SEPA
United States
Environmental Protection
Agency
The Exposome
and the Public:
Toxicity and Exposure Models
John Wambaugh, Kristin Isaacs, Katherine Phillips, Barbara Wetmore, January 23 - 26. 2020
Risa Say re, Antony Williams, Chris Grulke Alex Chao, and Jon Sobus *
Center for Computational Toxicology and Exposure Captiva Island, FL
Office of Research and Development
U.S. Environmental Protection Agency
Kristin Favela and Alice Yau
Southwest Research Institute
The views expressed in this presentation are those of the author
orcid org/0000-0002-4024-534X anc' c'ฐ not necessarily reflect the views or policies of the U.S. EPA
ExpoCast
exposure forecasting
AS
MS
January 25, 2020
American Society for
Mass Spectrometry
Unravelling the Exposome
-------
Credit: the Research Triangle Founda^j
Research conducted by a combination of Federal
scientists (including uniformed members of the
Public Health Service); contract researchers; and
postdoctoral, graduate student, and post-
baccalaureate trainees
Office of Research and Development
oEPA US EPA Office of Research and Development
I I + C+o + /-*o
United States
Environmental Protection
Agency
The Office of Research and Development (ORD) is the scientific research arm of EPA
562 peer-reviewed journal articles in 2018
Research is conducted by ORD's four national centers, and three
offices organized to address:
Public health and en v. assessment; comp. tox. and exposure;
env. measurement and modeling; and en v. solutions and
emergency response.
13 facilities across the United States
ORD Facility in
Research Triangle Park, NC
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vvEPA
United States
Environmental Protection
Agency
Chemical Regulation in the United States
Park et al. (2012): At least 3221 chemical
signatures in pooled human blood samples, many
appear to be exogenous
A tapestry of laws covers the chemicals people
are exposed to in the United States (Breyer, 2009)
Chemical safety testing is primarily for food
additives, pharmaceuticals, and pesticide active
ingredients (NRC, 2007)
Different levels of testing depending on
chemical category
GIVE A DOG A PHONE
Technology for our furry friends
NewScientist
wnnjr cwrtwu^
We've made
150,000 new chemicals
*
We touch them,
we wear them, we eat them
But which ones should
we worry about?
SPECIAL REPORT, page 14
THE GOOD FIGHT CHAMBER OF SECRETS IS IT ALIV E?
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3 of 29
Office of Research arid Development
November 29, 2014
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oEPA
United States
Environmental Protection
Agency
Chemical Regulation in the United States
Most other chemicals, ranging from industrial
waste to dyes to packing materials, are
covered by the Toxic Substances Control Act
(TSCA) which is administered by the EPA
Tens of thousands of chemicals are listed with the
Environmental Protection Agency (EPA) for
commercial use in the United States, with an
average of 600 new chemicals listed each year"
U.S. Government Accountability Office
Thousands of chemicals on the market were
"grandfathered" in without assessment
Judson et al. (2009), Egeghy et al. (2012),
Wetmore et al. (2015)
| Office of Research arid Development
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Cortisol
Simvastatin .
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Rappaport et al. (2014)
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oEPA
United States
Environmental Protection
Agency
Chemical Risk = Hazard x Exposure
The U.S. National Research Council (1983)
identified chemical risk as a function of both
inherent hazard and exposure
Addressing thousands of chemicals requires
"new approach methodologies" (NAMs*):
1. High throughput hazard characterization
(Dix et al., 2007, Collins et al., 2008)
2. High throughput exposure forecasts
(Wambaugh et al., 2013, 2014)
3. High throughput toxicokinetics (i.e., dose-
response relationship) linking
hazard and exposure
(Wetmore et al., 2012, 2015)
mg/kg BW/day
A
Potential Hazard
from in vitro with
Reverse
Toxicokinetics
Potential
Exposure Rate
| Office of Research and Development
*Kavlock et al. (2018)
Lower
Risk
Medium
Risk
Higher
Risk
-------
oEPA
United States
Environmental Protection
Agency
High-1 hroughput Bioactivity
Screening Projects
/.
With high throughput "toxicity" screening we attempt to estimate
points of departure in vitro using high throughput screening (HTS)
Tox21: Examining >8,000 chemicals using ~50 assays intended to
identify interactions with biological pathways (Schmidt, 2009)
ToxCast (Toxicity Forecast): For a subset (>3000) of Tox21
chemicals EPA has measured >1100 additional assays-endpoints
(Kavlock et ol.f 2012)
Most assays conducted in dose-response format (identify 50%
activity concentration - AC50 - and efficacy if data described by a
Hill function, Filer et ol.f 2016)
All data are public: http://comptox.epa.gov/dashboard/
| Office of Research and Development
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oEPA
United States
Environmental Protection
Agency
Chemical Bioactivity Data
Data from the ToxCast and Tox21 projects are available through the dashboard
https://comptox.epa.gov/dashboard/
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DETAILS
EXECUTIVE SUMMARY
PROPERTIES
ENV. FATBTRANSPORT
HAZARD
> ADME
~ EXPOSURE
-r BIOACTIVITY
TOXCAST: SUMMARY
PUBCHEM
TOXCAST DATA
TOXCAST: MODELS
SIMILAR COMPOUNDS
GENRA (BETA)
Bisphenol A
80-05-7 I DTXSID7020182
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| Office of Research arid Development
-------
^ Chemical Bioactivity Data
United States
Environmental Protection
Agency
Data from the ToxCast and Tox21 projects are available through the dashboard
S Chemistry Dashboard X
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X
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&EPA
Environmental Protection Home Advanced Search Batch Search Lists v Predictions Downloads
Agency
Copy t
Submit Comment
https://comptox.epa.gov/dashboard/
DETAILS
EXECUTIVE SUMMARY
PROPERTIES
ENV. FATBTRANSPORT
HAZARD
> ADME
~ EXPOSURE
-r BIOACTIVITY
TOXCAST: SUMMARY
PUBCHEM
TOXCAST DATA
TOXCAST: MODELS
SIMILAR COMPOUNDS
GENRA (BETA)
Bisphenol A
80-05-7 I DTXSID7020182
Searched by DSSTox Substance Id.
Chemical Activity Summary Q
OTOXCAST DATA
O ASSAY DETAILS
background measurement
cell morphology
dna binding
steroid hormone
trans-pc
cell cycle
cytokine
II adhesion molecules
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AC50 (uM)
AC50 (uM): 2.41
Scaled top: 4.08 ฎ
Assay Endpoint Name: NVS_ADME_rCYP2C13
Gene Symbol: Cyp2c13
Organism: rat
Tissue: NA
Assay Format Type: biochemical
Biological Process Target: regulation of catalytic
activity
Detection Technology: Fluorescence
Analysis Direction: positive
Intended Target Family: cyp
Description Data from the assay component
NVS_ADME_rCYP2C13 was analyzed into 2
assay endpoints. This assay endpoint,
NVS_ADME_rCYP2C 13, was analyzed in the
positive fitting direction relative to Acetonitrile as
the negative control and baseline of activity. Using
a type of enzyme reporter, loss-of-signal activity
can be used to understand changes in the
enzymatic activity as they relate to the gene
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8 of 29
Office of Research and Development
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oEPA
United States
Environmental Protection
Agency
In Vitro - In Vivo Extrapolation
(IVIVE)
IVIVE is the use of in vitro experimental data to predict phenomena in vivo
IVIVE-PK/TK (Pharmacokinetics/Toxicokinetics):
Fate of molecules/chemicals in body
Considers absorption, distribution, metabolism, excretion (ADME)
Uses empirical PK and physiologically-based (PBPK) modeling
IVIVE-PD/TD (Pharmacodynamics/Toxicodynamics):
Effect of molecules/chemicals at biological
target in vivo
Assay design/selection important
Perturbation as adverse/therapeutic effect,
reversible/ irreversible effeccts
Both contribute to in vivo effect prediction
| Office of Research and Development
Rodents: in vivo
Normalization of dose
PBPK models
NRC (1998)
Humans: in vivo
Testable predictions
Extrapolation
using PD and
PBPK models
Rodents: in vitro
Comparative testing
Humans: in vitro
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oEPA
United States
Environmental Protection
Agency
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(90/615)
The Margin Between Exposure and Hazard
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estimated or measured
average concentrations
associated with the LOAEL
in animal studies
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Humans with chronic
exposure reference values
(solid circles)
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containing the chemical
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populations
~
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MBP MEHP PFOA 2,4-D
(8/615) (35/615) (24/615) (10/615)
The five chemicals (as of 2011) with plasma biomonitoring AND ToxCast data... what do we do about the other 1000's?
| Office of Research and Development Ay I ward and Hays (2011)
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vvEPA Most Chemicals Lack Data on Exposure and Toxicokinetics
United States
Environmental Protection
Agency
The Ntlimul Amlmu* of
SCIENCES ENGINEERING MEDICINE
USING
21 ST CENTURY
SCIENCE
TO IMPROVE
RISK-RELATED
EVALUATIONS
NASEM (2017)
Hazard
High-Throughput
Risk
Prioritization
Toxicokinetics
Exposure
"Translation of high-throughput data into risk-
based rankings is an important application of
exposure data for chemical priority-setting.
Recent advances in high-throughput
toxicity assessment, notably the ToxCast
and Tox21 programs... and in high-
throughput computational exposure
assessment [ExpoCast] have enabled
first-tier risk-based rankings of
chemicals on the basis of margins
of exposure" - National Academies
of Sciences,
Engineering, and
Medicine (NASEM)
11 of 29
Office of Research and Development
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r/crM
Makes Use of
Exposure NAM Class
Description
Traditional Approach
Measurement
Toxicokinetics
Models
Descriptors
Evaluation
Machine
Learning
Measurements
New techniques including screening analyses
capable of detecting hundreds of chemicals
present in a sample
Targeted (chemical-specific) analyses
Toxicokinetics
High throughput methods using in vitro data to
generate chemical-specific models
Analyses based on in vivo animal studies
-
HTE Models
Models capable of making predictions for
thousands of chemicals
Models requiring detailed, chemical- and
scenario-specific information
-
Chemical Descriptors
Informatic approaches for organizing chemical
information in a machine-readable format
Tools targeted at single chemical analyses by
humans
-
Evaluation
Statistical approaches that use the data from
many chemicals to estimate the uncertainty in
a prediction for a new chemical
Comparison of model predictions to data on a
per chemical basis
Machine Learning
Computer algorithms to identify patterns
Manual Inspection of the data
-
Prioritization
Integration of exposure and other NAMs to
identify chemicals for follow-up study
Expert decision making
12 of 29
Office of Research and Development
Wambaugh et al., (2019)
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oEPA
United States
Environmental Protection
Agency
High Throughput Toxicokinetics (HTTK)
Most chemicals lack public toxicokinetic-related data (Wetmore et al., 2012)
In vitrotoxicokinetic data + generic toxicokinetic model
= high(er) throughput toxicokinetics
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httk
| Office of Research arid Development
Metabolism
Renal Clearanc
-------
oEPA
United States
Environmental Protection
Agency
Open Source Tools and Data for HTTK
https://CRAN.R-proiect.orq/packaqe=httk
G include a table in a script in r - C X I ซ rmarkdown-cheatsheet X I J* Defining toxicological tipping pc X <|| CRAN - Package httk X +
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httk: High-Throughput Toxicokinetics
Functions and data tables for simulation and statistical analysis of chemical toxicokinetics ("TK") as in Pearce et al. (2017) . Chemical-specific in vitro data have been
obtained from relatively high throughput experiments. Both physiologically-based ("PBTK") and empirical (e.g.. one compartment) "TK" models can be parameterized for several hundred chemicals
and multiple species. These models are solved efficiently, often using compiled (C-based) code. A Monte Carlo sampler is included for simulating biological variability (Ring et al., 2017
'l and measurement limitations. Calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017
1. These functions and data provide a set of tools for in vitro-in vivo extrapolation ("IVIVE") of high throughput screening data (e.g.. Tox21. ToxCast) to real-world
exposures via reverse dosimetry (also known as "RTK") (Wetmore et al,. 2015 V
Version: 1.10.1
Depends: R (> 2.10)
Imports: deSolve. msm. data.table, survey, mvtnomi. truncnomi. stats, graphics, utils, magrittr
Suggests: ggplot2. krntr, rmarkdown. R.rsp. GGallv. splots. scales. EnvStats. MASS. RColorBrewer. Teachini
gmodels. colorspace
Published: 2019-09-10
Author: John Wambaugh [aut. ere]. Robert Pearce [aut], Caroline Ring [aut], Greg Honda [aut], Mark Sfeir
Wetmore [ctb], Woodrow Setzer [ctb]
Maintainer: John Wambaugli
BugReports: https: 7aithub.com USEPA CompTox-ExpoCast-httk
License: GPL-3
URL: littps: ATOvy.epa.gov/chemical-researchrapid-chemical-exposiu'e-and-dose-research
NeedsCompilation: yes
Materials: NEWS
CRAN checks: httk results
Downloads:
R package "httk
i/
downloads 806/month
Reference manual: httk.pdf
Vignettes: Honda et al. (2019V. Updated Armitage et al. (2014^1 Model
Pearce et al, (2017) Creating Partition Coefficient Evaluation Plots
Ring et al. (2017^ Age distributions
Ring et al. ("2017~> Global sensitivity' analysis
Ring et al. C2017) Global sensitivity' analysis plotting
Open source, transparent, and peer-reviewed
tools and data for high throughput
toxicokinetics (httk)
Available publicly for free statistical software R
Allows in vitro-in vivo extrapolation (IVIVE) and
physiologically-based toxicokinetics (PBTK)
Human-specific data for 944 chemicals and rat-
specific data for 171 chemicals
Described in Pearce et al. (2017)
-------
oEPA
United States
Environmental Protection
* gency
Chemical Prioritization NAMs
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High throughput in vitro
screening can estimate doses
needed to cause bioactivity
(e.g., Wetmore et al.; 2015)
Exposure intake rates can
be inferred from
biomarkers
(e.g., Ring et al., 2018)
Chemicals Monitored by CDC NHANES
(Most chemicals do not have monitoring data - Egeghy et al. 2012)
| Office of Research and Development
mg/kg
Potential
Hazard from
in vitro with
Reverse
Toxicokinetic
s
Potential
Exposure
Rate
3W/day
Lower Medium Higher
Risk Risk Risk
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What Do We Know About Exposure?
Environmental Protection
Agency
Biomonitoring Data
Centers for Disease Control and Prevention (CDC) National Health and Nutrition Examination Survey
(NHANES) provides an important tool for monitoring public health
Large, ongoing CDC survey of US population: demographic, body measures, medical exam,
biomonitoring (health and exposure),...
Designed to be representative of US population according to census data
Data sets publicly available (http://www.cdc.gov/nchs/nhanes.htm)
Includes measurements of:
Body weight
Height
Chemical analysis of blood and urine
iinanes
I-,,. i . _ | _ National Health and Nutrition Examination Survey
| Office of Research and Development
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vvEPA
What Do We Know About Exposure?
Exposure Models
United States
Environmental Protection
Agency
Human chemical exposures can be coarsely grouped into ''near field" sources that are close to the
exposed individual (consumer or occupational exposures) 'far-field' scenarios wherein individuals are
exposed to chemicals that were released or used far away (ambient exposure) (Arnot et al., 2006).
A model captures knowledge and a hypothesis of how the world works (MacLeod et al., 2010)
EPA's EXPOsure toolBOX (EPA ExpoBox) is a toolbox created to assist individuals from within
government, industry, academia, and the general public with assessing exposure
Includes many, many models (https://www.epa.gov/expobox)
"Now it would be very remarkable if any system existing in the real world could be exactly represented
by any simple model. However, cunningly chosen parsimonious models often do provide remarkably
useful approximations... The only question of interest is 'Is the model illuminating and useful?'"
- George Box
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Office of Research and Development
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vvEPA EPA s ExpoCast (Exposure Forecast) Project
United States
Environ
Agency
Environmental Protection the framCWOrk
r
Space of
Chemicals
Chemicals
with
Monitoring
Data
Dataset 1
Dataset 2
Apply calibration and estimated uncertainty to
other chemicals
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Calibrate
models
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Chemicals
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Available Exposure Predictors
| Office of Research arid Development
3
Evaluate Model Performance
and Refine Models
FEMN
AEMN
CEMN
We use Bayesian methods to incorporate multiple models into consensus predictions for
1000s of chemicals within the Systematic Empirical Evaluation of Models (SEEM)
(Wambaugh et al., 2013, 2014; Ring et al., 2018)
Hurricane path
prediction is an
example of
integrating
multiple models
-------
oEPA
United States
Environmental Protection
Agency
Arnot
UNIVERSITY OF
MICHIGAN
UC DAVIS
UNIVERSITY OF CALIFORNIA
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Jon Arnot, Deborah H. Bennett, Peter P. Egeghy, Peter Fantke, Lei Huang, Kristin K. Isaacs, Olivier Jolliet,
Hyeong-Moo Shin, Katherine A. Phillips, Caroline Ring, R. Woodrow Setzer, John F. Wambaugh, Johnny Westgate
Predictor
Reference(s)
Chemicals
Predicted
Pathways
EPA inventory Update Reporting and Chemical Data
Reporting (CDR) (2015)
US EPA (2018)
7856
All
Stockholm Convention of Banned Persistent Organic
Pollutants (2017)
La!las (2001)
248
Far-Field Industrial and
Pesticide
EPA Pesticide Reregistration Eligibility Documents (REDs)
Exposure Assessments (Through 2015)
Wetmore et al. (2012, 2015)
239
Far-Field Pesticide
United Nations Environment Program and Society for
Environmental Toxicology and Chemistry toxicity model
(USEtox) Industrial Scenario (2.0)
Rosenbaum et al. (2008)
8167
Far-Field Industrial
USEtox Pesticide Scenario (2.0)
Fantke et al. (2011, 2012, 2016)
940
Far-Field Pesticide
Risk Assessment IDentification And Ranking (RAIDAR)
Far-Field (2.02)
Arnot et al. (2008)
8167
Far-Field Pesticide
EPA Stochastic Human Exposure Dose Simulator High
Throughput (SHEDS-HT) Near-Field Direct (2017)
Isaacs (2017)
7511
Far-Field Industrial and
Pesticide
SHEDS-HT Near-field Indirect (2017)
Isaacs (2017)
1119
Residential
Fugacity-based INdoor Exposure (FINE) (2017)
Bennett et al. (2004), Shin et al. (2012)
645
Residential
RAIDAR-ICE Near-Field (0.803)
Arnot et al., (2014), Zhang et al. (2014)
1221
Residential
USEtox Residential Scenario (2.0)
Jolliet et al. (2015), Huang et al. (2016,2017)
615
Residential
USEtox Dietary Scenario (2.0)
Jolliet et al. (2015), Huang et al. (2016),
Ernstoff et al. (2017)
8167
Dietary
| Office of Research and Development
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vvEPA
Reverse Dosimetry (Tan et al., 2006)
United States
Environmental Protection
Agency
Median chemical intake rates (mg / kg body weight
/day) were inferred from:
NHANES urine (Wambaugh et al, 2014,
Ring et al. 2017)
NHANES serum/blood either using HTTK
clearance (Pearce et al., 2017)
Literature clearance estimates were used for
methodologically challenging chemicals not
suited to HTTK
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20 of 29
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office of Research and Development Ringet al. (2018) Inferred Chemical Intake Rates (mg/kg BW/day)
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oEPA
Pathway-Based Consensus Modeling of NHANES
United States
Environmental Protection
Agency
Machine learning models
were built for each of four
exposure pathways
Pathway predictions can be
used for large chemical
libraries
Use prediction (and accuracy
of prediction) as a prior for
Bayesian analysis
Each chemical may have
exposure by multiple
pathways
] Office of Research and Development
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Each point
indicates a
different chemical
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Intake Rate (mg/kg BW/day) Inferred from
NHANES Serum and Urine
Pathway(s)
o Consumer
~ Consumer, Industrial
Consumer, Pesticide
A Consumer, Pesticide, Industrial
V Dietary, Consumer
ฆ Dietary, Consumer, Industrial
Dietary, Consumer, Pesticide
A Dietary, Consumer, Pesticide, Industrial
~ Dietary, Pesticide, Industrial
~ Industrial
Pesticide
Pesticide, Industrial
Ring et a I., 2018
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oEPA
United States
Environmental Protection
Agency
Consensus Modeling of Median Chemical Intake
We predict relevant pathway(s), median
intake rate, and credible interval for each
of 479,926 chemicals
Of 687,359 chemicals evaluated, 30%
have low probability for exposure via any
of the four pathways
They are considered outside the
"domain of applicability"
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>0.1 mg/kg bw/day
4: Aro
10 103
Chemical Rank
| Office of Research arid Development
Pathway(s)
O All Four
~ Cons., Ind.
O Cons., Pest.
A Cons., Pest., Ind.
V Consumer
ฆ Diet., Cons.
Diet., Cons., Ind.
A Diet., Cons., Pest.
~ Diet., Ind.
~ Diet., Pest.
O Diet., Pest., Ind.
Dietary
+ Industrial
X Pest., Ind.
O Pesticide
Ring et a I., 2018
-------
oEPA
United States
Environmental Protection
Agency
Consensus Modeling of Median Chemical Intake
We predict relevant pathway(s), median
intake rate, and credible interval for each
of 479,926 chemicals
Of 687,359 chemicals evaluated, 30%
have low probability for exposure via any
of the four pathways
They are considered outside the
"domain of applicability"
There is 95% confidence that the median
intake rate is below 1 [xg/kg BW/day for
474,572 compounds.
This 95% nterval reflects confidence
in the median estimate - not the most
highly exposed individuals
| Office of Research and Development
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1880 chemicals
>0.1 mg/kg bw/day
k: Aro
10 103
Chemical Rank
Pathway(s)
O All Four
~ Cons., Ind.
O Cons., Pest.
A Cons., Pest., Ind.
V Consumer
ฆ Diet., Cons.
Diet., Cons., Ind.
A Diet., Cons., Pest.
~ Diet., Ind.
~ Diet., Pest.
O Diet., Pest., Ind.
Dietary
+ Industrial
X Pest., Ind.
O Pesticide
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<0.1 _mg/kg bw/day_
474572 chemicals
<1 yg/kg bw/day
103 1 x 105 2x 10s 3x 10s 4x 105
Chemical Rank
5x1
Ring et a I., 2018
-------
oEPA
United States
Environmental Protection
Agency
Consensus Modeling of Median Chemical Intake
We predict relevant pathway(s), median
intake rate, and credible interval for each
of 479,926 chemicals
Potentially helpful for identifying
chemicals when suspect screening
Need to broaden the monitoring data -
this is based on only 114 chemicals!
Likewise, broader data can better inform
chemical pathway domain of applicability
| Office of Research arid Development
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1880 chemicals
>0.1 mg/kg bw/day
k: Aro
10 103
Chemical Rank
Pathway(s)
O All Four
~ Cons., Ind.
O Cons., Pest.
A Cons., Pest., Ind.
V Consumer
ฆ Diet., Cons.
Diet., Cons., Ind.
A Diet., Cons., Pest.
~ Diet., Ind.
~ Diet., Pest.
O Diet., Pest., Ind.
Dietary
+ Industrial
X Pest., Ind.
O Pesticide
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478046 chemicals
<0.1 _mg/kg bw/day_
474572 chemicals
<1 yg/kg bw/day
103 1 x 105 2x 10s 3x 10s 4x 105 5x1
Chemical Rank
Ring et a I., 2018
-------
oEPA
United States
Environmental Protection
Agency
Reducing Model Uncertainty with
Expanded Biomonitoring
120 Tentative
Chemical IDs
309 Tentative
Chemical Class IDs
Southwest Research institute
25 of 29
10"5 10"3 10"1
logio(/jg/mL)
| Office of Research and Development
Suspect screening analysis of pooled
samples of human blood
Analytical chemistry work by Kristin
Favela and Alice Yau of Southwest
Research Institute (SWRI)
Informatics team (EPA) led by
Katherine Phillips includes Alex Chao,
Barbara Wetmore, Risa Sayre, Jon
Sobus, Kristin Isaacs
Expected to be
seen in serum
Strata
Female > 45
Female < 45
Male < 45
Male > 45
Study design by
Lesa Ay I ward
and John
Wambaugh
-------
vvEPA
United States
Environmental Protection
Agency
We aren't
especially
interested in
cholesterol, or
glucose, or even
aspirin
Removing the "Background" from Blood
hmP Human Metabofome Database: X +
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Browse Search * Downloads About ~ Contact Us
Q Search
if TMIC The Wlotabolomlcs
Innovation Centre
Specializing in ready to use metabolomics kits.
Showing metabocard for Perfluorooctanoic acid (HMDB0059587)
However, without
categorization the
ubiquitous
"metabolome"
contains things like
PFOA (at right)
Identification Taxonomy Ontology Physical properties Spectra Biological properties Concentrations Links References XML
Show Metabolites with Similar Structures
Jump To Section
Record Information
Version
4.0
Status
Detected and Quantified
Creation Date
2012-10-25 14:44:47 UTC
Update Date
2019-07-23 07:12:32 UTC
HMDB ID
HMDB0059587
Secondary Accession
Numbers
HMDB59587
Metabolite Identification
Common Name
Perfluorooctanoic acid
% ^
dx
26 of 29
Office of Research and Development
-------
oEPA
United States
Coarsely Categorizing the Metabolome
Environmental Protection
Agency
A categorize metabolome database is under
development by Risa Sayre, Chris Grulke, Antony
Williams, Jon Sobus, and Alex Chao
We have identified five categories of chemical origin
(based on Rappaport etal. (2014) of small molecules
found in human blood biomonitoring samples:
1) endogenous metabolome
2a) exogenous nutrients
2b) markers of exposure to exogenous nutrients
Liquid Chromatography (n=95)
Endogenous
Nutrient
Pharmaceutical
Xenobiotic
Gas Chromatography (n=120)
3a) xenobiotics (pharmaceuticals, pesticides, and others)
3b) markers of exposure to xenobiotics
| Office of Research and Development
Work led by Risa Sayre (EPA/UNC/ORISE)
-------
&EPA Inferring Exposure from the Exposome
United States '
Environmental Protection
Agency
SEEM analyses rely upon exposure inferences from NHANES urine and blood biomonitoring
Kristin Isaacs and team are developing publicly available tools to automate that inference
Working with Robin Dodson and the Silent Spring Institute to generalize methods to correlate chemical
concentrations in dust with urine and exposure
For exposure inference from blood we need to know the clearance, volume of distribution
We can do this with HTTK!
However, toxicokinetic (TK) IVIVE has limitations:
Relatively slow throughput (1000 chemicals in last decade)
Quantitative Structure-Property Relationship (QSPR) models are being developed
and evaluated as part of a collaborative study led by Nisha Sipes (NTP)
In vitro methods are less than ideal for volatile chemicals
Generic inhalation TK IVIVE model has been developed (Linakis et al., submitted)
QSPR models can be evaluated specifically for volatile chemicals with measured data
Office of Research and Development
S! SimulationsPlus
SCIENCE + SOFTWARE = SUCCESS
0^0
Amot i
National Toxicology Program
U.S. Department of Health and Human Services
JffLS
Advancing Science. Improving Lives
9 O \
-------
vvEPA
United States
Environmental Protection
Agency
Summary
A tapestry of laws covers the chemicals people are exposed to
in the United States (Breyer, 2009)
Many chemicals, ranging from industrial waste to dyes to
packing materials, are covered by the recently updated Toxic
Substances Control Act (TSCA) and administered by the EPA
New approach methodologies (NAMs) are being developed to
prioritize these existing and new chemicals for testing
Calibrated high throughput exposure predictions are available,
but rely heavily on the NHANES sampling library - reducing
uncertainty and model evaluation depends on better
understanding the whole exposome
mg/kg BW/day
Potential hazard
from in vitro
converted to dose
by HTTK
Potential
Exposure Rate
Lower
Risk
Medium
Risk
Higher
Risk
29 of 29
Office of Research and Development
The views expressed in this presentation are those of the authors
and do not necessarily reflect the views or policies of the U.S. EPA
-------
ExpoCast Project
Exposure Forecasting)
Center for Computational Toxicology and Exposure
Linda Adams Richard Judson Mike Tornero-Velez
Alex Chao*
Daniel Dawson*
Mike Devito
Kathie Dionisio
Jen Korol-Bexell*
Anna Kreutz*
Charles Lowe*
Katherine Phillips
Christopher Eklund Ann Richard
Peter Egeghy
Marina Evans
Chris Grulke
Hongtai Huang*
Mike Hughes
Kristin Isaacs
Ashley Jackson*
Risa Sayre*
Mark Sfeir*
Jane Ellen
Simmons
Marci Smeltz*
Jon Sobus
Rusty Thomas
Elin Ulrich
Dan Vallero
Barbara Wetmore
John Wambaugh
tfl !m I ,aS
Antony Williams
CEMM
Hongwan Li
Xiaoyu Liu
Seth Newton
John Streicher*
Mark Strynar
BhoiisdK:
?s aim
- ,^t |
PLEASE EMAIL ME IF YOU
SHOULD BE ON THIS LIST
AND YOU ARE NOT
<ฆ "ซe
-v..
Swfzi cypnotex
Southwest Research Institute
an evoTec company
^Trainees
Collaborators
Arnot Research and Consulting
Jon Arnot
Johnny Westgate
integrated Laboratory Systems
Kamel Mansouri
Chang
Toxicology Program
Steve Ferguson
Nisha Sipes
Ramboll
Harvey Cleweil
Silent Spring Institute
Robin Dodson
Simulations Plus
Michael Lawless
Southwest Research Institute
Alice Yau
Kristin Favela
Summit Toxicology
Lesa Ay I ward
Technical University of Denmark
PeterFantke
ToxStrategies
Caroline King
Unilever
Beate Nicol
Cecilie Rendal
Ian Sorre
United States Air Force
HeatherPangburn
Matt Linakis
University of California, Davis
Deborah Bennett
University of Michigan
Olivier Jolliet
University of Texas, Arlington
Hyeong-Moo Shin
-------
References
vvEPA
United States
Environmental Protection
Agency
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Office of Research and Development
------- |