Systematic Empirical Evaluation 

Models (SEEM) Analysis and Hig

Throughput Exposure (HTE)

Estimates

Systematic Empirical Evaluation 

Models (SEEM) Analysis and Hig

Throughput Exposure (HTE)

Estimates

Presented by John Wambaugh

Office of Research and Development

National Center for Computational Toxicology

Presented by John Wambaugh

Office of Research and Development

National Center for Computational Toxicology
Presentation Roadma

Introduction

High Throughput Exposure

(HTE) Estimation

High Throughput Toxicokinetics

(HTTK) and Reverse

Toxicokinetics (RTK)

Ongoing Research

• Framework overview

• HTE Forward Data and Models• HTE Reverse Data and Models

• SEEM Analysis and HTE Estimates

Future Appli
Goals for High Throughput Ex

• Incorporate multiple models into consensus predictio

1000s of chemicals

• Evaluate/calibrate predictions with available measure

data across many chemical classes

• Empirically estimate uncertainty in predictions
Model Evaluation

Chemical Manufacture

Consumer

Building Materials

Products, Articles,

Building Materials Environme

al Release

Air, Dust, Surfaces Food 

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

Data and

Models
Illustration of the SEEM Fram

EstimateUncertainty

EDSPChemicalsQSARs andHTE Data

Biomonitoring

Data

Inferred (Reverse) Exposure

Model 1Model 2

...

Cm

Apply calibration and uncertainty to

other chemicals

Evaluate Model Performa

and Refine Models

Forward Predictions

Exposure

Inference

Dataset 1Dataset 2

...
P1P2P3P4

U1

U2

φ11φ21φ31φ32φ42

ܷଵ ൌ ߮ଵଵܲଵ ൅ ߮ଶଵܲଶ ൅ ߮ଷଵܲଷ

ܷଶ ൌ ߮ଷଶܲଷ ൅ ߮ସଶܲସ

ሻߠ ,ߤlogNormalሺ

Parent Exposure

Urinary

Excretion of

Analyte

data1

Summarized

NHANES Data

data2

φ22 

Inferred Exposures
Multiple regression models:

SEEM is a Linear Regress

Log(Parent Exposure) = a + m * log(Model Prediction) + b* Near 
Multiple regression models:

Average value

across all

chemicals

SEEM is a Linear Regress

Log(Parent Exposure) = a + m * log(Model Prediction) + b* Near 
Multiple regression models:

SEEM is a Linear Regress

 Can have multipl

m1, m2, ... for mu

(and combination

 First generation w

(i.e., RAIDAR per 

release), mUSEtox, 

mProductionVolume

Regression

coefficient

(or weight)

for different

model

predictions

Log(Parent Exposure) = a + m * log(Model Prediction) + b* Near 
Multiple regression models:

SEEM is a Linear Regress

 First generation SEEM also used early version of

ACToR UseDB to construct a binary (1 or 0) “near

field” source indicator

 Chemicals with use indicated in consumer

products, pharmaceuticals, personal care

products, fragrance, and food additive

 Effect of Near vs. Far Field was single most

important predictor for first generation SEEM

analysis

 Outperformed USEtox and RAIDAR for NHANES

chemicals (those models may be moreappropriate for persistent chemicals or ecologicalendpoints)

Weight for

descriptor

Log(Parent Exposure) = a + m * log(Model Prediction) + b* Near 
Log(Parent Exposure) = a + m * log(Model Prediction) + b* Near 

Multiple regression models:

SEEM is a Linear Regres

ε 

Re

un

th

Inferred Exposure

Weighted HTE Model Predictions
R2 ≈ 0.14

First Generation SEEM Forw

Predictions vs. Inferred Exp

(ES&T, 2013)
Second Generation SEEM A

 Indoor/consumer use is the primary determinant of 

exposure in First Generation Analysis (ES&T, 2013)

 The predictive ability of this simple, near‐field expos

forecast exposure raises the question:

• What other simple heuristics might be used to cla

chemicals?

 Can’t exactly answer that, but what we can ask is:

• Given the variety of high throughput exposure de

(data), which factors best explain exposures infer

the available NHANES biomonitoring data?
High Throughput Descripto

NHANES
Using Akaike Information Cri

(AIC) to Avoid Over‐fittin

 In first generation analysis we were

evaluating existing models with the

available (few) chemicals

 Now we are trying to build a model

using essentially the same number of

chemicals: there is a danger of over‐

fitting

 AIC (1974) is a statistical measure of

model

 Penalize goodness of fit with number of

parameters needed to achieve that fit

 Builds confidence that a model is

appropriate for extrapolation to othercircumstances

 Model does not just describe the noise in a

particular data set Noisy data an

Over‐fitting

Y

X
Not All Descriptors Are U

 The averag

AIC (smalle

for models different nparameterexplaining 

different coof chemica

 The predic

in the opti

with highefrequencie

represente

circles, and 

lower freq

lighter circ
Not All Descriptors Are U

 The average r

(smaller is bemade with diof parameter1500 differenof chemical e

 The predictor

the optimal mhigher frequerepresented band those witfrequencies b

 As a sanity ch

random variafrom binomiawith probabil10% of obtainselected as opdescriptors in model
Correlation for Second Gene

Model
Num

Heuristic Description

Inferred NHA

Chemical Expo

(106)

ACToR “Consumer use & 

Chemical/Industrial Process 

use”

Chemical substances in consumer products (e.g., toys, personal care products, clothes, furniture, and home-care products) that are also used in industrial manufacturing processes. Does not include food or pharmaceuticals.

37 

ACToR “Chemical/Industrial 

Process use with no 

Consumer use”

Chemical substances and products in industrial manufacturing processes that are not used in consumer products. Does not include food or pharmaceuticals

14 

ACToR UseDB “Pesticide 

Inert use” Secondary (i.e., non-active) ingredients in a pesticide which serve a purpose other than repelling pests. Pesticide use of 

these ingredients is known due to more stringent reporting standards for pesticide ingredients, but many of these chemicals appear to be also used in consumer products

16 

ACToR “Pesticide Active use” Active ingredients in products designed to prevent, destroy, 

repel, or reduce pests (e.g., insect repellants, weed killers, and disinfectants).

76 

TSCA IUR 2006 Total 

Production Volume Sum total (kg/year) of production of the chemical from all sites that produced the chemical in quantities of 25,000 pounds or 

more per year. If information for a chemical is not available, it is assumed to be produced at <25,000 pounds per year.

106 

High‐throughput exposure he
(ES&T, 2013)

R2 ≈ 0.14 

Improvement of Second ove

Generation SEEM
CDC, Fourth National Exposure Report (2011)

NHANES Data Breaks Dow

Demographics

 Will diff

demogra

different 
Predictors Do Not Vary Betw

Groups

 The vertica

the 95% creacross the 1exposure scfrom the NH

 SHEDS‐HT s
Exposure Predictions for 7968 EDSP C

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

framework
Exposure Predictions for 7968 EDSP C

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

below the NHANES limits of detection (LoD)

 Dotted lines indicate 25%, median, and 75% of the LoD distribution
Exposure Predictions for 7968 EDSP C

 Chemicals currently monitored by NHANES are distributed throughpu

predictions

 Chemicals with the first and ninth highest 95% limit are monitored by 
Exposure Predictions for 7968 EDSP C

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

UseDB for any of the four use category heuristics
Conclusions

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

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

calibration and estimate of uncertainty

 The second generation calibrated meta‐model can estimate rel

of chemical exposures for 7968 EDSP chemicals

• This includes thousands of chemicals with no other data on human expos

• Same factors are predictive across demographics characterized by NHANE

 Different demographics have different mean (overall) exposure

• There are demographic‐specific aspects not currently described by availab

 Upcoming third generation SEEM analysis:

• Developing new data sources for consumer products• Developing and evaluating new mechanistic HT models for exposure from 

and indoor environment (e.g., SHEDS‐HT)

• Should help decrease uncertainties and increase confidence in extrapolat
External CollaboratorsJon Arnot (ARC)Deborah Bennett (University of California, Irvine)

Alicia Frame (Dow Chemical)Anran Wang (NCSU)

Olivier Jolliet (UJade Mitchell (MBarbara Wetmo

Kathie DionisioPeter EghehyKristin IsaacsRichard JudsonThomas KnudsenChantel NicolasRobert PearceJames RabinowitzWoody SetzerCory StropeDan ValleroJohn Wambaugh

ExpoCast Team Chemical Safety for Sustainab

CollaboratorsMichael BreenRocky GoldsmithChris Grulke

Haluk OzkayJon SobusMark StrynaCecilia Tan

*Trainees

*

*

*

*

* Elaine Hubal

CSS Deputy National Program DirectorTina BahadoriCSS National Program Director

Acknowledgemen

*
Presentation Roadma

Introduction

High Throughput Exposure

(HTE) Estimation

High Throughput Toxicokinetics

(HTTK) and Reverse

Toxicokinetics (RTK)

Ongoing Research

Future Appli
Introduction

High Throughput Exposure

(HTE) Estimation

High Throughput Toxicokinetics

(HTTK) and Reverse

Toxicokinetics (RTK)

Ongoing Research

• RTK for in vitro HTS data

• Evaluation of Confidence

and Domain of Applicabil

Future Appli

Presentation Roadma