oEPA
United States
Environmental Protection
Agency
gency
High Throughput Inhalation
Toxicokinetics with the HTTK R Package
John Wambaugh
Center for Computational Toxicology and Exposure
Office of Research and Development
U.S. Environmental Protection Agency
Progress for o Stronger Future
http://orcid.ore/0000-0002-4024-534X
Computational Toxicology
Community of Practice Webinar
March 26, 2020
The views expressed in this presentation are those of the author
and do not necessarily reflect the views or policies of the U.S. EPA
-------
oEPA
Greg Honda
United States
Environmental Protection
Agency
Miyuki Breen
Mark Sfeir
Software Engineer
Matt Linakis (AFRL)
Heather Pangburn (AFRL)
Jeffery Gearhart (AFRL)
Nisha Sipes (NTP)
Kristin Isaacs
Marina Evans
Miyuki Breen
Human Variability
Inhalation
Oral
Absorption
Human
Gestation
Dustin Kapraun Tom Knudsen
Richard Judson Annie Lumen
(FDA)
cyprotex
HTTKTeam *A
Cyprotex
(lab work)
Russell Thomas
Barbara Wetmore
Mike Devito
David Murphy
Katherine Coutros
Ann Richard
Risa Sayre
Chris Grulke
Mike Devito
TK
Database
Structure-Based
Predictions
Dermal
Derek Angus Briana Franz
Maria Bacolod Jon Gilbert
Akshay Teresa Sierra
Badrinarayanan Bradley Snodgrass
Adam Brockman Chris Strock
Roger Dinallo
Marina Evans Tom Moxon Beate Nicol
(Unilever) (Unilever)
Funded by EPA's Office of Research and Development and
Office of Science Coordination and Policy
Nisha Sipes (NTP) Richard Judson
Rogelio Tornero-Velez Jon Arnot (ARC)
Daniel Dawson Kamel Mansouri (ILS
Stephen Ferguson Michael Lawless
(NTP) (Simulations Plus)
Brandall Ingle (ICF) Prachi Pradeep
Alumni
Robert Pearce Greg Honda Cory
Woody Setzer Strope
Caroline Ring Jimena Davis
Chantel Nicolas
-------
Credit: the Research Triangle Founda^j
3 of 71
oEPA US EPA Office of Research and Development
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
Office of Research and Development
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
-------
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 depending on 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?
Wostwiqlfnw The greatest *vw find Ajt#ici.il worm could
is. also wtuous of fotrly human bon« be first dugiUJaninMl
4 of 71
Office of Research and Development
November 29, 2014
-------
vvEPA
United States
Environmental Protection
Agency
Toxic Substances Control Act (TSCA)
Most other chemicals, ranging from industrial waste
to dyes to packing materials, are covered by the 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)
"Tens of thousands of chemicals
Environmental Protection Age
use in the United States, with
chemicals listed
U.S. Government Accountability
Schmidt, C. W. (2016)
5 of 71
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
Replacing Animal Testing with NAMs
Administrator of the EPA: "I am directing
leadership and staff in the Office of Chemical
Safety and Pollution Prevention and the Office of
Research and Development to prioritize ... the
reduction of animal testing while ensuring
protection of human health and the
environment."
"These new approach methods (NAMs), 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"
Office of Research and Development
} O *
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON. D C 20460
September 10, 2019
THE AOMtNlSTRATOR
MEMOKAMH M
SI BJECT: Directive to Prioritize Efforts to Remittee Animal/J esting
vui
FROM:
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. Knvironmental 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 Frank 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 five 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~he 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 predictivity than current animal
models.
-------
oEPA
United States
Environmental Protection
Agency
New Approach Methodologies (NAMs)
There are roughly 10,000 TSCA-relevant chemicals in
commerce
Traditional methods are too resource-intensive to
address all of these
NAMs include:
High throughput screening (ToxCast)
High throughput exposure estimates (ExpoCast)
High throughput toxicokinetics (HTTK)
TSCA Proof of concept: Examine ~200 chemicals with
ToxCast, ExpoCast and HTTK
HTTK was rate limiter on number of chemicals
mg/kg BW/day
A
Potential Hazard
from in vitro with
Reverse
Toxicokinetics
Potential
Exposure Rate
From Models
"A Proof-of-Concept Case Study Integrating Publicly Available Information
to Screen Candidates for Chemical Prioritization under TSCA" (EPA, 2019)
Office of Research and Development
Lower
Risk
Medium Higher
Risk Risk
-------
*S5L For the Kids at Home
Home Safari - Ocelot Sihil - Cincinnati Z
& BOTANICAL GARDEN
Home Safari
While the Cincinnati Zoo is closed and kids are home
from school, let us help make your children's hiatus from
school fun and educational.
Join us for a Home Safari Facebook
Live each day at 3pm EDT where we
will highlight one of our amazing
animals and include an activity you
can do from home
8 Of 71
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
Risk
Assessment
in I he Federal
(»overiimeiit:
Managing
the Progress
NRC (1983)
Three Components for Chemical Risk
Hazard
Chemical Risk
Exposure
Dose-Response
(Toxicokinetics
/Toxicodynamics)
Office of Research and Development
The National Academy of Sciences, Engineering and Medicine
(1983) outlined three components for determining chemical risk.
-------
vvEPA
United States
Environmental Protection
Agency
High-1 hroughput Risk Prioritization
(A
\WJ t -
iaa /\
\ To*?/
hgsc Z
\
\
m
I
IWINTP
- r tar
National Institute of
Environmental Health Sciences
•- Notenal twicobgy ftapam
TOXICITY TESTING IN THE 21 ST CENTURY
A VISION AND A STRATEGY
NCATS
Hazard
Chemical Risk
High throughput screening
(HTS) for in vitro bioactivity
potentially allows
characterization of
thousands of chemicals for
which no other testing has
occurred
NRC (2007)
Dose-Response
(Toxicokinetics
/Toxicodynamics)
Exposure
10 Of 71
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
High-1 hroughput Risk Prioritization
(A
\ To*?/
hgsc Z
- - X.
)
IWINTP
•- Notenal twicobgy ftapam
• ¥
\
TOXICITY TESTING IN THE 21 ST CENTURY
A VISION AND A STRATEGY
ST
ggjjfc, 4
*
K
I
NCATS
NRC (2007)
Hazard
Chemical Risk
Dose-Response
(Toxicokinetics
/Toxicodynamics)
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),
2017
Exposure
11 Of 71
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
In Vitro - In Vivo Extrapolation
(IVIVE)
IVIVE is the use of in vitro data to predict phenomena in vivo
7~his can be broken down into two components:
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
Office of Research and Development
Rodents: in vivo
Normalization of dose
PBPK models
NRC (1998)
Humans: in vivo
Testable predictions
Extrapolation
using PD anil
PBPK models
Rodents: in vitro
Comparative testing
Humans: in vitro
-------
oEPA
United States
The Need for Toxicokinetics NAMs
Environmental Protection
Agency
Most chemicals do not have TK data (Wetmore et al., 2015)
300
250
200
150
100
50
0
ToxCast Chemicals
Examined
Chemicals with
Traditional in vivo TK
Chemicals with High
Throughput TK
ToxCast Phase I (Wetmore et al. 2012)
ToxCast Phase II (Wetmore et al. 2015)
Office of Research and Development
Bell etal. (2018)
-------
^EEs NAMs for Toxicokinetics
Environmental Protection
Agency
In order to address greater numbers of chemicals we collect high throughput
toxicokinetic (HTTK) data (for example, Rotroff et al., 2010, Wetmore et al., 2012, 2015)
HTTK methods have been used by the pharmaceutical industry to determine range of
efficacious doses and to prospectively evaluate success of planned clinical trials (Jamei,
et al.,2009; Wang, 2010)
The primary goal of HTTK is to provide a human dose context for bioactive
concentrations from HTS (that is, in vitro-in extrapolation, or IVIVE) (for example,
Wetmore et al., 2015)
A secondary goal is to provide open source data and models for evaluation and use by
the broader scientific community (Pearce et al, 2017a)
14 of 71
Office of Research and Development
-------
vvEPA
In Vitro Data for HTTK
United States
Environmental Protection
Agency
Cryo-preserved
hepatocyte
suspension
Shibata etol. (2002)
\
-~
-~
-~
¦=~ n"
Cryo-preserved
Hepatocytes
(10 donor pool for
human)
The rate of disappearance of
parent compound (slope of
line) is the hepatic clearance
(|iL/min/106 hepatocytes)
Add Chemical
(1 and 10 |iM)
Remove Aliquots
at 15, 30, 60, 120
min
Analytical
Chemistry
T
We perform the assay at 1
and 10 |iM to check for
saturation of metabolizing
enzymes.
50
100
150
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)
15 of 71
Office of Research and Development
-------
oEPA
In Vitro Data for HTTK
United States
Environmental Protection
Agency
Cryo-preserved
hepatocyte
suspension
Shibata etol. (2002)
Rapid Equilibrium
Dialysis (RED)
Waters et ol. (2008)
\
T
f£5i=- 1
Cryo-preserved
Hepatocytes
(10 donor pool for
human)
Add Chemical
(1 and 10 nM)
Remove Aliquots
at 15, 30, 60, 120
min
Analytical
Chemistry
i
*
Double-wells
connected by semi-
permeable
membrane on a
Rapid Equilibrium
Dialysis (RED) Plate
Office of Research and Development
s *
Add plasma (6
donor pool for
human) to one
well
>=> -r-
Add chemical
F
ub.p
C
Incubate plates to
allow wells with
and without
protein to come
to equilibrium
Determine
concentration in
both wells
(analytical
chemistry)
well 1
C
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 n
clinical trials (Wang,
2010)
well 2
-------
oEPA
United States
Environmental Protection
Agency
Cryo-preserved
hepatocyte
suspension
Shibata etol. (2002)
In Vitro Data for H I I K
Rapid Equilibrium
Dialysis (RED)
Waters et ol. (2008)
\
T
&=¦ 1
Cryo-preserved
Hepatocytes
(10 donor pool for
human)
Add Chemical
(1 and 10 nM)
Remove Aliquots
at 15, 30, 60, 120
min
Analytical
Chemistry
i
*
Double-wells
connected by semi-
permeable
membrane on a
Rapid Equilibrium
Dialysis (RED) Plate
Office of Research and Development
s *
Add plasma (6
donor pool for
human) to one
well
>=>
Add chemical
F
ub.p
C
Incubate plates to
allow wells with
and without
protein to come
to equilibrium
Determine
concentration in
both wells
(analytical
chemistry)
well 1
C
Most chemicals do not
have TK data - we use
in vitro HTTK methods
adapted from pharma
to fill gaps
Environmental
chemicals:
Rotroff et al. (2010)
35 chemicals
Wet mo re et al. (2012)
+204 chemicals
Wet mo re et al. (2015)
+163 chemicals
Wambaugh et al. (2019)
+389 chemicals
well 2
-------
vvEPA
United States
Environmental Protection
Agency
High Throughput Toxicokinetics (HTTK)
In vitrotoxicokinetic data + generic toxicokinetic model
= high(er) throughput toxicokinetics
^ V # V # V H nl
CL
metab
.
4
1 2 ¦=> wk ¦¦ :¦-! *
Primary
Compartment
Gut Lumen
—>
CL
>=>
az.
vabs
httk
18 of 71
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
Open Source Tools and Data for HTTK
https://CRAN.R-proiect.orq/packaqe=httk
CRAN - Package httk X +
0 i cran.r-projectorg/web/packages/httk/index.html
¦" Apps Absence Request f Trave! Request For...
httk: High-Throughput Toxicokinetics
- a x
Q. ~ o a o I & :
REMD
'-HTTK \S. Confluence Q Bitbucket -.£¦ CompTox Dashboard EHP 0 Change Password
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 't and measurement limitations. Calibrated methods are included
for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 I These functions and data provide a set of tools for in vitro-in vivo extrapolation ("TVIVE") of
high throughput screening data (e.g.. Tox21, ToxCast) to real-world exposures via reverse dosimetr- filtn t-np-n-n -ii '"RTTT"'', ft -al—">0* s -.--rnrl n 1 'trwi Vfr* 71 Vi
Version:
Depends:
Imports:
Suggests:
Published:
Author:
Maintainer:
BugReports:
License:
URL:
Needs Comp ilation:
Citation:
Materials:
CRAN checks:
Downloads:
2.0.1
R (> 2.10)
deSolve, msm. data.table, survey, mvtnorm. truncnorm. stats, graphics, utils, i
ggplot2. knitr. rmarkdown. R.rsp. GGallv. gplots. scales. EnvStats. MASS. RC
ggregel, dplvr. forcats. saatr. stools. sridExtra
2020-03-02
John Wambaugh [aut, ere], Robert Pearce © [aut], Caroline Ring [a|
[ctb], Barbara Wetmore [ctb], Woodrow Setzer [ctb]
John Wambaugh
https:' github .c om U SEPA C ompTox-Exp oCast-httk
GPL-3"
https: ' vvvAv.epa.gov ckemical-research rapid-chemical-exo0sure-and-dose-re
yes
httk citation info
NEWS ,
httk results
R package "httk"
downloads 806/month
Reference manual: httk.cdf
Vignettes: Frank et al. 12018 Creating I VIVE Figure I'Fig. 6>
Honda et al. i'2019'i: Updated Armitage et al. i2014'> Model
Lmakis et al. (Submitted): Analysis and Figure Generation
Pearce et al. i'2017'i: Creating Partition Coefficient Evaluation Plots
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 987 chemicals
Described in Pearce et al. (2017a)
-------
vvEPA
Why Build Another Generic PBTKTool?
United States #
Environmental Protection
Agency
SimCYP
ADMET Predictor / GastroPlus
PK-Sim
IndusChemFate
httk
Maker
SimCYP Consortium /
Certara
Simulations Plus
Open Systems
Pharmacology
Cefic LRI
US EPA
Reference
Jamei et al. (2009)
Lukacova et al., (2009)
Eissing et al., (2011)
Jongeneelen et al., (2013)
Pearce et al. (2017a)
Availability
License, but inexpensive for research
License, but inexpensive for research
Free:
http://www.open-systems-
pharmacology.org/
Free:
http://cefic-lri.org/lri_toolbox/induschemfate/
Free:
https://CRAN.R-project.org/package=httk
Open Source
No
No
GitHub
No
CRAN and GitHub
Default PBPK Structure
Yes
Yes
Yes
Yes
Yes
Population Variability
Yes
Yes
Yes
No
Yes
Batch Mode
Yes
Yes
Yes
No
Yes
Graphical User
Interface
Yes
Yes
Yes
Excel
No*
Built-in Chemical-
Specific Library
Many Clinical Drugs
No
Many pharmaceutical-
specific models available
15 Environmental Compounds
980 Pharmaceutical and
ToxCast Compounds
lonizable Compounds
Yes
Yes
Yes
No
Yes
Export Function
No
No
Matlab and R
No
SBML and Jarnac
R Integration
No
No
Yes(2017)
No
Yes
Easy Reverse
Dosimetry
Yes
Yes
Yes
No
Yes
20 of 71
*Both PLETHEM (Scitovation) and Web-ICE (NICEATM) provide GUI's to HTTK and other models
Office of Research and Development Pre-computed HTTK results are also available at https://comptox.epa.gov/dashboard
-------
vvEPA
United States
Environmental Protection
Agency
TOX1COLCXJICALSCENCES 126< 15—15 (2012)
doi: 10.1093Ao\sciykfr295
Advance Access publication November 1. 2011
Open Source,Veri lable, Reproducible
Physiologically Based Pharmacokinetic Model Use in Risk
Assessment—Why Being Published Is Not Enough
Eva D. McLanahan.* 1 Hisham A. El-Masri.t Lisa M. Sweeney.i Leonid Y. Kopylev,|| Harvey J. Clewell,§ John F. Wambaugh.'
and P. M. Schlosserjl
"Although publication of a PBPK model in a peer-
reviewed journal is a mark of good science, subsequent
evaluation of published models and the supporting
computer code is necessary for their consideration for
use in [Human Health Risk Assessments]"
21 of 71
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
TOXICOLOGICAL SCONCES 126(1). 5-15 (2012)
doi: 10.1093Aoxsti/Wr295
Advance Access publication November 1. 2011
Open Source,Veri lable, Reproducible
Physiologically Based Pharmacokinetic Model Use in Risk
Assessment—Why Being Published Is Not Enough
Eva D. McLanahan.* 1 Hisham A. EI-Ma-sri.t Lisa M. Sweeney.i Leonid Y. Kopylev,J| Harvey J. Clewell,§ John F. Wambaugh.'
and P. M. Schlosserjl
''Although publication of a PBPK model in a peer-
reviewed journal is a mark of good science, subsequent
evaluation of published models and the supporting
computer code is necessary for their consideration for
use in [Human Health Risk Assessments]"
The White House
Office of the Press Secretary
For Immediate Release
May 09, 2013
Executive Order — Making Open and
Machine Readable the New Default
for Government Information
EXECUTIVE ORDER
MAKING OPEN AND MACHINE READABLE THE NEW DEFAULT
FOR GOVERNMENT INFORMATION
By the authority vested in me as President by the Constitution and the laws of
the United States of America, it is hereby ordered as follows:
Section J. General Principles. Openness in government strengthens our
democracy, promotes the delivery of efficient and effective services to the
public, and contributes to economic growth. As one vital benefit of open
government, making information resources easy to find, accessible, and usable
"...the default state of new and modernized Government information
resources shall be open and machine readable."
Office of Research and Development
-------
^S:£s5L Doing Statistical Analysis with HTTK
Environmental Protection
Agency
If we are to use HTTK, we need confidence in predictive ability
In drug development, HTTK methods estimate therapeutic doses for clinical studies
- predicted concentrations are typically on the order of values measured in clinical
trials (Wang, 2010)
¦ For most compounds in the environment there will be no clinical trials
Uncertainty must be well characterized
¦ We compare to in vivo data to get empirical estimates of HTTK uncertainty
¦ Any approximations, omissions, or mistakes should work to increase the estimated
uncertainty when evaluated systematically across chemicals
23 of 71
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
To evaluate a chemical-specific TK model for ''chemical x" you
can compare the predictions to in vivo measured data
Can estimate bias
1 Can estimate uncertainty
Can consider using model to extrapolate to other situations
(dose, route, physiology) where you don't have data
Building Confidence inTK Models
LO
c
o
'+->
03
i—
+->
C
o>
u
c
o
u
~o
O)
>
1—
O)
LO
O
X
X
.X
X
X
X
X
Chemical
Specific
Model
Predicted Concentrations
24 of 71
Office of Research and Development
Cohen Hubal et al. (2018)
-------
vvEPA
United States
Environmental Protection
Agency
To evaluate a chemical-specific TK model for ''chemical x" you
can compare the predictions to in vivo measured data
Can estimate bias
1 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
Building Confidence inTK Models
LO
c
o
'+->
03
i—
+->
C
o>
u
c
o
u
~o
O)
>
1—
O)
LO
_Q
O
X
X
.X
X
X
X
X
Chemical
Specific
Model
Predicted Concentrations
25 of 71
Office of Research and Development
Cohen Hubal et al. (2018)
-------
vvEPA
United States
Environmental Protection
Agency
To evaluate a chemical-specific TK model for ''chemical x" you
can compare the predictions to in vivo measured data
Can estimate bias
1 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
Building Confidence inTK Models
LO
c
o
'+->
03
i—
+->
C
o>
u
c
o
u
~o
CD
>
1—
O)
LO
_Q
O
LO
c
o
'+->
03
i—
+->
C
o>
u
c
o
u
~o
O)
>
1—
O)
LO
O
X
X
X
X
X
X
Chemical
Specific
Model
Predicted Concentrations
x
x
x
x
x
x
Generic
Model
Predicted Concentrations
26 of 71
Office of Research and Development
Cohen Hubal et al. (2018)
-------
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
1 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
LO
c
o
'+->
03
i—
+->
C
o>
u
c
o
u
~o
CD
>
1—
O)
LO
_Q
O
LO
c
o
'+->
03
i—
+->
C
o>
u
c
o
u
~o
O)
>
1—
O)
LO
_Q
O
X
X
X
X
X
X
Chemical
Specific
Model
Predicted Concentrations
x
x
x
x
x
x
Generic
Model
Predicted Concentrations
Cohen Hubal et al. (2018)
-------
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
1 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
LO
c
o
'+->
03
i—
+->
C
o>
u
c
o
u
~o
CD
>
1—
O)
LO
_Q
O
LO
c
o
'+->
03
i—
+->
C
o>
u
c
o
u
~o
O)
>
1—
O)
LO
_Q
O
X
X
X
X
X
X
Chemical
Specific
Model
Predicted Concentrations
x
A y
/
Z
Z z
X
X
X
X
X
Generic
Model
Predicted Concentrations
Cohen Hubal et al. (2018)
-------
oEPA
United States
Environmental Protection
Agency
In Vivo TK Database
https://github.com/USEPA/CompTox-PK-CvTdb
EPA has developed a public database of concentration
vs. time data for building, calibrating, and evaluating TK
models
Curation and development ongoing, but to date
includes:
¦ 198 analytes (EPA, National Toxicology Program,
literature)
¦ Routes: Intravenous, dermal, oral, sub-cutaneous,
and inhalation exposure
Standardized, open source curve fitting software
invivoPKfit used to calibrate models to all data:
https://github.com/USEPA/CompTox-ExpoCast-invivoPKfit
Other: 12 7
expired air
38 17
442 147
muscle
3 1
b cod
62 19 V 2
plasma
103
327
36 10
adipose
A
feces 4 1
urine 59 14
Office of Research and Development
Sayre et al. (accepted at Scientific Data)
-------
vi-EPA
United States
Environmental Protection
Agency
-V«,li?rii,73nn AuIIVar
NEIL GAIMAN
Office of Research and Development
For the Kids at Home
| Mouse Circus 0
0\A £evj>rc\4£
THE OFFICIAL NEIL &ALMAN WEBSITE
FOR YOUNGER, HEADERS
http://www.mousecircus.com/
Go to the videos section
for the author reading the
entirety of The Graveyard
Book and Coraline. Creepy
but great for the right kid!
The #1 New Ynri' Timet Bestseller and Winner of the 2000 Hiiro Award
With illustrations by Dave McKhan
-------
oEPA
United States
Environmental Protection
Agency
O
O
Where Do I Get
R is freely available from the Comprehensive R Archive Network (CRAN)
https://cloud.r-proiect.org/
It is often helpful to set an
environmental variable that points to a
personal library of R packages, for me,
on Windows, I have the ' user variable"
R_LIBS_USER set to
"c:/users/jwambaug/Rpackages"
Many people like to use a graphical user
interface (GUI) such as RStudio, which
also may be freely available to you:
https://rstudio.com/
Office of Research and Development
The Comprehensive R Archive Network
Download and Install R
Precompiled binary distributions of the base system and contributed packages. Windows and Mac users most likely want one of
these versions of R:
• Download R for Linux
• Download R for fMac~) OS X
• Download R for Windows
R is part of many Linux distributions, you should check with your Linux package management system in addition to the link
above.
Source Code for all Platforms
Windows and Mac users most likely want to download the precompiled binaries listed in the upper box, not the source code. The
sources have to be compiled before you can use them. If you do not know what this means, you probably do not want to do it!
• The latest release (2020-02-29, Holding the Windsock) R-3.6.3.tar.gz. read what's new in the latest version.
• Sources of R aloha and beta releases (daily snapshots, created only in time periods before a planned release).
• Daily snapshots of current patched and development versions are available here. Please read about new features and bug
fixes before filing corresponding feature requests or bug reports.
• Source code of older versions of R is available here.
• Contributed extension packages
Questions About R
• If you have questions about R like how to download and install the software, or what the license terms are, please read our
answers to frequently asked questions before you send an email.
-------
E2£AMPLE: Getting Started with HTTK
Environmental Protection
A9ency Install HTTK from the command line
, ,, , (GUI's like RStudio also provide menus for this)
> install.packages("httk") '
Installing package into *c:/Users/jwambaug/Rpackages'
(as *lib' is unspecified)
Please select a CRAN mirror for use in this session
trying URL 1https://cloud.r-proj ect.org/bin/windows/contrib/3.6/httk_2.0.1.zip 1
Content type 'application/zip' length 10127063 bytes (9.7 MB)
downloaded 9.7 MB
package *httk' successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\j wambaug\AppData\Local\Temp\Rtmp4STebz\downloaded_packages
> library(httk)
Warning message:
package *httk' was built under R version 3.6.3
> packageVersion ("httk")and functions
[1] *2.0.1'
Load the HTTK data, models,
Check what version you are using
Office of Research and Development
-------
vvEPA
What you can do with
R Package "httk"?
United States
Environmental Protection
Agency
¦ Allows prediction of internal tissue concentrations from dose regimen (oral and
intravenous)
¦ Allows conversion of in vitro concentration to doses
¦ A peer-reviewed paper in the Journal of Statistical software provides a how-to guide
(Pearce et al., 2017a)
¦ You can use the built in chemical library or add more chemical information
(examples provided in JSS paper)
¦ You can use specific demographics in the population simulator (Ring et al., 2017)
¦ You can control the built in random number generator to reproduce the same
random sequence (function set.seedQ)
33 of 71
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
lO
lO
Does My Chemical Have
HTTK Data?
> library(httk)
> get cheminfo()
List all CAS numbers for all
chemicals with sufficient data
Is a chemical available?
> "80-05-7" %in% get_cheminfo()
[1] TRUE
[1] "2971-36-0" "94-75-7"
TT
94-82-6
"90-43-7"
"1007-2
CO
1
[6] "71751-41-2" "30560-19-
^ I! I!
135410-
20-7" "34256-82-1"
"50594-
66-6"
All data on chemicals A, B, C
[11] "15972-60-8" "116-06-3"
TT
834-12-
8" "33089-61-1"
"101-05
-3"
subset(get cheminfo(in
[16] "1912-24-9" "86-50-0"
TT
131860-
33-8" "22781-23-3"
"1861-40-1" ...
fo="all"),Compound%in%
c("A","B","C"
) )
> get cheminfo(info=MallM) <
*=>
List all information
pKa
pKa
Human
Human
Human
DSSTox
Clint
Funbound
Substance
Compound CAS logP
Accept Donor
MW Clint
pValue
plasma
Id
Formula
Substance Type
2,4-6 94-75-7 2.81
2.81
221.03 0
0.149
0.04
DTXSID0020442 C8H6CI203
Single Compound
2,4-db 94-82-6 3.53
4.5
249.09 0
0.104
0.01
DTXSID7024035 C10H10CI203
Single Compound
2-phenylphenol 90-43-7 3.09
10.6
170.211 2.08
0.164
0.04
DTXSID2021151 C12H10O
Single Compound
6-desisopropylatrazine 1007-28-9 1.15
1.59
173.6 0
0.539
0.46
DTXSID0037495 C5H8CIN5
Single Compound
34 of 71
Office of Research and Development
-------
A E PA [i2£Msfl[p[Uig IVIVE Oral Equivalent Dose
Environmental Protection
Agency
#State-state oral equivalent dose (mg/kg BW/day) to produce 0.1 uM serum concentration for human, 0.95
quantile, for Acetochlor (calculated value):
> calc_mc_oral_equiv(0.1,chem.cas=M34256-82-lM)
uM concentration converted to mgpkgpday dose for 0.95 quantile.
95%
0 . 04530
#State-state oral equivalent dose (mg/kg BW/day) to produce 0.1 uM serum concentration for rat, 0.95
quantile, for Acetochlor (calculated value):
> calc_mc_oral_equiv(0.1,chem.cas="34256-82-1",species="Rat")
uM concentration converted to mgpkgpday dose for 0.95 quantile.
95%
0.1376
#State-state oral equivalent dose (mg/kg BW/day) to produce 0.1 uM serum concentration for human, 0.95
quantile, for Acetochlor (published value):
> get_lit_oral_equiv(0.1,chem.cas=M34256-82-l")
Human uM concentration converted to mg /kg bw/day dose.
[1] 0.6750
35 of 71
Office of Research and Development
-------
IVIVE with HTTK:
Environmental Protection _ . . ^
Agency Frank et al. (2018)
Toxicology and Applied Pharmacology 354 (2018) 81-93
Contents lists available at ScienceDirect
Toxicology and Applied Pharmacology
ELSEVIER journal homepage: www.elsevier.com/locate/taap
Defining toxicological tipping points in neuronal network development^
Christopher L. Frank , Jasmine P. Brown '2, Kathleen Wallace1, John F. Wambaugh ,
Imran Shah , Timothy J. Shafer ''
a Integrated Systems Toxicology Division. National Health and Environmental Effects Research Laboratory, EPA Research Triangle Park NC, USA
b National Center for Computational Toxicology, EPA. Research Triangle Park, NC, USA
H)
Chock lor
updates
36 of 71
Office of Research and Development
-------
vi-EPA IVIVE with HTTK:
United States
Environmental Protection _ . . ^
Agency Frank et al. (2018)
Toxicology and Applied Pharmacology 354 (2018) 81-93
ssMsaSl
• '¦*>
Defining 1
Christopher
Imran Shah
a Integrated Systems 1
b National Center for
37 of 71
Office o
Contents lists available at ScienceDirect
Fig. 6. Comparison between predicted plasma levels for critical
concentrations and ft nvt> estimates from the httk model. For
those chemicals with 1) tn vuro predicted critical concentra-
tions. 2) in *n*> smdfces indicating neurological effect, and 3)
available toaucoimeoc data the time-integrated plasma con-
cent ration (area under the curve or AUC) was predicted for the
LOLL associated with each chemical-specific study. The che
mical-specific prediction is indicated by the first four letters of
each chemicals name. There were two available studies for each
chemicaL The identity ("perfect predictor") line is Indicated by
a solid black line, while the dashed lines indicate ten-fold above
and below perfect prediction. Because all in vuro treatments
were exposed for the same amount of tune, (he relationship
between nominal m vttro concentration and time-integrated
concentration is a constant.
10~s 10"* 10
fn vivo AUC estimated wilh HTTK (jjM'day)
Chtardiazeposcide' ChlorpyriJos" Dielcfrin
Chemical
i Chbrpromacine Diazepam Diethylstiltsstrcl
-------
oEPA
United States
Environmental Protection
Agency
O
O
Vignettes in R
A vignette is R terminology for an example or walk-through that provides the code and outputs for doing a task in R.
> vignette (package="httkn) S i List all vignettes for a specific package
Frank2018
Honda2019
LinakisSubmitted
Pearce2017
Ring_2 017_vignette0 6_aerplotting
Ring_2 017_vignette02_evalmodelsubpop
Ring_2 017_vignette03_paper_fig2
Ring_2 017_vignette04_paper_fig3
Ring_2 017_vignette01_subpopulations
Ring_2 017_vignette05b_plothowgatej ohnson
Ring_2 017_vignette_05a_virtualstudypops
Wambaugh2 018
Wambaugh2 019
Frank et al. (2018): Creating IVIVE Figure (Fig. 6) (source, html)
Honda et al. (2019): Updated Armitage et al. (2014) Model (source, html)
Linakis et al. (Submitted): Analysis and Figure Generation (source, html)
Pearce et al. (2017): Creating Partition Coefficient Evaluation Plots (source, html]
Ring et al.
Ring et al.
Ring et al.
Ring et al.
Ring et al.
Ring et al.
Ring et al.
|2017): AER plotting (source, html)
|2017): Evaluating HTTK models for subpopulations (source, html]
12017) : Generating Figure 2 (source, html)
12017) : Generating Figure 3 (source, html)
12017): Generating subpopulations (source, html)
|2017): Plotting Howgate/Johnson data (source, html)
|2017) : Virtual study populations (source, html)
Wambaugh et al. (2018) : Creating All Figures (source, html)
Wambaugh et al. (2019): Creating Figures for the Manuscript (source, html)
38 of 71
Office of Research and Development
-------
O&^MtPLEs Vignettes in R
Environmental Protection
Agency
A vignette is R terminology for an example or walk-through that provides the code and outputs for doing a task in R.
> vignette("Frank2 018")
C © 127.0.0.1:10590/Iibrary/httk/doc/Frank2018.html
-------
oEPA
United States
Environmental Protection
Agency
O
O
Vignettes in R
A vignette is R terminology for an example or walk-through that provides the code and outputs for doing a task in R.
> vignette("Frank2018")
R -rank et a . (2018): Great ng X
-------
oEPA
United States
Environmental Protection
Agency
O
O
Vignettes in R
A vignette is R terminology for an example or walk-through that provides the code and outputs for doing a task in R.
> vignette("Frank2 018")
ft -tank et a . (2018): Great ng x
C CD 127.0.0.1:1G590/lib j
H Frank et al (2018): Creaong Fig. X
f- "> C © 127.0.0.1:10590/libi
Apps © Absence Request ~ Trawl •?: Apps Q Absence Request f Travi
Fran
(IVIV
John F. W
Septemb*
from "Defini
Christopher
Shafer
Toxicology
https:/7doi.o
Abstrac
Measuring e
screening la
interneuron;
Load the
knitr: :op1l
library(gc
## gdata:
## gdata:
##
## Attach:
## The fol
##
## noti
'fanlc et ai. (2018); Creat ng Fc. X ¦+•
-> C © 127.0.0.1:10590/li brary/httk/doc/Frank2018.html
::: Apps © Absence Request f Traw«l Request For... REMD-HTTK • Confluence O B tbuckel * CompTox Dashboard EHP ^ Change Password
chemical. The identity ("perfect predictor") line is indicated by a solid black line, while the dashed lines indicate
ten-fold above and below perfect prediction. Because all in vitro treatments were exposed for the same amount
of time, the relationship between nominal in vitro concentration and time-integrated concentration is a constant.
Fig.AUC <- ggplot(data=chem.table) +
geom_segment(color="grey",aes(x=AUC,y=Lower.95..ci,xend=AUC,yend=Higher.95..CI))+
# geom_point(aes(x=AUC,y=CriticaL.concentration,coLor="ChemicaL"))+
geom_text(aes(x=AUC,y=Critical.concentration,label=Compound.abbrev,color=Chemical)) +
scale_y_logl0(label=scientific_10,limits=c(10A-7,100)) +
scale_x_logl0(label=scientific_10,limits=c(10A-7,100)) +
annotation_logticks() +
geom_abline(slope=l, intercept=0) +
geom_abline(slope=l, intercepts,linetype="dashed") +
geom_abline(slope=l, intercept=-l,linetype="dashed") +
xlab(expression(paste(italic("In vivo")," AUC estimated with HTTK (uM*day)"))) +
ylab(expression(paste(italic("In vitro")," predicted Critical Cone. (uM)"))) +
scale_color_brewer(palette="Set2") +
theme_bw() +
theme(legend.position="bottom")
print(Fig.AUC)
~ O 0 O i
41 of 71
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
O
O
Vignettes in R
A vignette is R terminology for an example or walk-through that provides the code and outputs for doing a task in R.
> vignette("Frank2018")
ft -tank et a . (2018): Creat r>g ;»g X
j
annota)
geom_a
geom_a
geom_«
xlab(«
ylab(e
scale
theme
theme (I
print(Fig.
-a L
¦ IJ I'll "I I I ' Inn) I I I ImJ I I 111 ml I I 1111 n| I i I linil i I i IihJ I I i limj I t 111 ml
nical
10"4 10~2 10
In vivo AUC estimated with HTTK (uM'day)
Chlordiazepoxide* a Chlorpyrifos* Diazepam
Chlorpromazine Di-(2-ethylhexyl) phthalate Dieldrin
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
O
O
Vignettes in R
CRAN - Package httk X +
O i cran.r-projectorg/web/packages/httk/index.html
!" Apps Absence Request f Trave! Request For... 5" REMD
httk: High-Throughput Toxicokinetics
-ax
Q. ~ O 0 o I & :
'-HTTK \S. Confluence o Bitbucket -.£¦ CompTox Dashboard EHP 0 Change Password
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 't and measurement limitations. Calibrated methods are included
for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 ). These functions and data provide a set of tools for in vitro-in vivo extrapolation ("TVIVE") 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:
Depends:
Imports:
Suggests:
Published:
Author:
Maintainer:
BugReports:
License:
URL:
Needs Comp ilation:
Citation:
Materials:
CRAN checks:
Downloads:
Reference manual:
Vignettes:
2.0.1
R(> 2.10)
deSolve, msm. data.table, survey, mvtnorm. truncnorm. stats, graphics, utils, magrittr. purrr. methods
ggplot2. knitr. miaik&owii. R.rsp. GGallv. gpiots. scales. EnvStats. MASS. RColorBrewer. TeachmsDemcs. classlnt. ks. strinsr. reshape. reshace2. gdata, viridis. CeiisRegMod. gmodels, colorspace. ccwplot.
ggrepeL dplvr. forcats. saatr. stools. sridExtra
2020-03-02
JohnWambaugh [aut, ere], Robert Pearce [aut], Caroline Ring [aut], Greg Honda [aut], Mark Sfeir [aut], Matt Linakis [aut], Jimena Davis [ctb], James Sluka [ctb], Nisha Sipes
[ctb], Barbara Wetmore [ctb], Woodrow Setzer [ctb]
John Wambaugh
https:' gitkub .c om LT SEPA C ompTox-Exp oCast-httk
GPL-3"
https: vwwwepa.gov chemical-research rapid-chemical-exp0sure-and-dose-research
yes
httk citation info
NEWS
httk results
https://CRAN.R-proiect.orq/packaae=httk
httk.cdf
Frank eta., r 2 Q1S :¦: Creating I VIVE Figure fFig. 6>
Honda et al. i'2019'i: Updated Armitage et al. i2Q14"> Model
Lmakis et al. (Submitted): Analysis and Figure Generation
Pearce et al. i'2017'i: Creating Partition Coefficient Evaluation Plots
Vignettes are also available from the
CRAN web-page and help (package="httk" )
-------
oEPA
United States
Environmental Protection
Agency
Exhibition: The Advent of the Artist'.
For its fifth season, the Louvre's Petite
Galerie—a space dedicated to art and
cultural education— is holding an
exhibition titled The Advent of the Artist'
Discover artworks from Delacroix,
Rembrandt or Tintoretto.
For the Kids at Home
Remains of the Louvre's Moat
The Louvre was originally a fortress built by the French
king Philippe Auguste. It was intended to reinforce the
defenses that the king had ordered to be built in 1190 to
protect Paris from attack via the Seine. Today, visitors
can walk around the original perimeter moat and view
the piers that supported the drawbridge.
Lcr^ni
& i
https://www.louvre.fr/en/visites-en-liqne
Virtual Tours of the Louvre
Egyptian Antiquities
Collections from the Pharaonic period are
displayed on the east side of the Sully
wing, on the ground floor and 1st floor.
Office of Research and Development
Galerie d'Apollon
The Galerie d'Apollon, situated above the Petite
Galerie, was destroyed by fire in 1661 and rebuilt by Le
Vau. The ceiling, begun by Le Brun, is a homage to the
Sun King, Louis XIV. The central panel, Apollo Slaying
the Serpent Python, is by Delacroix (1851). The gallery
was recently restored.
-------
A E PA [i2£Msfl[p[Uig TK Statistics
United States
Environmental Protection _ . . . _ . . _ . . r __ . . , . r . x
Agency Calculate the mean, AUC, and peak concentrations for a 28 day study (default)
> calc_stats(chem.cas="34256-82-1")
Human plasma concentrations returned in uM units.
AUC is area under plasma concentration curve in uM * days units with Rblood2plasma =
$AUC
[1] 3.541
$peak
[1] 0.8966
$mean
[1] 0.1265
#Oops, I meant to do a rat, not a human study:
> calc_stats(chem.cas=M34256-82-l", species="rat")
Rat plasma concentrations returned in uM units.
AUC is area under plasma concentration curve in uM * days units with Rblood2plasma = .
$AUC
[1] 1.287
$peak
[1] 0.4182
$mean
[1] 0.04596
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
Getting Help
Within R: type "help(httk)
Office of R€
~ I ~ Q
R Documentation
Q web2etsevierproofcerr X Q web2.elsev»erproofcer.T X r R: High-Throughput To • X
-) C O © 127.0.0.1:24930/library/httk/html/hrtlc-package.html
Apps DSSto* " Confluence • JESEE A EHP s Battelle Box
httk-package {htrkj
High-Throughput Toxicokinetics httkt High-Throughput Toxicokinetics
Description
Functions and data tables for sunulation and statistical analysis of chemical toxicokinetics ("TK") using data obtained from relatively high
throughput, in vitro studies Both phvsiologically-based ("PBTK") and empuical (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-basecl)
code A Monte Carlo sampler is included for simulating biological variability and measurement limitations. Functions are also provided for
exporting "PBTK" models to "SBML" and "JARNAC" for use with other simulation software These functions and data provide a set of
tools for in vitro-in vivo extrapolation ("IVTVE") of high throughput screening data (e g ToxC'ast) to real-world exposures via reverse
dosimetry (also known as "RTK") Functions and data tables for simulation and statistical analysis of chemical toxicokinetics ("TK",) using
data obtained from relatively high throughput, ui vitro smdies Both physiologically-based ("PBTK") and empirical (e.g.. one compartment)
"TK" models can be parameterized for several hundred chemicals and multiple species. These models tire solved efficiently, often using
compiled (C-based) code A Monte Carlo sampler is mcluded for simulating biological variability and measurement limitations Functions
are also provided for exporting "PBTK models to "SBML" and "JARNAC" for use with other simulation software. These functions and
data provide a set of tools foi m vitro-m vivo extiapolatiou ("IVTVE") of high throughput screening data (e.g.. ToxCast) to real-world
exposures via reverse dosimetry (also known as "RTK")
Author(s)
Mauitamer John Wambaugh - wambaugh john ft epa gov-
Robert Pearce 4 pearce robert S epa gov
Caroline Ring
Nisha Sipes
Junena Davis
R Woodrow Setzer
See Also
Useful links
https cfpub epa gov si si_public_record_report cfhrdirEntryId=311211
https www epa gov chemical-research rapid-chemical-exposure-and-dose-research
https doi org 10 1093 toxsci kfvl71
https doi org 10 1093 toxsci kfvllS
[Package hrtk version 1 6 Index]
-------
oEPA
United States
Environmental Protection
Agency
Getting Help
Within R: type "help(httk)
Office of R€
Q web2etsevierproofcersT X Q w
"> C O © 127.0.0.1:24930
••• Apps ^ DSSto* • Confluence
httk-package {httk}
High-Thro
Description
Functions and data tables for simulat
throughput, ui vitro studies Both ph.
parameterized for several hundred cl
code A Monte Carlo sampler is incli
exporting "PBTK" models to "SBMI
tools for in vitro-m vivo extrapolatio
dosimetry (also known as "RTK") F
data obtained from relatively high th
"TK" models can be parameterized f
compiled (C-based) code A Monte (
are also provided for exporting "PB1
data provide a set of tools for in vitr
exposures via reverse dosimetry (al<
Author(s)
Mamtatner John Wambaugh - vvanil
Robert Pearce 4 pearce robert S epa g
C aroline Ring
Nisha Sipes
Jimena Da\is
R Woodrow Setzer
See Also
Useful links
https cfpub epa gov si si_public_re
Imps www.epa gov chenncal-reseai
https doi org 10 1093 toxsci kf\* 171
https doi org 10 1093 toxsci kfv 11S
Q web2.elsev>erproofcen- X Q *veb2.eisevierproofcen- x R R: High-Throughput To X
<- C O I© 127.0.0.1:24930/library/httk/html/00lndex.html
Apps ^ DSStox • Confluence J£SEE A &HP Q Battelle Box
High-Throughput Toxicokinetics
~ a
Documentation for package 'httk* version 1.6
DESCRIPTION file
User guides, package vignettes and other documentation
• Package NEWS
link-package
httkpop - pac ka g c
age dist smooth
age draw smooth
available rblood2plasma
blood mass correct
blood weight
bmiagc
bodv surface area
bone mass age
bram mass
calc analytic ess
calc hepatic clearance
ca Ionization
calc mc ess
calc_mc_oral_equiv
Help Pages
ABCDEGHIJKLMNQPRSIW
High-Tliroughput Toxicokinetics link High-Throughput Toxicokinetics
httkpop Yirnial population generator for HTTK
-- A --
Add a table of chemical information for use m making httk predictions.
Smoothed age distributions by race and gender
Draws ages from a smoothed distribution for a given gender race combination
Find the best available ratio of the blood to plasma concentration constant.
-- B --
Find average blood masses by age
Predict blood mass
CDC BMI-for-age charts
Predict body surface area
Predict bone mass
Predict bram mass
-- C --
Calculate the analytic steady state concentration.
Fmd the steady state concentration and the day it is reached
Calculate the elimination rate for a one compartment model
Calculate the hepatic clearance
Calculate the ionization
Fmd the monte carlo steady state concentration
Calculate Monte Carlo Oral Equivalent Dose
You can go straight
to the index with
help(pac ka ge ="httk")
-------
oEPA
United States
Environmental Protection
Agency
Getting Help
Within R: type "help(httk)
Office of R€
Q web2etsevierproofcer.t X Q
-> C O (D 127.0.0.1:24930
••• Apps ^ DSSto* • Confluence
httk-package {httk}
High-Thro
Description
Functions and data tables for simulat
throughput, ui vitro studies Both ph
parameterized for several hundred cl
code A Monte Carlo sampler is incli
exporting "PBTK" models to "SBMI
tools for in vitro-m vivo extrapolatio
dosimetry (also known as "RTK") F
data obtamed from relatively high th
"TK" models can be parameterized f
compiled (C-based) code A Monte (
are also provided for exporting "PB1
data provide a set of tools for in vitrc
exposures via reverse dosimetry (als
Author(s)
Maintainer John Wambaugh - vvanil
Robert Pearce 4 pearce robertrtepa g
Carol me Ring
Nisha Sipes
Jimena Da\is
R Woodrow Setzer
See Also
Useful links
https cfpub epa gov si si_public_re
https www.epa gov chemical-reseai
https doi org 10 1093 toxsci kf\* 171
https doi org 10 1093 toxsci kfv 11S
0 wefc>2.elsev»erproofcer*
C O I® 127.
Apps ^ DSStox • Cc
DESCRIPTION fil<
User guides, packai
• Package NEWS
link-package
httkpop - pac ka g c
age dist smooth
age draw smooth
available rblood2plasma
blood mass correct
b|ood_\vetg|it
bnnage
bodv surface area
bone mass age
bram mass
calc analytic ess
calccss
rnlc_^liminaiigii_iatc
c a lc hepatic c learanc e
caloomzation
calc mc ess
calc_mc_oral_equiv
- ~ X
Q web2elcevierproofcent' X Q web2 elsevierproofcer- X R R: Vignettes and other X
O O CD 127.0.0.1 24930/library/httk/doc/mdex.html
Apps £ DSStox • Confluence JESEE A EHP Q Battelle Box
Vignettes and other documentation
-------
oEPA
United States
Environmental Protection
Agency
Getting Help
Within R: type "help(httk)
Office of R€
Q web2etsevierproofcen- X Q w
-> C O (D 127.0.0.1:24930
••• Apps ^ DSSto* • Confluence
httk-package {httk}
High-Thro
Description
Functions and data tables for simulat
throughput, ui vitro studies Both ph
parameterized for several hundred cl
code A Monte Carlo sampler is incli
exporting "PBTK" models to "SBMI
tools for in vitro-m vivo extrapolatio
dosimetry (also known as "RTK") F
data obtained from relatively high th
"TK" models can be parameterized f
compiled (C-based) code A Monte (
are also provided for exporting "PB1
data provide a set of tools for in vitrc
exposures via reverse dosimetry (als
Author(s)
Maintainer John Wambaugh - vvanil
Robert Pearce 4 pearce robertrtepa g
Carol me Ring
Nisha Sipes
Jimena Da\is
R Woodrow Setzer
See Also
Useful links
https cfpub epa gov si si_pubhc_re
https www.epa gov chemical-reseai
https doi org 10 1093'toxsci kfvl 71
https doi org 10 1093 toxsci kfv 11S
0 web2.elsev»erproofcenr
C O I® 127.
Apps DSStox • Cc
DESCRIPTION fil<
User guides, packa
Package NEWS.
link-package
httkpop - pac ka g c
age dist smooth
age draw smooth
available rblood2plasma
blood mass correct
b|ood_\vetg|it
bnnage
bodv surface area
bone mass age
bram mass
calc analytic css
calccss
rnlc_^liminaiigii_iatc
c a lc hepatic c learanc e
caloomzation
calc mc ess
calcmcoralequiv
Q web2 elsevierproofcent X Q
C O ® 127.0.0.1:249;
Apps DSStox * Confluence
Ln
httk: supplemental vignette glob
httk supplemental vignette
httk supplemental vignette heigli
link supplem enta l_vi gnetre
httk. .supplemental
link supplemental vignette sen
httk vigner
httk vignetteC
httk-
httk. \ ii
httk vignene05b
hnk.vi:
link vignette 0
Q web2 eisevirfproofcen- X Q web2.ei5ev'erproofcenT X R AER plotting
CO© 127.0.0.1:24930/iibrary/httk/doc/vignette06_aerplotting. html
Apps ^ DSStox * Confluence JESEE A EHP Q Batteile Box
~ a o
AER plotting
Caroline Ring
2017-06-08
This vignette contains the code necessary to create the AER (and OED and exposure) heatmaps contained in
the paper
First, let's load some useful packages
library("data.table')
library('gplots')
#>
*> Attaching package: 'gpLots'
#> The foLLwing object is masked from 'package:stats':
#>
*> Louess
Library( ggplot2')
library*'httk*)
The vignette about model evaluations for subpopulations produced data files for each subpopulation
containing Css percentiles for each chemical in the HTTK data set As described in the paper, for each
chemical, an oral equivalent dose (OED) can be computed using the 95th percentile Css and a ToxCast AC50.
The OED is an estimate of the dose that would induce bioactivity Then, this OED can be compared to an
estimate of exposure for the same chemical The ratio of OED to exposure is called the activity-exposure ratio,
or AER if the AER is 1 or less then exposure to this chemical may be high enough to induce bioactivity if the
AER is much more than 1 then there probably isn't enough exposure to this chemical to cause bioactivity The
AER is thus an estimate of risk
Computing OEDs
The first step is to read in the Css percentile data. Wfe'll go ahead and do this for all 10 subpopulations.
JtSet sone basic parameters far wfttc#! data set to use
poormetab <- TRUE
f up.censor <- TRUE
model <• '3compartmentss'
all the subpopulations
ExpoCast.groups <- c('Total',
Age.6.11*,
Age.12.19',
•Age.20.65',
Age.GT65',
*8MZgt3r«
'BMIle38'.
-------
50 of 71
oEPA
United States
Environmental Protection
Agency
Getting Help
Within R: type "help(httk)
ease also feel free to email me at wambaugh.iohn@epa.gov
hrtk-package [httkj
Office of Re
Useful links
htTps cfpub epa go1
htrps www. epa go\
Imps doiorglOlO
Imps doi org 10 10
and exposure) heatmaps co
Main tamer John \Y
Robert Pearce pear
iroline Ring
Nisha Sipes
Jiniena Davis
R Woodrow Setzer
data files for each subpopulation
As described in the paper for each
percentile Css and a ToxCast AC50
OED can be compared to an
is called the activity-exposure ratio
be high enough to Induce bioactivlty If the
ire to this chemical to cause bioactivlty The
and do this for all 10 subpopulations
Functions and data t
throughput, in ntio
parameterized for se
code A Monte Car U
exporting "PBTK" n
tools for in vitro-in *,
dosimetry (also
data obtained from r
'TK" models can be
compiled (C-based)
are also provided fbi
data provide a set of
exposuies via revets
-------
vvEPA
United States
Environmental Protection
Agency
A General Physiologically-based Toxicokinetic
(PBTK) Model
•cardiac
•GFR
kidney
metab
liver
rest
Gut Lumen
Gut Blood
Kidney Tissue
Kidney Blood
Rest of Body
Body Blood
Liver Tissue
Liver Blood
Lung Tissue
Inhaled Gas
Lung Blood
u
httk" includes a generic PBTK model
Some tissues (for example, arterial blood) are simple compartments,
while others (for example, kidney) are compound compartments
consisting of separate blood and tissue sections with constant
partitioning (that is, tissue specific partition coefficients)
Some specific tissues (lung, kidney, gut, and liver) are modeled
explicitly, others (for example, fat, brain, bones) are lumped into the
''Rest of Body" compartment.
The only ways chemicals "leave" the body are through metabolism
(change into a metabolite) in the liver or excretion by glomerular
filtration into the proximal tubules of the kidney (which filter into the
lumen of the kidney).
51 of 71
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
Standard httk 2.0.1 PBTK Model
Lung Tissue
Lung Blood 0, jiun»r
Gut Tissue
Gut Blood
Ar-
CL
Liver Tissue
Liver Blood
t
¦metabolism
Kidney Tissue
Tissue Blood
Qiiv,
ilMa
>
3
Qgf
Rest-of-Body
Rest-of-Bodv Blood
¦^richly
New HT-PBTK Models
We are working to augment the basic HT-PBPTK model with
new PBTK models
¦ For example, inhalation PBTK will allow for calculation of
"inhalation equivalent doses" instead of oral equivalents
Each model will be released publicly upon peer-reviewed
publication
Pre-publication models can be shared under a material transfer
agreement (MTA)
¦ We assume there will be coding errors and over-simplifications,
so each publication involves curation of evaluation data from
the scientific literature and through statistical analysis
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency n ,
! Inhaled Air j Exhaled Breath I
L j
4alv
Mucous
Alveolar Space
¦'¦alv
Lung Blood
¦cardiac
Lung Tissue
¦gut
iLk
gutabs
metabolism
(MM Elim)
¦liver
¦rest
¦kidney
Gut Lumen
Gut Tissue
Gut Blood
Liver Tissue
Liver Blood
Body Tissue
Body Blood
Kidney Tissue
Kidney Blood
Office of Research and Development
Generic Gas Inhalation Model
Inhalation is an important route of exposure, particularly for
occupational settings
''Development and Evaluation of a High Throughput
Inhalation Model for Organic Chemicals'7 by Linakis et al. was
just accepted at Journal of Exposure Science and
Environmental Epidemiology
The structure of the inhalation model was developed from
two previously published physiologically-based models from
Jongeneelen etal. (2011) and Clewell etal. (2001)
The model can be parameterized with chemical-specific in
vitro data from the HTTK package for 917 chemicals in human
and 181 chemicals in rat
Model was made publicly available with the release of httk
v2.0.0 in February 2020
-------
oEPA
United States
Environmental Protection
Agency
Developing Models with the CvT Database
Access to in vivo concentration vs. time data
made it easier to identify coding and other
modeling errors
142 exposure scenarios across 41 volatile
organic chemicals were modeled and compared
to published in vivo data for humans and rat
Overall RMSE was 0.69, R2 was 0.54 for full
concentration time-course across all chemicals
and both species
2.5-
cn
c
o
4—'
ta
k-
c
o
o
o 0.01
o
T3
a>
>
w
0)
M
A
O
O)
o
_l
-2.5j
-5.0-
Office of Research and Development
Regression slope: 0.59
Regression RA2:0.54
Recession RMSE: 0 69
RMSE (vs. Identity): 0.87
% Missing:0 93%
-5 0
Log{Simulated Concentrations)
Linakis et al. (accepted)
Species
— Overall
— Human
— Rat
-------
Developing
Environmental Protection
Agency
Access to in vivo concentration vs. time data
made it easier to identify coding and other
modeling errors
142 exposure scenarios across 41 volatile
organic chemicals were modeled and compared
to published in vivo data for humans and rat
Overall RMSE was 0.69, R2 was 0.54 for full
concentration time-course across all chemicals
and both species
R2 was 0.69 for predicting peak concentration
Office of Research and Development
Pyrene Rat BL
Pyrene Rat BL t
Models with the CvT Database
0 2 4
Log(Simulated Max Concentration)
Linakis et al. (accepted)
Species
— Overall
— Human
— Rat
• * t ^FuranRatBL
f * * 2H-Perfluoropropane Human VBL
* \ *Furan Rat BL
• \
* s2H-Perfluoropropane Human VBL
\ "Furan Rat BL
i 2H-Perfluoropropane Human VBL
2H-Perfluoropropane Human VBL
Regression slope: 0.81
Regression RA2 0.69
Fiegression RMSE: D_45
RMSE (vs. Identity) 0.5
-------
Developing
Environmental Protection
Agency
Access to in vivo concentration vs. time data
made it easier to identify coding and other
modeling errors
142 exposure scenarios across 41 volatile
organic chemicals were modeled and compared 6
to published in vivo data for humans and rat
Overall RMSE was 0.69, R2 was 0.54 for full a,
'
Office of Research and Development
Models with the CvT Database
Pyrene Rat BL
Pyrene Rat BL
Tetrahydrofuran Human EB
•Decane Rat BL
*-2H-Perfluoropropane Human VBL
^.2H-Perfluoropropane Human VBL
*~Furan Rat BL
* 2H-Perfluoropropane Human VBL
-2H-Perfluoropropane Human VBL
Regression slope: 0.97
¦ Furan Rat BL
Regression RA2 0.79
Regression RMSE: 0.49
RMSE (vs Identity): 0.55
Log(Simulated AUC)
Linakis et al. (accepted)
-------
oEPA
United States
Environmental Protection
Agency
Developing Models with the CvT Database
Access to in vivo concentration vs. time data
made it easier to identify coding and other
modeling errors
Access to in vivo concentration vs. time data
also made it easier to find fault with specific
data sets
Correct
Used 4h
exposure instead
of 2h
Used mg/m3
dose units
instead of ppm
CO
LU
-a
a>
jo
3
E
GO
o
O
1,3-Butadiene (Human, 5ppmfor2h in EB)
7.5
E
Cl
a.
.1 5.0
CO
k—
c
0J
o
2.5
0.0
/
/
• •
Office of Research and Development
Figure
2 3 4 5
Time (h)
from Matt Linakis (AFRL)
-------
oEPA
United States
Environmental Protection
Agency
Developing Models with the CvT Database
Access to in vivo concentration vs. time data
made it easier to identify coding and other
modeling errors
Access to in vivo concentration vs. time data
also made it easier to find fault with specific
data sets
to
c
o
ro
+¦»
£
d)
O
c
o
o
¦o
o
>
0)
JO
O
O)
o
Species
— Overall
— Human
— Rat
Units on axis in
paper figure
caption were
wrong
Isopropanol Rat VBL
2,2-Dichloro-1,1,1-trifluoroethane Rat VBL
2,2-Dichloro-1,1,1 -trifluoroethane Rat VBL
• Carbon t
• Carbon tetrachloride Rj
Office of Research and Development
Methyl ethyl ketone Hoftjly V
\ ' .'"v
Kr*
• Methjhol Rat l?fr «•
Tetrachloroethylene HuAan EB •
• Tetrachloroatff l^p» Hurgay
• 2,»-B
Tetrahydrofuran Hurrtfe
y •* •«
VhV/.'h
•••'h'*' • ••Fura^?
.• ?> • f
at bl
• 2H-
ronane Human VE
n-Hexane Rat BL
Trich oroethv ene R
Trichlor
Trichloro
V •ujcf* •• • •• #
• Trichloroethfl^ffirRjl 0»a
-------
oEPA
United States
Environmental Protection
Agency
Using the PBPK Solver
> solve pbtk(chem.name="bisphenol a", plots=TRUE)
Human amounts returned in umol and concentration retu
AUC is area under plasma concentration in uM * days u
Rblood2plasma = 0.79.
time Agutlumen Cgut Oliver Cven
1 0.00000 3.066e+02: 0.00000 0.000e+00 0.000e+00 C
2 0.00001 3.065e + 02 0.14490 4.420e-05 5.000e-09 C
3 0.01042 1.778e + 02: 71.93000 2.389e+01 2 . 896e-01
4 0.02083 1.031e+02 72.91000 4.930e+01 6.929e-01
5 0.03125 5.978e + 01 59.22000 5.922e+01 9.241e-01
6 0.04167 3.4 66e + 01 45.55000 5.813e+01 9.967e-01
7 0.05208 2. 010e-i-01 34.87000 5.188e+01 9.783e-01
8 0.06250 1.165e+01 27.10000 4.416e+01 9.207e-01
9 0.07292 6.757e+00 21.62000 3.683e+01 8.536e-01
10 0.08333 3.918e+00 17.79000 3.061e+01 7.910e-01
11 0.09375 2.2 72e+00 15.12000 2.566e+01 7.380e-01
12 0.10420 1.317e+00 13.28000 2.186e+01 6.955e-01
13 0.11460 7.638e-01 11.99000 1.903e+01 6.625e-01
14 0.12500 4.429e-01 11.10000 1.694e+01 6.372e-01
15 0.13540 2.568e-01 10.47000 1.543e+01 6.179e-01
JL
Office of Research and Development
Agutlumen
time
Clung
tr*
Ckidney
CD
O
o
o
Cart
time
Cgut
time
Crest
CD
O
o
o
o
~
CO
o
p
-------
A E PA [i2£A[Ml[p[Uig Multiple Ways to Use Functions
Environmental Protection
A9ency By chemical name:
> calc_analytic_css(chem.name="bisphenol a", model="pbtk")
Plasma concentration returned in uM units.
[1] 1.173
By CAS number:
> calc_analytic_css(chem.cas="80-05-7", model="pbtk")
Plasma concentration returned in uM units.
[1] 1.173
You can change the parameters (for example, compromised renal filtration):
> p <- parameter!ze_pbtk(chem.cas="80-05-7")
> p$Qgfrc <- p$Qgfrc/10
> calc_analytic_css(parameters=p, model="pbtk")
Plasma concentration returned in uM units.
[1] 1.197
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
Standard httk 1.10.0 PBTK Model
Lung Tissue
Lung Blood Q, jiiiu»i
Gut Tissue
Gut Blood
Ar-
CL
Liver Tissue
Liver Blood
t
¦metabolism
Kidney Tissue
Tissue Blood
Qiiv,
ilMa
Qgf
Rest-of-Body
Rest-of-Bodv Blood
¦richly
Non-Exposed Skin Tissue
Non-Exposed Skin Blood
Exposed Skin Blood
Exposed Skin Tissue
Media
Dermal Exposure Route
EPA, Unilever
61 of 71
Office of Research and Development
New HT-PBTK Models
-------
vvEPA
United States
Environmental Protection
Agency
Inhaled air
Gas Inhalation
Exposure Route
(Linakis et a I., 2020)
Standard httk 1.10.0 PBTK Model
Lung Tissue
Lung Blood 0> ,i.ii.i»l
Gut Tissue
Gut Blood
CL,
Liver Tissue
Liver Blood
¦<@> t
¦metabolism
Kidney Tissue
Tissue Blood
Qi™
ikida
Rest-of-Body
Rest-of-Body Blood
¦richly
^^je^usecT
Non-Exposed Skin Tissue
Non-Exposed Skin Blood
Exposed Skin Blood
Exposed Skin Tissue
Media
Dermal Exposure Route
EPA, Unilever
¦*!
Lung Arterial Blood
Gut Tissue
Gut Blood
CL,
Liver Tissue
Liver Blood
¦metabolism
0<5F
Kidney Tissue
Tissue Blood
Rest-of-Body
Rest-of-Body Blood
Lung Tissue
Lung Blood
62 of 71
Office of Research and Development
New HT-PBTK Models
¦^lung
Inhaled Aerosol
Aerosol Inhalation
Exposure Route
(with APEX model)
EPA, AFRL
Gut Lumen
¦metabolism
'liver
Gut Tissue
Gut Blood
Kidney Tissue
Liver Tissue
Liver Blood
Rest-of-Body Blood
Rest-of-Body
Tissue Blood
Lung Tissue
Lung Blood
-------
oEPA
United States
Environmental Protection
Agency
Inhaled air
Gas Inhalation
Exposure Route
(Linakis et a I., 2020)
Standard httk 1.10.0 PBTK Model
Lung Tissue
Lung Blood 0> ,i.ii.i»l
Gut Tissue
Gut Blood
CL,
Liver Tissue
Liver Blood
*=
% t
metabolism
Kidney Tissue
Tissue Blood
Oliver
Rest-of-Body
Rest-of-Body Blood
¦richly
Non-Exposed Skin Tissue
Non-Exposed Skin Blood
Exposed Skin Blood
Exposed Skin Tissue
Media
Dermal Exposure Route
EPA, Unilever
¦*!
Lung Arterial Blood
Gut Tissue
Gut Blood
CL,
Liver Tissue
Liver Blood
¦metabolism
0<5F
Kidney Tissue
Tissue Blood
Rest-of-Body
Rest-of-Body Blood
Lung Tissue
Lung Blood
+o—>
Left Ventricle:
AfCh Vs
,iOLJkU
Left A
S>
New HT-PBTK Models
_2a
4 ^wit
¦®gut
Oliver
^kidne^
¦^richly
¦^¦lung
Aerosol Inhalation
Exposure Route
(with APEX model)
EPA, AFRL
Human Gestational Model
EPA, FDA
Venous Blood
> H
"s. =¦•<
2.
G)
c
H 3
= CD
Arterial Blood
Office of Research and Development
Fetus
I Inhaled Aerosol ~L
| Gut Limien |
Gut Tissue
Gut Blood
CL,
Liver Tissue
Liver Blood
¦metabolism
0<3F
Kidney Tissue
Tissue Blood
Rest-of-Body
Lung Tissue
Lung Blood
Venous Blood
Arterial Blood
Mother
n
Oliver
Rest-of-Body Blood
^richly
4 bbpitbbb
-------
oEPA
HTTK Limitations
United States
Environmental Protection
Agency
Oral absorption
¦ 100% assumed, but may be very different
¦ In silico models not necessarily appropriate for environmental chemicals
¦ Honda et al. (in preparation) developing QSAR using new in vitro data for ToxCast Chemicals
64 of 71
Office of Research and Development
-------
oEPA
HTTK Limitations
United States
Environmental Protection
Agency
Oral absorption
Hepatic Clearance (CLjnt)
¦ Not isozyme-specific (Isozyme-specific metabolism assays not HT)
¦ Ten donor pool in suspension for 2-4 h misses variability and low turnover compounds
¦ Isozyme abundances and activity: varies with age, ethnicity (at least) (Yasuda et al. 2008, Howgate
et al. 2006, Johnson et al. 2006)
¦ Parent chemical depletion only
¦ In silico predictions of isozyme-specific metabolism? Not easy!
¦ Though ADMET Predictor can do this for some isozymes, training data is mostly for
pharmaceuticals
65 of 71
Office of Research and Development
-------
oEPA
HTTK Limitations
United States
Environmental Protection
Agency
Oral absorption
Hepatic Clearance (CLjnt)
Plasma binding assay (Fup)
¦ Plasma protein concentration variability (Johnson et al. 2006, Israili et al. 2001)
¦ Albumin or AAG binding?(Routledge 1986)
66 of 71
Office of Research and Development
-------
oEPA
HTTK Limitations
United States
Environmental Protection
Agency
Oral absorption
Hepatic Clearance (CLjnt)
Plasma binding assay (Fup)
Analytical chemistry
¦ Must be able to develop method for each compound
¦ Working to develop QSARs for other compounds
67 of 71
Office of Research and Development
-------
oEPA
HTTK Limitations
United States
Environmental Protection
Agency
Oral absorption
Hepatic Clearance (CLjnt)
Plasma binding assay (Fup)
Analytical chemistry
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
68 of 71
Office of Research and Development
-------
oEPA
HTTK Limitations
United States
Environmental Protection
Agency
Oral absorption
Hepatic Clearance (CLjnt)
Plasma binding assay (Fup)
Analytical chemistry
Relatively slow throughput (1000 chemicals in last decade)
In vitro methods are less than ideal for volatile chemicals
• Generic inhalation TK IVIVE model has been developed (Linakis et alv submitted)
• QSPR models can be evaluated for volatile chemicals with measured data
69 of 71
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
HTTK Limitations
Oral absorption
Hepatic Clearance (CLjnt)
Plasma binding assay (Fuo)
Analytical chemistry
Relatively slow throughput (1000 chemicals in last decade)
In vitro methods are less than ideal for volatile chemicals
HTTK QSPR Evaluation Team
SimulationsPlus
SCIENCE + SOFTWARE = SUCCESS
Arnot
National Toxicology Program
U.S. Department of Health and Human Services
MILS
Advancing Science, Improving Lives
^£DSX
&
2
<
33
V
PRO^
Z
uj
o
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
Conclusions
HTTK allows dosimetric adjustment of high-throughput
screening (HTS) data across thousands of chemicals with
open source, free, and evaluated software
Comparison predicted concentrations and data is
a valuable approach for evaluation and establishing
confidence
¦ A new database of in vivo concentration vs. time data
has being developed (Sayre et al., in press)
¦ Can characterize model bias and uncertainty
Guided in part by "CvT" database, a generic inhalation
model has been developed (Linakis et al., in press)
mg/kg BW/day
A
Potential hazard
from in vitro
converted to
dose by HTTK
Potential
Exposure Rate
Lower Medium Risk
Risk
Higher
Risk
Office of Research and Development
The views expressed in this presentation are those of the author
and do not necessarily reflect the views or policies of the U.S. EPA
-------
Southwest Research Institute
ExpoCast Proj
(Exposure Forecast
Center for Computational Toxicology and Exposure
Linda Adams
Miyuki Breen*
Alex Chao*
Daniel Dawson*
Mike Devito
Kathie Dionisio
HpF- ' 1 6i... i.
Christopher Eklund
Peter Egeghy
Marina Evans
Chris Grulke
Hongtai Huang*
Mike Hughes
Kristin Isaacs
Ashley Jackson*
Richard Judson
Jen Korol-Bexell*
Anna Kreutz*
Charles Lowe*
Katherine Phillips
A~~ Richard
Sayre*
Mark Sfeir*
Jane Ellen
Simmons
Marci Smeltz*
Jon Sobus
Mike Tornero-Velez
Rusty Thomas
E in Ulrich
Dan Vallero
Barbara Wetmore
John Wambaugh
Antony Williams
Center for
Environmental
Measurement
and Modeling
Hong wan Li
Xiaoyu Liu
Seth Newton
EsBUfcs
is m
cyprotex
Progress for o Stronger Future
Trainees
Collaborators
Arnot Research and Consulting
Jon Arnot
Johnny Westgate
Integrated Laboratory Systems
Kamel Mansouri
Xiaoqing Chang
National Toxicology Program
Steve Ferguson
Nisha Sipes
Ram bo
Harvey Clewell
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
Peter Fantke
ToxStrategies
Caroline Ring
Unilever
Beate Nicol
Cecilie Rendal
ian Sorrell
United States Air Force
HeatherPangburn
Matt Linakis
University of California, Davis
Deborah Bennett
University of Michigan
Olivier Jofliet
University of Texas, Arlington
Hyeong-Moo Shin
-------
oEPA
United States
Environmental Protection
Agency
1 Breyer, Stephen. Breaking the vicious circle: Toward effective risk
regulation. Harvard University Press, 2009
1 Clewell, Harvey J., et al. "Comparison of cancer risk estimates for vinyl
chloride using animal and human data with a PBPK model." Science of
the Total Environment 274.1-3 (2001): 37-66.
1 Cohen, EA Hubal, et al. "Advancing internal exposure and
physiologically-based toxicokinetic modeling for 21st-century risk
assessments." Journal of exposure science & environmental
epidemiology (2018).
1 Collins FS, Gray GM, Bucher JR. Transforming environmental health
protection. Science. 2008;319:906-907. [PMC free article] [PubMed]
1 Egeghy, Peter P., et al. "The exposure data landscape for manufactured
chemicals." Science of the Total Environment 414 (2012): 159-166.
1 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.
1 Frank, Christopher L., et al. "Defining toxicological tipping points in
neuronal network development." Toxicology and applied
pharmacology 354 (2018): 81-93.
1 Honda et al., "Using the concordance of in vitro and in vivo data to
evaluate extrapolation assumptions" PLoS ONE 14.5 (2019): e0217564.
1 Jamei, Masoud, et al. "The Simcyp® population-based ADME
simulator." Expert opinion on drug metabolism & toxicology 5.2 (2009):
211-223.
1 Jongeneelen, Frans, and Wil Ten Berge. "Simulation of urinary
excretion of 1-hydroxypyrene in various scenarios of exposure to
polycyclic aromatic hydrocarbons with a generic, cross-chemical
predictive PBTK-model." International archives of occupational and
environmental health 85.6 (2012): 689-702.
Office of Research and Development
References
1 Judson, Richard, et al. "The toxicity data landscape for environmental
chemicals." Environmental health perspectives 117.5 (2009): 685-695.
1 Kavlock, Robert J., et al. "Accelerating the pace of chemical risk
assessment." Chemical research in toxicology 31.5 (2018): 287-290.
1 Linakis, et al. "Development of a Generalized Inhalation Model for use
with the High-Throughput Toxicokinetics (httk) Package in R", Journal
of Exposure Science and Environmental Epidemiology, in press
1 Lukacova, et al.. "Prediction of modified release pharmacokinetics and
pharmacodynamics from in vitro, immediate release, and intravenous
data." The AAPS journal 11.2 (2009): 323-334.
1 McLanahan, Eva D., et al. "Physiologically based pharmacokinetic
model use in risk assessment—why being published is not enough."
Toxicological Sciences 126.1 (2012): 5-15.
1 National Research Council. (1983). Risk Assessment in the Federal
Government: Managing the Process Working Papers. National
Academies Press.
1 National Research Council. (2007). Toxicity testing in the 21st century:
a vision and a strategy. National Academies Press.
1 National Research Council. Exposure Science in the 21st Century: a
Vision and a Strategy. National Academies Press, 2012.
1 Park, Youngja H., et al. "High-performance metabolic profiling of
plasma from seven mammalian species for simultaneous
environmental chemical surveillance and bioeffect monitoring."
Toxicology 295.1 (2012): 47-55.
1 Pearce, Robert, et al. "httk: R Package for High-Throughput
Toxicokinetics." Journal of Statistical Software, (2017a)
1 Pearce, Robert G., et al. "Evaluation and calibration of high-throughput
predictions of chemical distribution to tissues." Journal of
pharmacokinetics and pharmacodynamics 44.6 (2017b): 549-565.
1 Ring, Caroline L, et al. "Identifying populations sensitive to
environmental chemicals by simulating toxicokinetic variability."
Environment International 106 (2017): 105-118.
1 Rotroff, Daniel M., et al. "Incorporating human dosimetry and exposure
into high-throughput in vitro toxicity screening." Toxicological Sciences
117.2 (2010): 348-358.
1 Sayre, Risa et al., "Database of pharmacokinetic time-series data and
parameters for 144 environmental chemicals", Scientific Data, in press
1 Schmidt, Charles W. "TOX 21: new dimensions of toxicity testing."
Environmental health perspectives 117.8 (2009): A348.
1 Schmitt, Walter. "General approach for the calculation of tissue to
plasma partition coefficients." Toxicology in Vitro 22.2 (2008): 457-467.
1 Shibata, Y., et al. (2002). Prediction of hepatic clearance and availability
by cryopreserved human hepatocytes: an application of serum
incubation method. Drug Metabolism and Disposition, 30(8), 892-896
1 Wambaugh, John F., et al. "Evaluating In Vitro-ln Vivo Extrapolation of
Toxicokinetics." Toxicological Sciences 163.1 (2018): 152-169.
1 Wambaugh, John F., et al. "Assessing Toxicokinetic Uncertainty and
Variability in Risk Prioritization", submitted.
1 Wang, Ying-Hong. "Confidence assessment of the Simcyp time-based
approach and a static mathematical model in predicting clinical drug-
drug interactions for mechanism-based CYP3A inhibitors." Drug
Metabolism and Disposition 38.7 (2010): 1094-1104.
1 Wetmore, Barbara A., et al. "Integration of dosimetry, exposure and
high-throughput screening data in chemical toxicity assessment." Tox.
Sciences (2012)
1 Wetmore, Barbara A., et al. "Relative impact of incorporating
pharmacokinetics on predicting in vivo hazard and mode of action from
high-throughput in vitro toxicity assays." toxicological sciences 132.2
(2013): 327-346.
1 Wetmore, Barbara A., et al. "Incorporating high-throughput exposure
predictions with dosimetry-adjusted in vitro bioactivity to inform
chemical toxicity testing." Toxicological Sciences 148.1 (2015): 121-
136.
1 Waters, Nigel J., et al. "Validation of a rapid equilibrium dialysis
approach for the measurement of plasma protein binding." Journal of
pharmaceutical sciences 97.10 (2008): 4586-4595
------- |