 

interact at two points within the ER i.e., two hydrogen-bonding groups of a chemical within a 

specified distance interacting with receptor protein in different ER sub-pockets. However, it is 

also known that chemicals that contain only one hydrogen-bonding group, such as nonylphenols, 

bind ER and cause subsequent gene activation and in vivo effects (Jobling et al., 1996; Seki et 

al., 2003b). Katzenellenbogen et al. (2003) and references there-in describe the “dynamic and 

plastic character of ER” in their description of ER binding sub-pockets. Through their larger 

body of work they have identified three primary ER binding sub-pockets (referred to further in 

this paper as sites A, B, and C; Figure 2) each with different requirements for hydrogen bonding. 

This information proves useful for formulating and testing hypotheses of how chemicals with 

only one potential hydrogen-bonding group may interact with the ER, and how this interaction 

with the ER may vary dependent upon the character of the hydrogen-bonding group. The focus 

of this paper concerns chemical interactions at site A and B; site C is most relevant to 
17

investigations of potent synthetic ER antagonists (see Katzenellenbogen et al., 2003 and 

references therein).

Various (Q)SAR, docking, and conformational models also provide insights as to how 

chemicals, primarily steroidal compounds or compounds with relatively high ER binding 

affinity, may interact with the ER (e.g., Weise and Brooks, 1994; Tong et al., 1997; Shi et al., 

2002). A review of (Q)SAR models developed largely from higher affinity ER binding data is 

found in Schmieder et al., (2003a). These models, however, do not appear adequate for 

interpreting interaction of compounds with low ER binding affinities. In the approach described 

in this paper, systematic measurements of chemicals with low binding affinity were undertaken 

to establish a (Q)SAR training set that reflected current understanding of the ER binding domain 

and was representative of the chemical groups in the specific pesticide inventories of interest.

The Regulatory Chemical Domain

There were several aspects to consider in developing an expert system to prioritize food 

use pesticide inert ingredients and antimicrobial active ingredients for EDSP Tier I screening. 

First, the structures of the chemicals found in the inventories needed to be characterized and 

existing information examined to determine the extent to which ER binding had previously been 

measured for the types of chemicals found on the inventories. Where sufficient information was

lacking, a systematic approach (Schmieder et al., 2003b) to identify key chemicals for testing 

was established to develop a training set representative of the diverse chemicals in the two 

chemical inventories of interest. An iterative approach included strategic selection of chemicals 

for measuring binding affinity within chemical groups, assessment of results and trends within 

chemical groups in the context of the ER binding hypotheses, and additional selection of 
18

chemicals for measuring binding affinity until sufficient information was gathered to make a 

prediction for all the chemical groups in the inventories. 

Characterization of Inert and Antimicrobial Inventories. To establish the chemical 

structures within the food use pesticide inert ingredients (FI) and antimicrobial active ingredients 

(AM) inventories, the Chemical Abstract System (CAS) number for each compound was 

obtained and then cross-referenced to existing databases containing quality assured chemical 

structures (see SOP for Chemical Characterization, Appendix I). The FI inventory contained 893 

total entries, of which 393 were discrete chemical structures (93% organic, 6% inorganic, 1% 

organometallic). The 500 remaining entries in the inventory included polymers of mixed chain 

length, chemical mixtures, and undefined substances (e.g., sand, blood, tannins, cod liver oil). 

The AM inventory contained a total of 299 substances of which 211 were discrete chemical

structures (72% organic, 25% inorganic, 3% organometallic) and 88 were polymers, mixtures,

and undefined substances. The chemical sub-classes represented in these inventories included 

chemicals without rings (i.e., “acyclic” chemicals) such as alcohols, amines, quaternary amines, 

acyclic sulfates, borates, carboxy esters, aldehydes, ketones, and phosphoric, carboxylic, and 

sulfonic acids. Chemical groups with ring structures (i.e., “cyclic” chemicals) included 

alkylbenzenesulfonic acids, alkylphenols, alkyloxyphenols, chlorobenzenes, hydrofurans, cyclic 

hexanones and hexanols, imidazolidines, isothiazolines, oxazoles, parabens, cyclic pentanones, 

phenones, pyrrolidines, phthalates, salicylates, sorbitans, sulfonic acid dyes, triazines, and 

chemicals with multiple functional groups further divided into “mixed phenols” if a ring 

hydroxyl group was present, or “mixed organics” if there was no a hydroxyl group. 

The Chemical Inventory Domains of Interest and Existing Information. The early 

recognition that some chemicals interact with the ER and alter the endocrine system has led to 
19

the development of large databases of ER binding affinity. The majority of early work in this 

area came from drug design and the search for highly potent estrogen antagonists for potential 

use in breast cancer therapies. Compounds in these databases tend to be chemicals with binding 

affinities comparable to the strong binding of the natural ligand and have structures similar in 

steric and electronic characteristics. Consequently, the diversity of structures in these databases 

is limited with respect to defining a (Q)SAR domain for chemicals not designed a priori to be 

biologically active (e.g., pesticidally inert ingredients) or with chemicals designed for a primary 

biological activity not associated with endocrine effects (e.g., many classes of pesticide active 

ingredients). Consequently, these existing data are of limited use in developing (Q)SAR models

to estimate ER binding affinity for inventories of pesticide and industrial chemicals. 

While a previous review suggested that there was limited binding data relevant for the 

inventories of interest (Schmieder et al. 2003a), to ensure any existing binding affinity and/or 

transcription studies relevant to the inventories of interest were incorporated in the current effort, 

the literature was re-reviewed to determine if chemical groups in the FI and AM inventories had 

been evaluated previously. The overlap of FI and AM chemical structures with ER binding data 

reported in the literature was found to be minimal. The chemicals with binding data reported in 

the literature represented chemical classes that comprised less than 4% of the chemicals in the FI 

and AM inventories. Consequently, it was determined that a (Q)SAR model based on literature 

data would not have a domain applicable to the regulatory inventories of interest.

The Receptor Binding Assay Domain 

The historical focus on identifying chemicals with strong binding affinities for potential 

drug development resulted in much of the early ER binding affinity data to be generated using 
20

“cutoffs” in chemical concentrations, with the result that many classes of chemicals with lower 

binding potential were categorized as non-binding, even though their binding potential was not 

fully characterized. Consequently, initial (Q)SAR models for ER binding that incorporated these 

data in their training sets produced erroneous ER binding affinity predictions for lower affinity 

chemicals when predicted values were compared to empirical data derived from assays 

optimized for detection of low affinity binding (Schmieder et al., 2004).

To address this issue, the research described here reflects an emphasis on low affinity 

measurements for chemicals with a wide array of physical-chemical properties, using ER binding 

and transactivation assays optimized for detecting low affinity effects (for details see Schmieder 

et al., 2004). The resulting evaluation of binding affinity at higher substrate concentrations than 

had been typically employed required the development of methods to evaluate chemical 

solubility in the assay medium and to interpret binding curves using additional assays, e.g. Ki 

determinations or gene activation. 

Assays used to develop the training sets were designed to detect low, but measurable,

chemical-ER interactions and optimized for the types of chemicals found in the specific 

regulatory inventories of interest. The inventories of concern contain diverse structures with 

widely varying physical-chemical properties (e.g., Log Kow of < -1.0 to > 8.0; neutral organics; 

weak acids; organometallics, etc), thus chemical behavior in the assays used in developing the 

binding data is an important consideration. To limit false negative interpretation of measured 

ER-binding (i.e., incorrectly assuming a chemical had no potential to bind to the ER), chemicals 

were tested up to the limit of solubility in the assay media employed. Thus, the maximum test 

concentrations is determined on a chemical-by-chemical basis as chemical availability in test 

media will vary with structure, aqueous solubility, Log Kow, non-specific protein binding, pKa, 
21

etc. (see Heringa et al., 2004 for protein effects on chemical availability in endocrine assays). To 

illustrate the point, the maximum solubility of a chemical of Log Kow 1.3 in a protein enriched 

test system can be -1 Log M, while a chemical of Log Kow 6 may be insoluble above -4 Log M. 

Chemical effects on the assay components (e.g., protein denaturation) as well as assay effects on 

chemical availability (e.g., pH affecting chemical speciation, total protein content altering 

chemical availability) are also important to consider to minimize misinterpretation of results, 

especially in cell-free binding assays. To limit false positive interpretations (i.e. incorrectly 

assuming a chemical is capable of binding to the ER), representative chemicals within each 

structural class evaluated across the associated Log Kow range, were tested in transactivation 

assays to confirm ER-mediated gene activation. For chemical-ER interaction determined in 

whole-cell or tissue assays, chemical-specific cytotoxicity, or solubility in absence of 

cytotoxicity, were used to set maximum test concentrations. These recommendations for testing 

chemicals with a wide array of physical-chemical properties are also outlined in a draft OECD 

Detailed Review Paper on Environmental Endocrine Disruptor Screening: the Use of Receptor 

Binding and Transcriptional Activation Assays for Fish (OECD, 2009c – draft in review).

Chapter III. Regulatory Inventories of Interest and Expert System Model Domain

Overlaying Model Domains and Regulatory Inventories

The majority of (Q)SAR models reported in the literature (more than 15,000 models for a 

wide array of endpoints) are simple statistical relationships between endpoint data and molecular 

descriptors (Bio-Loom, 2006). The primary limitation of these statistical models is that 

chemicals can produce the same endpoint activity even though the toxicity mechanisms by which 
22

they interact with biological systems are completely different. For example, one chemical may 

bind to the ER via a hydrogen bond donating group and another may bind via a hydrogen bond 

accepting group or hydrophobic forces. When data from chemicals that elicit different 

mechanisms involving a common biological effect are combined statistically in a single (Q)SAR 

model, the model is likely to give spurious results unless the domain is narrowly defined

(Bradbury et al., 2003). 

Consistent with the OECD principles for (Q)SAR validation, evaluation of a (Q)SAR 

model for specific regulatory purposes should include an evaluation of the mechanism of 

chemical-biological interaction (e.g., molecular initiating event in an adverse outcome pathway; 

Figure 1) in addition to an evaluation of how well the endpoint value is predicted by the model. 

In general, statistical evaluations have not been successful in characterizing the knowledge that 

discriminates interaction mechanisms (Bradbury et al., 2003; OECD, 2009a). The OECD 

Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship 

((Q)SAR) Models (OECD, 2004a) offers several examples of statistical clustering approaches for 

defining the domain of (Q)SAR models using simplified structure spaces. However, these 

methods should be used with caution because the dimensionality of chemical structure space is 

still not well defined. 

As stated previously, a primary concern is how well the domain of a (Q)SAR model 

represents the breadth of chemical structures included in the regulatory inventory of interest (see 

Chapter II The Regulatory Chemical Domain). In the current effort, the regulatory applicability 

issue is addressed by subdividing inert ingredients and antimicrobial pesticides into respective 

subgroups that have specific chemical attributes mechanistically related to ER binding. As 

empirical data was generated for chemicals within the subgroups, it was used to develop
23

“testable rules,” similar to those used for structural alerts and other SAR techniques. The training 

sets (i.e., the knowledge base) evolved as "expert rules” were tested with additional chemicals in 

the inventory to determine if there was sufficient data to span the chemical structures in specified

subgroups. In this manner, the training set for the expert system was systematically tested to 

ensure the knowledge base represented the structure space of the two regulatory domains of 

interest, i.e. food use inert ingredients and antimicrobial pesticides.

Expanding the ER Binding Affinity Knowledge Base

The FI and AM knowledge base was established by selecting chemicals to investigate 

binding mechanisms as well as chemicals to expand the structural domain to overlap the 

regulatory inventories. Prototype expert rules were established to address structural features or 

chemical properties associated with binding types (see Chapter II. The ER Binding Domain). 

Initial evaluation of these rules revealed that none of the FI or AM chemicals contained two 

hydrogen-bonding groups at the distances required for high affinity interactions (e.g., A-B 

binding of estradiol and similar high affinity chemicals with hydrogen-bonding distances of 10.2 

to 11 Å as described in Serifimova et al., 2007). Thus, no testing was conducted for chemicals 

with these structural characteristics. Instead, testing efforts were concentrated on better defining 

chemical-ER binding within individual sub-pockets, specifically Site A and Site B in the ER 

ligand binding domains.

Both inventories contain a significant number of chemicals with at least one possible 

hydrogen bonding substituent. Due to the importance of hydroxyl groups in hydrogen bond 

interactions in Site A, a series of alkylphenols were tested for ER binding. While mostly parasubstituted alkylphenols were tested, a sufficient diversity of other phenols was included to allow 
24

sub-grouping with respect to branched versus n-chain and ortho, meta, and para substitution. A 

group of alkylphenols, from the basic structure phenol with no substituents, to the 12 carbon 

chain p-dodecylphenol, covering a Log Kow range from 1.5 to 8, were examined. All chemicals 

within the series, were found to bind ER. Typical binding displacement curves for this series of 

chemicals are shown in Figure 3A along with that of the endogenous hormone 17ß-estradiol

(E2). The EC50s for each chemical is compared to that of the positive control (i.e., displacement 

of [3H]-E2 from the ER by unlabelled E2) to calculate the relative binding affinity (RBA) for all 

chemicals relative to E2 at 100% (see Schmieder et al., 2004 for details). 

A subset of chemicals, representing the entire alkylphenol series and the associated Log 

Kow extremes, were tested in trout liver tissue slices to determine whether apparent ER binding 

resulted in gene activation. Transactivation in trout liver slices was demonstrated across the Log 

Kow range of 1.5 to 8, using phenol and p(n,t)-alkylphenols with C1 to C5, C8, C9, and C12 

carbon chains (e.g., see Figure 3B for representative vitellogenin mRNA induction data for 

phenol, 4-n-butylphenol, 4-t-octylphenol and 4-dodecylphenol). 

The potential for steric hindrance in ER binding for alkylphenols was also investigated, 

with only the 2,6-ditertbutyl substituted compound shown to be incapable of binding, 

presumably due to inability of the phenolic hydroxyl group to get close enough to the A site to 

form a hydrogen bond. Additionally, a group of p-alkoxy phenols was also tested. All were 

determined to bind to the ER. 

The ER binding affinity of alkylphenols was found to increase linearly with Log Kow 

from 1.5 to ~4.5 and then remain nearly constant with increasing lipophilicity up to a Log Kow 8

(Figure 4). The bilinear relationship between ER binding affinity and Log Kow is consistent 

with the hypothesis that binding can be increased through hydrophobic forces in the center of the 
25

ER sub-pockets. Because Log Kow is readily calculable from structure and has a mechanistic 

rationale, the RBA - Log Kow relationship allows one to predict from the structure of an 

untested chemical whether it belongs within the bounds of a tested chemical group and, if so, 

whether it would be predicted to bind ER and produce vitellogenin in a trout liver slice. 

Several chemical groups, which contain hydrogen bond acceptor substituents, were also 

investigated for their potential to interact at ER sub-pocket Site B. Initially, an extensive series of 

p-alkylanilines were tested, analogous to the alkylphenols, to evaluate the potential role of 

hydrogen bond interactions in receptor binding. The p-alkylanilines from Log Kow of 1.3 to 5.2 

were found to bind ER in a relationship similar to that found with alkylphenols (Figure 4). 

Similar p-alkoxyanilines also bound to the ER and followed the same relationship with Log Kow 

(data not shown). The alkylanilines and the alkoxyanilines, however, show less affinity for ER 

when compared to alkyl- and alkoxyphenols of the same alkyl chain length (e.g., note ER RBA

is ~0.001% for an alkylaniline of Log Kow 5 vs. ER RBA of ~0.01% for an alkylphenol of Log 

Kow 5 in Figure 4). In this regard, aniline itself was determined to not bind to the ER whereas 

phenol did bind and produce gene activation. 

The p-alkylphenol and p-alkylaniline chemical series shown in Figure 4 represent “lead” 

groups for low affinity hydrogen-bond donors in A Site, and low affinity hydrogen bond

acceptors at B site. Additional chemical groups likely to interact at Site A, as hydrogen-bond 

donors, include parabens, salicylates with Log Kows of 1.8 to 5.5. Some of these compounds 

have previously been identified in the literature as capable of binding human ER and/or 

activating human ER mediated gene expression (e.g., Routledge et al., 1998; Miller et al., 2001; 

Schultz et al., 2000; Leskinen et al., 2005; Morohoshi et al., 2005; Terasaka et al., 2006) and 

inducing ER-mediated production of vitellogenin in fish (e.g., Alslev et al., 2005; Bjerregaard et 
26

al., 2003). Although details are not presented here, many additional chemical classes in the FI 

and AM inventories that have hydrogen-bond acceptor substituents (e.g., phenones, phthalates, 

ring-substituted benzoates) were tested in the current study and determined to have rainbow trout 

ER affinities similar to those of alkylanilines with similar Log Kows. 

Ultimately, a wide variety of chemicals were tested to represent all the chemical 

subgroups in the FI and AM inventories. Chemicals in the training sets were selected across the 

respective Log Kow ranges of each subgroup in the inventories. In contrast to the groups 

previously mentioned, these remaining chemical subgroups did not have compounds capable of 

binding to the ER at the assay limit of detection. These included both neutral and ionic organic

chemicals, acyclic chemicals, and organometallic compounds. The chemicals tested that showed 

no ER binding affinity (RBA<0.00001%) are listed by group/subgroup in Table 1. The results 

suggest that chemicals with no binding affinity do not contain moieties with sufficient hydrogen 

bonding capability to interact with ER or are excluded from the binding site because of 

hydrophilic moieties. 

Some of the chemical groups that did not bind to the ER (i.e., RBA < 0.00001%) 

contained a large number of chemicals. As mentioned previously, it is important to ensure the 

structure space within FI and AM inventory chemical groups is represented in the training set 

especially when a limited number of tested chemicals are representing a much large chemical 

group. For instance, there were more than 70 alkylaromatic sulfonic acids in the inventories. 

Figure 5 shows representative chemical structures that were tested from this group; spanning a 

Log Kow range from -0.62 to 5.67. Another group, the sulfonic acid dyes, were tested over the 

Log Kow range of -1.0 to 6.04 and found not to bind ER (Figure 6). Figure 7 shows the same 

sulfonic acid dyes presented in Figure 6 with some additional sulfonic acid dyes from the 
27

inventory that were not tested (Figure 7 structures in white background), but predicted to have an 

RBA less than 0.00001%, based on being within the Log Kow of the knowledge base and thus

within the domain of dyes found to have measured RBA < 0.00001%. Figure 7 also illustrates

how chemicals in the training set can be compared to any untested inventory chemical thereby 

allowing users of the expert system to evaluate the adequacy of the model for their prioritization 

application.

Another important characteristic of the alkylaromatic sulfonic acids, and the sulfonic acid 

dyes is that these chemicals are charged in the assay solutions. A chemical carrying a charge is 

less likely to partition across membranes or into hydrophobic pockets. It is also possible that 

charged chemicals interact with and denature receptor protein. Chemicals with a sulfonic acid 

group, found in both alkylaromatic sulfonic acids and sulfonic acid dyes, possess an estimated 

pKa of 0.70, which would suggest complete ionization to charged species in the pH 7.4 buffer 

media. Interestingly, while most chemicals in these charged groups (Figure 5 and 6) did not give 

an indication of binding, occasionally chemicals with a net ionic charge gave apparent binding 

displacement (see OBS and DBS in Figure 5A- ER binding displacement graphs). However, 

follow-up experiments to detect the nature of the binding interaction (see Ligand Binding 

Characterization Studies and kaempferol data in Schmieder et al., 2004 for details) and/or assays 

using trout slices designed to detect results of ER agonism or antagonism (Figure 5B) at the 

tissue level, showed no evidence of ER-mediated gene activation or interruption of estradiol 

driven vitellogenin induction, respectively. As previously mentioned, it is highly likely that 

‘apparent’ binding of charged compounds that do not translate into gene activation, or ‘apparent’ 

binding that is subsequently shown to be non-competitive at the ER ligand binding domain, is 

instead due to charged species denaturing the ER protein in binding assays and causing release of 
28

[

3

H]-estradiol (i.e., non-competitive, endogenous ligand displacement). Because this ‘apparent’ 

binding can result in release of the ligands used in displacement assays and yield false positives 

(e.g., OBS and DBS in Figure 5A), it is recommended that apparent ER binding for charged 

chemicals be confirmed using higher order assays. Additional chemical groups possessing a 

charge in assay media include those with carboxylic acid moieties (with an estimated pKa of 4.1 

making them completely ionized at pH 7.4), and quaternary amines with a permanent charge. 

Representatives of these groups tested in the assays were all negative (RBA < 0.00001%; Table 

1). 

In addition to chemical charge, another important consideration is the potential for 

chemical hydrolysis in the assay solution, resulting in formation of a chemical species in the 

assay system. For example, the borate esters did not bind ER (RBA < 0.00001%) and this 

inactivity may be explained by the formation of an acyclic species with a Log Kow less than that 

needed for interaction with ER (i.e., these acids likely hydrolyze resulting in the formation of 

boric acid).

Some chemical groups not found in the inventories were tested to increase confidence in 

understanding structural attributes that preclude the possibility of ER binding. Negative binding 

results (RBA < 0.00001%) and lack of agonist or antagonist activities in trout slices are shown in 

Figure 8 (A & B) for non-ring substituted benzoates with Log Kow values of 3.59 to 4.76. This 

finding is in contrast to ER binding activity (RBA > 0.00001%) found for similar benzoate 

structures that have additional substituents at position 2, or position 4, or position 2, 3, and 4 on 

the benzene ring. 

It is beyond the scope of this summary to recount all the subgroups of chemicals tested. 

Nonetheless, this iterative process to establish the FI and AM training sets was continued until all 
29

the respective groups were examined. There are chemicals on the inventories that could not be 

easily assigned to a single subgroup. These chemicals have multiple functional groups; e.g., 

“mixed phenols” if they also contain at least one hydroxyl moiety making them capable of 

hydrogen-bond donation and interaction at site A; or “mixed organics” if they do not contain a 

hydroxyl-ring substituent but contain more than one substituent that might be a hydrogen-bond 

acceptor. A total of 16 mixed phenols were tested in the ER binding assay. Of these, three

showed some affinity for the ER (e.g., RBA > 0.00001%) and 13 had no affinity for ER (RBA < 

0.00001%). Additionally, there were a total of 47 mixed organics tested, with five showing 

binding (RBA > 0.00001%). As with other groups, some of the chemicals in the mixed phenols 

and mixed organics training sets are not in the FI or AM inventories but were tested to 

investigate hypothesized binding mechanisms. Structures of mixed phenols and mixed organics 

in the current training set are shown in Appendix II and III, respectively, to illustrate the 

diversity of these chemicals. While the majority of mixed phenols and mixed organics that have 

been tested thus far do not bind to the trout ER, there is not sufficient data at this time to further 

categorize these compounds into subgroups which bind ER and those which do not, or to 

hypothesize structural parameters associated with the activity of those compounds. 

There are additional groups for which a limited number of chemicals were tested; e.g., 

substituted benzoates, gallates, and thiophosphate esters. While the current training set contains 

sufficient knowledge to make predictions for these chemicals within the respective inventories, 

there is insufficient knowledge to make predictions for chemicals within these groups that are 

outside the chemical domain on the FI and AM inventories. If a chemical is classified in one of 

these subgroups and it is not an exact match to a training set structure the chemicals is 

determined to be outside of the model domain and an “unknown binding potential” outcome is 
30

assigned. As additional chemical inventories are evaluated and the training set expanded to 

represent a larger number and greater diversity of mixed phenols, mixed organics, and other 

groups under represented subgroups, it should be possible to gain a better understanding of 

substituent patterns that result in ER binding and provide the means to add additional rules to the 

expert system. 

A Rule-Based Expert System to Predict ER Binding Affinity

Chemical structures in heterogeneous inventories are inherently hierarchical. When some 

chemical groups behave quite differently from others, or the information known about each 

group varies substantially, rule-based expert systems offer one approach to estimating specific 

properties of an entire array of chemicals. Creating decision trees that reflect the hierarchy and 

applying expert rules and localized (Q)SAR models for specific chemical structures has 

numerous advantages in a prioritization scheme. Perhaps most important is the advantage of 

maintaining transparency of the estimates by describing the logic for grouping the chemicals, 

providing the localized (Q)SAR model or expert rules that were used to make the estimate, 

providing empirically measured data for chemicals in the associated training set, and providing 

literature values and citations for other chemicals in the same group to support the estimates. 

These data enable users of the expert system to discuss the scientific basis for the all estimates, 

and justify modifying a specific estimate if new knowledge becomes available to the user. 

Figure 9 presents an overview of the expert system’s decision tree for ER binding 

affinity. The current version examines each chemical in the respective inventories (see Figure 9, 

“Start Here”) and places them into groups of inactive chemicals (if the answer to query I is ‘No’, 

query II is ‘Yes’, or query III is ‘Yes’), into “drug-like” groups of chemicals that have the 
31

potential for high affinity ER binding (‘Yes’ to query IV), or into groups of chemicals that may 

have weak-to-moderate binding affinity depending on specific properties or structural features

and answers to queries V, VI, and VII. Each of these major queries is sub-divided as needed to 

more explicitly describe chemical subgroups associated with a binding type and properties of the 

training set chemicals associated with RBA > 0.00001% or < 0.00001% . Examples of questions 

that are used to categorize chemicals beyond what is shown in summary Figure 9 are provided in 

Figures 10, 11, and 12, which are expansions of queries I, V, and VII, with explanatory text 

provided below. 

Regarding query I, it has been generally accepted that chemicals must have at least one 

cycle (e.g., saturated or aromatic carbon ring) to competitively bind at the ER, although 

previously, there was limited evidence to fully evaluate this assumption. This general “testable 

rule” is mechanistically reasonable because flexibility and binding affinity, in general, vary 

inversely (Katzenellenbogen et al., 2003 and references therein; Serafimova et al., 2007). Cyclic 

and multi-cyclic chemicals tend to be less flexible, at least in the absence of long alkyl chains. 

Given the large percentage of acyclic chemicals in the inventories of interest, numerous acyclic 

chemicals were tested and none had measureable binding (i.e., none with RBA > 0.00001%). 

These included both ionic (quaternary amines, sulfates, and phosphates) and non-ionic acyclic 

structures (e.g., alcohols, amines, esters; see Figure 10). Specific acyclic chemicals tested are 

listed in Table 1. Therefore, query I (see Figure 9) in the expert system asks whether or not the 

chemical contains a cycle. If it does not contain a cycle, the chemical is not predicted to bind to 

the trout ER. 

Query II in Figure 9 asks if a cyclic chemical contains a charge. If it does, and it belongs 

to one of the groups listed in Figure 9 and within the specified Log Kow ranges the chemical is 
32

predicted to have an RBA < 0.00001%. If the answer to query II in Figure 9 is ‘No’, then the 

decision tree invokes the next query. 

An uncharged cyclic chemical moving on to query III in Figure 9, is examined to 

determine if it belongs to any one of the subgroups of chemicals for which members within the 

Log Kow ranges tested thus far had RBA < 0.00001%. There are a variety of steric and 

electrostatic reasons why these groups cannot bind to the ER. Generally, the absence of 

hydrogen bonding groups, or inappropriate geometry explains the failure of these chemicals to 

bind to ER (Katzenellenbogen et al., 2003 and references therein). Suffice it to say, these 

inactive groups are well-defined and the training sets showed no members of these groups have 

had an RBA > 0.00001%. The “known inactive” subgroups include: alkyl benzthiols, p,n-alkyl 

fluorobenzenes, a hindered alkylphenol, benzamides, borate esters, benzoates lacking ring 

substituents, bis-anilines, hydrofurans (alcohol and ketone), imidazolidines, isothiazolines, 

mono-cyclic hydrocarbons, oxazoles, n-alkyl phenones, pyrrolidiones, sorbitans, and triazines 

(Figure 9; Table 1). Note that the range of Log Kow covered by the tested chemicals within each 

specific chemical group is shown in Figure 9. A chemical within the group, but outside the 

tested Log Kow range is not within the domain of the expert system and no binding potential is 

assigned. 

The chemicals that do not meet the requirements of the first three queries belong to 

groups of chemicals for which some members of their respective subgroup bind ER. Chemicals 

within these subgroups with the greatest binding affinity to the ER receptor are those that mimic 

steroids and have two oxygen atoms available for hydrogen bonding at the specific distances 

dictated by the ER receptor. Chemicals that meet these requirements are removed from the other 

subgroups for further evaluation (Query IV); note that none of the chemicals currently in the FI 
33

or AM inventories have structural characteristics defined in this query. Chemicals that do not 

meet the requirements for high affinity binding are further evaluated by the expert system

(Figure 9; note arrow going from ‘No’ in query IV and on to query V). 

To evaluate these remaining subgroups, chemicals are next queried against a set of 

“special rules”. Query V was established for chemicals with unique structural features in the 

training set (see Figure 11; note that none of the FI or AM chemicals satisfy rules for DDT-like, 

tamoxifen-like, and multicyclic hydrocarbons groups) and/or to ensure coverage of FI and AM 

inventory chemicals (i.e., p-alkylchlorobenzene and thiophosphate ester groups). Currently 

training sets for these subgroups are sufficient to represent the FI and AM inventories; however, 

the expert system will not make a prediction for any chemicals in these subgroups that are 

outside the model domain. However, users of the expert system could over ride the system if 

they determined structures in the training set are sufficiently similar to a compound of interest

and can be used as analogs to assign a RBA classification. 

If a chemical does not meet any of the requirements for the first five queries, it is 

evaluated further to determine if it has structural features that make it likely to interact at site A 

or B. Initially the chemical’s Kow is estimated. We have noted that of over 350 chemicals tested 

in the training set, no chemical with a Log Kow less than 1.3 has been shown to competitively 

bind with ER. Thus, chemicals with a Log Kow less than 1.3 are assigned a RBA of less than 

0.00001%. While low affinity binding does not appear to occur for any chemical with Log Kow 

< 1.3, the reverse cannot be said; i.e., it is not the case that all chemicals with Log Kow > 1.3 

will bind ER. There are more specific (more restrictive) Log Kow ranges for binding activity that 

apply within many of the subgroups. These ranges are listed in Figure 9 for each group/subgroup 

as applicable. 
34

The sixth query identifies chemicals with Log Kow > 1.3 that have a phenolic substituent

as a hydrogen bond donating group; these chemicals are presumed to have the potential to bind at 

Site A in the ER (VI in Figure 9). This includes the p-alkylphenol series previously described, palkoxyphenols, parabens, and salicylates. Specific Log Kow ranges for active chemicals (RBA > 

0.00001%) in each subgroup are listed. Details of each training set are not included here but ER 

binding and trout liver slice transactivation data comparable to that shown for the alkylphenols in 

Figure 3A & 3B, as well as depictions of structures of each training set vs. the inventory 

structure (as in Figure 7) are available for all the groups listed in Figure 9. It is this latter 

information that is especially important to the evaluation of mixed phenols. While the presence 

of the phenolic group makes it a possible hydrogen-bond donor, with potential binding at Site A,

there are additional substituents present that could hinder binding activity. As described 

previously, currently there is insufficient information to establish general rules for these types of 

chemicals. A chemical falling in this group is first evaluated for an exact match to an existing 

mixed phenol in the training set chemical (Appendix II) and is assigned the appropriate RBA. 

Only 3 of the 13 mixed phenols tested have been found to bind ER above the RBA detection 

limit of 0.00001%. However, if a chemical is not an exact match to a tested mixed phenol, it is

considered to have an ‘unknown binding potential’. 

Query VII in Figure 9 identifies chemicals with aniline, ester, or other polar substituents

that can serve as a hydrogen bond acceptor at the B site in the ER (Figure 2). The expanded 

query is shown in Figure 12. The knowledge base of chemicals currently thought to bind at this 

site contains: p-alkylanilines, p-alkoxyanilines, phthalates, branched phenones, p-alkyl 

cyclohexanols and hexanones, and various ring-substituted benzoates. Again, each one of these 

groups has specific Log Kow ranges where ER binding has been measured. Similar to the mixed 
35

phenols in query VI, query VII for ‘mixed organics’ compares a compound to the mixed organic 

training set (Appendix III); if there is an exact structural match the corresponding RBA values 

are assigned. A mixed organic chemical that is not in the training set is characterized as having 

an “unknown binding potential” (i.e., the chemical is characterized as outside the model 

domain); however, as discussed above the user can override the system if they determine a 

chemical(s) in the training set can serve as an analog. 

Using the rules summarized in Figure 9, the expert system was applied to the FI and AM 

inventories. The predictions obtained from the system are summarized in Table 2. For the FI 

inventory, of 393 discrete chemicals, 378 are predicted to have a RBA less than 0.00001% (the 

limit of detection in the assay) and 15 are predicted to have an RBA of >0.00001%. For the AM 

inventory, of 211 discrete chemicals, 196 are predicted to have an RBA less than 0.00001% and 

15 are predicted to have an RBA greater than 0.00001%. Note that some of the structures 

predicted to have RBA < 0.00001% fell into groups that had some chemicals that bind ER, but 

the specific structure in question did not have a high enough Log Kow to bind ER. For example, 

4-cyclohexanols of Log Kow 2.4 to 3.8 in the training set were found to bind ER, but 

cyclohexanols with Kows below this cutoff did not bind to the ER. 

As mentioned previously, if the RBA for any chemical cannot be predicted, the chemical 

contains structural features which are not included in the chemicals in the current training set and 

are not currently in the model domain. The approach, however, is based on a perspective that the 

domain of the expert system knowledge base can be expanded with strategic testing of additional

chemicals in inventories of regulatory interest. This hypothesis based, strategic expansion of the 

expert system’s domain provides a systematic basis to incorporate additional chemical diversity. 
36

Chapter IV. Accessibility of the Expert System

Expert Systems for ER Binding Affinity

One of the more straight forward approaches to developing an expert system for a 

complex endpoint is to use decision trees with the queries based on simple “if, then else” logic. 

In current chemical decision trees, the conditions placed on individual queries in order for them 

to be logically true can range from simple presence or absence of a general substructure to a 

complex parameter range derived from still other molecular models. As illustrated in this work, 

a decision tree model has been developed that incorporates simple chemical substructure queries, 

i.e., expert rules for when to invoke (Q)SAR models for ER binding affinity. 

The reason expert system technology is so important is that SARs are typically most

reliable for chemicals that interact through the same mechanisms. This underlying SAR

principle would mean that the user would have to be familiar with the multidimensional 

interactions of chemicals with biological systems. Obviously, this complex reality places an 

undue burden on users of the models. The solution is to encode the expert knowledge of 

chemical interactions and provide the user with a fully transparent description of what 

considerations were made, how the estimate was calculated from these considerations, and 

literature citation(s) to underpin the expert rules. 

The use of decision trees facilitates regulatory acceptance because they inform the user 

about how well the decision tree model covers the domain of the chemicals to be assessed 

(usefulness) and, at the same time, provide a transparent explanation for the basis of the 

estimated endpoint. Explaining the “why” behind each chemical prediction in the decision tree 

is crucial to subsequent discussions in a prioritization application. For example, since binding to 
37

a receptor has many specific constraints that preclude structures from fitting within the receptor 

“pocket(s),” a large number of chemical groups are expected to be “nonbinding.” To build a 

decision tree to estimate ER binding affinity, a series of queries to identify nonbinding groups of 

chemicals was established and combined with a series of more specific rules to group large 

categories of chemicals with a range of binding affinities. Each of these categories was further 

classified and quantitative models to estimate binding affinity for the entire category was 

included. Even more specific structural requirements were added to identify steroid-like 

chemicals of which many had large binding affinities similar to drugs and potent estrogenic 

chemicals. 

The expert system for ER binding affinity was created with specific chemical inventories 

and domains. The expert system can be easily expanded as additional data become available on 

chemicals from additional domains. The expert system model was also developed with the 

presumption that the model could be used manually for individual chemicals, or totally 

automated on a small personal computer for ranking larger lists of chemicals. The query 

language for the logical grouping of chemicals as well as the estimation of ER binding affinity is 

written in a syntax that is understandable and transferable to computer languages. As discussed 

in Chapter I, upon completion of the expert system, consistent with SAP review comments, EPA 

envisions including the expert system in OECD’s (Q)SAR toolbox.

Ongoing Research: Human ER Binding Affinity and Gene Activation

The work reported here focuses development of an expert system based on rainbow trout 

ER binding and transactivation assays. Research in progress is evaluating the applicability of the 

expert system for predicting relative binding affinity to human ERα. Specifically, a subset of the 
38

FI and AM chemicals are being tested with a binding assay using full-length recombinant human 

ERα and a transactivation assay using human T47D-KBLuc cells. Chemicals are selected for 

human ER in vitro testing based on predictions from the current expert system to provide 

coverage of each chemical subgroup and Log Kow range within the FI and AM inventories. 

Preliminary data suggests that the same groups of chemicals bind ER from both species, 

although additional subgroups remain to be evaluated. Within the subgroups evaluated thus far, 

the trend is that fewer members of a chemical group bind to the human ER than rainbow trout 

ER (e.g., a more restrictive Log Kow range for binding human ER within a group). 

These findings support previous studies that noted a concordance across species (fish, rat, 

human) when similar assay systems are compared (i.e., chemical affinity for recombinant ER 

from species A vs. recombinant ER from species B, or chemical affinity for cytosolic ER from 

species A vs. cytosolic ER from species B, or comparing affinities measured in assays with 

similar total protein concentrations and chemical availability). Binding affinities measured using 

recombinant ERs (generally with low total assay protein) are typically orders of magnitude 

greater than affinities measured using ER derived from cytosolic preparations where total assay 

protein can be 10 times greater. This is likely due to differences in non-specific binding of 

chemicals to proteins (Schmieder and Henry, 1988) other than estrogen receptor protein (see 

Heringa et al., 2004 for more discussion of the influence of assay total protein on measured ER 

activity). For instance, RBAs for select alkylphenols and monohydroxy parabens tested using 

ER from rat uterine cytosol (Blair et al., 2000) are within same order of magnitude of affinities 

determined in this study and in published literature for rainbow trout ER when receptors are 

isolated from liver cytosol (Olsen et al., 2005). Consistent with the relationships noted above, 

ER binding affinities for these same chemicals, determined using recombinant ERs from either 
39

human or trout origin, are often an order of magnitude greater than those determined using 

cytosolic receptors. Other researchers have also shown that there is good ER binding 

concordance across species for lower affinity chemicals (Matthews et al., 2000; Petit et al., 1997; 

Olsen et al., 2005). However care must be taken when comparing studies if cutoffs in maximum 

concentration tested differ between studies, which will confound the data interpretation.

The comparative work on human ER for FI and AM chemicals will continue, using the 

systematic approach described, to characterize any differences, and if needed. tailor expert

systems that are specific to ‘a vertebrate’ (i.e., fish or human) or to establish an expert system 

that will be protective of the most sensitive species for a specific chemical subgroup.

V. Conclusions

The EDSTAC (USEPA, 1998a) and SAB/SAP (USEPA, 1999) outlined the strengths and 

limitations of using (Q)SARs as an effects-based component in future prioritization schemes for 

chemicals that have little or no empirical effects data. The strengths of using (Q)SAR systems 

include the means to rapidly predict biological activities of large numbers of chemicals, at little 

or no cost, and thereby avoid the need to prioritize on the basis of “no data.” The EDSTAC also 

concluded that (Q)SARs can provide the means to prioritize chemicals in a transparent and

consistent manner that avoids the problem of comparing data derived from different 

experimental systems. The EDSTAC noted that (Q)SARs that predict receptor binding and/or 

gene transcription activation, for example, have the same strengths and limitations as the 

experimental data derived from in vitro assays. In that regard, the EDSTAC cautioned that use 

of experimental or predicted receptor binding or gene activation data does not necessarily predict 

in vivo responses, nor does it encompass other mechanisms of endocrine disruption. As guiding 
40

principles for development and use of (Q)SARs for priority setting the EDSTAC also 

recommended that applicable chemical domains be sufficiently diverse for their intended 

application, the training sets be developed using the most complete and accurate data sets 

available, and that (Q)SARs be validated and used only within the range of conditions for which 

they were developed and evaluated. 

The OECD Principles for the Validation of (Q)SARs (OECD, 2004a) provide a detailed 

framework to address the perspectives developed by the EDSTAC. The OECD principles 

include documentation of a well-defined biological endpoint; a mechanistic basis for the model; 

a defined domain of model applicability; appropriate measures of goodness of fit, robustness and 

predictability; and an unambiguous algorithm. Transparency is a critical characteristic of the 

OECD (Q)SAR validation principles – particularly in the context of the mechanistic plausibility 

of the predictions and the reasonableness of estimated values compared to experimentally 

derived data; the chemical domain of a (Q)SAR application; and the regulatory purpose of the 

(Q)SAR. 

The ER binding expert system described here represents an encapsulation of both 

undirected research published in the literature from the past two decades as well as directed 

research over the past four years to tailor (Q)SAR methods to provide effects information to 

support future development of prioritization schemes for Tier 1 EDSP screening. Development 

of the systems was guided by the OECD principles of (Q)SAR validation and the (Q)SAR-based 

expert system reported here is derived from a training set of food use inert ingredients and 

antimicrobial active ingredient pesticides. When combined with estimates of exposure potential, 

consistent with EDSTAC (USEPA, 1998a) and SAB/SAP (USEPA, 1999) recommendations, the 

expert system provides a complementary effect-based means to prioritize food use inert 
41

ingredients and antimicrobial pesticides for future screening and testing. Given that this 

(Q)SAR system is specific to ER binding, Tier 1 prioritization would be relevant for those Tier 1 

assays that are specific to, or capable of, detecting potential interactions with the estrogen 

system; e.g., uterine cytosolic ER binding assay, ER transactivation assay, uterotrophic (rat) 

assay, pubertal female (rat) assay, and the fish short term reproduction assay 

(http://www.epa.gov/scipoly/oscpendo/pubs/fish_fs.pdf). 

The expert system for ER binding reported here can be readily expanded to address 

additional chemical inventories. Prioritization of chemicals for Tier 1 assays designed to detect 

potential interactions with the androgen of thyroid hormone systems would require 

corresponding expert systems specific to those receptors. While the current expert system is 

specific to ER binding affinity, the underlying approach for developing the system could be 

applied to the development of training sets for other nuclear receptors to further expand hazardbased prioritization schemes. 

Research and development on HTPS and bench-level in vitro and (Q)SAR methods had 

not sufficiently progressed for use in the initial round of EDSP priority setting. Contingent on 

the SAP’s review, and completion of the corresponding human ER binding and gene activation 

data sets to confirm the preliminary findings of concordance between rainbow trout and human 

data, the (Q)SAR-expert system would provide effects information relevant to the estrogen 

hormone system for use in subsequent rounds of EDSP priority setting for food use inert 

ingredients and antimicrobial pesticides An option for future Tier 1 screening of chemicals 

identified through a (Q)SAR-informed prioritization scheme for chemicals with little or no 

existing in vivo data could include a phased approach in which in vitro Tier 1 assay data is

collected and evaluated to inform prioritization decisions on the in vivo Tier 1 screening assays.
42

In conclusion, the Agency is exploring the expansion of the existing expert system to 

non-food use inert ingredients, which include many of the same chemical classes represented by 

the food use inert ingredient and antimicrobial inventories. In an iterative process, bench-level or 

HTPS data will be used to develop and validate an evolving (Q)SAR-based expert system for 

predicting estrogen-based effects data. The same principles of training set and (Q)SAR 

development could also be systematically applied to specified inventories to address other 

receptor-based mechanisms of endocrine disruption; e.g. the androgen hormone system. 

Decisions regarding future efforts will include an evaluation of forthcoming HTPS data and the 

extent to which current HTPS and/or bench level assays methods will require any modification to 

support development of appropriate (Q)SAR training sets for chemicals with low receptor 

binding affinity. Ongoing research measuring in vivo responses to chemicals with low ER 

binding affinity will also inform interpretation of predicted or empirically measured in vitro data 

in EDSP prioritization and screening.
43

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Conformations and Their Role in Biological Activity.” Journal of Steroid Biochemistry 

Molecular Biology. 50: 61-73.

Yokota, H., Abe, T., Nakai, M., Murakami, H., Eto, C., and Yakabe, Y. 2005. “Effects of 4-tertPentylphenol on the Gene Expression of P450 11ß-Hydroxylase in the Gonad of Medaka 

(Oryzias latipes).” Aquatic Toxicology. 71: 121-132. 
48

Figure 1. An estrogen receptor (ER)-mediated reproductive impairment adverse outcome 

pathway (from Schmieder et al., 2004) The pathway demonstrates typical responses that might 

be observed at various levels of biological organization and the types of assays (in vitro or in 

vivo) from which observations along the pathway can be made. In this example, chemical 

binding to the rainbow trout ER, the point of pathway initiation, is used to develop a quantitative 

structure-activity relationship (in silico model). 
49

Figure 2. The estrogen receptor ligand binding domain showing sub-pockets A, B, C where 

chemical ligand hydrogen bonding interactions occur. Receptor protein amino acids involved in 

interactions with chemical ligands are indicated by letter and sequence number. 17ß-Estradiol is 

shown in the binding pocket (after Katzenellenbogen, et al., 2003 and references therein, and 

pers. comm.).

R 394

E 353 H 524

C

T 347

HO

OH

CH3 H

H H

H

A B

R 394

E 353 H 524

C

T 347

HO

OH

CH3 H

H H

H

A B
Figure 3. Binding and gene expression of p-alkylphenols in rainbow trout estrogen receptor 

(rtER) in vitro assays. A) rtER binding to receptor derived from liver cytosol determined by 

displacement of [3

H]-17ß-estradiol (E2) for: unlabelled 17ß-estradiol (E2,), phenol (), 4-

ethylphenol (), 4-n-butylphenol (), 4-n-octylphenol (), 4-t-octylphenol (), 4-nnonylphenol (), and 4-t-nonylphenol isomer mixture (); and, B) rtER mediated gene 

expression in trout liver slices; symbols are matched for positive control 17ß-estradiol (left side 

of each figure) and phenol or substituted p-alkylphenol (right side of each figure) measured in 

the same experiment; phenol (, left Y-axis scale); 4-n-butylphenol (, right Y-axis scale); 4-

tert-octylphenol (, middle right Y-axis scale); and 4-dodecylphenol (, far right Y-axis scale).

Log Concentration (M)

ctrl -10 -9 -8 -7 -6 -5 -4 -3 -2

VTG mRNA copies/400 ng total RNA 

1e+5

1e+6

1e+7

1e+8

1e+4

1e+5

1e+6

1e+7

1e+8

1e+4

1e+5

1e+6

1e+7

1e+8

1e+4

1e+5

1e+6

1e+7

B

0

10

20

30

40

50

60

70

80

90

100

110

ctrl -10 -9 -8 -7 -6 -5 -4 -3 -2 -1

Log Concentration (M)

[3H]-E2 Binding (%)

A
Figure 4. Relationship between rainbow trout estrogen receptor binding (Relative Binding Affinity; RBA) and Log Kow for chemicals

interacting at Site A (e.g., p-alkylphenols; blue diamond) or Site B (e.g., p,n-alkylanilines; green triangle).

0.000001

0.00001

0.0001

0.001

0.01

0.1

1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

Log Kow

Relative Binding Affinity
52

Figure 5. Structures of alkylaromatic sulfonic acids, abbreviations, and Log Kow (measured or 

estimated) and results of rainbow trout estrogen receptor (rtER) binding assays illustrating: (A) 

absence of rtER binding for PTS (), EBS (), 2NSA (), NSA () from -7 logM to solubility 

for each, and apparent binding for OBS () and DBS (), compared to 17ß-estradiol (E2) (-10 

to -6 logM, ); and confirmed lack of trout liver slice vitellogenin (VTG) mRNA induction for 

OBS and DBS in: (B) agonist assay with E2 (-10 to -7 logM, symbols match chemical exposures 

on the same day) or 6 concentrations of DBS (, left Y-axis scale) or OBS (, right Y-axis, -

5.25 to -3 logM); and (B) antagonist assay with a maximum induction concentration of E2 (-8 

logM) combined with each of 5 concentrations of DBS (-5.3 to -3.5 logM,∇, left Y-axis scale), 

or E2 (-7 logM) combined with each of 5 concentrations of OBS (-5 to -3 logM, , far right Yaxis scale).

Log Kow = 0.38 calc

S

O

O

OH

H3C

Log Kow = 0.63 msrd

OH

S

O O

2NSA

Log Kow = 0.92 calc

S

O

O

OH NSA

Log Kow = -0.62 msrd

H3C S

OH

O

O

PTS

Log Kow = 3.56 calc

S

O

O

OH

H3C

OBS

Log Kow = 5.67 calc

S

O

O

OH

H3C

EBS DBS

Log Concentration (M)

ctrl -10 -9 -8 -7 -6 -5 -4 -3 -2

VTG mRNA copies/400 ng total RNA 1e+4

1e+5

1e+6

1e+4

1e+5

1e+6

1e+7

1e+8

1e+3

1e+4

1e+5

1e+6

1e+7

1e+8

Log Concentration (M)

ctrl -10 -9 -8 -7 -6 -5 -4 -3 -2

[3H]-E2 Binding (%)

0

10

20

30

40

50

60

70

80

90

100

110

120

A) B)
53

Figure 6. Structures of sulfonic acid dyes, abbreviations, and Log Kow, and results of rainbow 

trout estrogen receptor (rtER) assays illustrating absence of : (A) rtER binding for 9 dyes AYE 

(); SYE (); RAC (); MIT (∇); ABL (); FBR (); BCA (); BAD ();WRB (), 

compared to 17ß-estradiol (E2) (); and, (B) trout liver slice vitellogenin (VTG) mRNA 

induction for 2 dyes compared to E2 positive control in agonist assays WRB () and AYE ();

and, B) antagonist assay showing no change in the maximum VTG mRNA in response to -8 log 

M () E2 when combined with AYE from -5 logM to -3 logM.

Log Kow = 6.04 

H3C

Cl

N

N

OO

HO

S

OO

O

WRB

Log Kow = 3.84 

OS

O

O

S

OO

O

BAD

Log Kow = 3.41 

OS

O O

N CH3

N

OO

OH

BCA

Log Kow = 3.09 

S

O O

OH S O

O OH

NH

N

N

HO

OH

N

NH

NH N

N

N

HO

HO

N

NH

N

FBR

Log Kow = 3.03 

O- S

O

O N

H3C

S

OO

O

N+

S OO O H3C

ABL

Log Kow = 1.89 

Cl

Cl

NH

O

NH

Cl

O Cl

S O

O

OMIT

Log Kow = 0.08 

HO

N

N

O

H3C

S

O

O

OH

H3C

S

O O

OH

RAC

Log Kow = -0.44 

OO S

O

N

N

HO

S

OO

O

SYE

Log Kow = -1.00 

OS O

O N

N

N

H

N

S

OO

O HO

OO

AYE

Log Concentration (M)

ctrl -10 -9 -8 -7 -6 -5 -4 -3 -2

[3H]-E2 Binding (%)

0

10

20

30

40

50

60

70

80

90

100

110

120

Log Concentration (M)

VTG mRNA copies/400 ng total RNA 

ctrl -10 -9 -8 -7 -6 -5 -4 -3 -2

1e+3

1e+4

1e+5

1e+6

1e+7

1e+8

1e+5

1e+6

1e+7

1e+5

1e+6

1e+7

1e+8 A) B)
54

Figure 7. To provide transparency at each query in the expert system existing data in the knowledge-base (i.e., data in the training 

set) is compared to food use inert ingredient and antimicrobial pesticide inventory chemicals. The example shows the sulfonic acid 

dyes structures in the knowledge base (grey shaded structures at top of figure) and nine representative chemicals from the food use 

inert ingredient inventory; the first inventory structure shown is also on the antimicrobial inventory. Chemical structures with no 

shading have not been assayed for ER binding. Chemicals are presented in increasing order of Log Kow. Since all dyes shown are 

within the Log Kow of the knowledge base, they are in the domain and predicted to have RBA < 0.00001%. 

Sulfonic Acid Dyes

Log Kow = -1.00 c Log Kow = 0.08 c

Log Kow = 3.03 c Log Kow=3.09 c Log Kow = 3.41 c Log Kow = 3.84 c Log Kow = 6.04

Log Kow = -0.44 c Log Kow = 1.89 m

OS O

O N

N

N

H

N

S

OO

O HO

OO

O- S

O

O N

H3C

S

OO

O

N+

S OO O H3C OS

O

O

S

OO

S O O O

OH S O

O OH

NH

N

N

HO

OH

N

NH

NH N

N

N

HO

HO

N

NH

N

OS

O O

N CH3

N

OO

OH

H3C

Cl

N

N

OO

HO

S

OO

O

HO

N

N

O

H3C

S

O

O

OH

H3C

S O O

OH

Cl

Cl

NH

O

NH

Cl

O Cl

O S

O

ORainbow Trout Training Set

Inventory

Log Kow = 3.03 c Log Kow = 4.12 c Log Kow = 4.12 c Log Kow = 4.19 c

Log Kow = -1.00 c Log Kow = 0.73 c

OS O

O N

N

N

H

N

S

OO

O HO

OO

O- S

O

O N

H3C

S

OO

O

N+

S OO O H3C

H3C

Cl

N

N

OO

HO

S OO

O

S

O O

ON N

OH NH2

S

O

O O- HO

N

N

O

H3C

S

O

O

OH

H3C

S O O

OH

S

O O

HO

HO

N N

S

O

O HO

O

HO HO

N

N

S

O

HO O

Cl

O

OHO

N

N

S

O

O- O

Cl

ON

N S

O

O

OS O O

OOO S

O

N

N

HO

S O

-

O

O

Log Kow = -1.00 c Log Kow = 0.08 c

Log Kow = 6.04
55

Figure 8. Structures of non-ring substituted benzoates, Log Kow and their abbreviations and 

results of rainbow trout estrogen receptor (rtER) binding assays illustrating: (A) absence of rtER 

binding for BB (), IAB (), NHE (∇), PB (), and PEB () from -6 logM to solubility for 

each benzoate compared to 17ß-estradiol (E2) (-10 to -6 logM, ); and, (B) lack of trout liver 

slice vitellogenin (VTG) mRNA induction for IAB in agonist assay with E2 (-10 to -5 logM, ) 

or 6 concentrations of IAB (-4.5 to -2.5 logM, , left Y-axis scale); and, B) antagonist assay 

with a maximum induction concentration of E2 (-8logM ()) combined with each of 6 

concentrations of IAB from -4.5 to -2.5 logM () compared to positive control E2 (-10 to -5 

logM, ) response slices run in the same experiment. 

O

O

CH3

BB

Log Kow = 3.84 msrd

O

O CH3

CH3

IAB

Log Kow = 4.15 msrd

O

O

CH3

NHE

Log Kow = 4.76 est

O

O

PB

Log Kow = 3.59 msrd

O

O

PEB

Log Kow = 4.01 msrd

Log Concentration (M)

VTG mRNA copies/400 ng total RNA 

ctrl -10 -9 -8 -7 -6 -5 -4 -3 -2

1e+3

1e+4

1e+5

1e+6

1e+7

1e+4

1e+5

1e+6

1e+7

Log Concentration (M)

ctrl -10 -9 -8 -7 -6 -5 -4 -3 -2

[3H]-E2 Binding (%)

0

10

20

30

40

50

60

70

80

90

100

110

120

A) B)
56

Figure 9. The expert system decision tree based on rainbow trout estrogen receptor binding affinity and trout liver slice 

transactivation knowledgebase. 

Start Here:

Chemical List

e.g., Food Use Inerts,

Antimicrobials

Contains 

a Cycle

Yes Contains 2 OH,

or OH and =0, at 

Spec. Dist

Log KOW <1.3

“Special 

Rule”

Applies

Site “A”

Contains Phenol

Fragment 

Log Kow Range

4-Alkylanilines ( 1.4 - 5.1)

4-Alkoxy Anilines ( 2.6 - 3.7)

Phthalates ( 2.5 - 6.8)

Phenones-(branched) ( 2.7 - 4.8)

4-Alkyl cyclohexanols ( 2.4 - 3.8)

4-Alkyl cyclohexanones ( 2.4 - 3.8) *

2-; 4-; or 2,4,6-Benzoates

(substitution-dependent Log Kows)

IV

Possible Low Affinity

Site A-Type, Site B-Type

Site “B”

Contains “Specified”

Fragment

Log Kow Range

Alkylphenols (1.5 - 8.0)

Alkoxy phenols (2.3 - 4.3)

Parabens (1.9 - 5.4)

Salicylates (2.5 - 5.1)

Belongs to 

known Active

Sub-class

RBA > 0.00001%

Yes

No

VI

Unknown 

Binding Potential

No

Yes

Yes

No

Yes

Belongs to 

known Active

Sub-class

Belong to untested class

Unknown Binding Potential

Mixed Phenols

No

VII

No

Belongs to 

known Inactive 

Sub-class

Log Kow Range 

4-Alkylbenzthiols ( 2.9 - 4.2)

4-n-Alkylfluorobenzenes ( 2.3 - 4.9)

Alkylphenol (2,6-subst) ( 4.2)

Benzamides ( 1.7 - 2.1) 

Borate Esters (-0.5 - 1.0)

Benzoates (non-ring subst) ( 3.6 - 4.2)

Bis-anilines ( 1.6 - 6.2)

Hydrofurans (alcohol & ketone) ( 0.0 - 1.2)

Imidazolidines (-0.9 - 0.6) 

Isothiazolines ( 0.6 - 2.5)

Mono-cyclic Hydrocarbons ( 2.7 - 4.6)

Oxazoles (<0.3)

Phenones (n-alkyl) ( 1.6 - 4.1)

Pyrrolidiones (-0.4 - 3.3)

Sorbitans ( 3.2 - 5.9) 

Triazines (-0.4 - 3.4)

Yes

DDT-Like 

Tamoxifen-Like

Multicyclic hydrocarbons

Alkylchlorobenzenes

Thiophosphate Esters

Yes

No

No

Yes

RBA > 0.00001%

RBA < 0.00001%

I

RBA < 0.00001%

Yes

No

V

III

Contains

a Charge

No

Yes

II

Alkylaromatic Sulfonic Acids 

Log Kow (-0.6 - 5.7)

Sulfonic Acid Dyes 

Log Kow (-0.4 - 6.0)

Exact match to Mixed Phenols 

Training Set Structure and 

measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Unknown 

Binding Potential

Mixed Organics

No

Exact match to Mixed Organics 

Training Set Structure and 

measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Unknown 

Binding Potential

Exact match to Group 

Training Set Structure 

and measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Meets specific distance 

criteria for: 

Sites “A-B”,

Sites “A-C”, or

Sites “A-B-C”

Contains some 

attenuating feature,

steric or other

High Binding Affinity 

“A-B”;“A-C” or “A-B-C”

RBA < 0.00001%

Low Affinity Binder

“A-B”,“A-C” or “A-B-C”

Strength of 

attenuation factors 

Yes

No

Strong

Weak

Start Here:

Chemical List

e.g., Food Use Inerts,

Antimicrobials

Contains 

a Cycle

Yes Contains 2 OH,

or OH and =0, at 

Spec. Dist

Log KOW <1.3

“Special 

Rule”

Applies

Site “A”

Contains Phenol

Fragment 

Log Kow Range

4-Alkylanilines ( 1.4 - 5.1)

4-Alkoxy Anilines ( 2.6 - 3.7)

Phthalates ( 2.5 - 6.8)

Phenones-(branched) ( 2.7 - 4.8)

4-Alkyl cyclohexanols ( 2.4 - 3.8)

4-Alkyl cyclohexanones ( 2.4 - 3.8) *

2-; 4-; or 2,4,6-Benzoates

(substitution-dependent Log Kows)

IV

Possible Low Affinity

Site A-Type, Site B-Type

Site “B”

Contains “Specified”

Fragment

Log Kow Range

Alkylphenols (1.5 - 8.0)

Alkoxy phenols (2.3 - 4.3)

Parabens (1.9 - 5.4)

Salicylates (2.5 - 5.1)

Belongs to 

known Active

Sub-class

RBA > 0.00001%

Yes

No

VI

Unknown 

Binding Potential

No

Yes

Yes

No

Yes

Belongs to 

known Active

Sub-class

Belong to untested class

Unknown Binding Potential

Mixed Phenols

No

VII

No

Belongs to 

known Inactive 

Sub-class

Log Kow Range 

4-Alkylbenzthiols ( 2.9 - 4.2)

4-n-Alkylfluorobenzenes ( 2.3 - 4.9)

Alkylphenol (2,6-subst) ( 4.2)

Benzamides ( 1.7 - 2.1) 

Borate Esters (-0.5 - 1.0)

Benzoates (non-ring subst) ( 3.6 - 4.2)

Bis-anilines ( 1.6 - 6.2)

Hydrofurans (alcohol & ketone) ( 0.0 - 1.2)

Imidazolidines (-0.9 - 0.6) 

Isothiazolines ( 0.6 - 2.5)

Mono-cyclic Hydrocarbons ( 2.7 - 4.6)

Oxazoles (<0.3)

Phenones (n-alkyl) ( 1.6 - 4.1)

Pyrrolidiones (-0.4 - 3.3)

Sorbitans ( 3.2 - 5.9) 

Triazines (-0.4 - 3.4)

Yes

DDT-Like 

Tamoxifen-Like

Multicyclic hydrocarbons

Alkylchlorobenzenes

Thiophosphate Esters

Yes

No

No

Yes

RBA > 0.00001%

RBA < 0.00001%

I

RBA < 0.00001%

Yes

No

V

III

Contains

a Charge

No

Yes

II

Alkylaromatic Sulfonic Acids 

Log Kow (-0.6 - 5.7)

Sulfonic Acid Dyes 

Log Kow (-0.4 - 6.0)

Exact match to Mixed Phenols 

Training Set Structure and 

measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Unknown 

Binding Potential

Mixed Organics

No

Exact match to Mixed Organics 

Training Set Structure and 

measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Unknown 

Binding Potential

Exact match to Group 

Training Set Structure 

and measured RBA

RBA > 0.00001%

RBA < 0.00001%

No

Yes

Meets specific distance 

criteria for: 

Sites “A-B”,

Sites “A-C”, or

Sites “A-B-C”

Contains some 

attenuating feature,

steric or other

High Binding Affinity 

“A-B”;“A-C” or “A-B-C”

RBA < 0.00001%

Low Affinity Binder

“A-B”,“A-C” or “A-B-C”

Strength of 

attenuation factors 

Yes

No

Strong

Weak

Meets specific distance 

criteria for: 

Sites “A-B”,

Sites “A-C”, or

Sites “A-B-C”

Contains some 

attenuating feature,

steric or other

High Binding Affinity 

“A-B”;“A-C” or “A-B-C”

RBA < 0.00001%

Low Affinity Binder

“A-B”,“A-C” or “A-B-C”

Strength of 

attenuation factors 

Yes

No

Strong

Weak
57

Figure 10. Expansion of Decision Tree Query I. When the answer to the question “Contains a 

Cycle” is ‘No’, the chemical is determined to be acyclic. The system then asks if the chemical 

contains a charge. Whether or not the chemical contains a charge, the same conclusion of an 

RBA < 0.00001% is reached. This figure lists the specific chemical groups in the training set 

from which this conclusion is based. No rainbow trout estrogen receptor binding was detected 

for any of the 25 acyclic compounds tested across all chemical groups listed. Note that acyclic 

compounds with “mixed functional groups” tested contained multiple substituents from many of 

the groups listed below as well as carboxylic acid and sulfonic acid substituents. Detection limit 

of the assay under test conditions in this study is 0.00001% relative to estradiol at 100% (RBA < 

0.00001%). 

Acyclic

I

Contains a 

Net Charge Ionic surfactants

• Phosphoric Acids

• Quat Amines

• Sulfates

• Mixed Functional Groups

Non-Ionic Structures

• Alcohols

• Amines

• Ketones

• Aldehydes

• Borates

• Carboxy Esters

• Mixed Functional Groups

Yes

No

RBA < 0.00001%

RBA < 0.00001%

Acyclic

I

Contains a 

Net Charge Ionic surfactants

• Phosphoric Acids

• Quat Amines

• Sulfates

• Mixed Functional Groups

Non-Ionic Structures

• Alcohols

• Amines

• Ketones

• Aldehydes

• Borates

• Carboxy Esters

• Mixed Functional Groups

Yes

No

RBA < 0.00001%

RBA < 0.00001%
58

Figure 11. Expansion of Decision Tree Query V – Special Structure Rules. Chemical queries are used to assign chemicals to 

groups/subgroups witin Query V. A chemical satisfying the general group query is then assessed for an exact chemical match to a 

training set chemical structure and assigned the binding affinity of that tested chemical. If an exact match is not found for the 

following groups, then “unknown binding potential is assigned: DDT-type, multicyclic hydrocarbons, tamoxifen-type and 

thiophosphate esters. However, the 4-alkyl halobenzenes were sufficiently tested to establish sub-groups based upon Log Kow cutoffs. 

For the 4-alkylchlorobenzenes, the Log Kow cutoff is 4.5, above which binding was observed (RBA > 0.00001%) for Log Kow 4.5 to 

5.5. No 4-alkylchlorobenzenes of Log Kow > 5.5 was tested, so an “unknown binding potential” is assigned for chemicals in this 

group with a Log Kow > 5.5. Two additional groups, alkylbromobenzenes and alkyliodobenzenes have not been tested and are 

currently out of the domain of this expert system. Detection limit of the assay under these conditions is 0.00001% relative to estradiol 

at 100% (RBA < 0.00001%). 

Special Structure 

Rules

V

DDT-Like

Selection Rules

Yes RBA > 0.00001%

No

Yes

Cyclic(>=2) 

Hydrocarbons

Selection Rules

Yes

No

RBA < 0.00001%

RBA > 0.00001%

Yes

No

Tamoxifen-Like

Selection Rules

Yes

No

Alkyl Halobenzenes

Selection Rules

Contains Cl

No

No

RBA < 0.00001%

Yes

Yes

Yes

Yes

No

Thiophosphate Ester

Selection Rules

No

Exact match to DDT Type 

TrSet Structure with 

RBA > 0.00001%

Unknown 

Binding Potential

No

Exact match to Cyclic (>=2)

Hydrocarbon TrSet Structure

with RBA > 0.00001%

Exact match to Cyclic (>=2)

Hydrocarbon TrSet Structure

with RBA < 0.00001%

Yes

Unknown 

Binding Potential

No

RBA > 0.00001% Yes Exact match to 

Tamoxifen Type TrSet Structure 

with RBA > 0.00001%

Unknown 

Binding Potential

No

RBA < 0.00001%

RBA > 0.00001% Yes

No

Exact match to Thiophosphate

Ester TrSet Structure with 

RBA > 0.00001%

Yes

Unknown 

Binding Potential

No

Exact match to Thiophosphate

Ester TrSet Structure with 

RBA < 0.00001%

Yes

Log KOW 

< 4.5

RBA > 0.00001%

No

Yes

Contains Br

Contains I

Unknown 

Binding Potential

Unknown 

Binding Potential

Unknown 

Binding Potential

Log KOW 

4.5 – 5.5

Unknown 

Binding 

Potential

Yes

No

Special Structure 

Rules

V

DDT-Like

Selection Rules

Yes RBA > 0.00001%

No

Yes

Cyclic(>=2) 

Hydrocarbons

Selection Rules

Yes

No

RBA < 0.00001%

RBA > 0.00001%

Yes

No

Tamoxifen-Like

Selection Rules

Yes

No

Alkyl Halobenzenes

Selection Rules

Contains Cl

No

No

RBA < 0.00001%

Yes

Yes

Yes

Yes

No

Thiophosphate Ester

Selection Rules

No

Exact match to DDT Type 

TrSet Structure with 

RBA > 0.00001%

Unknown 

Binding Potential

No

Exact match to Cyclic (>=2)

Hydrocarbon TrSet Structure

with RBA > 0.00001%

Exact match to Cyclic (>=2)

Hydrocarbon TrSet Structure

with RBA < 0.00001%

Yes

Unknown 

Binding Potential

No

RBA > 0.00001% Yes Exact match to 

Tamoxifen Type TrSet Structure 

with RBA > 0.00001%

Unknown 

Binding Potential

No

RBA < 0.00001%

RBA > 0.00001% Yes

No

Exact match to Thiophosphate

Ester TrSet Structure with 

RBA > 0.00001%

Yes

Unknown 

Binding Potential

No

Exact match to Thiophosphate

Ester TrSet Structure with 

RBA < 0.00001%

Yes

Log KOW 

< 4.5

RBA > 0.00001%

No

Yes

Contains Br

Contains I

Unknown 

Binding Potential

Unknown 

Binding Potential

Unknown 

Binding Potential

Log KOW 

4.5 – 5.5

Unknown 

Binding 

Potential

Yes

No
59

Figure 12. Expansion of Decision Tree Query VII – Site B- Belongs to known Active Class. A chemical not satisfying any previous 

query is queried for structural fragments that would place it in one of the chemical groups listed in the figure. Once assigned to a 

group its Log Kow is compared to the specific Log Kow range of group members with an RBA > or < 0.00001% assigned. If the Log 

Kow falls outside of specified ranges the chemical is considered to have an “unknown binding potential”. If a chemical contains more 

than one specific fragment associated with B-site binding it is considered a “mixed organic” chemical. The chemical is then assessed 

for an exact match to a training set structure. If there is an exact match the associated, measured RBA is assigned. If the chemical does 

not match a chemical in the training set, the chemical is out of the expert system domain and an “unknown binding potential” is 

assigned.

Site B

Specific Fragment

4-Alkylanilines 

4-n-Alkoxylanilines 

Phthalates 

Phenones – (branched) 

4-Alkyl cyclohexanols

4-Alkyl 

cyclohexanones*

*4-Alkyl cyclohexanones

positive activity suspected 

to be mediated through 

metabolic conversion to the 

respective 4-alkyl 

cyclohexanol in cytosol.

VII

Belongs

to known Active 

Sub-class

Yes

No

GROUP SPECIFIC LogKow Cutoffs

Log Kow Range

4-Alkylanilines (1.4 – 5.1)

4-n-Alkoxylanilines (2.6 – 3.7)

Phthalates (2.5 – 6.8)

Phenones – (branched) (2.7 – 4.8)

4-Alkyl cyclohexanols (2.4 – 3.8)

4-Alkyl cyclohexanones* (2.4 – 3.8)

RBA > 0.00001%

Yes

No

Unknown 

Binding Potential

• Mixed Organics

RBA < 0.00001%

RBA > 0.00001% Yes

No

Exact match to Mixed 

Organics Training Set 

Structure with 

RBA > 0.00001%

Yes

Unknown Binding Potential No

Exact match to Mixed 

Organics Training Set 

Structure with 

RBA < 0.00001%

2-; 4-; or 2,4,6-Benzoates 

GROUP SPECIFIC 

LogKowCutoffs

Log Kow

Range

4-Alkylanilines < 1.4

4-n-Alkoxylanilines < 2.6

Phthalates > 6.8

4-Alkyl cyclohexanols < 2.4

4-Alkyl cyclohexanones* < 2.4

No

RBA < 0.00001%

Yes

RBA < 0.00001%

RBA > 0.00001% Yes

No

Exact match to 2-, 4-, or 2,4,6-

Benzoates Training Set 

Structure with 

RBA > 0.00001%

Yes

Unknown Binding Potential No

Exact match to 2-, 4-, 2,4,6-

Benzoates Training Set 

Structure with 

RBA < 0.00001%

Site B

Specific Fragment

4-Alkylanilines 

4-n-Alkoxylanilines 

Phthalates 

Phenones – (branched) 

4-Alkyl cyclohexanols

4-Alkyl 

cyclohexanones*

*4-Alkyl cyclohexanones

positive activity suspected 

to be mediated through 

metabolic conversion to the 

respective 4-alkyl 

cyclohexanol in cytosol.

VII

Belongs

to known Active 

Sub-class

Yes

No

GROUP SPECIFIC LogKow Cutoffs

Log Kow Range

4-Alkylanilines (1.4 – 5.1)

4-n-Alkoxylanilines (2.6 – 3.7)

Phthalates (2.5 – 6.8)

Phenones – (branched) (2.7 – 4.8)

4-Alkyl cyclohexanols (2.4 – 3.8)

4-Alkyl cyclohexanones* (2.4 – 3.8)

RBA > 0.00001%

Yes

No

Unknown 

Binding Potential

• Mixed Organics

RBA < 0.00001%

RBA > 0.00001% Yes

No

Exact match to Mixed 

Organics Training Set 

Structure with 

RBA > 0.00001%

Yes

Unknown Binding Potential No

Exact match to Mixed 

Organics Training Set 

Structure with 

RBA < 0.00001%

2-; 4-; or 2,4,6-Benzoates 

GROUP SPECIFIC 

LogKowCutoffs

Log Kow

Range

4-Alkylanilines < 1.4

4-n-Alkoxylanilines < 2.6

Phthalates > 6.8

4-Alkyl cyclohexanols < 2.4

4-Alkyl cyclohexanones* < 2.4

No

RBA < 0.00001%

Yes

RBA < 0.00001%

RBA > 0.00001% Yes

No

Exact match to 2-, 4-, or 2,4,6-

Benzoates Training Set 

Structure with 

RBA > 0.00001%

Yes

Unknown Binding Potential No

Exact match to 2-, 4-, 2,4,6-

Benzoates Training Set 

Structure with 

RBA < 0.00001%
60

Table 1. Training set chemicals within chemical sub-groups for which no rainbow trout estrogen 

receptor binding was observed at an RBA detection limit of 0.00001%.

Acyclic Chemicals CAS Chemical Name LogKow

Acyclic borates 121437 Trimethylborate -1.00

Acyclic alcohol 77996 1,1,1-tris(hydroxymethyl)propane -1.00

Acyclic alcohol 111273 1-hexanol 2.03

Acyclic alcohol 104767 2-ethyl-1-hexanol 3.01

Acyclic alcohol 112301 1-decanol 4.24

Acyclic amine 112572 1,2-ethanediamine, n-(2-aminoethyl)-n'-2-(2-aminoethyl)aminoethyl -1.00

Acyclic amine 78900 1,2-diaminopropane -0.91

Acyclic amine 2783177 1,12-diaminedodecane 3.51

Acyclic carboxy ester 97632 Ethyl methacrylate 1.94

Acyclic carboxy ester 689894 Methylsorbate 1.96

Acyclic aldehyde 4313035 (2E,_4E)-2,4-heptadienal 1.86

Acyclic ketone 504201 2,6-dimethyl-2,5-heptadien-4-one 2.68

Acyclic mixed 67436 Diethylenetriamine pentaacetic acid -1.00

Acyclic mixed 139899 Trisodium (2-hydroxyethyl)ethylenediaminetriacetic acid -1.00

Acyclic mixed 55406536 3-iodo-2-propynyl N-butylcarbamate 2.65

Acyclic mixed 38916426 DL-Aspartic acid, N-(3-carboxy-1-oxo-3-sulfopropyl)-N-octadecyl-, tetrasodium salt 5.33

Acyclic mixed 67633630 N-ethyl-N,N-dimethyl-3-[(1-oxoisooctadecyl)amino]-1-propanaminium, ethyl sulfate 6.43

Acyclic mixed 637398 triethanolamine hydrochloride -1.00

Acyclic mixed 42808366 Octadecanoic acid, 9(or_10)-(sulfooxy)-, 1-butylester, sodium salt 6.76

Acyclic phosphoric acid 4672382 propylphosphonic acid 0.28

Acyclic quat.amine 35141367 1-Propanaminium, N,N,N,-trimethyl-3(trimethoxysilyl)-, chloride; 1-propanaminium, -1.00

Acyclic quat.amine 56375792 tributylmethylammonium chloride 0.24

Acyclic quat.amine 112005 N-dodecyltrimethylammonium chloride 1.22

Acyclic sulfate 126921 sodium 2-ethylhexylsulfate 2.75

Acyclic sulfate 142314 sodium octylsulfate 2.88

Cyclic 

groups/subgroups

Alkylaromatic Sulfonic Acids 657841 sodium_p-toluenesulfonate -0.62

Alkylaromatic Sulfonic Acids 98691 4-ethylbenzenesulfonic_acid 0.38

Alkylaromatic Sulfonic Acids 532025 naphthalene-2-sulfonic_acid_sodium salt 0.63

Alkylaromatic Sulfonic Acids 130143 Naphthalene-1-sulfonic acid, sodium salt 0.92

Alkylaromatic Sulfonic Acids 6149037 4-Octylbenzenesulfonic acid, sodium salt 3.56

Alkylaromatic Sulfonic Acids 27176870 Dodecylbenzenesulfonic_acid_ 5.67

Alkyl Benzthiols 106456 p-toluene_thiol 2.88

Alkyl Benzthiols 2396681 4-tert-butylthiophenol 4.19

p-Alkylfluorobenzene 462066 Fluorobenzene 2.27

p-Alkylfluorobenzene 403394 1-fluoro-4-isopropylbenzene 3.71

p-Alkylfluorobenzene 28593148 4-fluoropentylbenzene 4.90

Benzamide 10546700 N-propylbenzamide 1.72

Benzamide -- 2-methyl-n-propyl-benzamide 2.08

Benzoates - non-ring subst 93992 Phenylbenzoate 3.59

Benzoates - non-ring subst 136607 n-butylbenzoate 3.84

Benzoates - non-ring subst 6789884 Hexyl benzoate; Benzoic acid, n-hexyl ester 3.84

Benzoates - non-ring subst 94473 Phenethylbenzoate 4.01

Benzoates - non-ring subst 94462 Isoamylbenzoate 4.15

Bis-Anilines 101779 4,4'-diaminodiphenylmethane 1.59

Bis-Anilines 54628216 4,4'-diamino-2,2'-dimethylbibenyl 3.57

Bis-Anilines 4073987 4,4'-methylenebis(2,6-dimethylaniline) 4.01

Bis-Anilines 101688 1,1'-methylenebis 4-isocyanato benzene 4.08

Bis-Anilines 101611 4,4'-methylenebis[N,N-dimethyl]aniline 4.45

Bis-Anilines 2390592 Ethanaminium,N-[4-[bis[4-(diethylamino)phenyl]methylene]-2,5-cyclohexadien-1-ylidene]-N-ethyl-,chloride 5.78

Bis-Anilines 2716101 4,4'-[1,4-phenylenebis(1-methylethylidene)]bis-benzenamine 6.04

Bis-Anilines 13680358 4,4'-methylenebis(2,6-diethylanilne) 6.15

Cyclic Alcohols - other 96413 Cyclopentanol 0.71

Cyclic Ketones - other 120923 Cyclopentanone 0.38

Cyclic Ketones - other 78591 Isophorone 1.70

Cyclic Ketones - other 4694126 2,4,4-trimethylcyclopentanone 1.86
61

Cyclic Ketones - other 76222 (+/-)-Camphor 2.18

Furans- sugar 57501 Sucrose -1.00

hydroFurans 97994 tetrahydrofurfuryl_alcohol 0.02

hydroFurans 105215 5-butyldihydrofuran-2(3H)-one 1.15

Imidazolidines 116256 dimethyl-1-hydroxymethylhydantoin -0.86

Imidazolidines 16079882 1-bromo-3-chloro-5,5-dimethyl hydantoin 0.39

Imidazolidines 77485 1,3-dibromo-5,5-dimethyl-2,4-imidazolidinedione 0.54

Imidazolidines 118525 1,3-dichloro-5,5-dimethylhydantoin 0.55

Isothiazolines 2634335 1,2-benzisothiazol-3-one 0.61

Isothiazolines 26530201 2-octyl-3(2H)-isothiazolone 2.45

Mono-Cyclic Hydrocarbons 108883 Toluene 2.73

Mono-Cyclic Hydrocarbons 106423 p-Xylene (anhydrous) 3.15

Mono-Cyclic Hydrocarbons 98828 Cumene 3.66

Mono-Cyclic Hydrocarbons 5989275 (R)-(+)-Limonene 4.57

Oxazoles 6542376 1-aza-3,7-dioxabicyclo(3.3.0) octane-5-methanol -1.00

Oxazoles 7747355 5-ethyl-1-aza-3,7-dioxabicyclo(3.3.0)-octane 0.26

Phenones - n-chain 98862 Acetophenone 1.63

Phenones - n-chain 942927 n-hexanophenone 3.64

Phenones - n-chain 1671756 Heptanophenone 4.13

Pyrrolidiones 872504 1-methyl-2-pyrrolidone -0.38

Pyrrolidiones 2687947 N-octylpyrrolidone; 1-Octyl-2-pyrrolidone 3.31

Sorbitans 1338392 Sorbitan,_monododecanoate_(9CI) 3.15

Sorbitans 1338438 (z)-sorbitan,_mono-9-octadecenoate 5.89

Sulfonic Acid Dyes 1934210 Acid_Yellow_23 -1.00

Sulfonic Acid Dyes 2783940 C.I._Food_Yellow_3 -0.44

Sulfonic Acid Dyes 25956176 C.I. Food Red 17 0.08

Sulfonic Acid Dyes 3567257 Sulcofuron-sodium, monohydrate 1.89

Sulfonic Acid Dyes 3844459 C.I. Acid Blue 9, disodium salt 3.03

Sulfonic Acid Dyes 4404437 Fluorescent Brightener 28 3.09

Sulfonic Acid Dyes 5281049 Pigment Red 57-1 3.41

Sulfonic Acid Dyes 27344418 Disodium 4,4'-bis(2-sulfostyryl)biphenyl 3.84

Sulfonic Acid Dyes 7023612 2-Naphthalenecarboxylicacid,4-[(5-chloro-4-methyl-2-sulfophenyl)azo]-3-hydroxy-,calcium salt(1:1) 6.04

Triazines 7673098 N,N',N-trichloro-1,3,5-triazine-2,4,6-triamine -0.38

Triazines 2893789 Dichloro-s-triazinetrione 1.28

Triazines 5915413 6-chloro-n(1,1-dimethylethyl)-n'-ethyl-1,3,5-triazine-2,4-diamine 3.06

Triazines 28159980 N'-tert-butyl-n-cyclopropyl-6-(methylthio)-1,3,5-triazine-2,4-diamine 3.38
Table 2. Number of discrete chemicals in the food use inert ingredient (FI) and antimicrobial 

pesticide (AM) inventories that are predicted to have rainbow trout estrogen receptor binding 

affinities below or above an RBA of 0.00001% based on the expert system depicted in Figure 9.. 

 

FI AM

Number of chemicals in Acyclic, Charged, or Known Inactive Groups predicted to have RBAs < 

0.00001%

Acyclics (all groups) 230 122

Alkylaromatic Sulfonic Acids 78 3

Sulfonic Acid Dyes 9 1

Sorbitans 7 0

Monocyclic Hydrocarbons 7 0

Cyclic caged Hydrocarbons 1 0

Pyrrolidiones 3 0

Hydrofurans 3 0

n-Alkyl Phenones 1 0

Oxazoles 0 3

Triazines 1 13

Isothiazolines 3 7

Imidazolidines 0 8

Cyclic Inorganics 0 3

2,6-subst Alkylphenol 1 0

Cyclic Pentanones/Others 2 0

Number of Chemicals in Site A, B, or Special Rule Groups predicted to have RBAs < 0.00001% 

because of Log Kow specific cut-offs

4-Cyclohexanones of Log Kow <2.4 1 0

4-Cyclohexanols of Log Kow <2.4 1 0

4-Chlorobenzenes of Log Kow <4 1 0

Number of Mixed Functional/Heteratom Chemicals with Log Kow < 1.3 and/or measured 

RBA < 0.00001%

Mixed Phenols 7 3

Mixed Organics 20 29

Organometallics 2 4

Total Discrete Chemicals with RBAs predicted to be <0.00001% 378 196

Chemicals predicted to have RBAs > 0.00001% by satisfying Site A, B, or Special Rule queries 

and Log Kow > group-specific cut-off

Alkyl Phenols 3 9 

Alkoxy Phenols 1 0

Parabens 3 0

Salicylates 1 0

Phthalates 3 0

Thiophosphate Esters 1 0

Mixed Functional/Heteratom Chemicals with Log Kow >1.3 and measured RBA > 0.00001%

Mixed Phenols 2 6 

Mixed Organics 1 0

Total Discrete Chemicals with RBAs predicted to be >0.00001% 15 15

___________________________________________________________________________

Total Discrete Chemicals in the Inventory 393 211