Slide 1

FIFRA Scientific Advisory Panel Meeting

Integrated Bioactivity and Exposure Ranking: A 

Computational Approach for the Screening of 

Chemicals in the Endocrine Disruptor Screening 

Program

Arlington, VA

December 2 – December 5, 2014
Slide 2

High Throughput Exposure (HTE) 

and 

High Throughput Toxicokinetics 

(HTTK)

John Wambaugh

U.S. E.P.A., Office of Research and Development
Presentation Roadmap

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Introduction

Estrogen Receptor 

Bioactivity

Androgen Receptor 

Bioactivity

Exposure

Toxicokinetics/Dosimetry

Integrated Bioactivity 

Exposure Ranking

Future Directions
Introduction

 ExpoCast is an initiative to develop the necessary approaches and tools for 

rapidly predicting exposure for thousands of chemicals (Cohen-Hubal, et 

al., 2010)

Environmental Fate and Transport

Consumer Use and Indoor Exposure

 July, 2014 FIFRA SAP “New High 

throughput methods to estimate 

chemical exposure”:

 High Throughput Exposure (HTE) 

predictions for 7968 chemicals, 

White paper Section 4

 High throughput toxicokinetics 

(HTTK), White paper Section 5

“All cases are unique, and very similar to others.”

― T.S. Eliot

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014
In vitro Bioactivity, HTTK, and in Vivo Toxic 

Doses

Comparison of HTTK predicted 

oral equivalent doses (box 

and whisker plots in 

mg/kg/day) with doses for 

no effect and low effect 

groups in animal studies

Lowest Observed Effect Level

No Observed Effect Level 

(NEL)

NEL/100

Estimated chronic exposure 

levels from food residues 

are indicated by vertical 

red lines. All values are in 

mg/kg/day.

Judson et al. (2011)

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014
In Vitro Bioactivity, In Vivo Toxicokinetics, and 

Exposure for Humans

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

 Studies like 

Wetmore et al. 

(2012),addressed 

the need for 

toxicokinetic data

Section 5, Fig. 5.1, p. 87
In Vitro Bioactivity, In Vitro Toxicokinetics, and 

Exposure for Humans

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

 Studies like 

Wetmore et al. 

(2012),addressed 

the need for 

toxicokinetic data

 As in Egeghy et al. 

(2012), there is a 

paucity of data 

for providing 

context to HTS 

data

Section 5, Fig. 5.1, p. 87
Exposure Science in the 21st Century

 2012 NRC report:

 New tools needed 

for screening and 

prioritization of 

chemicals for 

targeted toxicity 

testing 

 New, focused 

exposure 

assessments or 

monitoring studies 

needed

 Better 

quantification of 

population 

vulnerability 

needed

Figure from Egeghy et al. (2012), 

“The exposure data landscape for manufactured chemicals”

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 4.2, p. 78
Exposure Forecasting (ExpoCast)

 Develop the tools and data 

necessary to rapidly quantify 

human and ecological exposure 

potential of chemicals

 Focus is distinct from many existing 

exposure tools that support either 

screening level assessments on a 

per chemical basis or full 

regulatory risk assessment

 a prediction is a specific statement

 a forecast is a probabilistic 

statement

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

e.g. Judson et al., (2011)

No 

Priority

No 

Priority

Lower

Priority

Medium

Priority

Higher

Priority

Section 4.2, p. 78
Goals for High Throughput Exposure

• Incorporate multiple models into consensus predictions for 

1000s of chemicals

• Evaluate/calibrate predictions with available measurement 

data across many chemical classes

• Empirically estimate uncertainty in predictions

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 4.1, p. 77
Applying ExpoCast

 There are 

1000s of 

chemicals to 

which we 

might be 

exposed

 How can we 

use ExpoCast 

to pick 

chemicals 

with more 

likely 

exposure?

 What about 

uncertainty?

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Chemicals That Are Potentially In the Environment

Images from Thinkstock

Section 4.1, p. 77
Applying ExpoCast

 The CDC 

targets some 

chemicals for 

exposure 

biomonitoring

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

NHANES

Images from Thinkstock

Section 4.1, p. 77
Applying ExpoCast

 They find 

evidence of 

high exposures 

for some 

chemicals

 Moderate 

exposures for 

others

 And many 

chemicals are 

below the limit 

of detection

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Images from Thinkstock

Section 4.1, p. 77
Applying ExpoCast

 We use the 

chemical 

descriptors and 

high level use 

information 

(ACToR UseDB) 

that is available 

for thousands 

of EDSP 

chemicals to 

organize the 

NHANES 

chemicals

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Moderate Exposure High Exposure < Limit of Detection

Images from Thinkstock

Section 4.1, p. 77
Applying ExpoCast

 We can then 

predict which 

chemicals 

without 

monitoring 

data are most 

like high, 

moderate, and 

low exposure 

NHANES 

chemicals

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

There will still be 

other chemicals 

without 

characteristics that 

are predictive of 

NHANES chemicals

Moderate Exposure High Exposure < Limit of Detection

Images from Thinkstock

Section 4.1, p. 77
Exposure Space

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 4.2, Figure 4.2, p. 81

Chemical Manufacture

Consumer

Products, Articles, 

Building Materials Environmental 

Release

Food Air, Soil, 

Water

Air, Dust, 

Surfaces

Human

Direct Use

(e.g., lotion)

Residential Use

(e.g. ,flooring)

MONITORING

DATA

RECEPTORS

MEDIA

Biomarkers 

of Exposure

Media Samples

Waste
Exposure Space

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 4.2, Figure 4.2, p. 81

Chemical Manufacture

Consumer

Products, Articles, 

Building Materials Environmental 

Release

Food Air, Soil, 

Water

Air, Dust, 

Surfaces

Near-Field

Direct

Near-Field 

Indirect

Human

Dietary Far-Field

Direct Use

(e.g., lotion)

Residential Use

(e.g. ,flooring)

MONITORING

DATA

RECEPTORS

MEDIA

EXPOSURE 

PATHWAY

(MEDIA + RECEPTOR)

Biomarkers 

of Exposure

Media Samples

Waste
Exposure Space

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 4.2, Figure 4.2, p. 81

Chemical Manufacture

Consumer

Products, Articles, 

Building Materials Environmental 

Release

Food Air, Soil, 

Water

Air, Dust, 

Surfaces

Near-Field

Direct

Near-Field 

Indirect

Human

Dietary Far-Field

Direct Use

(e.g., lotion)

Residential Use

(e.g. ,flooring)

MONITORING

DATA

RECEPTORS

MEDIA

EXPOSURE 

PATHWAY

(MEDIA + RECEPTOR)

Biomarkers 

of Exposure

Media Samples

Waste

Data and 

Models
Exposure Space

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 4.2, Figure 4.2, p. 81

Chemical Manufacture

Consumer

Products, Articles, 

Building Materials Environmental 

Release

Food Air, Soil, 

Water

Air, Dust, 

Surfaces

Near-Field

Direct

Near-Field 

Indirect

Human

Dietary Far-Field

Direct Use

(e.g., lotion)

Residential Use

(e.g. ,flooring)

MONITORING

DATA

RECEPTORS

MEDIA

EXPOSURE 

PATHWAY

(MEDIA + RECEPTOR)

Biomarkers 

of Exposure

Media Samples

Waste

Data and 

Models

• Biomarkers reflect all pathways, not just those in forward model –

missed pathways contribute to estimated uncertainty
Systematic Empirical Evaluation 

of Models (SEEM)

 There are four basic steps in the SEEM framework

1. Forward prediction of exposures, which involves model curation and 

parameterization

2. Inference of exposures from monitoring data

3. Systematic evaluation and calibration of the predictions against the 

inferred exposures

4. Extrapolation of the calibrated model predictions and estimated 

uncertainty to chemicals with no monitoring data. 

 To achieve these aims the SEEM framework used Bayesian 

formalism and multivariate, linear regression for 

demonstrating and evaluating predictive ability

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 4.2, p. 78-79
Illustration of the SEEM Framework

Estimate 

Uncertainty

EDSP 

Chemicals

QSARs and 

HTE Data

Biomonitoring

Data

Inferred (Reverse) Exposure 

Model 1

Model 2

...

Calibrate 

models

Apply calibration and uncertainty to 

other chemicals

Evaluate Model Performance

and Refine Models

Forward Predictions

Exposure 

Inference

Dataset 1

Dataset 2

...

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 4.2, Figure 4.1, p. 79
SEEM Evolution – Human Exposure

Model and Predictors

• Existing complex fate and 

transport models have low 

correlation to measured 

exposures

• Near field factor most 

important

• Simple, readily available data

• Better correlation to 

measured exposures

• Similar predictions across 

demographics

1st Gen

Calibration/Evaluation Data SEEM Conclusion

2nd Gen

USEtox

RAIDAR

Near Field / Far Field

NHANES

Urine

Data

Production Volume

Use Categories

Production Volume

Phys-Chem Properties

NHANES

Urine

Data

3rd Gen

• Analysis in progress

SHEDS-HT

Literature 

Models

CPcat Database

NHANES

Urine and 

Blood

Data

Pesticide REDs

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 4.2, p. 79-82
 

 

 

i i

i

i

M W

mg/kg/day mg/kg/day 0

M W00

Parent

Products

1

2

3

4

 

day

g

*

g

mg

70 kg

1

mg/kg/day creatine

creatine

i

i



Steady-state assumption

Linking NHANES Urine Data and Exposure

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Generalization of LaKind

and Naiman (2008)

Section 4.2, p. 79
 

 

 

i i

i

i

M W

mg/kg/day mg/kg/day 0

M W00

Parent

Products

1

2

3

4

 

day

g

*

g

mg

70 kg

1

mg/kg/day creatine

creatine

i

i



Steady-state assumption

Linking NHANES Urine Data and Exposure

Observations

(CDC NHANES 

urine samples)

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Generalization of LaKind

and Naiman (2008)

Section 4.2, p. 79
 

 

 

i i

i

i

M W

mg/kg/day mg/kg/day 0

M W00

Parent

Products

1

2

3

4

 

day

g

*

g

mg

70 kg

1

mg/kg/day creatine

creatine

i

i



Generalization of LaKind

and Naiman (2008) Steady-state assumption

Linking NHANES Urine Data and Exposure

Observations

(CDC NHANES 

urine samples)

Unknowns (we choose to use Bayesian analysis 

via Markov Chain Monte Carlo or MCMC)

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 4.2, p. 79
Mapping of NHANES Parents to Analytes

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Parent chemical

Chemical measured

in urine

Lots of unknowns, but MCMC 

can try many possibilities (JAGS 

and STAN samplers used)

Section 4.2, p. 79
Limit of Detection (LOD)

 If observations < analytic detection limits: model as left 

censored observations from lognormal population 

distribution

Fraction < LOD

 Parameters for distribution: 

log geometric mean (ln(GM)) 

and standard deviation

• Also estimate these with 

MCMC

 Generally, these estimates 

have greater uncertainty

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 4.2, p. 79
Log(Parent Exposure) = a + m * log(Model Prediction) + b* Use Indicator + ε

Multiple regression models:

SEEM is a Linear Regression

ε ~ N(0, 2)

Residual error, 

unexplained by the 

regression model

Any pathways missed 

by forward models 

increase this error

Inferred Exposure

Weighted HTE Model Predictions

FIFRA SAP: Integrated Bioactivity Exposure 

Ranking Dec 2-5, 2014

Section 4.2, p. 82-83
Exposure Predictions for 7968 Chemicals

 These are the current calibrated and evaluated predictions from the SEEM 

framework

Upper 95%

Prediction

Median

Prediction

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 4.2, Figure 4.4, p. 83
Exposure Predictions for 7968 Chemicals

 We focus on the median and upper 95% predictions because the lower 95% is 

below the NHANES limits of detection (LoD)

 Dotted lines indicate 25%, median, and 75% of the LoD distribution

Upper 95%

Prediction

Median

Prediction

NHANES

LoD

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 4.2, Figure 4.4, p. 83
Exposure Predictions for 7968 Chemicals

 Chemicals currently monitored by NHANES are distributed throughput the 

predictions

 Chemicals with the first and ninth highest 95% limit are monitored by NHANES

NHANES

LoD

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 4.2, Figure 4.4, p. 83
Exposure Predictions for 7968 Chemicals

 The grey stripes indicate the 4182 chemicals with no use indicated by ACToR

UseDB for any of the four use category heuristics

NHANES

LoD

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 4.2, Figure 4.4, p. 83
High Throughput Exposure 

Summary

 We used the SEEM framework to compare HTE factors to the NHANES 

data for a subset of chemicals in order to determine an empirical 

calibration and estimate of uncertainty

 The second generation calibrated meta-model can estimate relative levels 

of chemical exposures for 7968 chemicals

• These include thousands of chemicals with no other data on human exposure

• Same factors are predictive across multiple demographics (including age and gender) 

characterized by NHANES

 New data and models should help decrease uncertainties and increase 

confidence in extrapolation

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014
High Throughput Exposure 

Future Directions

 Developing new methods to draw inferences from CDC blood/serum 

samples

 Developing new data sources for consumer products

 Developing and evaluating new mechanistic HT models for exposure from 

consumer use and indoor environment (e.g., SHEDS-HT)

 Coordinating with biomonitoring studies to obtain additional data on 

both:

• Chemicals expected to be similar to those currently measured at high levels

• Chemicals unlike any that are currently being monitored

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014
Presentation Roadmap

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Introduction

Estrogen Receptor 

Bioactivity

Androgen Receptor 

Bioactivity

Exposure

Toxicokinetics/Dosimetry

Integrated Bioactivity 

Exposure Ranking

Future Directions
High Throughput Toxicokinetics (HTTK)

 Toxicokinetics (TK) provides a bridge between HTS and HTE by 

predicting tissue concentrations due to exposure 

 HTTK methods have been used by the pharmaceutical industry to 

determine range of efficacious doses and to prospectively evaluate 

success of planned clinical trials

 HTTK data can be applied to perform in vitro-in vivo extrapolation 

(IVIVE) or “reverse dosimetry” in order to provide a human dose 

context for in vitro concentrations from HTS

• Reverse dosimetry can approximately convert in vitro HTS results to 

daily doses needed to produce similar levels in a human

• This allows direct comparisons with exposure

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 5
ToxCast in vitro AC50s

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

 One point for each chemical-in vitro assay combination with a 

systematic (Hill function) concentration response curve

Wetmore et al. (2012)

Section 1, Figure 1.2, p. 11
Oral dose in

(mg/kg/day)

Sum of hepatic 

and renal 

clearance

(mg/kg/day)  























l u b int

int

u b l u b Q F *

GFR*F Q *F *

oral dose rate

Cl

Cl

Cs s

Steady-State Plasma Concentration

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

 In vitro plasma protein 

binding and metabolic 

clearance assays allow 

approximate hepatic 

and renal clearances to 

be calculated

 At steady state this 

allows conversion from 

concentration to 

administered dose

 No oral absorption/ 

bioavailability included

Section 5, p. 89
Add plasma (6 

donor pool for 

human) to one 

well

Add chemical Determine 

concentration in 

both wells 

(analytical 

chemistry)

Double-wells 

connected by semipermeable 

membrane on a 

Rapid Equilibrium 

Dialysis (RED) Plate

Incubate plates to 

allow wells with 

and without 

protein to come 

to equilibrium

.

. .

.

. .

.

. .

.

.

2

1

,

well

well

u b p

C

C

F 

1 2

RED Method: 

Waters et al. (2008)

 Data on ToxCast chemicals initially collected at Hamner 

Institutes 

 Published:

 Rotroff et al. (2010) - Pilot study using 38 Phase I ToxCast Chemicals

 Wetmore et al. (2012) - Remainder of easily analyzed Phase I chemicals

 Wetmore et al. (2013) - Rat TK for 50 ToxCast/ToxRefDB compounds

 Wetmore et al. (2014) – Assessed variability in metabolism for a dozen 

ToxCast compounds

Plasma Protein Binding

(Fraction Unbound in Plasma)

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 5, p. 89
Cryopreserved

Hepatocytes

(10 donor pool for 

human)

Add Chemical

(1 and 10 μM)

Remove Aliquots 

at 15, 30, 60, 120 

min

Analytical 

Chemistry

The rate of disappearance of 

parent compound (slope of 

line) is the hepatic clearance

(μL/min/106 hepatocytes)

We perform the assay at 1 

and 10 μM to check for 

saturation of metabolizing 

enzymes.

10 μM

1 μM

0 50 100 150

Time (min)

Log Conc (μM)

Cryopreserved hepatocyte

Method: Shibata et al. (2002)

Intrinsic Hepatic Clearance

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

 Data on ToxCast chemicals 

initially collected at Hamner 

Institutes

Section 5, p. 89
Steady-State Model is Linear

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Steady-state Concentration (

m

M)

Daily Dose (mg/kg/day) 0

Prediction

 























l u b int

int

u b l u b Q F *

GFR*F Q *F *

oral dose rate

Cl

Cl

Cs s  Can calculate predicted steady-state concentration (Css) 

for a 1 mg/kg/day dose and multiply to get concentrations 

for other doses

Slope = Css for 1 mg/kg/day

Section 5, Figure 5.3, p. 91
Steady-State In Vitro-In Vivo 

Extrapolation (IVIVE)

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Oral Equivalent Daily Dose

Steady-state Concentration (mM) = in vitro AC50 0

Prediction

 Swap the axes

 Can divide bioactive concentration by Css for for a 1 mg/kg/day dose to get oral equivalent dose

Slope = mg/kg/day per Css

1 mg/kg/day

Wetmore et al. (2012)

Section 5, Figure 5.3, p. 91
 It appears harder to prioritize on bioactive in vitro 

concentration without in vivo context

ToxCast in vitro AC50s

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Wetmore et al. (2012)

Section 1, Figure 1.2, p. 11
 Translation from in vitro to steady-state oral equivalent doses 

allow greater discrimination between effective chemical 

potencies

HTTK Oral Equivalents

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Wetmore et al. (2012)

Section 1, Figure 1.2, p. 11
Reverse Dosimetry with HTTK

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

High 

Throughput 

In Vitro 

Bioactive 

Concentration

Simulated 

Human

In Vivo

Doses Populations 

that are More 

Sensitive

HTTK

in vitro

data

Monte Carlo

Simulation of Biological

Variability

Combination of 

higher exposure 

and sensitivities 

Images from Thinkstock

Section 5, p. 89
Variability in Steady-State TK Model

 In vitro clearance (μL/min/106 hepatocytes) is scaled to a whole organ clearance 

using the density of hepatocytes per gram of liver and the volume of the liver 

(which varies between individuals)

 Glomerular filtration rate (GFR) and blood flow to the liver (Ql) both vary from 

individual to individual

 Further assume that measured HTTK parameters have 20% coefficient of variation

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

 























l u b int

int

u b l u b Q F *

GFR*F Q *F *

oral dose rate

Cl

Cl

Cs s

(Passive) Renal 

Clearance

Hepatic Clearance 

(Metabolism)

Section 5, p. 89
Monte Carlo (MC) Approach to Variability

SimCYP (Pharma) Approach

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

 























l u b int

int

u b l u b Q F *

GFR*F Q *F *

oral dose rate

Cl

Cl

Cs s

Probability

log Liver Flow (Ql)

Probability

log Glomerular Filtration Rate (GFR)

Probability

log Liver Volume

Probability

log Clint

in vitro

Probability

log fub

Probability

Css

Wetmore et al. (2012)

Section 5, Figure 5.4, p. 92
FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014 Oral Equivalent Daily Dose

Steady-state Concentration (mM) = in vitro AC50 0

Median

Predicted Css

 The higher the predicted Css, the lower the oral equivalent dose, so the upper 95% predicted Css

from the MC has a lower oral equivalent dose

Lower 95%

Predicted Css

Upper 95%

Predicted Css

Section 5, Figure 5.3, p. 91

Steady-State In Vitro-In Vivo 

Extrapolation (IVIVE)
Evaluation of HTTK Performance and Domain 

of Applicability

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

 Through comparison to in 

vivo data, a cross-validated 

(random forest) predictor 

of success or failure of 

HTTK has been constructed

 The largest single class of 

chemicals fall into the 

category of “On the order” 

(within approximately a 

factor of three)

 More likely to overestimate 

(conservative error) than 

underestimate plasma 

concentrations from an 

exposure
HTTK Summary

 HTTK methods developed for pharmaceuticals have been adapted to 

environmental testing

 We can infer daily doses that produce plasma concentrations equivalent to 

bioactive concentrations

• Can identify environmentally-relevant in vitro concentrations for high throughput screening

 All methods and data publicly available: R package “httk” and two manuscripts 

submitted to peer-reviewed journals (one guide to using R package and one 

statistical evaluation of performances)

 Chemical specific analytical chemistry is a rate limiting step: throughput is higher 

than in vivo testing but not as fast as high throughput bioactivity screening

• 100s of chemicals per year

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 5
HTTK Future Directions

 Collecting new HTTK data on chemicals using improved (serial dilution of plasma) 

methodology for measuring plasma binding

 Expanding populations variability analysis to include sensitive population groups to 

go beyond NHANES (current MC simulations focus on adults)

 Current in vivo data for evaluation of models is from heterogeneous studies –

ongoing efforts to collect data from limited in vivo studies and to organize data 

from larger, systematic studies (e.g., National Toxicology Program) into computable 

format

 Improved evaluation data will allow better assessment of predictive ability and 

determination of domain of applicability

FIFRA SAP: Integrated Bioactivity Exposure Ranking Dec 2-5, 2014

Section 5