Page 1 of 3 FIFRA SCIENTIFIC ADVISORY PANEL

SESSION TITLE: Integrated Bioactivity and Exposure R anking: 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 chem ical’s potential estr ogen bioactivity is based on a computational model integratin g data from 18 high th roughput ToxCast assays measuring several endpoints along t he 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 t he Endocrine Disruptor Screening Program (EDSP) framework, EPA is reviewing the scient ific strengths and limitations of the ER model described in the white paper to: i) prio ritize 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 t he following charge questions relevant to

Section 2 of the white paper and estrogen bioactivity:

a. How clearly has EPA described the co mputational 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 lim itations 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 guidelin es 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 us ed for evaluating published in vivo studies?

d. Based on all the data presented in Se ction 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 AUCPage 2 of 3 model to distinguish and prioriti ze chemicals based on potential estrogen bioactivity?

e. Based on all the data presented in Se ction 2 on ER AUC model performance including characterization of reference chemicals, concordance with in vivo uterotrophic results, and comparison with Tier 1 a ssay endpoints, what are strengths and limitations of the ER AUC model to contribute to the weight of

evidence determination of a chemic al’s potential estrogen bioactivity?

f. Based on all the data presented in Se ction 2 on ER AUC model performance including characterization of reference chemicals, concordance with in vivo uterotrophic results, and comparison with Tier 1 a ssay 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 chem ical’s potential andr ogen bioactivity is based on a computational model integratin g 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 t he 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 follo wing charge questions relevant to Section 3 of the white paper and androgen bioactivity:

a. How clearly has EPA described the co mputational 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 lim itations 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 guidelin es to evaluate concordance between ARPage 3 of 3 AUC model scores of in vitro bioactivity, and the in vivoandrogenic 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 Secti on 3 on AR AUC model’s performance, what are strengths and limitations of us ing 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 toxi cokinetics and exposure esti mates (Sections 4 and 5), the IBER approach was used to rank c hemicals based on the ratio between the bioactivity dose range, and the expected ex posure range (Section 6). The IBER approach extends point estimates of bioacti vity, toxicokinetics, and exposure for a chemical, to distribution ranges based on uncertainty and populat ion 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 t he white paper and the IBER approach:

a. How clearly has EPA described the comput ational tools in Section 6 to develop IBER values, including modeling uncer tainty and population variability?

b. What are strengths and lim itations of using the IBER approach to prioritize chemicals for further EDSP screening based on the ratio between the ER bioactivity dose range, and t he expected exposure range?

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

chemicals for further EDSP screeni ng based on the ratio between the AR bioactivity dose range, and t he expected exposure range?