ENVR 500 Environmental Processes, Exposure, and Risk Assessment:New Approach Methodologies for Chemical Risk Assessment


A EPA
November 19,2019
United States
Environmental Protection
Agency
ENVR 500 Environmental Processes, Exposure, and Risk Assessment:
New Approach Methodologies
for Chemical Risk Assessment
Center for	Computational
Office of Researc
U.S. Environmental
Disruptive Innovation in Chemical Evaluation
The views expressed in this presentation are those of the author
orcid.org/0000-0Q02-4024-534X and do not necessarily reflect the views or policies of the U.S. EPA

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• 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
|[|fm Office of Research and Development
vvEPA	US EPA Office of Research and Development
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)
Different testing requirements exist for food
additives, pharmaceuticals, and pesticide active
ingredients (NRC, 2007)
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?
Wostwiqlfnw	The greatest *vw find	Ajt#ici.il worm could
is. also wtuous	ofe-arty human bones	be first dugiUJaninMl
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Office of Research arid Development
November 29, 2014

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vvEPA
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)
Thousands of chemicals on the market were
"grandfathered" in without assessment
Judson et al. (2009), Egeghy et al. (2012), Wetmore et al. (2015)
GAO
March 2013
United States Government Accountability Office
Report to Congressional Requesters
TOXIC SUBSTANCES
EPA Has Increased
Efforts to Assess and
Control Chemicals but
Could Strengthen Its
Approach
"Tens of thousands	of chemical
Environmental	Protection	Age
use in the United States,	wit
chemicals	listed
U.S. Government Accountability
GAO
Accountability • Integrity • Reliability
GAO-13-249
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Office of Research arid Development
March, 2013

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oEPA
United States
Environmental Protection
Agency
NRC (1983)
I hree Components for Chemical Risk
Dose-Response
(Toxicokinetics
/Toxicodynamics)
The National Academy of Sciences, Engineering and Medicine (1983)
| office of Research and Development outlined three components for determining chemical risk.

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TSCA 2.0
A New Era in
Chemical Risk Management
After decades of dysfunction, the Toxic Substances Control Act has been
overhauled with provisions that promise better protection against potentially
harmful chemicals. © Dmytro Grankin/Alamy; Alexander Aldatov/Alamy
vvEPA
United States
Environmental Protection
Agency
Toxic Substances
Control Act (TSCA)
TSCA was updated in June, 2016 to allow more rapid
evaluation of chemicals (Frank R. Lautenberg
Chemical Safety for the 21st Century Act)
New approach methodologies (NAMs) are being
considered to inform prioritization of chemicals for
testing and evaluation (Kavlock et al., 2018)
EPA has released a "AWorking Approach for
Identifying Potential Candidate Chemicals for
Prioritization" (September, 2018)
Schmidt, C. W. (2016). TSCA 2.0: A new era in
chemical risk management", Environmental
Health Perspectives, A182-A186.
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Office of Research and Development

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oEPA
Chemical Risk
United States
Environmental Protection
Agency
• The U.S. National Research Council (1983) identified chemical
risk as a function of both inherent hazard and exposure
• To address thousands of chemicals, we need NAMs that can
inform prioritization of chemicals most worthy of additional
study
• High throughput risk prioritization needs:
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)
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Office of Research and Development
Hazard x Exposure
mg/kg BW/day
A
Potential Hazard
from in vitro with
Reverse
Toxicokinetics
Potential
Exposure Rate
Lower
Risk
Medium
Risk
Higher
Risk

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vvEPA
United States
Environmental Protection
Agency

r
HP

TOXICITY TESTING IN THE 21 ST CENTURY
A VISION AND A STRATEGY



ht iT , S.VV •

v \ ¦ i At \ &
I III LI u

NRC (2007)
High- hroughput Risk Prioritization
Dose-Response
(Toxicokinetics
/Toxicodynamics)
High-Throughput
Risk
Prioritization
\
High throughput screening
(HTS) for in vitro bioactivity
potentially allows
characterization of thousands
of chemicals for which no
other testing has occurred
8 of 53
Office of Research and Development
To perform high throughput risk prioritization, we need all three components

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vvEPA
United States
Environmental Protection
Agency
High-throughput Screening
Hertzberg and Pope (2000):
• "New technologies in high-throughput screening have significantly increased throughput and reduced
assay volumes..."
Kaewkhaw et al. (2016)
"...new fluorescence
methods, detection
platforms and liquid-
handling technologies/'
Typically assess many
chemicals with a signal
readout (e.g., green
fluorescent protein).
Positive
Control
Titration of
Potential Hits
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Office of Research arid Development

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vvEPA
United States
Environmental Protection
Agency
The Margin Between Exposure and Hazard
1000
u
E=
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u
e
0.001
Triclosan
(90/615)
1000
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5
MBP MEHP PFOA 2,4-D
(8/615) (35/615) (24/615) (10/615)
^ estimated or measured
average concentrations
associated with the LOAEL
in animal studies
O NOAEL in animal studies
£ Humans with chronic
exposure reference values
(solid circles)
X Volunteers using products
containing the chemical
+ Biomonitored occupational
populations
General populations
10 of 53
Aylward and Hays (2011)
Office of Research arid Development	Journal of Applied Toxicology 31 741-751

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oEPA
United States
Environmental Protection
Agency
High-1 hroughput Bioactivity
Screening Projects
/.
A
'4
•j
J
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: For a subset (>2000) of Tox21 chemicals ran
>1100 additional assays (Kavlock et al., 2012)
Most assays conducted in dose-response format (identify
50% activity concentration - AC50 - and efficacy if data
described by a Hill function, Filer et al., 2016)
All data are public: http://comptox.epa.gov/dashboard/
| Office of Research and Development
I
Tox}J }
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vvEPA
United States
Environmental Protection
Agency
New Approach Methodologies (NAMs)
There are roughly 10,000 TSCA-relevant
chemicals in commerce
Considering the inclusion of new
approach methodologies (NAMs). These
NAMs include:
• High throughput screening (ToxCast)
• High throughput exposure estimates
(ExpoCast)
• High throughput toxicokinetics
(HTTK)
Chemical
Research in
Toxicology
© Cite This: Chem. Res. Toxicol. 2018, 31, 287-290
pubs.acs.orgA
Accelerating the Pace of Chemical Risk Assessment
J. ___ J. J. J. c
Robert J. Kavlock, Tina Bahadori, Tara S. Barton-Maclaren, Maureen R Gwinn, Mike Rasenberg,
and Russell S. Thomas*"
ABSTRACT: Changes in chemical regulations worldwide have
increased the demand for new data on chemical safety. New
approach methodologies (NAMs) are defined broadly here as
including in silico approaches and in chemico and in vitro assays,
as well as the inclusion of information from the exposure of
chemicals in the context of hazard [European Chemicals
Agency, "New Approach Methodologies in Regulatory Science",
2016]. NAMs for toxicity testing, including alternatives to
animal testing approaches, have shown promise to provide a
large amount of data to fill information gaps in both hazard
and exposure. In order to increase experience with the new
data and to advance the applications of NAM data to evaluate
the safety of data-poor chemicals, demonstration case studies
Accelerating the Pace of Chemical Risk Assessment
APCRA«r.»
• TSCA Proof of concept: Examine ~200 chemicals with ToxCast, ExpoCast and HTTK
• HTTK was rate Iimiter on number of chemicals
• "A Proof-of-Concept Case Study Integrating Publicly Available Information to Screen Candidates for
Chemical Prioritization under TSCA"
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Office of Research and Development
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oEPA
United States
Environmental Protection
Agency
Replacing Animal Testing with NAMs
"To aggressively pursue a reduction in animal testing, I am
directing leadership and staff in the Office of Chemical
Safety and Pollution Prevention and the Office of Research
and Development [ORD] to prioritize ... the reduction of
animal testing while ensuring protection of human health
and the environment."
"These new approach methods (NAMs), nclude any
technologies, methodologies, approaches or combinations
thereof that can be used to provide information on
chemical hazard and potential human exposure that can
avoid or significantly reduce the use of testing on animals"
• NAMs for filling information gaps for decision-making
• integrating data steams into chemical risk assessment
• making the information publicly available
~| Office of Research and Development
y"os*>»
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UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON. D C 20460
September 10, 2019
THE ADMINISTRATOR
MKMOKAMU M
Andrew R. Wheeler
Administrator
TO: Associate Deputy Administrator
General Counsel
Assistant Administrators
Inspector General
Chief Financial Officer
Chief of Staff
Associate Administrators
Regional Administrators
During my March 2019 all-hands address. I reiterated the U.S. Knvironmcntal Protection
Agency s commitment to move away from animal testing. We are already making significant
efforts to reduce, replace and refine our animal testing requirements under both statutory and
strategic directives. For example, the Toxic Substances C ontrol Act. amended June 22. 2016. by
the f rank R. Uutcnbcrg Chemical Safety for the 21" Century Act. requires the EPA to reduce
reliance on animal testing. Also, Objective 3.3 of the FY 21)19-2022 U.S. EPA Strategic Plan
outlines a commitment to further reduce the reliance on animal testing within live years. More
than 200,000 laboratory animals have been saved in recent years as a result of these collective
efforts.
Scientific advancements exist today that allow us to better predict potential hazards for risk
assessment purposes w ithout the use of traditional methods that rely on animal testing. These new
approach methods (N AMs), include any technologies, methodologies, approaches or combinations
thereof that can be used to provide information on chemical hazard and potential human exposure
that can avoid or significantly reduce the use of testing on animals. I~hc benefits of NAMs are
extensive, not only allowing us to decrease animals used while potentially evaluating more
chemicals across a broader range ol potential biological effects, but in a shorter timeframe with
lewer resources while olten achieving equal or greater biological prediciivity than current animal
models.
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vvEPA
We Still Need Toxicokinetics and Exposure
United States
Environmental Protection
Agency
The Ntlimul Amlmu* of
SCIENCES • ENGINEERING • MEDICINE
USING
21 ST CENTURY
SCIENCE
TO IMPROVE
RISK-RELATED
EVALUATIONS
NASEM (2017)
"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)
14 of 53
Office of Research and Development
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vvEPA
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
mg/kg
Potential
Hazard from
in vitro with
Reverse
Toxicokinetic
s
Potential
Exposure
Rate
3W/day
15 of 53
Office of Research and Development
Ring et al. (2017)
Lower Medium Higher
Risk Risk Risk
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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
Normalization of dose
PBPK models
NRC (1998)
Humans: in vivo
Testable predictions
Extrapolation
using PD and
PBPK models
Comparative testing
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vvEPA
United States
Environmental Protection
Agency
High Throughput Toxicokinetics (HTTK)
In vitrotoxicokinetic data + generic toxicokinetic model
= high(er) throughput toxicokinetics
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vvEPA
United States
Environmental Protection
Agency
In Vitro Data for HTTK
\
Cryo pre served
hepatocyte
suspension
Shibata et ol.
(2002)
9^
-~
-~
-~
-~
;
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Hepatocytes
(10 donor pool)
Add Chemical
(1 and 10 |iM)
T
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120 min
Analytical
Chemistry
J
The rate of
disappearance of
parent compound
(slope of line) is the
hepatic clearance
(|iL/m in/106
hepatocytes)
50
100
150
We perform the
assay at 1 and 10 |iM
to check for
saturation of
metabolizing
enzymes.
Most chemicals do
not have TK data -
we use in vitro HTTK
methods adapted
from pharma to fill
gaps
In drug development,
HTTK methods allow
IVIVE to estimate
therapeutic doses for
clinical studies -
predicted
concentrations are
typically on the order
of values measured in
clinical trials (Wang,
2010)
18 of 53
Office of Research and Development
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oEPA
United States
Environmental Protection
Agency
Cryo pre served
hepatocyte
suspension
Shibata et ol.
(2002)
In Vitro Data for HTTK
Rapid
Equilibrium
Dialysis (RED)
Waters et ol.
(2008)
\

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ni
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Hepatocytes (1 and 10 \xM) Aliquots at
(10 donor pool) 15, 30, 60,
i a 120 min
Analytical
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Double-wells
connected by
semi-permeable
membrane on a
RED Plate
Add plasma Add chemica|
(6 donor pool)
to one well
F
well 1
Incubate
plates
come to
equilibrium
ub,p
c
• Most chemicals do
not have TK data -
we use in vitro HTTK
methods adapted
from pharma to filJ
gaps
• In drug development,
HTTK methods allow
IVIVE to estimate
therapeutic doses for
clinical studies -
predicted
Determine concentrations are
concentration typically on the order
in both wells of values measured in
(analytical clinical trials (Wang,
chemistry) 2010)
| Office of Research arid Development
well 2
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oEPA
United States
Environmental Protection
Agency
Cryo pre served
hepatocyte
suspension
Shibata et ol.
(2002)
In Vitro Data for HTTK
Rapid
Equilibrium
Dialysis (RED)
Waters et ol.
(2008)
\

^ as
T
&=¦ 1
Cryopreserved Add Chemical Remove
Hepatocytes (1 and 10 \xM) Aliquots at
(10 donor pool) 15, 30, 60,
i a 120 min
Analytical
Chemistry
*

* $

Double-wells
connected by
semi-permeable
membrane on a
RED Plate
Add plasma Add chemica|
(6 donor pool)
to one well
F
well 1
Incubate
plates
come to
equilibrium
ub,p
c
Determine
concentration
in both wells
(analytical
chemistry)
| Office of Research arid Development
well 2
Most chemicals do
not have TK data -
we use in vitro HTTK
methods adapted
from pharma to filJ
gaps
Environmental
chemicals:
Rotroff et al. (2010) 35
chemicals
Wetmore et al. (2012)
+204 chemicals
Wetmore et al. (2015)
+163 chemicals
Wambaugh et al. (2019)
+389 chemicals
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vvEPA
United States
Environmental Protection
Agency
Building Confidence inTK Models
To evaluate a chemical-specific TK model for ''chemical x" you can
compare the predictions to in vivo measured data
• Can estimate bias
• Can estimate uncertainty
• Can consider using model to extrapolate to other situations
(dose, route, physiology) where you don't have data
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Model
Predicted Concentrations
21 of 53
Office of Research and Development
Cohen Hubal et al. (2018)
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vvEPA
United States
Environmental Protection
Agency
Building Confidence inTK Models
To evaluate a chemical-specific TK model for ''chemical x" you can
compare the predictions to in vivo measured data
• Can estimate bias
• Can estimate uncertainty
• Can consider using model to extrapolate to other situations
(dose, route, physiology) where you don't have data
However, we do not typically have TK data
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22 of 53
Office of Research and Development
Cohen Hubal et al. (2018)
-------
vvEPA
United States
Environmental Protection
Agency
Building Confidence inTK Models
To evaluate a chemical-specific TK model for ''chemical x" you can
compare the predictions to in vivo measured data
• Can estimate bias
• Can estimate uncertainty
• Can consider using model to extrapolate to other situations
(dose, route, physiology) where you don't have data
However, we do not typically have TK data
We can parameterize a generic TK model, and evaluate that
model for as many chemicals as we do have data
• We do expect larger uncertainty, but also greater confidence in
model implementation
• Estimate bias and uncertainty, and try to correlate with
chemical-specific properties
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23 of 53
Office of Research and Development
Cohen Hubal et al. (2018)
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oEPA
United States
Environmental Protection
Agency
Building Confidence inTK Models
• To evaluate a chemical-specific TK model for ''chemical x" you can
compare the predictions to in vivo measured data
• Can estimate bias
• Can estimate uncertainty
• Can consider using model to extrapolate to other situations
(dose, route, physiology) where you don't have data
• However, we do not typically have TK data
• We can parameterize a generic TK model, and evaluate that
model for as many chemicals as we do have data
• We do expect larger uncertainty, but also greater confidence in
model implementation
• Estimate bias and uncertainty, and try to correlate with
chemical-specific properties
• Can consider using model to extrapolate to other situations
(chemicals without in vivo data)
~| Office of Research and Development
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-------
oEPA
United States
Environmental Protection
Agency
Building Confidence inTK Models
• To evaluate a chemical-specific TK model for ''chemical x" you can
compare the predictions to in vivo measured data
• Can estimate bias
• Can estimate uncertainty
• Can consider using model to extrapolate to other situations
(dose, route, physiology) where you don't have data
• However, we do not typically have TK data
• We can parameterize a generic TK model, and evaluate that
model for as many chemicals as we do have data
• We do expect larger uncertainty, but also greater confidence in
model implementation
• Estimate bias and uncertainty, and try to correlate with
chemical-specific properties
• Can consider using model to extrapolate to other situations
(chemicals without in vivo data)
~| Office of Research and Development
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vvEPA
United States
Environmental Protection
Agency
• We estimate clearance from two
processes - hepatic metabolism
(liver) and passive glomerular
filtration (kidney)
• This appears to work better for
pharmaceuticals than other
chemicals:
• ToxCast chemicals are
overestimated
• Non-pharmaceuticals may be
subject to extrahepatic
metabolism and/or active
transport
Evaluation Example
O)
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oEPA
United States
Environmental Protection
Agency
Toxicokinetic Triage: When DoesTK IVIVE
Through comparison to in vivo data, a cross-
validated (random forest) predictor of success or
failure of HTTK has been constructed
All chemicals can be placed into one of seven
confidence categories
• Added categories for chemicals that do not
reach steady-state or for which plasma binding
assay fails
Plurality of chemicals end up in the ''on the order"
bin (within a factor of 3.2x) which is consistent
with Wang (2010)
| Office of Research and Development
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Triage Category
-------
oEPA
United States
Environmental Protection
Agency
Different crayons
have different
colors...
Until I open the
box, I don't know
what colors I
have...
...especially if my
six-year-old has
been around.
| Office of Research arid Development

Uncertainty
[wr re rent
Brilliant
GbJare
Irayola
CRAYONS
BtJtU-IN SHARPENER
,u>:v
tip A h
^CjtfpSLI
64 CRAYON B
-------
\/EPA \Asi ki^i hi I ii"v
United States T dl IdUllll/
Environmental Protection *
Agency
Different crayons
have different
colors...
The ''average"
color may not
even be in the
box!
Didere
BrvlU;
Col1
29 of 53
Office of Research arid Development
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oEPA
United States
Environmental Protection
Agency
Different crayons
have different
colors...
The "average"
color may not
even be in the
box!
¦Kjj
P]!tliyy
m
U
im
Variability
| Office of Research arid Development
-------
vvEPA
United States
Environmental Protection
Agency
Correlated Monte Carlo
sampling of physiological
model parameters built
into R "httk" package
(Pearce et al., 2017):
Sample NHANES
biometrics for
actual individuals:

Population simulator for HTTK
nnanes
National Health and Nutrition Examination Survey
Sex
Race/ethnicity
Age
Height
Weight
Serum creatinine
31 of 53
Office of Research and Development
Slide from Caroline Ring (ToxStrategies)
Ring et al. (2017)
-------
vvEPA
United States
Environmental Protection
Agency
Correlated Monte Carlo
sampling of physiological
model parameters built
into R "httk" package
(Pearce et al., 2017):
Sample NHANES
biometrics for
actual individuals:
Sex
Race/ethnicity
Age
Height
Weight
Serum creatinine

Population simulator for HTTK
nnanes
National Health and Nutrition Examination Survey
~
Regression equations from literature
(McNally et al., 2014)
(+ residual marginal variability)
(Similar approach used in SimCYP [Jamei et al. 2009], GastroPlus,
PopGen [McNally et al. 2014], P3M [Price et al. 2003], physB [Bosgra et al. 2012], etc.)
32 of 53
Office of Research and Development
Slide from Caroline Ring (ToxStrategies)
Ring et al. (2017)
-------
vvEPA
United States
Environmental Protection
Agency
Correlated Monte Carlo
sampling of physiological
model parameters built
into R "httk" package
(Pearce et al., 2017):
Sample NHANES
biometrics for
actual individuals:
Sex
Race/ethnicity
Age
Height
Weight
Serum creatinine

Population simulator for HTTK
nnanes
National Health and Nutrition Examination Survey
~
Predict physiological
quantities
Tissue masses
Tissue blood flows
GFR (kidney function)
Hepatocellularity
Regression equations from literature
(McNally et al., 2014)
(+ residual marginal variability)
(Similar approach used in SimCYP [Jamei et al. 2009], GastroPlus,
PopGen [McNally et al. 2014], P3M [Price et al. 2003], physB [Bosgra et al. 2012], etc.)
33 of 53
Office of Research and Development
Slide from Caroline Ring (ToxStrategies)
Ring et al. (2017)
-------
oEPA
United States
Environmental Protection
Agency
Risk-Based Ranking for Total NHANES Population
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Potential
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in vitro with
Reverse
Toxicokinetics
Potential
Exposure
Rate
& Sr
M 9
| Office of Research and Development
CorflpcufKt
Lower Medium Higher
Risk Risk Risk
Ring et al. (2017)
-------
oEPA
United States
Environmental Protection
Agency
Life-stage and Demographic Variation in Exposure
• Wambaugh et ol. (2014) made steady-
state inferences of exposure rate
(mg/kg/day) from NHANES data for
various demographic groups
-0.5
0.5
Change in Exposure
Relative to Total Population
| Office of Research and Development
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Ring et ol. (2017)
-------
oEPA
United States
Environmental Protection
Agency
Life-stage and Demographic Variation in K
• Ring et ol. (2017) made demographic-
specific predictions of change in plasma
concentrations for a 1 mg/kg bw/day
exposure
v~>
rt3
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0.5
Change in Toxicokinetics
Relative to Total Population
| Office of Research and Development
.a

///

-------
oEPA
United States
Environmental Protection
Agency
• Can calculate
margin
between
bioactivity and
exposure for
specific
populations
Life-stage and Demographic Variation in Risk Priority
mg/kg BW/day
A
Potential Hazard
from in vitro with
Reverse
Toxicokinetics
Potential Exposure
from ExpoCast
-0.5
0.5
Change in Risk Relative to
Total Population
| Office of Research and Development
Lower Medium Risk Higher
Risk Risk
v~>
rt3
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CU
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*
-------
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 « rmarkdown-cheatsheet X I Defining toxicological tipping pc X <|| CRAN - Package httk X +
O £}" ® cran.r-project.org/web/packages/httk/indGX.html Q. ~ Q H S3 0 £
Apps (ft; Confluence yy DSStox u Chemicals Dashboa... EKP @ ORD Travel Request,,, Q Article Request Q Graphics Request £ ChemTrack Q https://cranlogs.r-p... ^ CSS REMD RACT 0 mec_ssy_sub
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 ("IVTVE") of high throughput screening data (e.g., Tox21, ToxCast) to real-world
exposures via reverse dosimetry (also known as "RTK") (Wetmore et al.. 2015 1.
Version: 1.10.1
Depends: R(> 2.10)
Imports: deSolve. msm. data.table, survey, mvtnorm. truncnomi. stats, graphics, utils, magrittr
Suggests: ggplot2. knitr. rmarkdown. R.rsp. GGallv. splots. scales. EnvStats. MASS. RColorBrewer. TeachingDei
gmodels. colorspace
Published: 2019-09-10
Author: John Wambaugh [aut. ere]. Robert Pearce [aut], Caroline Ring faut]. Greg Honda [aut], Mark Sfeir [aut]
Wetmore [ctb], Woodrow Setzer [ctb]
Maintainer: John Wambaugh
BugReports: https: sithub.com USEPA CompTox-ExpoCast-httk
License: GPL-3
URL: https: .mvw.epa.gov/chemical-researchrapid-chemical-exposure-aiid-dose-research
Needs Compilation: y es
Materials: NEWS
CRAN checks: httk results
Downloads:
downloads 474/month
Reference manual: httk.pdf
Vignettes: Honda et al, (20191: Updated Armitage et al. (2014"! Model
Pearce et al, (20171 Creating Partition Coefficient Evaluation Plots
Ring et al. ("20171Age distributions
Ring et al. ("20171 Global sensitivity' analysis
Ring et al. ("20171 Global sensitivity analysis plotting
Ring et al. ("20171Height and weight spline fits and residuals
R package "httk
if
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
-------
vvEPA
United States
Environmental Protection
Agency
High throughput screening (Dix et
al., 2006, Collins et al., 2008) + in
vitro-in vivo extrapolation (IVIVE,
Wetmore et al., 2012, 2015) can
predict a dose (mg/kg bw/day) that
might be adverse
Risk = Hazard x Exposure
High-Throughput
Risk
Prioritization
39 of 53
Office of Research and Development
NRC (1983)
-------
vvEPA
United States
Environmental Protection
Agency
High throughput screening (Dix et
al., 2006, Collins et al., 2008) + in
vitro-in vivo extrapolation (IVIVE,
Wetmore et al., 2012, 2015) can
predict a dose (mg/kg bw/day) that
might be adverse
Risk = Hazard x Exposure
High-Throughput
Risk
Prioritization
Need methods to forecast exposure for
thousands of chemicals
(Wetmore et al., 2015)
High throughput models exist to
make predictions of exposure via
specific, important pathways such
as residential product use and diet
40 of 53
Office of Research and Development
NRC (1983)
-------
oEPA
Limited Available Data for Exposure
Estimation
Most chemicals lack public exposure-related data beyond production volume (Egeghy et alv 2012)
United States
Environmental Protection
Agency
10OQ0*
ProiiuGlran Use
Vokjme Category
r -DDd Cherninal Waler
Use Cone.
Air BrcirTvarkar
Ccnc Cone
| Office of Research arid Development
Data Type
-------
v>EPA
United States
Environmental Protection
Agency
Understanding Exposure is a Systems
Problem
USE and RELEASE
Consumer
Products and Durable
Goods
Direct Us^ Residential Use
Chemical Manufacturing and Processing
Environmental
Release
[e.g., surface cleaner) (e.g. ,flo
MEDIA
EXPOSURE
(MEDIA + RECEPTOR)
RECEPTOR
Exposure event unobservable: Can try to predict exposure by characterizing pathway
Some pathways have much higher average exposures: In home "Near field" sources
significant (Wallace, eta!., 1987)
42 of 53
Office of Research and Development
-------
oEPA
New Approach Methodologies for Exposure Science
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
•
•
•
•
•
•
| Office of Research and Development
Wambaugh et al., (2019)
-------
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
-------
oEPA
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
45 of 53
Office of Research and Development
-------
vvEPA
Evaluation NAMs: The SEEM Framework
United States
Environmental Protection
Agency
r
Space of
Chemicals
r
Chemicals
with
Monitoring
Data
Dataset 1
Dataset 2
Apply calibration and estimated uncertainty to
other chemicals
¦M
m
cc
0
JS£
fO
V!
Exposure
Ml inference
hi
CD
Model 1
Model 2
Estimate ¦
Uncertainty
Calibrate
models
•
\
Different
Chemicals
J <
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
-------
vi-EPA
United States
Environmental Protection
Agency
_ 2-
C
CD
"o
Q3
O
O
c 0-
o
CO
CO
8M -
O)
0
DC
Wambaugh et al. (2014)
I I l < I
47 of 53
Office of Research arid Development
Heuristics of Exposure
— Total
— Female
— Male
— ReproAgeFemale
— 6-11_years
— 12-19_years
— 20-65_years
— 66+years
— BMI_LE_30
BMI GT 30
R2 ~ 0.5 indicates that we can predict
50% of the chemical to chemical
variability in median NHANES
exposure rates
Same five predictors work for all
NHANES demographic groups
analyzed - stratified by age, sex, and
body-mass index:
• Industrial and Consumer use
• Pesticide Inert
• Pesticide Active
• Industrial but no Consumer
use
• Production Volume
-------
vvEPA
United States
Environmental Protection
Agency
Correlation is Not Causation
Wambaugh et al. (2014) found that "pesticide inerts"
had higher than average levels in biomonitoring data,
while "pesticide actives" had lower than average
In World War II, there Royal Air Force (UK) wanted to
armor planes against anti-aircraft fire
• Initial proposal was to place armor wherever
bullet holes were most common
• Mathematician Abraham Wald pointed out that
they were looking at the planes that had returned
• See Drum, Kevin (2010) "The Counterintuitive
World"
Pesticide inerts have many other uses, but there are
more stringent reporting requirements for pesticides
• Exposure is occuring by other pathways
48 of 53
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
Knowledge of Exposure Pathways Limits
High Throughput Exposure Models
"In particular, the
assumption that 100%
of [quantity emitted,
applied, or ingested] is
being applied to each
individual use scenario
is a very conservative
assumption for many
compound / use
scenario pairs."
This is an open access amide published under an ACS AuthorChoice License. which pe-rmits
copying and redistribution of the article or any adaptations for fion-commercial pur poses.
Article
p\j b 3 j cs.ofg/est
Risk-Based High-Throughput Chemical Screening and Prioritization
using Exposure Models and in Vitro Bioactivity Assays
Hyeong-Moo Shin,*' Alexi Emstoff,*'^ Jon A. Arnot,"-L'* Barbara A Wetmore,V Susan A Csiszar,s
Peter Fantkc,* Xianming Zhang,0 Thomas E. McKonc,*'^ Olivier Jolliet/ and Deborah H. Bennett*
49 of 53
Office of Research arid Development
-------
oEPA
United States
Environmental Protection
Agency
Chemical Use Identi les Relevant Pathways
>2000 chemicals with Material Safety Data Sheets
(MSDS) in CPCPdb (Goldsmith etol., 2014)
CO
ro
u
0)
u
IS)

< "i
Apparel
Auto and Tires
Baby
Beauty
Craft and Party
Electronics
Grocery
Health
Home
Home Improvement
Patio and Garden
Pets
Sports and Outdoors
T oys
Some pathways have
much higher average
exposures!
Near Field Occ
Ecological
| Office of Research arid Development
Near field sources have been known to be important at least since 1987 -
see Wallace, et al.
-------
oEPA Chemical Property NAMs
I I rt I + /N/4 C B ™
United States
Environmental Protection
Arz^n
SCIENTIFIC DATAIK
OPEN
ELSEVIER
Contents lists available at ScienceDirect
Food and Chemical Toxicology
journal homepage: www.elsevier.com/locate/foodchemtox
Development of a consumer product ingredient database for chemical /¦) Cr()ssMjlt
exposure screening and prioritization
M.-R. Goldsmith'*, CM. GrulkeR.D. Brooksb, T.R. Transuec, Y.M. Tan A. Frame'10, P.P. Egeghy ',
R. Edwards d, D.T. Chang8, R. Tornero-VelezK. Isaacs3, A. Wangac, J. Johnsona, K. Holm3, M. Reich',
J. Mitchells, DA Valleroa, L Phillipsa, M. Phillipsa, J.F. Wambaugha. R.S. Judsona,
T.J. Buckleya, C.C. Dary"
Data Descriptor: The Chemical and
Products Database, a resource for
exposure-relevant data on
chemicals in consumer products
Kathie L. Dionisio1, Katherine Phillips1, Paul S. Price1, Christopher M. Grulke2,
Antony Williams2, Derya Biryol1'3, Tao Hong* & Kristin K. Isaacs1
Broad "index" of chemical uses
ELSEVIER
Contents lists available at ScienceDirect
Toxicology Reports
journal homepage: www.elsevier.com/locate/toxrep
Exploring consumer exposure pathways and patterns of use ¦.
for chemicals in the environment
Kathie L Dionisio11, Alicia M. Frame11'1, Michael-Rock Goldsmitha-2,
John F. Wambaughb, Alan Liddell'-3, Tommy Catheyd, Doris Smith",
James Vailb, Alexi S. Ernstoff , Peter Fantkee, Olivier Jolliet',
Journal of Exposure Science and Environmental Epidemiology (2018) 28, 216-222
0 2018 Nature America, Inc. past of Sponger Mature. AM rights reserved 1559-0631/18
w.nature.com ijes
ORIGINAL ARTICLE
Consumer product chemical weight fractions from
ingredient lists
Kristin K. Isaacs1, Katherine A. Phillips', Derya Biryol1-2, Kathie L Dionisio1 and Paul S. Price1
| Office of Research and Development
Measurement of chemicals in
consumer products
https://comptox.epa.gov/dashboard
-------
oEPA
What is "High Throughput"?
United States
Environmental Protection
Agency
• Tox21: Testing one assay across 10,000 chemicals takes 1-2 days, but only 50 assays have been
developed so far that can run that fast
• ToxCast: ~1100 off-the-shelf (pharma) assay-endpoints tested for up to 4,000 chemicals over the past
decade, now developing new assays as well
HTS tox assays often use single readout, such as fluorescence, across many chemicals, measuring
concentration for toxicokinetics or exposure requires chemical-specific methods...
• ExpoCast: Ring et al. made in silico predictions for ~480,000 chemicals from structure, but based on
NHANES monitoring for ~120 chemicals
• Quantitative non-targeted analysis (NTA) may eventually provide greater evaluation data to
reduce uncertainty
• HTTK: In vitro data on 944 chemicals collected for humans, starting with Rotroff et al. (2010)
• Work continues to develop in silico tools, e.g. Sipes et al. (2016)
52 of 53
Office of Research and Development
Our work is not done
-------
oEPA
United States
Environmental Protection
Agency
Summary
A tapestry of laws covers the chemicals people are exposed to
in the United States (Breyer, 2009)
Most other 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
All data are being made public:
• The CompTox Chemicals Dashboard (A search engine for
chemicals) http://comptox.epa.gov/
• R package "httk": https://CRAN.R-project.org/package=httk
| Office of Research and Development
mg/kg BW/day
Potential hazard
from in vitro
converted to dose
by HTTK
Potential
Exposure Rate
Lower
Risk
Medium
Risk
Higher
Risk
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)
NCCT
Chris Grulke
Richard Judson
Ann Richard
Risa Sayre*
Mark Sfeir*
Rusty Thomas
John Wambaugh
Antony Williams
NRMRL
Xiaoyu Liu
NHEERL
Linda Adams
Christopher
Ecklund
Marina Evans
Mike Hughes
Jane Ellen
Simmons
Tamara Tal
NERL
Cody Addington*
Namdi Brandon*
Alex Chao*
Kathie Dionisio
Peter Egeghy
Hongtai Huang*
Kristin Isaacs
Ashley Jackson*
Jen Korol-Bexell*
Anna Kreutz*
Charles Lowe*
Seth Newton
*Trainees
Katherine Phillips
Paul Price
Jeanette Reyes*
Randolph Singh*
Marci Smeltz
Jon Sobus
John Streicher*
Mark Strynar
Mike Tornero-Velez
Elin Ulrich
Dan Vallero
Barbara Wetmore
Collaborators
Arnot Research and Consulting
Jon Arriot
Johnny Westgate
Institut National de I'Environnement et des Risques
(INERIS)
Frederic Bois
Integrated Laboratory Systems
Kamel Mansouri
National Toxicology Program
Mike Devito
Steve Ferguson
Nisha Sipes
Ramboll
Harvey Cleweli
ScitoVation
Chantel Nicolas
Silent Spring Institute
Robin Dodson
Southwest Research Institute
Alice Yau
Kristin Favela
Summit Toxicology
Lesa Aylward
Technical University of Denmark
Peter Fantke
Tox Strategies
Caroline Ring
Miyoung Yoon
Unilever
Beate Nicol
Cecilie Rendal
Ian Sorrell
United States Air Force
Heather Pangburn
Matt Linakis
University of California, Davis
Deborah Bennett
University of Michigan
Olivier Jolliet
University of Texas, Arlington
Hyeong-Moo Shin
-------
oEPA
United States
Environmental Protection
Agency
References
Cohen Hubal, EA, et al. "Advancing internal exposure and
physiologically-based toxicokinetic modeling for 21st-
century risk assessments." Journal of exposure science &
environmental epidemiology (2018).
Eissing, Thomas, et al. "A computational systems biology
software platform for multiscale modeling and simulation:
integrating whole-body physiology, disease biology, and
molecular reaction networks." Frontiers in physiology 2
(2011): 4.
Frank, Christopher L., et al. "Defining toxicological tipping
points in neuronal network development." Toxicology and
applied pharmacology 354 (2018): 81-93.
Honda, Gregory S., et al. "Using the concordance of in vitro
and in vivo data to evaluate extrapolation assumptions."
PloS one 14.5 (2019): e0217564.
Jamei, et al. "The Simcyp® population-based ADME
simulator." Expert opinion on drug metabolism & toxicology
2009b;5:211-223
Jongeneelen, Frans J., and Wil F. Ten Berge. "A generic,
cross-chemical predictive PBTK model with multiple entry
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model and comparison of predictions with experimental
results." Annals of occupational hygiene 55.8 (2011): 841-
864.
Breyer, Stephen. Breaking the vicious circle: Toward
effective risk regulation. Harvard University Press, 2009
Collins FS, Gray GM, Bucher JR. Transforming
environmental health protection. Science. 2008;319:906-
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Dix David, et al. "The ToxCast program for prioritizing
toxicity testing of environmental chemicals." Toxicol Sci.
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Egeghy, P. P., et al. (2012). The exposure data landscape for
manufactured chemicals. Science of the Total Environment,
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Judson, Richard S., et al. "In vitro screening of
environmental chemicals for targeted testing prioritization:
the ToxCast project." Environmental health perspectives
118.4 (2009): 485-492.
Kavlock, R. J., et al. (2018). Accelerating the pace of
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Mansouri, Kamel, et al. "OPERA models for predicting
physicochemical properties and environmental fate
endpoints." Journal of cheminformatics 10.1 (2018): 10.
McLanahan, Eva D., et al. "Physiologically based
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(2011): 5-15.
Lukacova
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Dva, Viera, Walters. Woltosz, and Michael B. Bolger.
effective risk regulation. Harvard University Press, 2009
Egeghy, P. P., et al. (2012). The exposure data landscape for
manufactured chemicals. Science of the Total Environment,
414, 159-166.
Filer, Dayne L.. "The ToxCast analysis pipeline: An R package
for processing and modeling chemical screening data." US
Environmental Protection Agency: http://www. epa.
gov/ ncct/toxcast/f iles/MySQL%
20Database/Pipeline_Overview. pdf (2014)
Hertzberg, R. P., & Pope, A. J. (2000). High-throughput
screening: new technology for the 21st century. Current
opinion in chemical biology, 4(4), 445-451.
Ingle, Brandall L., et al. "Informing the Human Plasma
Protein Binding of Environmental Chemicals by Machine
Learning in the Pharmaceutical Space: Applicability Domain
and Limits of Predictability." Journal of Chemical
Information and Modeling 56.11 (2016): 2243-2252.
Jamei, et al. "The Simcyp® population-based ADME
simulator." Expert opinion on drug metabolism & toxicology
2009;5:211-223
Kaewkhaw, R., et al. (2016). Treatment paradigms for
retinal and macular diseases using 3-D retina cultures
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linestreatment design using PSC-Derived 3-D retina
cultures. Investigative ophthalmology & visual science,
57(5), ORSFI1-ORSFI11.
Kavlock, Robert, et al. "Update on EPA's ToxCast program:
providing high throughput decision support tools for
s I ric U
i no rr
+¦ 11
rocoa rr*
h i v
-------