# An Effects

- based Expert System to Predict E strogen Receptor Binding Affinity for Food Use Inert I ngredients and Antimicrobial Pesticides: Application in a Prioritization Scheme for Endocrine Disruptor Screening

7/2 7 /2009

U.S. Environmental Protection Agency O ffice of Pesticide Programs Washington DC 20460 M id
- C ontinent E cology D ivision N ational H ealth and E nvironmental E ffects L aboratory O ffice of R esearch and D evelopment Duluth MN 55804 R 394 E 353 H 524 C T 347

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H H H A Bi 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 ( Q SARs ) 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 ingre dients 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 i ssue is the development of prioritization techniques for chemicals, such as inert ingredients and some antimicrobial active ingredients that have minimal existing toxicological information. This F ederal I nsecticide F ungicide and R odenticide A ct Scientific A dvisory P anel (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 r ule

- b ased e xpert

s ystem for predicting estrogen receptor (ER) binding affinity was developed by t he 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 ) ( O ECD, 2004 a ) and was the subject of the O ECD Expert Consultation to Evaluate an Estrogen Receptor Binding Affinityii Model for Hazard Identification ( O ECD, 2009 a )

. 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 exp lanation 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 L evel 1 of the OECD E ndocrine D isruption T esting and A ssessment Con ceptual 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 recommenda tions , 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 mean s .iv DISCLAI MER None of the discussion s 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

- settin g 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

. " M easured 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 T ABLE OF C ONTENTS P AGE C HAPTER I. P RIORITIZING C HEMICALS FOR SCREENING AND A SSESSMENT : AN A PPLICATION FOR THE E NDOCRINE D ISRUPTOR S CREENING P ROGRAM
................................ ................... 1 Background

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

................................ .... 3 Prioritizing Chemicals for Endocrine Disruptor Screening

................................ ....... 6 Evolution of the Expert System

................................ ................................ .............. 11 C HAPTER II . M ECHANISTIC B ASIS O F T HE E XPERT S YSTEM T O P REDICT R ELATIVE E STROGEN R ECEPTOR (ER) B INDING A FFINITY
................................ ............................... 1 2 Overview of the Approach to Build an Effects

- Based Expert S yste m .................... 1 2 ER Binding Affinity: An Indicator of Potential Reproductive Effects

.................... 14 The ER Binding Domain

................................ ................................ ......................... 1 6 The Regulatory Chemical Domain

................................ ................................ .......... 17 Characterization of Inert and Antimicrobial Inventories

............................ 1 8 The Chemical Inventory Domain s of Interest and Existing Information

..... 18 The Receptor Binding Assay Domain

................................ ................................ ..... 19 C HAPTER III. R EGULATORY I NVENTORIES O F I NTEREST A ND E XPERT S YSTEM M ODELING D OMAI N
................................ ................................ ................................ ............................... 2 1 Overlaying Model Domains and Regulatory Inventories

................................ ........ 2 1 Expanding the ER Binding Affinity Knowledge Base

................................ ........... 2 3 A Rule

- Based Expert System to Predict ER Binding Affinity

................................ 3 0 C HAPTER IV. A CCESSIBILITY OF THE E XPERT S YSTEM
................................ ................... 3 6 Expert Systems for ER Binding Affinity

................................ ................................ . 3 6 Ongoing Research : Human ER Binding Affinity and Gene Activation

.................. 3 7 C HAPTER V. C ONCLUSIONS ................................ ................................ ................................ 39vi References ................................ ................................ ................................ ........................... 4 3 Figures & Tables ................................ ................................ ................................ ................ 4 81 Chapter I. Prioritizing Chemicals for Screening and Assessment: an Application for the Endocrine Disruptor Screening Program This chapter briefly reviews national and international eff orts to formulate approaches for prioritizing large chemical inventories for toxicity screening, testing, and risk assessment. T he development and application of structure activity relationships (SARs) and quantitative structure activity relationships (QSA Rs) 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 antimic robial 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

. T here are more ne w 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 strat egic 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 Da ta 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/oecds ids/ sidspub.html) , there are r easons to lessen the dependence on in vivo testing all chemicals for all endpoints . These reasons includ e 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 r ecent reports reflect the growing recognition that more efficient testi ng 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 ha zard 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 QSAR s to estimate relevant endpoint values to form prioritization schemes

. The OECD member countries have long recognize d the potential of ( Q ) SAR to establish appropriate priorities for the conduct of initial hazard assess ment s 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 , 2004 a ) and have compiled case studies to illustrate how mem ber 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 foc uses on fully utilizing an integrated approach to testing and assessment. This vision is consistent with the Toxicology Testing in the 21 st Century report published by the National Research Council (N RC, 2007). The future goal is to move toward a hypothes is

- 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 adva nces 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 s cientific 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 meta bolites 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 i s needed to assess and subsequently manage risks

. T he 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 di fferent 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 US EPA 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

. US EPA’s P esticide Program, in collaboration with OPPT, has applied these metho ds in situations where there is limited data. In the assessment of inert ingredients, for example, SARs have been used in identifying stru cturally

- similar c ompounds to form categories of chemicals. This use of SAR allows for the testing of selected membe rs of a category with the results then being applied to the other members of the group. Additionally SAR principl e s are used to identify chemical “analogs” (chemicals that are closely related to the chemical of interest) for compounds where there is limite d 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 e cological risk assessment, QSAR programs such as ASTER ( USEPA, 2009a) and ECOSAR ( USEPA, 2009 b ) 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 Oncolog ic ( USEPA , 2005 a ) , 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 ide ntification of potential toxicological endpoints of concern and thereby target testing needs or follow

- up actions

. Antimicrobial active pesticide ingredients generall y 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 fo r 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 004 5; http://www.regulations.gov/fdmspublic/ContentViewer?objectId=0900006480665444&dispositi on=attachment&contentType=pdf )6 T he 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 antimicro bial pesticides for screening of endocrine disruptor potential based on their potential to bind to the estrogen receptor (ER) . This expert system provid es 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 scr eens , 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 ( s orting and p rioritization with existing data and/or ( Q ) SAR systems) and Level 2 ( i n 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 , 2004 b ). In the US, the Food Quality Protectio n 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 Endocrine7 Disruptor Scree ning 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 Advisor y Panel ( SA P )

. 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 c onsistent with recommendations f rom 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 w ere 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, 1 998 b ) and generally supported by a joint committee of the SAB/SAP ( USEPA , 1999). With regard to sorting and prioritization, t he EDSTAC ( USEPA , 1998 a ) 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 28 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) chemic als 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 p riority

- 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 info rmation, 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 w ere available to support prioritization. The Agency subsequently formulated a framework generally consistent with the EDSTAC’s recommendations ( USEPA , 1998 a ), which was presented to the SAB/SAP ( http://yosemite.epa.gov/sab/sabproduct.nsf/C8ABD41 0E357DBCF85257193004C42C4/$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, conc luded 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 , 1998 a ), 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 the9 universe of chemicals th e 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 , 2005 b ). The Agency further determined that the first 50 to 100 chemicals selected for Tier 1 screening would be drawn from pesticide active ingredients a s well as pesticide i nert 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 , 2007 a ,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 , 2009 c ). Consistent with EDSTAC and SAB/SAP re commendations 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 High

Throughput 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 t he 58 active ingredients in the initial group of 67 pesticide chemicals that will be subject to Tier 1 screening. Several H TPS assays directly relate to estrogen, androgen, thyroid and aromatase activity ; r esults for the pesticide active10 ingredients will be available at [ http://www.epa.gov/comptox/toxcast ]. Future HTPS efforts w ill 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 antimic robial 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 p aper outlines the development of a (Q) SAR

- based expert system and how it ca n 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 T ier 1 screening was evaluated using t he OECD Principles for the Validation o f ( Q ) SARs ( OECD, 2004 a ). 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 , 1998 a ). 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 firs t 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 explained11 mechanistically and how the estimate is reasonable based on data for comparable chemicals. The

secon d 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 des cribe 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 contex t. 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 May 15

- 16 , 2008 in Utrecht, The Netherlan ds) an earlier iteration of this work, a chemical sub

- class approach to form a decision support system for ER binding, was presented (OECD , 2009 b ) [see http://www.olis.oecd.org/olis/2009doc.ns f/linkto/ env - jm

mono(2009)4 ]. The findings from this workshop indicate d 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 de veloped by OECD for use by risk assessors around the globe to facilitate the use of ( Q ) SAR systems documented in accordance12 with OECD ( Q ) SAR validation principles ( OECD , 2004 a ). Subsequent to th is workshop, an OECD Expert Consultation to Evaluate an Estrogen Receptor Binding Affinity Model for Hazard Identification (OECD , 2009 a ) 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 ca tegories 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 or ganization (Figure 1) . The pathway progress es from the molecular initiating event, through cell and tissue level gene transcription and translation, continu ing through organ effects
to an adverse outcome observed in the individual or a population. With a p lausible pathwa y to an adverse outcome described , a rationale is provided for using the molecular initiating event as a basis for prioritiz ing chemicals for further screening with EDSP Tier I assays , which incorporate

en d points at higher l evels of biologic al organizatio n . The specific in vitro assays used to develop the expert system are : i) m easur ed chemical binding to the rainbow trout ER to detect the13 potential for a chemical to initiat e 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 C hapter II present the key components o f the approach to developing the expert system. The first section presents ER b inding a ffinity a s an indicator of p otential

r eproductive e ffects

. 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 d iscusses th e 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 m easuring chemical

- ER binding for compounds in the chemical domain to ensure the collection of accurate data is presented

. Chapter II I describe s how understanding of the ER binding domain, chemical domain, and assay domain in Chapter II is applied to establish an ER binding t raining set r epresentative 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 II I also describes how the knowledge gained from the training set can be “coded” into systematic logic rules that establish a rule

- based expert system. C hapter IV of the report describes how the expert rules are implemented in a decision
tree to s upport prioritization of chemicals in the two inventories for Tier 1 screening

. Chapter IV14 also summarizes on - going research on human ER binding of a strategically chosen subset of food use inert ingredients and antimicrobial pesticides.

ER Binding Affi nity: An Indicator of Potential Reproductive Effects There are multiple protein target s 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 effort s over the past two decades in the area of endocrine disruption ha ve focused on increasing understanding of how chemicals perturb these endogenous hormone systems. Despite the compl exity 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 (Fig ure 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 con ceptual 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 wil l 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 prioriti zation 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 interacti ons and feedback between15 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 wit h 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, a s 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 supp ort 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 t he 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 b een shown to be ER responsive to parent chemical s and their metabolite s ( Shilling and Williams, 2000; Schmieder et al. , 2000; Schmieder et al. , 2004) . The liver slice assay provides confirm ation that the ER binding translates to gene activation or antagoni sm a t the next , higher level of biological organization along the ER

mediated 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 T he ER Binding Domain The energ etic 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 struct ures 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 estab lish 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 che mical 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 in teracting with receptor protein in di fferent 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 intera ct 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 to17 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. , 200 2 )

. 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 th is p aper , systematic measurements of chemicals with low binding affinity were undertaken to establish a ( Q ) SAR training set tha t 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 we re several aspects to consider in developing an expert syst em to prioritiz e food use pesticide inert ingredients and antimicrobial active ingredients for EDSP Tier I screening. First, the structures of the chemicals found in the inventories need ed 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 divers e chemicals in the two chemical inventories of interest

. An iterative approach include d 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 hypothese s , and additional selection of18 chemicals for measuring binding affinity until sufficient information wa s gathered to make a prediction for all the chemical groups i n the inventor ies

. Characterization of Inert and Antimicrobial Inventories. To establish the chemical structures within the food use pesticide inert ingredients (FI) and antimicrobial ac tive ingredients (AM) inventories, the Chemical Abstract System (CAS ) number for each compound was obtained a nd 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 disc rete 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 in ventory 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 i ncluded chemicals without rings ( i.e. , “acyclic” chemicals) such as alcohols, ami n es, 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 alky l benzenesulfonic acids, alkylphenols, alkyloxyphenols, chlorobenzenes, hydro furans, cyclic hexanones and hexanols, imidazolidines, isothiazolines, oxazoles, parabens, cyclic pentanones, phenones, pyrrolidines, phthalat es, salicylates, sorbitans, sulfonic acid dyes, triazines, and chemicals with multiple functional groups further divided into “mixed phenols” i f a ring hydroxyl group was present, or “mixed organics” if there was no a hydroxyl group

. The Chemical Inventor y Domain s of Interest and Existing Information

. The early recognition that some chemicals interact with the ER and alter the endocrine system has led to19 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 de fining 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 ingredient s). Consequently , these existing data are of limited use in developing (Q)SAR model s 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. 2003 a ), 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. T he 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 l iterature 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 using20 “ 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 produce d 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 (Schmie der 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 l ow affinity effects (for details see Schmieder et al. , 2004). The resulting evaluation of binding aff inity at higher substrate concentrations than had been typically employed require d 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; organo metallics, 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 et c . ( 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, chem ical

- 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, 2009 c

– draft in review )

.
Chapter III. Regulatory Inventories of Interest and Expert System Model Dom ain

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 which22 they interact with biological systems are completely different. For example, one che mical 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 involv i ng a common biological effect are combined statist ically 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, e valuation 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 val ue 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, 2009 a ) . The OECD Guidance Document on the Validation of (Qua ntitative) Structure

- Activity Relationship (( Q ) SAR ) Models (OECD, 2004 a ) 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 ca ution 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 o f interest ( see Chapter II T he R egulatory C hemical D omain). In the current effort, the regulatory applicability issue is addressed by subdivid ing inert ingredients and antimicrobial pesticides into respective sub groups that have specific chemical attributes mechanistically related to ER binding

. As empirical data was generated for chemicals within the subgroups, it was used to develop23 “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 subgroup s . In this manner , the training set fo r the expert system was systematically tested to ensure the k nowledge 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 an d AM knowledge base was established by selecting chemicals to investigate bindi ng mechanisms as well as chemicals to expand the structural domain to overlap the regulatory inventories

. Prototype e xpert rules were established to addr ess 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 requ ired for high affinity interactions ( e.g. , A

- B binding of estradiol and similar high affinity chemicals with hydrogen

- bo nding distances of 10.2 to 11 Å as described in Serifimova et al. , 2007 ) . Thus, no testing was conducted for chemicals with these struct ural 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 para sub stituted alkylphenols were tested, a sufficient diversity of other phenols was included to allow24 sub

- grouping with respect to branch ed versus n

- chain and ortho , meta , and para substitution. A group of a lkylphenols , from the basic structure phenol with no s ubstituents, 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 3 A alon g with that of the endogenous hormone 17 ß

- estradiol (E2) . The EC50s for each chemical is compared to that of the positive control ( i.e. , displacement of [ 3 H]

- E2 from the ER by unlabelled E2) to calculate the relative binding affinity (RBA) for all chemical s relative to E2 at 1 00% (see Schmieder et al. , 2004 for details). A subset of chemicals , represent ing 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)

- alkylphenol s with C1 to C5, C8, C9, and C12 carbon chains ( e.g. , see Figure 3 B fo r 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 compoun d 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. A dditionally, a group of p - alkoxy phenols was also tested

. A ll 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 the25 ER sub

- pockets . Because Log Kow is readily calculable from structure and ha s 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 vite llogenin 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, a n extensive series of
p - alkylanilines were tested, an alogous to the alkylphenols, to evaluate the potential role of hydrogen bond i nteractions 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)

. Simila r 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 alkoxy phenols 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 a lkylphenol of Log Kow 5 in Figure 4 ). In this regard , aniline itself was determined to not bind to the ER whereas phenol did bind a nd produce gene activation

. The p - alkylphenol and p - alkylaniline chemical series shown in Figure 4 represent “lead” groups for low affinity hydrogen

- bond donor s in A Site, and low affinity hydrogen bond acceptor s at B site . Additional chemical groups likely to interact at Site A , as hydrogen

- bond donors , include parabens , salicylates with Log Kow s 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 h uman ER mediated gene expression ( e.g. , Routledge e t 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 et26 al. , 2003). Although detail s 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

- subst ituted 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. C hemicals in the training sets were selected a cross the respective Log Kow ranges of each subgroup in the inventories . In contrast to the groups previously mentioned, the se 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. 0 0001 % ) are listed by group/ sub group in Table 1. The results suggest that chemicals with no binding af finity 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 chemical s . 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 chemical s are represent ing a much l arge chemical group. For instance, there were more than 70 alkylaromatic sulfonic acids in the inventories. Figure 5 shows representative chemical structure s 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 the27 inventory that were not tested (Fi gure 7 structures in white background) , but predicted to have an RBA less than 0.00001%, b ased 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 illustrate s how chemica ls 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 sulfoni c acid dyes is that these chemicals are charged in the assay solution s . 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 a nd 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 specie s in the pH 7.4 buffer media. Interestingly, w hile 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 5 A

- ER binding displacement gra phs ). 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 a gonism or antagonism (Figure 5 B) 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 th at

‘ apparent ’ binding of charge d 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 denatur ing the ER protein in binding assays and causing release of28 [ 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 re commended 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 ). I n addition to chemical charge , another important consideration is the potential for chemi cal hydrolysis in the assay solution , resulting in formation of a chemical species in the assay system
. For example, t he 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 K ow 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 substi tuen ts 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 sub groups of chemicals tested. Nonetheless, this iterative process to establish the FI and AM training sets was continu ed until all29 the respective g roups were examined. There are chemicals on the inventories that could not be easily assigned to a single sub group. These chemicals have multiple functional groups ; e.g. , “mixed phenols” if they also contain a t 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 mix ed 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 o rganics in the current training set are shown in Appendix II and III, respectively, to illustrate the diversity o f these chemicals. While the majority of mixed phenols and mixed organics that have been tested thus far do not bind to the trout ER, t here is not sufficient data at this time to further categorize these compounds into subgroups which bind ER and those which do not, or to hypothesi ze structural parameters associated with the activity of those compounds

. T here are additional groups for which a limited number of chemicals were test ed ; 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 insu fficient knowledge to make prediction s 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 is30 assigned. A s additional chemical inventories are e valuated and the training set expanded to represent a larger number and greater diversity of mixed phe nols , mixed organics , and other groups under represented subgroups , it should be possible to gain a better understanding of substituent patterns that result in ER b inding and provide the means to add additional rules to the expert system
.

A Rule
- Based Ex pert 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

numero us 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 d iscuss 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 the31 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 answer s 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% . Exa mples 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 me chanistically 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 abse nce 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 ; s ee Figure 10 )

. Specific acyclic chemicals tested are listed in Table 1. Therefore, q uery 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. Q uery 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 th e specified Log Kow ranges the chemical is32 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

. A n 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

. G enerally, the absence of hydrogen bonding groups, or inappropriate geometry explains the failure of these chemicals to bind to ER ( Katz enellenbogen 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 ha d 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 t he range of Log Kow covered by the tested chemica ls 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 require ments of the first three queries belong to groups of chemicals for which some members of the ir respective sub group bind ER . C hemicals within these subgroups with the greatest binding affinity to the ER receptor are those that mimic steroids and have two o xygen atoms available for hydrogen bonding at the specific distances dictated by the ER receptor. Chemicals that meet these requirements are removed from the other sub groups for further evaluation (Query IV) ; note that none of the chemicals currently in t he FI33 or AM inventories have structural characteristics defined in this query

. C hemicals that do not meet th e requirement s 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 evaluat e these remaining sub groups, 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 1 1 ; note that none of the FI or AM chemica ls 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 suff icient 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 determine d structures in the training set are sufficiently similar to a compound of interest and can be used as analog s 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 determ ine 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 com petitively 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. , i t is not the case th at all chemicals with Log Kow > 1.3 will bind ER. T here are more specific (more restrictive) Log Kow ranges for binding activity that apply within many of the sub groups. 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 ha ve the potential to bind at Site A in the ER (VI in Figure 9 ). This includes the p

- alkylphenol ser ies previously described, p

alkoxyphenols, 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 transac tivation data comparable to that shown for the alkylphenols in Figure 3A & 3B , as well as depict ions 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 inf ormation 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 bindin g 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 se t chemical (Appendix II) and is assigned the appropriate RBA. O nly 3 of the 13 mixed phenols tested have been found to bind ER above the RBA detection limit of 0.00001%. However, i f a chemical is not an exact match to a tested mixed phenol , it is considered to have an ‘unknown binding potential’

. Q uery 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 mixed35 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 matc h 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, t he expert system was applied to the FI and AM inventories

. T he predictions obtained fr o m 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 1 5 are predicted to have an RBA of > 0.00001% . For the AM inventory, of 211 discrete che micals, 1 96 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

c yclohexanols with Kows below this cutoff did not bind to the ER. As mentioned previ ously, 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 perspecti ve 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 bas is to incorporate additional chemical diversity

.36 Chapter IV . Accessibility of the Expert System

Expert System s 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 quer ies 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 th at 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 thr ough 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 cover s the domain of the chemicals to be assessed (usefulness) and, at the same time, provid e 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 to37 a receptor h as 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 th e 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 assay s. Research in progress is evaluating the applicability of the expert system for predicting relative binding affinity to human ERα. Specifically, a subset of the38 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) w hen similar assay systems are compared ( i.e. , c hemical affinity for recombinant ER f rom 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) . B inding 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 wh ere total assay protein can be 10 times greater. This is likely due to differences in non

- specific binding of chemicals to proteins (S chmieder and Henry, 1988 ) other than estrogen receptor protein (see Heringa et al. , 2004 for more discussion of the influe nce of assay total protein on measured ER activi ty ). 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 stud y 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 the se same chemicals , determined using recombinant ERs from either39 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 low er 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 int er pretation

. The co mparative 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 system s that are specific to ‘a vertebrate’ ( i.e. , fish or human) or to establish an ex pert system that will be protective of the most sensitive species f or a specific chemical subgroup.

V. Conclusions The EDSTAC ( USEPA, 1998 a ) 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 p redict 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 limitation s 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 mechan isms of endocrine disruption. As guiding40 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 evalua ted. The OECD P rinciples for the Validation of (Q)SAR s (OECD , 2004 a ) provide a d etailed framework to address the perspectives developed by the EDSTAC. T he 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 unambig uous algorithm. Transparency i s a critical characteristic of the OECD (Q)SAR validation principles

– particularly in the context of the mechanistic plausibility of the predictions and the reaso nableness 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 h ere 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 t h e (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 w ith estimates of exposure potential, consistent with EDSTAC ( USEPA, 1998 a ) and SAB/SAP ( USEPA, 1999) recommendations, t he expert system provide s a complementary effect

- based means to pr ioritize food use inert41 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 trans activation 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 exper t syste ms 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 hazard

based prioritization schemes . R esearch 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 settin g . Contingent on the SAP’s review, and completi on 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 w ould provide effects information relevant to the estrogen hormone system fo r use in subsequent rounds of EDSP priority setting for food use inert ingredi ents 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

e xisting 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 screen ing 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 a n iterative process, bench

- level or HTPS data will be used to develop and validate an evolving (Q)SAR

- based expert system for predict ing 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 recep tor
- 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 R EFERENCES Alslev, B., B. Korsgaard, and P. Bjerregaard. 2005. “Estrogenicity of Butylparaben in Rainbow Trout Oncorhynchus mykiss Exposed via Food and Water.” Aquatic Toxicology. 72: 295

- 304. Bio

- Loom, “Ver. 1.5 Using CLogP.” 2006. Biobyte Corporation, Claremont , CA, USA.. http://www.biobyte.com Bjerregaard, P., Andersen, D. N., Pedersen, K. L., Pedersen, S. N., and Korsgaard, B. 2003. “Estrogenic Effect of Propylparaben (propylhydroxybenzoate) in Rainbow Trout Oncorhynchus mykiss After Exposure via Food and Water.” Computational Biochemistry and Physiology. Part C. 136: 309

- 317. Blair, R. M., Fang, H., Branham, W., Hass, B., Dial, S., Moland, C., Tong, W., Shi, L., Perkins, R., and Sheehan, D. 2000. “The Estrogen Receptor Relative Binding Affinities of 188 Natural and Xenochemicals: Structural Diversity of Ligands.” Toxicological Sciences. 54: 138

- 153. Bradbury S. P., Russom, C.L., Ankley, G.T., Schultz, T. W., and Walker, J.D. 2003. “Overview of Data and Conceptual Approaches for Derivation of Quantitative Structure

- Activity Relationships for Ecotoxicological Effects of Organic Chemicals” Environmental Toxicology and Chemistry

. 22(8): 1789

- 1798. Bradbury S. P., Feijtel, T. C., and Van Leeuwen, C. J. 2004. “Meeting the Scientific Needs of Ecological Risk Assessment in a Regulatory Context.” Environmental Science and Technology. 38: 463A

- 470 A Gilroy, D. J., Miranda, C. L., Siddens, L. K., Zhang, Q., Buhler, D. R., and Curtis, L. R. 1996. “ Dieldrin Pretreatment Alters [ 14 C]dieldrin and [ 3 H]7,12

- dimethylbenz[ a ]anthracene Uptake in Rainbow Trout Liver Slices.” Fundamental and Applied Toxicology. 30: 187

- 193. Gray, M. A., Niimi, A. J., and Metcalfe, C. D. 1999. “ Factors Affecting the Development of Testis

- ova in Medaka, Oryzias latipes , Exposed to Octylphenol.” Environmental Toxicolog y and Chemistry. 18: 1835

– 1842. Heringa, M. , Schreurs, R. , Busser, F. , Van Der Saag, P. , Van Der Burg, B. , and Hermens, J. 2004. “ Toward More Useful In Vitro Toxicity Data With Measured Free Concentrations

. ” Environ mental Sci ence & Techno logy 38 : 6263

– 6270. Jobling, S., Sheahan, D., Osborne, J. A., Matthiessen, P., and Sumpter, J. P. 1996. “Inhibition of Testicular Growth in Rainbow Trout ( Oncorhynchus mykiss ) Exposed to Estrogenic Alkylphenolic Chemicals.” Environ mental Toxicolog y and Chemistry. 15 : 194

- 202. Katzenellenbogen, J., Muthyala, R., and Katzenellenbogen, B. S. 2003. “Nature of the Ligand

binding Pocket of Estrogen Receptor α and ß: T he Search for Subtype

- selective Ligands and Implications for the Prediction of Estrogenic Activity.” Pure and Applied Chemistry. 75 : 2397

- 2403.44 Leskinen, P., Michelini, D., Picard, D., Karp, M., and Virta, M. 2005. “Bioluminescent Yeast Assays for Detecting Estrogenic and Androgenic Activity in Different Matrices.” Chemosphere. 61: 259

- 266. Matthews, J., Celius, T., Halgren, R., and Zacharewski, T. 2000. “Differential Estrogen Receptor Binding of Estrogenic Substances: a Species Comparison.” Journal of Steroid Biochemistry and Molecular Biology. 74: 223

- 234. Miller, D., Wheals, B. B., Beresford, N., and Sumpter, J. P. 2001. “Estrogenic Activity of Phenoli c Additives Determined by an In Vitro Yeast Bioassay.” Environmental Health Perspective s . 109(2): 133

- 138. Morohoshi, K., Yamamoto, H., Kamata, R., Shiraishi, F., Koda, T., and Morita, M. 2005. “Estrogenic Activity of 37 Components of Commercial Sunscreen Lotions Evaluated by In Vitro Assays. Toxicology In Vitro. 19 : 457

- 469. National Research Council (NRC). 2007. Toxicity Testing in the 21 st Century: A Vision and a Strategy. National Academy of Sciences, Washington, D.C. http://www.nap.edu/catalog.php?record_id=11970#toc Olsen, C., Meussen

- Elholm, E., Hongslo, J., Stenersen, J., and Tollefsen, K. 2005. " Estrogenic Effects of Environmental Chemicals: A n Interspecies Comparison.” Computational Biochemistry and Physiology. Part C. 141 : 267

- 274. Organization for Economic Cooperation and Development, ( OECD), 2004a. The Report From The Expert Group On (Quantitative) Structure

- Activity Relationships [(Q)SARs] On The Principles For The Validation Of (Q)SARs

. Series on Testing an d Assessment. No. 49. ENV/JM/MONO(2004)24. 17

- Dec

- 2004. http://www.oecd.org/dataoecd/33/37/37849783.pdf OECD, 2004 b. Chemical Testing Guidelines for Endocrine Disruptors: Conceptual Framework. Jan. 2004. http://www.oecd.org/document/58/0,3343,en_2649_34377_2348794_1_1_1_1,00.html OECD. 2008. Workshop on Integrated Approaches to Testing and Assessment. Series on Testing and Assessment No. 88. ENV/JM/MONO(2008(10). 27

- May

- 2008

. http://www.oecd.o rg/dataoecd/45/52/40705314.pdf OECD, 2009a. Report of the Expert Consultation to Evaluate an Estrogen Receptor Binding Affinity Model for Hazard Identification

. Task Force on Hazard Assessment

. ENV/JM/HA/RD(2009)1

. 2

- March

- 2009 107pp. (to be declassified 31

- Jul

- 2009). OECD. 2009b

. Report Of The Workshop On Structural Alerts For The OECD (Q)SAR Application Toolbox

. Series on Testing and Assessment No. 10 1 . Feb., 2009. ENV/JM/MONO(2009)4

. http://www.olis.oecd.org/olis/2009doc.ns f/linkto/env

- jm

- mono(2009)445 OECD , 2009c . Draft Detailed Review Paper on Environmental Endocrine Disruptor Screening: The Use of Receptor Binding and Transcriptional Activation Assays for Fish. Series on Testing and Assessment. No. XX. May . 2009. http://www.oecd.org/document/12/0,3343,en_2649_34377_1898188_1_1_1_1,00.html (public comment period ended 29

- May

- 2009) Petit, F., LeGoff, P., Cravedi, J. P., Valotaire, Y., and Pakdel, F. 1997. " Two Complementary Bioassays for Screening the Estrogenic P otency of Xenobiotics: Recombinant Yeast for Trout Estrogen Receptor and Trout Hepatocyte Cultures.” Journal of Molecular Endocrinology. 19 : 321

- 335. Routledge, E. J., Parker, J., Odum, J., Ashby, J., and Sumpter, J. 1998. “Some Alkyl Hydroxy Benzoate Pre servatives (Parabens) Are Estrogenic.” Toxicology and Applied Pharmacology. 153 : 12

- 19. Schmieder, P.K. and Henry, T.R. 1988. “Plasma Binding of n

- B utanol, P henol, N itrobenzene, and P entachlorophenol in Rainbow Trout and Rat: a Comparative Study.” Computational Biochemistry and Physiol ogy. 91C (2): 413

- 418. Schmieder, P., Tapper, M., Linnum, A., Denny, J., Kolanczyk, R., and Johnson, R. 2000. “Optimization of a Precision

- cut Trout Liver Tissue Slice Assay as a Screen for Vitellogenin Induction: Comparison of Slice Incubation Techniques.” Aquatic Toxicology. 49: 251

- 268. Schmieder, P., Ankley, G., Mekenyan, O., Walker, J., and Bradbury, S. 200 3a. “QSAR Models for Predicting ER Binding Affinity of Structurally Diverse Chemicals.” Environmental Toxicology and Chemistry

. 22(8): 1844

- 1854. Schmieder, P., Mekenyan, O., Bradbury, S., and Veith, G. 2003b. “QSAR Prioritization of Chemical Inventories for Endocrine Disruptor Testing.” Pure and Applied Chemistry. 75 (11/12): 2389

- 2396. Schmieder, P. K., Tapper, M. A., Denny, J. S., Kolanczyk, R. C., Sheedy, B. R., Henry, T. R., and Veith, G. D. 2004. “Use of Trout Liver Slices to Enhance Mechanistic In terpretation of Estrogen Receptor Binding for Cost

- effective Prioritization of Chemicals within Large Inventories.” Environmental Science and Technology. 38(23): 6333

- 6342. Schultz, T.W., Sinks, G., and Cronin, M. 2000. “Effects of Substituent Size and Di mensionality on Potency of Phenolic Xenoestrognes Evaluated with a Recombinant Yeast Assay.” Environmental Toxicology and Chemistry. 19: 2637

- 2642

. Serafimova, R., Todorov, M., Nedelcheva, D., Pavlov, T., Akahori, Y., Nakai, M., and Mekenyan, O. 2007. “QS AR and M echanistic I nterpretation of E strogen R eceptor B inding”. SAR and QSAR in Environmental Research

. 18: 389

- 421. Seki, M., Yokota, H., Matsubara, H., Maeda, M., Tadokoro, H., and Kobayashi, K. 2003a. “Fish Full Life Cycle Testing for the Weak Estrogen 4

- t
- pentylphenol on Medaka ( Oryzias latipes ).” Environmental Toxicology and Chemistry. 22 : 1487

- 1496.46 Seki, M., Yokota, H., Maeda, M., Tadokoro, H., Kobayashi, K. 2003b. “Effect of 4

- N onylphenol and 4

- tert
- O ctylphenol on the Sex Differentiation in Medaka ( Oryzias latipes ).” Environmental Toxicology and Chemistry. 22: 1507

– 1516. Shi, L., Tong, W., Fang, H., Perkins, R., Wu, J., Tu, M., Blair, R., Branham, W., Waller, C., Walker, J., and Sheehan, D. M. 2002. “An Integrated ‘4

- Phase’ Approach for Setting En docrine Disruption Screening Priorities

- Phase I and II Predictions of Estrogen Receptor Binding Affinity.” SAR and QSAR in Environmental Res earch

. 13(1): 69

- 88

. Shilling, A. and Williams, D. 2000. “Determining Relative Estrogenicity by Quantifying Vitellogenin Induction in Rainbow Trout Liver Slices.” Toxicolog y and Applied Pharmacology. 164: 330

- 335. Terasaka, S., Inoue, A., Tanju, M., and Kiyama, R. 2006. “Expression Profiling of Estrogen

R esponsive Genes in Breast Cancer Cells Treated with A lkyl phenols, C hlorinated P henols, P arabens, or B is

- and B enzoylphenols for Evaluation of Estrogenic Activity.” Toxicological Letters. 163: 130

- 141. Tong, W., Perkins, R., Strelitz, R., Collantes, E., Keenan, S., Welsh, W., Branham, W., and Sheehan D. M. 1997

. “Quantitative

- Structure Activity Relationships (QSARs) for Estrogen Binding to the Estrogen Receptor: Prediction Across Species.” Environmental Health Perspective s . 105 : 1116

- 1124. United States Environmental Protection Agency (USEPA) . 1998a. Endocrine Disruptor Screening and Testing Advisory Committee (EDSTAC) Final Report: Chapter 4: Priority Setting. Aug. 1998. http://www.epa.gov/scipoly/oscpendo/pubs/edspoverview/finalrpt.htm USEPA

. 1998b. EDSP : FRN: Proposed Statement of Policy. Vol. 63, No. 248. Dec. 1998. 71542

- 71568. http://www.epa.gov/endo/pubs/122898frnotice.pdf USEPA

. 1999. Review of the EPA’s Proposed Environmental EDSP

. July 1999. http://yosemite.epa.gov/sab/sabproduct.nsf/C8ABD410E357DBCF85257193004C42C4/$File/ec 13.pdf USEPA

. 2002. EDSP : FRN: Proposed Chemical Selection Approach for Initial Screening. Vol. 67, No. 250. Dec. 2002. 79611

- 79629. http://www.epa.gov/scipoly/oscpendo/pubs/12

- 02

- frnotice.pdf USEPA. 2005a. OncoLogic User’s Manual: An Expert System for Prediction of the Carcinog enic Potential of Chemicals. Ver. 6.0. March 2005. http://www.epa.gov/oppt/sf/pubs/onco

- user - man.pdf USEPA. 2005b

. Endocrine Disruptor Screening Program (EDSP): Federal Registry Notice (FRN): Chemical Selection Approach for Initial Screening. Vol. 70, No. 186. Sep. 2005. 56449

- 56465. http://www.epa.gov/fedrgstr/EPA

- TOX/2005/September/Day

- 27/t19260.pdf47 USEPA. 2007a. EDSP: FRN: Draft List of Initial Pesticide Active Ingredients and Pesticide Inerts to be Considered for Screening under the Federal Food, D rug and Cosmetic Act. Vol. 72, No. 116. June 2007. 33486

- 33503. http://www.epa.gov/endo/pubs/draft_list_frn_061807.pdf USEPA. 2007b

. EDSP: FRN: Draft Policies and Procedures. Vol. 72, No. 239. Dec. 2007. 70842

- 70862. http://www.epa.gov/scipoly/oscpendo/pubs/draft_policies_frn.pdf USEPA. 2008a. EDSP: FRN: Data Requirements for Antimicrobial Pesticides. Part III. 40 CFR Parts 158 and 161. Vol. 73, No. 196. Oct. 2008 . http://edo cket.access.gpo.gov/2008/E8

- 23127.htm USEPA . 2008b. 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. EPA

- HQ

- OPP

- 2008

- 0110

- 0045. http://www.regulations.gov/fdmspublic/component/main?main=DocketDetail&d=EPA

- HQ

OPP

- 2008

- 0110 USEPA

. 2009a. ASsessment Tools for the Evaluation of Risk (ASTER). http://www.epa.gov/med/Prods_Pubs/aster.htm USEPA

. 2009b. Ecological Structure Activity Relationships (ECOSAR). Feb. 2009. http://www.epa.gov/oppt/newchems/tools/21ecosar.htm USEPA. 2009 c. EDSP: FRN: Final List of Initial Pesticide Active Ingredients and Pesticide Inert Ingredients to be Screened under the Federal Food, Drug, and Cosmetic Act. Vol. 74, No. 71. April 2009. 17579

- 17585. http://www.epa.gov/endo/pubs/final_list_frn_041509.pdf Wiese, T. E., and Brooks, S. C. 1994. “Molecular Modeling of Steroidal Estrogens: Novel 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

- tert
P entylphenol on the Gene Expression of P450 11 ß

- H ydroxylase 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 e t 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 bindi ng 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. 1 7ß

- 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 CH 3 

### H

H H H 

# A B R 394 E 353 H 524 

# C T 347

HO OH CH 3 

### H

H H H 

# A BFigure 3. Binding and gene expression of p

- alkylphenols in rainbow trout estrogen receptor (rt ER ) in vitro assays. A) rtER binding to receptor derived from liver cytosol determined by displacement of [ 3 H]

- 17ß

- e stradiol (E2) for: unlabelled 17ß

- estradiol (E2,  ), phenol (  ), 4

- ethylphenol (  ), 4

- n - butylphenol (  ), 4

- n - octylphenol (  ), 4

- t
- octylphenol (  ), 4

- n nonylphenol (  ), 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 - alk ylphenol (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 -2VTG 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

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 ctrl Log Concentration (M)[3H]-E2Binding (%) AFigure 4. Relationship between rainbow trout estrogen receptor binding (Relative Binding Affinity; RBA) and Log K ow for chemicals interacting at Site A ( e.g. , p - alkyl phenols ; 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 KowRelative Binding Affinity52 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 scal e) 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 Y

axis scale).

Log Kow = 0.38 calc S O O O H

C H 3

Log Kow = 0.63 msrd O H S O O 2NSA

Log Kow = 0.92 calc S O O O H NSA Log Kow = - 0.62 msrd C H 3 S O H

O O PTS

Log Kow = 3.56 calc S O O O H

C H 3 OBS

Log Kow = 5.67 calc S O O O H

C H 3 DBS EBS

Log Concentration (M) ctrl -10 -9 -8 -7 -6 -5 -4 -3 -2VTG 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 B) A)53 Figure 6. Structures of sulfonic acid dyes, abbreviations, and Log Kow, and results of r ainbow 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 V TG mRNA in response to

- 8 log M (  ) E2 when combined with AYE from

- 5 logM to

- 3 logM

.

Log Kow = 6.04 C H 3 Cl
N N O O O H

S O O O WRB

Log Kow = 3.84 O S

O O S O O O BAD

Log Kow = 3.41 O S

O O C H 3 N N
O O O H BCA Log Kow = 3.09 S O O O H S O O O H N H
N
N O H O H N N H N N H N N O H

O H N N H N FBR

Log Kow = 3.03 O - S O O N C H 3

S O O O N + S O O O C H 3 ABL Log Kow = 1.89 Cl Cl

N H O

N H Cl

O Cl
S O O O MIT Log Kow = 0.08 O H
N N O C H 3

S O O O H C H 3 S O O O H RAC

Log Kow =
- 0.44 O S O O N N O H

S O O O SYE

Log Kow =
- 1.00 O S O O N N N H
N S O O O O H O O 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) ctrl -10 -9 -8 -7 -6 -5 -4 -3 -2VTG mRNA copies/400 ng total RNA

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 B) A)54 Figure 7. To provide transparency at ea ch 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 s ulfonic a cid d yes structures in the knowledge base (grey shad ed 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. C hemical 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 = 3.41 c Log Kow = 3.84 c Log Kow =3.09 c Log Kow = 6.04 Log Kow = - 0.44 c Log Kow = 1.89 m O S O O N N N H
N S O O O O H O O

O - S O O N C H 3

S O O O N + S O O O C H 3 O S O O S O O O S O O O H S O O O H N H
N
N O H O H N N H N N H N N O H

O H N N H N O S O O C H 3 N N
O O O H C H 3 Cl
N N O O O H

S O O O O H
N N O C H 3

S O O O H C H 3 S O O O H Cl Cl

N H O

N H Cl

O Cl
S O O O 

### Rainbow Trout Training Set

Inventory

Log Kow = 3.03 c Log Kow = 4.12 c Log Kow = 4.19 c Log Kow = 4.12 c Log Kow = - 1.00 c Log Kow = 0.73 c O S O O N N N H
N S O O O O H O O

O - S O O N C H 3

S O O O N + S O O O C H 3 C H 3 Cl
N N O O O H

S O O O S O O O N N O H N H 2 S O O O - O H
N N O C H 3

S O O O H C H 3 S O O O H S O O O H O H N N S O O O H

O O H O H N N S O

O O H

Cl O O O H N N S O

O O -

Cl O N N S O O O S O O O O S O O N N O H

S O - O O

Log Kow = 0.08 c Log Kow = - 1.00 c

Log Kow = 6.0455 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 inductio n 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 f rom - 4.5 to

- 2.5 logM (  ) compared to positive control E2 (

- 10 to

- 5 logM,  ) response slices run in the same experiment.

O
O
C H 3 BB

Log Kow = 3.84 msrd O
O C H 3
C H 3 IAB

Log Kow = 4.15 msrd O
O

C H 3 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) ctrl -10 -9 -8 -7 -6 -5 -4 -3 -2VTG mRNA copies/400 ng total RNA

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 trans activation 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 K OW <1.3

“ Special

Rule ” Applies

Site “ A ” Contains Phenol Fragment

Log Kow Range 4
- Alkylanilines ( 1.4
- 5.1) 4
- Alkoxy Anilines ( 2.6
- 3.7) Phthalates ( 2.5
- 6.8) Phenones
- (branched) ( 2.7
- 4.8) 4
- Alkyl c yclohexanols ( 2.4
- 3.8) 4
- Alkyl cyclohexanones ( 2.4
- 3.8) * 2
- ; 4
- ; or 2,4,6
- Benzoates (substitution
- dependent Log Kows ) IV

Possible Low Affinity Site A
- Type, Site B
- Type

Site “ B ” Contains “ Specified ” Fragment Log Kow Range Alkylphenols (1.5
- 8.0) Alkoxy phenols (2.3
- 4.3) Parabens (1.9
- 5.4) Salicylates (2.5
- 5.1) Belongs to known Active

Sub
- class RBA > 0.00001% Yes

No VI

Unknown Binding Potential No

Yes

Yes

No Yes

Belongs to known Active

Sub
- class

Belong to untested class Unknown Binding Potential Mixed Phenols

No VII No Belongs to known Inactive Sub
- class

Log Kow Range

4
- Alkylbenzthiols ( 2.9
- 4.2) 4
- n - Alkylfluorobenzenes ( 2.3
- 4.9) Alkylphenol (2,6
- subst) ( 4.2) Benzamides ( 1.7
- 2.1) Borate Esters (
- 0.5
- 1.0) Benzoates (non
- ring subst ) ( 3.6
- 4.2) Bis
- anilines ( 1.6
- 6.2) Hydrofurans (alcohol & ketone ) ( 0.0
- 1.2) Imidazolidines (
- 0.9
- 0.6) Isothiazolines ( 0.6
- 2.5) Mono
- cyclic Hydrocarbons ( 2.7
- 4.6) Oxazoles (<0.3) Phenones (n
- alkyl) ( 1.6
- 4.1) Pyrrolidiones (
- 0.4
- 3.3) Sorbitans ( 3.2
- 5.9) Triazines (
- 0.4
- 3.4) Yes DDT
- Like Tamoxifen
- Like Multicyclic hydrocarbons Alkylchlorobenzenes Thiophosphate Esters

Yes No

No Yes

RBA > 0.00001% RBA < 0.00001% I

RBA < 0.00001%

Yes No V III Contains a Charge No

Yes II

Alkylaromatic Sulfonic Acids

Log Kow (
- 0.6
- 5.7) Sulfonic Acid Dyes

Log Kow (
- 0.4
- 6.0)

Exact match to Mixed Phenols Training Set Structure and measured RBA RBA > 0.00001% RBA < 0.00001% No Yes

Unknown Binding Potential Mixed Organics No Exact match to Mixed Organics Training Set Structure and measured RBA RBA > 0.00001% RBA < 0.00001% No Yes Unknown Binding Potential Exact match to Group Training Set Structure and measured RBA RBA > 0.00001% RBA < 0.00001% No Yes Meets specific distance criteria for: Sites “ A
- B ” , Sites “ A
- C ” , or Sites “ A
- B
- C ”

Contains some attenuating feature,

steric or other

High Binding Affinity

“ A
- B ” ; “ A
- C ” or “ A
- B
- C ”

RBA < 0.00001%

Low Affinity Binder

“ A
- B ” , “ A
- C ” or “ A
- B
- C ” Strength of attenuation factors Yes No

Strong Weak Start Here: Chemical List e.g., Food Use Inerts , Antimicrobials Contains a Cycle Yes Contains 2 OH, or OH and =0, at Spec. Dist

Log K OW <1.3

“ Special

Rule ” Applies

Site “ A ” Contains Phenol Fragment

Log Kow Range 4
- Alkylanilines ( 1.4
- 5.1) 4
- Alkoxy Anilines ( 2.6
- 3.7) Phthalates ( 2.5
- 6.8) Phenones
- (branched) ( 2.7
- 4.8) 4
- Alkyl c yclohexanols ( 2.4
- 3.8) 4
- Alkyl cyclohexanones ( 2.4
- 3.8) * 2
- ; 4
- ; or 2,4,6
- Benzoates (substitution
- dependent Log Kows ) IV

Possible Low Affinity Site A
- Type, Site B
- Type

Site “ B ” Contains “ Specified ” Fragment Log Kow Range Alkylphenols (1.5
- 8.0) Alkoxy phenols (2.3
- 4.3) Parabens (1.9
- 5.4) Salicylates (2.5
- 5.1) Belongs to known Active

Sub
- class RBA > 0.00001% Yes

No VI

Unknown Binding Potential No

Yes

Yes

No Yes

Belongs to known Active

Sub
- class

Belong to untested class Unknown Binding Potential Mixed Phenols

No VII No Belongs to known Inactive Sub
- class

Log Kow Range

4
- Alkylbenzthiols ( 2.9
- 4.2) 4
- n - Alkylfluorobenzenes ( 2.3
- 4.9) Alkylphenol (2,6
- subst) ( 4.2) Benzamides ( 1.7
- 2.1) Borate Esters (
- 0.5
- 1.0) Benzoates (non
- ring subst ) ( 3.6
- 4.2) Bis
- anilines ( 1.6
- 6.2) Hydrofurans (alcohol & ketone ) ( 0.0
- 1.2) Imidazolidines (
- 0.9
- 0.6) Isothiazolines ( 0.6
- 2.5) Mono
- cyclic Hydrocarbons ( 2.7
- 4.6) Oxazoles (<0.3) Phenones (n
- alkyl) ( 1.6
- 4.1) Pyrrolidiones (
- 0.4
- 3.3) Sorbitans ( 3.2
- 5.9) Triazines (
- 0.4
- 3.4) Yes DDT
- Like Tamoxifen
- Like Multicyclic hydrocarbons Alkylchlorobenzenes Thiophosphate Esters

Yes No

No Yes

RBA > 0.00001% RBA < 0.00001% I

RBA < 0.00001%

Yes No V III Contains a Charge No

Yes II

Alkylaromatic Sulfonic Acids

Log Kow (
- 0.6
- 5.7) Sulfonic Acid Dyes

Log Kow (
- 0.4
- 6.0)

Exact match to Mixed Phenols Training Set Structure and measured RBA RBA > 0.00001% RBA < 0.00001% No Yes

Unknown Binding Potential Mixed Organics No Exact match to Mixed Organics Training Set Structure and measured RBA RBA > 0.00001% RBA < 0.00001% No Yes Unknown Binding Potential Exact match to Group Training Set Structure and measured RBA RBA > 0.00001% RBA < 0.00001% No Yes Meets specific distance criteria for: Sites “ A
- B ” , Sites “ A
- C ” , or Sites “ A
- B
- C ”

Contains some attenuating feature,

steric or other

High Binding Affinity

“ A
- B ” ; “ A
- C ” or “ A
- B
- C ”

RBA < 0.00001%

Low Affinity Binder

“ A
- B ” , “ A
- C ” or “ A
- B
- C ” Strength of attenuation factors Yes No

Strong Weak Meets specific distance criteria for: Sites “ A
- B ” , Sites “ A
- C ” , or Sites “ A
- B
- C ”

Contains some attenuating feature,

steric or other

High Binding Affinity

“ A
- B ” ; “ A
- C ” or “ A
- B
- C ”

RBA < 0.00001%

Low Affinity Binder

“ A
- B ” , “ A
- C ” or “ A
- B
- C ” Strength of attenuation factors Yes No

Strong Weak57 Figure 10. Expansion of Decision Tree Query I

. W hen the answer to the question “ Contains a Cycle ” is ‘ No ’ , the chemical is determined to be acyclic. The system then ask s 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. N o rainbow tro ut 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 a s well as carboxylic acid and sulfonic acid substituents. Detection limit of the assay under t est 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 e xact chemical match to a training set chemical structure and assigned the binding affin it y of that tested chemical. If an exact match is not found for the following groups, then “unknown binding potential is assigned: DDT

- t ype, m ulticyclic hydrocarbons, t amoxifen

- t ype and t hiophosphate e sters. However, the 4

- a lkyl halobenzenes were sufficien tly tested to establish sub

- groups based upon Log Kow cutoff s. 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, alkyl bromo benzenes 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 RBA > 0.00001% Yes

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 RBA > 0.00001% Yes

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 No59 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. O nce assigned to a group it s 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 a n “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 e xact 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” i s 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 c hemicals within chemical sub

- groups for which no rainbow trout estrogen receptor binding was observed at an RB A 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 phospho ric 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 1.8661 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 3.38Table 2. Number of discrete c hemicals 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 Fig ure 9.

. FI AM Number of che micals 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 Phe nones 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

- Cyclohexa nones 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 Inventor y 393 211