Toward a 3rd Generation SEEM

Analysis: New Data and Mode

Toward a 3rd Generation SEEM

Analysis: New Data and Mode

Presented by Kristin Isaacs, Ph.D.

National Exposure Research Laboratory

Office of Research and Development

Presented by Kristin Isaacs, Ph.D.

National Exposure Research Laboratory

Office of Research and Development
Presentation Roadma

Introduction

High Throughput Exposure

(HTE) Estimation

High Throughput Toxicokinetics

(HTTK) and Reverse

Toxicokinetics (RTK)

Ongoing Research

• A

E

• T

SH

Future Appli
New Data and Models

Should Lead to Improved Human Exposure 

 Second generation SEEM analysis was based on heuristics dete

from algorithmically generated use categories

• Explained ~50% of the chemical to chemical variance in exp

indicated by NHANES but did not vary significantly between

demographics

 New data sources should allow richer chemical descriptors

• May explain more of exposures

• May characterize demographic differences

 New mechanistic models should be better suited to extrapolat

predictive ability can be demonstrated

 Preparing to perform third generation SEEM analysis
Better Models and Data Sh

Reduce Uncertainty

Image from Little et al. (2012), see also Nazaroff e

Bennett et al. (2012), Wenger and Jolliet (2012

Uncertainty/Variability of NHANES Biomonitoring

~10% Far field

(Industrial) Releases

~60% Indoor /

Consumer Use

Indirect Exposure Direct Expo

• Consolida

Activities D

• ACToR Us

CPcatDB• SHEDS‐HT
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

...
Illustration of the SEEM Fram

EstimateUncertainty

EDSPChemicalsQSARs andHTE Data

Biomonitoring

Data

Inferred Exposure

Model 1Model 2

...

Camo

Apply calibration and uncertainty

to other chemicals

Weighted HTE Model

Predictions

Exposure

Inference

Dataset 1Dataset 2

...
Introduction

 New data for high‐throughput forward pre

of human exposure

• Consumer product characterization

• Chemical use information

 New model for high‐throughput estimatio

near‐field exposure

• Stochastic Human Exposure and Dose Simulat

Model‐High Throughput

 Use of new data and models in a 3rd Gene

SEEM analysis
Consumer Product Character

 Consumer Product Chemical Profile Database (CPCPdb)

• Goldsmith et al., Food and Chemical Toxicology, 2014

• Curated from retailer‐provided material data safety sheets (MSDS

automated and manual methods

• Chemical ingredients by product, including % composition where 

• Mapped to consumer product retail category, product name avai

additional categorizations

• Totals: 1797 chemicals in 8921 products

 Household Product Database (HPDB) ‐ National Library of Me

• MSDS‐based

• Totals: 3864 chemicals/substances in 12073 products

• Tiered product categorization
Mapping Chemical Ingredien

Product Categories

Gol
Product Categories of NHA

Chemicals

Gol
CPCatDB: Chemical and

Product Category Databa

 There is a further need to catalog how chemica

used

 One major input to exposure modeling

 Information exists but was widely dispersed

 ACToR can help bring this data together

 Create CPCat database

• Chemical use categories

• Product use categories

• Mapping from chemicals to products

 ACToR UseDB and CPCPdb integrated into CPCa
General Classes of Chemi

Use Categories in CPCatD

 General use associated categories

• e.g., used in lipsticks, as a pesticide

 Functional‐use categories

• e.g., a solvent

 Product‐use categories

• e.g., chemicals used in bathtub toys

 Therapeutic‐use categories

• e.g., antibiotics

 Industrial sector‐use categories

• e.g., mining
Use Category Harmonizatio

CPCatDB

 Each data source has its own category hierarc

 Categories from data sources were manually 

to one or more harmonized categories called 

“cassettes”

 Each CPCat cassette is made up of one or mo

describing the product or usage

 Cassettes and source categories can provide

additional heuristics/models for evaluation w

the SEEM framework
CPCat data sources

(>40,000 unique chemicals inc

Original data source

Class of

categories

Original

categories

CPCat

cassettes

ACToR Data Sets and Lists General‐use 131 173 

ACToR UseDB General‐use 15 15 CDR 2012:

Consumer General‐use 34 36 

Industrial Function Functional‐use 34 27 Industrial Sector Industrial sector‐use 42 43 

DfE Functional‐use 11 9 Dow Functional‐use 19 18 DrugBank Therapeutic‐use 582 460 2006 IUR General‐use 19 24 

KemI Functional‐use 61 31 NICNAS General‐use 17 17 

Retail Product Categories Product‐use 359 191 SPIN:

detpcat General‐use 781 284 

Industrial Sector Industrial sector‐use 580 221 NACE Industrial sector‐use 57 52 

UC62 General‐use 61 59 

Toxome Functional‐use 16 16 
SHEDS‐HT: Overview

 Stochastic Human Exposure and Dose

Simulation Model (SHEDS) – High Throu

(HT)

 Developed from ORD’s higher‐tier SHED

Multimedia exposure model

 Produces population‐level distributions 

exposures to chemicals with near‐field 

 Calculates aggregate exposures to chem

via multiple pathways, scenarios, and ro
SHEDS Approach

Ou

0.01

0.1

1

10

100

1000

10000

0 20 

Dose

• Populati• Populati

0.01

0.1

1

10

100

0 20 

Exposure

Algorithms

• Calculate Individual Daily Exposures/Doses:

Input Databases 

• Census• Human Activity•Food Consumption•Usage Data•Exposure Factor Distributions•Chemicals in Consumer 

Products

Dermal

Inhalation

Non‐Dietary Ingestion

Dietary Ingestion

Total Intake
Background and History: SH

Multimedia

1999

2001

SHEDS‐

Multimedia

version 2

(SAS and C++)

2002‐2005

Develop/apply

SHEDS‐Wood for CCA

2 FIFRA SAPs

2004‐2006

Develop

SHEDS‐

Multimedia v. 3

8/2007

FIFRA SAP review

of SHEDS‐

Multimedia

version 3

2007‐2009:

Multimedia V.4,

Including dietary

module

2011

SHED

Develo

paramet

(SA

2010:

FIFRA SAP review

of SHEDS‐MM

version 4

MethodsApplications

Xue et al.

Hand‐to‐mouth data

Xue et al.

Object‐to‐mouth data

Mokhtari et al. Sensitivity methods

Özkaynak 

Prototype m

compar

Xue et al. Longitudinal methods

Glen et al. Longitudinal methods 

Hore et al.

Chlorpyrifos

Stout and Mason

Chlorpyrifos

Buck et al.

Chlorpyrifos

Zartarian et al.

Chlorpyrifos

Zartarian et al.

CCA

Xue et al. CCA

Zartarian et al. CCA

Stout et al. Diazinon

Georgopoulos et al. Arsenic

Xue et al. Arsenic

Tulve et al. Pyret

Zartarian 

Özkaynak et al. Soil and d

Xue e

Glen and Isaacs

Sensitivity analysis me

1999‐2000

SHEDS‐

Pesticides

version 1
SHEDS‐HT: Motivation and D

Features

 Broad applicability, yet minimally parameterized

 Retention of critical knowledge and algorithms of SHEDS‐

 Flexible input data structure

• Allow for various tiers of inputs (no constraint on data

chemicals; no unnecessary assumptions for data‐poor

chemicals)

• Allow for straightforward update of predictions as mo

become available

 Extensible/Scalable

• Add new exposure scenarios or routes to expand dom

applicability without significant recoding

• Add new chemicals easily
SHEDS‐MM to SHEDS‐H

• High‐tier

• Minute‐level time resolution

• Best for single/few chemicals

• SAS‐based

• Separate dietary/residential modules

• 40+ interface screens• ~200 inputs/distributions

• Longitudinal• Potentially long run times for large populations

SHEDS‐MM SHE

• Reduced temporal resolution

(daily)

• Variance decomposition

based sensitivity: elimination

of minor inputs

• Additional exposure

scenarios (direct exposures

to consumer products;

emission from articles)

• Reduced version of indoor

emissions fugacity module

• Low‐to‐mid • Appropriate 

• Retains pop

information

• R‐based (fre

• Fast execut

per chemica

• Combined d

aggregate p

• Extendable 
Forward Prediction of Exp

Chemical Manufacture

Environmen

Release

Food 

Dietary Far‐Field

MONITORING

DATA

RECEPTORS

MEDIA

EXPOSURE

PATHWAY

(MEDIA + RECEPTOR)

Media Samples

Waste

Data and

Models

Consumer

Building Materials

Products, Articles,

Building Materials

Air, Dust, Surfaces

Near‐Field

Direct

Near‐Field

Indirect

Human

Dietary

Direct Use

(e.g., lotion)

Residential Use(e.g. ,flooring)

Biomarkers

of Exposure
Forward Prediction of Exp

SHEDS‐HT Pathways

Chemical Manufacture

Environmen

Release

Food 

Dietary Far‐Field

MONITORING

DATA

RECEPTORS

MEDIA

EXPOSURE

PATHWAY

(MEDIA + RECEPTOR)

Media Samples

Waste

Data and

Models

Consumer

Building Materials

Products, Articles,

Building Materials

Air, Dust, Surfaces

Near‐Field

Direct

Near‐Field

Indirect

Human

Dietary

Direct Use

(e.g., lotion)

Residential Use(e.g. ,flooring)

Biomarkers

of Exposure
Exposure Fields, Scenarios, Rout

Data Streams Included in SHED

Pathway Scenarios Routes Pathway‐Specific Data S

Near‐field

direct

Dermal exposure during householduse of consumer productsInhalation of vapor during use of

consumer products

Inhalation of aerosol mass during

use of consumer products

DermalInhalationIngestion (directingestion plusincidental non‐dietary ingestion viahand‐to‐mouth

transfer)

Consumer product chemical compos

Consumer product use patternsChemical propertiesTime‐activity dataVarious exposure factor distribution

Near‐field

indirect

Application of consumer products

to household surfaces, air, or petsEmission of chemicals fromconsumer articles or buildingproducts

DermalInhalationIngestion (object tomouth transfer plusincidental non‐dietary ingestion via

hand‐to‐mouth

transfer)

Consumer product chemical composChemical emission rates from article

Consumer product use patternsConsumer product article or building 

Chemical fate and transport informa

Time‐activity data and videography Various exposure factor distribution

Dietary

Consumption of contaminated food

or drinking water (from agriculturalchemical use or leaching from foodpackaging)

Dietary ingestion Chemical concentrations in foods an

Population food and water intakes
SHEDS‐HT: Data and Mod
SHEDS‐HT Modules

 Direct Pathway

• Dermal, inhalation (vapor and aerosols), and ingestion routes

• Formulated similarly to other available models; probabilistic

• Dependent on category‐specific frequency of consumer product use, 

prevalence, mass of product used, and product chemical compositio

 Dietary Pathway

• Consistent with SHEDS‐MM dietary methods reviewed during the 20• Consumption data: NHANES‐What We Eat In America (1999‐2006)

• Combined with food and water residue distributions to produce daily 

exposures
SHEDS‐HT Exposure Mod

 Fate and Transport (Source‐to‐Concentration)

• Starting point: fugacity‐based indoor fate and transport model developed for u

Multimedia (based on Bennett and Furtaw, ES &T, 2004)

• Reduced fugacity model using variance decomposition‐based sensitivity analys

Isaacs, Env Model and Software, 2012). Critical parameters retained: Vapor prerates on surfaces, air exchange rates, Kow, solubility

 Indirect Pathway

• Fugacity module is used to model the fate and transport of chemical within a h

prevalence (PR) and frequency of consumer product use

• Individuals breathe indoor air or touch contaminated surfaces post‐use, resulti

via inhalation, dermal, object‐to‐mouth, and hand‐to‐mouth routes

Air

Surfaces

Particles

Air 

Exchange

Deposition/

Resuspension

Cleaning
Initial Case Study: Chemicals in Consumer 

and Pesticides in Foods and Drinking W

 CPCPdb and HPDB

• This case study focused on “consumable” products

• Usable ingredient data were extracted for 2177 consumer 

chemicals

 Products mapped to ~250 harmonized consumer pro

categories appropriate for assigning exposure scenaruse

• Direct/indirect scenarios

• Product use data were generated by category; best guess a

made when no data available

 Distributions of residues in foods and drinking water 

obtained for 330 pesticides from the United StatesDepartment of Agriculture’s Pesticide Data Program 

databases (1997‐2011)
Model Parameterizatio

CAS #XXX‐XX‐X

ARTS AND CRAFTS PAINT

BODY PAINT

PENS AND MARKERS

DETAILING

AUTO FLUIDS AND ADDITIVES

SURFACE CLEANER

LIME REMOVER

AIR FRESHENER

ADHESIVE

DEGREASER

BLEACH

LAUNDRY DETERGENT

BUBBLE SOLUTION

EXTERIOR CLEANER

INTERIOR INSECTICIDE

SHAVING CREAM

SHAMPOO

SELF

TANNER

HAND LOTION HAND SOAP

FOOT CARE

HAIR STYLING

BAR SOAP

BODY WASH

BATH OIL/SALTS

BODY LOTION

CONDITIONER

DEPILATORY

DEODORANT

EYE MAKEUP

FACE CREAM

FACE MAKEUP

FACE WASH

DIRECT INHALATION

OF AEROSOL OR

VAPOR

DIRECT DERMAL

INDIRECT VIA

INHALATION,DERMAL ANDINCIDENTAL INGESTION

PREVALENCE

FREQUENCY

M

CHEMICAL‐SPECIFIC PROPERTIES

CONSUMER PRODUCT CATEGORIES

EXPOSURE SCENARIOS

PRODUCAND/OR EXPOSURPARAME

PE

PER

SKIN

CHEMICAL

DIETARY

CONCENTRATIONS

IN FOODS CONCENTRATION

IN WATER

DIRECT INGESTION 
Initial Case Study: Summar

Chemical Exposure Scenar

Scenario Number of

Chemicals

Number of

Consumer Product

Categories

Num

Pro

Dietary 330 ‐ 

Direct Dermal 2203 229 Direct Ingestion 303 6 Direct Inhalation

of Aerosol

1099 62 

Direct Inhalation

of Vapor

1979 164 

Indirect 945 77 

TOTAL 2507 246 
Initial Case Study Results: SHE

Results for 2507 Chemicals wit

field Source Exposure Pathw

Percentiles of tota

intake dose for all 

Mean total intake dos

cohort, sorted by the 

children ages 0‐5 year
Initial Comparisons with 

SHEDS‐HT predicted chemical intake doses compared to oral equivalent intake doses 

NHANES biomarker data for 39 chemicals (Wambaugh et al., ES & T, 2013). Left: SHED

dose versus biomonitoring‐based predicted median intakes. Right: SHEDS 5th,50th ,95

predictions compared to median inferred predictions using NHANES with uncertaintie
Planned Next Steps

 Focus on evaluation to inform development prioritie

• Evaluation of individual components with chemic

from new and ongoing field studies

• Full evaluation with SEEM framework (3rd genera

analysis)

 Expansion of chemical domain

• Additional scenarios

– Emission of chemicals from articles, food packaging, others

• Additional consumer product ingredient data

 Identifying and incorporating other available sources 

consumer product use data

 Robust route‐ and chemical‐specific dose forward pr

absorption, distribution, metabolism, excretion (ADM
Third Generation Analy

 Little et al. model (2012)

 RAIDAR and USEtox near‐field exposures

 Other new literature models

Chemical Information

from CPCPdb/CPCatDB

(updated/new heuristics)

Systematic Evaluation Allows Model

Evaluation/Refinement and

Extrapolation to

Updated Consensus Predictions

Multiple Exposure Pathways

Predicted by SHEDS‐HT

Other Near

Pred
 New data describing chemical consumer product

compositions, consumer product use, and chemical 

recently been identified and curated

 These data have been used to support the developm

new high‐throughput mechanistic, multipathway mohuman exposure

 These new models and data will be used to build new

consensus models within the SEEM framework that

potentially better describe the variance among chemhuman exposure, and reduce the uncertainty in consmodel predictions

Summary
Kathie DionPeter EgheKristin IsaacRichard JudThomas KnChantel NicRobert PeaJames RabWoody SetzCory StropeDan ValleroJohn Wamb

ExpoCa

Elaine HubalCSS Deputy National Program DirectorTina BahadoriCSS National Program Director

Acknowledgemen

Halûk Özkaynak , ORDPeter Egeghy, ORDRocky Goldsmith, ORDGraham Glen, AlionChris Grulke, ORDRaina Brooks, SSCDaniel Vallero, ORDLuther Smith, Alion

SHEDS-HT Development Team

*

Jianping Xue, ORDValerie Zartarian, ORDHalûk Özkaynak, ORD

SHEDS-MM Development Team

*

CPcat Team

Kathie Dionisio, ORD Alicia M. Frame, DowRocky Goldsmith, ORDAlan Liddell, NCSUTommy Cathey, Lockheed Martin Doris Smith, SEE James Vail, SEE Alexi Ernstoff, U Denmark Peter Fantke, U Denmark Olivier Jolliet, U MichiganRichard Judson, ORDJohn Wambaugh, ORD

*

*

*

*