-------
vvEPA
Research Challenge and Need
United States
Environmental Protection
Agency
''The normal development of the fetus, infant, and child can be disrupted by relatively low doses of
certain chemicals. These developmental stages are 'windows of susceptibility' when there is
increased vulnerability to the effects of toxic chemicals." Birnbaum (2010)
Too many chemicals to do traditional approaches of developmental toxicity testing
Need for reliable alternative approaches (that is, NAMs) for
- Hazard: Efficient screening of chemicals for developmental toxicity potential
- Toxicokinetics: Determination of concentration in key tissues as a function of time
- Risk based prioritization for more detailed evaluations
5 of 32
Office of Research and Development
-------
^EEs Assessing Chemical Risk
Environmental Protection
Agency
We wish to link chemical exposure to adverse Exposure
responses
Default for
limited
information
6 of 32
Office of Research and Development
Response
Barton (2005)
-------
oEPA
United States
Environmental Protection
Agency
Assessing Chemical Risk
We wish to link chemical exposure to adverse
responses
Both the window of susceptibility (that is, the timing
of the toxicodynamics) and the toxicokinetics
occurring during that window must be addressed
These analyses involve combining quantitative
descriptions of the tissue dosimetry (that is,
pharmacokinetics), window of susceptibility, and
dose-response behavior within that window
A major challenge for any modeling, but especially life
stage modeling, is how to obtain data sets for model
parameterization, calibration, and evaluation
Exposure
*
Pharmaco- /Toxicokinetics
Fate of molecules/chemicals in body
Considers absorption, distribution,
metabolism, excretion (ADME)
*
Pharmaco- /Toxicodynamics
Effect of molecules/chemicals at
biological target in vivo
Perturbation as adverse/therapeutic
effect, reversible/ irreversible effects
*
Response
Office of Research and Development
Barton (2005)
-------
oEPA
United States
Environmental Protection
Agency
Windows of Susceptibility
Transplacental carcinogenesis
The timing of
exposure matters.
Lowered birth weight
Preterm delivery
J
Congenital malformations
Spontaneous abortions
Subfertility
Menstrual disorders
Solid lines indicate the most
probable timing of exposure for a
particular outcome, dotted lines
indicate less probable but still
possible timing of exposure. Arrows
suggest that a defined cutoff point
for exposure to a specific outcome
is not known.
Pre-conception 0 1
(fertilization)
Office of Research and Development
]—I—I—
Months of gestation
8 9 Neonatal period
(birth)
Selevan et al. (1987)
-------
oEPA
Hazard, Exposure, and Toxicokinetics
United States
Environmental Protection
Agency
Chemical risk to the public health can be
assessed through consideration of hazard,
exposure and toxicokinetics (dose-response)
Most of chemicals have little or no data on
hazard, exposure, and toxicokinetics, see
Judson et ol. (2009),
Egeghy et ol. (2012),
Wetmore et ol. (2015)
Generating data for thousands
of chemicals requires
''new approach
methodologies" (NAMs)
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
xand 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) in
2017
Exposure
9 of 32
Office of Research and Development
NRC (1983)
-------
vvEPA
United States
Environmental Protection
Agency
New Approach Methods for
I "oxicodynamic Windows of Susceptibility
Palmer et al. (2013) and (2017) reported on in vitro biomarker
assays for rapid and targeted screening of chemicals for
developmental toxicity based on changes in cellular metabolism as
early signals
Specifically, the assay determines the in vitro concentration of the
test compound that is associated with developmental toxicity
potential (dTP)
Assays have been shown to have good accuracy,
sensitivity, specificity, and high concordance to
existing in vivo models
Zurlinden et al. (2020) describes incorporation of
these assays into ToxCast screening program
? 2013 Wiley Periodicals, Inc.
Birth Defects Research (Part B) 98:343-363 (2013)
Palmer et al. 2013
Original Article
Establishment and Assessment of a New Human
Embryonic Stem Cell-Based Biomarker Assay for
Developmental Toxicity Screening
Jessica A. Palmer,* Alan M. Smith, Laura A. Egnash, Kevin R. Conard, Paul R. West, Robert E. Burrier,
Elizabeth L.R. Donley, and Fred R. Kirchner
Stemina Biomarker Discovery, Inc., Madison, Wisconsin
ELSEVIER
Contents lists available at ScienceDirect
Reproductive Toxicology
journal homepage: www.elsevier.com/locate/reprotox
A human induced pluripotent stem cell-based in vitro assay predicts
developmental toxicity through a retinoic acid receptor-mediated
pathway for a series of related retinoid analogues
Jessica A. Palmer *, Alan M. Smith, Laura A. Egnash1, Michael R. Colwell,
Elizabeth L.R. Donley, Fred R. Kirchner, Robert E. Burrier
Stemina Biomarker Discovery, Inc., 504 S. Rosa Rd., Madison, Wl 53719, USA
Palmer et al. 2017
10 of 32
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
New Approach Methods for
I oxicodynamic Windows of Susceptibility
Zurlinden et al. (2020) describes incorporation of Palmer et al. (2013, 2017) assays into the
ToxCast screening program
Multi-well Plates
of Chemicals
••• ••••
••• ••••
~••• ••••
•••• •
O G*H • i ' Qnff
tfi
o
Q.
E
re
(A
ro
r*.
ro
IDIIDIIHII
~ ©••••
Q ] tUa* Only
686 records
single conc.
379 records
conc, response
m
TJ
c
3
o
a
E
o
O
in
CO
o
1,114,338
data points
invitrodb
I-- ¦'
data points
, i
1 L^-i
LeYtti
tcpl
Virtual Plates
o
o
o
!' O
m
o
m
@K J
yj J
m
oo
oo
§
oo
o
CD
q0
01
u
a
a
#4
S
w O
N
J
r
T
T
X
i-04 D. 001 0,03 0.
Concentration (uM)
Zurlinden et al. (2020)
11 of 32
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
KNOWN: In vitro Measured Internal Exposure (MM)
associated with Developmental Toxicity
Test Compound #1 Test Compound #n
4
In vitro assay
[e.g., Palmer et al. 2013, 2017]
Exposure Range
M',nTer.itnnop
Exposure Range
M r 1 T
i Tprarnfi.
Exposure Range
Exposure Range
Non Teratogen
Exposure Range
T m-atnrifn
Exposure Range
T pratnnpri
Exposure Range
T pj-atnnpn
Exposure Rang
Teratogen
1.0
0.8S
Ornithine/Cystine Cell
Ratio Viability
Teratogenicity
Potential:
Ornithine/Cystine Ratio
Teratogenicity
Threshold
T eratogenicity
Potential
Cell Viability
JliML
In vitro concentration [|iM]
Identified for developmental
toxicity potential
-Corresponds to internal target
site concentration in vivo (uM)
12 of 32
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
UNKNOWN: In vivo Relevant External Exposure (nrig/kg)
associated with Developmental Toxicity
Specific Research Goal:
What is the level of in vivo external exposure (mg/kg) that
yields the corresponding internal exposure levels (|iM)
that are shown to be associated with developmental
toxicity in vitro?
Essence of In vitro to In vivo
Extrapolation (IVIVE)
¥
*
^-Corresponds to internal target
site concentration in vivo (|iM)
13 of 32
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
V
e
n
0
u
s
b
1
o
0
d
Need a tool that bridges
Internal Exposure 4-4 External Exposure:
Physiologically Based Pharmacokinetic or Toxicokinetic (PBPK or PBTK) Modeling
lung
heart
adipose
Aibrain ¦A
testes
bone
muscle
skin
<-
<¦
<
liver
Elimination/
Metabolism
stomach
| pancreas
¦jg spleen"
a
r
t
e
r
i
a
kidney
Elimination
0 5 10 15 X '& 30 35
Tltffi
45 50 55
Predict target tissue
concentration
0 S 10 15 202SM3S 40 4S 35 3S
"lire
Predict plasma
concentration
Mathematical
description of
what the body
does to the drug
(Pharmacokinetics)
Model comprises
of physiological
parameters and
chemical-specific
parameters
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
Utility of l-ully Parameterized PBPK:
Forward Dosfmetry
External Exposure
*
CD
m
a.
adipose
brain
bone
muscle
stomach
Elimination/
Metabolism
kidney
Eliminalion
Forward Dosimetry
Internal
Exposure
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
Utility of Fully Parameterized PBTK:
Reverse Dosimetry
External Exposure
*
*
CD
m
a.
adipose
brain
bone
muscle
stomach
Elimination/
Metabolism
kidney
Eliminalion
Reverse Dosimetry
Internal
Exposure
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
UNKNOWN: External Exposure (mg/kg)
associated with Developmental Toxicity
Specific Research Goal:
What is the level of in vivo external exposure
(mg/kg) that yields the corresponding internal
exposure levels (pM) that are shown to be
associated with developmental toxicity in vitro?
In vitro to In vivo Extrapolation
using
PBTK Modeling
*
*
^-Corresponds to internal target
site concentration in vivo (pM)
17 of 32
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
High Throughput Toxicokinetics (HTTK)
Most chemicals lack public toxicokinetic-related data (Wetmore et al., 2012)
In vitrotoxicokinetic data + generic toxicokinetic model
= high(er) throughput toxicokinetics
^ v # v ^ v #
p i
£
i 2^ ¦=> ..¦=>
Gut Lumen
Primary
Compartment
si,
Oral Absorption
httk
Office of Research and Development
Metabolism
Renal Clearance
-------
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 J* Defining toxicological tipping pc X <|| CRAN - Package httk X +
C £> ® cran.r-project.org/web/packagGs/httk/indGX.html Q. ~ o H O B i
Apps (§ Confluence DSStox u Chemicals Dashboa... EHP © ORD Travel Request,,. Q Article Request Q Graphics Request £ ChemTrack Q https://cranlogs.r-p... ^ CSSREMDRACT [3 niec_s\y_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 ("IVIVE") of high throughput screening data (e.g.. Tox21. ToxCast) to real-world
exposures via reverse dosimetry (also known as "RTK") (Wetmore et al,. 2015 V
Version: 1.10.1
Depends: R (> 2.10)
Imports: deSolve. msm. data.table, survey, mvtnomi. truncnomi. stats, graphics, utils, magrittr
Suggests: ggplot2. krntr, rmarkdown. R.rsp. GGallv. splots. scales. EnvStats. MASS. RColorBrewer. Teachini
gmodels. colorspace
Published: 2019-09-10
Author: John Wambaugh [aut. ere]. Robert Pearce [aut], Caroline Ring [aut], Greg Honda [aut], Mark Sfeir
Wetmore [ctb], Woodrow Setzer [ctb]
Maintainer: John Wambaugli
BugReports: https: 7aithub.com USEPA CompTox-ExpoCast-httk
License: GPL-3
URL: littps: ATOvy.epa.gov/chemical-researchrapid-chemical-exposiu'e-and-dose-research
NeedsCompilation: yes
Materials: NEWS
CRAN checks: httk results
Downloads:
R package "httk"
downloads 806/month
Reference manual: httk.pdf
Vignettes: Honda et al. (2019V. Updated Armitage et al. (2014^1 Model
Pearce et al, (2017) Creating Partition Coefficient Evaluation Plots
Ring et al. (2017^ Age distributions
Ring et al. ("2017~> Global sensitivity' analysis
Ring et al. C2017) Global sensitivity' analysis plotting
l>...- ^ >1 : yr\i n\ , ^
Open source, transparent, and peer-reviewed
tools and data for high throughput
toxicokinetics (httk)
Available publicly for free statistical software R
Allows in vitro-in vivo extrapolation (IVIVE) and
physiologically-based toxicokinetics (PBTK)
Human-specific data for 944 chemicals and rat-
specific data for 171 chemicals
Described in Pearce et al. (2017a)
-------
oEPA
United States
Environmental Protection
Agency
H" HI K Model Cali bration and
Evaluation
/
Volume of
c
1100 Distribution
£2
Q
"D A
Q> 1
-------
vvEPA
United States
Environmental Protection
Agency
'HTTK' R-Package Extended to Pregnancy
1-Comp
A.
CI,
we 11-stirred '
Primary Compartment
3-Comp
B.
CL
Rest of Hotly
Qliver + Qin
Figure 1: Models (A) 1 compartment, (B) 3compartment
mass-balance, Qrest is defined as the difference betweer
kidney, and gut. Variable names are defined in Table 1.
Pearce et al. 2017a
c.
PBTK
Rest of Body
Body Blood
Gut Tissue
Lung Blood
Liver Tissue
Liver Blood
and (C) pbtk. In order to preserve
Qcardiac and the flow to the liver,
Maternal/Fetal PBTK
PLOSIONE
RESEARCH ARTICLE
Empirical models for anatomical and
physiological changes in a human mother and
fetus during pregnancy and gestation
Dustin F. Kapraun 1 *, John F. Wambaugh©2, R. Woodrow Setzer j2, Richard
S. Judson J2
1 National Center for Environmental Assessment, US Environmental Protection Agency, Research Triangle
Park, North Carolina, United States of America, 2 National Center for Computational Toxicology, US
Environmental Protection Agency, Research Triangle Park, North Carolina, United States of America
I
I
J
Kapraun et al. 2019
21 of 32
Office of Research and Development
-------
SEPA Representative Physiological
Parameter Changes in the Mother
United States
Environmental Protection
Agency
tissue
— Modified Logistic Model
Abduljalil et al. (2012) Model
— • Gaohua et al. (2012) Model
* ¦ ¦ * Luecke et al. (1994) Model
Dallmann et al. (2017) Model
I Curated Data (Abduljalil et al., 2012)
— Cubic Growth Model
Abduljalil et al. (2012) Model
— • Luecke et al. (1994) Model
¦ ¦ ¦ * Dallmann et al. (2017) Model
I Curated Data (Abduljalil et al., 2012)
I
Placenta volume
1 Linear Model
Abduljalil et al. (2012) Model
Gaohua et al. (2012) Model
Luecke et al. (1994) Model
Dallmann et al. (2017) Model
Curated Data (Abduljalil et al., 2012)
— Cubic Model
— — Abduljalil et al. (2012) Model
— • Gaohua et al. (2012) Model
¦ ¦ • * Growth Data (Hytten & Leitch, 1971)
J Curated Data (Abduljalil et al., 2012)
10 15 20 25 30 35 40
Gestational Age (weeks)
800
700
600
3 500
E
| 400
> 300
200
100
0
Maternal plasma volume
10 15 20 25 30 35 40
Gestational Age (weeks)
0 5 10 15 20 25 30 35 40
Gestational Age (weeks)
1Q1_ Adipose tissue mass
0 5 10 15 20 25 30 35 40
Gestational Age (weeks)
¦70
> 3.5
Maternal Body We
500-
450-
j- 400 -
3
CD
15 350-
a.
3
300-
250-
200 H
Maternal cardiac outbut
— Cubic Model
Abduljalil etal. (2012) Model
— ¦ Gaohua et al. (2012) Model
¦ • • • Dallmann et al. (2017) Model
I Curated Data (Abduljalil et al., 2012)
10 15 20 25 30 35
Gestational Age (weeks)
40
100
90
80
70-
60-
50
40
30
Maternal kidney blood flow
— Cubic Model
— — Linear Transition Model
— • Abduljalil et al. (2012) Model
4 • * Dallmann et al. (2017) Model
5 Curated Data (Abduljalil et al., 2012)
10 15 20 25 30
Gestational Age (weeks)
35
40
200
180
160
c 140
120
100
° 80
Maternal glomerular filtratio
i rate
— Quadratic Model
Abduljalil etal. (2012) Model
— ¦ Dallmann et al. (2017) Model
• • • • Pearce et al. (2016) Model
I Curated Data (Abduljalil et al., 2012)
10 15 20 25 30
Gestational Age (weeks)
35
40
50
40
' 30
; 20
10
Proportional-to-Volume Model
Linear Transition Model
Gaohua et al. (2012) Model
Dallmann et al. (2017) Model
Zhang et al. (2017) Model
//
Placental blp
//
10
15 20 25 30
Gestational Age (weeks)
35
40
22 of 32
Office of Research and Development
Kapraun et al. (2019)
-------
SEPA Representative Physiological
Parameter Changes in the Fetus
United States
Environmental Protection
Agency
Quadratic Growth Model
Gompertz Model
Luecke et al. (1994) Model
Zhang et al. (2017) Model
Abduljalil et al. (2018) Model
Curated Data (Abduljalil et al., 2018)
Power Law Model
Gompertz Model
Luecke et al. (1994) Model
Zhang et al. (2017) Model
Abduljalil et al. (2018) Model
Curated Data (Abduljalil et al., 2018)
Gompertz Model
Abduljalil et al. (2012) Model
Wosilait et al. (1992) Model
Dallmann et al. (2017) Model
Curated Data (Abduljalil et al., 2012)
Fetal Volume
Fetal kidney mass
Fetal liver mass
10 15 20 25 30 35 40
Gestational Age (weeks)
Gp<;tatinnal Anp (wppkO
Gestational Age (weeks)
Logistic Model
Kiserud et al, (2000) Model
Zhang etal. (2017) Model
Data (Kiserud et al., 2000)
Logistic Model
Data (Mielke and Benda, 2001)
Logistic Model
Abduljalil et al. (2012) Model
Luecke et al. (1994) Model
Luecke et al. (1994) Adjusted Model
Dallmann et al. (2017) Model
Curated Data (Abduljalil et al., 2012)
Fetal blood flow
through the
ductus arteriosus
Fetal blood flow
through
the placenta
Amniotic fluid
volume i
0 5 10 15 20 25 30 35 40
Gestational Age (weeks)
Gestational Age (weeks)
Gestational Age (weeks)
4000
3500
3000
_ 2500
.§ 2000
400
200
0
23 of 32
Office of Research and Development
Kapraun et al.
(2019)
-------
vvEPA
United States
Environmental Protection
Agency
Generic Gestational FB I K Model
Fetal
Maternal
r-
±
s
a
|
m
?•
T
ft
¦Q
3"
r
S
£
a
f*
§
X
a
ft «
B *
a s
a =
S»
S +-
O-"—
A
Venous Blood
f R
g E
¦i fc
f-
£ fi
0 CL
3 5
* <
1 S
¦Q
I
I
I *
1 1
a a
sr
. j
4
f-
E
a
3
3
f
Arterial Blood
9"
Venous Blood
i. J
i. j
Ti
O
£
S
3
3d
n
a.
8
' 1
i- i
-3
•Q
fi-
Sl
a
3
&
4
B
S-3
5
+
P
•C3
r
<5
8 z
*0
~
o
ir.
rt
Arterial Blood
24 of 32
Office of Research and Development
-------
vvEPA
Maternal/Fetal HTTK Model:
Features
United States
Environmental Protection
Agency
Description of fetal physiology and the evolving fetal circulatory system in pregnancy
PBPK models
¦ Temporal changes in maternal and fetal physiological parameters (e.g. body weight,
blood flow rate, and compartment volumes) informed by the most current human
experimental data available
¦ Designed to simulate ADME in mother and fetus from 13 weeks gestation to term.
Placental/fetal transfer is described using partition coefficients which might be sufficient
for many chemicals
25 of 32
Accommodates analysis (IVIVE/forward/reverse dosimetry) for >900 chemicals
Office of Research and Development
-------
vvEPA
Maternal/Fetal HTTK Model:
Not Included
United States
Environmental Protection
Agency
Changes in maternal metabolic enzyme expression levels and activity
¦ Changes in fetal metabolic enzyme expression levels and activity
¦ Changes in renal clearance capacities in fetus across gestational age
¦ Changes in plasma protein binding for both mom and fetus
Placental metabolism contributions
¦ Placental barrier descriptions (permeability rate constants or active transporter
function to determine extent of fetal exposure might be important for some
chemicals)
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
Forward Dosimetry Evaluations
Generic HTTK Model to Predict ATRA Kinetics in Humans
all-fra/is-retinoic acid (ATRA)
Sample
Studies
Dose
PK parameters
Observe
d
Predicted
Predicted/
Observed
Ratio
Ozpolat et
al. 2003
1.2 mg/kg
Cmax (nM)
AUC(0,°°) (nM*h)
1.3 ±1.2
3.0±2.6
0.1
3.12
0.1
1
Thudi et al.
2011
0.14 mg/kg
Cmax (uM)
AUC(0,°°) (nM*h)
0.1±0.04
0.3±0.1
0.02
0.4
0.2
1
Peng et al.
2014
0.3 mg/kg
Cmax (uM)
AUC(0,°°) (nM*h)
0.5±0.1
1.2±0.4
0.04
0.8
0.1
1
With minimal model inputs (Fup & Clint), the
generic model:
Well predicted the Area Under the Curve
Underpredicted the Cmax by a factor of 10
~i
o.o
Agutlumen
i r 1 r
0.5 1.0 1.5 2.0
time
Clung
time
Ckidney
Atubules
l 1 1 r
0.0 0.5 1.0 1.5 2.0
Cart
Cgut
i i i i r
0.0 0.5 1.0 1.5 2.0
time
Crest
Cplasma
O -
o z
0.0
Cven
Cliver
~i 1 1 r
0.5 1.0 1.5 2.0
time
Ametabolized
AUC
~i 1 r
0.5 1.0 1.5 2.0
Sample model outputs for ATRA following oral dosing
27 of 32
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
Forward Dosimetry Predictions during Pregnancy
Generic Maternal/Fetal HTTK Model
First trimester includes very dynamic changes in
physiology which are difficult to be characterized
quantitatively in a physiological model
c
o
•4—1
c
(D
O
c
o
o
CO
E
(/)
ro
CL
l.E-02
8.E-03
all-trans retinoic acid
1 _
/ Pre-pregnancy at
/ steady state
©
Maternal
Fetal
© ©
Pregnancy
50
100 150 200 250
BOO
Time (days)
Influence of pregnancy related
physiological changes on whole
body chemical kinetics
~Trimester1
(tt) ~Trimester2
^3) ~Trimester3
Pregnancy related physiological changes for ATRA results in a dilution effect of chemical
internal dosimetrics
28 of 32
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
Maternal/Fetal HTTK Model
Predictions for Retinoid Analogues
Normalized initial plasma concentration for each retinoid analogue
ATRA Maternal plasma
Maternal dosimetry
100
150 200
The gestation period (days)
250
ACITRETIN Maternal plasma
Maternal dosimetry
100
150 200
The gestation period (days)
250
Decrease in maternal
plasma
concentrations for
retinoid analogues
ranged from 8-15%
9-cis-retinoic acid Fetal plasma
ACITRETIN Fetal plasma
Fetal dosimetry
100
150 200
The gestation period (days)
250
Office of Research and Development
Fetal dosimetry
J
The gestation period (days)
Decrease in Fetal
plasma
concentrations for
retinoid analogues
ranged from 4-9%
-------
vvEPA
United States
Environmental Protection
Agency
Reverse Dosimetry Predictions during Pregnancy
[Scales Linearly]
c
o
¦4—'
ro
+->
c
CD
u
c
o
u
CO
CO
ro
External exposure (mg/kg) levels
all-trans retinoic acid
1
/ Pre-pregnancy at
/ steady state
©
Maternal
Fetal
© ©
Pregnancy
50
100
150
200
250
300
Time (days)
(t?) Trimester 1
Trimester 2
© Trimester 3
Over the course of pregnancy it takes higher in vivo exposure doses to yield the same in vitro
measured developmental toxicity potential estimates - depending on the extent of decrease in
maternal plasma concentration during pregnancy
30 of 32
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
Model Predicted External Exposures Associated with
Developmental Toxicity
Retinoid analogs
in vitro
Developmental
toxicity potential
(dTP, nM)
[Palmer et al. 2017]
Corresponding HTTK
predicted lowest
external exposure
in vivo (mg/kg/day)
all-trans retinoic acid
19 (±15)
2.20E-03
13-cis-retinoid acid
65 (±35)
6.34E-03
9-cis-retinoic acid
36 (±9)
3.51E-03
Etretinate
1694 (±1537)
9.59E-02
Acitretin
ND
-
Retinol
191536(±108464)
4.05E+01
TTNPB
62 (±38)
NA*
*chemical-specific model does not reach steady state for the given
inputs
Real-Life Exposure
(mg/kg)
~
IZZI
DevTox External Exposure
In vivo (mg/kg)
i 1—-
^Tt1 T2 T3?)
Dose (mg/kg)
PBPK
<
Reverse
dosimetry
in vitro DevTox
assay (pM)
Internal Dose
in vivo (pM)
n
a;
T3
v
C
03
CC
\7
¦>
31 of 32
Office of Research and Development
-------
oEPA Project Summary and Next Steps
United States
Environmental Protection
Agency
¦ In vivo external exposure doses associated with developmental toxicity (as measured in
vitro) for retinoid analogues were determined using HTTK modeling platform
¦ HTTK pregnancy model allowed for the study of the effects of physiological changes on
chemical kinetics.
¦ HTTK pregnancy model implications stands to have more confidence for chemicals that
have physiological parameters as the most influential determinant of maternal-fetal
disposition
¦ Future efforts include gathering available environmental exposure levels for activity-to-
exposure ratio determinations
¦ In-progress pregnancy PBTK models when characterized fully will serve to be an
invaluable tool for understanding pregnancy related changes on chemical kinetics
Office of Research and Development
-------
L
Acknowledgements
/
Dustin Ka
Un Jung
RobertPearce
mm--wiTrK5jeir
Richard Judson
Tom Knudsen
Nicole Kleinstreuer
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 or FDA
t
we are here
-------
oEPA
United States
Environmental Protection
Agency
Barton, Hugh A. "Computational pharmacokinetics
during developmental windows of susceptibility."
Journal of Toxicology and Environmental Health,
Part A 68.11-12 (2005): 889-900.
Birnbaum, Linda S. "TSCA reform under way in
Congress." (2010) Environmental Health
Perspectives 118: A106-A106.
Egeghy, P. P., et al. (2012). The exposure data
landscape for manufactured chemicals. Science of
the Total Environment, 414, 159-166.
Judson, Richard, et al. "The toxicity data landscape
for environmental chemicals." Environmental
Health Perspectives 117.5 (2008): 685-695.
Kapraun, Dustin F., et al. "Empirical models for
anatomical and physiological changes in a human
mother and fetus during pregnancy and
gestation." PloS one 14.5 (2019).
National Research Council. (1983). Risk
Assessment in the Federal Government: Managing
the Process Working Papers. National Academies
Press.
Office of Research and Development
References
National Academies of Sciences, Engineering, and
Medicine. (2017). Using 21st century science to
improve risk-related evaluations. National
Academies Press.
Palmer, Jessica A., et al. "Establishment and
assessment of a new human embryonic stem
cell-based biomarker assay for developmental
toxicity screening." Birth Defects Research Part B:
Developmental and Reproductive Toxicology 98.4
(2013): 343-363.
Palmer, Jessica A., et al. "A human induced
pluripotent stem cell-based in vitro assay predicts
developmental toxicity through a retinoic acid
receptor-mediated pathway for a series of related
retinoid analogues." Reproductive Toxicology 73
(2017): 350-361.
Pearce, Robert, et al. "httk: R Package for High-
Throughput Toxicokinetics." Journal of Statistical
Software, (2017a)
Pearce, Robert G., et al. "Evaluation and calibration
of high-throughput predictions of chemical
distribution to tissues." Journal of
Pharmacokinetics and Ppharmacodynamics 44.6
(2017b): 549-565.
Selevan, Sherry G., and Grace K. Lemasters. "The
dose-response fallacy in human reproductive
studies of toxic exposures." Journal of
OccupationalMmedicine 29.5 (1987): 451-454.
Wambaugh, J. F. et al. (2018). "Evaluating in vitro-
in vivo extrapolation of toxicokinetics."
Toxicological Sciences, 163(1), 152-169.
Wetmore, Barbara A., et al. "Integration of
dosimetry, exposure and high-throughput
screening data in chemical toxicity
assessment." Toxicological Sciences (2012)
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.
Zurlinden, Todd J., et al. "Profiling the ToxCast
library with a pluripotent human (H9) stem cell
line-based biomarker assay for developmental
toxicity." Toxicological Sciences (2020).
-------