# Endocrine

Policy Forum

November 25 , 2014

Docket Id ent ification Number EPA

- HQ

- OPP

- 2014

- 0614

Dr. Fred Jenkins Office of S cience Coordination and Policy (7201M) Environmental Protection Agency US EPA Headquarters 1200 Pennsylvania Ave., NW Washington, D.C. 20460

- 0001

Post ed to Federal eRulemaking Portal: http://www.regulations.gov

RE: EPF c omments submitted with regard to the FIFRA SAP on integrated bioactivity and exposure ranking

Dear Dr. Jenkins :

The Endocrine Policy Forum ( EPF) 1 is committed to assisting its members in complying with EPA’s Endocrine Disruptor Screening Program (EDSP)

. EPF has focused on compl ying with the EDSP List 1 Tier 1 Test Orders and is preparing for subsequent Tier

2 testing as necessary. The EPF sup ports the development and use of scientifically sound methods for priority setting for the EDSP. Herein we consider and provide constructive
feedback on the EPA’s White Paper developed for public release and SAP review,

“ Integrated bioactivity and exposure ranking: A computational approach for the prioritization and screening of chemicals in the Endocrine Disruptor Screening Program ” (hereafter “SAP White P aper ” )

.

At the c onceptual level, w e support EPA’s data

- driven approach to employ scient ifically
valid methods to combine high

- throughput (HTS) bioactivity data via modeling

, convert
in vitro activities to systemic exposures using high

- throughput toxicokinetics (HTTK),
and then to compare these data to high

- throughput exposure (H T E) predictio ns to derive integrated bioactivity and exposure ranking (IBER) values to aid in the prioritization of

1 The Endoc rine Policy Forum is a consortium of CropLife America and American Chemistry Council members and non

- member stakeholders formed to address the aggregate needs of the group with respect to technical guidance and regulatory policy in response to the implemen tation of the EPA Endocrine Disruptor Screening Program

. Working with the EPA, our goal is to maximize the efficient and effective use of the Agency 's and our members' time and resources.EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 2

chemicals for screening under the EDSP program

. We generally understand the EPA approach to determine the estrogen receptor area under the curve (ER AU C), androgen receptor area under the curve (AR AUC), and derivation of the IBER. However, the information EPA released in its white paper, and appendices, is insufficient to support an independent review. Specifically, EPA's models currently lack enough transparency to
evaluate their strengths and weaknesses. The data sources, including the identification of the set of ToxCast data used (e.g.

, the public release date , R scripts, and other supporting
information ) should have been provided for stakeholders to better interpret and/ or recapitulate these models. Further, the step

- wise function of these models, and how the range of ER and AR AUC values relate to in vitro potency have not been adequately
defined. These limitations suggest the utility of these m odels for prioritization cannot currently be adequately independently characterized by external scientists. Nevertheless, the methods are encouraging and appear to address several key challenges faced in interpreting HTS results for determining activity, including potential assay interference that may lead to different outcomes across a suite of HTS assays , including ER and AR

assays.

In the attached set of detailed comments , primarily focused on the charge questions with some overarching observations

, w e provide analysis and perspectives on the SAP White Paper as well as recommendations for improving the scientific basis of priority setting for chemical screening utilizing high

- throughput tools. The work described in the SAP White Paper clearly advances the science of prioritization by defining the relative distance between predicted exposure and predicted bioactivity, with emphasis on
estrogen

- or androgen

- receptor driven bioactivity. However, in their current state, the ER
and AR AUC models are not de scribed in enough technical and mathematical detail, and as such, may appear as “black box” toxicology. In order for stakeholders to responsibly
review these models for use in decision

- making, it will be necessary to describe all terms in these models (e. g., sign(slope), how T

- scores are applied) and the specific reasons for model choices (e.g., linear model). These further explanations and cl arifications will be
critical in not only advancing the science and future application of these high

- throughput

me thods

, but also in achieving confidence with stakeholders. We view this work as a conceptual advance in the science of EDSP prioritization, but future application of these models will require critical review by stakeholders, which is not currently possibl e. F urther explanation of these models also comports with EPA’s stated objectives of advancing data quality and transparency.

It is important for the SAP to provide clear direction to EPA to guide the Ag ency's research on developing and validating the data sets and prediction models. Thank you inEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 3

advance for your consideration of our comments. If you have any questions please

contact me at emihaich@nc.rr.com or 9 19

- 479

- 6947.

Sincerely,

Ellen Mihaich, Ph.D, DABT
Scientific Coordinator Endocrine Policy Forum

# Endocrine

Policy Forum

Endocrine Policy Forum (EPF) Comments on

Integrated bioactivity and exposure ranking: A computational approach for the prioritization and screening of chemicals in the

Endocrine Disruptor Screening Program

( EPA

- HQ

- OPP

- 2014

- 0614 )EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 2

Table of Contents

1

. Overarchin g Comment........................................................................ 3
A. T he ER and AR AUC Models of HTS Endocrine Activity

............... ..... ... 3
B

. Integrated bioactivity exposure ranking

...... ................ ........................... 4 C

. Application of a Scientific Confidence Framework (e.g., Cox et al., 2014)

.... 4
D. Abbreviated Time for Review and Analysis of EPA’s White Paper and Related Materials

........ ........................ . ............................................ . . .......... .... 5

2. EPF Response to Charge Questions Concerning Estrogen Bioactivity...

....... . . .. ... . 6 A. Charge Question 1.a

....................................................... .. ....................... .. ........ 6 B. Charge Question 1.b

........................................................ ....................... .. ....... 10 C. Charge Question 1.c............................................................................

.. . .......... 12 D. Charge Question 1.d..............................................................................

.. ........ 13 E. Charge Question 1.e.................................................................................

... ..... 16 F. Cha rge Question 1.f..................................................................................

... .... 17

3. EPF Response to Charge Questions Concerning Androgen Bioactivity.....

........ ... 19 A. Charge Question 2.a

.................................. ..................... ................. ............ .... 19
B. Charge Question 2.b

....................................................... ....................... ....... ... 21 C. Charge Question 2.c

....................................................... .... .............. ........... .... 22
D. Charge Question 2.d

....................................................... ........................ ....... .. 22

4. EPF Response to Charge Questions Concerning the integrated bioactivity exposure ranking (IBER)

...... . ....... ................................................................... .... .. 23 A. Charge Question 3.a

.......................................................................... . ... ... ....... 23
B. Charge Question 3.b and 3.c

.......................... ......................................... . ...... 23

5. References

.................................................................................................... ... . ..... 28

6. Appendix A : Print

- out of the iCSS Dashboard Data for DES

............... .............. 30EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 3

1. Overarching Comments

A. The ER and AR AUC Models of HTS Endocrine Activity

The EPA’s data

- driven model approach to combine HTS assay activity within the estrogen/anti

- estrogen or androgen/anti

- androgen pathways addresses many of the challenges of using HTS in vitro data as prioritization information for EDSP : inclusion of all orthogonal or related assay data due to the perhaps unknown limitations and strengths of certain technologies; development of a modeling approach that att empts to use all o f these data (use of a penalty) with fewer, rather than more assumptions; and separation of cytotoxicity and assay interference from in vitro endocrine activity driven by ER or AR signaling. However, several areas require improvement and /or further explanation before the utility of the ER and AR AUC models can be independently evaluated and verified
for their fit

- for

- purpose use in prioritization for further EDSP screening in the EDSP21
framework.

1. Transparency, transferability, and user

- interface with the model are currently lacking. Specifically, the EPA has not described which release of the ToxCast data was used to develop the ER and AR AUC scores reported ; it is inferred that
the latest re

- release of the ToxCast data was used, and t hese data have only recently been posted by the EPA (end of October 2014), and without appropriate guidance and time for stakeholders or independent peer reviewers on the SAP to
review these data.

2. The EPA has not provided enough detail of the function o f the model to recapitulate it, nor have they provided the R scripts or a user interface which are necessary to objectively and independently evaluate and verify the model.

3. The ER and AR AUC models do not incorporate potential in vitro interaction with

st eroidogenesis , a mechanism that may drive apical effects on estrogen and androgen systems . This is a major toxicological deficiency with current use of these models for prioritizing chemicals that may interfere with estrogen and androgen signaling.

4. Expl anations of how the ER and AR AUC range of values relate to a potency
distribution for reference chemicals are currently lacking, and are necessary to improve understanding of how the model might actually be used for a prioritization task

. The White Pape r would be strengthened by demonstration ofEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 4 how prioritization (or the setting of a benchmark level for delineating "priority"

from "non

- priority") would be accomplished . For example for ER, use of an AUC = 0.7 correspo nding to phytoestrogen activity or s etting the IBER benchmark at the IBER of a phytoestrog e n or a chemical of low priority would be helpful

. The proposed cut

- off for positivity of AUC = 0.1 by EPA has not been scientifically justified; specifically, EPA has not provide d enough information

r egarding the pathway potency that might be expected to correspond to such a proposed cut

- off level

.

5. The AR AUC model is too preliminary to evaluate charge question 1d on the use of the model to distinguish and prioritize chemicals on the basis of in vitro androgen bioactivity. No predictivity can be reported for the model because comparisons to in vivo or “true positive” data are not available.

B. Integrated bioactivity exposure ranking

The IBER approach represents a major advance in systematic pri oritization of
chemicals . The integration of bioactivity results with exposure information provides a scientifically sound approach for use in priority setting for the EDSP. The general concept is well

- conveyed : decreased priority corresponds to increase d distance between the upper 95% confidence limit on potential oral human exposure and the oral equivalent dose corresponding to the 5% bioactivity level. While such an approach can account for uncertainty and variability, upper bounds and lower bounds ar e clearly not as stable as central estimates. For comparison across compounds for use in priority setting, central estimates of both exposure and activity should be considered. The use of upper bound and lower bound estimates likely implies greater precis ion than is warranted by the datasets and models,

What is less obvious is how the IBER distribution will be used to establish further priorities for screening , as pharmaceuticals rank quite high in IBER, with a clear break
from all other chemicals. Demo nstration of low and high priority using IBER would improve stakeholder capability to evaluate it as a fit

- for

- purpose prioritization tool.

C. Application of a Scientific Confidence Framework (e.g., Cox et al., 2014)

The EPA’s program for exposure

- activity profiling includes some components of the Scientific Confidence Framework described by Cox et al. (2014); however, the program would benefit by adopting all aspects of the Scientific Confidence Framework.
The framework is designed to document s ci entific confidence in high throughputEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 5 screening (HTS) assays and subsequent pre diction models

. The major components of the
S cientific F ramework include:

1) A naly tical validation

- the biological basis and analytical performance of each

assay should be understood

, including the chemical domain of applicability and assay performance

.

2) Q ualification

– to ensure transparency, a defined algorithm for each prediction
model is needed that includes measures of goodness

- of

- fit, robustness and predictivity

, a s well as model limitations

. This includes characteriz ation of prediction models in sufficient detail to facilitate review, reconstruction and independent verification of results . In the S AP W hite P aper, the models are not characterized sufficiently to allow full review and independent verification. This aspect of the current program requires additional work.

3) U tilization

- justifying applications where there is sufficient scientific confidence to support the use of the prediction model. The lev el of scientific confidence needed for HTS assays and prediction models is “use dependent” (i.e., the confidence needed for prioritization is less than including HTS data/models in a weight of evidence assessment or replacing some EDSP Tier 1 assays)

. We understand that EPA is in the pr ocess of implementing this step and we look forward to the opportunity to review the Agency's findings.

When available, the assay validity results, the model code, training and test sets and model performance results will enable independent verification and objective and thorough peer review. Such information should ensure that the scientific evidence and documentation is sufficient to support the proposed uses for EDSP prioritization. Unfortunately, since this informatio n has not yet been made available by EPA, a thorough and independent scientific evaluation is not possible.

D . Abbreviated Time for Review and Analysis of EPA’s White Paper and Related
Materials

As with the four S AP meetings held in 2013 and t he one held in July 2014, EPA has again provided its documents to the public only 3 weeks prior to the SAP, giving stakeholders only 14 or 15 days total to comprehensively review the SAP White Paper and develop and submit written comments. Moving forward

, adequate time for public
review and comment would result in more meaningful stakeholder involvement for the Agency. The short time for review is exacerbated by the “soft” re

- release of ToxCast data at the end of October, and the use of unpublished HTTK v alues for ToxCast Phase IIEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 6 chemicals. The models presented incorporate new data, not all of which is publically or easily available, and as such the time provided t o review the presented models was
insufficient.

2. EPF Response to Charge Questions Conc erning Estrogen Bioactivity

Charge Question 1.a: How clearly has EPA described the computational tools in Section 2.1 (i.e., high throughput assays and models) used to estimate ER agonist and antagonist bioactivity?

EPA's impetus for the described conce ptual approach to generate area under the curve (AUC) scores for chemicals, corresponding to a profile of potential estrogenic or anti

estrogenic activity, is based on the need to employ a method that can combine results from different types of assay s in a manner that proves useful in prioritizing chemicals for further endocrine screening. The general introduction of this model, and the assay data input for the model, are well

- described. While we commend EPA for its initiative in developing such an appro ach, and believe we understand the main features of the modeling, several shortcomings are apparent. For example, the specific function of the
model to generate the AUC scores, and filtering using the Z

- score and T

- score, require s further description and utilities for independent experts , other agencies, the private sector, and SAP peer reviewers , among others, to be able to utilize and evaluate the model. The current presentation of the model represents an initial work

- in

- progress rather than a tool
tha t other toxicologists and information scientists can use and explore in order to define strengths, limitations, and inputs that may have the most significant (or least significant) impacts on the model output

.

Specific points for clarification that would have significantly improved presentation clarity include:

i. A complete and separate section to describe the Z

- score approach to distinguishing cell stress and cytotoxicity

- related assay interference (page 18 of 111).

We commend the effort that EPA has inve sted toward distinguishing cytotoxicity from
apparent estrogenic or anti

- estrogenic activity, a major source of assay interference which
hinders appropriate interpretation of prioritization information. However, the section describing the technical d etails of the Z

- score approach requires the inclusion of more information before it can be considered a reproducible and reliable approach open to adaptation.EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 7

a. The collection of 35 assays in the ToxCast assay battery used to detect

cytotoxicity and cell s tress should be explicitly described in a table or appendix, including assay source, assay endpoint name, range of concentrations evaluated in screening, technology or assay platform, and target information. By providing specific information , oth ers might be able to regenerate these Z

- scores, and also suggest revision, either deletion or addition, of certain assays to this cytotoxicity battery from either assays that are already run within the ToxCast screening program, or assays currently
outside the purview of ToxCast.

b. The global cytotoxicity median absolute deviation ( MAD ) calculation should be specifically demonstrated, perhaps in a separate appendix or via issuance of an R program and flat input data file (.csv) such that others could verify this value of 0.26 log units.

c. A visual graphic should be provided to demonstrate the distribution and meaning of Z

- scores; i.e., a typical value range that suggested overlap with cytotoxicity should be defined for each assay (as illustrated for ATG_ERa_TR ANS in a separate section of the document on page 33), or across all assays.

d. Example Z

- score calculations should be provided.

ii. A complete table of derived numerical values, including the symbol and definition. Such a table would include some of the descr iptions given in the text, and in some cases expand these descriptions. Terms such as the F matrix, R j vector (pages 21

- 22), penalty(R), etc. There are many terms to track, and complete definitions provided in one table, in words and mathematically, woul d assist in understanding. The R j vector is not well

- described at all; a re these values
the vector of 2 6 activity values (in log micromolar units) (page 23)

. More detail on these terms, and examples, would be helpful.

iii. A complete description of how the si gn (slope) term is used to discount assays or sets of assays active at lower concentrations than the remaining assays. The description provided on page 22 is not adequate to understand how the data are being discounted mathematically. This "discounting" may significantly impact the predicted bioactivity.EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 8 iv. A bulleted summary or workflow diagram of the order of operations in the model.

A bulleted summary or workflow diagram would help explain the steps, and their order, for the model to generate scores for use in prioritization. Expansion of Figure 2.7 (page 34 of 111) to include each mathematical step following collection of the vector of assay AC50 values would be helpful to understand for instance when the penalty(R) is applied in relationship to T

- filt ering and Z

- score filtering. Without this logical work flow, it is difficult to recapitulat e the model,

or identify the steps w ith the greatest impact on the ultimate result.

v. Release of an interactive utility concurrently with the White Paper de scribing the
model. It would be useful to understand how different chemical

- assay inputs affect the model score by having a user interface, like the one described on page 101 of 111. In the absence of the ability to interact with the model and
demonstrat e how different chemical

- assay inputs affect model scores, it is not possible to provide a complete assessment of the strengths and weaknesses of the model as requested by Charge Question 1b. Thus, the SAP White Paper may be somewhat premature based on the pr ogress of the model development and presentation.

vi. Release of R

- code and sample input files concurrently with the White Paper describing the model. Also

, EPA should concurrent ly release R

- scripts and easy

to

- use flat input files to regenerate model scores, at a minimum for the reference
chemicals

, so as to enable a thorough and independent peer review

. Lacking such information, it would be appropriate to state that the model l acks
transparen cy , and is more of a “black box” than a verifiable, open source

program . With the issuance of R

- scripts and files for analysis, a technical discourse could be developed on the validity and merit of the model. Release of

pre

- named, flat files for call

- in to R would be most appropriate, as this would not require the installation of additional programs beyond R or experience with database management software, e.g. MySQL, or access to a server to run a MySQL database. The fewer barriers to assessment of the model, the better for interaction with the stakeholder community.

vii. Sample table comparing the results of the AUC model and the Uterotrophic assay with a better description of relative potency. Table 2.12 (page 57) is necessarily
“blinded ” for chemical name due to the draft nature of List 1 Tier 1 weight of evidence evaluations. However, it would be appropriate to list reference chemical activity in the ER AUC model versus the Uterotrophic assay outcome (either in
the literature or run as positive controls) in order to understand the translation of

in vitro to in vivo potency of potential estrogenic or anti

- estrogenic chemicals.EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 9 As the majority of reference chemicals used for optimization of the ER AUC

model were “Inactive” or “Very Weak ,” it is important to understand if “Very Weak” agonist (and perhaps at some point antagonist ) potency in the model will
ever be relevant for in vivo hazard, regardless of the level of exposure.

viii. Avoidance of the term pseudo

- receptor (page 20 of 111)

. The term “pseudo

receptor” introduces unnecessary confusion. In drug design, for example, “pseudoreceptor models” are used to combine features of receptor and ligand modeling techniques (Tanrikulu & Schneider, 2008). These models have nothing
to do with interference. “Interference target ,” “non

- ER entrypoint” or some other more descriptive term would d istinguish receptor

- related targets f rom entrypoints into the ER pathway system that have unknown biological importance or reflect
cytotox icity or nonselective activity. Calling these activity targets “pseudo

receptors” gives a level of biological relevance that does not reflect current biological understanding of estrogen signaling.

ix. Differences and similarities with the published ER AUC m odel (Rotroff et al. 2014) should be delineated in the text or in an appendix. It appears that the
model deviates from Rotroff et al., ( 2014), at least in minor ways. For full
transparency, there should be text included to explain in what ways the SAP
White Paper model and published model may be distinct. The explanation in
Rotroff et al. ( 2014 ) is structured very differently than that in the White Paper

.

x. Explanation of next analysis steps in prioritization, e.g. “down

- selection” of
chemicals. Figure 2.7 (page 34) defines populations of chemicals based on ER pathway activity, followed by T

- score and Z

- score filtering. However, it is unclear what happens to each population of chemicals. For instance, what happens to “Active” chemicals with “Low T?” Are these chemicals of low concern, and as such deprioritized for further assessment? There is a lack of clear and specific language about what the binning demonstrated in this graph means
for decision

- making, both scientifically and in regulatory application s.

xi. Explanation of how the model was corrected to handle empirical observations, or what is expected from the model considering the empirical observations. On pages 34

- 35, causes of crosstalk and cross

- reactivity in the ER AUC model are described by techn ology platform, e.g.

, ATG, NVS, and Tox21 assays in the ER AUC model. What is unclear is how these assertions based on analysis of assay data should (a) inform interpretation of ER AUC model scores, or (b) change the function of the model or suggest model corrections. How are these observationsEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 10 of crosstalk and cross

- reactivity translated into revisions to the model or

interpretation of the results?

Charge Question 1 .b: What are the strengths and limitations of the ER AUC model’s ability to identify refe rence chemicals that include a variety of structures and have a wide range of in vitro ER bioactivities?

i. Strength : In clusion of as many assay endpoints as possible to inform an estrogen

or anti

- estrogen hypothesis. In clusion of multiple assays with inhe rent design limitations is a major strength of the approach. This helps to provide context to potential assay false positives.

ii. Strength : In clusion of a systematic method for consideration of cytotoxicity and
cell stress. The Z

- score approach, or some s imilar approach, is absolutely needed to provide appropriate context for the concentrations required to elicit endocrine activity in vitro

.

iii. Limitation : Narrow discussion (page 36 of 111) and presentation of structural
classes evaluated with the ER AUC mod el. Based on the current presentation of structural diversity included in the chemical set used to evaluate the ER AUC model, it may not be possible to assess model capability to handle a diversity of chemical structures. Several questions arise at revie w of the structure class discussion on page 36, including:

a. How many structure classes were assigned in the library? This
information do es not appear to be provided in Appendix 3.

b. Do the reference structures evaluated using high

- throughput in vitro
bi oactivity assays reflect current understanding of the structure classes capable of perturbing the ER pathway, i.e. based on the systems like the ER Expert System?

c. The AUC model seems to lead to compressed scores for substances that have potencies that dif

fer by several orders of magnitude. Although the
model correctly predicts higher AUC scores for very strong agonists, including 17β

- estradiol, diethylstilbestrol, etc., versus weaker agonists, the

s cores do not reflect the vast differences in potencies . For example, th e model provides AUC scores of 1.01 for 17β

- estradiol and 0.77 genistein

, yet in terms of EC50 values 17β

- estradiol is 3

- 4 orders of magnitude more potent in ER binding assays than genistein. Furthermore, the AUCEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 11 difference between 17β

- estradiol and estro ne (1.01 versus 0.987) is

difficult to reconcile with the 2 orders of magnitude difference in EC50
values reported for these substances typically based on ER standard

assays. On the other hand, if the difference in AUC values for 17β

estradiol versus estr one reflects two orders of magnitude difference in EC50 potency, then the AUC difference between 17β

- estradiol and methyltestosterone (1.01 versus 0.44) would translate to 57 orders of magnitude difference in EC50 values, which is far greater than the

appr oximately 6 orders of magnitude usually reported. It is thus unclear how potency information is conveyed by the AUC scoring scale, whether that scale is compressed or expanded relative to standard measurements of potency, and hence, whether the model prov ides a scientifically justified approach for portraying the resolution between chemicals of different
structural class and in vitro or in vivo activity in estrogen activity assays

. These ambiguities might be resolved and the clarity of the information

co nveyed by the model bolstered by a more thorough discussion of the AUC scoring scale and the values produced by the different chemistries
tested.

iv. Limitation : The current model does not include input from effects on steroidogenesis pathways . Perturbation of metabolic pathways involved i n steroid hormone production is not currently included in this model, and it is unclear how they will be inc orporated. As this represents a major node for xenobiotics to
modulate est rogen pathways, this is a deficit in the current model that should be addressed in subsequent iterations of this general approach. Reference chemicals known to perturb estrogen pathways via effects on ster oidogenesis should be evaluated; thus, any new steroidogenesis model should intersect with estrogen and androgen models, rather than being distinct

. The Future Directions section of the White Paper mentions modeling of the steroidogenesis pathway using a “variant of the EDSP Tier 1 T47D guideline assay” (page 99). This may be an error, as
the re is not a T47D guideline assay for steroidogenesis; rather, we believe the EPA meant the H295R steroidogenesis assay (OCSPP 890.1550).

v. Limitation : The assays generally lack metabolic capacity; therefore, metabolic activation/detoxification are not facto red into the scoring system. Some
substances administe red by the oral route may be essentially fully inactivated upon absorption, and thus the AUC results, if interpreted as an indicator of relative potency in vivo would greatly overestimate the potency of such a
subs tance. The opposite would hold for a substance that requires metabolic activation to form a moiety that binds to the receptor.EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 12

Charge Question 1 .c: EPA used data from published in vivo studies that are methodologically consistent with EDSP Tier 1 guidel ines to evaluate concordance
between ER AUC model scores of in vitro bioactivity and the in vivo uterotrophic response studies (Section 2.2.1). What are the strengths and limitations of the curation methods and quality standards used for evaluating publish ed in vivo
studies?

i. Limitation : A thorough description of the test article or chemical administered should be a requisite for inclusion in this literature curation of Uterotrophic

assays. The availability, or unavailability , of any analytical chemistry on the diet or administered dosing solution should be noted. A lack of information regarding test substance purity and source should decrease the quality rating of the study, and should be considered when systematically reviewing the validity of any study . Review or reporting regarding test substance purity is a primary element of the ToxRTool rating for the systematic review of the literature ( https://eurl

- ecvam.jr c.ec.europa.eu/about

- ecvam/archive

publications/toxrtool ). GLPs also require that the dose preparations be analyzed for composition to validate actual doses administered. We suggest that NICEATM attempt to harmonize the systematic curation of the litera ture with ToxRTool to improve international acceptance of this work.

ii. Limitation : Minimum endpoints included in acceptable studies should be clearly specified. Minimum endpoints should have included body weight so that both
relative and absolute uterine w eight can be used for evaluation of the assay findings. S ystemic toxicity, baseline husbandry and experimental conditions can influence endocrine function in the estrogen, androgen and thyroid
systems ; thus

, sufficient information should be provided to he lp distinguish primary endocrine

- mediated effects from indirect, secondary effects due to alterations in other systems/organs. A lso, description of the uterine weights, including whether or not the uterus was wet or blotted at necropsy, should be

annotate d.

iii. Limitation : Criteria for determination of study outcomes should be clearly specified. The curation methodology should also include a complete description of the criteria for different study outcomes, i.e.

, positive or negative , or equivocal . Were th e conclusions of the individual publications used, or were the results evaluated by a reviewer using the predetermined criteria established in the OCSPP 890.1600 test guideline? Did the reviewers evaluate the acceptability of vehicle control values set fo rth in the OCSPPEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 13 890.1600 test guideline? Were there limits on the acceptable coefficient of

variation (CV) values for given endpoints?

iv. Limitation : Verification of laboratory proficiency. The OECD uterotroph i c assay test guideline requires that each lab oratory conducting the uterotrophic assay demonstrate proficiency either by periodically conducting the assay with reference compounds (every 6 months) or by running a concurrent positive control in each assay. The laboratory is also required to conduct a baseline positive control study with a minimum of 4 doses. http://www.epa.gov/endo/pubs/uterotrophic_OECD_guideline.pdf

v. Limitation : Specification of the birth date and age of the animals used in the immature assay. The OECD test guideline requires that fo r the uterotrophic assay with immature animals the day of birth must be specified. Furthermore, the OECD test guideline requires that dosing begin immediately after weaning (e.g., postnatal day 18 (with the day of birth being postnatal day 0), and complet ed on postnatal day 21. The test guideline states that dosi ng should cease prior to PND 25 because, after this age, endogenous estrogen levels begin to rise leading to an increase in uterine weight and an increase in the group standard deviations

.

It is unclear how consideration of these limitations may have affected the summary results presented in Table 2.7 (page 45 of 111).

Charge Question 1 .d: Based on all the data presented in Section 2 on ER AUC model performance including characterization of r eference chemicals, and
concordance with in vivo uterotrophic results, what are strengths and limitations of using the ER AUC model to distinguish and prioritize chemicals based on potential estrogen bioactivity?

i. Strength : The ER Pathway model performanc e relative to Uterotrophic reference
chemicals. A clear strength of this approach is its predictive value; from Table 2.9, false positive and false negative rates were extremely low using reference chemicals, with excellent sensitivity and specificity sco res. However, it should be noted that this model will be improved when the high

throughput assay battery is improved via addition of more metabolic capacity. The importance of amending the high

- throughput battery to include metabolism as an input for thi s model cannot be overstated (as mentioned on pages 51 and 54 of the White Paper).EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 14

ii. Limitation : Lack of consideration of a relative potency cut

- off and uncertain

potency value scale

. For chemicals with an ER agonist AUC score equal to 1,
LEL values for U terotrophic reference chemicals ranged 5 orders of magnitude (Figure 2.14, page 52

- of note, the Figure legend should be revised to appropriately indicate that the ER AUC scores are on the X

- axis ) . For high

- level binning of chemicals, compression of the ER agonist AUC scores into a smaller window, with a threshold of 0.1, may be appropriate. For prioritization of chemicals for further testing, it may be difficult to accurately distinguish potent estrogenic activity from relatively weak estrogenic activit y. Thus, the actual task of prioritizing hundreds or thousands of chemicals for
further screening using the ER AUC model may represent a highly sensitive approach, but not a very specific one with respect to the potential ER

- or AR

mediated responses of a substance in in vivo models. Further, it may be appropriate to consider a relative potency cut

- off, i.e. relative potency below some value may not be of any concern based on receptor biology (Borgert et al. 2013) . A demonstration of how to benchmark the model results to
chemicals with in vitro estrogenic activity that do not demonstrate in vivo activity (e.g., no activity in the uterotrophic assay) based on low potency (high

K d with ER) should be included.

a. The importance of this limitation can be appr eciated from considering an interesting example. Potent androgens have been used as reference chemicals to validate various assays for detecting potential estrogenic activity, including validation of estrogen receptor transactivation assays
under ICCVAM a nd the current estrogen expert system under ToxCast. Rough estimates of relative potency can be derived from these as well as the published literature, a few examples of which appear in the following
table.

Reference Chemical Relative Potency Estimate 1 Assay / [Citation]

Estriol 6.67E

- 02 ERTA( α ) [Chen et al. 2004 3 ]
2

- OH

- E 2 2.00E

- 02 ERTA( α ) [Chen et al. 2004 3 ]
Testosterone 2.00E

- 05 ERTA( α ) [Chen et al. 2004 3 ]
Testosterone 7.01E

- 06 BG1

- Luc ERTA Mean EC 50 [ICCVAM, 2011]EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 15 19

- Nortestosterone 2 2.06E

- 06 BG1

- Luc ERTA Mean EC 50 [ICCVAM, 2011] Methyl testosterone 5.24E

- 06 BG1

- Luc ERTA Mean EC 50 [ICCVAM, 2011] Tamoxifen 5.05E

- 05 BG1

- Luc ERTA Mean EC 50 [ICCVAM, 2011] Genistein 4.00E

- 04 ERTA( α ) Ranhotra et al., 2005 Genistein 1.13E

- 05 BG1

- Luc ERTA Mean EC 50 [ICCVAM, 2011]
Estrone 1.57E

- 02 BG1

- Luc ERTA Mean EC 50 [ICCVAM, 2011] Ethinyl Estradiol 4.8E

- 01 BG1

- Luc ERTA Mean EC 50 [ICCVAM, 2011]
DES 1.63E

- 01 BG1

- Luc ERTA Mean EC 50 [ICCVAM, 2011]

1 Mean EC 50 Estradiol

- 17  ÷ Mean EC 50 of Test Chemical

2 non

- aromatizable androgen

3 Data derived from published graphs

From this table, it is apparent that in vitro potencies relative to e stradiol

- 17  range 6 orders of magnitude. The potencies of the two aromatizable androgens, testosterone and methyltestosterone, are at the extreme low end of this range and no m ore potent than the non

- aromatizable androgen 19

- nortestosterone, indicating that the potency of these steroids at the estrogen receptor is not due to a metabolic transformation to an estrogenic molecular structure. These and/or similar androgens are weakl y uterotrophic; however their uterotrophic activity is blocked by anti

- androgens but not by anti

estrogens, indicating an anabolic effect on uterine tissue mediated via uterine androgen receptors (Schmidt et al., 1976; Schmidt & Katzenellenbogen, 1979;

Ber i et al., 1998). Genistein is one to two orders of magnitude more potent than the androgens; the endogenous estrogens estrone and estriol are two to three orders of magn itude more potent than the phyto estrogens, and; the synthetic pharmaceutical estrogens ethinyl estradiol and DES are one order of magnitude more potent still. In contrast, Appendix 3 of the White Paper listsEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 16 estrogen agonist AUC values that differ by less than 3

- fold for these or similar

chemicals, despite much lower AUC values for other ch emicals.

Reference Chemical AUC Genistei n 0.77 Diadzein 0.623
Estradiol

- 17  1.01
Estrone 0.987
DES 1.01
17

- methyltestosterone 0.441

From these values, the AUC methodology seems to suggest little potency difference between the primary endogenous estrogen, potent synthetic estrogens, phytoestrogens that are marginally active in vivo , and androgens that are not estrogenic in vivo . It is thus unclear whether, and if so how, the AUC methodology would prioritize substances with "true" potent ial
estrog enic activity differently from "true" non

- estrogens or low potency
estrogens for further screening.

Charge Question 1 .e: Based on all the data presented in Section 2 on ER AUC model performance including characterization of reference chemicals, concord ance with in vivo uterotrophic results, and comparison with Tier 1 assay endpoints, what are strengths and limitations of the ER AUC model to contribute to the weight of evidence determination of a chemical’s potential estrogen bioactivity?

i. Strength : Tox Cast bioactivity data, and the ER AUC model, should be considered in the weight of evidence for potential estrogen bioactivity because these data appear to inform relevant molecular

- initiating event hypotheses. In principle, high

- throughput bioactivity da ta in general should be considered in any weight of evidence approach. However, as noted above, we have concerns about the AUC methodology and the lack of transparency of model inputs, which, at the present time, prevents independent verification. Furthe rmore, it is important to maintain context for these data as screening level information: higher levels of information, e.g. in vivo studies, should generally be considered more relevant for potential endocrine activity if these data are
available.

ii. Limita tion : Confusion regarding how shifts on the x

- axis (potency shifts) affect
the overall AUC score. It is not clear if two equivalent AUCs, one AUC derived from a right

- shifted dose

- response curve and one AUC derived from a
left

- shifted dose

- response curve, would result in the same AUC score. ThisEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 17 should be explained e xplicitly in the SAP White Paper. If these two AUCs

would result in the same score, then this presents a limitation to consideration of relative potency using this method. Using the AUC method wou ld appear to conflict with a prioritization method that ideally prioritizes chemicals with greater potency at this receptor over chemicals that may act with low potency
if at all.

iii. Limitation : Transferability of the method. Until stakeholders can interact with and modulate the results of the ER AUC model, it is not transferable enough to apply to weight of evidence determinations.

iv. Limitation : It is unclear how an incomplete set of assay data would be used with the ER AUC model or independent of the ER A UC model. As no single assay consistently drives the ER AUC score, it would appear that it is difficult to parse the assay battery down to a fewer number of assays. Thus, how would an incomplete high

- throughput screening battery for a chemical be handled ? Can inputs for some subset of the assays be used with the ER AUC model, or would a separate consideration of these data be needed for a weight

- of

evidence determination? What is the forward applicability of this model in
circumstances in which not all of these assay data are available for a
chemical?

Charge Question 1 .f: Based on all the data presented in Section 2 on ER AUC model performance including characterization of reference chemicals, concordance
with in vivo uterotrophic results, and comparis on with Tier 1 assay endpoints, what are strengths and limitations of using the ER AUC model to substitute for EDSP Tier 1 ER binding, ER transactivation, or Uterotrophic assays for the purpose of characterizing a chemical’s potential estrogen bioactivity?

i. Combined Strength and Limitation: Though there was a high degree of
concordance between the ER AUC model a nd the Uterotrophic results, the extension of the model beyond the reference chemicals focused predominantly on chemicals with negative Uterotrophic results (Table 2.12). More confidence in the model predictivity would result from consideration of a higher number of chemicals which demonstrated some estrogenic activity in

vivo

.

ii. A notable exception is found in the mention of a single false negativ e substance, octamethylcyclotetrasiloxane, known as D4. The White Paper ( page 51 ) carefully notes the possible reasons for the apparent lack ofEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 18 detection in the ER AUC model , including the possibility that due to the high

volatility of D4, test solutions c ontained insufficient concentrations of the chemical to elicit a response. If this occurred, the result would indicate a true false negative. Also mentioned is the possibility that D4 has insufficient potency to exhibit estrogenic potential in this syste m. Here again, it is critical to distinguish between in vitro activity as identified by the computational system from biologically relevant potency (e.g.

, uter o trophic response)

.

D4 has been evaluated in two

- generation reproduction studies considered to be definitive Tier 2 tests per the EDSP (Dow Corning, 2001). In such bioassays, D4 fails to elicit a pattern of responses indicative of estrogen agonism. Consistent with this lack of clear estrogenic effect, D4 exhibits very low potency in sensitive in vivo and in vitro screening

- level assays for detecting estrogenic activity, the results of which are summarized in the following table.

Assay Positive

Control [Positive

Control] [D4 at 50% response] Citation

RUA (F344) Weight EE 2.6E

- 07 McKim et al.
200 1

RUA (F344) Weight DES 5.1E

- 07 McKim et al.
2001

RUA (SD) Weight EE 1.71E

- 06 McKim et al.
2001

RUA (SD) Weight DES 1.85E

- 06 McKim et al.
2001

RUA (SD) Weight Coumestrol 2.17E

- 02 McKim et al.
2001

MUA (B6C3F1) EE 6.0E

- 06 (Weight)
2.5E

- 06 (Peroxidase) He et al.

2003

ERTAα 17β

- E 5.0E

- 06 Quinn et al.
2007EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 19

ERBAα EE 1.14E

- 04 Quinn et al.
2007

ERBAα DES 7.4E

- 04 Quinn et al.
2007

ERBAβ EE, DES 0 Quinn et al.
2007

RUA: Rat Uterotrophic Assay MUA: Mouse Uterotrophic Assay ERTA: Estrogen receptor transactivation assay

ERBA : Estrogen receptor binding assay

Assuming that D4 was present in the test solutions near nominal concentrations, the failure of the computational system to indicate an estrogenic “hit” for D4 would be
consistent with its lack of in vivo estrogen agonism in the 2

- generation reproduction
studies and its very low potency measured in other in vitro and in vivo screening assays. In that case, the term “false negative” may be misleading, as t he computational system may be correctly distinguishing a chemic al that does not warrant further screening, much less testing, based on potential estrogenic activity. Although androgenic potential was not addressed in the White Paper, D4 also lacks any detectable activity in the Hershberger assay (Quinn et al., 2007).

3. EPF Response to Charge Questions Concerning Androgen Bioactivity

Charge Question 2 .a: How clearly has EPA described the computational tools in Section 3.1 (i.e., high throughput assays and models) used to estimate AR agonist and antagonist bioactivi ty?

Many of t he same recommendations and questions are relevant in the AR AUC section as cited above in the ER AUC section (1a).

One point to note, there are some clarifications required in the document that avoid confusion. For example:

 The EPA shou ld cite which version of ToxCast was used for the data included in this white paper. Given that the ToxCast and ExpoCast are still undergoing updates, it is important for stakeholders to refer to the correct version of these databases if trying to recreat e or verify analyses by the EPA.EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 20

 Table 3.1 (page 62) references the 9 androgen receptor (AR) assays used for the

ToxCast analysis. This list includes two AR binding assays

– NVS_NR_hAR (human androgen receptor) and NVS_NR_rAR (rat androgen receptor); h owever, on page 67, the legend for the figures lists the two AR binding assays as NVS_NR_hAR (human androgen receptor) and NVS_NR_cAR (chimp androgen receptor). ToxCast phase 2 contains data on all three AR binding assays, so it is unclear which two assay s were used in this analysis and why the third assay was
discarded.

 It appears that ToxCast Phase 3 data may have been used in the EPA’s analysis. For example, for the reference chemicals, diethylstilbestrol is included as an AR antagonist (page 63); ho wever, the two antagonist assays (Tox 21_AR beta

lactamase antagonist reporter gene and Tox21_AR luciferase

- MDAKB2 antagonist reporter gene) were negative for diethylstilbestero l in ToxCast Phase 2 (see Appendix A ). While it is commendable that the EPA ha s used the most recent data and continues to refine their approach, it would be useful to cite the
version of ToxCast used in this SAP White Paper . This poses a problem for stakeholders as ToxCast Phase 3 was released very recently and is not yet
available in the CSS Dashboard. Thus, the data is not yet in a “user friendly” format, making it difficult for outside experts from stakeholder group s or independent peer reviewers to verify the data which were the basis for the analyses.

 In Table 3.3 (page 69 o f 111) , three colors are used in the “AUC agonist (R1)” and “AUC antagonist (R2)” columns (green, yellow and red), but there is no description in the table legend to disclose the significance of these colors. Red appears to indicate negative (inactive) r esponses, but some AUC = 0 values are shaded yellow (yellow includes AUCs from 0

– 0.35; whereas green indicates higher AUC values from 0.55

– 1.09).

 In some places in the document, the EPA states (p. 69) that “Overall, there were
20/1855 chemicals with A R agonist (R1) AUC ≥ 0.1 and 93 chemicals with AR antagonist (R2) AUC ≥ 0.1”; however, in other parts of the text (page 74), it states: “Chemicals in the EDSP universe with AR agonist (R1) or antagonist (R2) scores > 0.05 are higher priority candidates fo r additional endocrine screening and testing.” While these AUC values are not mutually exclusive, the criterion should
be consistent and scientifically justified to warrant additional screening and testing. If these AUC values are referring to different aspects of analysis (i.e., the text was meant to refer to two different AUCs), the rationale for using two different criteria should be clearly presented.EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 21

 The figure legend for Figure 3.5 (page 75) references “AR agonist (R1) AUC”,

when the graph appears to illustrate “AR an tagonist (R2) AUC

. ” There is also a
typo graphical error on the AUC score in this legend.

Charge Question 2.b: What are strengths and limitations of the AR AUC model’s ability to identify reference chemicals that include a variety of structures and ha ve a wide range of in vitro AR bioactivities?

One of the strengths of the EPA’s approach is the inclusion of multiple methods to differentiate positive AR

- related responses (agonism or antagonism) from assay
interference in the AR

- related in vitro assays (i.e., false positive results due to cross

reactivity, cell stress, etc.). This is particularly important with the loss

- of

- signal responses in the antagonist assays, which are sometimes due to cytotoxicity. Appendix 7 was very helpful in clarifying the AR results for the reference chemicals as well as identifying
sources of interference.

A current limitation to the predictive capability of the AR AUC model is a lack of assay biomarkers of steroidogenesis perturbation. Modulation of AR bioactivity is not the only mechanism by which xenobiotic exposure may result in apical changes in whole animal assays like the Hershberger assay or multigeneration reproduction studies. In the event these data are available for a chemical subject to screening, whole anima l data with endocrine function should be considered more biologically

- relevant.

Currently, the reference set of chemicals is too small to build sufficient confidence in this approach for the three goals stated by the Agency. Additional reference compounds are needed to strengthen the conclusions. Reference chemicals must span a wide range of potencies and include a greater number of inactive compounds. Figures such as those in Appendix 7 would be useful for future reference compounds too.

The AR AUC mod el as presented represents a very encouraging but incomplete effort. Without model performance definitions, i.e.

, representation of true positive, false positive, true negative, and false negative findings when compared to the Hershberger assay, the model is not in a state that can be fully evaluated for its predictive capability. Of the reference chemical set, only three were complete ly inactive , and two of these three were phthalates (same chemical class). A greater chemical diversity should be

represen ted in the reference set of chemicals before determination of model success in delineating a wide range of in vitro bioactivities.EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 22 Charge Question 2.c: EPA plans to use data from published in vivo studies that are methodologically consistent with EDSP T ier 1 guidelines to evaluate concordance
between AR AUC model scores of in vitro bioactivity, and the in vivo androgenic and antiandrogenic responses (Section 3.2.1). What are strengths and limitations of the planned curation methods and quality standards for evaluating published in vivo
studies?

The inclusion of published studies will be beneficial to the EPA’s goal of planning to replace in vivo Tier 1 EDSP assays with ToxCast

- based in vitro assays. EPA should consider using a data reliability tool (e.g. , ToxRTool) for a systematic evaluation of publications prior to their inclusion in the analysis. Confirmation of chemical identity and purity is critical as well as dose concentration confirmatory analyses for in vivo
studies.

As part of this process, t he EPA should pay close attention to systemic toxicity or methodological approaches that could jeopardize the relevance and/or reliability of the data. Also, as mentioned in the text, the EPA should conduct the analysis ( in vitro vs. in

vivo AR data) usin g chemicals that span a range of potencies. In addition, it is critical that the EPA include more than three inactive compounds in their analysis. Sufficient

sa mple sizes are critical to maintain variance within a reasonable range.

In addition, page 73 s tates that “Tier 1 in vivo Hershberger Assay (OCSPP 890.1400; OECD no. 441) is designed to detect androgen agonist and antagonist activities indicated by any change in fi ve accessory sex tissue weights

. " Generally, the Hershberger assay requires statistic ally significant changes in two or more accessory sex tissue weights and the appropriate direction change in all accessory sex tissues for the assay to be deemed positive.

Other applicable comments appear in response to charge question 1

. c above. The sa me deficiencies in the curation method are present for the approach to the Hershberger assay curation project.

Charge Question 2.d: Based on the data presented in Section 3 on AR AUC model’s performance, what are strengths and limitations of using the A R AUC model to distinguish and prioritize chemicals based on potential androgen bioactivity?

As stated above for ER AUC outputs , there are some questions about relative potency with respect to AR AUCs. The order of AUC Agonist (R1) (or antagonist (R2)) l isted in Table 3.3 (page 69) is reported to reflect potency (AC50 values

– page 22

- 23). Again, compression of scores seems to be operative with the AUC model. If AUC is an indication of potency, then it is a bit surprising that the relative potencies of the strong AR agonists are so similar: 17beta trenbolone (AUC = 1.09) 17

- methyltestosteroneEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 23 (AUC = 1.08), 5alpha

- dihydrotestosterone (AUC = 1.09), norethinodrone (AUC = 1.02)
and testost erone propionate = (AUC = 1.01) in light of results from the literatu re which indicate significant potency differences. In addition, 17alpha

- estradiol has an AUC of 0.61, which is approximately 60% of the strong agonists. Is there any concern that the relative potencies will be difficult to compare because different assay systems are used?

Please refer to our previous comments

, earlier in this document, on relative potency considerations, transferability, and questions regarding the forward utility of the model in cases where not all assay data are available for a particular chemical. The AR AUC
mod el is preliminary and not at a stage that it can be evaluated for prioritization potential, particularly in the absence of model performance information.

4. EPF Response to Charge Questions Concerning the integrated bioactivity exposure ranking (IBER)

Charge Question 3 .a: How clearly has EPA described the computational tools in Section 6 to develop IBER values, including modeling uncertainty and population variability?

i. Population variability as modeled by Monte Carlo analysis is explained well in
the text, especially when referencing July SAP materials on high

- throughput exposure modeling.

ii. Explanations of uncertainty and general variability (page 94) are far too limited, and mostly relegate these issues to “default” from another publication, or
futur e research direction. Thus, these areas require further development and explanation, and should be considered in a draft stage.

iii. The IBER approach is generally well explained and meets the objective of defining the distance between potential predicted haz ard and exposure for any given chemical. However, it is unclear if the activity comparator, 5% of the bioactivity dose range, is for all high

- throughput bioactivity, or for just estrogen or androgen bioactivity. This should be clearly explained.

Charg e Questions 3

. b and 3 .c: What are the strengths and limitations of using the IBER approach to prioritize chemicals for further EDSP screening based on the ratio between ER/AR bioactivity dose range and the expected dose range?

i. Strength : This is an infor mation

- based prioritization approach that is generally transparent, and EPA is to be commended for the IBER's scientific rigor.EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 24 ii. Strength : There is a clear recognition of different scenarios that might arise from

the QSAR models and there are statement s provided for how each scenario will be interpreted, including one in which a chemical is set aside following consistent prediction as ER

- inactive in a majority of all models.

iii. Limitation : A practical demonstration of how the prioritization process would proceed is unclea r because there is a clear separation in IBER scores between the top IBER values (for pharmaceuticals) and the rest of the IBER scores. The highest priority IBER scores were for steroids and pharmaceuticals, with
a clear break in the distribution of these scores.

Example from Appendix 3 of the ER IBER Scores. The steroidal estrogens cluster together (IBER < 1). Then, the distribution of IBER values quickly advance in magnitude, with daidzein, a dietary phytoestrogen, represented with an ER IBER = 90. Including requisite variability and uncertainty, which values in the range of 90 warrant consideration for further screening in the EDSP ? The EPA should consider providing additional rationale to illustrate the prioritization proce ss using IBER. Note: Endosulfan (ER IBER 2.055965) and Trifluralin (ER IBER 417.8785 ) were both List 1 chemicals

. Perhaps the results of screening these compounds could be taken into consideration in how they would have been prioritized.

Chemical Name ER IBER (Appendix 3), the top 30 values sorted ascending

Tamoxifen citrate 9.65E

- 09
17alpha

- Estradiol 0.000116
17alpha

- Ethinylestradiol 0.000769
Diethylstilbestrol 0.000925

Basic Blue 7 0.005917 2,2

- Bis(4

- hydroxyphenyl)

- 1,1,1

- trichloroethane 0.00768 6

17beta

- Estradiol 0.067728

Estrone 0.093178

4

- Hydroxytamoxifen 0.121667

Norethindrone 1.779332

Endosulfan 2.055965
Pyridaben 4.813092
Bisphenol AF 27.29529

Prodiamine 47.56313

17

- Methyltestosterone 56.21679
Bisphenol A 62.22238

Flumetralin 86.1 036

Daidzein 90.40641EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 25

2

- Phenylphenol 171.9054

CJ

- 013610 184.9567

17beta

- Trenbolone 190.023
Clorophene 323.2731

Trifluralin 417.8785

SB236057A 467.1451 2,2',4,4'

- Tetrahydroxybenzophenone 570.9987
Propylparaben 951.7878

5HPP

- 33 1517.262
C8

- 18

- Alky dimethylbenzyl ammonium chlorides 1557.882
Chlorethoxyfos 2256.855

Ethalfluralin 2450.251

Additional example of the AR IBER score distribution from Appendix 3. There is a clear break between the steroidal androgens and the rest of the chemical set. U sing a suitable benchmark chemical, EPA should consider highlighting what IBER value would warrant consideration for further screening in the EDSP

. Note: List 1 chemicals in this table are Endosulfan (AR IBER 1101.125), Linuron (AR IBER 4357.942), Resme thrin (AR IBER 5522.567) , and Propiconazole (AR IBER 643056 ) . List 2 chemicals are Triflumizole (AR IBER 775.7125) and Coumaphos (AR IBER 722723.4).

Chemical Name AR IBER (Appendix 3), the top 53 values sorted ascending Testosterone propionate 0.00010 1

Basic Blue 7 0.00119

17beta

- Trenbolone 0.002949
5alpha

- Dihydrotestosterone 0.013169
Pyridaben 0.023351

17

- Methyltestosterone 0.029335

Norethindrone 0.788686

Triclosan 3.643363

17beta

- Estradiol 6.614861

Emamectin benzoate 35.64566
Clorophene 41. 2542

Dicloran 68.1402
17alpha

- Estradiol 110.4586 2,2',4,4'

- Tetrahydroxybenzophenone 131.8482

MGK

- 264 168.1495

Estrone 298.3226EPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 26 17alpha

- Ethinylestradiol 503.5311

Triticonazole 649.4789
Propanil 661.7377
Diethylstilbestrol 770.9295

Triflumizole 775 .7125
Oryzalin 844.4524

4

- Hydroxytamoxifen 886.5688

Tamoxifen citrate 943.1446

Tetraconazole 954.7428
Bisphenol A 980.4187

Endosulfan 1101.125
Digoxin 1209.293
Quinoxyfen 1724.291
Napropamide 3307.225

Fenitrothion 3358.944
Fipronil 3732.393

Lin uron 4357.942

Resmethrin 5522.567

Oxadiazon 22868.31

Fluoxastrobin 35728.56
Cycloheximide 41209.32

Fluazinam 47290.31 2,2

- Bis(4

- hydroxyphenyl)

- 1,1,1

- trichloroethane 48511.66

Oxazepam 84784.17

Imazalil 92725.96

Difenoconazole 100189.9
Bisphenol AF 105041.8

Fenbuconazole 139115.1

Butachlor 147440.1
Methoxychlor 203594.8
Dimethomorph 254275.1
4,4',4

- Ethane

- 1,1,1

- triyltriphenol 264370.9

Fenhexamid 335429

Fenarimol 463649.7

Phosalone 479440.4
Propiconazole 643056
Coumaphos 722723.4

a. Since a n IBER for a given substance is highly dependent upon the

exposur e estimate, EPA should provide an opportunity for submission of aEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 27 refined exposure estimate as part of OSRI, before requiring further EDSP

screening.

b. EPA should anticipate that as part o f OSRI data, well conducted published and guideline studies will likely be available for many compounds. How
will this other scientifically

- relevant information be integrated with the
HTS/AUC results for consider ation in this prioritization process prior to
the issuance of test orders ? How would ER or AR AUC scores that conflict with guideline study information or other scientifically relevant
information be handled?

c. One of the learnings from List 1 testing confirms negative endocrine activity outcomes f

or chemicals previously tested in higher tier assays. That suggests that these higher tier assays ( i.e. , OECD Test Guideline 416 with enhanced endpoints, and OECD Test Guideline 443) with negative results should be sufficient to assess potential endocrine activity and, thus, chemicals with negative data from these assays should not be placed onto future lists to go through the screening process.

iv. Limitation : A lack of metabolism in the bioactivity assays and limited consideration of metabolism in reverse t oxicokinetic approaches. The most pressing research need to improve the utility of the IBER approach is better consideration of metabolism in both bioactivity and exposure estimates. The impact of metabolic clearance in the HTTK reverse dosimetry approac h is fairly well understood by incorporating test material loss rates determined in human hepatocytes (Wetmore et al. 2012). Little can be done to accurately determine metabolic loss in the HTS ER/AR assays that have already been conducted. One approach , however, to estimate metabolism in these bioactivity assays would be to scale the hepatocyte

- based metabolic clearance determined in the HTTK approach to the relative CYP levels (or other relevant metabolic system, i.e. : UDPGT) in bioactivity assay cells vs. liver
cells.

v. Limitation : IBER approach is not currently transferable to stakeholders due to a lack of access to the ER and AR AUC models and the latest reverse toxicokinetic information. While the HTTK approach is well described in the July SAP do cumentation (and related publications), it relies on a fairly high

end PBPK modeling tool (SimCYP) to provide probabilistic distributions of systemic exposure. Specific details of the PBPK model used ( i.e. : model code) have not been published. Also, uti lization of a more mainstreamEPF Comments for Bioactivity and Exposure Ranking SAP

N ovember 25, 2014 Page 28 modeling application would allow for a broader implementation of this

approach with other scientists.

vi. Limitation : Use of the upper 95% confidence limit on potential oral human exposure and the oral equivalent dose correspon ding to the 5% bioactivity level has not been shown to be superior to use of central estimates. While use of upper bound on exposure and lower bound on potency can account for uncertainty and variability, upper bounds and lower bounds are clearly not as

s table as central estimates. For comparison across compounds for use in priority setting, central estimates of both exposure and activity should be considered. The use of upper bound and lower bound estimates likely implies greater precision than is warran ted by the datasets and models

.



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- derived prediction models: Lessons learned from an endocrine case study. Regul. Toxicol. Pharmacol. 69, 443

- 450.
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Appendix A

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- out of the iCSS Dashboard Data for DES. Accessed 11/14/2014.EPF Comments for Bioactivity and Exposure Ranking SAP

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