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FIFRA SCIENTIFIC ADVISORY PANEL 

SESSION TITLE: Integrated Bioactivity and Exposure Ranking: A Computational Approach for 

Prioritization and Screening of Chemicals in the Endocrine Disruptor Screening Program 

FIFRA SAP MEETING DATE(S): Dec. 2 – Dec. 5, 2014 

CHARGE QUESTIONS: 

ESTROGEN BIOACTIVITY 

1) EPA’s proposed approach for quantifying a chemical’s potential estrogen bioactivity is 

based on a computational model integrating data from 18 high throughput ToxCast 

assays measuring several endpoints along the estrogen receptor (ER) signaling 

pathway. The computational model outputs are expressed as area under the curve 

(AUC) scores for ER agonist (R1) and antagonist (R2) bioactivity. Before routinely 

using the ER computational model in the Endocrine Disruptor Screening Program 

(EDSP) framework, EPA is reviewing the scientific strengths and limitations of the ER 

model described in the white paper to: i) prioritize chemicals for further EDSP screening 

and testing based on estimated bioactivity, ii) contribute to the weight of evidence 

evaluation of a chemical’s potential bioactivity, and iii) substitute for specific endpoints in 

the EDSP Tier 1 battery. Please address the following charge questions relevant to 

Section 2 of the white paper and estrogen bioactivity: 

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? 

b. What are strengths and limitations of the ER AUC model’s ability to identify 

reference chemicals that include a variety of structures and have a wide range of 

in vitro ER bioactivities? 

c. EPA used data from published in vivo studies that are methodologically 

consistent with EDSP Tier 1 guidelines 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 strengths and limitations of the curation 

methods and quality standards used for evaluating published in vivo studies? 

d. Based on all the data presented in Section 2 on ER AUC model performance 

including characterization of reference chemicals, and concordance with in vivo

uterotrophic results, what are strengths and limitations of using the ER AUC 
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model to distinguish and prioritize chemicals based on potential estrogen 

bioactivity? 

e. 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 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? 

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 comparison 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? 

ANDROGEN BIOACTIVITY 

2) EPA’s proposed approach for quantifying a chemical’s potential androgen bioactivity is 

based on a computational model integrating data from nine high throughput ToxCast 

assays measuring several endpoints along the androgen receptor (AR) signaling 

pathway. The computational model outputs are expressed as area under the curve 

(AUC) scores for AR agonist (R1) and antagonist (R2) bioactivity. Before routinely 

using the AR computational models in the EDSP framework, EPA is reviewing the 

scientific strengths and limitations of the AR AUC model described in this white paper 

to: i) prioritize chemicals for further EDSP screening and testing based on estimated 

bioactivity, and ii) contribute to the weight of evidence evaluation of a chemical’s 

potential bioactivity. Please address the following charge questions relevant to Section 

3 of the white paper and androgen bioactivity: 

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 

bioactivity? 

b. What are strengths and limitations of the AR AUC model’s ability to identify 

reference chemicals that include a variety of structures and have a wide range of 

in vitro AR bioactivities? 

c. EPA plans to use data from published in vivo studies that are methodologically 

consistent with EDSP Tier 1 guidelines to evaluate concordance between AR 
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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? 

d. Based on the data presented in Section 3 on AR AUC model’s performance, 

what are strengths and limitations of using the AR AUC model to distinguish and 

prioritize chemicals based on potential androgen bioactivity? 

INTEGRATED BIOACTIVITY EXPOSURE RANKING (IBER) 

3) For Endocrine Disruptor Screening Program (EDSP) chemicals with ToxCast estrogen 

receptor (ER) and androgen receptor (AR) bioactivity scores (Section 2 and 3), and 

ExpoCast high throughput toxicokinetics and exposure estimates (Sections 4 and 5), 

the IBER approach was used to rank chemicals based on the ratio between the 

bioactivity dose range, and the expected exposure range (Section 6). The IBER 

approach extends point estimates of bioactivity, toxicokinetics, and exposure for a 

chemical, to distribution ranges based on uncertainty and population variability. 

Chemical rankings are based on the ratio of the lower range of the bioactive dose, to 

the upper range of the exposure estimate. Please address the following charge 

questions relevant to Section 6 of the white paper and the IBER approach: 

a. How clearly has EPA described the computational tools in Section 6 to develop 

IBER values, including modeling uncertainty and population variability? 

b. What are strengths and limitations of using the IBER approach to prioritize 

chemicals for further EDSP screening based on the ratio between the ER 

bioactivity dose range, and the expected exposure range? 

c. What are strengths and limitations of using the IBER approach to prioritize 

chemicals for further EDSP screening based on the ratio between the AR 

bioactivity dose range, and the expected exposure range?