Inference (Reverse Prediction) o

Human Exposure

Inference (Reverse Prediction) o

Human Exposure

Presented by R. Woodrow Setzer

National Center for Computational Toxicology

Office of Research and Development

Presented by R. Woodrow Setzer

National Center for Computational Toxicology

Office of Research and Development
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
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

• Biomarkers reflect all pathways, not just those in forward m

pathways contribute to estimated uncertainty

Data and

Models

Reverse Inference of Expo
Goal

 To develop human exposure estimates 

population level from biomonitoring da

characterize the uncertainty of thoseestimates.
Chemicalsthat Could

be Modeled(1936)

ProductioDataIUR (6759 

with prod

>25,000 

CPRI (242 

with tota

NHANES

Chemicals for First

Generation (2127)

31 51

Data Availability for Evaluating 

Predictions

 Ground‐truth with CDC

NHANES urine data

 Many chemicals had

median conc. below thelimit of detection (LoD)

 Most chemicals >LoD

not high productionvolume

 82 chemicals inferred

for Wambaugh et al.(2013) – First generation

 Adding more chemicals

(106 currently), dozensmore expected withserum model – Secondgeneration
NationalHealthAndNutritionExaminationSurvey

• NATIONAL averages for “descript

and nutritional statistics for sex, r

Hispanic origin, and age subdomapopulation”.

• Annual survey, data released on

2

cycle.

• Oversampling:

• Age 12

‐19, 60+

• Mexican

‐American, black pop

• In 2000

‐2006, pregnant wome

everyone

< 130% of poverty le

• Blood and urine samples analyze

than 1/person) for

a large list of

environmental and related chem

list varies over sample year, ages 
Population Subgroups Ana

 Total population Sex

• Females• Females of child‐bearing age (16 – 49 y.o.)

• Males

 Age

• 6‐11 year olds

• 12‐19 year olds

• 20‐65 year olds

• 66+ years old

 Obesity

• BMI ≤ 30

• BMI > 30
Exposure Absorbed Dose

Absorption

Bioavailability

Metabolism

Elimination

Distribution

Urine Productio

Product

Composition /

Chemical Use /

Occurrence

Production

Volume / Release

Behavior Patterns

Fate and

Transport Factors

Emissivity, etc.

Factors Contributing to U

Concentration of Environm

Chemicals and Metabolit
Exposure Absorbed Dose

Absorption

Bioavailability

Metabolism

Elimination

Distribution

Urine Productio

Product

Composition /

Chemical Use /

Occurrence

Production

Volume / Release

Behavior Patterns

Fate and

Transport Factors

Emissivity, etc.

Factors Contributing to U

Concentration of Environm

Chemicals and Metabolit

Domain of Reverse In
From NHANES Sample to Estim

Exposure

 a complex, multistage sample: individuals in sample

represent different numbers of people in the population

• Statistical analyses must accommodate sampling

weights and strata.

• ~2000 urine samples evaluated for environmental

chemicals

– rare high values (e.g. from extreme occupational exposures

are likely to be missed.
Estimate Exposure in Two 

 Stage 1: Use statistical methods appropriate to NHANES desi

estimate population summaries like quantiles or log(geomet

for urinary analytes

 Stage 2: Summaries from Stage 1 are data to estimate expos

parent chemicals.
Challenges and Assumpt

 Many observations < LOD

• Assume lognormal population distribution and tr

observations as censored

 Mapping from parent chemical to urine analyte 

• Assume all mass observed, and fraction of parent 

analyte unknown.

 Population variability includes a component du

discrete exposure events and clearance

• Need to assume steady‐state exposure at individu

 Urine voids vary in volume

• Scale analytes to creatinine concentration, and us

sex, ethnicity – adjusted value for daily creatinineexcretion.
Limit of Detection

 Observations < analytic detection limits: modeled as left

censored observations from lognormal population

distribution

Fraction < LOD

• Data: # < LOD, total 

• Parameters for distr

ln(geometric mean (

• SD ~ other chemical

complete data

• Generally, these est

greater uncertainty
Mapping of NHANES Paren

Analytes

14

Parent c

Chemicain urine
     

i i

i

i MW

mg/kg/day mg/kg/day 0 MW0 0

Parent

Products

12

3

4

  day g * gmg 70 kg 1 mg/kg/day creatine creatine i i 

LaKind and Naiman (2008)

Steady‐state assumption

Linking NHANES Urine Data 

Exposure
P1P2P3P4

U1

U2

Geometric Mean:

Exposure toParent Compound

GeometricMean: UrinaryExcretion of

Analyte

data1

Data from First St

data2

Stage Two Model
P1P2P3P4

U1

U2

φ11φ21φ31φ32φ42

ܷଵ ൌ ߮ଵଵܲଵ ൅ ߮ଶଵܲଶ ൅ ߮ଷଵܲଷ

ܷଶ ൌ ߮ଷଶܲଷ ൅ ߮ସଶܲସ

Geometric Mean:

Exposure toParent Compound

GeometricMean: UrinaryExcretion of

Analyte

data1

Data from First Sta

data2

φ22 

Stage Two Model
P1P2P3P4

U1

U2

φ11φ21φ31φ32φ42

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ሻߠ ,ߤlogNormalሺ

Geometric Mean:

Exposure toParent Compound

GeometricMean: UrinaryExcretion of

Analyte

data1

Data from First Sta

data2

φ22 

Stage Two Model
Bayesian vs. Classical Stat

 Why use Bayesian Statistics?

• Bayesian statistics emphasizes the uncertaint

the estimates

• It is computationally simpler in this complicat

model

 Second Generation estimates use the R

package rstan (Stan Development Team, 2

to carry out Hamiltonian Monte Carlo

Sampling from the model posterior.
Evaluating Model Goodness‐o
Model Fits to Urine Analyt
Estimates of Exposure
Contributions to U• Geometry of relation

parent chemical and 

• Number of samples b

Impact of Uncertainty on Est
Estimates of Population G

Absorbed Dose of Parent Che

Total Population and 6‐11 Yea
Summary

 We have used data on urine analytes fr

NHANES to infer US population geometmean exposures to a range chemicals

 The model allows quantification of the

uncertainty of the exposure inferences.
Reserve Slides
P1P

2

P

3

P

4

U1

U

2

φ11φ21φ31φ32φ42

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ሻߠ ,ߤlogNormalሺ௜	~	ܲ

Geometric Mean

Exposure RateParent Compound

Geometric Mean

Rate of UrinaryExcretion of

Metabolite

data1

Data from First

Stage If ‘good’ estim

logNorm

௝~	ݕ

If many observatData are counts lod (Ngtlod) and 

1) SDj is sampled 

smoothed disestimates of p

ln(y)).

2) Compute Pr{o

Uj , SDj }:

3) The number o

lod is binomia

size and

p

= P

Pralod௝

ൌ

න

ܰ

ஶ

୪୬	ሺ௟௢ௗ

ೕሻ

Ngtlod௝ ~ Bino

Data are estima

means,

yj

, and e

errors, sej

:

data

2

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φ22

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0	 ൑ 	 ߮௜௝ 	൑ 1