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FIFRA Scientific Advisory Panel Meeting

January 29- February 1, 2013

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 an approach to determine the 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 uses existing information including physicochemical 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 throughput 

(HTP) testing. Thus, a number of specific current and emerging technical 

methods are 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 processes used to a) 

assign a lower priority or exclude chemicals from EDSP screening based on their 

physicochemical properties; b) expand the estrogen receptor (ER) Expert System 

(ES) originally built for prioritizing food use pesticide inert ingredients and 

antimicrobial active ingredients (SAP, 2009a,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 Adverse Outcome Pathway (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 androgen receptor based pathway or 

perturbation of the thyroid axis). The approach presented here is anchored by the 
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Organization of Economic Cooperation and Development (OECD) QSAR 

Validation Principles 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 which chemicals to be screened earlier in 

the EDSP Tier 1 battery of assays. According to the OECD QSAR Validation 

Principles, a screening 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 ESv1 and 2 given that the majority 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 within the 

prioritization methodology. 

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 logical sequence of filters, and 
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Whether changes to the approach could set priorities more 

efficiently and effectively 

Question 2. Physicochemical Properties (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 exclusion criteria for each 

physicochemical property as 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 influence 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: 

Whether there are additional chemical characteristics that should 

be considered (e.g., volatility),

Whether there are additional data sources for each parameter 

identified (e.g., chemaxon for pKa values, EpiSuite for half life 

values, etc.), and how to consider multiple data sources in a weight 

of evidence approach.

Whether the exclusion criteria cut offs and the confidence that 

application of the exclusion criteria will reasonably identify those 

substances that are untestable and/or unable to trigger the 

molecular initiating event of 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)
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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 have been generated to cover pesticide non-food use inert ingredients (i.e.,

ESv2). In addition, an analysis has determined that the ESv2 can cover ~70 

percent of the EDSP Chemical Universe that includes 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 

scientifically 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 

indicate any considerations unique to ESv2 that indicate the approach 

used to develop ESv1 (SAP, 2009a) 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, if 

the in vitro assay TrSet 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 Data (Sections 4-6)

The agency is proposing to use the HTP data to expand the ER expert system (i.e.,

ESv3). Therefore, it is important to understand how the HTP data are generated 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, in evaluating computational tools, the agency will consider 

internationally accepted science principles including the OECD QSAR validation

principles of “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 Endocrine 

Disruptor Screening and Testing Advisory Committee (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 principles 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 uncertainties 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 measured endpoint, to the degree possible. The following 

questions relate to the transparency of the HTP techniques (e.g., how the data are
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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 analysis 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 results 

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 interpretation, 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 include in your 

comments:

The adequacy of the approach to generate the data plots; how and

what parameters are calculated, etc.,

How the background data were used to establish the control level,

Whether 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 appropriateness of the statistical 

analyses.

4.3 Would you recommend other considerations or approaches to analyzing

the HTP data?
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Question 5. Performance Evaluation of the HTP ER Binding and 

Transcriptional Activation Assays Against a Set 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 

toxicology components of the prioritization process is being evaluated. The 

performance of each HTP assay has been evaluated using a set of welldocumented 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 comment 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 of 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 sections 5 and 6, please 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 reasons 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 pathway, the agency compared a set of 

chemicals evaluated with 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). 
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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 ESv1/2. 

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)

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Section 8 describes a category base strategy (as used to build the original ESv1) to 

expand coverage training set data for the entire EDSP inventory (i.e., the OECD 

principle that addresses the “domain of application”). The 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 (analysis 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 ESv3. 

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, please 

comment on the strengths and limitations 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 Vitro Testing and Computer Based Simulations: Addressing 

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

there is no proposal presented in the white paper. Discussion of the issue 

of active metabolites in examples are presented in Section 7.4.3)
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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. The agency would like to 

take advantage of the growing knowledge of assays systems as well as existing 

metabolic profiles, computation techniques 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 metabolites

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 development 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 application 

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

simulations.

9.1 Please 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 events (e.g., other AOPs 

for perturbing estrogen, androgen receptor, and thyroid hormone 

systems)? 

9.2 What lessons 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?