## 1 FIFRA Scientific Advisory Panel Meeting

Jan uary 29

- February 1, 20 13

CHARGE QUESTIONS

Scientific Issues Associated with Prioritizing the Universe of Endocrine

Disruptor Screening Program (EDSP) Chemicals Using Computational Toxicology Tools

The agency intends to screen those chemicals which have the greatest potential to interact with the endocrine systems in humans and other species. To that end

, EPA has proposed a n approach to determine t he order in which to screen certain classes of chemicals ( e.g., drinking water contaminants and pesticide inert ingredients, including fragrances) using the Tier 1 battery under EPA’s Endocrine Disruptor Screening Program (EDSP)

. This proposed chemical prioritization

process use s existing information including physico chemical properties and exposure information together with information from computational toxicology

methods

, including in silico expert systems, effects

- based chemical category and

read

- across concepts, and empirical in vitro low throughput and high throu ghput (HTP) testing. Thus

, a number of specific current and emerging technical

methods a re involved in the proposed chemical prioritization process to cover the large EDSP chemical inventory

.

The purpose of this FIFRA Scientific Advisory Panel review is to obtain input and

recommendations on the scientific concepts, principles and process es used to a) assign a lower priority or exclude chemicals from EDSP screening based on their physicochemical properties; b ) expand the estrogen receptor ( ER ) E xpert System (ES) originally built for prioritizing food use pesticide inert ingredients and antimicrobial active ingredients (SAP, 2009 a,b ) to cover nonfood inert ingredients ( i.e., ESv2) and determine the domain of applicability for Safe Drinking Water Act ( SDWA ) chemicals and inert ingredient fragrances ; c ) develop an approach to expand chemical coverage to the larger EDSP chemical inventory and build ESv3 by identifying the chemical domain for SDWA chemicals and fragrances

not covered by ESv2 ; and d) consider HTP performance and applicability as training set data in building ESv3

.

Building on the 2009 SAP of EPA’s ER expert system (i.e., ESv1) , this SAP

review will use an ER

- mediated A dverse O utcome P athway (AOP) as the organizing framework . The focus of this review will be on the specific ER pathway

. This review will seek advice on whether the approach presented here can be used generically for any chemical category

- based expert system for other molecular initiating events ( e.g., the androg en receptor based pathway or perturbation of the thyroid axis). The approach presented here is anchored by the

## 2 Organization of Economic Cooperation and Development ( OECD ) QSAR

Validation P rinciples to ensure an overall transparent process that result in a

well

- founded approach that achieves a defined regulatory objective, in this case

an effects

- based approach to prioritize whic h chemicals to be screened earlier in

the EDSP Tier 1 battery of assays

. According to the OECD QSAR Validation Principles, a scree ning assay should use a well

- defined endpoint for which there is adequate confidence that measured endpoint differences are directly attributable to differences between chemical structures tested, rather than a reflection of attributes of the assay system ( e.g., how the biological or physical aspects of the assay system may affect chemicals differently, or how the endpoint data were calculated and interpreted).

To determine the extent to which HTP ER assay binding and transactivation data are appropriate to inform expansion of the ER expert system, the panel will be

asked to comment on whether EPA clearly and transparently described how the HTP data were collected, processed, and interpreted (sections 4, 5 and 6) . The panel will also be asked to comment on the characteristics of the data derived

from the HTP ER assays in the context of expanding and developing ESv3 for use in determining EDSP Tier 1 screening priorities , consistent with characteristics of data used to develop E Sv 1 and 2 given that the major ity of environmental chemicals will have no ER affinity or very low affinity for the receptor ( i.e., to have confidence that negative in vitro results indicate inactivity for the molecular initiating event (MIE) within the AOP )

.

The Charge Questions to the SAP will begin with a general consideration of the

overall prioritization decision logic and conceptual approach followed by more specific questions concerning the specific steps or components w ithin the prioritization methodol ogy.

Question 1

. Overall Prioritization Approach (Section 2.2; also see Section

9 )

1.1 Please comment on appropriateness of the overall conceptual approach for prioritizing the EDSP chemical inventory ( note : subsequent charge questions address issues associated with specific components of the prioritization approach) . In your comments, please address:

The extent to which EPA’s description of the process transparently captures and describes the key technical steps of the prioritization

scheme and whether there are other scientific considerations the agency should incorporate into its EDSP prioritization scheme;

The robustness of the scientific support for the overall approach and, in particular, the log ical s equence of filters, and

## 3 Whether changes to the approach could set priorities more efficiently and effectively

Question 2

. Physicochemical Propert ies ( Section 2 .2.2 ) Not every chemical necessarily needs to undergo EDSP screening and testing.

Decisions of whether to screen a chemical for endocrine interaction are made in part based on information about a chemical’s inherent properties ( e.g., pKa values, molecular weight, reactivity/stability, corrosivity, known functional groups, and charged species). EPA may use this information to identify untestable chemicals or those that are unlikely to pose systemic effects ( e.g., endocrine perturbation ). Application of filters based on physiochemical properties enables EPA to reduce the chemical universe that would need further evaluation (as illustrated in the white paper

, from ~10,000 chemicals to ~5000 or less).

2.1 Please comment on the proposed e xclusion criteria for each physicochemical property a s appropriate for identifying chemicals that are untestable and/or unable to elicit systemic toxicity. Please include a

discussion of the extent to which varying the range of exclusion criterion

can inf luence confidence in identifying chemicals that are untestable and/or unable to elicit systemic effects ( i.e., those substances that are unlikely to trigger the molecular initiating event). Please include in your

comments:

W hether there are additional chemical characteristics that should be considered ( e.g., volatility)

,

W hether there are additional data sources for each parameter identified (e.g., chemaxon for pK a values , EpiSuite for half life values, etc. )

, and how to consider multiple data sources in a weight of evidence approach.

Whether t he exclusion criteria cut offs and the confidence that application of the exclusion criteria will reasonably identify those

substance s that are untestable and/or un able to trigger the

molecular initi atin g event o f the ER pathway . Also please discuss options for evaluating cases where uncertainty in a chemical’s specific parameter range overlaps a p

- chem filter cut off

. Please consider in your comments the following exclusion criteria: pKa values < 2 or >11.5; hydrolysis half life of <40 days and charged species of 99% or greater at pH 7

Question 3

. ER Expert System Development ( Section 3)

## 4 The ER ES was originally built based on a training set (TrSet) of chemicals using

in vitro assays specifically optimized to measure a well defined endpoint as indicated by ER binding and gene activation and to cover the domain of applicability (in the case of ESv1 food use pesticidal inert ingredients and antimicrobial active ingredients ) . This ES was the subject of a 2009 FIFRA SAP

review. Since that review additional work has been done and additional TrSet

data ha ve been generated to cover pesticide non

- food use inert ingredients (i.e., ESv2). In addition, an analysis has determine d that the ESv2 can cover ~70 percent of the EDSP Chemical Universe that includ es SDWA chemicals and fragrances

.

3. 1 Please comment on the approach used to build ESv2 through chemical testing and effect

- based category and read across methods to provide a

s cientifically defensible approach to predict ER binding potential for a larger number of chemical groups, particularly with regard to defining new groups from within the Mixed Organics and Mixed Phenols. Please

indi c ate any considerations unique to ESv2 that indicate the approach used to develop ESv1 (SAP, 2009 a ) needs to be modified.

3. 2 Building from the ESv1 training set, please comment on the level of scientific confidence that a chemical is unlikely to initiate the ER AOP, i f

the in vitro assay TrSe t data shows no activity . Please comment on the extent to which the level of confidence may vary by chemical category.

Questions 4

. HTP ER Binding and ER Transactivation D ata ( Sections 4

- 6)

T he agency is proposing to use the HTP data to expand the ER expert system ( i

. e

., ES v3) . Therefore

, it is important to understand how the HTP data are generate d and interpreted for the intended use . As endorsed by the 2010 SAP on Integrated Approaches to Testing and Assessment Strategy: Use of New Computational and Molecular Tools, i n evaluating computational tools, the agency will consider internationally accepted science principles including the OECD QSAR validation principles o f “ Clear, Defined Biological Endpoint, Mechanistic Interpretation, Unambiguous Algorithm, Goodness of Fit and Domain of Applicability.” These principles are consistent with and complement the recommendations of the E ndocrine

D isruptor S creening and T esti ng A dvisory C ommittee (EDSTAC) and considered flexible to be broadly applicable to predictive models or computational tools which may

be based on HTP or other in vitro data. The principle s particularly applicable to this charge question are : a well

- defined endpoint and mechanistic interpretation. A “well defined endpoint” is intended to ensure clarity in the endpoint data used to build predictive models, chemical categories, or read

- across. Any predictions made from the data will inherently contain all unc ertainties and limitations in the data measurement and interpretation. The intent of “ mechanistic interpretation ” is to ensure a mechanistic association between the attributes of a chemical and its interaction with the biological system resulting in the me asured endpoint, to the degree possible. The following questions relate to the transparency of the HTP techniques ( e.g., how the data are

## 5 processed and interpreted and the extent of reliability and consistency within an assay and among HTP assays )

.

4.1 Given the importance of a well

- defined endpoint to determining if/how endpoint data are appropriately used, the panel is asked to comment on:

Whether the assays are sufficiently described so that others can reconstruct the assay conditions and data analy sis and to what

extent additional information would be useful ?

Whether EPA has described sufficiently the important experimental conditions that affect the assay measurements, and whether EPA has sufficiently discussed their potential impact on assay resul ts

and interpretation of the results?

Whether the chemical library ( e.g., purity and analysis), chemical exposure ( e.g., solvents used, chemical dilutions), and plates and plate layouts provides information necessary for data interpretation?

The adequacy of test concentrations (maximum and minimum concentration tested) and cutoffs, and whether the concentration cutoffs affected some assays results more than others, especially in the context of false negatives.

4.2 With respect to data interp retation, please comment on the approach for defining an active chemical ( i.e., to initiate the ER AOP ) and an inactive

chemical ( i.e., unlikely to initiate the ER AOP ) compound and whether the

method of data interpretation is adequately described and if the rationale for the approach is sufficiently presented. Please i nclude in your

comments:

The adequacy of the approach to generate the data plots; how and what parameters are calculated, etc

. ,

H ow the background data were used to establish the control l evel

,

W hether the process for identifying assay interference is adequately described

,

Adequacy of data normalization, outlier identification, curve fitting, background subtractions, and all other data processing and calculation techniques , and a ppropriateness of the statistical analyses

.

4.3 Would you recommend other considerations or approaches to analyz ing

the HTP data ?

## 6 Question 5. Performance Evaluation of the HTP ER Binding and Transcriptional Activation Assays Against a S et of Reference Chemicals ( Section 6)

An important aspect of the Agency’s Proposed Chemical Prioritization Approach for the EDSP is to minimize the occurrence of false negatives and to have confidence that all potentially active chemicals ( i.e., those that can trigger the MIE) are identified . Therefore, the performance of each of the computational

toxic ology components of the prioritization process is being evaluated. The performance of each HTP assay has been evaluated using a set of well

docu mented reference chemicals that represent a diversity of chemical classes and range of potencies. Additional performance metrics have also been evaluated that take into account a number of parameters that are unique to HTP technology.

5.1 Please comme nt on the selection of reference chemicals and whether they are sufficient to assess the performance of each HTP assay for ER agonists . How well do the reference chemicals represent the range of potencies needed to establish the reliability and relevance o f these assays for use in EDSP chemical prioritization for the inert ingredients

, fragrances

, and SDWA chemicals ?

5.2 Please comment on whether the Agency’s evaluation of the performance of the HTP ER binding and activation assays has considered and accurately assessed all relevant aspects of the assays . For example, have signal

- to

- noise ratios, background subtraction and interferences been adequately characterized when describing the strengths and limitations of the assay?

5.3 Based on analyses in s ections 5 and 6, p lease comment on the comparative performance of the 8 HTP assays for detecting ER reference agonists (e.g

., do some assays perform differently, and if so to what

extent ?) Was sufficiently detailed information provided explaining the likely reason s for assay differences , when they are observed ( e.g., cutoffs, assay interferences, background differences)?

Question 6. Analysis of ER Expert Training Set and HTP **in vitro** Assay Data ( Section 7)

To better understand assay responses to a chemical and whether it has the potential to activate the ER

- mediated pathw ay, the agency compared a set of

chemicals evaluated wit h the ER HTP in vitro assays with data on the same set of chemicals included as part of the in vitro training set data used to build the ER

ESv1 /2 (ES TrSet).

## 7 6.1 Please comment on the agency’s assessment of how HTP in vitro assays performed with respect to expectations of performance for chemicals within chemical categories when compared with the in vitro training set

data used to build the ES v1/2

.

6.2 Please comment on EPA's approach to comparing the ES TrSet data with

the HTP data and the interpretations regarding discordant results between the two assay approaches. Please recommend other considerations or approaches to conducting this comparative analysis?

6.3 Given that there may be varying occurrences of false negatives and false positives based on different sources of training set data , please include in your comments options for addressing these uncertainties for different categories in the context of building ESv3 ?

Question 7 . Category

- Based Approach and Strategic Testing to Expand the

ES applicability domain with additional **in vitro** data ( Section 8)

. Section 8 describe s a category base strategy (as used to build the original ESv1 ) to expand cover age training set data for the entire EDSP in ventor y ( i.e., the OECD principle that addresses the “ domain of application ”) . Th e approach described in Section

8 is considered relevant to any in vitro assay data that is applicable for measuring a well defined end point within an AOP under study (in this case, an ER

- mediated pathway).

7.1 Please comment on the adequacy and efficiency of the category based approach to select chemicals for testing to expand the training sets in terms of covering the ~1700 chemicals not covered in the domain of ESv2.

7.2 Based on the HTP assay performance (an alysis of reference chemicals and comparative analysis with the ES TrSet), please comment on the adequacy of the HTP data for advancing the ER expert system’s rules to cover the additional groups of chemicals in ESv 3

.

7.3 To the extent there are differences between the ES TrSet and HTP data in detecting the ability of chemicals to initiate the ER AOP, especially in the

context of minimizing ‘false negatives’ for low potency compounds, p lease

comment on the strengths and lim itations of combining data from the different assays to generate training set data for building the structure

based rules within the ESv3. What are the strengths and limitations of using training set data from assays that have the same or similar degree

of sensitivity.

Question 8

. In V itro T esting and Computer Based Simulations : Addressing

Active Metabolites ( Note: This question is looking toward the future and

t here is no proposal presented in the white paper

. D iscussion of the issue of active metabolites in examples are presented in Section 7 .4.3 )

## 8 In the context of building a category rule

- based approach, it is important to understand if the biological activity is attributed to the parent compound or a metabolite. It is also important to understand if a metabolite of an ‘inactive’ parent compound may be able to trigger the ER MEI. T he agency would like to take advantage of the growing knowledge of assays systems as well as existing

metabolic profiles, computation techniq ues for predicting metabolites and understanding of ER

- binding structure activity relationships to address this area of uncertainty

.

8 .1 Please provide any initial thoughts on how varying empirical and/or computational techniques could be employed to account for metabolite s that may trigger the ER MIE

.

Question 9

. Use of the AOP and Category

- Based Testing Strategies for Other Endocrine Pathways (Chapter 9)

The agency plans to implement an EDSP chemical prioritization scheme that will

use both exposure and effect based metrics. The current paper focused on development of an effect

- based approach using the ER pathway and relevant in

vitro assays. This prioritization methodology draws on several fundamental concepts generally applicable to the dev elopment of computational methods

. These concepts include understanding inherent chemical properties as well as

chemical structure and bioactivity relationships, use of the AOP concept and category

- based read across methods to guide the development and ap plication

of in vitro testing using lower or higher throughput methods and computer

- based

simulations

.

9 .1 P lease comment on whether the principles and concepts used to develop the ER expert system are generally applicable for any category

- based prioritization system for other molecular initiating event s ( e.g.

, other AOPs for perturbing estrogen, androgen receptor, and thyroid hormone systems)?

9 .2 What lesson s have been learned from the development of the ER focused prioritization model that will facilitate more efficient and effective development of an effect

- based prioritization model for the androgen pathway.

9 .3 What will be the challenges in developing a prioritization using in vitro methods and computer based simulations for the thyroid hormone system ?