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 humanderived

Data uncertainty (potency)

Other biology not included

(See next slide)

Default for now

HapMap cell-line

experiments may help

Pharmacokinetics

Note: Data is humanderived

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