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US EPA CSS-HERA
Board of
Scientific
Counselors
Chemical Safety
Subcommittee
Meeting
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
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CSS Session 2 Slides
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The work presented within represents US EPA Office of Research and Development research
activities. Material includes both peer reviewed, published results and work-in-progress
research. Please do not cite or quote slides.
-------
Table of Contents
OCSPP-TSCA Inventory: Prioritization Proof of Concept (Richard Judson) 3
Developmental Neurotoxicity (DNT) in vitro Battery as an Alternative to DNT in vivo Guideline Studies
Used by OPP (Tim Shafer) 24
Implementing a Workflow for Exposure Screening of Drinking Water Contaminants of Concern (Kristin
Isaacs) 48
Application of NAMs and AOPs to Surface Water Surveillance and Monitoring in the Great Lakes (EPA
Region 5) and a Western River (EPA Region 8) (Daniel Villeneuve) 78
The work presented within represents US EPA Office of Research and Development research activities. Material
includes both peer reviewed, published results and work-in-progress research. Please do not cite or quote slides.
-------
vyEPA
OCSPP-TSCA Inventory: Prioritization Proof of
Concept
Richard Judson, PhD
BOSC Meeting
February 3, 2021
The views expressed in this presentation are those of the author and do not
necessarily reflect the views or policies of the U.S. EPA
-------
X
Prioritization and Pre-prioritization
Many organizations face the problem that they have too many chemicals to
evaluate given the available resources
One solution is to use a data-driven approach to prioritize chemicals for
detailed assessments
OCSPP:TSCA High and low priority chemicals
OCSPP: EDSR potential endocrine disruptors
OW: Candidate Contaminant List (CCL)
OW: Chemicals in biosolids
Health Canada: Domestic Substances List (DSL)
Minnesota Department of Health: Chemicals of concern to children
-------
^S>ER^V TheTSCA Prioritization Problem
Under the LautenbergAct, 2016 Amendment toTSCA (*):
EPA must establish a risk-based process to determine which chemicals it will prioritize
for assessment, identifying them as either high or low" priority substances.
High priority - the chemical may present an unreasonable risk of injury to health or
the environment due to potential hazard and route of exposure, including to
susceptible subpopulations
Low priority - the chemical use does not meet the standard for high-priority
Assessments for High Priority chemicals must be completed in 3 years, requiring a complete
data package at the beginning
The TSCA Active Inventory contains over 33,000 chemicals
CompTox resources can provide key inputs to aid this prioritization process
(*) https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/highlights-key-provisions-frank-r-lautenberg-chemical
3
-------
X
J he Comp ox Opportunity
CCTE staff have been developing resources with data on large numbers of
chemicals covering hazard, exposure, toxicokinetics and physico-chemical
properties
Traditional Animal Toxicology: ToxRefDB.ToxValDB
In Vitro Hazard:ToxCast, specific models for endocrine pathways
Exposure: ExpoCast (SEEM), CPCat & CPDat, models of use
Toxicokinetics: HTTK
PhysChem: OPERA models of physchem and other properties
Experience building large-scale integrative models
-------
vvEPA
Implementation of the Proof-of-Concept Study
Operationalized long-term strategy through development of the
Public Information Curation and Synthesis (PICS) approach
Integrates information from a variety of sources to better understand the
landscape of publicly available information for large numbers of chemical
substances
Synthesizes information across key scientific domains used to evaluate
chemical risks
Consistent with the Strategic Plan
Implementation of Alternative Test
integrate NAMs to fill gaps when traditional testing data are not available
5
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vvEPA
Defining Intended Application of PICS
Approach
The PICS approach was intended to:
Understand the landscape of publicly-available information on the over 33,000 substances on the active inventory
Provide a transparent and reproducible process for integrating available information and identifying potential
information gaps
Increase efficiency and manage workload by focusing expert review on substances that may have a greater potential
for selection as high- or low-priority candidates
Create a flexible and sustainable process that can adapt to scientific advances and continual generation of new safety-
related information
Organize the process into modular workflows that can be readily updated or adapted to address prioritization needs
under other mandates
The PICS approach was not intended to:
Replace the formal TSCA prioritization or risk evaluation processes
Create a ranked list of substances
Signal that the EPA has concerns with particular substances or categories of substances
Supplant expert judgment and review
Utilize confidential business information
Incorporate systematic review of information to address study and data quality
6
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vvEPA
Schematic of PICS Approach Within the
Candidate Selection Process
TSCA Active
Inventory
(-33,000
chemicals)
Public Information Curation and
Synthesis (PICS) Approach
Scientific
Information
Domain
Availability
Metric
Metric
(SDM)
(1AM)
Subset of the
TSCA Active
Inventory
OCSPP Expert Review
and Analysis
Identification of
Candidate Chemical
7
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vvEPA
Proof-of-Concept Chemicals (POC 238)
The process was carried out on the complete TSCA Active Inventory
For illustration, a total of 238 substances selected from the curated,
non-confidential active TSCA inventory
Selection based on the following:
Proposed set of 20 high- and 20 low-priority candidate substances
Substances from the 2014 update to the TSCA Work Plan
Substances with known relevance to each of the scientific domains
Subset of chemical substances listed in the FDA's Substances Added
to Food inventory and EPA's Safer Chemical Ingredients List (SCIL)
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vvEPA
Proof-of-Concept: Data QA/QC
Proof-of-Concept
(238 Chemicals)
Data QA/QC
Scientific Domain Metric
Information Availability Metric
I
Specific data domain and data source error rates
'Data QA plan for TSCA active inventory
ฆFIE estimates for data QC
>QA of massive amounts of data is an ongoing challenge
Proof of Concept
0 20 40 60 80 100
Information Availability Metric
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vvEPA
Proof-of-Concept: Metrics
0 20 40 60 80 100
Information Availability Metric
10
-------
Scientific Domain Metric
Seven scientific domains were selected based on:
Previous use in TSCA prioritization activities (i.e., TSCA workplan)
Statutory language in the amended TSCA
Consultation with OCSPP management and staff
Tiered workflows for each scientific domain designed based on
the current state of the science
The overall scientific domain metric is determined by summing
the results from the individual scientific domain workflows
11
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vvEPA
Overall Scientific Domain Metric
12
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vvEPA
Information Availability Metric
Included in PICS approach to evaluate the amount of information
available for use in any future chemical substance risk evaluation
Needed because detailed risk assessments cannot be carried out
without sufficient data
Based on the potentially relevant information for exposure, human
health and ecological toxicity
Modifying criteria (based on OPPT new chemicals program and
consultation with OPPT technical staff) applied to make the score
context-specific
Incorporates "information gathering flags" to highlight data types used
in specific scientific domain metrics as well as possible data gaps
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*>EPA
Information Availability Metric
TSCA Active Inventory
Modifying Criteria
T
Potentially Relevant Studies:
1 Acute Mammalian Toxicity%
ฆ Repeat-dose Mammalian Toxicity
(subchronic or chronic)%
1 Developmental Toxicity%
1 Reproductive Toxicity%
ฆ Genotoxicity%
1 Carcinogenicity%
ฆ Skin Sensitization or Eye
Corrosivity%
1 Acute Aquatic Ecotoxicity#
1 Chronic Aquatic Ecotoxicity#
Exposure
I
Chemical Intermediate AND
Short Environmental Half-Life
(Hours)
Potentially Relevant Studies:
Acute Mammalian Toxicity%
Repeat-dose Mammalian Toxicity
(subchronic orchronic)%
Developmental Toxicity%
Reproductive Toxicity%
Genotoxicity%
Carcinogenicity%
Skin Sensitization or Eye
Corrosivity%
Acute Aquatic Ecotoxicity#
Exposure
Low Water Solubility
(< 0.1 mg/L)*
MW> 1000 OR
Exempt Polymers
Potentially Relevant Studies:
Acute Mammalian Toxicity%
Repeat-dose Mammalian Toxicity
(subchronic orchronic)%
Developmental Toxicity%
Reproductive Toxicity%
Genotoxicity%
Carcinogenicity%
Skin Sensitization or Eye
Corrosivity%
Exposure
Potentially Relevant Studies:
Skin Sensitization or Eye
Corrosivity%
Exposure
Information Availability Metric = /"(Potentially Relevant Studies Available)
*Criteria based on Sustainable Futures Manual (EPA-748-B12-001); #includes multiple trophic
level data; %Not required if chemical has an authoritative human hazard assessment
I4
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vvEPA
Proof-of-Concept Results
High priority chemicals have larger scientific domain scores than the low priority
"Safe" Chemical sets (e.g. food ingredients) tend to have low scientific domain scores
The POC chemicals have larger than average information availability
Proof of Concept
o
-t'
0
c
CD
E
o
Q
o
c
92
'o
w
o
o
o
CO
o
CD
o
o
C\J
O -
o POC not in TSCA 10/90
TSCA 10
o TSCA 90
v Low Priority
a High Priority
ฃ ^
s>
o
o
o
I 8
V
ฃ
O Q
8. W
e
&
ฅ
w
0
7
wv
V
TSCA 10
O TSCA 90
O Other
High Priority
Candidates
^ Low Priority
Candidates
20
40
60
80
100
Information Availability Metric
Information availability vs. scientific domain metrics for the
POC238 set of chemical substances. Positions of points are
staggered for ease of visualization.
O
"55
c
CD
E
o
D
o
c
0
o
cn
o
o
o
00
o
CD
O
o
CNJ
^TSCA High
O TSCA 90
# TSCA POC
A TSCA Low
O Food Ingredients
SCIL
O SCIL Full Green
O TSCA Active
-0-
20
40
r~
60
80
100
Information Availability Metric
Distributions of metric scores for selected chemical substance lists. For
each list, the point shows the median scientific domain and
information availability metrics. The whiskers span 90% of the
distributions. Data here is taken from the lists across the TSCA Active
Inventory. Uses data from the complete TSCA active inventory.
IS
-------
vvEPA
Proof-of-Concept Results
The larger the value, the fewer the number of chemicals with that type of information
Ecotoxicology, n euro toxicology BAF medium confidence have largest amount of missing data
Human Hazard : acute ]
Human Hazard : subchromc |
Human Hazard : chronic |
Human Hazard : reproductive ~|
Human Hazard : developmental |
Human Hazard : repeat dose |
Human Hazard : neurotoxicity |
Ecological Hazard : repeat dose vertebrate |
Ecological Hazard : repeat dose invertebrate |
Ecological Hazard : repeal dose plant |
Ecological Hazard : acute vertebrate |
Ecotogical Hazard : acute invertebrate |
Ecological Hazard : acute plant |
Genotoxicity: Only predicted genetox data |
Genoioxicity : No genetox data or predictions |
Cancer: No cancer data ~|
Sensitization/lrritation : skin irritation ]
Sensitization/1 rritation ; eye irritation 1
Sensitization/lrrrtation : skin sensitization |
Susceptible Population : No exposure predictions |
Bioaccumulation : No BAF data or models |
Bioaccumulalion : BAF medium confidence |
Bioaccumulation : BAF lew confidence |
I I 1 1 1 1
0.0 0.2 0.4 0.6 0.8 1.0
16
Fraction of POC with IG Flag
-------
Example: Compare Lwo Chemicals
CASRN
Name
Scientific Domain Metric
Information Availability Metric
IG flag human hazard (missing mammalian
hazard data)
IG flag ecological hazard (missing eco hazard
data)
Human hazard-to-exposure ratio metric
Ecological hazard metric
Carcinogenicity metric
Genotoxicity metric
Susceptible population metric
Persistence bioaccumulation metric
Sensitization / irritation metric
HER repeat dose
POD in vivo oral repeat dose
Human exposure (SEEMS)
Ecological min POD
Genotoxicity call
Carcinogenicity call
Skin sensitization metric
Eye irritation metric
Skin irritation metric
Volatile
4435-53-4
3-Methoxybutyl acetate
15.9
60
subchronic, chronic, developmental
acute plant, repeat dose invertebrate,
repeat dose vertebrate
2.3
2.0
0 (no data)
1
2
1
1
13253000
100 mg/kg-day
0.0000075 mg/kg-day
0.71 mg/L
non-genotoxic
L
L
No
71-43-2
Benzene
70.5
93
developmental, reproductive
acute plant, acute invertebrate
2.7
2.0
4
4
4
2
3
11374
0.015 mg/kg-day
0.0000013 mg/kg-day
0.49 mg/L
genotoxic
Group I: carcinogenic to humans
L
H
H
Yes
17
-------
X
Challenges
Data sources are limited
Many chemicals do not have data in any source
Only public data was used, i.e., no CBI data
Largely only use data from other compilations, i.e., do not carry out targeted literature
search and data extraction
Manual data QA/QC is time and resource intensive for thousands of chemicals
CCTE is developing automated pipelines and web-based manual QC tools
Apples and oranges tradeoffs
How to weigh relative concerns of hazard, exposure, physchem properties?
This is finally a policy decision
18
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Summary
The PICS approach was developed to better understand the landscape of publicly
available information for large numbers of chemical substances
It combines results from domain-specific workflows that reflect the overall degree of
potential concern related to human health and the environment with the amount of
relevant information
It is intended to focus expert review on substances that may have a greater potential
for selection as high- or low-priority candidates
The proof-of-concept case study demonstrated that the PICS approach generally
resulted in higher metrics for the high-priority candidates as compared to the low-
priority candidates and identified areas for potential information gathering
The method and software are flexible and can be customized for other prioritization
applications
19
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vvEPA
Data Curation and QC iger Learn
General - John Cowden (NCCT), Richard Judson (NCCT), Amar Singh (NCCT)
QC Data Integration and QA Automation Workgroup - Richard Judson (NCCT), Jeremy Dunne
(NCCT), Amar Singh (NCCT), Chris Grulke (NCCT)
Human Health Hazard/Risk Assessment Workgroup - Johanna Congleton (NCEA), Urmila Kodavanti
(NHEERL), Chris Lau (NHEERL), Mary Gilbert (NHEERL), Yu-Sheng Lin (NCEA), Dan Vallero (NHEERL),
Kelly Garcia (NCEA), Carolyn Gigot (NCEA), Andrew Greenhalgh (NCEA), Allison Eames (NERL)
Ecological Toxicity Data Workgroup - Dale Hoff (NHEERL), Colleen Elonen (NHEERL), Leslie Hughes
(NHEERL), Anita Pascocello (NHEERL)
Exposure Data Workgroup - Katherine Phillips (NERL), Janet Burke (NERL), Abhishek Komandur
(NERL), Ashley Jackson (NERL), Lauren Koval (NERL)
Genotoxicity Data Workgroup - David DeMarini (NHEERL), Maureen Gwinn (NCCT), Catherine
Gibbons (NCEA), Sarah Warren (NHEERL), Jeff Dean (NCEA), Anita Simha (NCCT), Nagu Keshava
(NCEA)
Chemistry Data Workgroup - Kent Thomas (NHEERL), Michael Gonzalez (NRMRL), Doug Young
(NRMRL), Chris Grulke (NCCT)
-------
^>EPA Proof-of-Concept iger IIearn
General - Maureen Gwinn (NCCT), Richard Judson (NCCT), Amar Singh (NCCT)
Information availability - Tony Williams (NCCT), Jeremy Dunne (NCCT), Jason Lambert
(NCCT)
Human Hazard-to-Exposure Ratio - Katie Paul-Friedman (NCCT), John Wambaugh (NCCT),
Elaina Kenyon (NHEERL), Kristin Isaacs (NERL), Jason Lambert (NCCT)
Susceptible Population Exposure - Kathie Dionisio (NERL), Kristin Isaacs (NERL), John
Wambaugh (NCCT)
Carcinogenicity/Genotoxicity - Grace Patlewicz (NCCT), David DeMarini (NHEERL),
Catherine Gibbons (NCEA), Jeffry Dean (NCEA), Anita Simha (NCCT), Nagu Keshava (NCEA),
Todd Martin (NRMRL), Sarah Warren (NHEERL)
Eco Hazard - Dan Villeneuve (NHEERL), Carlie La Lone (NHEERL), Todd Martin (NRMRL)
Persistence/bioaccumulation - John Nichols (NHEERL), Lawrence Burkhard (NHEERL), Eric
Weber (NERL)
Skin sensitization/irritation and Eye irritation - Todd Martin (NRMRL), Leora Vegosen
(NRMRL)
Draft Deliberative - do not cite or quote 2 I
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oEPA
Developmental Neurotoxicity (DNT) in vitro Battery as
an Alternative to DNT in vivo Guideline Studies Used by
OPP
Tim Shafer
Board of Scientific Counselors Subcommittee
Chemical Safety for Sustainability and
Health and Environmental Risk Assessment National Research Programs
Virtual Meeting
February 3, 2021
The subsequent presentation has been cleared by the Office of Research and Development but is not Agency Policy. This
presentation contains unpublished data.
Progress for o Stronger Future
-------
oEPA
Outline
I. (Re)-lntroduction to CSS Research on alternative approaches for developmental
neurotoxicity (DNT) hazard assessment
II. International Efforts on use of NAMs for DNT hazard assessment
III. Application of NAMs to OCSPP issues.
IV. Future Directions
-------
oEPA
Status of DNT NAMs Research in CSS
2008 2019 2022
ฆ1
Assay Development .
Assay
Assay Implementation
-------
oEPA
Issues with in vivo DNT studies
"Triggered'* test- Only requested if concern for neurotoxicity
Expensive- ~$l,000,000/chemical
Time-consuming- takes 1-2 years to complete
Ethically questionable- Estimated ~1000 animals/test
Value of Information
High variability; low precision
Not often used (~25%) for point of departure values for risk assessment*
Only ~150 compounds have DNT Guideline Studies
Problem for OPPTS and OPP
*Raffaele et al. The use of developmental neurotoxicity data in pesticide risk assessments. Neurotoxicol Teratol. 2010 Sep-Oct;32(5):563-72.
4
-------
xv EPA
Addressing the limitations of the DNT Guideline Study
by using Phenotypic Screens
Critical Processes of Nervous
System Development
-> J
i
Synaptogenesis
w
Proliferation
Differentiation
/ ]f Neurite growth
f
ฆ> *
Cognition
& Behavior
Myelination Neural network
formation & function
Migration
5
-------
The EPA Assay Battery
Proliferation
Apoptosis
Neurite initiation
Neurite initiation
Neurite maturation
Synaptogenesis
Network formation
(MEA)
Behavior/Anatomy
Each assay has concurrent assessments of
cell health/viability and has been vetted
with assay positive controls as well as by
testing DNT reference compounds.
-human neuroprogenitors (hNPl)
-human neuroprogenitors (hNPl)
-human neurons (hN2, iCell )
-rat primary neural culture
-rat primary neural culture
-rat primary neural culture
-rat primary neural culture
-zebrafish
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oEPA
High Content Imaging: Overview
Automated microscopy providing data at the level of the individual cell
High throughput: automated data acquisition and analysis in multi-well plates
High content; large amounts of data from a single image.
Multiwell Culture Immunocytochemistry Image Acquisition Image Analysis
Feature Extraction
Epifluorescence microscope and digital camera in a box
Automated stage movement, exposure, and focusing capabilities
Computer algorithms analyze the images to provide cell-based data (e.g. size, shape, location, fluorescence
intensity)
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oEPA
Measurement of Network Formation in vitro using
Microelectrode Array (MEA) Recording
Bis-1
Mean Firing Rate # Active Electrodes Burst Rate # Actively Bursting
(spikes/min) (bursts/min) Electrodes
"Brain-on-a-Chip": Complex 2D model
Rat cortical neural networks
Contains neurons & glia cells
Spontaneous activity
Develops rapidly in vitro
Follow network development overtime
Integrates activity of multiple processes
A snapshot in time of neural network activity in one well.
Each box represents the electrical activity of neurons on 1
electrode in the array.
Control
0.03
8
-------
oEPA
International Efforts on DNT NAMs
SOT Todetrf
Uvy J_ loxicology
www.toxsci. oxfordjournals.org
TOXICOLOGICAL SCIENCES, 167(1), 2019,4S-57
doi: 10.1093/toxsc i/kfy211
Advance Access Publication Date: November 23,2018
Forum
Table 2. Proposed Assays for Evaluation As an In Vitro DNT Battery
FORUM
International Regulatory and Scientific Effort for
Improved Developmental Neurotoxicity Testing
Magdalini Sachana,*'1 Anna Bal-Price,t Kevin M. Crofton* Susanne H.
Bennekou,5 Timothy J. Shafer,11 Mamta Behl," and Andrea Terron1"
Towards regulatory DNT testing: Alternative methods
Figure 1. Timeline of efforts to develop and implement new alternative methods for developmental neurotoxicity.
Process
Assays
References
Proliferation
hNPl
Harrill et al. (2018)
NPC1
Baumann et al. (2016)
and Barenys et al.
(2017)
UKN1
Balmer et al. (2012)
Apoptosis
hNPl
Harrill et al. (2018)
Migration
NPC2
Baumann et al. (2016)
and Barenys et al.
(2017)
UKN2
Nyffeler et al. (2017)
Neuron differentiation
NPC3
Baumann et al. (2016)
and Barenys et al.
(2017)
Oligodendrocyte
NPCS/6
Baumann et al. (2016)
differentiation &
and Barenys et al.
maturation
(2017)
Neurite outgrowth
iCell gluta hN2
Harrill et al. (2018)
UKN 4 & 5
Kruget al. (2013)
NPC4
Baumann et al. (2016)
and Barenys et al.
(2017)
Synaptogenesis
Rat primary
Harrill et al. (2018)
synaptogenesis
Network formation
MEA-NFA
Brown et al. (2016) and
Frank et al. (2018)
-------
A EPA DNT NAMs Provide Good Coverage of Neurodevelopmerrta! Processes
Proliferation
hNPl
Apoptosis p ,
1 Apop
Differentiation
UKN2
NPC3-5
UKN2 ...
NPC2 Migration
Synaptogenesis
Syriap
Neurite growth
UKN4&5
RatCort_NOG
iCell NOG
MEA-NFA
MEA-AcN
Myelination
NPC6
Neural network
formation & function
Aschner of a I., 2016
-------
oEPA
OECD/EFSA-EPA Collaboration
Assays
Synaptogenesis
Chemical Proliferation Ann ptosis Neurrte Outgrowth
Class | Differentiation j Migration
Growth
Net Fen |Behavior
ABCOE 1 2 3 4 S 6789 101112 13 14 IS 1617 18 19202122232425 26 2728 29 3031
Species: ฆHuman ฆRodent ฆAlternative
Development of a Guidance Document for the use of DNT alternative assays in
Integrated Approaches for Testing and Assessment (lATAs)
Guidance for incorporation of in vitro assays into lATAs
Case Studies
Draft Guidance document expected mid 2021
-------
oEPA
Use of DNT NAMs at EPA
I. Screening Level information
APCRA, TSCA, PFAS
II. Understanding species differences
Data from DNT NAMs provided to OPP to help understand rodent-human differences in response to
chemicals since the battery has both rodent and human assays
III. Structure-activity relationships
OPP requested data from selected assays on a set of structurally similar compounds
A DNT Guideline study existed for one compound ("compound X")
Assays were selected based on the of activity of compound X in Guideline Study.
Structurally similar compounds were tested in vitro
OPP will use the data from the in vitro screens in WOE approach to deciding whether or not to
request DNT guideline studies on the other compounds (Decisions are in progress).
IV. Weight of Evidence approaches
Organophosphates
12
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oEPA Organophosphates and DNT
Organophosphate insecticides are currently regulated based on inhibition of
acetylcholinesterase (AChE):
Primary Questions:
1) Does the DNT battery indicate that this may not be health protective?
2) Can data from the DNT battery contribute to a WOE approach for OPs?
13
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oEPA Organophosphates and DNT
Study Design:
Test 27 Organophosphate insecticides in the EPA DNT assays
8 Parent/oxon pairs
Concentration-response up to 100 |iM
Pipeline results through TCPL to generate AC50 values
Use HTTK to convert AC50 values to AED50 values
Compare to BMD/BMDL10 values based on AChE inhibition
Assays:
Proliferation
Apoptosis
Neurite initiation
Neurite initiation
Neurite maturation
Synaptogenesis
Network formation
(MEA)
Behavior/Anatomy
human neuroprogenitors (hNPl)
human neuroprogenitors (hNPl)
human neurons (hN2)
rat primary neural culture
rat primary neural culture
rat primary neural culture
rat primary neural culture
zebrafish (data analysis pending)
14
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xv EPA
OPs demonstrate differential responses in the HCI
assays.
Color Key
-6 -2 2
Value
Activity Type
NOG initiation, rat
Synaptogenesis/maturation, rat
NOG initiation, hN2
Apoptosis/viability, hNP1
Proliferation, hNP1
Diazoxon_TT0000177G01 j
Acephate_EPAPLT0167A01 '
Dicrotophos_TT0000177H03
Fosthiazate_TT0000177B04
Malaoxon_TT0000177B03
Profenofos_TT0000177A01
Tebupirimfos_TT0000177C02
ฆ
OmethoateJTOOOOl77C04
Methamidophos_EPAPLT0167A08
ฆ
Ethoprop_TT0000177D01
Dichlorvos_TT0000177C01
m
Diazinon EPAPLT0170D06
ฆ
Chlorpyrifos oxon_EX000378
Phosmet_TT0000177C03 2
ฆ
ฆ
ฆ
Phorate_TT0000177F02
ฆ
Dimethoate_E PAPLT0167G06
Trichlorfon EPAPLT0170D03
ฆ
ฆ
Chlorethoxyfos_TT0000177G03
Tribufos_TT0000177F03 3
Naled_TT0000177E03
Terbufos TT0000177E01
ฆ
ฆ
ฆ
ฆ
ฆ
ฆ
ฆ
ฆ
ฆ
ฆ
Pirimiphos-methyl_TT0000177D03
ฆ
ฆ
ฆ
Chlorpyrifos_EX000384
Malatt>ion_EPAPLT0167G08
ฆ
Coumaphos_TT0000177A02
Z-T etrachlorvi nphos_TT0000177B 01 4
Bensu lide_TT0000177A03
S J /pV'/'/
////////a
-
v//x
Cluster 1: negative or with effects in 1-3 endpoints.
Cluster 2: effects on 5 or more assay endpoints
Cluster 3: OP samples with effects on all HCI assay
activity types except for NOG initiation in hN2 cells
and synaptogenesis n cortical cells
Cluster 4: widespread effects across activity types
\7 *
/ V /' /'
c? cf^ cP
15
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oEPA
Most OPs decreased MEA NFA activity
Color Key
i : m
-6-2 2 6
Value
Oxon structure
1
>-E
C
u
Activity Type
Cytotoxicity
General
Bursting
Network Connectivity
T erbufos_TT0000177E01
Malathion_TT0000177D02
Chlorpyrifos_EX000384
Naled_TT0000177E03
Tebupirimfos_TT0000177C02
Pirimiphos-methyl_TT0000177D03
Chlorpyrifos_TT0000177E02
Bensulide_TT0000177A03
Coumaphos_TT0000177A02
Chlorethoxyfos_TT0000177G03
Tribufos_TT0000177F03
Trichlorfon_TT0000177F01
Phorate_TT0000177F02
Diazinon_TT0000177H01
Malathion_EPAPLT0167G08
Dimethoate_TT0000177H02
Phosmet_TT0000177C03
Ethoprop_TT0000177D01
Chlorpyrifos oxon_EX000378
Z-Tetrachlorvinphos_TT0000177B0
T richlorfon_EPAPLT 0170D03
Dimethoate EPAPLT0167G06
Chlorpyrifos oxon J IU0001//GU2
Acephate_TT0000177A04
Malaoxon_TT0000177B03
Methamidophos_TT0000177B02
Diazoxon_TT0000177G01
Diazinon_EPAPLT0170D06
Acephate_EPAPLT0167A01
Dicrotophos_TT0000177H03
FosthiazateJT0000177B04
Dichlorvos_TT0000177C01
Profenofos_TT0000177A01
Omethoate_TT0000177C04
Methamidophos_EPAPLT0167A08
xO xO ^ X<> xO x<> xO
r r mS= v ซฆ< ;
^ Af
<#' t.f
Top active cluster of OPs contains oxon
and non-oxon structures.
These OPs, like the assay performance
controls and many other compounds,
appear to generally decrease all activity
types and most assay endpoints.
Bottom cluster with minimal actives
appears somewhat driven by cytotoxicity
in the LDH assay.
Negative- 0 assay endpoints altered
Equivocal-1-3 assay endpoints altered
Positive- >3 assay endpoints altered
16
-------
xv EPA
Overall, there was agreement between the HCI and
MEAJMFA assays
DTXSID
Chemical
MEA NFA
HCI
Neg Equiv
Pos
1
2
3
4
DTXSID8023846
Acephate
X X
X
DTXSID9032329
Bensulide
X
ฆ
H
DTXSID2032344
Chlorethoxyfos
X
X
DTXSID4020458
Chlorpyrifos
x,x
ฆ
m
DTXSID 1038666
Chlorpyrifos
X
X
X
oxon
DTXSID2020347
Coumaphos
X
X
DTXSID9020407
Diazinon
X
X
X
DTXSID5037523
Diazoxon
X
X
DTXSID5020449
Dichlorvos
X
X
DTXSID9023914
Dicrotophos
X
X
DTXSID7020479
Dimethoate
X
X
DTXSID4032611
Ethoprop
X
X
DTXSID0034930
Fosthiazate
X
X
DTXSID9020790
Malaoxori
X
X
DTXSID4020791
Malathion
X
X
DTXSID6024177
Methamidophos
X X
X
DTXSID 1024209
Naled
X
X
DTXSID4037580
Omethoate
X
X
DTXSID
Chemical
Neg Equiv
Pos
EM
3
D
DTXSID4032459
Phorate
X
X
DTXSID5024261
Phosmet
X
X
DTXSID0024266
Pirimiphos-methyl
X
X
DTXSID3032464
Profenofos
X
X
DTXSID 1032482
Tebupirimfos
X
X
DTXSID2022254
Terbufos
X
X
DTXSID 1024174
Tribufos
X
X
DTXSID0021389
Trichlorfon
X
X
DTXSID 1032648
Z-
Tetrachlorvinphos
X
X
Equiv or Pos in MEA NFA and negative in HCI: Acephate, diazoxon,
dicblorvos, dicrotophos, fosthiazate, malaoxon, omethoate, profenofos
Positive in MEA NFA and negative in HCI: Ethoprop
Positive in HCI and negative in MEA NFA: OP chemical (methamidophos)
was neg/equiv in the MEA NFA
If activity is observed in the HCI assays, it is likely that the OP chemical
will also be active in the MEA NFA.
-------
xv EPA
v
V
For some OPs, DNT-NAM AC50 < bioactivity estimate
from the rest of ToxCast.
5th-%ile ToxCast AC50 ~ Min DNT-NAM AC50
Burst
DNT-NAM battery may provide a more potent estimate of
bioactivity for substances with minimum DNT-NAM AC50
< 5th percentile of filtered ToxCast AC50 values:
Chlorpyrifos and chlorpyrifos oxon
Acephate
Dichlorvos
Terbufos
Diazoxon
Methamidophos
Suggests that the DNT-NAM battery, in covering
some new biology not previously in ToxCast, may
yield bioactivity threshold concentrations lower
than what is already available for some
neuroactive substances in ToxCast.
Chlorpyrifos
Acephate
Dichlorvos
Phorate
Terbufos
Naled
Phosmet
Diazoxon
Ethoprop
Omethoate
Fosthiazate
Tribufos
Chlorethoxyfos
Dicrotophos
Chlorpyrifos oxon
Profenofos
Pirimiphos-rnethyl
Malaoxon
Methamidophos
Diazinon
Tebupirimfos
Z-Tetrachlorvinphos
Malathion
Coumaphos
Dimethoate
Bensulide
Trichlorfon
ฆn-
ฆ n>
m-
-CD-
-Q!
m
~ ฆ -HD
i n
I
-~>
-CD-
i ~-
T
*
HZZ~-
D
-{EJD-
-CZE-
I I
a:
i
-10 12
log 10 micromolar value
A.
14211093
8 f1209
52/1420
39/880
79/1280
205/1196
57/1395
14/957
43 / 970
3/407
40 / 934
119/1181
74 / 949
7/927
178/982
92/998
106 / 992
41/1245
7/1178
89/1472
135 / 946
96/488
115/1442
205/1509
23/1228
412/2148
99/1390
5 18
-------
oEPA
AED50 to BMD/BMDL10 comparisons
human
1000
100
10
1
0.1
0.01
0.00H
1e-04
1000
100-
10-
H
0.1
0.0H
0.001
1e-04J
(C
o
-E
E
Q
Q.
2
Q.
O
-E
LU
rat
% o
o not selective
selective
ฆ NA
BMD10
BMDL10
huBMDIO
huBMDLIO
O ฐ fSSSi
wm ฐ ฎ
o ฐ c.|
o
t=2| oฐ
" ' OD O
o
Hum, AED50, hum cells
Rat, AED50, rat cells
huRat, AED50, rat cells
19
-------
oEPA X Summary of the AED50 to BMD/BMDL comparison
Chemicals with AED50
values ป> BMD/BMDL
comparator
Chemicals with lowest
AED50 within 1 loglO
order of magnitude of
BMD/BMDL comparator
Chemicals with lowest AED50 approaching BMD/BMDL
comparator
Missing in vitro data for
comparison
Rat/HuRat
Coumaphos, diazoxon,
dicrotophos, ethoprop,
fosthiazate, omethoate
acephate, bensulide,
chlorpyrifos, chlorpyrifos
oxon, diazinon,
dimethoate, malathion,
methamidophos, and
phorate
dimethoate and methamidophos (lower quartile of huRat
AED50 values
dichlorvos (huRat AEDcn: onlv one positive rat assav
endpoint) overlaps with the BMDL10 value, and it was not
based on selective bioactivity in the DNT-NAM battery.
malathion (huRat AEDcn (selective) for also approach the
BMD/BMDL10 values.
Malaoxon (negative in
all assays)
Human
bensulide, chlorpyrifos,
chlorpyrifos oxon,
coumaphos, diazinon,
dimethoate, malathion,
methamidophos,
phosmet, pirimiphos-
methyl, tribufos, and
trichlorfon
dichlorvos, onlv two AEDcn values are available for
comparison, and these values are centered around the
BMD10/10 and BMDL10/10 values.
terbufos, onlv 3 human AEDcn values are available for
comparison, and the lowest one of these values
approaches the BMD10/10 value.
Negative in all assays
with human cells:
Acephate, diazoxon,
dicrotophos, ethoprop,
fosthiazate, omethoate,
phorate, profenofos,
and tebupirimfos
Malaoxon was negative
in all assays.
-------
oEPA
AEDs from DNT NAMS are more sensitive than LOAELs for
other compounds
OXFORD
SOT T(>c-etrf
OV7 1 loxicology
www.toxsci.oxfordjournals.org
TOXICOLOGICAL SCIENCES, 169(2), 2019, 436-45S
doi: 10.1093/toxsci/kfz052
Advance Access Publication Date: February 28, 2019
Research Article
AED Min EC50 AED Min Tppt A LOAEL \7 Min Dose Tested
Evaluation of Chemical Effects on Network Formation
in Cortical Neurons Grown on Microelectrode Arrays
Timothy J. Shafer,*'1 Jasmine P. Brown,*'2 Brittany Lynch,1"
Sylmarie Davila-Montero,* Kathleen Wallace,* and Katie Paul Friedmanง
Even though AEDs were not more sensitive than BMDLs
for OPs, DNT NAMs can still be sensitive indicators of
potential disruption of nervous system development
DDT
Carbofuran
Boscalid
Moliriate
Bifenthrin
Simvastatin
<0
o
E S-Bioallethrin
1-
-5 -4
-3-2-10 1 2
Iog10-mg/kg/day value
21
-------
oEPA
Overall conclusion
The development of a DNT-NAM battery for assessing potential DNI -related
effects:
Provides an opportunity to overcome some of the challenges with the in vivo DNT guideline study
Evaluates critical processes underlying neurodevelopment
Incorporating human relevant information.
Represents a significant advancement toward developing a DNT-NAM battery for DNT evaluation.
Is currently being utilized for a variety of regulatory decision-making processes at EPA
22
-------
oEPA
Future Di rections
I. Continue to Improve Current Assays
I. Scale up to higher throughput
II. Increase # compounds tested
II. Contribute to Development of AOPs (CSS 4.2.4)
III. Incorporate Next Generation Technologies
IV.Incorporate 3D Models
23
-------
oEPA X Collaborators
EPA
Theresa Freudenrich
Kathleen Wallace
Jasmine Brown
Chris Frank
Stephanie Padilla
Josh Harrill
Megan Culbreth
Bill Mundy (retired)
Kevin Crofton (retired)
Katie Paul-Friedman
Richard Judson
Anna Lowit (OPP)
Monique Perron (OPP)
Liz Mendez (OPP)
Sarah Dobreniecki (OPP)
Support:
EPA CSS Research Program
EPA Pathway Innovation Projects
University of Konstanz
Marcel Leist
Johanna Nyffeler
Diisseldorf
Ellen Fritsche
-------
f/EPA
United States
Environmental Protection
Agency
ExpoCast
exposure forecasting
Implementing a Workflow for Exposure Screening of
Drinking Water Contaminants of Concern
Kristin Isaacs
US EPA CSS-HERA Board of Scientific Counselors
Chemical Safety Subcommittee Meeting
February 2-5, 2021
The views expressed in this presentation are those of the author and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
-------
4>EPA
United States
Environmental Protection
Agency
Background
CRADA
COOPERATIVE RESEARCH AND
DEVELOPMENT AGREEMENT
mi
DEPARTMENT
OF HEALTH
ฃ
5
33
\
^fcD S7^
&
PRO*^0
ro
z
HI
a
The US Environmental Protection Agency's Center for
Computational Toxicology and Exposure (CCTE) and the
Minnesota Department of Health (MDH) are collaborating to
use new chemical data generated from scientific approaches
such as read-across, QSAR, high-throughput toxicology
screening, and computational modeling of exposure and
toxicokinetics to prioritize chemicals for further evaluation
and inform risk assessment
CCTE and MDH finalized a formal Cooperative Research and
Development Agreement (CRADA) in 2019
CRADA has a goal of addressing up to five MDH chemical
evaluation activities
2 of 28
Office of Research and Development
US EPA OSS-HERA BOSC Meeting - February 2-5, 2021
-------
4>EPA
United States
Environmental Protection
Agency
Problem: MDH CEC Initiative
^CLEAN
TM WATER
LAND &
LEGACY
AMENDMENT
mi
DEPARTMENT
OF HEALTH
Through its Contaminants of Emerging Concern (CEC) initiative, the
Minnesota Department of Health (MDH) collaborates with partners and
the public to identify contaminants of interest in drinking water
Substances that have been released to, found in, or have the potential
to enter Minnesota waters, and:
Real or perceived health threat,
No current Minnesota human health-based guidance
New information that increases the level of concern
3 of 28
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
oEPA
United States
Environmental Protection
Agency
Problem: MDH CEC Initiative
MDH CEC
Nomination
Eligibility
Toxicity
Exposure
Screening
Screening
Through its Contaminants of Emerging Concern (CEC) initiative, the
Minnesota Department of Health (MDH) collaborates with partners and
the public to identify contaminants of interest in drinking water
Substances that have been released to, found in, or have the potential
to enter Minnesota waters, and:
Real or perceived health threat,
No current Minnesota human health-based guidance
New information that increases the level of concern
Substances selected via a nomination process, followed by:
* Screening-level evaluation and ranking of nominated chemicals
based on exposure and toxicity potential
Screening informs selection of contaminants for an in-depth
toxicological review and guidance development
4 of 28
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
oEPA
United States
Environmental Protection
Agency
Problem: CEC Exposure Screening
MDH CEC
Nomination
Eligibility
Toxicity
Screening
\ 1
Exposure
Exposure screening was identified by MDH as a high-
priority workflow for implementation under the CRADA
Past approach: manual exposure screening by MDH staff
Data identification is time-consuming process (multiple
days to a week for 1 chemical)
Disparate data sources
Synthesis can be challenging
Scoring is also manual: tedious/unreproducible
Many chemicals are data-poor based on traditional
approaches (for example, existing regulatory exposure
assessments, traditional monitoring data)
5 of 28
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US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
&EPA
United States
Environmental Protection
Agency
Approach
Establish collaboration between MDH and CCTE accelerate the exposure screening
process
Develop a proof-of-concept automated workflow for scoring chemicals and reporting
results according to MDH screening criteria
Incorporate New Approach Methodologies (NAMs) for exposure from ORD's Exposure
Forecasting (ExpoCast) project
Apply workflow to two chemical lists
87 chemicals previously manually evaluated by MDH (for assessment of workflow
performance)
171 proof-of-concept chemicals of interest to MDH and EPA
6 of 28
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
4>EPA
United States
Environmental Protection
Agency
CEC Exposure Screening Criteria
Uses components of the US EPA's Office Water Candidate
Contaminant List (CCL) methodology and incorporates the
recommendations from MDH Stakeholder Task Group
Considers data and criteria associated with multiple
domains, including
Chemical identity and use
Chemical properties
Chemical emissions and disposal
Chemical occurrence in environment, drinking water,
and food
Human exposure potential
Incorporates MN information where possible
7 of 28
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
oEPA
United States
Environmental Protection
Agency
CEC Exposure Screening Criteria
Uses components of the US EPA's Office Water Candidate
Contaminant List (CCL) methodology and incorporates the
recommendations from MDH Stakeholder Task Group
Main Scoring Criteria
Persistence and Fate
Release Potential
Occurrence
Unadjusted
Score
Scoring Adjustments (+/-)
Chemical Identity
Exposure Potential
Detection Frequency
Score
Adjustments
Final Score
Considers data and criteria associated with multiple
domains, including
Chemical identity and use
Chemical properties
Chemical emissions and disposal
Chemical occurrence in environment, drinking water,
and food
Human exposure potential
Incorporates MN information where possible
Office of Research and Development
Evaluates and scores chemicals using algorithm developed
by MDH (primary unadjusted score + score adjustments=
final score)
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
4>EPA
United States
Environmental Protection
Agency
Eight Classes of NAMs for Exposure from the
ExpoCast Project
ELSEVIER
Current Opinion in Toxicology
Available online 31 July 2019
In Press, Journal Pre-proof (?)
^ Toxicology
ซv
New Approach Methodologies for Exposure
Science
John F. Wambaugh 1A S3, jane C. Bare 2, Courtney C. Carignan 3, Kathie L Dionisio 4, Robin E.
Dodson 5| 6, Olivier Jolliet7, Xiaoyu Liu 8, David E. Meyer 2, Seth R. Newton 4, Katherine A. Phillips 4,
Paul S. Price4, Caroline L. Ring9, Hyeong-Moo Shin 10,Jon R. Sobus4 TamaraTal u, Elin M. Ulrich
4, Daniel A. Vallero 4, Barbara A. Wetmore 4, Kristin K. Isaacs 4
Chemical descriptors that provide information on chemicals in an
exposure context (e.g., how chemicals are used)
Machine-learning approaches that use these descriptors to fill gaps in
existing data
High-throughput exposure models that address various pathways
High-throughput measurements that fill gaps in monitoring data
High-throughput approaches that measure or predict chemical
toxicokinetics
New evaluation frameworks that integrate models and monitoring to
provide consensus exposure predictions
All these pieces together provide can accelerate high-
throughput risk-based chemical prioritization
9 of 28
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
&EPA Workflow Design and Implementation
United States
Environmental Protection
Agency
Data Curat ion
MN-specific documents and other source
documents extracted and curated into ORD's
research databases via the Factotum curation
application.
QA, document provenance, audit tracking
ORD's
"Factotum'
' Curation Application
1
fete
ORD "Research1
" Databases
10 of 28
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
4>EPA
United States
Environmental Protection
Agency
Workflow Design and Implementation
Data Curat ion
MN-specific documents and other source
documents extracted and curated into ORD's
research databases via the Factotum curation
application.
a ป ^
=ix
Other public data streams, e.g.
USGS webservices or datasets
not yet incorporated into formal
ORD databases
11 of 28
Office of Research and Development US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
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4>EPA
United States
Environmental Protection
Agency
Workflow Design and Implementation
Data Curat ion
MN-specific documents and other source
documents extracted and curated into ORD's
research databases via the Factotum curation
application.
875 Thousand Chemicals
Product/Use Categories Assay/Gene
Identifier substring search
Other public data streams, e.g.
USGS webservices or datasets
not yet incorporated into formal
ORD databases
CompTox Chemicals Dashboard
"Workflow-Specific Data Mart"
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
4>EPA
United States
Environmental Protection
Agency
Workflow Design and Implementation
Data Curat ion
MN-specific documents and other source
documents extracted and curated into ORD's
research databases via the Factotum curation
application.
QA, document provenance, audit tracking
ORD's "Factotum" Curation Application
ORD "Research" Databases
a ป ^
=ix
Other public data streams, e.g.
USGS webservices or datasets
not yet incorporated into formal
ORD databases
t
R
L. A
Data retrieval arid caching
875 Thousand Chemicals
Product/Use Categories Assay/Gene
Identifier substring search
CompTox Chemicals Dashboard
"Workflow-Specific Data Mart"
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
4>EPA
United States
Environmental Protection
Agency
Workflow Design and Implementation
Data Curat ion
MN-specific documents and other source
documents extracted and curated into ORD's
research databases via the Factotum curation
application.
QA, document provenance, audit tracking
ORD's "Factotum" Curation Application
Main Scoring Criteria
Persistence and Fate
Release Potential
Occurrence
Unadjusted
Score
Scoring Adjustments (+/-)
Chemical Identity*
Exposure Potential
Detection Frequency
Score
- Adjustments
Final Score
Other public data streams, e.g.
USGS webservices or datasets
not yet incorporated into formal
ORD databases
t
R
k. A
>r~
Data retrieval arid caching
Chemical scoring
Summary report and data
table generation
875 Thousand Chemicals
Product/Use Categories Assay/Gene
Identifier substring search
CompTox Chemicals Dashboard
"Workflow-Specific Data Mart"
Automated Reporting and Data
Generation for In-Depth Assessment
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
4>EPA
United States
Environmental Protection
Agency
Curation of Chemical Use Descriptors with Factotum
We are using informatics approaches to
obtain and curate chemical use
descriptor information
Public data sources: reports, consumer
product ingredient data, etc.
Utilizing standard curation/QA
procedures and tools
Currently supports EPA's Chemical and
Products Database
Integrates with ORD's chemical
curation workflows
Allowed us to curate many MN-specific
documents for use in the workflow
Raw Public
Documents
"Factotum"
Curation
Application
Document Loading, Data
Extraction, Chemical and
Product Curation
Curated
Research
Database
FOR CHEMICAL EMERGENCY
Evaluation of Ergonomics,
Chemical Exposures, and
Ventilation at Four Nail Salons
HHE Report No. 2015-0139-3338
15 of 28
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US EPA CSS-HERA BOSC Meeting - February
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4>EPA
United States
Environmental Protection
Agency
Multimedia Monitoring Database (MMDB)
ORD research database of measurements from over 20 public data sources
Includes data from several EPA programs, California state monitoring
programs, the FDA, the Comparative Toxicogenomics Database, the EU's
Information Platform for Chemical Monitoring Data (IPCHEM), the National
Health and Nutrition Examination Survey (NHANES), the USDA, the
International Council for the Exploration of the Sea (ICES), and the
International Council of Chemical Associations' Long-Range Research Initiative
(ICCA-LRI)
Harmonized to chemical identifier and media (e.g., drinking water, surface
water, human blood or urine, soil, food, and ecological species).
Developed in collaboration with OPPT
Contains over 250 million individual data records covering over 3200 unique
chemicals
Basis for future QSAR-like models for occurrence in different media
Manuscript for submittal for peer-reviewed publication in internal EPA clearance
16 of 28
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
oEPA
United States
Environmental Protection
Agency
* Incorporate
Exposure
NAM data
Data Source Summary
Chemical Identity and Use
Chemical Identifiers and Synonyms
EPA-ORD's CompTox Chemicals Dashboard/Underlying Databases
Uses
EPA-ORD's Chemicals and Products Database1 (CPDat)
Uses
EPA's Chemical Data Reporting (CDR) Consumer, Commercial, Industrial uses
National Production Volume
EPA-ORD's CompTox Chemicals Dashboard (Underlying data)
Uses
EPA Safer Chemical Ingredients List
Chemical Properties
Measured Properties
EPA-ORD's CompTox Chemicals Dashboard/Underlying Databases
Predicted Properties
EPA-ORD's CompTox Chemicals Dashboard (OPERA QSAR Models4)
Predicted Wastewater Treatment Removal
EPA's Estimation Program Interface Suite (EPI-Suite)
Transformation Products
EPA-ORD's CompTox Chemicals Dashboard/Underlying Databases
Chemical Emissions and Disposal
Pesticide Releases
National Agricultural Statistic Service
Chemical Releases
EPA's Toxics Release Inventory
Down-the-Drain Releases
EPA's SHEDS-HT model
Chemical Occurrence in Environment, Drinking Water, and
Food
Occurrence in Environmental Media, Including Drinking and Surface
Water
EPA-ORD Multimedia Monitoring Database (MMDB)
Occurrence in US Water
US Geological Survey (USGS) Water Quality Portal data, via its application programming interface (API)
Occurrence in MN Water
Custom Database developed by USGS for MDH
Occurrence in MN Water
MN-specific reports, curated into EPA's chemical databases
Occurrence in Food
US Department of Agriculture (USDA) Pesticide Data Program
Occurrence in Food
US Food and Drug Administration (FDA) Substances Added to Food Database
Occurrence in Food
US Food and Drug Administration (FDA) Indirect Food Additives Database
Human Exposure
Intake Exposures Inferred from Biomonitoring Data
Biomonitoring Data
Consumer Exposure Predictions
General Population Exposures
Presence on Biomonitoring Lists
EPA-ORD's CompTox Chemicals Dashboard/Underlying Databases
EPA-ORD Multimedia Modeling Database (MMDB)
EPA-ORD's SHEDS-HT Model
EPA-ORD's Systematic Empirical Evaluation of Models (SEEM) Consensus Predictions
Biomonitoring California
17 of 28
Office of Research and Development US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
oEPA
United States
Environmental Protection
Agency
The automated workflow was applied to the 258
chemicals (87 evaluated by MDH previously, 171 on
the current proof-of-concept list)
Also defined an "Information Availability Score" ฃ
o
All data collection, scoring, and report/table writing ^
were completed in approximately 18 hours >,
03
"ro 2.0-
<
c
0
CD
1
M
c
1.2
18 of 28
Office of Research and Development
Results
CRADA List
Data Needed
MDH Evaluated
Benzophenone
t Tributyl phosphate
* Triphenyl phosphate
Anthraquinone
t
Benzene
L, ., . 1-Butanol
Hexachlorobenzene.
. VEthylbenzene
Aniline y
. ' 4-Nonylphenol. branched
Bromoform
* ^yclopenta[g]-2^enzogyian-4-9Tf 6.7.8^exahy(fro^t.^^^l]^a0mitl?y^
Carbon disulfide* 3-Methvlindole* ^Nicotine
' . | Copper
ป Ethoprop Aluminum
ฆ ' t # * * Zinc
Meprobamate Estrone lron
4-Nonylphenol^, Arsenic Lead
11 -otone Cotinine Metformin Phenol
5-MethyMH-benzotnazole ahซr\*MercUry
Dimethipm # ^ j Cadmium
Carbadox Hexabromocyclododecane
Bis(4-hydroxyphenyl)methane
~ / %
|-Androstene-3.17-dione* Cotinine Metformin Phenol
..* .1 . % ....
4
Final Score
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
oEPA
United States
Environmental Protection
Agency
The automated workflow was applied to the 258
chemicals (87 evaluated by MDH previously, 171 on
the current proof-of-concept list)
Also defined an "Information Availability Score'' ฃ
o
All data collection, scoring, and report/table writing ^
were completed in approximately 18 hours >,
Many of the chemicals with the highest scores (>5) '3
have already been screened by MDH. r?
ro 2.0
<
c
0
CD
1
M
c
19 of 28
Office of Research and Development
Results
CRADA List
Data Needed
MDH Evaluated
Benzophenone
Triphenyl phosphate
1-Butanol
Tributyl phosphate
Anthraquinone
Benzene
Hexachlorobenzene. -
. VEthylbenzene
Aniline y
, ' 4-Nonylphenol. branched
Bromoform *
X;yclopenta[g]-2^benzoฃyian-4-9^.6J.8^exahylro^t^^^l]^a0mงtl?y^
^ ^ 3-Methylindole * Nicotine
Carbon disulfide-
ป
-
.* .1
t .
Ethoprop Aluminum
Meprobamate Estrone
Iron
Copper
Zinc
4-Nonylphenol .ArsenicLead
Cotinine |Metformir| Phenol
4-Androstene-3.17-dione
.* 5-MethyMH-benzotriazole Mercury
Dimethipin ] *Cadm,um
Hexabromocyclododecane
Dimethipir
Carbadox
Bis(4-hydroxyphenyl)methane
Final Score
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
oEPA
United States
Environmental Protection
Agency
The automated workflow was applied to the 258
chemicals (87 evaluated by MDH previously, 171 on
the current proof-of-concept list)
Also defined an "Information Availability Score" ฃ
o
All data collection, scoring, and report/table writing ^
were completed in approximately 18 hours >,
Many of the chemicals with the highest scores (>5) '3
have already been screened by MDH.
Identified several other chemicals that have not
undergone explicit exposure screening process
by MDH but have been identified as priority to
evaluate via assessments outside the CEC E
.... >-1.6
initiative ฃ
c
03
ro 2.0
<
c
o
CD
1.2
20 of 28
Office of Research and Development
Results
CRADA List
Data Needed
MDH Evaluated
Benzophenone
t Tributyl phosphate
* Triphenyl phosphate
Anthraquinone
Benzene
L. ., . 1-Butanol
Hexachlorobenzene.
. VEthylbenzene
Aniline y
, ' 4-Nonylphenol. branched
Bromoform *
X;yclopenta[g]-2^benzoฃyian-4-9^.6.7.8^exahy'lro^t^^^l]^
-------
oEPA
United States
Environmental Protection
Agency
The automated workflow was applied to the 258
chemicals (87 evaluated by MDH previously, 171 on
the current proof-of-concept list)
Also defined an "Information Availability Score'' ฃ
o
All data collection, scoring, and report/table writing ^
were completed in approximately 18 hours >,
Many of the chemicals with the highest scores (>5) '3
have already been screened by MDH. r?
TO 2.0
Identified several other chemicals that have not
undergone explicit exposure screening process g
by MDH but have been identified as priority to "ฆ*=
evaluate via assessments outside the CEC E
.... >-1.6
initiative ฃ
_E
There were 82 chemicals that did not have enough
data for main unadjusted scores to be calculated
36 had positive exposure scoring adjustment
(might be priority for additional data
collection/curation)
21 of 28
Office of Research and Development
Results
CRADA List
Data Needed
MDH Evaluated
. .* .1
t .
.1.
a
Benzophenone
*1
hraq
Benzene
Tributyl phosphate
* Triphenyl phosphate
Anthraquinone
lj ., . 1-Butanol
Hexachlorobenzene.
. VEthylbenzene
Aniline y
. ' 4-Nonylphenol. branched
Bromoform
^yclopenta[g]^benzoฃyian--h3^67 8-^exahylro^^^^^l]Jxa0mงtliayf-
rbbn disulfide 3-Methylindole' Nicotine
' . | Copper
ป Ethoprop Aluminum
' t # * * Zinc
Meprobamate Estrone lfon -
*.
4-Androstene-3.17-dione
4-Nonylphenol^, Arsenic J.ead
e-j. h-oione Cotinine Metformin Phenol
; 5-MethyM H -benzot nazol e Mercury
Dimethipm # # j Cadmium
Carbadox Hexabromocyclododecane
Bis(4-hydroxyphenyl)methane
Final Score
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
oEPA
United States
Environmental Protection
Agency
Initial Evaluation of Automated Workflow and Manual
Results
Excellent agreement between scores in
Persistence and Fate and Occurrence domains
Codeine
Copper sulfate
Imazapyr
2-Propen-l-ol
DVquat
1- Br o mo pro pane
Diphenhydramine
Formaldehyde
2- Methox^thanol
Fluconazole
Biphe rryl
Cobalt
Trimethoprim
Hexabromocyclodlodec-..
Nicotine
CNoroacet c add
Bromoform
DicNcfoacetic add
Dibromoacetic acid
Trichloroacetic add
Methy paraben
Menthol
Dimethipin
Propyl paraben
Diethyl ene glycol
Ethoprop
HHCB
Androstenedione
Benzophenone
Tributyl phosphate
Anthraquinone
~ neomycin
Sulfathiazole
Warfarin
Fluoxetine
Amrtriptyline
Metoprolol
D ecabrcmoci phenyl.-
Endothall
Triclopvr
Triphenyl phosphate
Bifenthrin
Hydroquinone
Oxyfluorfen
Tr is (2-b Litoxy ethyl)_.
Persistence
and Fate
Manual Score
Workflow Score
22 of 28
Office of Research and Development
US EPA CSS-HEFRA BOSC Meeting - February 2-5, 2021
-------
'trA
United States
Environmental Protection
Agency
Initial Evaluation of Automated Workflow and Manual
Results
Excellent agreement between scores in
Persistence and Fate and Occurrence domains
HHCB
Benzophenone
Tributyl phosphate
Anthraquinone
Tris(2-butoxyethyl)...
5-methyl-lH-...
Codeine
Tramadol
Fluconazole
Trimethoprim
Metformin
Nicotine
Ethoprop
Bupropion
Li neomycin
Sulfathiazole
Fluoxetine
Carbadox
Triclopyr
Triphenyl phosphate
Bifenthrin
Oxyfluorfen
Androstenedione
Amitriptyline
Warfarin
0
Occurrence
Manual Score
Workflow Score
23 of 28
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
oEPA
United States
Environmental Protection
Agency
Initial Evaluation of Automated Workflow and Manual
Results
Excellent agreement between scores in
Persistence and Fate and Occurrence domains
Somewhat poorer alignment in the Release
Potential domain
Tris(2...
HHCB
5-methyl-lH...
Bifenthrin
Formaldehyde
Benzophenone
TributyL.
Bi phenyl
Trimethoprim
Bupropion
Copper sulfate
Nicotine
Propyl paraben
Codeine
Tramadol
Fluconazole
Fluoxetine
Triphenyl...
Ethoprop
Dimethipin
Anthraqui none
~ neomycin
Oxyfluorfen
0
10
Release
Potential
Manual Score
Workflow Score
24 of 28
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
oEPA
United States
Environmental Protection
Agency
Initial Evaluation of Automated Workflow and Manual
Results
Excellent agreement between scores in
Persistence and Fate and Occurrence domains
Somewhat poorer alignment in the Release
Potential domain
Poor agreement in score adjustments (i.e.,
detection frequency, human exposure
potential)
Difference in estimates of detection
frequencies in MMDB and MN sources
New exposure information from ExpoCast
HHCB
Cobalt
Anthraquinone
Tris(2-butoxyethyl) phosphate
Nicotine
Triphenyl phosphate
Propyl paraben
Benzophenone
Methyl paraben
Fluoxetine
Decabromodiphenyl ether
Bifenthrin
Triclopyr
Metformin
Imazapyr
Formaldehyde
Tributyl phosphate
Chloroaceticacid
Dichloroacetic acid
Dibromoacetic acid
Trichloroacetic acid
Endothall
5-methyl-lH-benzotriazole
Androstenedione
Copper sulfate
Menthol
Diphenhydramine
Tramadol
Hexabromocyclododecane
Bromoform
Trimethoprim
Hydroquinone
Fluconazole
Amitriptyline
Carbadox
Codeine
2-Propen-l-ol
Biphenyl
Bupropion
Ethoprop
Dimethipin
Diethylene glycol
2-Methoxyethanol
Oxyfluorfen
Warfarin
Metoprolol
Sulfathiazole
Diquat
Score
Adjustments
Manual Score
Workflow Score
25 of 28
Office of Research and Development
US EPA CSS-HEFRA BOSC Meeting - February 2-5, 2021
-------
4>EPA
United States
Environmental Protection
Agency
Initial Evaluation of Automated Workflow and Manual
Results
Excellent agreement between scores in
Persistence and Fate and Occurrence domains
Somewhat poorer alignment in the Release
Potential domain
Poor agreement in score adjustments (i.e.,
detection frequency, human exposure
potential)
Difference in estimates of detection
frequencies in MMDB and MN sources
New exposure information from ExpoCast
Reflected in final scores
Cobalt
Chloroacetic acid
Dibromoacetic acid
Trichloroacetic acid
HHCB
Copper sulfate
Tris(2-butoxyethyl) phosphate
Formaldehyde
Bromoform
Dichloroacetic acid
Benzophenone
5-methyl-lH-benzotriazole
Tributyl phosphate
Bifenthrin
Triclopyr
Nicotine
Anthraquinone
2-Methoxyethanol
Propyl paraben
Codeine
Imazapyr
Hexabromocyclododecane
Biphenyl
Methyl paraben
Metformin
Trimethoprim
Menthol
Bupropion
Endothall
Tramadol
Diphenhydramine
Fluoxetine
Fluconazole
Triphenyl phosphate
2-Propen-l-ol
Diethylene glycol
Diquat
Androstenedione
Decabromodiphenyl ether
1-Bromopropane
Ethoprop
Sulfathiazole
Carbadox
Amitriptyline
Hydroquinone
Oxyfiuorfen
Lincomycin
Warfarin
Dimethipin
Metoprolol
O.CX) 2.00 4.00 6.00 8.00
Final Score
Manual Score
Workflow Score
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
&EPA
United States
Environmental Protection
Agency
Next Steps
Continue evaluations
Closer look at differences across the data domains
Are there priority data sources to be added?
Incorporation of additional data streams into workflow
Integration into workflow of MN-specific water measurement database
Additional exposure NAMs, including machine-learning models for media
occurrence built using the MMDB monitoring descriptors
ORD toxicologists are working with MN to gather hazard data (including data
from NAMs) for data-poor nominated CECs and those identified as having high
exposure potential
27
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February
-------
&EPA
United States
Environmental Protection
Agency
Impact
This workflow allows MDH health scientists to accelerate and expand exposure
screening evaluations, freeing resources to complete the more complex aspects of
exposure assessment
Large libraries of chemicals relevant to MDH can be rapidly screened for a priori
identification of new potential nominees (something that has never been feasible)
The implemented workflow has formed a basis for exposure screening under another
MDH regulatory program, the Toxic Free Kids initiative (implementation now underway,
MDH concurrently developing screening algorithm in collaboration with ORD)
ORD has had initial conversation with Office of Water to discuss potential use of a
similar automated workflow approach for future CCL phases
28 of 28
Office of Research and Development
US EPA CSS-HERA BOSC Meeting - February 2-5, 2021
-------
CRADA earn
{Exposure Forecasting)
EPA-ORD MDH
Kathie Dionisio Christopher Greene
Jill Franzosa Helen Goeden
Kristin Isaacs David Bell
Jason Lambert Sarah Johnson
Monica Linnenbrink James Jacobus
Katie Paul-Friedman
Amar Singh
Jonathan Taylor Wall
Antony Williams
-------
ExpoCast Project
(Exposure Forecasting)
Collaborators
CCTE
Linda Adams
Miyuki Breen*
Alex Chao*
Dan Dawson*
Mike Devito
Kathie Dionisio
Christopher Ecklund
Marina Evans
Peter Egeghy
Michael-Rock Goldsmith
Chris Grulke
Mike Hughes
Kristin Isaacs
Richard Judson
Jen Korol-Bexell*
Anna Kreutz*
Charles Lowe*
Seth Newton
Katherine Phillips
Paul Price
Tom Purucker
Ann Richard
Caroline Ring
Marci Smeltz*
Jon Sobus
Risa Sayre*
MarkSfeir*
Mark Strynar
Zach Stanfield*
Rusty Thomas
Mike Tornero-Velez
El in UI rich
Dan Vallero
John Wambaugh
Barbara Wetmore
Antony Williams
CEMM
Xiaoyu Liu
CPHEA
Jane Ellen Simmons
CESER
David Meyer
Gerardo Ruiz-Mercado
Wes Ingwersen
Trainees
Arnot Research and Consulting
Jon Arnot
Johnny Westgate
Institut National de I'Environnement et des
Risques (INERIS)
Frederic Bois
Integrated Laboratory Systems
Kamel Mansouri
National Toxicology Program
Steve Ferguson
Nisha Sipes
Ramboll
Harvey Clewelll
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
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
Application of NAMs and AOPs to Surface
Water Surveillance and Monitoring in the Great Lakes
(ERA Region 5) and a Western River (ERA Region 8)
Daniel L. Villeneuve, US EPA, Office of Research and Development, Center for Computational Toxicology and
Exposure, Great Lakes Toxicology and Ecology Division
Progress for o Stronger Future
The views expressed in this presentation ore those of the author and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
-------
v>EPA Problem/Need
Regions, states, tribes, and communities are monitoring an ever-growing list of
contaminants in water and other environmental matrices.
Established water quality standards / guidelines are lacking for many of the
chemicals detected.
Uncertainty about whether the chemicals detected are likely to be harmful at the
concentrations detected
Need to focus limited resources available for monitoring, research, and/or
source reduction on the substances most likely to cause adverse effects.
Even with extensive contaminant monitoring, undetected compounds and
mixtures leave uncertainty about whether assessments based on individual
chemicals are sufficiently protective.
-------
Role for NAMs
In the absence of traditional animal toxicity data, NAMs can provide a
provisional, protective (?), benchmark to support risk-based prioritization
When traditional animal toxicity data are limited (scope of endpoints or
taxa), NAMs can protect against mode of action-based toxicities that may
be overlooked in traditional guideline studies or QSARs.
NAMs can be used to directly test complex mixtures, providing bioactivity
data that account for unknowns and cumulative/integrated effects.
3
-------
xv EPA
EPA Region 5
Great Lakes Restoration Initiative - Emerging Contaminants
FY2010 - FY2014
Great Lakes Restoration Initiative
Action Plan
Focus Area 1: Toxic Substances and Areas of Concern
Goal 5: The health and integrity of wildlife populations and habitat are protected from adverse
chemical and biological effects associated with the presence of toxic substances in the Great
Lake Basin.
Identify significant sources and impacts of new toxics to the Great Lakes
ecosystem , in order to devise and implement effective control strategies.
Great Lakes
RESTORATION* *
Great Lakes Restoration Initiative
Action Plan II --
*'ป & '&ฆ'* &
1 VI
K
~
ฆ
njtembrt 201.4 ' " v' .
Focus Area 1: Toxic Substances and Areas of Concern
Increase knowledge about contaminants in Great Lakes fish and wildlife
Identify emerging contaminants and assess impacts on Great Lakes fish and wildlife
4
-------
Chemical monitoring
Land cover
~ Open water
~ Urban(open and
low intensity)
ฆ Urban (medium and
high intensity)
~ Forest, shrubland,
herbaceous, and barren
~ Planted/cultivated
~ Wetland
A Stream sampling site
- - State/province boundary
Site watershed boundary
Great Lakes watershed
boundary
ake Su,oe,
200 Kilometers
709 water samples collected 2010-2013
57 Great Lakes tributaries
38 sites sampled 1-2 times
19 sites sampled 7-64 times
Analyzed for 67 organic contaminants
Water quality benchmarks (27/67 = 40%)
In vivo toxicity data (34/67 = 51%)
ToxCast data (54/67 = 81%)
Which chemicals are of concern?
Where are we most likely to see impacts?
What kinds of effects might we expect to see?
5
-------
oEPA
Which chemicals?
4-Nonylphenol, branched
4-( 1,1,3,3-Tetramethylbutyl jphenol
4-Cumylphenol
4-Octylphenol
Pantachlorophenol
Atrazine
Metolachlor
Metalaxyl
Biornacil
Prometon
3f4-Dichlorc>pheny1 isocyanste
Bisphenol A
2-tert-Butyl -4-methoxyphenol
5- Methyl -1H -benzotr iazole
Diazinon
Chlorpyrifos
Dichtorvos
Carbard
DEET
Carbazole
Cumane
2,6-D imeth yl naphthalene
2-Meth yl naphtha lene
1 -Methyl naphtha lene
Triclosan
p-Cresol
2,2*4,4-Tetrabromodiphenyl ether
TIX PR
Trisf2-butewyetliyI) phosphate
Tribute phosphate
Caffeine
Benzo(a)pyrene
Fluoranthene
Pyrene
Anthracene
Naphthalene
Phenanthrene
Benzophenone
6-Acetyl-1,1,2,4,4,7 -hexameth yttetralin
Isoquinoline
Indole
D-Limonene
Triphenyl phosphate
Diethyl phthalate
Tris (2-c h toroethy I) pnos phate
Bromoform
Methyl salicylate
1,4-DichkSrobenzene
Isophorone
Tetrach loroeth yl ene
4-tert-Oclylphenol monoethoxylate
4-Nonylphenol monoethoaylate
4-Nonyl phenol, branched
4-( 1,1,3,3-Tetramethylbutyl )phenol
4-tert-Octylphenol diethoxylate
4-Nonyl phenol diethoxylate
Bisphenol A
1,4-Diehlorobenzane
Sites
10
1
3
2
1
23
27
4
a
7
10
21
2
12
Q
0
1
8
47
12
2
7
17
13
7
9
1
6
21
17
21
23
SO
28
15
17
24
17
5
3
3
3
11
16
16
2
4
5
4
3
0
0
10
1
14
15
21
5
ToxCast
Maximum EARaioC^
T raditional
Maximum Toxicily Quotient per Site
I
CD
D--
ED-
_1D"
r-r-E
d&=~.
o-
-~
EJ-
m
-ED
B-
-03-
m--
Q--
-CD-
EAR =
TQ =
-o-
-i 1 1 r~
i 1 1 1 r
1
-------
xv EPA
Which sites?
20
J0 15H
TO
si
ฃ
03
6 io-
-a
ฃ
0-
# Samples
Lake Superior
Lake Michigan
Lake Huron
Lake Erie
Lake Ontario
1iiiiiiir
.is
-|iiir
-iiiiiiiiir
tiiiiir11-!iiiiiiiiiir
=^T37Sฎccc ซq j=l i o = a:ci>!aiiii!0 TO 0> 03 ซ 03 a
15? <5ซooo Cyag85Soฃe5ic^osฎ 3 to ELSiEife to^ ooS'iio^rtOn^ifn^-o $ as
SS^CQ O^TO c 03Q. NT3 qn
ty ~ cn> wl -j jj = j q- p. J <-
^ci-c 00 ฃ O ฃ ฃ ca ซ2-^ -i= 10 3 for each site.
Sites link to sources and stakeholders
7
-------
ซปEPA What effects?
~ ~ K
A C
Mixture of chemicals
detected at a site.
B.
EABjY|jXture
~ A
Assay 1
At*
Assay 2
Assay 3
~ ~
Assay 4
C.
EAR
AOP-l
EAR
AOP-2
EAR
AOP-3
Assay
KE1
Assay 2
KE2
KE3
KE4
Assay 3
Assay 4
KE10
KE11
KE3
KE4
KES
KE6
KE7
KES
KE9
D.
AOP Network
KE5
KE6
KE1
KE2
KE3
KE4
AOl
KE10
KE11
%
KE7
KES
KE9
A02
Considers cumulative effects of
detected chemicals
Assume additivity within each
ToxCast assay/endpoint
Assay endpoints map to key events
Redundant KEs not double-counted
Considers cumulative impacts of
multiple pathway perturbations on
potential adverse outcomes.
-------
oEPA
What Effects?
Assay endpoints associated with higher EARs
NVS NR hER
OT_ERa_EREGFP_0120
OT_ERa _ERE G F P__0480
NVS_NR_hCAR_Antagonist
NVS_NR_mERa
ATG ERa_TRANS_up
NVS_ENZ_hPDE4A1
AT G_ERE_CI S_up
0T_ ER_ E Ra E Rb_0480
AC EA_T47D_80h r_Positive
ATG_Sox_CIS_up
NVS NR__bER
ATG_PXRE_CIS_up
OT_ ER_E RbE Rb_0480
OT_ ER_E RaE Ra_1440
OT_ ERE RaE Ra_0480
NCCT_TPO__AUR_dn
OT_ER ERbERb_1440
NVS_ADME rCYP2C11
NCCT_ HEK293T_Ce IITite rGLO
CLD_CYP1A2_24hr
C LD~CY P3A4_48h r
APR_HepG2_p53Act_72h_dn
TOX21 _ER a_LUC_B G1 _Agon ist
C LD_CYP2B6_24h r
CLD CYP3A4_6hr
CEETOX H295R ESTRONE_up
NVS_GPC R_hAdoRA 1
NVS NR hAR
OT_ ER_E RaE Rb_1440
"CLD CYP2B66hr
NVS_MP_hPBR
NVS_ENZ_oCOX2
NVS_GPCR_gLTB4
NVS ADME_rCYP2A1
ATG~PXR_TRANS_up
ATG NRF2_ARE_CIS_up
NVS_ADME_hCYP2C19
NVS_ADME hCYP2B6
CLD_CYP2B6_48hr
TOX21 _E Ra_B LA_Agon ist_ratio
TOX21_ARE_BLA_agonist_ratio
NVS_MP_rPBR
ATG_VDRE CIS_up
ATG_RXRb_TRANS_up
TOX21 MMP ratio down
# Sites
21
22
22
33
21
35
23
34
24
27
21
21
49
33
21
22
26
33
21
13
23
27
17
3 6
27
27
26
21
22
3 3
47
34
23
21
21
34
34
31
33
47
35-
38
49
34
33
33-
AOP
Associated
Undefined
5
5
2
4
16
2
13
9
7
2
4
33
12
5
7
10
10
1
6
4
2
1
14
4
1
5
3
6
10
6
6
1
2
1
16
17
7
3
5
6
13
10
14
15
14
Associated AOPs / AOP networks
Activation,
NADPH
oxidai
Histpne
deacet^lase
Inhibattoo
Direct
mitochondrial
inhibition
Inhibition, increased,
cyclooxygenase ros
activity production
Activation,
nicotinic
acetylcholine
receptor
V)
Testicular
toxicity
Reproductive
failure
Reproductive
faiure
V
Mixture 3: 4-Nonylphenol, branched;
Atrazine; Metolachlor; 5 sites
Reduced,
reproductive
success
Decreased,
population
trajectory
Death/faiure,
, colony
Decreased,
fecundity y
Altered,
larval
development
Activation
Androgen ppar?
receptor, / \
ant^gorjitsm
ฆy?
Impairment
of
reproductive
capacity
Agon ism.
estrogen
/ฆ^cepthr
f / \
VKOR
inhibition
I !
Altered,
reproductive ^productive
behaviour or9ans
Impaired,
fertility
mpaired lmFfired
development recruitment,
of population
trajectory
10*
10~3 10~2
EAR^jteMixture
-------
oEPA
GLRI-CECs, On-going research
NAMs-based prioritization being applied to other data sets
Fill gaps when water quality benchmarks and in vivo toxicity data are lacking or limited
Additional GLRI data sets
Other USGS monitoring studies (including drinking water)
Risk-based prioritization (incorporating NAMs) is now being applied to over
800 organic contaminants detected over 10 years of CEC monitoring
Includes water, sediment, passive samplers, mussels, fish
Help inform nomination of potential chemicals of mutual concern as defined through Annex 3 of
binational Great Lakes Water Quality Agreement.
10
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Analyte
A 2,4-D
O Caffeine
O DEET
~ Gabapentin
~ Lamotrigine
~ Metformin
A Metolachlor ESA
~ Sulfamethoxazole
Indicator
Personal care
~ Pesticide
ฆ Pharmaceutical
2013 National Park Service and USGS measured contaminants along
Colorado River between Arches NP and Canyonlands NP
Variety of pharmaceuticals, pesticides and persona! care products detected
Greatest concentrations at the Moab WWTP discharge
Detectable concentrations extended > 15 km downstream
oEPA
EPA Region 8
Waste-water treatment upgrade, Moab, UT
Arches Motional Park
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Moab WWTP
Colorado River at
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oEPA
Three prominent activities were detected
Estrogen-like (important to reproduction)
Glucocorticoid-iike (important to stress response)
PPARy activation (involved in regulation of body fats)
Screened samples using the Attagene trans-Factorial
ToxCast assay platform
Screens for activation of 24 different nuclear receptors
What about chemicals that weren't monitored
assay
12
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xv EPA
EPA Region 8
Waste-water treatment upgrade, Moab, UT
Northern Colorado Plateau Network us. 5 1
Leaving Traces in Park Waters
Contaminants of emerging concern on the northern Colorado Plateau
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Maintaining pristine water quality is crucial to both visitor experience and
ecosystems in the national parks. New research shows that even individual
park visitors can help make a positive difference by eliminating waste well
away from water sources and avoiding contact with low-flow waters.
Northern Colorado Plateau
Network parks where CECs
were sampled:
Arches NP
Moab UT
5000 year-round residents
>1 million visitors per year
Moab WWTP
Originally built in the 1950s
Upgraded 1996 (trickling filter, chlorine disinfection)
Ammonia and nutrient violations with
increasing tourism pressure and age
2018 new WWTP (activated sludge, UV disinfection)
Parks and tourism are important to
the local economy
Would the treatment upgrade reduce the loading of bioactive CECs to the Colorado River?
13
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oEPA
Bioactivity Screening with Attagene
WWTP Outflow- 2014
Assay aat-iiTicalicn
ฆ Endocrine
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Xenotoclc metabolism
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WWTP Outflow 2019
Assay Ossification
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Metabolism
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Six sites, once per year
Biological activities observed (ER, GR, PPARg) were consistent with
pilot years.
Activity was greatest at the WWTP outflow, diminished rapidly
downstream.
M oab April 201 8
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SEPA Targeted Bioassays
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ER Activity
New WWTP online
Ma the son Wetland
WWTP Outflow
Below WWTP
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GR Activity
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12 sites, bi-monthly, spring to fall over two years
- ER activity declined shortly after
WWTP replacement
- A little lag
- Possibly trending back up in summer
- Much lower immediately downstream
- PPARy activity not
detected in targeted assay
- Slightly less sensitive
- GR activity declined immediately
after WWTP replacement
- Only detected at WWTP outflow
15
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Chemical Monitoring
1,7-Diinethyl-iaiithiiie
1O-Hydroxy-amitriptylinc
Abacavir
Acetaminophen
Acyclovir
Albuterol
Amitriptyliuc
Amphetamine
Atenolol
Bupropion
Caffeine
Carbamate pine
Carisoprodol
Cimetidinc
March
2019
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1.0 0.8 0.6 0.4
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Only partial heat map shown
2018
- 62 (out of 131) chemicals detected at outflow
2019
- 36 (out of 131) chemicals detected at outflow
- Generally lower concentrations than 2018
Consistent with bioassay results
Detections and concentrations quickly decrease
away from WWTP
Guanylurea increased in 2019
- WWTP transformation product of metformin
- Metformin below detection limits
- Recent studies in our lab suggest very low
toxicity to aquatic organisms
16
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oEPA
Good news!
Community investments in upgraded WWTP infrastructure
appear to have had a positive effect on the loading of biologically
active contaminants to the Colorado River.
In vitro bioactivities (ER, GR, and PPARy) reduced and rapidly decline
downstream
Fewer contaminants and lower concentrations
Caged-fish survival drastically improved
Additional contaminant and bioactivity monitoring, if desired,
can be focused in close proximity to the WWTP outflow
Some on-going sample collection in 2020-2021 monitor trends in ER-
and GR- activity
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oEPA
Conclusions
Practical applications of NAMs and NAMs data in chemical safety assessment
is not limited to prospective assessments of individual chemicals.
NAMs data can help inform risk-based screening based on environmental
monitoring, particularly where traditional toxicity benchmarks are lacking.
NAMs can be applied to evaluate complex mixtures with both known and
unknown compositions.
NAMs applications can aid in environmental decision-making
18
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SEPA Acknowledgements
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vปEPA References
Corsi SR, De Cicco LA, Villeneuve DL, Blackwell BR, Fay KA, Ankley GT, Baldwin AK. Prioritizing chemicals of ecological
concern in Great Lakes tributaries using high-throughput screening data and adverse outcome pathways. Sci Total
Environ. 2019 Oct 10;686:995-1009. doi: 10.1016/j.scitotenv.2019.05.457.
Cavallin JE, Battaglin WA, Beihoffer J, Blackwell BR, Bradley PM, Cole AR, Ekman DR, Hofer RN, Kinsey J, Keteles K,
Weissinger R, Winkelman DL, Villeneuve DL. Effects-Based Monitoring of Bioactive Chemicals Discharged to the
Colorado River before and after a Municipal Wastewater Treatment Plant Replacement. Environ Sci Technol. 2021 Jan
19;55(2):974-984. doi: 10.1021/acs.est.0c05269.
Great Lakes Restoration Initiative, Action Plan, https://www.glri.us/sites/default/files/glri actionplan.pdf
Great Lakes Restoration Initiative, Action Plan II. https://www.glri.us/sites/default/files/glri-action-plan-2.pdf
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