# Section 6.1:

Basic Approach for IBER



# & Section 6.2: Modeling Uncertainty and Variability

Richard Judson National Center for Computational Toxicology Office of Research and Development Slide 1 of 25

# Presentation Roadmap

Introduction Estrogen Receptor Bioactivity Androgen Receptor Bioactivity Exposure

Toxicokinetics/Dosimetry

Integrated Bioactivity Exposure Ranking

Future Directions FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 2 of 25

# Integrated Bioactivity Exposure Ranking (IBER)

-In Vitro assays

- Bioactivity Concentration

- Use in vitro toxicokinetics

- Convert to Bioactivity Dose



### - Compare with modeled Exposure Section 6, p. 92
FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 3 of 25

# Issues To Consider

 Approach uses worst- case scenarios • Confidence intervals (variability and uncertainty) 

# are calculated • Use appropriate 95% or 99% limits on exposure, PK, PD 

#  Prioritize chemicals with closest overlap between exposure and bioactivity

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 4 of 25 

### Section 6

# IBER – Integrated Bioactivity Exposure Ranking

Section 6 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 5 of 25

# The Tools of IBER
High -throughput Bioactivity and Pharmacokinetics

High -throughput Exposure 

# +

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 6 of 25 

### Section 6

# Summary of Uncertainty and Variability

Components for IBER Uncertainty Variability Pharmacodynamics Note: Data is human derived Data uncertainty (potency)



### Other biology not included (See next slide) Default for now 

### HapMap cell - line experiments may help 

### Pharmacokinetics Note: Data is human derived 

### Data uncertainty (plasma

protein binding, intrinsic clearance). 

### Model variability in liver function as f

(age, sex, body weight) 

### Exposure Note: Model is parameterized using NHANES data 

### Includes uncertainty in

biomonitoring data 

### NHANES- derived variability SHEDS- like models can be used

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 7 of 25 

### Section 6

# Pharmacodynamics Issues

8 

###  Each curve also has data and model uncertainty
 Use many in vitro assays
 Potency varies by cell type and technology
 Use model to integrate and provide uncertainty estimate

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 8 of 25

# Case Study One

 Bisphenol A was active at some concentration for 17 of 18 ER- related assays Assay Conc. NVS_NR_bER_ACC 0.19 NVS_NR_hER_ACC 0.20 NVS_NR_mERa_ACC 0.27 OT_ER_ERaERa_0480_ACC 1.27 OT_ER_ERaERa_1440_ACC 1.34 OT_ER_ERaERb_0480_ACC 0.23 OT_ER_ERaERb_1440_ACC 0.25 OT_ER_ERbERb_0480_ACC 0.23 OT_ER_ERbERb_1440_ACC 0.19 OT_ERa_EREGFP_0120_ACC 0.33 OT_ERa_EREGFP_0480_ACC 0.52 ATG_ERa_TRANS_up_ACC 0.03 ATG_ERE_CIS_up_ACC 0.05 Tox21_ERa_BLA_Agonist_ratio_ACC 1.88 Tox21_ERa_LUC_BG1_Agonist_ACC 0.14 ACEA_T47D_80hr_Positive_ACC 0.16 Tox21_ERa_BLA_Antagonist_ratio_ACC 13.27 Tox21_ERa_LUC_BG1_Antagonist_ACC 1000000 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 9 of 25 

### Section 6

# Case Study One

 A mathematical model was used to integrate all ER assays into a single predicted bioactive concentration

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 10 of 25 

### Section 6

# Case Study One

 The error bar indicates the span between the median and the minimum plausible bioactive concentration

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 11 of 25 

### Section 6

# Case Study One

 Reverse dosimetry based on HTTK data was used to predict an oral equivalent dose that would cause the ACC in plasma for the 95 - percentile, most sensitive adult

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 12 of 25 

### Section 6

# Case Study One

 Based on the ACToR UseDB descriptors and production volume, a median exposure for similar NHANES chemicals can be predicted 

### Heuristic Bisphenol A Consumer & 

### Industrial Use Yes Industrial Use Only No Pesticide Inert No Pesticide Active No Production Volume > 1 billion lbs/year

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 13 of 25 

### Section 6

# Case Study One

 Due to the large uncertainty, the upper 95% limit of the exposure estimate credible interval is used 

### Heuristic Bisphenol A Consumer & 

### Industrial Use Yes Industrial Use Only No Pesticide Inert No Pesticide Active No Production Volume > 1 billion lbs/year

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 14 of 25 

### Section 6

# Case Study One

 ANSES (2013) BPA Receipts, 200 ng/ kgBW/d (Workers) and 10 ng/kg BW/d (Consumers )  LaKind and Naiman (2011) Estimated Exposure to BPA from NHANES data in ng/kg BW/day): Demographic LaKind and

Naiman (2011 ) ExpoCast

Geometric Mean Median ExpoCast

Geometric Mean Upper 95% Total 35.1 25.0 2193 Age 6- 11y 54 63 4984 Age 12 - 19y 48 59 5169 Age 20- 39y* 38.5 57 6056 Age 40- 59y* 28.9 57 6056 Age >=60y 27.3 66 84221 Male 39.6 38 3132 Female 31.2 12 1125 *ExpoCast makes single prediction for Age 20 - 59y FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 15 of 25 

### Section 6

# Case Study Two

 Triclosan was bioactive at some concentration for 6 of 9 AR- related assays Assay Conc. NVS_NR_hAR_ACC 1.87 NVS_NR_cAR_ACC 10.84 OT_AR_ARSRC1_0480_ACC 10.93 OT_AR_ARSRC1_0960_ACC 1000000 ATG_AR_TRANS_up_ACC 1000000 Tox21_AR_BLA_Agonist_ratio_ACC 78.59 Tox21_AR_LUC_MDAKB2_Agonist_ACC 1000000 Tox21_AR_BLA_Antagonist_ratio_ACC 25.85 Tox21_AR_LUC_MDAKB2_Antagonist_ACC 30.90

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 16 of 25 

### Section 6 AR

# Case Study Two

 A mathematical model was used to integrate all AR assays into a single predicted bioactive concentration

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 17 of 25 

### Section 6 AR

# Case Study Two

 The error bar indicates the span between the median and the minimum plausible bioactive concentration

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 18 of 25 

### Section 6 AR

# Case Study Two

 Reverse dosimetry based on HTTK data was used to predict an oral equivalent dose that would cause the ACC in plasma for the 95 - percentile, most sensitive adult

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 19 of 25 

### Section 6 AR

# Case Study Two

 Based on the ACToR UseDB descriptors and production volume, a median exposure for similar NHANES chemicals can be predicted 

### Heuristic Triclosan Consumer & 

### Industrial Use Yes Industrial Use Only No Pesticide Inert No Pesticide Active Yes Production Volume < 25,000 lbs/year

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 20 of 25 

### Section 6 AR

# Case Study Two

 Due to the large uncertainty, the upper 95% limit of the exposure estimate credible interval is used 

### Heuristic Triclosan Consumer & 

### Industrial Use Yes Industrial Use Only No Pesticide Inert No Pesticide Active Yes Production Volume < 25,000 lbs/year

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 21 of 25 

### Section 6 AR

# Case Study Two

 EPA Triclosan Occupational and Residential Exposure Assessment (2008) μg/ kgBW/day exposures: Demographic Mage (2007) Schafer (2004) Geigy (1981)

Mean Geigy (1981)

95% ExpoCast

Geometric Mean Median ExpoCast

Geometric Mean Upper 95% Total 2.5 2.9 2.9 4.5 0.0012 0.085 Age 6- 11 1.6 1.9 1.7 2.4 0.0079 0.17 Age 12 - 19 2.7 3.2 4.1 6.2 0.0015 0.11 Age 20- 59 2.9 3.2 3.0 4.7 0.0015 0.11 Age >=60 1.9 2.2 2.1 3.3 0.002 0.083 Male 3.1 3.8 3.6 5.6 0.0011 0.074 Female 2.0 2.1 2.1 3.4 0.0016 0.11

 Triclosan exposures underestimated by ExpoCast because most pesticide active ingredients exposures are significantly lower than exposures for other chemical classes FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 22 of 25 

### Section 6 AR

# IBER for ER Only show subset (of 290) with ER bioactivity

EDSP Dashboard & Appendix 3, Table 6.3 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 23 of 25

# IBER for AR Only show subset (of 290) with AR bioactivity

EDSP Dashboard & Appendix 3, Table 6.3 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 24 of 25

# Summary for IBER
 A method to rank chemicals by exposure- bioactive ratio  Uses in vitro assay data to derive a concentration at which pathway- based bioactivity occurs  Uses in vitro toxicokinetics to convert to an oral 

# equivalent dose  Protective (i.e., conservative/worst scenarios)  Uses models to estimate exposure, given assumptions 

# about near-field use  All quantities include estimates of uncertainty and variability 

#  For AR and ER, most chemicals showing overlap between exposure and bioactivity are drugs or natural hormones Section 6 FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2 -5, 2014 Slide 25 of 25