SEPA
United States
Environmental Protection
Agency
November 7, 2019
Disruptive Innovation in Chemical Evaluation
Rapid Chemical Exposure
and Dose Research
John Wambaugh
Center for Computational
Office of Rese
U.S. Environmental Protection Agency
The views expressed in this presentation are those of the author
orcid.org/0000-0Q02-4024-534X and do not necessarily reflect the views or policies of the U.S. EPA
-------
• 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
EHiH Office of Research and Development
vvEPA US EPA Office of Research and Development
United States
Environmental Protection
Agency
•The Office of Research and Development (ORD) is the scientific research arm of EPA
•562 peer-reviewed journal articles in 2018
• Research is conducted by ORD's four national centers, and three
offices organized to address:
• Public health and en v. assessment; comp. tox. and exposure;
env. measurement and modeling; and en v. solutions and
emergency response.
•13 facilities across the United States
ORD Facility in
Research Triangle Park, NC
-------
vvEPA
United States
Environmental Protection
Agency
Chemical Regulation in the United States
Park et al. (2012): At least 3221 chemical
signatures in pooled human blood samples, many
appear to be exogenous
A tapestry of laws covers the chemicals people
are exposed to in the United States (Breyer, 2009)
Different testing requirements exist for food
additives, pharmaceuticals, and pesticide active
ingredients (NRC, 2007)
GIVE A DOG A PHONE
Technology for our furry friends
NewScientist
wnnjr cwrtwu^
We've made
150,000 new chemicals
*
We touch them,
we wear them, we eat them
But which ones should
we worry about?
SPECIAL REPORT, page 14
THE GOOD FIGHT CHAMBER OF SECRETS IS IT ALIV E?
Wostwiqlfnw The greatest *vw find Ajt#ici.il worm could
is. also wtuous ofe-arty human bones be first dugiUJaninMl
3 of 67
Office of Research arid Development
November 29, 2014
-------
vvEPA
United States
Environmental Protection
Agency
Chemical Regulation in the United States
Most other chemicals, ranging from industrial waste
to dyes to packing materials, are covered by the Toxic
Substances Control Act (TSCA)
Thousands of chemicals on the market were
"grandfathered" in without assessment
Judson et al. (2009), Egeghy et al. (2012), Wetmore et al. (2015)
GAO
March 2013
United States Government Accountability Office
Report to Congressional Requesters
TOXIC SUBSTANCES
EPA Has Increased
Efforts to Assess and
Control Chemicals but
Could Strengthen Its
Approach
"Tens of thousands of chemical
Environmental Protection Age
use in the United States, wit
chemicals listed
U.S. Government Accountability
GAO
Accountability • Integrity • Reliability
GAO-13-249
4 of 67
Office of Research arid Development
March, 2013
-------
oEPA
United States
Environmental Protection
Agency
Chemical Regulation in the United States
TSCA was updated in June, 2016 to allow more rapid
evaluation of chemicals (Frank R. Lautenberg
Chemical Safety for the 21st Century Act)
New approach methodologies (NAMs) are being
considered to inform prioritization of chemicals for
testing and evaluation (Kavlock et al., 2018)
EPA has released a "AWorking Approach for
Identifying Potential Candidate Chemicals for
Prioritization" (September, 2018)
130 STAT. 448
PUBLIC LAW 114-182—JUNE 22, 2016
| Office of Research arid Development
Public Law 114-182
114th Congress
An Act
Juno 22. 2016
[H.R 2&TAI
Frank K
Lint. iil« i
Chemical Safety
for the 21«t
Century Act
15 USC 2601
not#.
To modernize the Toxic Substance* Control Act, and for other purpono*
lie it enacted by the Senate and House of Representatives of
the United States of America in Congress assembled,
SECTION 1, SHORT TVTLEi TABU! OF CONTENTS.
(a) SHORT Title.—This Act muv be cited an the "Frank R.
Lautenberg Chemical Safety for the 21st Century Act".
-------
oEPA
Chemical Risk
United States
Environmental Protection
Agency
• The U.S. National Research Council (1983) identified chemical
risk as a function of both inherent hazard and exposure
• To address thousands of chemicals, we need NAMs that can
inform prioritization of chemicals most worthy of additional
study
• High throughput risk prioritization needs:
1. High throughput hazard characterization
(Dix et al., 2007, Collins et al., 2008)
2. High throughput exposure forecasts
(Wambaugh et al., 2013, 2014)
3. High throughput toxicokinetics (i.e., dose-response
relationship) linking hazard and exposure
(Wetmore et al., 2012, 2015)
6 of 67
Office of Research and Development
Hazard x Exposure
mg/kg BW/day
A
Potential Hazard
from in vitro with
Reverse
Toxicokinetics
Potential
Exposure Rate
Lower
Risk
Medium
Risk
Higher
Risk
-------
oEPA
United States
Environmental Protection
Agency
NRC (1983)
I hree Components for Chemical Risk
Dose-Response
(Toxicokinetics
/Toxicodynamics)
The National Academy of Sciences, Engineering and Medicine (1983)
| office of Research and Development outlined three components for determining chemical risk.
-------
vvEPA
United States
Environmental Protection
Agency
r
HP
TOXICITY TESTING IN THE 21 ST CENTURY
A VISION AND A STRATEGY
ht iT , S.VV •
v \ ¦ i At \ &
I III LI u
NRC (2007)
High- hroughput Risk Prioritization
Dose-Response
(Toxicokinetics
/Toxicodynamics)
High-Throughput
Risk
Prioritization
\
High throughput screening
(HTS) for in vitro bioactivity
potentially allows
characterization of thousands
of chemicals for which no
other testing has occurred
8 of 67
Office of Research and Development
To perform high throughput risk prioritization, we need all three components
-------
vvEPA
United States
Environmental Protection
Agency
High-throughput Screening
Hertzberg and Pope (2000):
• "New technologies in high-throughput screening have significantly increased throughput and reduced
assay volumes..."
Kaewkhaw et al. (2016)
"...new fluorescence
methods, detection
platforms and liquid-
handling technologies/'
Typically assess many
chemicals with a signal
readout (e.g., green
fluorescent protein).
Positive
Control
Titration of
Potential Hits
9 of 67
Office of Research arid Development
-------
oEPA
United States
Environmental Protection
Agency
High-1 hroughput Bioactivity
Screening Projects
/.
A
'4
•j
J
We attempt to estimate points of departure in vitro using
high throughput screening (HTS)
Tox21: Examining >8,000 chemicals using ~50 assays
intended to identify interactions with biological pathways
(Schmidt, 2009)
ToxCast: For a subset (>2000) of Tox21 chemicals ran
>1100 additional assays (Kavlock et al., 2012)
Most assays conducted in dose-response format (identify
50% activity concentration - AC50 - and efficacy if data
described by a Hill function, Filer et al., 2016)
All data are public: http://comptox.epa.gov/dashboard/
| Office of Research and Development
I
Tox}J }
-------
vvEPA
United States
Environmental Protection
Agency
The Margin Between Exposure and Hazard
1000
u
E=
o
u
e
0.001
Triclosan
(90/615)
1000
100 3
u
e
o
u
m
10
0-1
0.01
0.001
(A
TO
o
E
3
5
MBP MEHP PFOA 2,4-D
(8/615) (35/615) (24/615) (10/615)
^ estimated or measured
average concentrations
associated with the LOAEL
in animal studies
O NOAEL in animal studies
£ Humans with chronic
exposure reference values
(solid circles)
X Volunteers using products
containing the chemical
+ Biomonitored occupational
populations
General populations
11 of 67
Aylward and Hays (2011)
Office of Research arid Development Journal of Applied Toxicology 31 741-751
-------
vvEPA
United States
Environmental Protection
Agency
i he CompTox Chemicals Dashboard
® Chemrstry Dashboard I Home
0 A comptox.epa.goydashboard
Apps ^ Absence Request @ Travel Request For.. Q| R.EMD-HTTK u Confluence Q Stbuctcet
«~ ~ ~ a x
v>EPA
United States
Environmental Protection Home Advanced Search Batch Search bsts v Predictions Downloads
Agency
875 Thousand Chemicals
(s l\
I Product/Use Categories Assay/Gene
\1>
X^_pro2^/
~ Identifier substring search
See what people are saying, reac the dashboard comments!
Cite the Dashboard Publication dick here
Latest News
Read more news
August 9th 2019 - New release (3.0.9) in time for ACS Fall Meeting
August 14th 2019 at 4:39:37 PM
A new version of the Dashboard has been released in time for the ACS Fall meeting Included in this release are updates to data in the ToxVal
database, an update to the in vitro database (version 3.2), and the re'-ease also addresses a number of minor bugs and includes a short list of
additional functionality as descnbed in the Release Notes here.
-4
~
12 of 67
Office of Research arid Development
https://comptox.epa.gov/dashboard
-------
oEPA
United States
Environmental Protection
Agency
Chemical Bioactivity Data
• Data from the ToxCast and Tox21 projects are available through the dashboard
https://comptox.epa.gov/dashboard/
® Chemistry Dashboard X
e
~
X
O O A Secure | https://comptox.epa.gov/dashboard/dsstoxdb/results7search=DTXS(D7020182#bioactivity
~ Q
Q
Apps S Confluence ^ DSStox £ Chemistry Dashboard 1^ JESEE A EHP © ORD Travel Request Q Article Request
Q Graphics Request ® ChemTrack Q https://cranlogs.r-pi-
A Un'*ecl States
Environmental Protection Home Advanced Search Batch Search Lists v Predictions Downloads
!¦! m m Agency
^^^^^^^^hare ~ | Submit
~
DETAILS
EXECUTIVE SUMMARY
PROPERTIES
ENV. FATBTRANSPORT
HAZARD
> ADME
~ EXPOSURE
-r BIOACTIVITY
TOXCAST: SUMMARY
PUBCHEM
TOXCAST DATA
TOXCAST: MODELS
SIMILAR COMPOUNDS
GENRA (BETA)
Bisphenol A
80-05-7 I DTXSID7020182
Searched by DSSTox Substance Id.
Chemical Activity Summary Q
OTOXCAST DATA
O ASSAY DETAILS
10-
I o ShowAJI
+
CO
CD
e-
CL
nucJear receotor
w
51
T3
background measurement
5E
zs
s-
cell morphology
p
dna binding
7-
steroid hormone
•
a»
o
6-
transporter
O
•
Select a data point in the plot to see
ion channel
•
5-
goer
..
•f. *
associated details
oxid ©reductase
• •
/ • #
4-
kinase
•• •.
• . 1 •
protease
• , ,
cyp
• •• * .
2-
cell cycle
•
• • •
- v :
i
i. ,» AC50 (uNI)
cytokine
* * J *
.
cell adhesion molecules
• •
•
i i i 11 mi i i i 11 mi i i i ii imi i i i i 11
Ml i i 1 II Mil 1 1 1 Mill
I 0.00001
0.01 0.1 1
10 100 1QOO
i Download ~
| Office of Research arid Development
-------
vvEPA
United States
Environmental Protection
Agency
Chemical Bioactivity Data
Data from the ToxCast and Tox21 projects are available through the dashboard
® Chemistry Dashboard X
e
~
X
0 ^ A Secure | https://comptox.epa.gov/dashboard/dsstoxdb/results7search=DTXS(D7020182#bioactivity
~ Q
a
Apps S Confluence ^ DSStox £ Chemistry Dashboard 1^ JESEE EHP @ ORD Travel Request Q Article Request
Q Graphics Request ® ChemTrack Q https://cranlogs.r-pi-
A Un'*ecl States
Environmental Protection Home Advanced Search Batch Search Lists v Predictions Downloads
!¦! m m Agency
^^^^^^^^hare ~ | Submit
~
https://comptox.epa.gov/dashboard/
DETAILS
EXECUTIVE SUMMARY
PROPERTIES
ENV. FATBTRANSPORT
HAZARD
> ADME
~ EXPOSURE
-r BIOACTIVITY
TOXCAST: SUMMARY
PUBCHEM
TOXCAST DATA
TOXCAST: MODELS
SIMILAR COMPOUNDS
GENRA (BETA)
Bisphenol A
80-05-7 I DTXSID7020182
Searched by DSSTox Substance Id.
Chemical Activity Summary Q
OTOXCAST DATA
O ASSAY DETAILS
background measurement
cell morphology
dna binding
steroid hormone
trans-pc
cell cycle
cytokine
II adhesion molecules
* •
W • i
AC50 (uM)
AC50 (uM): 2.41
Scaled top: 4.08 ®
Assay Endpoint Name: NVS_ADME_rCYP2C13
Gene Symbol: Cyp2c13
Organism: rat
Tissue: NA
Assay Format Type: biochemical
Biological Process Target: regulation of catalytic
activity
Detection Technology: Fluorescence
Analysis Direction: positive
Intended Target Family: cyp
Description Data from the assay component
NVS_ADME_rCYP2C13 was analyzed into 2
assay endpoints. This assay endpoint,
NVS_ADME_rCYP2C 13, was analyzed in the
positive fitting direction relative to Acetonitrile as
the negative control and baseline of activity. Using
a type of enzyme reporter, loss-of-signal activity
can be used to understand changes in the
enzymatic activity as they relate to the gene
Ciirtharmr.ro thio ocoji anHnnint .-on hn
i Download ~
14 of 67
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
Chemical Bioactivity Data
Data from the ToxCast and Tox21 projects are available through the dashboard
® Chemistry Dashboard X
e
~
X
0 ^ A Secure | https://comptox.epa.gov/dashboard/dsstoxdb/results7search=DTXS(D3022409#bioactivity
~ Q
a
Apps S Confluence ^ DSStox £ Chemistry Dashboard 1^ JESEE A EHP @ ORD Travel Request Q Article Request
Q Graphics Request ® ChemTrack Q https://cranlogs.r-pi-
A Un'*ecl States
Environmental Protection Home Advanced Search Batch Search Lists v Predictions Downloads
!¦! m m Agency
^^^^^^^^hare ~ | Submit
~
https://comptox.epa.gov/dashboard/
DETAILS
EXECUTIVE SUMMARY
PROPERTIES
ENV. FATBTRANSPORT
HAZARD
ADME
~ EXPOSURE
-r BIOACTIVITY
TOXCAST: SUMMARY
PUBCHEM
TOXCAST DATA
TOXCAST: MODELS
SIMILAR COMPOUNDS
GENRA (BETA)
4,4'-Sulfonyldiphenol
80-09-1 | DTXSID3022409
Searched by DSSTox Substance Id.
Chemical Activity Summary Q
OTOXCAST DATA
O ASSAY DETAILS
background measurement
cell cycle
sieroid hormone
transporter
cyp
AC50 (uM)
—I—I II 11 111 T"
TTTTl 1 1 I I I I 111
AC50 (uM): 0.80
Scaled top: 2.45 ®
Assay Endpoint Name: ATG_ERa_TRANS_up
Assay Description: 117
Gene Symbol: ESR1
Organism human
Tissue: liver
Assay Format Type: cell-based
Biological Process Target: regulation of
transcription factor activity
Detection Technology: RT-PCR and Capillary
electrophoresis
Analysis Direction: positive
Intended Target Family: nuclear receptor
Description: Data from the assay component
ATG_ERa_TRANS was analyzed into 1 assay
endpoint This assay endpoint,
ATG_ERa_TRANS_up, was analyzed in the
positive fitting direction relative to DMSO as the
negative control and baseline of activity. Using a
type of inducible reporter, measures of mRNAfor
r-w Ho iicnri in nnrtorctonfl
i Download ~
15 Of 67
Office of Research and Development
-------
^EEs Physicochemical Properties
Environmental Protection
Agency
Measured and predicted physicochemical properties are available
• OPEn structure-activity/property Relationship App (OPERA) by Mansouri, et al. (2018)
https://comptox.epa.gov/dashboard/
* Chemistry Dashboard X
0 ^ 0 Not secure | comptox-prod.epa.gov/dashboard/dsstoxdb/results?search=triclosan#properties
e
$D 15
Apps ® Confluence ^ DSStox £ Chemistry Dashboard JESEE A EHP © QRD Travel Request Q Article Request Q Graphics Request ® ChemTrack Q https://cranlogs.r-pi
f,EPA
United States
Environmental Protection Home Advanced Search Batch Search Lists v Predictions Downloads
Agency
Copy t
DETAILS
EXECUTIVE SUMMARY
ENV. FATETTRANSPORT
HAZARD
ADME
~ EXPOSURE
t BIOACTIVITY
SIMILAR COMPOUNDS
GENRA (BETA)
RELATED SUBSTANCES
SYNONYMS
t LITERATURE
LINKS
Triclosan
3380-34-5 | DTXSID5032498
Searched by Approved Name-
Property
Summary v
i Download ~
Columns v
Summary
Property *
Experimental average »
Predicted average *
Experimental median ~
Predicted median ~
Experimental range ~
Predicted range ~
Unit ~
LogP: Octanol-Water
4.76 (1)
4.79
5.06
4.76
3.78 to 5.27
Melting Point
48.0 (11)
107
57.0
108
-40.0 to 57.5
73.4 to 141
°c
Boiling Point
-
352
345
-
327 to 374
ac
Water Solubility
3.45e-5 (1)
2.48e-5
2.15e-5
3.45e-5
4.54e-6 to 5.16e-5
mol/L
Vapor Pressure
"
9.72e-6
2.90e-6
4.53e-7 to 3.26e-5
mmHg
Flash Point
165
165
-
162 to 169
°c
Surface Tension
-
51.7
-
51.7
dyn/cm
Index of Refraction
-
1.63
-
1.63
Molar Refractivity
"
69.3
-
69.3
cmA3
16 of 67
Office of Research arid Development
-------
^iPo Bulk Download of Data
United States
Environmental Protection
Agency
Can download databases and spreadsheets of data using Batch Search
e
- ~ X
Chemistry Dashboard X
4- CO O Not secure | comptox-prod.epa.gov/dashboard/dsstoxdb/batch_search
Q
¦" Apps Confluence yy DSStox ® Chemistry Dashboard JESEE A EHP @ ORD Travel Request Q Articie Request Q Graphics Request £ ChemTrack Q https://cranlogs.r-p
x>EPA
United States
Environmental Protection Home Advanced Search Batch Search Lists v Predictions Downloads
Agency
https://comptox.epa.gov/dashboard/
Batch Search©
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Step Three: Select Download Data or Display Chemicals
Please enter one identifier per line
Select Input Type(s)
4 Identifiers
Chemical Name ©
® CASRN©
® InChlKey 0
0 DSSTox Substance ID ©
U InChlKey Skeleton ©
1 MS-Ready Formula(e) ©
-1 Exact Formula(e) ©
~ Monoisotopic Mass
Display All Chemicals
Enter Identifiers to Search (searches should be limited to <5000 identifiers)
aisghenol s
80-05-7
TRICLOSAN
Download Chemical Data
tsft;
Discover.
About/Disclaimer
Accessibility
Privacy
Connect.
ACToR
DSSTox
Downloads
Ask,
Contact
Help
17 of 67
Office of Research and Development
-------
^iPo Bulk Download of Data
United States
Environmental Protection
Agency
Can download databases and spreadsheets of data using Batch Search
/ ® Chemistiy Dashboard X
e -
~ X
O O O Not secure j comptox-prod.epa.gov/dashboard/dsstoxdb/batch_search
*a ~ Q
Q Q i
¦" Apps * Confluence yy DSStox ® Chemistry Dashboard 1^ JESEE EHP @ ORD Travel Request
Q Article Request Q Graphics Request * ChemTrack Q https://cranlogs.r-p-
&EPA
Environmental Protection Home Advanced Search Batch Search Lists v Predictions Downloads
Agency
Share •
https://comptox.epa.gov/dashboard/
Customize Results
~ select All
I—I Select All in Lists
Chemical Identifiers
< DTXSID©
* Chemical Name 0
~ CAS-RN ©
~ InChlKey 0
~ IUPAC Name ©
Structures
Mol File ©
~ SMILES 0
LJ inChl String ©
B MS-Ready SMILES ©
~ QSAR-Ready SMILES ©
Intrinsic And Predicted Properties
J Molecular Formula ©
J Average Mass ©
U Monoisotopic Mass ©
~ TEST Model Predictions ©
(s0 OPERA Model Predictions ©
Metadata
~ Curation Level Details ©
J NHANES/Predicted Exposure ©
U Data Sources ©
~ Include ToxVal Data Availability ©
1 J Assay Hit Count
Ll Number of PubMed Articles ©
U PuhChem Data Sources ft
0 40CFR355
Q A list of all PBDEs (Polybrominated diphenyl ethers)
1 -1A let of all PCBs (Polychlorinated biphenyls)
LJ A list of polycyclic aromatic hydrocarbons
LI Acute exposure guideline levels
1 J Algal Toxins
'—' Androgen Receptor Chemicals
U APCRA Chemicals for Prospective Anatysis
1 J APCRA Chemicals for Retrospective Analysis
LI APCRA Chemicals for Retrospective Analysis_App_List_448_Chemicals
~ ATSDR Minimal Risk Levels (MRLs) for Hazardous Substances
- i ATSDR Toxic Substances Portal Chemical List
~ Bisphenol Compounds
1J California Office of Environmental Health Hazard Assessment
1 Chemicals with interesting names
0 CMAP
'-I DNTScreening Library
LI Drinking Water Suspects, KWR Water. Netherlands
~ EDSP Universe
U EPA Chemicals associated with hydraulic fracturing
~ EPA Consumer Products Suspect Screening Results
U EPA Consumer Products Suspect Screening Results
J EPA Integrated Risk Information System (IRIS)
~ EPAPFAS Cross-Agency Research List
D EPA PFAS List of 75 Test Samples (Set 1)
—1 EPACPJH - EPA Cell Painting Reference Chemical Set
1 J EPAHFR - EPA Chemicals associated with hydraulic fracturing
'J EU Cosmetic Ingredients Inventory (Combined 2000/2006)
U EU Toxrisk Dataset
18 of 67
| Office of Research arid Development
-------
TSCA 2.0
A New Era in
Chemical Risk Management
After decades of dysfunction, the Toxic Substances Control Act has been
overhauled with provisions that promise better protection against potentially
harmful chemicals. © Dmytro Grankin/Alamy; Alexander Aldatov/Alamy
f Li A
r
hi
[_
AS - AV
> : ^
1 f
SEPA TSCA
United States ®
Environmental Protection
Agency
• The updated Toxic Substances Control Act (TSCA) has been
considering the inclusion of new approach methodologies (NAMs).
These NAMs include:
• High throughput screening (ToxCast)
• High throughput exposure estimates (ExpoCast)
• High throughput toxicokinetics (HTTK)
• "10,000 TSCA-relevant chemicals in commerce
• Proof of concept: ~200 chemicals with ToxCast, ExpoCast and HTTK
• HTTK was rate limiter on number of chemicals
• "A Proof-of-Concept Case Study Integrating Publicly Available
Information to Screen Candidates for Chemical Prioritization
underTSCA"
Office of Research and Development
Schmidt, C. W. (2016). TSCA 2.0: A new era in
chemical risk management", Environmental
Health Perspectives, A182-A186.
-------
oEPA
United States
Environmental Protection
Agency
Replacing Animal Testing with NAMs
"To aggressively pursue a reduction in animal testing, I am
directing leadership and staff in the Office of Chemical
Safety and Pollution Prevention and the Office of Research
and Development [ORD] to prioritize ... the reduction of
animal testing while ensuring protection of human health
and the environment."
"These new approach methods (NAMs), nclude any
technologies, methodologies, approaches or combinations
thereof that can be used to provide information on
chemical hazard and potential human exposure that can
avoid or significantly reduce the use of testing on animals"
• NAMs for filling information gaps for decision-making
• integrating data steams into chemical risk assessment
• making the information publicly available
~| Office of Research and Development
y"os*>»
} O *
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON. D C 20460
September 10, 2019
THE ADMINISTRATOR
MKMOKAMU M
Andrew R. Wheeler
Administrator
TO: Associate Deputy Administrator
General Counsel
Assistant Administrators
Inspector General
Chief Financial Officer
Chief of Staff
Associate Administrators
Regional Administrators
During my March 2019 all-hands address. I reiterated the U.S. Knvironmcntal Protection
Agency s commitment to move away from animal testing. We are already making significant
efforts to reduce, replace and refine our animal testing requirements under both statutory and
strategic directives. For example, the Toxic Substances C ontrol Act. amended June 22. 2016. by
the f rank R. Uutcnbcrg Chemical Safety for the 21" Century Act. requires the EPA to reduce
reliance on animal testing. Also, Objective 3.3 of the FY 21)19-2022 U.S. EPA Strategic Plan
outlines a commitment to further reduce the reliance on animal testing within live years. More
than 200,000 laboratory animals have been saved in recent years as a result of these collective
efforts.
Scientific advancements exist today that allow us to better predict potential hazards for risk
assessment purposes w ithout the use of traditional methods that rely on animal testing. These new
approach methods (N AMs), include any technologies, methodologies, approaches or combinations
thereof that can be used to provide information on chemical hazard and potential human exposure
that can avoid or significantly reduce the use of testing on animals. I~hc benefits of NAMs are
extensive, not only allowing us to decrease animals used while potentially evaluating more
chemicals across a broader range ol potential biological effects, but in a shorter timeframe with
lewer resources while olten achieving equal or greater biological prediciivity than current animal
models.
-------
vvEPA
United States
Environmental Protection
Agency
The Ntlimul Amlmu* of
SCIENCES • ENGINEERING • MEDICINE
Risk-Related Evaluations Report from National Academies of
Sciences, Engineering, and Medicine (NASEM)
"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
USING
21 ST CENTURY
SCIENCE
TO IMPROVE
RISK-RELATED
EVALUATIONS
NASEM (2017)
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"
21 of 67
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
* gency
Chemical Prioritization NAMs
OJ
i_
=3
u~t
O
Q.
X
10
"D
CD
U
T3
CD
oT *****"
CL. fD
"O
o \ 10-
CD >
m cQ
Q
c
E
| ^ 10"
*5
cr
~o
CD
+-»
03
to
JL
P
IS i
iu^r
High throughput in vitro
screening can estimate doses
needed to cause bioactivity
(e.g., Wetmore et al.; 2015)
Exposure intake rates can
be inferred from
biomarkers
(e.g., Ring et al., 2018)
Chemicals Monitored by CDC NHANES
mg/kg
Potential
Hazard from
in vitro with
Reverse
Toxicokinetic
s
Potential
Exposure
Rate
3W/day
22 of 67
Office of Research and Development
Ring et al. (2017)
Lower Medium Higher
Risk Risk Risk
-------
oEPA
United States
Environmental Protection
Agency
In Vitro - In Vivo Extrapolation
(IVIVE)
IVIVE is the use of in vitro experimental data to predict phenomena in vivo
• IVIVE-PK/TK (Pharmacokinetics/Toxicokinetics):
• Fate of molecules/chemicals in body
• Considers absorption, distribution, metabolism, excretion (ADME)
• Uses empirical PK and physiologically-based (PBPK) modeling
IVIVE-PD/TD (Pharmacodynamics/Toxicodynamics):
• Effect of molecules/chemicals at biological
target in vivo
• Assay design/selection important
• Perturbation as adverse/therapeutic effect,
reversible/ irreversible effeccts
Both contribute to in vivo effect prediction
| Office of Research and Development
Normalization of dose
PBPK models
NRC (1998)
Humans: in vivo
Testable predictions
Extrapolation
using PD and
PBPK models
Comparative testing
-------
vvEPA
United States
Environmental Protection
Agency
High Throughput Toxicokinetics (HTTK)
In vitrotoxicokinetic data + generic toxicokinetic model
= high(er) throughput toxicokinetics
V ^ V
¦=>
¦
j:V".
SI
0
l 2 ¦=> !•! ¦=>
*al
CL
metab
Primary
Compartment
Gut Lumen
—>
CL
vabs
httk
24 of 67
Office of Research arid Development
-------
vvEPA
United States
Environmental Protection
Agency
In Vitro Data for HTTK
\
Cryo pre served
hepatocyte
suspension
Shibata et ol.
(2002)
9^
-~
-~
-~
-~
;
Cryopreserved
Hepatocytes
(10 donor pool)
Add Chemical
(1 and 10 |iM)
T
Remove
Aliquots at
15, 30, 60,
120 min
Analytical
Chemistry
J
The rate of
disappearance of
parent compound
(slope of line) is the
hepatic clearance
(|iL/m in/106
hepatocytes)
50
100
150
We perform the
assay at 1 and 10 |iM
to check for
saturation of
metabolizing
enzymes.
Most chemicals do
not have TK data -
we use in vitro HTTK
methods adapted
from pharma to fill
gaps
In drug development,
HTTK methods allow
IVIVE to estimate
therapeutic doses for
clinical studies -
predicted
concentrations are
typically on the order
of values measured in
clinical trials (Wang,
2010)
25 of 67
Office of Research and Development
-------
oEPA
United States
Environmental Protection
Agency
Cryo pre served
hepatocyte
suspension
Shibata et ol.
(2002)
In Vitro Data for HTTK
Rapid
Equilibrium
Dialysis (RED)
Waters et ol.
(2008)
\
~n"
Sm
ni
Cryopreserved Add Chemical Remove
Hepatocytes (1 and 10 \xM) Aliquots at
(10 donor pool) 15, 30, 60,
i a 120 min
Analytical
Chemistry
*
s $
Double-wells
connected by
semi-permeable
membrane on a
RED Plate
Add plasma Add chemica|
(6 donor pool)
to one well
F
well 1
Incubate
plates
come to
equilibrium
ub,p
c
• Most chemicals do
not have TK data -
we use in vitro HTTK
methods adapted
from pharma to filJ
gaps
• In drug development,
HTTK methods allow
IVIVE to estimate
therapeutic doses for
clinical studies -
predicted
Determine concentrations are
concentration typically on the order
in both wells of values measured in
(analytical clinical trials (Wang,
chemistry) 2010)
| Office of Research arid Development
well 2
-------
oEPA
United States
Environmental Protection
Agency
Cryo pre served
hepatocyte
suspension
Shibata et ol.
(2002)
In Vitro Data for HTTK
Rapid
Equilibrium
Dialysis (RED)
Waters et ol.
(2008)
\
^ as
T
&=¦ 1
Cryopreserved Add Chemical Remove
Hepatocytes (1 and 10 \xM) Aliquots at
(10 donor pool) 15, 30, 60,
i a 120 min
Analytical
Chemistry
*
* $
Double-wells
connected by
semi-permeable
membrane on a
RED Plate
Add plasma Add chemica|
(6 donor pool)
to one well
F
well 1
Incubate
plates
come to
equilibrium
ub,p
c
Determine
concentration
in both wells
(analytical
chemistry)
| Office of Research arid Development
well 2
Most chemicals do
not have TK data -
we use in vitro HTTK
methods adapted
from pharma to filJ
gaps
Environmental
chemicals:
Rotroff et al. (2010) 35
chemicals
Wetmore et al. (2012)
+204 chemicals
Wetmore et al. (2015)
+163 chemicals
Wambaugh et al. (2019)
+389 chemicals
-------
vvEPA
United States
Environmental Protection
Agency
Building Confidence inTK Models
To evaluate a chemical-specific TK model for ''chemical x" you can
compare the predictions to in vivo measured data
• Can estimate bias
• Can estimate uncertainty
• Can consider using model to extrapolate to other situations
(dose, route, physiology) where you don't have data
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
28 of 67
Office of Research and Development
Cohen Hubal et al. (2018)
-------
vvEPA
United States
Environmental Protection
Agency
Building Confidence inTK Models
To evaluate a chemical-specific TK model for ''chemical x" you can
compare the predictions to in vivo measured data
• Can estimate bias
• Can estimate uncertainty
• Can consider using model to extrapolate to other situations
(dose, route, physiology) where you don't have data
However, we do not typically have TK data
LO
c
o
'+->
03
i—
+->
C
o>
u
c
o
u
~o
O)
>
1—
O)
LO
_Q
O
X
X
4
X
X
X
X
Chemical
Specific
Model
Predicted Concentrations
29 of 67
Office of Research and Development
Cohen Hubal et al. (2018)
-------
vvEPA
United States
Environmental Protection
Agency
Building Confidence inTK Models
To evaluate a chemical-specific TK model for ''chemical x" you can
compare the predictions to in vivo measured data
• Can estimate bias
• Can estimate uncertainty
• Can consider using model to extrapolate to other situations
(dose, route, physiology) where you don't have data
However, we do not typically have TK data
We can parameterize a generic TK model, and evaluate that
model for as many chemicals as we do have data
• We do expect larger uncertainty, but also greater confidence in
model implementation
• Estimate bias and uncertainty, and try to correlate with
chemical-specific properties
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
30 of 67
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
• 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
y
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
• 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
y
x
x /
A y
/
Z
Z z
X
X
X
Generic
Model
Predicted Concentrations
Cohen Hubal et al. (2018)
-------
vvEPA
United States
Environmental Protection
Agency
• We estimate clearance from two
processes - hepatic metabolism
(liver) and passive glomerular
filtration (kidney)
• This appears to work better for
pharmaceuticals than other
chemicals:
• ToxCast chemicals are
overestimated
• Non-pharmaceuticals may be
subject to extrahepatic
metabolism and/or active
transport
Evaluation Example
O)
O
¦O
-------
oEPA
United States
Environmental Protection
Agency
Toxicokinetic Triage: When DoesTK IVIVE
Through comparison to in vivo data, a cross-
validated (random forest) predictor of success or
failure of HTTK has been constructed
All chemicals can be placed into one of seven
confidence categories
• Added categories for chemicals that do not
reach steady-state or for which plasma binding
assay fails
Plurality of chemicals end up in the ''on the order"
bin (within a factor of 3.2x) which is consistent
with Wang (2010)
| Office of Research and Development
150'
03
O
1100
_c
0
I—
1
M—
0
1
(D
_o
E
^ 50-
\<3^
O'
,
140
,o
Wambaugh et al. (2015)
<&•
$-
Cf?'
oc
f-
&
Triage Category
-------
oEPA
United States
Environmental Protection
Agency
Different crayons
have different
colors...
Until I open the
box, I don't know
what colors I
have...
...especially if my
six-year-old has
been around.
| Office of Research arid Development
\
Uncertainty
[wr re rent
Brilliant
GbJare
Irayola
CRAYONS
BtJtU-IN SHARPENER
,u>:v
tip A h
^CjtfpSLI
64 CRAYON B
-------
\/EPA \Asi ki^i hi I ii"v
United States T dl IdUllll/
Environmental Protection *
Agency
Different crayons
have different
colors...
The ''average"
color may not
even be in the
box!
Didere
BrvlU;
Col1
36 of 67
Office of Research arid Development
-------
oEPA
United States
Environmental Protection
Agency
Different crayons
have different
colors...
The "average"
color may not
even be in the
box!
¦Kjj
P]!tliyy
m
U
im
Variability
| Office of Research arid Development
-------
vvEPA
United States
Environmental Protection
Agency
Correlated Monte Carlo
sampling of physiological
model parameters built
into R "httk" package
(Pearce et al., 2017):
Sample NHANES
biometrics for
actual individuals:
Population simulator for HTTK
nnanes
National Health and Nutrition Examination Survey
Sex
Race/ethnicity
Age
Height
Weight
Serum creatinine
38 of 67
Office of Research and Development
Slide from Caroline Ring (ToxStrategies)
Ring et al. (2017)
-------
vvEPA
United States
Environmental Protection
Agency
Correlated Monte Carlo
sampling of physiological
model parameters built
into R "httk" package
(Pearce et al., 2017):
Sample NHANES
biometrics for
actual individuals:
Sex
Race/ethnicity
Age
Height
Weight
Serum creatinine
Population simulator for HTTK
nnanes
National Health and Nutrition Examination Survey
~
Regression equations from literature
(McNally et al., 2014)
(+ residual marginal variability)
(Similar approach used in SimCYP [Jamei et al. 2009], GastroPlus,
PopGen [McNally et al. 2014], P3M [Price et al. 2003], physB [Bosgra et al. 2012], etc.)
39 of 67
Office of Research and Development
Slide from Caroline Ring (ToxStrategies)
Ring et al. (2017)
-------
vvEPA
United States
Environmental Protection
Agency
Correlated Monte Carlo
sampling of physiological
model parameters built
into R "httk" package
(Pearce et al., 2017):
Sample NHANES
biometrics for
actual individuals:
Sex
Race/ethnicity
Age
Height
Weight
Serum creatinine
Population simulator for HTTK
nnanes
National Health and Nutrition Examination Survey
~
Predict physiological
quantities
Tissue masses
Tissue blood flows
GFR (kidney function)
Hepatocellularity
Regression equations from literature
(McNally et al., 2014)
(+ residual marginal variability)
(Similar approach used in SimCYP [Jamei et al. 2009], GastroPlus,
PopGen [McNally et al. 2014], P3M [Price et al. 2003], physB [Bosgra et al. 2012], etc.)
40 Of 67
Office of Research and Development
Slide from Caroline Ring (ToxStrategies)
Ring et al. (2017)
-------
oEPA
United States
Environmental Protection
Agency
Risk-Based Ranking for Total NHANES Population
-S 10
CO
5 ao
O
c
u E 10"3
> cu
n- ^
cr co
llj o
"O g-
ro -o 10"7
.1 &
+-» U
CO ¦ —
LU "D
CD
rng/kg BW/day
Potential
Hazard from
in vitro with
Reverse
Toxicokinetics
Potential
Exposure
Rate
& Sr
M 9
| Office of Research and Development
CorflpcufKt
Lower Medium Higher
Risk Risk Risk
Ring et al. (2017)
-------
oEPA
United States
Environmental Protection
Agency
Life-stage and Demographic Variation in Exposure
• Wambaugh et ol. (2014) made steady-
state inferences of exposure rate
(mg/kg/day) from NHANES data for
various demographic groups
-0.5
0.5
Change in Exposure
Relative to Total Population
| Office of Research and Development
v~>
rt3
U
CU
u
co
NA
J&
&
///
^ ^ &
\°V J#"
ilor
AJacdfar
Aco'achlor
Propylpiira
Ci ci atophc
s
Ring et ol. (2017)
-------
oEPA
United States
Environmental Protection
Agency
Life-stage and Demographic Variation in K
• Ring et ol. (2017) made demographic-
specific predictions of change in plasma
concentrations for a 1 mg/kg bw/day
exposure
v~>
rt3
U
CU
u
co
-0.5
0.5
Change in Toxicokinetics
Relative to Total Population
| Office of Research and Development
.a
&
///
-------
oEPA
United States
Environmental Protection
Agency
• Can calculate
margin
between
bioactivity and
exposure for
specific
populations
Life-stage and Demographic Variation in Risk Priority
mg/kg BW/day
A
Potential Hazard
from in vitro with
Reverse
Toxicokinetics
Potential Exposure
from ExpoCast
-0.5
0.5
Change in Risk Relative to
Total Population
| Office of Research and Development
Lower Medium Risk Higher
Risk Risk
v~>
rt3
U
CU
u
co
J&
*
-------
oEPA
United States
Environmental Protection
Agency
Open Source Tools and Data for HTTK
https://CRAN.R-proiect.orq/packaqe=httk
G include a table in a script in r - C X I « rmarkdown-cheatsheet X I Defining toxicological tipping pc X <|| CRAN - Package httk X +
O £}" ® cran.r-project.org/web/packages/httk/indGX.html Q. ~ Q H S3 0 £
Apps (ft; Confluence yy DSStox u Chemicals Dashboa... EKP @ ORD Travel Request,,, Q Article Request Q Graphics Request £ ChemTrack Q https://cranlogs.r-p... ^ CSS REMD RACT 0 mec_ssy_sub
httk: High-Throughput Toxicokinetics
Functions and data tables for simulation and statistical analysis of chemical toxicokinetics ("TK") as in Pearce et al, (2017) . Chemical-specific in vitro data have been
obtained from relatively high throughput experiments. Both physiologically-based ("PBTK") and empirical (e.g.. one compartment) "TK" models can be parameterized for several hundred chemicals
and multiple species. These models are solved efficiently, often using compiled (C'-based) code. A Monte Carlo sampler is included for simulating biological variability (Ring et al., 2017
'l and measurement limitations. Calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017
1, These functions and data provide a set of tools for in vitro-in vivo extrapolation ("IVTVE") of high throughput screening data (e.g., Tox21, ToxCast) to real-world
exposures via reverse dosimetry (also known as "RTK") (Wetmore et al.. 2015 1.
Version: 1.10.1
Depends: R(> 2.10)
Imports: deSolve. msm. data.table, survey, mvtnorm. truncnomi. stats, graphics, utils, magrittr
Suggests: ggplot2. knitr. rmarkdown. R.rsp. GGallv. splots. scales. EnvStats. MASS. RColorBrewer. TeachingDei
gmodels. colorspace
Published: 2019-09-10
Author: John Wambaugh [aut. ere]. Robert Pearce [aut], Caroline Ring faut]. Greg Honda [aut], Mark Sfeir [aut]
Wetmore [ctb], Woodrow Setzer [ctb]
Maintainer: John Wambaugh
BugReports: https: sithub.com USEPA CompTox-ExpoCast-httk
License: GPL-3
URL: https: .mvw.epa.gov/chemical-researchrapid-chemical-exposure-aiid-dose-research
Needs Compilation: y es
Materials: NEWS
CRAN checks: httk results
Downloads:
downloads 474/month
Reference manual: httk.pdf
Vignettes: Honda et al, (20191: Updated Armitage et al. (2014"! Model
Pearce et al, (20171 Creating Partition Coefficient Evaluation Plots
Ring et al. ("20171Age distributions
Ring et al. ("20171 Global sensitivity' analysis
Ring et al. ("20171 Global sensitivity analysis plotting
Ring et al. ("20171Height and weight spline fits and residuals
R package "httk
if
Open source, transparent, and peer-
reviewed tools and data for high throughput
toxicokinetics (httk)
Available publicly for free statistical software
R
Allows in vitro-in vivo extrapolation (IVIVE)
and physiologically-based toxicokinetics
(PBTK)
Human-specific data for 944 chemicals and
rat-specific data for 171 chemicals
-------
vvEPA
United States
Environmental Protection
Agency
What Can We Do When We Don't HaveTK?
1000
u
E=
o
u
e
0.001
Triclosan
(90/615)
1000
100 3
u
e
o
u
m
10
0-1
0.01
0.001
(A
TO
o
E
3
5
MBP MEHP PFOA 2,4-D
(8/615) (35/615) (24/615) (10/615)
^ estimated or measured
average concentrations
associated with the LOAEL
in animal studies
O NOAEL in animal studies
£ Humans with chronic
exposure reference values
(solid circles)
X Volunteers using products
containing the chemical
+ Biomonitored occupational
populations
General populations
46 of 67
Aylward and Hays (2011)
Office of Research arid Development Journal of Applied Toxicology 31 741-751
-------
oEPA
United States
Environmental Protection
Agency
Using Predicted H K for Risk
Prioritization
A
\
Notional Institute of
\ To ypj J
r%3C Z— /©SNTP
NCATS
Sipes et al., (2017) used Simulations Plus ADMET Predictor to make in silico predictions of metabolism and protein binding:
10
15
,10 _
10'
Doses ranges for all 3925 Tox21 compounds
eliciting a 'possible'-to-'likely' human in vivo
interaction alongside estimated daily exposure
1015
>,
1010
03
"O
O)
O)
10
E
a>
o
O
en
o
Q
10"5
56 compounds with potential in vivo biological
interaction at or above estimated
environmental exposures
| Office of Research arid Development
-------
vvEPA
United States
Environmental Protection
Agency
What Can We Do When We Don't HaveTK?
ToxCast
CD -
LO -
it
o
X
ro
E
LU
>>
o
cs
o
it
LU
CO -
CM -
100
Perfluorooctanoic acid
Average Serum PFOA Concentration
m vivo
toxicity studies
¦ Attagene (cellular)
a BioSeek (cellular)
o NovaScreen (cell free)
o
o
o
i A
10 1
Potency (uM)
0.1
0.01
10
3 _
o
c
o
o 10'
c
CD
00
o
o
CM
&
5
^r
o
o
CM
CD
CL
CD
>
CD
O
O
CM
ZJ
CD
1
s
i
s_
i
a>
_>
_>
_>
o
h-
h-
r--
^r
x—
t—
t—
o
CO
UO
o
o
T—
\—
T—
CM
H—
Q
Q
Q
M—
o
CD
CD
CD
-C
r-
r-
r-
c
o
o
o
CD
o
o
o
-i—'
CM
CM
CM
h—
CD
o
~o
~o
5
5
5
>
Q Q
CD CD
r- r-
o o
o o
CM CM
±= ±= ±= ~o "o
CM
O
O
CM
tt=
o
J=
c
CD
-t—'
=5
CD
i
1
1
i
i
i
~CD
"(D
Id
3
~CD
~CD
C
C
c
C
C
~£=
>
>
>
>
>
CD
CO
CO
<
o
'
">
c
48 of 67
Office of Research arid Development
Wambaugh et alv (2013)
-------
vvEPA
United States
Environmental Protection
Agency
What Can We Do When We Don't HaveTK?
ToxCast
CD
LO
it
O
3
O
><
ro
E
LU
>-.
o
03
O
S CN
CO
Perfluorooctane sulfonic acid
¦ Attagene (cellular)
a BioSeek (cellular)
o NovaScreen (cell free)
o
o
o
Average Serum PFOS Concentration
m vivo
toxicity studies
100
10 1
Potency (uM)
0.1
0.01
a 10
,3 -
® 102 J
O
c
ro ,
I 101
"O 0
5 10 ^
CD
i 10
£Z
<
10
-1 _
i-2
CD
>
(D
>
CO CO CO CM CO
o o o o o
o o o o o
CM CM CM CM CM
JZ
h-
00
o
o
CM
O)
c
CD
JZ
O
"O
"O
CD
CD
CD
CD
CD
o
0
1
-*—>
C
A—*
c
-i—•
C
C
-i—•
c
>
>
>
>
>
CM
CM
O
=3
"O
0
1
Q_
~o
zs
"O
o
s
Q.
CD
CO
CO
<
o
-t—'
">
c
49 of 67
Office of Research arid Development
Wambaugh et a I., (2013)
-------
oEPA
United States
Environmental Protection
Agency
NHANES Gives Blood/Serum Levels of Chemicals
• Centers for Disease Control and Prevention
(CDC) National Health and Nutrition
Examination Survey (NHANES) provides an
important tool for monitoring public health
• Currently only have NHANES values from
Wambaugh et al. (2014) on dashboard
• Working to include all NHANES chemicals in
future dashboard release
• CDC NHANES data can be obtained from:
https://www.cdc.gov/exposurereport/index.html
One EPA X J Q PeopieSc X | G world wa X ¦<*. CompTc - X About th X | tjjjj GoToMe^ X | +
O O ® comptox-prod.epa.gov/dashboard/dsstoxdb/results?search=bisphenol%20... tV q b o a
I;; Apps < ® Confluence yy DSStox ». i Chemicals Dashboa... A EHP @ ORD Travel Request... Q Article Request @ Graphics Request
~
v>EPA
United States
Environmental Protection
Agency
| Office of Research arid Development
DETAILS
EXECUTIVE SUMMARY
PROPERTIES
ENV. FATE/TRANSPORT
HAZARD
> ADME
~ EXPOSURE
PRODUCT & USE CATEGORIES
CHEMICAL WEIGHT FRACTION
CHEMICAL FUNCTIONAL USE
TOXICS RELEASE INVENTORY
MONITORING DATA
EXPOSURE PREDICTIONS
PRODUCTION VOLUME
> BIOACTIVITY
SIMILAR COMPOUNDS
GENRA (BETA)
PCI ATFn CI IRQTAMTFC
Bisphenol A
80-05-7 | DTXSID7020182
Searched by Approved Name.
Q National Health and Nutrition Examination Survey
(NHANES) Inferences (mg/kg-bw/day)
^ Download ~
Demographic
Lower 95th Limit
Upper 95th Limit *
Median
Ages 6-11
3,80e-5
4.92e-5
4.33e-5
Ages 12-19
2.55e-5
3.38e-5
2.93e-5
Ages 20-65
2.79e-5
3.27e-5
3.02e-5
Ages 65+
1.91 e-5
2.31 e-5
2.10e-5
BMI > 30
2.38e-5
2J4e-5
2.55e-5
BMI < 30
3.02e-5
3.3Qe-5
3.16e-5
Repro. Age Females
2.83 e-5
3.31 e-5
3.06e-5
Females
2.58e-5
3.03 e-5
2.80e-5
Males
2.94e-5
3.37e-5
3.15e-5
Tota!
2.86e-5
3.08e-5
2.97e-5
10 records
-------
vvEPA
United States
Environmental Protection
Agency
New Exposure Data and Models
High throughput
screening + in vitro-
in vivo extrapolation
(IVIVE) can predict a
dose (mg/kg bw/day)
that might be
adverse
High-Throughput
Risk
Prioritization
Dose-Response
(Toxicokinetics
/Toxicodynamics)
51 of 67
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
New Exposure Data and Models
High throughput
screening + in vitro-
in vivo extrapolation
(IVIVE) can predict a
dose (mg/kg bw/day)
that might be
adverse
High-Throughput
Risk
Prioritization
Wetmore et al. (2012, 2015) have
provided in vitro and simulation
methods for characterizing
toxicokinetics
Dose-Response
(Toxicokinetics
/Toxicodynamics)
52 of 67
Office of Research and Development
-------
vvEPA
United States
Environmental Protection
Agency
High throughput screening (Dix et
al., 2006, Collins et al., 2008) + in
vitro-in vivo extrapolation (IVIVE,
Wetmore et al., 2012, 2015) can
predict a dose (mg/kg bw/day) that
might be adverse
Risk = Hazard x Exposure
High-Throughput
Risk
Prioritization
Need methods to forecast exposure for
thousands of chemicals
(Wetmore et al., 2015)
High throughput models exist to
make predictions of exposure via
specific, important pathways such
as residential product use and diet
53 of 67
Office of Research and Development
NRC (1983)
-------
oEPA
Limited Available Data for Exposure
Estimation
Most chemicals lack public exposure-related data beyond production volume (Egeghy et alv 2012)
United States
Environmental Protection
Agency
10OQ0*
ProiiuGlran Use
Vokjme Category
r -DDd Cherninal Waler
Use Cone.
Air BrcirTvarkar
Ccnc Cone
| Office of Research arid Development
Data Type
-------
v>EPA
United States
Environmental Protection
Agency
Understanding Exposure is a Systems
Problem
USE and RELEASE
Consumer
Products and Durable
Goods
Direct Us^ Residential Use
Chemical Manufacturing and Processing
Environmental
Release
[e.g., surface cleaner) (e.g. ,flo
MEDIA
EXPOSURE
(MEDIA + RECEPTOR)
RECEPTOR
Exposure event unobservable: Can try to predict exposure by characterizing pathway
Some pathways have much higher average exposures: In home "Near field" sources
significant (Wallace, eta!., 1987)
55 of 67
Office of Research and Development
-------
oEPA
New Approach Methodologies for Exposure Science
Makes Use of
Exposure NAM Class
Description
Traditional Approach
Measurement
Toxicokinetics
Models
Descriptors
Evaluation
Machine
Learning
Measurements
New techniques including screening analyses
capable of detecting hundreds of chemicals
present in a sample
Targeted (chemical-specific) analyses
•
•
•
•
Toxicokinetics
High throughput methods using in vitro data to
generate chemical-specific models
Analyses based on in vivo animal studies
•
-
•
•
HTE Models
Models capable of making predictions for
thousands of chemicals
Models requiring detailed, chemical- and
scenario-specific information
•
•
-
•
Chemical Descriptors
Informatic approaches for organizing chemical
information in a machine-readable format
Tools targeted at single chemical analyses by
humans
-
•
Evaluation
Statistical approaches that use the data from
many chemicals to estimate the uncertainty in
a prediction for a new chemical
Comparison of model predictions to data on a
per chemical basis
•
•
•
•
•
Machine Learning
Computer algorithms to identify patterns
Manual Inspection of the Data
•
•
•
-
Prioritization
Integration of exposure and other NAMs to
identify chemicals for follow-up study
Expert decision making
•
•
•
•
•
•
| Office of Research and Development
Wambaugh et al., (2019)
-------
What Do We Know About Exposure?
Environmental Protection
Agency
Biomonitoring Data
Centers for Disease Control and Prevention (CDC) National Health and Nutrition Examination Survey
(NHANES) provides an important tool for monitoring public health
Large, ongoing CDC survey of US population: demographic, body measures, medical exam,
biomonitoring (health and exposure),...
Designed to be representative of US population according to census data
Data sets publicly available (http://www.cdc.gov/nchs/nhanes.htm)
Includes measurements of:
• Body weight
• Height
• Chemical analysis of blood and urine
iinanes
I-,,. i . _ | _ National Health and Nutrition Examination Survey
| Office of Research and Development
-------
oEPA
What Do We Know About Exposure?
Exposure Models
United States
Environmental Protection
Agency
• Human chemical exposures can be coarsely grouped into ''near field" sources that are close to the
exposed individual (consumer or occupational exposures) 'far-field' scenarios wherein individuals are
exposed to chemicals that were released or used far away (ambient exposure) (Arnot et al., 2006).
• A model captures knowledge and a hypothesis of how the world works (MacLeod et al., 2010)
• EPA's EXPOsure toolBOX (EPA ExpoBox) is a toolbox created to assist individuals from within
government, industry, academia, and the general public with assessing exposure
• Includes many, many models
https://www.epa.gov/expobox
''Now it would be very remarkable if any system existing in the real world could be exactly represented by
any simple model. However, cunningly chosen parsimonious models often do provide remarkably useful
approximations... The only question of interest is 'Is the model illuminating and useful?'" George Box
58 of 67
Office of Research and Development
-------
vvEPA
Evaluation NAMs: The SEEM Framework
United States
Environmental Protection
Agency
r
Space of
Chemicals
r
Chemicals
with
Monitoring
Data
Dataset 1
Dataset 2
Apply calibration and estimated uncertainty to
other chemicals
¦M
m
cc
0
JS£
fO
V!
Exposure
Ml inference
hi
CD
Model 1
Model 2
Estimate ¦
Uncertainty
Calibrate
models
•
\
Different
Chemicals
J <
Available Exposure Predictors
| Office of Research arid Development
3
Evaluate Model Performance
and Refine Models
FEMN
AEMN
CEMN
We use Bayesian methods to incorporate multiple models into consensus predictions for
1000s of chemicals within the Systematic Empirical Evaluation of Models (SEEM)
(Wambaugh et al., 2013, 2014; Ring et al., 2018)
Hurricane path
prediction is an
example of
integrating
multiple models
-------
vi-EPA
United States
Environmental Protection
Agency
_ 2-
C
CD
"o
Q3
O
O
c 0-
o
CO
CO
8M -
O)
0
DC
Wambaugh et al. (2014)
I I l < I
60 of 67
Office of Research arid Development
Heuristics of Exposure
— Total
— Female
— Male
— ReproAgeFemale
— 6-11_years
— 12-19_years
— 20-65_years
— 66+years
— BMI_LE_30
BMI GT 30
R2 ~ 0.5 indicates that we can predict
50% of the chemical to chemical
variability in median NHANES
exposure rates
Same five predictors work for all
NHANES demographic groups
analyzed - stratified by age, sex, and
body-mass index:
• Industrial and Consumer use
• Pesticide Inert
• Pesticide Active
• Industrial but no Consumer
use
• Production Volume
-------
vvEPA
United States
Environmental Protection
Agency
Correlation is Not Causation
Wambaugh et al. (2014) found that "pesticide inerts"
had higher than average levels in biomonitoring data,
while "pesticide actives" had lower than average
In World War II, there Royal Air Force (UK) wanted to
armor planes against anti-aircraft fire
• Initial proposal was to place armor wherever
bullet holes were most common
• Mathematician Abraham Wald pointed out that
they were looking at the planes that had returned
• See Drum, Kevin (2010) "The Counterintuitive
World"
Pesticide inerts have many other uses, but there are
more stringent reporting requirements for pesticides
• Exposure is occuring by other pathways
61 of 67
Office of Research and Development
-------
&EPA Chemical Dashboard Provides SEEM Predictions
United States
Environmental Protection
Agency
Currently only have SEEM2 exposure
predictions from Wambaugh et al.
(2014) on dashboard
Working to include SEEM3 in future
dashboard release
SEEM3 Predictions can be obtained
from Ring et al. (2018) Supporting
Information:
https://pubs.acs.org/doi/10.1021/acs.est.8b04056
One EPA X © PeopleSc X | 3 world wa X • CompTc X About th X | GoToMe- X ¦+"
O i comptox-prod.epa.gov/dashboard/dsstoxdb/results?search=bisphenol%20... ~ o a o a *
¦¦¦ Apps Confluence DSStox li Chemicals Dashboa... A EHP @ ORD Travel Request... Q Article Request ^ Graphics Request
~ X
vvEPA
United States
Environmental Protection
Agency
| Office of Research arid Development
DETAILS
EXECUTIVE SUMMARY
PROPERTIES
ENV. FATE/TRANSPORT
HAZARD
~ ADME
~ EXPOSURE
PRODUCT & USE CATEGORIES
CHEMICAL WEIGHT FRACTION
CHEMICAL FUNCTIONAL USE
TOXICS RELEASE INVENTORY
MONITORING DATA
EXPOSURE PREDICTIONS
PRODUCTION VOLUME
~ BIOACTIVITY
SIMILAR COMPOUNDS
GENRA (BETA)
Bisphenol A
80-05-7 | DTXSID7020182
_ Searched by Approved Name.
e Exposure Predictions (mg/kg-bw/day)
JL Download ~
Demographic *
Median ~
95th Percentile *
Ages 6-11
6.30e-5
5.82e-3
Ages 12-19
2,68e-5
2.00e-3
Ages 20-65
2.05e-5
1.61 e-3
Ages 65+
1.61 e-5
2.18e-3
BMI > 30
1.69e-5
1.45e-3
BMI < 30
2,67e-5
2.26e-3
Repro. Age Females
1.11 e-5
1.57e-3
Females
1.11 e-5
9.09e-4
Males
3.89e-5
3.34e-3
Total
2.11 e-5
2.00e-3
-------
vvEPA
United States
Environmental Protection
Agency
Knowledge of Exposure Pathways Limits
High Throughput Exposure Models
"In particular, the
assumption that 100%
of [quantity emitted,
applied, or ingested] is
being applied to each
individual use scenario
is a very conservative
assumption for many
compound / use
scenario pairs."
This is an open access amide published under an ACS AuthorChoice License. which pe-rmits
copying and redistribution of the article or any adaptations for fion-commercial pur poses.
Article
p\j b 3 j cs.ofg/est
Risk-Based High-Throughput Chemical Screening and Prioritization
using Exposure Models and in Vitro Bioactivity Assays
Hyeong-Moo Shin,*' Alexi Emstoff,*'^ Jon A. Arnot,"-L'* Barbara A Wetmore,V Susan A Csiszar,s
Peter Fantkc,* Xianming Zhang,0 Thomas E. McKonc,*'^ Olivier Jolliet/ and Deborah H. Bennett*
63 of 67
Office of Research arid Development
-------
oEPA
United States
Environmental Protection
Agency
Chemical Use Identi les Relevant Pathways
>2000 chemicals with Material Safety Data Sheets
(MSDS) in CPCPdb (Goldsmith etol., 2014)
CO
ro
u
0)
u
IS)
< "i
Apparel
Auto and Tires
Baby
Beauty
Craft and Party
Electronics
Grocery
Health
Home
Home Improvement
Patio and Garden
Pets
Sports and Outdoors
T oys
Some pathways have much higher
average exposures!
Near Field Occ
Ecological
| Office of Research arid Development
Near field sources have been known to be important at least since 1987 -
see Wallace, et al.
-------
oEPA Chemical Property NAMs
I I rt I + /N/4 C B ™
United States
Environmental Protection
Arz^n
SCIENTIFIC DATAIK
OPEN
ELSEVIER
Contents lists available at ScienceDirect
Food and Chemical Toxicology
journal homepage: www.elsevier.com/locate/foodchemtox
Development of a consumer product ingredient database for chemical /¦) Cr()ssMjlt
exposure screening and prioritization
M.-R. Goldsmith'*, CM. GrulkeR.D. Brooksb, T.R. Transuec, Y.M. Tan A. Frame'10, P.P. Egeghy ',
R. Edwards d, D.T. Chang8, R. Tornero-VelezK. Isaacs3, A. Wangac, J. Johnsona, K. Holm3, M. Reich',
J. Mitchells, DA Valleroa, L Phillipsa, M. Phillipsa, J.F. Wambaugha. R.S. Judsona,
T.J. Buckleya, C.C. Dary"
Data Descriptor: The Chemical and
Products Database, a resource for
exposure-relevant data on
chemicals in consumer products
Kathie L. Dionisio1, Katherine Phillips1, Paul S. Price1, Christopher M. Grulke2,
Antony Williams2, Derya Biryol1'3, Tao Hong* & Kristin K. Isaacs1
Broad "index" of chemical uses
ELSEVIER
Contents lists available at ScienceDirect
Toxicology Reports
journal homepage: www.elsevier.com/locate/toxrep
Exploring consumer exposure pathways and patterns of use ¦.
for chemicals in the environment
Kathie L Dionisio11, Alicia M. Frame11'1, Michael-Rock Goldsmitha-2,
John F. Wambaughb, Alan Liddell'-3, Tommy Catheyd, Doris Smith",
James Vailb, Alexi S. Ernstoff , Peter Fantkee, Olivier Jolliet',
Journal of Exposure Science and Environmental Epidemiology (2018) 28, 216-222
0 2018 Nature America, Inc. past of Sponger Mature. AM rights reserved 1559-0631/18
w.nature.com ijes
ORIGINAL ARTICLE
Consumer product chemical weight fractions from
ingredient lists
Kristin K. Isaacs1, Katherine A. Phillips', Derya Biryol1-2, Kathie L Dionisio1 and Paul S. Price1
| Office of Research and Development
Measurement of chemicals in
consumer products
https://comptox.epa.gov/dashboard
-------
oEPA Chemical Dashboard Provides Use Information
United States
Environmental Protection
Agency
CPdat: The Chemical and Products
Database (Dionisio, et al. 2018)
Curated information on the
occurrence of chemicals
SCIENTIFIC DATA<
01111O1
OPEN
Received: 16 October 2017
Accepted: 30 April 2018
Published: 10 July 2018
Data Descriptor: The Chemical arid
Products Database, a resource for
exposure-relevant data on
chemicals in consumer products
Kathie L. Dionisio1, Katherine Phillips1, Paul S. Price1, Christopher M. Grulke2,
Antony Williams2, Derya Biryol1,3, Tao Hong* & Kristin K. Isaacs1
| Office of Research and Development
« Complex Chemicafs Dashboard X
e Ct
¦¦¦ Apps (Sj Confluence ^ DSStox *¦ Chemicals Dashboa..
A comptox-prod.epa.gov/dashboard/dsstoxdb/results?search=bisphenoi%20a#ex... ~ o a o a
A EHP @ ORD Travel Request... Q Article Request ^ Graphics Request
SEPA
United States
Environmental Protection
Agency
DETAILS
EXECUTIVE SUMMARY
PROPERTIES
ENV. FATE/TRANSPORT
HAZARD
> ADME
~ EXPOSURE
PRODUCT & USE CATEGORIES
CHEMICAL WEIGHT FRACTION
CHEMICAL FUNCTIONAL USE
TOXICS RELEASE INVENTORY
MONITORING DATA
EXPOSURE PREDICTIONS
PRODUCTION VOLUME
~ BIOACTIVITY
SIMILAR COMPOUNDS
GENRA (BETA)
RELATED SUBSTANCES
SYNONYMS
Bisphenol A
80-05-7 | DTXSID7020182
Searched by Approved Name.
Product and Use Categories (PUCs) Q
Download ~ Column
Product or Use Categorization
* Categorization type
* Number of Unique Products v
adhesive
CPCat Cassette
17
manufacturing, metals
CPCat Cassette
17
paint
CPCat Cassette
16
manufacturing, machines
CPCat Cassette
12
manufacturing, plastics
CPCat Cassette
11
building_material, flooring
CPCat Cassette
8
construction
CPCat Cassette
8
surface_treatment metals
CPCat Cassette
8
stabilizer
CPCat Cassette
7
bu i Id i ng_constru ction
CPCat Cassette
6
3 4 5 6 7 8
Showing 1 to 10 of 117 records
-------
oEPA
United States
Environmental Protection
Agency
Summary
A tapestry of laws covers the chemicals people are exposed to
in the United States (Breyer, 2009)
Most other chemicals, ranging from industrial waste to dyes to
packing materials, are covered by the recently updated Toxic
Substances Control Act (TSCA) and administered by the EPA
New approach methodologies (NAMs) are being developed to
prioritize these existing and new chemicals for testing
All data are being made public:
• The CompTox Chemicals Dashboard (A search engine for
chemicals) http://comptox.epa.gov/
• R package "httk": https://CRAN.R-project.org/package=httk
| Office of Research and Development
mg/kg BW/day
Potential hazard
from in vitro
converted to dose
by HTTK
Potential
Exposure Rate
Lower
Risk
Medium
Risk
Higher
Risk
The views expressed in this presentation are those of the authors
and do not necessarily reflect the views or policies of the U.S. EPA
-------
ExpoCast Project
Exposure Forecasting)
NCCT
Chris Grulke
Richard Judson
Ann Richard
Risa Sayre*
Mark Sfeir*
Rusty Thomas
John Wambaugh
Antony Williams
NRMRL
Xiaoyu Liu
NHEERL
Linda Adams
Christopher
Ecklund
Marina Evans
Mike Hughes
Jane Ellen
Simmons
Tamara Tal
NERL
Cody Addington*
Namdi Brandon*
Alex Chao*
Kathie Dionisio
Peter Egeghy
Hongtai Huang*
Kristin Isaacs
Ashley Jackson*
Jen Korol-Bexell*
Anna Kreutz*
Charles Lowe*
Seth Newton
*Trainees
Katherine Phillips
Paul Price
Jeanette Reyes*
Randolph Singh*
Marci Smeltz
Jon Sobus
John Streicher*
Mark Strynar
Mike Tornero-Velez
Elin Ulrich
Dan Vallero
Barbara Wetmore
Collaborators
Arnot Research and Consulting
Jon Arriot
Johnny Westgate
Institut National de I'Environnement et des Risques
(INERIS)
Frederic Bois
Integrated Laboratory Systems
Kamel Mansouri
National Toxicology Program
Mike Devito
Steve Ferguson
Nisha Sipes
Ramboll
Harvey Cleweli
ScitoVation
Chantel Nicolas
Silent Spring Institute
Robin Dodson
Southwest Research Institute
Alice Yau
Kristin Favela
Summit Toxicology
Lesa Aylward
Technical University of Denmark
Peter Fantke
Tox Strategies
Caroline Ring
Miyoung Yoon
Unilever
Beate Nicol
Cecilie Rendal
Ian Sorrell
United States Air Force
Heather Pangburn
Matt Linakis
University of California, Davis
Deborah Bennett
University of Michigan
Olivier Jolliet
University of Texas, Arlington
Hyeong-Moo Shin
-------
oEPA
United States
Environmental Protection
Agency
References
Cohen Hubal, EA, et al. "Advancing internal exposure and
physiologically-based toxicokinetic modeling for 21st-
century risk assessments." Journal of exposure science &
environmental epidemiology (2018).
Eissing, Thomas, et al. "A computational systems biology
software platform for multiscale modeling and simulation:
integrating whole-body physiology, disease biology, and
molecular reaction networks." Frontiers in physiology 2
(2011): 4.
Frank, Christopher L., et al. "Defining toxicological tipping
points in neuronal network development." Toxicology and
applied pharmacology 354 (2018): 81-93.
Honda, Gregory S., et al. "Using the concordance of in vitro
and in vivo data to evaluate extrapolation assumptions."
PloS one 14.5 (2019): e0217564.
Jamei, et al. "The Simcyp® population-based ADME
simulator." Expert opinion on drug metabolism & toxicology
2009b;5:211-223
Jongeneelen, Frans J., and Wil F. Ten Berge. "A generic,
cross-chemical predictive PBTK model with multiple entry
routes running as application in MS Excel; design of the
model and comparison of predictions with experimental
results." Annals of occupational hygiene 55.8 (2011): 841-
864.
Breyer, Stephen. Breaking the vicious circle: Toward
effective risk regulation. Harvard University Press, 2009
Collins FS, Gray GM, Bucher JR. Transforming
environmental health protection. Science. 2008;319:906-
907. [PMC free article] [PubMed]
Dix David, et al. "The ToxCast program for prioritizing
toxicity testing of environmental chemicals." Toxicol Sci.
2007;95:5-12
Egeghy, P. P., et al. (2012). The exposure data landscape for
manufactured chemicals. Science of the Total Environment,
414, 159-166.
Judson, Richard S., et al. "In vitro screening of
environmental chemicals for targeted testing prioritization:
the ToxCast project." Environmental health perspectives
118.4 (2009): 485-492.
Kavlock, R. J., et al. (2018). Accelerating the pace of
chemical risk assessment. Chemical research in toxicology,
31(5), 287-290.
Mansouri, Kamel, et al. "OPERA models for predicting
physicochemical properties and environmental fate
endpoints." Journal of cheminformatics 10.1 (2018): 10.
McLanahan, Eva D., et al. "Physiologically based
pharmacokinetic model use in risk assessment—why being
published is not enough." Toxicological Sciences 126.1
(2011): 5-15.
Lukacova
Office of Research and Development „ ,
Dva, Viera, Walters. Woltosz, and Michael B. Bolger.
effective risk regulation. Harvard University Press, 2009
Egeghy, P. P., et al. (2012). The exposure data landscape for
manufactured chemicals. Science of the Total Environment,
414, 159-166.
Filer, Dayne L.. "The ToxCast analysis pipeline: An R package
for processing and modeling chemical screening data." US
Environmental Protection Agency: http://www. epa.
gov/ ncct/toxcast/f iles/MySQL%
20Database/Pipeline_Overview. pdf (2014)
Hertzberg, R. P., & Pope, A. J. (2000). High-throughput
screening: new technology for the 21st century. Current
opinion in chemical biology, 4(4), 445-451.
Ingle, Brandall L., et al. "Informing the Human Plasma
Protein Binding of Environmental Chemicals by Machine
Learning in the Pharmaceutical Space: Applicability Domain
and Limits of Predictability." Journal of Chemical
Information and Modeling 56.11 (2016): 2243-2252.
Jamei, et al. "The Simcyp® population-based ADME
simulator." Expert opinion on drug metabolism & toxicology
2009;5:211-223
Kaewkhaw, R., et al. (2016). Treatment paradigms for
retinal and macular diseases using 3-D retina cultures
derived from human reporter pluripotent stem cell
linestreatment design using PSC-Derived 3-D retina
cultures. Investigative ophthalmology & visual science,
57(5), ORSFI1-ORSFI11.
Kavlock, Robert, et al. "Update on EPA's ToxCast program:
providing high throughput decision support tools for
s I ric U
i no rr
+¦ 11
rocoa rr*
h i v
------- |