A Proof-of-Concept Study
Integrating Publicly Available
Information to Screen
Candidates for Chemical
Prioritization under TSCA
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v>EPA
EPA Document# EPA/600/R-21-106
June 2021
United States Office of Chemical Safety and Pollution Prevention
Environmental Protection Agency Office of Research and Development
A Proof-of-Concept Case Study Integrating Publicly Available Information to
Screen Candidates for Chemical Prioritization under TSCA
June 2021
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Table of Contents
Acknowledgements 4
Disclaimer 4
Suggested Citation 4
Reviewers 5
List of Abbreviations and Acronyms 6
1. Executive Summary 8
2. Introduction 8
3. Background 9
3.1 Development and Implementation of New Approach Methods Under TSCA 9
3.2 Evaluating Existing Chemicals Under TSCA 10
3.3 Public Engagement 11
4. Public Information Curation and Synthesis (PICS) Approach 13
4.1 Overview of the PICS Approach 13
4.2 What the PICS Approach is Intended to Accomplish 15
4.3 What the PICS Approach is Not Intended to Accomplish 16
5. A Proof-of-Concept Case Study 17
5.1 Chemical Substance Selection, Curation and Quality Control 17
Chemical Structure and Identifier Mapping 17
Chemical Substance Selection 18
Chemical Substance Information Extraction and Quality Control 19
5.2 Scientific Domain Metric Assessment 19
Human Health Hazard-to-Exposure Ratio Domain 20
Carcinogenicity Domain 26
Genotoxicity Domain 28
Ecological Hazard Domain 33
Susceptible Human Population Domain 36
Persistence and Bioaccumulation Domain 40
Skin Sensitization and Skin/Eye Irritation Domain 48
5.3 Scientific Domain Metric Calculation 52
5.4. Information Availability Metric 52
5.5 Results of the Proof-of-Concept Analysis 54
Overall Evaluation 54
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5.6 Overall Limitations and Long-term Options 62
6. Summary 63
7. Conclusion 64
Appendix A. Proof-of-Concept (POC) Subset of the Non-confidential TSCA Active Inventory 66
Appendix B. Detailed Information on Data Sources used in the PICS Approach 76
Appendix C. Quality Assurance Recommendations to Efficiently Review Datasets to Support Candidate
Chemical Identification for TSCA 89
Overview 89
Procedures for QC review 89
Human Hazard Domain Workgroup Review Approach 90
Exposure Domain Workgroup Review Approach 91
Genotoxicity Domain Workgroup Review Approach 93
Bioaccumulation Subdomain Workgroup Review Approach 93
Ecological Hazard Domain Workgroup Review Approach 94
Skin Sensitization and Skin/Eye Irritation Workgroup Review Approach 94
Summary 95
Appendix D. Definition of Exposure Pathways for Calculating the Susceptible Population Domain
Metric 96
Appendix E. Public Information Curation and Synthesis (PICS) Output for Proof-of-Concept (POC)
Subset of the Non-confidential TSCA Active Inventory 98
Appendix F. Comparison of Individual Scientific Domain Metrics for the POC238 and Non-confidential
TSCA Active Inventory 101
Appendix G. Information Availability Metric Calculation 104
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Acknowledgements
This report was developed by the United States Environmental Protection Agency (U.S. EPA), Office of
Chemical Safety and Pollution Prevention (OCSPP) and the Office of Research and Development (ORD).
Disclaimer
This document has been reviewed in accordance with the U.S. Environmental Protection Agency policy
and approved for publication. Mention of trade names or commercial products does not constitute
endorsement or recommendation for use.
Suggested Citation
USEPA 2021. A Proof-of-Concept Case Study Integrating Publicly Available Information to Screen
Candidates for Chemical Pre-Prioritization under TSCA. June 2021. U.S. Environmental Protection
Agency, Office of Chemical Safety and Pollution Prevention and Office of Research and Development,
Washington, DC. EPA/600/R-21-106.
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Reviewers
This document has been provided for review to EPA scientists, and peer reviewed by independent
scientists external to EPA (listed below). A summary of comments and EPA's response to comments
received from the independent external peer reviewers is provided through EPA's Science Inventory
database of publicly released research products which is available by clicking here.
Dr. Tara S. Barton-Maclaren, Health Canada
Dr. Weihsueh A. Chiu, Texas A&M University
Dr. Helen M. Goeden, Minnesota Department of Health
Dr. Kerry W. Nugent, Australian Industrial Chemicals Introduction Scheme (formerly NICNAS)
Dr. Edward J. Perkins, Department of Defense
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List of Abbreviations and Acronyms
AD
Applicability Domain
AT SDR
Agency for Toxic Substances and Disease Registry (CDC)
BAF
Bioaccumulation Factor
BCF
Bioconcentration Factor
BER
Bioactivity-to-Exposure Ratio
CA
Chromosomal Aberration
CDC
Centers for Disease Control and Prevention
CMP
(Canadian) Chemicals Management Plan
CPCat
Chemical and Product Categories (database)
CPDat
Chemical and Product Database
CTS
Chemical Transformation Simulator (US EPA ORD)
DSSTox
Distributed Structure-Searchable Toxicity Database
DTXSIDs
DSSTox Substance Identifiers
ECHA
European Chemicals Agency
EcoSAR
Ecological Structure Activity Relationships
EFSA
European Food Safety Authority
EPA
US Environmental Protection Agency
ExpoCast
Exposure Forecasting
EUSES
European Union System for Evaluation of Substances
FDA
Food and Drug Administration
GHS
Globally Harmonized System of Classification and Labeling of Chemicals
HER
Human Hazard-to-Exposure Ratio
HESS
Hazard Evaluation Support System
HP VIS
High Production Volume Information System
HSDB
Hazardous Substances Data Bank
IAM
Information Availability Metric
IARC
International Agency for Research on Cancer
IG
Information Gathering
IRIS
Integrated Risk Information System
LLNA
Local Lymph Node Assay
MNT
Micronucleus Test
NAMs
New Approach Methods
NIOSH
National Institute for Occupational Safety and Health (CDC)
NTP
National Toxicology Program
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OCSPP Office of Chemical Safety and Pollution Prevention (OCSPP)
OECD Organization for Economic Cooperation and Development
OPP Office of Pesticide Programs (US EPA)
OPPT Office of Pollution Prevention and Toxics (US EPA)
ORD Office of Research and Development (US EPA)
PECs Predicted Exposure Concentrations
PICS Public Information Curation and Synthesis
PNECs Predicted no Effect Concentrations
POD Points-of-Departure
Pov Overall Chemical Persistence
PPRTV Provisional Peer-Reviewed Toxicity Values
QC Quality Control
QA Quality Assurance
QSAR Quantitative Structure-Activity Relationship
ROC Report on Carcinogenesis (NTP)
SCIL Safer Chemical Ingredients List
SDM Scientific Domain Metric
SEEM Systematic Empirical Evaluation of Models
TER TTC-to-Exposure Ratio
TEST Toxicity Estimation Software Tool (US EPA)
TSCA Toxic Substances Control Act
TSCA10 First Ten TSCA Work Plan Chemical Substances Selected for Evaluation
TSCA90 Chemical Substances from the 2014 Update to the TSCA Work Plan
POC238 238 Chemical Substances Selected for the POC Case Study
TTC Threshold of Toxicological Concern
UVCBs Unknown or Variable Composition, Complex Reaction Products and Biological
Materials
WHO World Health Organization
WOE Weight-of-Evidence
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1. Executive Summary
Regulatory agencies worldwide are looking to efficiently integrate information on
chemical substances1 in order to inform priorities for decisions and data requests. This document
updates the US Environmental Protection Agency's (EPA) long-term strategy described in the
Working Approach for Identifying Potential Candidate Chemicals for Prioritization2 and presents
the Public Information Curation and Synthesis (PICS) approach that integrates publicly-available
hazard, exposure, persistence, and bioaccumulation information for chemical substances. The
purpose of the PICS approach is to synthesize information from traditional and new approach
methods (NAMs)3 to understand the overall degree of potential concern as well as the relative
coverage of potentially relevant human health and ecological toxicity and exposure information
that could inform level of effort and resources that may be needed to evaluate that specific chemical
substance. The PICS approach is based on two dimensions. The first dimension, Scientific Domain
Metric (SDM), encompasses the synthesis of the traditional and NAM data to understand the
overall degree of potential concern related to human health and the environment. The second
dimension, Information Availability Metric (IAM), reflects the relative coverage of potentially
relevant human health and ecological toxicity and exposure information that could inform level of
effort and resources that may be needed to evaluate that specific chemical substance. The PICS
approach is not designed to replace the prioritization process described in TSCA but aims to
increase efficiency and focus expert review on chemical substances that may have a greater
potential for designation as a high- or low priority candidate. A proof-of-concept case study was
performed by applying the PICS approach to a subset of the TSCA active inventory. The results
demonstrate that the approach discriminated between high- and low priority candidate chemical
substances and identified potential information gaps. The PICS approach may be applied to large
numbers of chemical substances and is an important tool for efficiently integrating and
synthesizing large amounts of publicly available information, and aspects of the approach could
be adapted and applied to other prioritization decision contexts.
2. Introduction
Regulatory agencies worldwide need to make decisions on chemical substances4 based on
a set of defined criteria on specific hazards of concern, exposure to specific populations, or
1 Unless otherwise indicated, any references to "chemical" or "chemical substance" throughout this document means
a "chemical substance" as defined in TSCA Section 3(2).
2 https://www.epa.gov/sites/production/files/2018-09/documents/preprioritization white paper 9272018.pdf
3 https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/alternative-test-methods-and-strategies-reduce
The term NAMs was recently introduced to cover any in vitro, in silico, or in chemico technique used to provide
data or information for regulatory decision making.
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persistence and bioaccumulation in the environment. There is a need for a consistent, timely and
efficient approach to organize large numbers of chemical substances for further evaluation. This
document describes an approach to integrate publicly available information on the more than
33,000 chemical substances on the non-confidential TSCA active inventory5 to efficiently select
chemical substances for expert review prior to prioritization. The information in this document
expands on the long-term strategy previously described by the EPA6, continuing with the
development of the PICS approach for synthesizing information from traditional and NAMs in key
scientific domains. These domains include human health hazard to exposure ratio (incorporating
multiple specific toxicities), ecological hazard, carcinogenicity, genotoxicity, human exposure
(general and susceptible populations), persistence/bioaccumulation, skin sensitization, skin
irritation, and eye irritation. Of the seven domains used in the PICS approach, five were included
in the previous Working Approach for Potential Candidates. The additional two domains
(carcinogenicity; skin sensitization and skin/eye irritation) were included in the PICS approach
based on their use in the 2014 TSCA Work Plan7. The detailed process was tested in a proof-of-
concept case study. The PICS approach may help streamline the evaluation of chemical substances
by transparently and reproducibly synthesizing available information and identify potential data
gaps, and aspects of the approach could be adapted and applied to other prioritization decision
contexts.
This document presents a proof-of-concept approach for EPA and the broader scientific
community, and neither constitutes rulemaking by the EPA, nor can it be relied on to create a
substantive or procedural right enforceable by any party in litigation with the United States. Non-
mandatory language such as "should" provides recommendations and does not impose any legally
binding requirements. Similarly, statements about what EPA expects or intends to do reflect
general principles to guide EPA's activities and are not judgments or determinations as to what
EPA will do in any particular case.
3. Background
3.1 Development and Implementation of New Approach Methods Under TSCA
Section 4 (h) of the Toxic Substances Control Act (TSCA), as amended by the Frank R.
Lautenberg Chemical Safety for the 21st Century Act (P.L. 114-182), requires EPA to develop a
Strategic Plan to promote the development and implementation of alternative test methods and
strategies to reduce, refine or replace vertebrate animal testing and provide information of
5 This list can be found at https://comptox.epa.gov/dashboard/chemical lists/TSCA ACTIVE NCTI 0320
6 https://www.epa.gOv/sites/production/files/2018-09/documents/preprioritization white paper 9272018.pdf
7 TSCA Work Plan Methods Document 2012 (https://www.epa.gov/sites/production/files/2014-
03/documents/work plan methods document web final.pdf).
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equivalent or better scientific quality and relevance for assessing risks of injury to health or the
environment of chemical substances or mixtures. EPA's Strategic Plan to Promote the
Development and Implementation of Alternative Test Methods Within the TSCA Program8 was
released on June 22, 2018 and outlines the EPA's plan to reduce the use of vertebrates for chemical
substances regulated under TSCA. As part of this Strategic Plan, EPA describes incremental steps
for the development and integration of NAMs that are appropriate and fit-for-purpose for making
TSCA-related decisions (e.g., identifying candidates for prioritization, prioritization, risk
evaluations for new and existing chemical substances and other risk-based decisions). This multi-
year strategic plan includes criteria for determination of what would be considered NAMs by the
EPA and how they may be applied for evaluation of human health hazard, ecological hazard and
exposure. In addition to this, EPA has developed a NAMs workplan9 for reducing use of animals
in chemical testing in order to prioritize Agency efforts and resources toward activities that aim to
reduce the use of animal testing while continuing to protect human health and the environment.
This workplan expands EPA's discussion of the development and use of NAMs for support of
regulatory decision-making beyond TSCA and focuses on mechanisms for building confidence in
the implementation of NAMs.
3.2 Evaluating Existing Chemicals Under TSCA
Under Section 6(b) of TSCA, EPA is required to both prioritize and evaluate the risks of
existing chemical substances. The law contains specific timetables, minimum chemical substance
numbers, and general requirements for both prioritization and risk evaluation. Prioritization10 is a
9- to 12-month public process in which chemical substances are designated as either high- or low
priority for risk evaluation. A high priority chemical substance is one "that the Administrator
concludes, without consideration of costs or other non-risk factors, may present an unreasonable
risk of injury to health or the environment because of a potential hazard and a potential route of
exposure under the conditions of use, including an unreasonable risk to potentially exposed or
susceptible subpopulations identified as relevant by the Administrator." A low priority chemical
substance is one that "the Administrator concludes, based on information sufficient to establish,
without consideration of costs or other non-risk factors, that such chemical substance does not
8 Alternative Test Methods and Strategies to Reduce Vertebrate Animal Testing in TSCA
(https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/alternative-test-methods-and-strategies-
reduce).
9 More information can be found at https://www.epa.gov/chemical-research/epa-new-approach-methods-work-plan-
reducing-use-animals-chemical-testing
111 Final Rule, "Procedures for Prioritization of Chemicals for Risk Evaluation Under the Toxic Substances Control
Act," available at https://www.regulations.gov/document?D=EPA-HO-OPPT-2016-0636-0Q74.
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meet the [High priority] standard." A designation of a chemical substance as low priority indicates
that a risk evaluation is not warranted at that time.
TSCA requires that high priority chemical substances undergo risk evaluation to determine
whether a chemical substance presents an unreasonable risk of injury to health or the environment,
without consideration of costs or other non-risk factors, including an unreasonable risk to a
potentially exposed or susceptible subpopulation11 identified as relevant to the risk evaluation by
the Administrator, under the conditions of use12. The risk evaluation must take no longer than three
years with a possible six-month extension. If unreasonable risk is identified, EPA has two years
with a possible extension of two additional years to finalize regulations so the chemical substance
no longer presents such a risk.
On March 20, 2019, the EPA initiated the prioritization process for the first set of 20 highl-
and 20 low priority candidate chemical substances. The initiation of the prioritization process is
followed by a 90-day public comment period for submitting relevant information. Upon
completion of the public comment period, the EPA performs a screening review of the candidate
chemical substances based on hazard and exposure potential, persistence and bioaccumulation,
potentially exposed or susceptible subpopulations, storage near significant sources of drinking
water, the conditions of use, and the volume of the chemical substance manufactured or processed.
Based on the outcome of the screening review, the EPA will propose to designate a chemical
substance as either a high priority or low priority chemical substance and release the information,
analysis, and basis used to make the designation. The proposed designation will be followed by a
second 90-day comment period prior to finalizing the designation.
3.3 Public Engagement
On December 11, 2017, EPA held a public meeting to gain input regarding identification
of potential candidate chemical substances for prioritization. In preparation for this meeting, EPA
published a discussion document including possible approaches to inform the dialogue at the
11 "Potentially exposed or susceptible subpopulation," as defined in TSCA Section 3(12), means a group of
individuals within the general population identified by the Administrator who, due to either greater susceptibility or
greater exposure, may be at greater risk than the general population of adverse health effects from exposure to a
chemical substance or mixture, such as infants, children, pregnant women, workers or the elderly (15 U.S.C. 2602).
12 "Conditions of use" under TSCA means "the circumstances, as determined by the Administrator, under which a
chemical substance is intended, known, or reasonably foreseen to be manufactured, processed, distributed in
commerce, used or disposed of' (15 U.S.C. 2602). For purposes of prioritization, the Administrator may determine
that certain uses fall outside the definition of "conditions of use".
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meeting13. A Response to Comment document has been developed to address the comments14.
EPA received 43 relevant comments in the docket associated with the public meeting15. There
was consensus in the comments that EPA should proceed in a transparent manner with
opportunities for public participation. However, there was no consensus around one or more of the
proposed approaches the Agency presented. The most consistent support focused on use of the
2014 Update to the TSC A Work Plan16 as the starting point for identifying high priority candidates.
For low priority chemical substances, there was some support for using the chemical substances
on the Safer Chemical Ingredients List (SCIL)17, which was developed by EPA's Safer Choice
Program, as a starting point. There was general support for the integration of NAMs for filling
information gaps during the process to identify potential candidate chemical substances for
prioritization; however, there was some concern regarding the readiness of these approaches for
decision-making on prioritization for risk evaluation. There were opposing views regarding filling
information gaps and EPA's authority to request submission of information, the use of voluntary
submissions, when to request information, the quality of information, and how to use information
from other jurisdictions (e.g., the European Union's Registration, Evaluation, Authorisation, and
Restriction of Chemicals (REACH)).
On September 27, 2018, EPA released A Working Approach for Identifying Potential
Candidate Chemicals for Prioritization19, (to be called Working Approach for Potential Candidates
throughout this document) that described both short- and long-term strategies for selecting
candidate chemical substances for prioritization under TSCA. The long-term strategy in the
Working Approach for Potential Candidates was adapted from the TSCA 2012 Work Plan
process19, but incorporated scientific advances in relevant fields, integration of NAMs, and modern
information management technologies to integrate the large volume of information in an efficient,
automatable and reproducible manner. The strategies presented in the Working Approach for
Potential Candidates reflected public input received at the December 2017 meeting20 and through
13 Meeting materials for the December 11, 2017 Possible Approaches for Identifying Potential Candidates for
Prioritization Public meeting can be found here: https://www.epa.gov/assessing-and-managing-chemicals-under-
tsca/possible-approaches-identifving-potential-candidates.
14 https://www.epa. gov/sites/production/files/2018-
09/documents/publiccommentssummarv dec 11 preprioritization 927.pdf
15 The public comments received following the December 11, 2017 public meeting are available at
www.regulations.gov in docket EPA-HO-OPPT-2017-0586.
16 https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/tsca-work-plan-chemical-assessments-2014-
update
17 This list can be found at https://comptox.epa.gov/dashboard/chemical lists/SCILFULL
18 https://www.epa.gov/sites/production/files/2018-09/documents/preprioritization white paper 9272018.pdf
19 https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/tsca-work-plan-methods-document
211 https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/meetings-and-webinars-amended-toxic-
substances-control#12/l 1
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the public docket for that meeting21. EPA accepted a second round of public comments on the
proposed longer-term strategy. EPA received 26 unique comments in the docket22. Commenters
also noted that data gaps do not necessarily equate to data needs, and that EPA should not prioritize
solely based on information availability. Commenters included recommendations on specific
topics, including susceptible and sensitive populations and increasing the use of exposure data to
make this approach more risk-based. The comments also highlighted the need to clarify the
purpose of the long-term strategy as a means for increasing efficiency of the expert review required
for the selection of candidates for prioritization and not as a replacement for the formal
prioritization and risk evaluation steps in the process. Finally, there appeared to be misconceptions
about the difference between the bins outlined in the strategy and the chemical substance categories
the Agency regularly uses in the TSCA New Chemicals Program to group chemical substances
expected to show the same hazard characteristics. The present document and the PICS approach
are intended to address these comments.
4. Public Information Curation and Synthesis (PICS) Approach
4.1 Overview of the PICS Approach
The PICS approach updates and expands on the long-term strategy described in the
Working Approach for Potential Candidates document and integrates information from a variety
of sources to better understand publicly available information for these chemical substances. The
PICS approach synthesizes information from traditional methods and NAMs in key scientific
domains including human health hazard to exposure ratio (incorporating multiple specific
toxicities), ecological hazard, carcinogenicity, genotoxicity, exposure to susceptible populations,
persistence/bioaccumulation, skin sensitization, and skin/eye irritation. For each scientific domain,
a workflow was developed that specifies what information is utilized and the logic of how it is
integrated. The methodology underlying the individual workflows are designed to incorporate
scientific advances in each discipline and may differ from domain to domain. The domain-specific
workflows are described in detail in the subsequent sections. Consistent with the Strategic Plan to
Reduce the Use of Vertebrate Animals in Chemical Testing23, the PICS approach integrates NAMs
to fill gaps when traditional testing data are not available. In general, each workflow is based on
previously accepted methods for prioritizing chemical substances under TSCA24, with a focus on
the use of data from study types for which there is traditionally the most confidence in the
21 Further information can be found at docket EPA-HQ-OPPT-2017-0587.
22 Further information can be found at docket EPA-HQ-OPPT-2018-0659.
23 https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/strategic-plan-reduce-use-vertebrate-animals-
chemical
24 TSCA Work Plan Methods Document 2012 (https://www.epa.gov/sites/production/files/2014-
03/documents/work plan methods document web final.pdf).
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regulatory toxicology community (e.g., in vivo), followed by those with decreasing confidence
depending on the context for use (e.g., in vitro, in silico). Unless otherwise described, the domain-
specific workflows generally utilize conservative assumptions to reduce the potential for false
negatives at the initial screening stage. This document also presents potential options for future
work to improve the approach, as well as caveats and limitations.
High
Information,
High concern
Low
Information,
Low concern
1AM
Figure 1. Schematic of the Public Information Curation and Synthesis (PICS) approach. The approach
integrates publicly available information from seven scientific domains that represent human health and
environmental hazard topics into a Scientific Domain Metric (SDM), and the amount and type of data in
the Information Availability Metric (IAM). These two metrics are combined to give a visual display of the
degree of potential concern and availability of publicly available information for the chemical substances
assessed to inform future expert review of these chemical substances.
The PICS approach is based on two dimensions allowing visualization and separation of
the chemical substances along each dimension (Figure 1). The first dimension reflects the overall
degree of potential concern related to human health and the environment and is the integration of
the individual results from the domain-specific workflows. In the PICS approach, this dimension
is referred to as the Scientific Domain Metric (SDM).
The second dimension reflects the relative coverage of potentially relevant human health
and ecological toxicity and exposure publicly available information that could inform level of
effort and resources that may be needed to evaluate that specific chemical substance. This
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dimension is referred to as the Information Availability Metric (IAM). The level of effort and
resources is typically context specific and informed by expert judgment; however, an expert driven
approach is not scalable to apply to the thousands of chemical substances on the TSCA active
inventory at the initial screening stage. Therefore, a set of modifying criteria were used to inform
the set of potentially relevant human health and ecological toxicity information. The modifying
criteria were modeled after considerations used in the TSCA New Chemicals Program and include
a combination of functional use considerations, environmental half-life, water solubility,
molecular weight, and whether the chemical substance is a TSCA exempt polymer. The existence
of an authoritative human health assessment would also contribute to this metric. In the PICS
approach, the summary result from this dimension is referred to as the IAM.
The SDM and IAM are combined into a graphical representation of the PICS approach for
the chemical substances on the TSCA active inventory. In response to public comments, the PICS
approach moved away from the defined 'bins' of chemical substances that had been proposed in
the Working Approach for Potential Candidates. The PICS approach does not determine what a
result for a specific chemical substance represents, rather it provides a synthesis of the public
information available for individual chemical substances.
4.2 What the PICS Approach is Intended to Accomplish
The current non-confidential, active TSCA inventory contains over 33,000 chemical
substances25 with varying amounts and types of available information. Historical approaches that
search, compile, and manually evaluate relevant information are very time and resource intensive
and are not be feasible for large number of chemical substances. As part of the development of a
long-term strategy to inform selection of candidates for further review, an automated approach
was developed that extracts, stores, and integrates publicly available information from traditional
toxicology, exposure, and environmental fate-related studies, as well as NAMs. The approach
relies on an information management and technology infrastructure to efficiently and transparently
perform these functions and is one of possible tools that may inform candidate selection for
prioritization of TSCA inventory chemical substances. A representation of the PICS approach
within candidate identification is provided in Figure 2. The PICS approach is intended to
accomplish the following aims:
• Understand the landscape of publicly available information on the over 33,000 data poor
and data rich chemical substances on the TSCA active inventory and aid in identifying
candidates for prioritization;
• Provide a transparent and reproducible process for integrating available information and
identifying potential information gaps;
25 https://www.epa.gov/tsca-inventorv/tsca-inventorv-notification-active-inactive-rule
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• Increase efficiency and manage workload by focusing expert review on chemical
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 scientific information; and
• Organize the process into modular workflows that can be readily adapted to address
prioritization needs under other mandates.
4.3 What the PICS Approach is Not Intended to Accomplish
In order to manage expectations, it is also important to define what lies outside the domain of
the PICS approach. The PICS approach is not intended to:
• Replace the formal TSCA prioritization or risk evaluation processes;
• Create a ranked list of chemical substances;
• Signal that the EPA has concerns with particular chemical substances or categories of
chemical substances;
• Supplant expert judgment and review;
• Utilize confidential business information (CBI); or
• Incorporate systematic review of information to address study and data quality.
Approach for Identification of Candidate Chemicals
Identification of
Candidate Chemical
Substances
Figure 2. Schematic of the PICS Approach in Relation to Identifying High- and Low priority
Candidate Chemical Substances. The PICS approach is a tool that can be used to inform identification of
candidate chemical substances. The PICS approach combines results from domain-specific workflows and
the relative coverage of potentially relevant human health and ecological toxicity information to identify a
subset of the TSCA active inventory for additional expert review and analysis. Potential candidates
identified using this approach combined with those from other tools (e.g., OncoLogic™) followed by expert
review and weight-of-evidence (WOE) analysis is one approach that EPA can use to help select candidate
chemical substances for prioritization.
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
Expert review and
analysis
Other tools
Systematic Approach to
Data Analysis
Weight-of-Evidence
evaluation
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5. A Proof-of-Concept Case Study
A subset of the TSCA active inventory was selected to test the PICS approach in a proof-
of-concept (POC) case study. The subset of chemical substances was designed to evaluate the
impact of various workflows across the scientific domains and gauge the impact of different
modifying criteria on information availability. The chemical substances (described in further detail
below) focused on including a broad range of chemical substances with varying levels of available
information and included the initial proposed set of 20 high26- and 20 low priority27 chemical
substances. The PICS approach is intended to work broadly across the chemical substance
landscape, but this case study was designed for particular application within the TSCA active
inventory. Following the development of this approach, the PICS approach can also be applied to
the broader TSCA active inventory or adapted to other decision contexts.
The POC case study was also used to develop standard operating procedures (SOPs) for
quality control (QC) analysis of the large, Type 1 datasets28 required to apply the approach to the
TSCA active inventory. The SOPs are being implemented in an internal software system to assess,
track and correct any discrepancies between data used as input and the source documents or files
from which these data were obtained. The QC analysis was mainly focused on the data accuracy,
and not determining the study or data quality. Further review of data and study quality would be
performed by experts outside of the PICS approach prior to chemical substance selection. The QC
software system being developed will also accommodate in-depth, expert review of studies for
selected chemical substances and/or studies with summary values that trigger QC review.
5.1 Chemical Substance Selection, Curation and Quality Control
Chemical Structure and Identifier Mapping
Chemical substances on the non-confidential TSCA active inventory were downloaded
from the EPA website29. Chemical substances without a CAS Registry Number were removed,
and the remaining chemical substances mapped to Distributed Structure-Searchable Toxicity
(DSSTox) substance identifiers (DTXSIDs) in the ChemReg chemical registration system30, a
26 This list can be found at https://comptox.epa.gov/dashboard/chemical lists/TSCAHIGHPRI
27 This list can be found at https://comptox.epa.gov/dashboard/chemical lists/TSCALOWPRI
28 Type 1 sources were defined as data sources storing reasonably available and relevant information that could be
readily queried and extracted in a structured manner. This includes existing databases (and dashboards) that allow
the user to sift through information using a graphical user-interface, a direct query such as Structured Query
Language (SQL), orwebservice Application Programming Interfaces (APIs).
(https://www.epa.gov/sites/production/files/2018-09/documents/preprioritization white paper 9272018.pdf)
29 https://www.epa. gov/tsca-inventorv/how-access-tsca-inventorv
311 Grulke CM, Williams AJ, Thillanadarajah I, Richard AM. (2019). EPA's DSSTox database: History of
development of a curated chemistry resource supporting computational toxicology research. Computational
Toxicology 12:100096.
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database underpinning the CompTox Chemicals Dashboard3132. Any chemical substances with
conflicts between the TSCA identifiers and DSSTox records (e.g., discrepant CAS numbers or
chemical substance names) were placed in a queue for mapping review by trained chemists. The
mapped, non-confidential TSCA active inventory contained -33,000 chemical substances and
25,275 with structural information visible via the CompTox Chemicals Dashboard33.
Chemical Substance Selection
Chemical substances for the POC case study were selected from the mapped, non-
confidential TSCA active inventory by the scientific experts designing the domain specific
workflows and included:
• Initial proposed set of 20 high- and 20 low priority candidate chemical substances along
with the initial first ten TSCA Work Plan chemical substances selected for evaluation in
2016 (TSCA10)34;
• Chemical substances from the 2014 update to the TSCA Work Plan (TSCA90)35;
• Chemical substances with well-studied effects in each of the scientific domains;
• A subset of chemical substances listed in the Food and Drug Administration's (FDA)
Substances Added to Food inventory (formerly Everything Added to Foods in the United
States list) and EPA's Safer Choice Safer Chemical Ingredients List (SCIL).
From these lists, a total of 238 chemical substances, called the POC238 (listed in Appendix
A), was compiled for the development of workflows and metrics for each of the seven scientific
domains36. The POC238 contains some chemical substances for which an expected biological
response in one or more of the separate domains would serve as a reference for evaluation of how
accurately the PICS approach identified potential hazards or environmental concerns. The POC238
was selected to span a range in the degree of potential concern and information availability;
however, the overall information availability for the selected chemical substances was generally
higher than for the overall TSCA active inventory (see Figure 14).
31 Richard, AM (2004). DSSTOX website launch: Improving public access to databases for building structure-
toxicity prediction models. Preclinica 2(2): 103-108.
32 Williams AJ, Grulke CM, Edwards J, McEachran AD, Mansouri K, Baker NC, Patlewicz G, Shah I, Wambaugh
JF, JudsonRS, et al. (2017). The CompTox Chemistry Dashboard: a community data resource for enviromnental
chemistry. Journal of Cheminfonnatics 9(1):61.
33 Curated list of non-confidential substances on the active TSCA inventory
(https://comptox.epa.gov/dashboard/chemical lists/TSCA ACTIVE NCTI 0320). The list contains 33,364
chemical substances as of March 2020.
34 TSCA10 represents the first ten TSCA Work Plan chemical substances selected for risk evaluation in 2016.
35 TSCA90 represents the TSCA Work Plan chemical substances from the 2014 update.
36 The scientific domains include human hazard relative to exposure, ecological hazard, carcinogenicity,
genotoxicity, exposure to susceptible populations, persistence and bioaccumulation, skin sensitization and skin/eye
irritation
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Chemical Substance Information Extraction and Quality Control
A key component of the PICS approach is the curation of data collected from "Type 1"
data sources (as defined in the Working Approach for Potential Candidates). Type 1 data sources
are publicly available and readily searchable, enabling data extraction in a structured form. Hazard,
exposure, persistence, and bioaccumulation information was extracted from a range of Type 1
sources (listed in Appendix B). Curated traditional and NAM data were compiled and filtered prior
to being analyzed for QC. More details on specific filtering of information sources for the
individual workflows are described below.
QC analysis was performed on the data for the POC238 chemical substances to ensure the
curation accuracy from primary published sources to database repository format, inform the
development of formal quality assurance (QA) procedures, and obtain information on the scope
and resources needed to perform QC for the entire active TSCA inventory. Specific approaches
and considerations for the QC review are provided in Appendix C. The QC analysis focused on a
determination of accuracy of extraction of the information from the Type 1 sources and did not
examine or evaluate study conclusions. Additionally, no study quality considerations were
evaluated during QC review. Reviewers did not perform a critical analysis of experimental design,
statistical analyses, or data interpretation. Rather, reviewers compared the aggregated Type 1 data
to primary and secondary sources. Reviewers flagged data that could not be confirmed to the
primary source, even if the aggregated data matched the secondary source. However, certain
secondary sources, such as the ECOTOX knowledgebase, the Integrated Risk Information System
(IRIS), or chemical exposure data have existing QC or peer review processes. For these select
databases, confirmation to secondary source was sufficient to pass QC review.
Over 25,000 total records were identified, with nearly 17,000 data points (68%) associated
with primary sources. For this effort, a data point was deemed 'reviewed' if it matched the number
in the authoritative secondary source; primary source review was not required. As an example, a
point of departure (POD) extracted from the Integrated Risk Information System (IRIS) would
have been confirmed against the on-line IRIS database, but not tracked back to the source paper
for the POD. POD matching required that the chemical identity, POD value, and relevant metadata
(e.g., units, exposure route, species) were consistent. The case study developed methods for data
aggregation, curation, and evaluation, as well as QA recommendations to efficiently review Type
1 datasets.
5.2 Scientific Domain Metric Assessment
A comprehensive analysis of the publicly available information for the POC238 chemical
substances was performed following data curation and QC. The overall SDM is determined by
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summing the results from the individual scientific domain workflows described below for the
following domains: (1) human health hazard relative to exposure; (2) ecological hazard; (3)
carcinogenicity; (4) genotoxicity; (5) susceptible populations; (6) persistence and
bioaccumulation; and (7) skin sensitization and skin/eye irritation (Figure 3). Of the seven domains
used in the PICS approach, five were included in the previous Working Approach. The additional
two domains (carcinogenicity; skin sensitization and skin/eye irritation) were included in the PICS
approach based on their use in the 2014 TSCA Work Plan. Each of these workflows represent a
mechanism for making a determination of potential concern for a compound in each domain based
on publicly available data. These domains were selected based on their importance to
understanding human health and ecological hazard, human exposure (including susceptible
populations), past use in TSCA prioritization activities37, and/or the statutory language in the Frank
R. Lautenberg Chemical Safety for the 21st Century Act (P.L. 114-182)38.
Figure 3. The seven scientific domains used to evaluate the degree of potential concern related to human
health and the environment for each chemical substance. The overall SDM is the sum of the individual
workflows within each domain.
Human Health Hazard-to-Exposure Ratio Domain
The identification of 2014 TSCA Work Plan chemical substances included consideration of
human health hazard as well as information on exposure potential (TSCA 2012)39. As outlined in
the Working Approach for Potential Candidates, the workflow described for this domain proposes
the use of ratios of hazardous effect dose-response (e.g., point-of-departure) information to
37 TSCA Work Plan Methods Document 2012 (https://www.epa.gov/sites/production/files/2014-
03/documents/work plan methods document web final.pdf).
38 https://www.govinfo.gov/app/details/PLAW-l 14publl82 https://www.govinfo.gov/app/details/PLAW-
114publl82
39 TSCA Work Plan Methods Document 2012 (https://www.epa.gov/sites/production/files/2014-
03/documents/work plan methods document web final.pdf).
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exposure predictions. As point-of-departure doses for hazardous effects approach exposure
predictions, a greater degree of potential concern may be indicated, whereas doses for hazard and
exposure separated by many orders of magnitude may suggest a lower degree of potential concern.
IG Flag | 2. Tiering of sources described in the text.
Figure 4. Workflow associated with the human health hazard-to-exposure ratio domain. Inhalation data
used only if converted to mg/kg-day; tiering of sources described in the text. HER, hazard-to-exposure ratio
calculated based on a point-of-departure from an in vivo repeat dose toxicity study divided by the median
ExpoCast exposure estimate; BER, bioactivity-to-exposure ratio calculated based on the in vitro-to-in vivo
extrapolation (IVIVE)-adjusted bioactivity estimates divided by the median ExpoCast exposure estimate;
TER, threshold of toxicological concern-to-exposure ratio calculated based on the TTC divided by the
median ExpoCast exposure estimate; POD, point-of-departure; and IG Flag, information gathering flag.
The calculation of the human health hazard-to-exposure ratio (HER) domain metric is based
on a workflow that incorporates a tiered selection of hazard information as well as exposure
estimates from the EPA ExpoCast (Exposure Forecasting) modeling effort (Figure 4)40. The third
generation ExpoCast Systematic Empirical Evaluation of Models (SEEM3) exposure model41 is a
meta-model for aggregate population median dose intake rate and incorporates twelve different
411 https://www.epa.gov/sites/production/files/2014-12/documents/exposure forecasting factsheet.pdf
41 Ring CL, Arnot J A, Bennett DH, Egeghy PP, Fantke P, Huang L, Isaacs KK, Jolliet O, Phillips KA, Price PS,
Shin HM. (2018). Consensus modeling of median chemical intake for the US population based on predictions of
exposure pathways. Environmental Science & Technology 53(2):719-32.
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exposure predictors covering sources that are near42- and far-field43. Four distinct source-based
exposure pathways were considered: non-pesticide dietary, consumer products, far-field chemical,
and far-field industrial. Chemical substances with other exposure pathways are outside of the
domain of the models and are noted with an information gathering (IG) flag. IG flags are used to
bring attention to specific aspects of the workflow decisions that may impact the results and may
denote whether the data falls within the applicability domain of the model. SEEM3 is calibrated
using chemical substance intake rates from biomonitoring data from the Centers for Disease
Control and Prevention (CDC) National Health and Nutrition Examination Survey; (NHANES)44;
and are used in place of the SEEM3 predictions45. As further described in Ring et al. 201746,
NHANES was used as a more comprehensive dataset which allowed for incorporation of
interindividual variability, including across different demographics. PODs from dose-response
curves from traditional in vivo toxicity studies are divided by the median ExpoCast intake rate
estimate to provide a HER. The approach uses only POD values with units of mg/kg-bw/day from
repeat dose studies (including multiple specific toxicities, e.g., reproductive toxicity). Therefore,
the majority of included studies assessed the oral route of exposure, although other routes of
exposure were included if the units had been converted appropriately (e.g., inhalation exposure
concentration converted to an equivalent mg/kg-bw/day dose). The POD used for HER calculation
was either the minimum of the set, or if a human health-relevant POD estimate from an
authoritative regulatory agency was available (ATSDR, EFSA, EPA HEAST, EPA OPP, EPA
IRIS or EPA PPRTV), it was used in the analysis. When in vivo studies are not available, in vitro
bioactivity estimates from ToxCast are converted into an oral dose equivalent using high-
throughput toxicokinetic (HTTK) approaches47 48 (called the in vitro-Xo-in vivo extrapolation
(IVIVE) POD) and divided by the ExpoCast exposure estimate to provide a bioactivity-to-
42 Near-field represents exposures occurring proximal to use-field (e.g., sources inside the home, for example from
consumer products).
43 Far-field represents exposures occurring far from use or as a result of enviromnental emission (e.g., ambient
sources outside the home, for example from industrial releases).
44 Centers for Disease Control and Prevention. "Fourth report on human exposure to environmental chemical
substances, updated tables." US Department of Health and Human Services, Centers for Disease Control and
Prevention (2017).
45 Ring CL, Arnot J, Bennett DH, Egeghy P, Fantke P, Huang L, Isaacs KK, Jolliet O, Phillips K, Price PS, Shin
HM, Westgate JN, SetzerRW, Wambaugh JF. (2019). Consensus Modeling of Median Chemical Intake forthe U.S.
Population Based on Predictions of Exposure Pathways. Enviromnental Science and Technology 53(2):719-732.
46 Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF. (2017). Identifying populations sensitive to
enviromnental chemical substances by simulating toxicokinetic variability. Enviromnent International 106:105-118.
47 Pearce RG, Setzer RW, Davis JL, Wambaugh JF. (2017). Evaluation and calibration of high-throughput
predictions of chemical distribution to tissues. Journal of Pharmacokinetics and Pharmacodynamics 44(6): 549-565.
48 Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS. (2017). httk: R package for high-throughput
toxicokinetics. Journal of Statistical Software 79(4):1.
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exposure ratio (BER)49-50. Finally, when neither in vivo nor in vitro studies are available, the most
relevant threshold of toxicological concern (TTC) value is assigned when appropriate and divided
by the ExpoCast exposure estimate to provide a TTC-to-exposure ratio (TER)51. Note that TTC
values are in silico NAMs derived using the Toxtree software application (Ideaconsult Ltd)52 by
calculating the lower 95th-percentile POD for each of the classes of chemical substances
considered, and then applying a safety factor of 100. In the current application, this safety factor
is removed because lack of in vivo data is accounted for separately in the IAM. From a practical
standpoint, if this safety factor was left in place, a vast majority of chemical substances with only
a TTC value would be designated as high concern, regardless of exposure level.
Human Health Hazard-to-Exposnre Evaluation
A human HER domain metric is assigned in a tiered fashion based on the magnitude of the
HER, BER, or TER value. The order of preference is HER > BER > TER (i.e., if the HER is
available, it is used preferentially over BER and TER). For volatile substances, PODs from
traditional in vivo repeat dose toxicity studies that have units converted to mg/kg-bw/day are
utilized, followed by IVIVE POD estimates using in vitro bioactivity data from ToxCast and
toxicokinetic estimates from HTTK. EPA does not initially incorporate TTC values for volatile
substances since well-established TTC values for the inhalation route of exposure are not yet
available.
For each chemical, each metric was assigned a value in the range from 1 to 4, to allow
combining the metrics in a consistent way. This is adapted from the strategy used in the 2012
TSCA Work Plan Methodology53. Most of the metrics naturally fell into discrete categories from
low concern=l to high concern=4. For the HER and ecological hazard domains (see below) we
converted the continuous value to this scale using Formula 1:
Domain metric = 4 - 3 X ^oCHER/BER/TERj-^pCHER/BER/TERW
io5lo(HER/BER/TER)rnax-io5lo(HER/BER/TER)rnin v >
49 Paul-Friedman K, Gagne M, Loo LH, Karamertzanis P. Netzeva T, Sobanski T, Franzosa J A, Richard AM,
Lougee RR, Gissi A, Lee JJ, Angrish M, Dome JL, Foster S, Raffaele K, Bahadori T, Gwinn MR, Lambert J,
Whelan M, Rasenberg M, Barton-Maclaren T, Thomas RS. (2020). Utility of In Vitro Bioactivity as a Lower Bound
Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization. Toxicological Sciences 173(l):202-225.
511 Wetmore BA, Wambaugh JF, Allen B, Ferguson SS, Sochaski MA, Setzer RW, Houck KA, Strope CL, Cantwell
K, JudsonRS, LeCluyse E. (2015). Incorporating high-throughput exposure predictions with dosimetry-adjusted in
vitro bioactivity to inform chemical toxicity testing. Toxicological Sciences 148(1): 121-36.
51 Patlewicz G, Wambaugh JF, Felter SP, Simon TW, Becker RA. (2018). Utilizing Threshold of Toxicological
Concern (TTC) with high throughput exposure predictions (HTE) as a risk-based prioritization approach for
thousands of chemicals. Computational Toxicology 7:58-79.
52 Patlewicz G, Jeliazkova N, Safford RJ, Worth AP, Aleksiev B. (2008). An evaluation of the implementation of the
Cramer classification scheme in the Toxtree software. SAR and QSAR in Enviromnental Research 19(5-6):495-524.
53 TSCA Work Plan Chemicals: Methods Document, https://www.epa.gov/sites/production/files/2014-
03/documents/work plan methods document web final.pdf
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Note that the maximum and minimum values are taken across all chemicals with HER values so
that the domain metric is scaled relative to HER values. The first term (logio (HER/BER/TER))
uses an HER for a chemical, if available, followed by BER and TER. This sets the domain metric
of the chemical with the lowest HER (highest concern) to a value of 4 and sets the domain metric
of the chemicals with the highest HER (lowest concern) to 1 (Table 1). The minimum and
maximum HER values will be somewhat sensitive to the set of chemicals included, but these values
are taken from the 2838 out of the TSCA active inventory with value for either HER, BER or TER.
The largest HER is 5.03xl014 (1,2,5,6,9,10-hexabromocyclododecane) and the smallest value is
0.89 (ethenylsilanetriyl triacetate). A value of 0 is given in the absence of information and the
substance is flagged for future information gathering. The in vivo hazard data is derived from the
EPA ToxValDB database. The in vitro ToxCast data is obtained from the EPA invitroDB database.
These datasets as well as the toxicokinetic data parameters are publicly available through the EPA
CompTox Chemicals Dashboard54.
Table 1. Criteria used to calculate the human hazard to exposure ratio domain metric
Metric
HER, BER, or TER value1
0
No available data (hazard or exposure)
1
Result is on a continuum based on Formula 1, i.e., 1 = highest HER, BER,
TER (lowest concern); 4 = lowest HER, BER, TER (highest concern)
2
3
4
Information Gathering (IG) Flags: Note concerning key study types with no in vivo data (repeat dose,
reproductive, developmental); secondary source data; predicted data; lack of exposure data
'HER, hazard-to-exposure ratio calculated based on in vivo repeat dose toxicity studies divided by the
median ExpoCast exposure estimate; BER, bioactivity-to-exposure ratio calculated based on IVIVE
bioactivity estimates divided by the median ExpoCast exposure estimates; TER, TTC-to-exposure ratio
calculated based on the TTC divided by the median ExpoCast exposure estimate.
Limitations and Longer-term Options
When only data from acute in vivo studies was available, the data were not considered
sufficient for calculation of the HER, which uses hazard information from in vivo repeat dose
54 https://comptox.epa. gov/dashboard
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studies, including studies for specific endpoints including reproductive and developmental
toxicology. (Note though that the presence or absence of acute data is included in the IAM,
described below.) Ongoing research will be needed to determine how to utilize acute toxicity
information in the absence of repeat dose toxicity information to estimate a POD for HER
calculation. In the current implementation, POD values with typical inhalation units (mg/m3 or
ppm) have been excluded. Converting these inhalation values to the oral equivalent dose value
requires, at a minimum, knowing whether the chemical substance has local or systemic effects.
This information is not typically captured in the current Type 1 information sources and will
require either manual curation of the relevant studies, or development of a semi-automated
approach to select the appropriate exposure effect class. Similarly, conversion of dermal exposure
was not addressed for this case study. Future efforts could incorporate data from these additional
routes of exposure.
Calculation of the BER is influenced by selection of a minimum in vitro potency value
from high-throughput bioactivity screening data and the HTTK approaches used to derive an
administered equivalent dose. Although this is an area of ongoing research, current evidence
supports that this global bioactivity approach is conservative55 and that further efforts to refine
may provide additional pathway specific PODs (increase relevance). In the POC, features of the
concentration-response curves fit to the ToxCast high-throughput bioactivity data have been used
to identify a minimum potency value showing bioactivity. In future iterations, we propose
leveraging ongoing research on how to best identify the minimum credible in vitro potency values
from ToxCast and other sources of high-throughput bioactivity data (e.g. high-throughput
transcriptomics), as well as ongoing and iterative improvements in HTTK modeling. Selected
choices in HTTK modeling approaches can also include a consideration of interindividual
toxicokinetic variability, or not, depending on the scenario; in a conservative approach to an initial
screening of substances, use of estimated parameters for toxicokinetically-susceptible individuals
to derive administered equivalent doses may be informative.
Additionally, QSAR models were considered to estimate in vivo PODs as a fourth level in
the hazard estimation process (in vivo>IVI VE>Q S A R>TTC), but at the time of development, a
valid QSAR model was not available. Finally, the current TTC values are limited to oral exposures.
We are reviewing the latest research efforts related to the use of TTC for other routes of exposure,
and any future improvements to this approach may expand the domain of applicability of the TTC
55 Paul Friedman K, Gagne M, Loo LH, Karamertzanis P. Netzeva T, Sobanski T, Franzosa J A, Richard AM,
Lougee RR, Gissi A, Lee JJ, Angrish M, Dome JL, Foster S, Raffaele K, Bahadori T, Gwinn MR, Lambert J,
Whelan M, Rasenberg M, Barton-Maclaren T, Thomas RS. (2020). Utility of In Vitro Bioactivity as a Lower Bound
Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization. Toxicological Sciences 173(l):202-225.
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to incorporate these updates such as in Nelms and Patlewicz (2020)56. This may also help to
address limitations of this approach related to potential screening of compounds for which in vitro
assays (the basis of BER) or TTC (the basis of TER) do not perform well.
Carcinogenicity Domain
The probable or known carcinogenicity of a chemical substance was considered in
selecting the 2014 TSCA Work Plan chemical substances57. Carcinogenicity was not included as
a separate domain in the previous Working Approach for Potential Candidates, due to limited
availability of Type 1 carcinogenicity data sources. In the Working Approach for Potential
Candidates, the genotoxicity domain was considered as a surrogate for carcinogenicity. In the PICS
approach, carcinogenicity and genotoxicity are included as separate domains due to the fact that
carcinogenicity may be associated with non-genotoxic as well as genotoxic mechanisms.
The ability of an agent to cause cancer in humans is typically assessed using a weight-of-
evidence approach, considering exposure, epidemiology, animal cancer data, and mechanistic data,
including genotoxicity and pharmacokinetic/pharmacodynamic information. Major national and
international organizations convene expert panels to perform these evaluations, resulting in
authoritative assessments of the potential of agents to induce cancer in humans (e.g., IARC, IRIS).
EPA has its own guidelines for cancer that consider mechanistic data as an important component
of carcinogenicity58. In the absence of such evaluations for human cancer, the ability of the agent
to cause cancer in in vivo rodent models is an indication of the potential of an agent to be
carcinogenic to humans. Rodent chronic bioassays include the standard protocols established by
organizations such as the National Toxicology Program (NTP)59, as well as more generalized
guidance from institutions such as the Organization for Economic Cooperation and Development
(OECD)60. These data have been compiled in ToxValDB. The presence of lesions believed to have
resulted from carcinogenesis were used in a binary fashion in the scoring process. That is, the
potency of the carcinogen is not reflected in the evaluation process, just the evidence of
carcinogenicity (yes or no) (Figure 5).
56 Nelms MD, Patlewicz G. (2020) Derivation of new Thresholds of Toxicological Concern values for exposure via
inhalation for environmentally-relevant chemicals. Frontiers in Toxicology 2:5.
57 https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/tsca-work-plan-methods-document
58 https://www.epa.gov/sites/production/files/2013-09/documents/cancer guidelines final 3-25-05.pdf
https://www.epa.gov/sites/production/files/2013-09/documents/cancer guidelines final 3-25-05.pdf
59 NTP Toxicology/Carcinogenicity Study Overview, https://ntp.niehs.nih.gov/testing/tvpes/cartox/index.html
611 OECD Draft guidance http://www.oecd.org/chemicalsafetv/testing/44960015.pdf
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I Table (Table 2) ,
Figure 5. Tiered evaluation process associated with the carcinogenicity domain. The workflow begins with
the determination of human carcinogenicity from an authoritative source and ends at one of the red, dashed
line boxes. Blue, solid line boxes represent intermediate decision points. IG flag = information gathering
flag.
Carcinogenicity Evaluation
The carcinogenicity domain metric is determined from a two-tiered evaluation workflow
of the available carcinogenicity data in humans and/or animals. Chemical substances that have
not been evaluated for their ability to cause cancer in humans, or where there are no available
human data, are evaluated for the ability to cause cancer in animals. This is a binary result, not
based on the dose required to produce carcinogenicity, but based on the presence or absence of
carcinogenicity in a study (Figure 5/Table 2). Chemical substances with evidence of known human
carcinogenicity as determined by an authoritative source are given a value of 4; chemical
substances that have been determined to have evidence as possible or probable human carcinogens
are given a value of 3; chemical substances that have been shown to cause cancer in animals but
have not otherwise been assessed for their ability to cause cancer in humans are given a value of
2; and chemical substances with evidence indicating a low likelihood of carcinogenicity in either
humans or rodents based on negative data (e.g., a negative rodent cancer bioassay) are given a
value of 1. This category may also be termed as having inadequate or insufficient evidence of
carcinogenicity in an authoritative carcinogenesis assessment. A value of 0 is given in the absence
of data and an information gathering flag is included.
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Table 2. Criteria used to calculate the carcinogenicity domain metric
No available data for carcinogenicity
Evidence of low likelihood of carcinogenicity; inadequate or insufficient data
Evidence for animal carcinogenicity but not assessed for human
carcinogenicity
Evidence of possible or probable human carcinogenicity based on either
human epidemiology or animal toxicology data
Known human carcinogen
Carcinogenicity Determination
Limitations and Longer-term Options
There are limited data available for carcinogenicity of chemical substances. One limitation
of this approach is the lack of a published peer-reviewed automated predictive model for the
determination of carcinogenicity. OncoLogic™ 61, a computer system that evaluates the
carcinogenic potential of chemical substances, has not yet been modified to analyze large numbers
of chemical substances in a manner that can be readily incorporated into this approach. In the
future, the OncoLogic system could be adapted to meet this need and could be incorporated into
this workflow in a tiered manner. Indeed, there is an activity within the OECD Toolbox
Management Group which is investigating the feasibility of implementing the decision logic of
selected OncoLogic chemical classes into the OECD Toolbox. Currently, OncoLogic is used as
part of the expert review of compounds and incorporated into the weight-of-evidence assessment
of specific compounds.
Genotoxicity Domain
Genotoxicity is an important component of understanding chemical substance hazards.
Genotoxicity refers to the ability of agents to induce DNA damage, such as DNA strand breaks or
DNA adducts, as well as the ability to induce mutations, i.e., heritable changes in DNA sequence.
In the absence of carcinogenicity data, genotoxicity is often used as a surrogate. This document
evaluates chemical substances for genotoxicity by considering data from assays that collectively
detect mutations in bacteria or mammalian cells, as well as DNA damage in mammalian cells or
rodents. For the PICS approach, some consideration was given to including genotoxicity within
the same domain as carcinogenicity. However, it was determined that these should be considered
61 https://www.epa.gov/tsca-screening-tools/oncologictm-computer-SYStem-evaluate-carcinogenic-potential-
chemicals
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separately to incorporate not only the impact of nongenotoxic carcinogens, but also capture
genotoxic chemical exposures that may not have been assessed for cancer.
Since the initial EPA implementation of TSCA in 1976, many studies have assessed which
combinations of genotoxicity tests are the most predictive62, resulting in testing schemes
recommended by the OECD Genetic Toxicology Test Guidelines63, the International Conference
on Harmonization64, and the NTP65. Additional consideration has been given to entirely new
testing approaches, which do not rely on traditional assays66 67.
Most regulatory bodies in the U.S., such as the EPA and FDA, recommend the OECD
genetic toxicology guidelines. This testing includes a set of bacterial assays for gene mutation
using strains of Salmonella (the Ames strains) and strains of Escherichia coli WP2 and assays for
chromosomal mutation (in vitro chromosome aberration assay, mouse bone-marrow micronucleus
assay, and the mouse lymphoma Tk+/~ assay). The combination of assays identifies genotoxic
agents that produce primarily gene mutations, chromosomal mutations or both gene and
chromosomal mutations. A smaller number of chemical substances produce aneuploidy
(chromosome gain or loss), which is also detected by the chromosome aberration (CA) or mouse
bone-marrow micronucleus assays (MNT).
In the PICS approach, we considered that the genotoxicity of an agent can be sufficiently
assessed by evaluating data in the standard bacterial mutation assays (the Ames Salmonella and E.
coli WP2 strains) and the three principal assays for chromosomal mutation (in vitro chromosome
aberration assay, mouse bone-marrow micronucleus assay, and the mouse lymphoma Tk+/~ assay).
The selection of a subset of genotoxicity assays used in this approach was based on recent work
62 Eastmond DA, Hartwig A, Anderson D, Anwar WA, Cimino MC, Dobrev I, Douglas GR, Nohmi T, Phillips DH,
Vickers C. (2009). Mutagenicity testing for chemical risk assessment: update of the WHO/IPCS Harmonized
Scheme. Mutagenesis 24:341-349.
63 Guidance Document on Revisions to OECD Genetic Toxicology Test Guidelines (2015)
http://www.oecd.org/chemicalsafetv/testing/Genetic%20Toxicologv%20Guidance%20Document%20Aug%2031%2
02015.pdf
64 International Conference on Hannonisation. (2012). Genotoxicity Testing and Data Interpretation for
Pharmaceuticals Intended for Human Use. S2(R1).
http://www.ich.org/fileadmin/Public Web Site/ICH Products/Guidelines/Safetv/S2 Rl/Step4/S2R1 Step4.pdf
65 NTP Genetic Toxicology, https://ntp.niehs.nih.gov/testing/tvpes/genetic/index.html
66 Thomas RS, Philbert MA, Auerbach SS, Wetmore BA, Devito MJ, Cote I, Rowlands JC, Whelan MP, Hays SM,
Andersen ME, Meek ME, Reiter LW, Lambert JC, Clewell HJ 3rd, Stephens ML, Zhao QJ, Wesselkamper SC,
Flowers L, Carney EW, Pastoor TP, Petersen DD, Yauk CL, Nong A. (2013). Incorporating new technologies into
toxicity testing and risk assessment: Moving from 21st century vision to a data-driven framework. Toxicological
Sciences. 136:4-18.
67 Dearfield KL, Gollapudi BB, Bemis JC, Benz RD, Douglas GR, Elespuru RK, Johnson GE, Kirkland DJ,
LeBaron MJ, Li AP, Marchetti F, Pottenger LH, Rorije E, Tanir JY, Thybaud V, van Benthem J, Yauk CL, Zeiger
E, Luijten M. (2017). Next generation testing strategy for assessment of genomic damage: A conceptual framework
and considerations. Enviromnental and Molecular Mutagenesis 58:264-283.
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by Williams et al. 201968 which demonstrated that this subset of assays is sufficient to identify
99% of mutagens tested.
In the absence of experimental data, genotoxicity may be predicted using in silico
Quantitative Structure-Activity Relationship (QSAR) models for Ames mutagenicity or in silico
structural alerts for clastogenicity. This evaluation is similar for measured data but is tagged with
an IG flag for predicted data. The EPA Toxicity Estimation Software Tool (TEST) was used to
predict Ames mutagenicity, along with the OECD Toolbox, which includes DNA alerts for Ames,
CA and MNT; protein binding alerts for CA; in vitro mutagenicity (Ames test) alerts by Instituto
Superiore di Sanita (ISS), and in vivo mutagenicity (micronucleus) alerts by ISS. The Ames
mutagenicity module within the TEST software is based on a dataset of 6,512 chemical substances
that was compiled from several different sources as described in Hansen et al.69. After removal of
salts, mixtures, ambiguous compounds, and compounds without CAS numbers, the final dataset
consisted of 5,743 chemical substances. Several different approaches were used to derive TEST
predictions, including a hierarchical-clustering approach70, a nearest-neighbor approach, a Food
and Drug Administration approach, and a single-model approach. The profilers within the OECD
Toolbox are collections of structural alerts that have been compiled and developed by various
researchers and organizations. Most of the profilers incorporate the alerts devised by Ashby and
Tennant (1991)71, but additional alerts are included depending on the experimental data available.
DNA OASIS profilers include alerts derived from the training sets used for the TIMES expert
system, whereas the ISS alerts rely on the IS SCAN72 database. A consensus outcome from the
individual models culminates in the overall prediction conclusion generated for a given chemical
substance.
68 Williams RV, DM DeMarini, LF Stankowski Jr, PA Escobar, E Zeiger, J Howe, R Elespuru, KP Cross. (2019).
Are all bacterial strains required by OECD mutagenicity test guideline TG471 needed? Mutation Research
848:503081.
69 Hansen K, Mika S, Schroeter T, Sutter A, ter Laak A, Steger-Hartmann T, Heinrich N, Miiller KR. (2009).
Benchmark data set for in silico prediction of Ames mutagenicity. Journal of Chemical Information and Modelling
49(9):2077-81.
711 More details can be found at https://www.epa.gov/chemical-researcli/toxicitv-estimation-software-tool-test
71 Ashby J, Tennant RW. (1991). Definitive relationships among chemical structure, carcinogenicity and
mutagenicity for 301 chemicals tested by the U.S. NTP. Mutation Research 257(3):229-306.
72 Carcinogens database developed by ISS. http://old.iss.it/publ/anna/2008/l/44148.pdf
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Metric as described in the Genotoxicity Evaluation table; if multiple metrics are possible based on the data, the highest metric will be selected. I
Figure 6. Tiered evaluation process associated with the genotoxicity domain. The process evaluates the
potential mutagenicity and DNA damaging potential of a chemical substance as well as the potential
clastogenicity. IG, information gathering.
Genotoxicity Evaluation
The process for screening chemical substances for genotoxicity is shown in Figure 6/Table
3. Chemical substances with evidence of genotoxicity are evaluated based on results for gene
mutation in bacterial mutagenicity assays and any of the three assays for chromosomal mutations
(clastogenicity as described above). Chemical substances that have been determined to be
genotoxic experimentally (either as a gene or chromosomal mutagen) are given a value of 4;
chemical substances that are predicted to be genotoxic are given a value of 3; chemical substances
with inconclusive data are given a value of 2; chemical substances with data showing that the
chemical substance is not likely to be genotoxic are given a value of 1; and chemical substances
with no data are given a value of 0. Chemical substances with inconclusive results are also tagged
with an IG flag. If there are multiple data sources for a chemical substance, the highest metric is
selected, e.g., if a chemical substance is a predicted clastogen (metric of 3) and a mutagen based
on measured data (metric of 4), an overall metric of 4 was used. No attempts were made to evaluate
the quality and design of the studies, and the IG flag gives the end-user an opportunity to evaluate
the weight-of-evidence in situations that are deemed inconclusive. Combinations of the Ames or
clastogenicity information was used to determine the genotoxicity, which was converted to a
numeric scale as described in Table 3.
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Table 3. Criteria used to calculate the genotoxicity domain metric
No available data for genotoxicity
Evidence of low likelihood of genotoxicity - predicted or measured data
Inconclusive evidence of genotoxicity (predicted or measured)
Evidence of genotoxicity - predicted data
Evidence of genotoxicity - measured data
Genotoxicity Determination
Limitations and Longer-term Options
A limitation of the genotoxicity analysis was the reliance on secondary source data for
specific genotoxicity endpoints. The sources included authoritative assessments, data compilation
summaries, and publicly available review papers. For the purposes of the automated PICS
approach, secondary data sources were deemed acceptable but given an IG flag so that the expert
reviewer would have an awareness of the potential limitations of the data sources. Although
weight-of-evidence results from authoritative sources may be considered, this approach does not
perform a weight-of-evidence analysis for genotoxicity. For many of the chemical substances in
the database, there were genotoxicity data available from a variety of data sources. This screening
approach does not explicitly address the absolute number of positive or negative results or
contradictory results. These issues should be considered during the downstream expert review of
any candidate chemical substances (Figure 2). Similarly, a recent effort to develop a genotoxicity
hazard assessment framework using in silico tools was published73. This method was developed
to rapidly assess chemical hazard but is not designed as an automated approach for analysis of a
large number of chemicals at one time. While the published approach goes further to incorporate
expert review, many of the underlying assumptions on incorporating specific genotoxicity assays
support decisions made in the PICS approach. Longer-term options should also include recent
advances in the development of a consensus model using combinations of QSAR models and
structural alert predictions to strengthen the use of predictive results in the genotoxicity assessment
of compounds74.
73 Hasselgren C, Ahlberg E, et al. (2019). Genetic toxicology in silico protocol. Regulatory Toxicology and
Pharmacology 107:104403.
74 Pradeep P. Judson R, DeMarini, DM, Keshava N, Martin TM, Dean J, Gibbons CF, Sima A, Warren SH, Gwinn
MR, Patlewicz, G. (2021). Evaluation of existing QSAR models and structural alerts and development of new
ensemble models for genotoxicity using a newly compiled experimental dataset. Computational Toxicology
18:100167.
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Ecological Hazard Domain
The ecological hazard domain is intended to account for potential toxicity to a broad
diversity of wildlife and plants. Under typical approaches for ecological hazard classification of
chemical substances, for example under the Globally Harmonized System (GHS)75, only aquatic
toxicity is considered. However, it is common to consider data from at least three trophic levels of
organisms, generally a fish, an invertebrate (crustacean), and at least one plant or algae species76.
In cases where data for all three trophic levels are unavailable, additional uncertainty factors are
often applied to account for the fact that potentially sensitive classes of organisms with highly
distinct life histories and physiology have not been considered. Consistent with GHS,
experimentally-derived test data are preferred for derivation of the ecological hazard metric.
However, in cases where no experimentally derived PODs are available, QSAR models are used.
This well-established method for evaluating ecological hazards is the basis of the workflow
developed for the ecological hazard domain of the PICS approach.
The ecological hazard domain is a hazard-only approach based on measured or estimated
chronic and acute aquatic toxicity. In vivo aquatic toxicity data are collected from US EPA's
ECOTOX Knowledgebase77, eChemPortal78, and EFSA79. In cases where in vivo data are absent,
QSAR-based predictions of aquatic acute toxicity are derived from EcoSAR80 or TEST81. All data
are compiled into ToxValDB.
In the PICS approach (Figure 7), additional uncertainty factors are not applied when the
three trophic levels typically considered in GHS classification82 are not represented in the dataset.
However, there is a computational evaluation of the presence/absence of data for each trophic
level. In cases where one or more trophic levels of organisms are not represented, an alert is
provided in the form of an information gathering flag. This alert provides an indication that at least
one major group of potentially sensitive taxa are not currently considered as part of the ecological
hazard domain metric.
75 United Nations. (2017). Globally harmonized system of classification and labeling of chemical substances.
Seventh revised edition. United Nations, New York, NY, USA. ST/SG/AC.10/30/Rev.7
76 United Nations. (2017). Globally harmonized system of classification and labeling of chemical substances.
Seventh revised edition. United Nations, New York, NY, USA. ST/SG/AC.10/30/Rev.7
77 https://cfpub.epa.gov/ecotox/
78 https://www.echemportal.org/
79 http://www.efsa.europa.eu/
811 https://www.epa.gov/tsca-screening-tools/ecological-structure-activitv-relationsliips-ecosar-predictive-model
81 https://www.epa.gov/chemical-researcli/toxicitv-estimation-software-tool-test
82 United Nations. (2017). Globally harmonized system of classification and labeling of chemical substances.
Seventh revised edition. United Nations, New York, NY, USA. ST/SG/AC.10/30/Rev.7
33
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Figure 7. Workflow for derivation of the ecological hazard domain metric. Boxes with dashed red borders
indicate decision points and information gathering alerts that are appended to the metric. Acute POD values
are divided by 10 prior to comparison to chronic values. This aligns with a 10-fold difference in the category
cut-off concentrations for acute versus chronic values applied under the Globally Harmonized System5".
QSAR predicted values are considered only when no in vivo data are available and are used, unadjusted.
Ecological Hazard Evaluation
An ecological domain metric is based on the lowest POD value derived from available in
vivo data (acute and chronic; any endpoint or life stage) or QSAR predictions when in vivo POD
estimates are unavailable (Figure 7). Using the same rationale as developed for GHS classification,
in vivo acute values (expressed as LC50 or LD50) are divided by a factor of 10 to derive a final in
vivo POD estimate (POD,„ vivo). Chronic toxicity data (e.g., reported as NOEC, LOEC, NOEL,
LOEL, NOAEL, LOAEL) and QSAR-based estimates are used unadjusted. The minimum
PODqsar value is the minimum across all EcoSAR and TEST predictions (including both acute
and chronic models). In general, the chronic values are lower than the acute values, so chronic
QSAR values are most often used. The resulting minimum POD,„ vivo across all three categories
are then compared with the ecological hazard domain metric table (Table 4) to assign the final
value on a scale of 1-4 (low hazard to very high hazard). A value of 1 is also given when the POD
83 United Nations. (2017). Globally harmonized system of classification and labeling of chemical substances.
Seventh revised edition. United Nations, New York, NY, USA. ST/SG/AC.10/30/Rev.7
34
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is greater than the water solubility of the chemical84. In the absence of in vivo data, the minimum
PODqsar is selected. The domain metric was calculated in the same way as for human HER, i.e.
by scaling the continuous chemical level POD values onto the 1-4 scale, using Formula 2:
Domain metric = 4 — 3 x l°9iopoD iog10poDmin ^
l0dioPODmax—log-L0PODmin
Here the PODmin and PODmax are the minimum and maximum PODs across all
chemicals. As with HER, this assigns a metric value of 4 to the chemical with lowest POD and a
metric value of 1 to the chemical with the largest POD. The minimum and maximum POD
values will be somewhat sensitive to the set of chemicals included, but these values are taken
from the 5031 out of the TSC A active inventory with in vivo PODs for either acute or chronic
aquatic studies. The largest POD is 163,709 mg/L (phosphonic acid, [1,6-hexanediylbis
[nitrilobis(methylene)]]tetrakis) and the smallest value is 1.6xl0"19 (propanoic acid, 3-
(dodecylthio)-, 2,2-bis[[3-(dodecylthio)-l-oxopropoxy]methyl]-l,3-propanediyl ester).
Table 4. Criteria used to calculate the ecological hazard domain metric
Metric
Minimum Aquatic POD (mg/L)
0
No available data
1
Result is on a continuum based on Formula 2, i.e., 1 = highest POD (lowest
concern); 4 = lowest POD (highest concern)
2
3
4
Information Gathering (IG) Flags: predicted data; secondary source data
Limitations and Longer-term Options
One of the limitations of the ecological hazard domain metric is that, unlike the human
hazard-to-exposure ratio metric, the ecological hazard domain metric is solely based on hazard,
without consideration of exposure. This is primarily due to limited peer-reviewed, automated
ecological exposure estimation tools. While procedures to derive ecological exposure estimates
are routinely implemented, those approaches cannot currently be automated for thousands of
chemical substances. Consequently, the development of an appropriate automated framework for
84 Water solubility is predicted by OPERA (Mansouri et al., 2018;
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5843579/)
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ecological exposure estimation is viewed as a high priority for long-term improvement of the
ecological domain metric. If appropriate ecological exposure estimates could be generated or
obtained in an automated fashion, the ecological hazard domain metric could be adapted to use a
hazard-to-exposure ratio approach that parallels that used in the human health domain described
earlier.
A second limitation of the current ecological hazard domain metric is that it only considers
aquatic ecotoxicity data. Terrestrial ecotoxicity data for mammals is considered in the human
health hazard domain. However, ecotoxicity data for other terrestrial organisms (e.g., amphibians,
birds, reptiles, earthworms, insects, terrestrial plants) are not considered which is consistent with
the GHS approach85. The 2012 TSCA Work Plan Chemical substances methods86 only considered
aquatic ecotoxicity data in deriving a hazard metric. The greater reliance on aquatic ecotoxicity
data over terrestrial ecotoxicity data is based in part on the assumption that the aquatic
compartment is maximally vulnerable as a final receiving environment for many chemical
substances. Aquatic organisms are continuously immersed in the aqueous exposure media and
tend to have high exposure levels87. The focus on aquatic species is also based in part on the greater
availability of aquatic ecotoxicity data and the availability of well-established QSARs for
predicting aquatic toxicity.
Finally, consistent with the GHS approach, experimentally-derived PODs are preferred
over QSAR predictions. This is expected to be robust when a modest number of experimentally-
derived POD values are available. However, in cases where the experimental data are sparse, one
or a few poorly designed studies could lead to underestimation of hazard. Given that the PICS
approach is intended to assist in efficiently selecting chemical substances for subsequent expert
review, it is assumed that study quality and data sufficiency would be evaluated in the expert-
driven part of the process.
Susceptible Human Population Domain
The modernization of TSCA included a requirement for increased attention to susceptible
subpopulations, such as infants, children, pregnant women, workers, or the elderly. In the context
of TSCA, a "potentially exposed or susceptible subpopulation" is defined as a group of individuals
within the general population who, due to either greater susceptibility or greater exposure, may be
at greater risk than the general population of adverse health effects from exposure to a chemical
substance or mixture. Currently, children are the only susceptible subpopulation given separate
85 United Nations. (2017). Globally harmonized system of classification and labeling of chemical substances.
Seventh revised edition. United Nations, New York, NY, USA. ST/SG/AC.10/30/Rev.7
86 https://www.epa.gov/sites/production/files/2014-03/documents/work plan methods document web final.pdf
87 United Nations. (2017). Globally harmonized system of classification and labeling of chemical substances.
Seventh revised edition. United Nations, New York, NY, USA. ST/SG/AC.10/30/Rev.7
36
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consideration in the PICS approach, although additional subpopulations (e.g., pregnant women,
elderly) could be incorporated in the future if appropriate to the decision context.
Children may have higher exposure levels to environmental chemical substances than
adults, and life-stage dependent exposure sources and pathways can contribute to this
differential88. Infants and young toddlers have unique exposure sources such as breast milk and
formula. Children play close to the ground, and thus increased contact with the floor and a lower
height of the breathing zone results in increased exposure to chemical substances in dust and to
chemical substances emitted from flooring or from products applied to floors. Children display
increased hand and object mouthing behaviors, and thus can be more highly exposed to chemical
substances in consumer products applied to the body, residential surfaces, or in articles such as
toys. Children can also be more heavily exposed to environmental pollutants than adults due to
physiological factors; they consume more food and water and have higher inhalation rates per
pound of body weight than adults89.
Susceptible Population Evaluation
A susceptible population domain metric was developed that characterizes the potential for
differential exposure between children and the general population (Figure 8/Table 5). The
proposed susceptible subpopulation domain metric incorporates exposure from multiple sources
that contribute to an increased exposure of children relative to the general population (Figure 8).
Each exposure source is given an exposure differential score to semi-quantitatively represent the
magnitude of potential exposure differential between children and adults. Each chemical is
assessed using available data to determine whether it occurs in each exposure source and is
evaluated accordingly. The exposure source definitions, the data sources used to identify
associated chemicals, and the mechanism(s) by which the exposure source contributes to increased
exposure for children are summarized in Appendix D. These definitions are consistent with the
pathways used by ORD in high-throughput models of exposure90. Information in EPA's Chemical
and Product (CPDat)91 and Chemical Product Category (CPCat)92 databases or in EPA Chemical
Data Reporting (CDR) is used to determine which exposure sources are relevant for each chemical
88 Environmental Protection Agency (2006). A Framework for Assessing Health Risks of Enviromnental
Exposures to Children, EPA/600/R-05/093F.
89 Enviromnental Protection Agency (2002). Child Specific Exposure Factors Handbook, EPA-600-P-00-002B.
911 Ring CL, Arnot JA, Bennett DH, Egeghy PP, Fantke P, Huang L, Isaacs KK, Jolliet O, Phillips KA, Price PS,
Shin HM, Westgate JN, Setzer RW, Wambaugh JF. (2019). Consensus modeling of median chemical intake for the
U.S. population based on predictions of exposure pathways. Enviromnental Science and Technology 53(2):719-732.
91 Dionisio KL, Phillips K, Price PS, Grulke CM, Williams A, Biryol D, Hong T, Isaacs KK. (2018). The Chemical
and Products Database, a resource for exposure-relevant data on chemicals in consumer products. Scientific Data, 5:
1-9.
92 Dionisio KL, Frame AM, Goldsmith MR, Wambaugh JF, Liddell A, Cathey T, Smith D, Vail J, Ernstoff AS,
Fantke P, Jolliet O, Judson RS. (2015). Exploring consumer exposure pathways and patterns of use for chemicals in
the enviromnent. Toxicology Reports 2:228-37.
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substance. CPDat and CPCat contain use information from hundreds of data sources; CPDat
contains reported chemical substance data on thousands of consumer products (obtained from
product Safety Data Sheets or ingredient lists), while CPCat contains general chemical substance
use information for over 75,000 chemical substances from public manufacturer, government, or
industry chemical substance lists. Two exposure sources, breast milk and residential dust, are
characterized here via a recently published meta-analyses93 94.
CPCat and CPDat
Chemical and Product
Use Databases, 2016
Chemical Data Reporting,
published meta-analyses
Does the chemical have available
exposure source information?
Yes
No
Consumer Sources:
Other Products
! IG Flag | |
/ N
I Metric as described in
J Susceptible Population
^ Exposure Evaluation Table
Figure 8. Workflow of susceptible population domain metric based on the relevance of multiple exposure
pathways. The sources associated with individual chemical substances were identified using information in
EPA's Chemical and Product (CPDat) and Chemical Product Category (CPCat) databases, in EPA
Chemical Data Reporting (CDR) results, or in a published meta-analysis of chemical substances in
residential dust. Exposure sources are shown here in the middle of the figure, with the assigned value to the
right of the source representing the exposure differential score, which semi-quantitatively represents the
magnitude of contribution to the exposure differential between children and adults. IG: information
gathering flag.
93 Lehmann GM, LaKind JS, Davis MH, Hines EP, Marchitti SA, Alcala C, Lorber C. (2018). Environmental
chemicals in breast milk and formula: Exposure and risk assessment implications. Environmental Health
Perspectives 126:96001.
94Mitro SD, DodsonRE, Singla V, Adamkiewicz G, Elmi AF, Tilly MK, Zota AR. (2016). Consumer product
chemicals in indoor dust: A quantitative meta-analysis of U.S. studies. Enviromnental Science and Technology 50:
13611-11.
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Once it is determined that a chemical substance has available exposure source information,
exposure sources are evaluated for the chemical substance. If the exposure source is determined
for a substance, the value assigned to the source (represented in Figure 8 to the right of each
potential source) is added to the total for that chemical substance. The value associated with each
exposure source represents the relative magnitude of potential exposure differential between
children and adults. The total for each chemical substance can range from 1-18 and is used to
assign the susceptible population domain metric based on the ranges specified in Table 5.
Table 5. Criteria used to evaluate the susceptible population exposure domain metric.
Total Exposure Source Value
Chemical substance had no information in the exposure source data sources
Chemical substance had information in at least 1 data source but the reported
sources were not associated with evidence of potential for higher exposure for
children (i.e., not associated with the sources in Figure 8).
Chemical had information in at least 1 data source with a combined exposure
differential value corresponding to value < to the 50th percentile score for all
chemicals (using the workflow in Figure 8)
Chemical had information in at least 1 data source with a combined exposure
differential value corresponding to value between the 50th and 90th percentiles
scores (using the workflow in Figure 8)
Chemical had information in at least 1 data source with a combined exposure
differential value corresponding to value > the 90th percentile score (using the
workflow in Figure 8)
Information Gathering (IG) Flags: predicted data; secondary source data
Limitations and Longer-Term Options
A limitation of the susceptible population exposure domain is that only children's exposure
was included. Future updates may expand this component to include other susceptible populations
(e.g., workers, elderly). Further, the metric developed herein was designed to capture information
about exposure sources relevant to children, using publicly available exposure-relevant data that
has been compiled and curated by ORD. Collection and curation of product and monitoring data
by ORD is ongoing, and new data or data streams can be incorporated into this workflow when
they reach an acceptable level of quality review. It is likely that the available product and
monitoring data used to develop the susceptible population metric may not be representative of the
total chemical substance landscape to which children are exposed. For example, the children's
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products for which data are available in CPDat may not be representative of all products used by
children, and the chemical substances reported on safety data sheet for these products are not
necessarily representative of all the chemical substances in those products. Therefore, the lack of
a positive for a chemical substance data source does not necessarily imply a global negative (only
a negative for these data sources). Additionally, this domain could be further expanded through
incorporation of any future ExpoCast models as they are developed, including those that generated
consensus exposure predictions for individual cohorts (e.g., children, the elderly).
Persistence and Bioaccumulation Domain
Persistence refers to the tendency of a chemical substance to remain in the environment in
its original form, potentially resulting in exposures that last for a long period of time.
Bioaccumulation refers to the tendency of a chemical substance to accumulate in biota. Although
generally considered separately, these properties overlap to a substantial degree because molecular
features that tend to increase chemical persistence, such halogenation and/or steric features that
limit microbial degradation, often promote increased bioaccumulation. Chemical bioaccumulation
may occur in both terrestrial and aquatic environments; however, bioaccumulation assessments
are often focused on the potential for chemical accumulation in fish. This focus reflects the well-
known tendency of hydrophobic substances to partition out of the water column and into aquatic
biota. Fish may accumulate chemicals directly from water and by consuming contaminated food
items.
Persistence Evaluation
The workflow for evaluating persistence is based on ultimate biodegradation, which is defined as
complete mineralization resulting in the formation of carbon dioxide, water, mineral salts and
biomass and is measured in weeks (Figure 9). If measured biodegradation half-lives are available,
the persistence domain metric is derived based on this data. In the absence of measured data,
calculated ratings for ultimate biodegradability are obtained from BIOWIN3 (Ultimate Survey
Model) in EPI Suite95.
95 US EPA. (2019). Estimation Programs Interface Suite™ for Microsoft® Windows, v 4.11. United States
Enviromnental Protection Agency, Washington, DC, USA.
40
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Figure 9. Tiered evaluation process associated with the persistence domain metric. Persistence evaluation
is performed by comparing a half-life value for ultimate biodegradation. IG = information gathering.
Persistence evaluation is performed by comparing a half-life value from the workflow for overall
persistence to the criteria recommended in the 2012 TSCA Work Plan Chemicals: Methods
Document96 (Table 6).
Table 6. Criteria used to calculate the persistence domain metric
Metric
Experimental Half-Lives or
Calculated Rating for Ultimate
Biodegradation
Persistence Criteria
0
No data available
1
<1.75 - 2.25 weeks
t, < 60 days
/i
2
>2.25 - 2.75 weeks
60 days < t < 180 days
/i
3
>2.75 - 5 weeks
t, >180 days
/i
Information Gathering (IG) Flags: predicted data; secondary source data
Bioaccnmnlation Evaluation
Bioaccumulation is typically evaluated using a steady-state fish bioaccumulation factor
(BAF) or bioconcentration factor (BCF). The BAF (chemical substance concentration in
fish/chemical substance concentration in water; L/kg) quantifies chemical substance accumulation
96 U.S EPA. (2012). TSCA Work Plan Chemicals: Methods Document. U.S. Environmental Protection Agency,
Office of Pollution Prevention and Toxics, https://www.epa. gov/sites/production/files/2014-
03/documents/work plan methods document web final.pdf
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occurring in fish by all possible routes of exposure and is generally determined in field-collected
animals. The BCF (chemical substance concentration in fish/chemical substance concentration in
water; L/kg) quantifies accumulation occurring in a water-only exposure and is usually measured
in controlled laboratory studies. Because BCFs can be determined experimentally, they are often
used as a surrogate measure of bioaccumulation potential when measured BAFs are unavailable.
However, a measured BAF better represents the potential for chemical substance bioaccumulation
in a real-world setting.
A workflow for evaluating chemical substance bioaccumulation potential in fish is shown
in Figure 10. In most cases, this workflow leads to a BAF or BCF that can be evaluated against
previously defined criteria from the 2012 TSCA Work Plan Chemical Methods Document97 (Table
7). Possible exceptions include chemical substances that are outside the applicability domain of
predictive models (see below). Relative confidence in these BAF and BCF values is represented
by flags indicating "high," "intermediate," or "low" confidence. The structure of the workflow
represents several general considerations. First, preference is given to chemical substances for
which measured BAFs are available. If a measured BAF is not available, chemical substances for
which measured BCFs are available receive preference. A database of measured fish BAFs and
BCFs was published by Arnot and Gobas98. The BCFs assembled by these authors were evaluated
for data quality based on aspects of study design and the collection of supporting analytical
information. This database did not provide criteria for evaluating fish BAFs; however, methods
used to measure reported BAFs were compared to existing guidance for analysis of environmental
samples. For the purposes of the bioaccumulation domain metric, a "high quality BCF" refers to a
BCF that was scored 1 or 2 in the Arnot-Gobas database across the entire set of evaluation criteria
(labeled "acceptable confidence" by the authors), while a "high quality BAF" refers to a BAF that
was judged by the same authors to be of "acceptable quality." Either of these bioaccumulation
metrics can be used as the basis for evaluating bioaccumulation potential, and the resulting values
are flagged as "high confidence."
If a measured BAF or BCF is not available, the chemical substance is passed on to
predictive BCF modeling. The workflow assumes that all structures are neutral at environmental
pH values. In most cases, this represents a conservative assumption; that is, BCFs predicted under
this assumption are likely to be higher than the actual BCF values associated with these
compounds. However, exceptions to this general rule are known to exist.
97 U.S EPA. (2012). TSCA Work Plan Chemical Methods Document. U.S. Enviromnental Protection Agency,
Office of Pollution Prevention and Toxics, https://www.epa. gov/sites/production/files/2014-
03/documents/work plan methods document web final.pdf
98 Arnot J A, Gobas, FA. (2006). A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF)
assessments for organic chemicals in aquatic organisms. Enviromnental Reviews 14:257-297.
42
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The two models used to predict bioconcentration of neutral chemical substances are the
Arnot-Gobas QSAR model" (henceforth "Arnot-Gobas model" without citation) included in the
BCFBAF module of EPI Suite ver. 4.11, and the OPERA BCF QSAR model3 (henceforth
"OPERA BCF model" without citation) developed by the EPA ORD. The Arnot-Gobas model is
a one-compartment mass-balance description that predicts bioconcentration from competing rates
of uptake and elimination, while the OPERA BCF model employs a ^-nearest neighbor approach
to calculate a BCF from measured values for chemical substances that exhibit molecular similarity
to the chemical substance. Before using either model, the chemical substance is evaluated using
the KOWWIN model in EPI Suite to determine whether it possesses a predicted log Kow value >
9. If the predicted log Kow is > 9, the chemical substance is flagged as "low confidence." This
designation reflects uncertainty in both the log Kow estimate and the modeled bioconcentration
prediction.
The workflow is structured so that the Arnot-Gobas model is implemented first. This model
provides several BCF metrics. For this workflow, we focused on BCF prediction for lower trophic
level fish, assuming biotransformation. This focus reflects the fact that most standardized in vivo
BCF tests are performed using small fish species or juveniles of larger species. If the Arnot-Gobas
returns a value, the chemical substance is passed on for evaluation of bioaccumulation potential.
If not, the chemical substance is passed to the OPERA BCF model.
If the OPERA model does not return a value, the process terminates with an IG flag, which
indicates that there are no data (measured or predicted) available. This outcome is anticipated for
some inorganic chemical substances, metals, organometallic chemical substances and mixtures. If
OPERA returns a value, a determination is made whether the chemical substance falls within the
model's applicability domain (AD). A chemical substance that falls within the AD is passed on for
evaluation. A chemical substance that falls outside the AD is flagged as "low confidence" and
then passed on for evaluation. Any chemical substance for which the evaluation is based on a
predicted BCF is flagged "medium confidence" unless it has been flagged "low confidence" at
some earlier step (e.g., because it possesses a log Kow value > 9). This designation reflects the
fact that BCF prediction models have been developed and calibrated using data for a relatively
small number of industrial chemical substances (< 1000). In addition, predicted BCFs do not
account for potential food web effects (i.e., biomagnification).
99 Arnot J A, Gobas F. (2003). A generic QSAR for assessing the bioaccumulation potential of organic chemical
substances in aquatic food webs. QSAR and Combinatorial Science. 22:337-345.
43
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Yes
Is a high quality BCF or BAF available
No
Assume the chemical is
neutral at environmental pH
values and proceed
Is a high quality BAF available?
If >1
value
No
I
j value r
Calculate median BAF
If 1 value
/ \
| Metric as described in
the bioaccumulation
If >1
value
evaluation table.
(\ value r
Is a high quality BCF available? I »l
^
Calculate median BCF
If 1 value
i
_J Designate as "high
| confidence"
-A
Is the EPI Suite-predicted log Kow > 9
Yes; proceed, but designate as "low confidence"
Predict BCF using EPI Suite (lower trophic level fish,
predicted using Arnot and Gobas model with QSAR-
predicted biotransformation)
Does EPI Suite return a value? j— ~J^^Predict^BCFusing^OPERA^^
Yes; proceed to
bioaccumulation
evaluation
I
Does OPERA return a value?
¦+| IG flag j
Is the chemical in the OPERA AD?
Yes
| No; proceed, but designate as "low confidence"
Metric as described in the bioaccumulation
evaluation table.
I
I
I IG flag; designate as "medium confidence" unless |
I
designated as "low confidence" at an earlier step
I
Figure 10. Tiered evaluation process associated with the bioaccumulation domain; high quality BAF or BCF refer to scoring in the Arnot and Gobas
database (see text for details). BCF = bioconcentration factor; BAF = bioaccumulation factor; Kow = octanol/water partition coefficient; QSAR =
quantitative structure activity relationship; AD = applicability domain; and IG = information gathering flag.
44
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Table 7. Criteria for bioaccumulation domain metric
No data available
BCF or BAF1
1000-5000
< 1000
Limitations and Longer-Term Options
EPA is currently in the process of adopting a new approach for evaluating persistence based
on the potential half-life in air, water, soil, and sediment. The approach factors the partitioning
characteristics of the chemical substances and potential removal pathways based on standard
physical-chemical substance properties and environmental fate parameters. Once adopted, this
approach may be included in the persistence component of this workflow.
Measured BAFs for some poorly metabolized compounds may exceed measured BCFs by
an order of magnitude or more due to biomagnification of chemical residues (i.e., higher
concentrations at successively higher trophic levels)100. These differences are also apparent in
modeled BAF and BCF values. Given this fact, as well as the preference of measured BAFs over
measured BCFs noted above, it is reasonable to ask whether preference should be given to
predicted BAFs over predicted BCFs. Because predicted BAFs may exceed modeled BCFs,
particularly for chemicals of special concern from a bioaccumulation perspective, they represent a
more conservative metric of bioaccumulation. Predicted BAFs are well suited, therefore, to
performing risk assessments for individual chemicals. The current focus on predicted BCFs was
motivated by the fact that the number of published BCFs for fish greatly exceeds the number of
published BAFs. The use of a BCF prediction model therefore results in comparable modeled and
measured values, which is important if both data sources are used to perform a relative evaluation
of bioaccumulation potential for many chemicals. To evaluate this question further, Costanza et
al.101 predicted BCFs and BAFs for 6,034 chemicals using the Arnot-Gobas model, and then
11111 Arnot JA, Gobas, FA. (2006). A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF)
assessments for organic chemicals in aquatic organisms. Enviromnental Reviews 14:257-297.
1111 Costanza J, Lynch DG, Boethling RS, Arnot JA. (2012). Use of the bioaccumulation factor to screen chemicals
for bioaccumulation potential. Enviromnental Toxicology and Chemistry 31:2261-2268.
45
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compared these values to criteria used by EPA to screen chemicals for bioaccumulation potential
("not bioaccumulative" = BCF or BAF < 1000; "bioaccumulative" = 1000 < log BCF or log BAF
< 5000; "highly bioaccumulative" = BCF or BAF > 5000). The results showed that for 86% of
chemicals there was no change in bioaccumulation rating when using the BAF rather than the BCF.
This finding suggests that for screening-level assessments, modeled BCFs and BAFs generally
lead to the same conclusion.
The inability of current modeling approaches to adequately predict bioaccumulation of
ionizable compounds represents a well-recognized research need. Process-based models that
describe uptake and accumulation of ionizable compounds in fish have been described by several
authors102 103. Additional models have been developed specifically for per- and polyfluorinated
alkylated substances (PFAS), many of which are > 99% ionized at environmental pH values104.
Ionizable chemicals represent a particularly difficult challenge in the field of predictive
bioaccumulation modeling. Guidance provided in EPI Suite (BCFBAF Help menu, 7.1
Bioconcentration Factor (BCF), 7.1.1 Estimation Methodology) indicates that the Arnot-Gobas
model is not intended for use with ionizable compounds. Instead, the guidance recommends using
an alternative model which bins chemicals based on their estimated log Kow values (neutral form).
BCFs predicted by this model for such compounds tend to be relatively low (maximum log BCF
of 1.75). Additional guidance indicates, however, that this model should not be used for
compounds possessing specific molecular features. These features include the presence of an
aromatic azo group (a structural component of many pigments and dyes), a charged metal species
(esp., mercury or tin), or a long chain alkyl group (e.g., many cationic and anionic surfactants).
Not given on this list of molecular features is the presence of a fluorine group. Nevertheless, this
model appears to be poorly suited for ionizable PFAS compounds, several of which have been
shown to accumulate in fish (log BCFs > 3)105.
The BCF dataset used to train the OPERA BCF model contains a small (< 70) number of
ionizable compounds. In principal, these measured BCFs reflect the net result of all processes
responsible for bioconcentration including chemical speciation in the water and fish, and uptake
and accumulation of all relevant chemical species. Given the small number of chemicals in the
1112 Erickson RJ, McKim JM, Lien GJ, Hoffman AD, Batterman, SL. (2006) Uptake and elimination of ionizable
organic chemicals at fish gills. II. Observed and predicted effects of pH, alkalinity, and chemical properties.
Enviromnental Toxicology and Chemistry 25:1522-1532.
1113 Annitage JM, Arnot JA, Wania F, Mackay D. (2013). Development and evaluation of a mechanistic
bioconcentration model for ionogenic organic chemicals in fish. Enviromnental Toxicology and Chemistry 32:115-
128.
1114 Ng CA, Hungerbuhler K. (2013). Bioconcentration of perfluorinated alkyl acids: How important is specific
binding? Enviromnental Science and Technology 47:7214-7223.
1115 Martin JW, Mabury SA, Solomon KR, Muir DCG. (2003). Bioconcentration and tissue distribution of
perfluorinated acids in rainbow trout (Oncorhynchus mykiss). Enviromnental Toxicology and Chemistry 22:196-
204.
46
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training set, however, there is a relatively high likelihood that a given ionizable chemical will fall
outside the applicability domain of the QSAR. Moreover, as indicated by the exclusionary criteria
given in EPI Suite, some ionizable compounds may require special attention.
Anticipating future acceptance of a process-based model for ionizable compounds, we may
consider possible modifications to the decision tree used to evaluate chemical bioaccumulation
potential. The first step in a revised tree would be to determine whether a chemical is substantially
ionized at environmental (5 - 9) pH values. In a recent review, it was suggested that pH effects
on uptake and accumulation of ionizable chemicals by fish are likely to be minor unless the extent
of ionization in bulk water exceeds 90%106. Of special concern are weak acids and bases for which
an accurate estimate of pKa would be required. Ideally, this estimate would be obtained using an
open-source software tool that has been evaluated against measured data as well as existing
proprietary software. An OPERA pKa model may provide such a tool107. If exclusionary criteria
are still required, they would be applied at an early stage in the decision process. For some
chemical classes (e.g., PFAS), the decision tree may direct the user to employ a model specifically
designed for such compounds. BCFs predicted by a general model for ionizable compounds could
be handled in a manner analogous to that for BCFs presently generated by the Arnot-Gobas model.
Assuming further that the OPERA BCF model will be updated and trained using newly available
data for ionized chemicals, we may anticipate a situation wherein BCFs can be predicted by a
process-based model and by the OPERA BCF model. This would require some type of process
for averaging these predictions or choosing one in preference to the other. Presently, due to a lack
of data for model calibration and evaluation, these models cannot be applied with confidence to
the broad range of ionizable structures contained on the TSCA inventory108. It is anticipated that
these or similar models can be incorporated into future bioaccumulation evaluation efforts.
Combined Persistence and Bioaccumulation Domain Metric
The combined persistence and bioaccumulation domain metric (Table 8) is obtained by adding the
separate metrics from the persistence and bioaccumulation workflows (Figures 9 and 10) and
1116 Armitage JM, Erickson RJ, Luckenbach T, Ng CA, Prosser RS, Arnot J A, Schirmer K, Nichols JW. (2017).
Assessing the bioaccumulation potential of ionizable organic compounds: current knowledge and research priorities.
Enviromnental Toxicology and Chemistry 36(4):882-897.
1117 Mansouri K, Cariello, NF, Korotcov A, Tkachenko V, Grulke CM, Sprankle CS, Allen D, Casey WM,
Kleinstreuer NC, Williams AJ. (2019) Open-source QSAR models for pKa prediction using multiple machine
learning approaches. Journal of Cheminfonnatics 11:60.
1118 Franco A, Ferranti A, Davidsen C, Trapp S. (2010) An unexpected challenge: Ionizable compounds in the
REACH chemical space. International Journal of Life Cycle Assessment. 15:321-325.
47
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described in Tables 6 and 7. This process is consistent with the method recommended in the 2012
TSCA Work Plan Chemical Methods document109.
3-4
1 -2
0
6
5
Combined Score
No data available
Low
High
Very High
Moderate
Skin Sensitization and Skin/Eye Irritation Domain
Skin sensitization and skin/eye irritation are important potential hazards of chemical
substances that are of concern for human health. These are addressed here in a separate domain
due to different routes of exposure and different data sources for these endpoints. Ocular and
dermal exposures can occur through a variety of sources, particularly occupational exposures as
well as consumer exposures
Local effects are changes at the site of contact (skin, eye, mucous
membrane/gastrointestinal tract, or mucous membrane/respiratory tract) as a result of exposure to
a chemical substance. Such changes after a single exposure may be categorized as irritant or
corrosive, depending on the severity and reversibility of the outcomes. Corrosive substances are
those which may destroy living tissues with which they come into contact. Irritant substances are
non-corrosive substances which, through immediate contact with the tissue may cause
inflammation.
Skin sensitization denotes the immune-mediated hazards associated with human allergic
contact dermatitis and/or rodent contact hypersensitivity. Allergic contact dermatitis is the clinical
term that indicates the presence of skin erythema and edema that result from delayed type IV cell-
mediated skin hypersensitivity.
1119 U.S EPA. (2012) TSCA Work Plan Chemicals: Methods Document. U.S. Enviromnental Protection Agency,
Office of Pollution Prevention and Toxics, https://www.epa. gov/sites/production/files/2014-
03/documents/work plan methods document web final.pdf
48
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Figure 11. Tiered evaluation process associated with the skin sensitization, skin/eye irritation domain
metric. IG = information gathering flag; L = low; M = medium; H = high; VH = very high.
Skin Sensitization and Skin/Eve Irritation Evaluation
The proposed skin sensitization and skin/eye irritation domain metric incorporates GHS
hazard codes or hazard categories from the ECHA Classification, Labelling and Packaging (CLP)
Regulation and agencies of several countries (e.g., Canada, Japan, Denmark), as well as study-
level REACH registration data from ECHA via OECD's eChemPortal. GHS classification and
labeling information was extracted from websites of the environmental or occupational health
agencies of individual countries. The study-level data in eChemPortal were obtained by searching
for the endpoints of eye irritation (in vivo and in vitro), skin corrosion (in vitro), skin irritation (in
vivo), and skin sensitization (in vivo local lymph node assay (LLNA), in vivo non-LLNA and in
vitro).
The GHS classifications were converted to a 4-level ranking of Low (L), Moderate (M),
High (H), and Very High (VH), which was then converted to a numerical scale of 1-4 (L=l,
VH=4). The ECHA experimental data were converted to the same scale using a mapping from the
result summary sentences provided by each study. An expert-derived dictionary was created that
mapped each unique result sentence to a scoring level. The criteria for determining metrics are
shown in Table 9 below. These criteria were based on the EPA's Design for the Environment
Program (DfE) Alternatives Assessment Criteria for Hazard Evaluation110111.
1111 US EPA (2011) Design for the Environment Program Alternatives Assessment Criteria for Hazard Evaluation
Version 2.0. https://www.epa.gov/saferchoice/alternatives-assessment-criteria-hazard-evaluation. Accessed 09/24/18
111 Vegosen L, Martin TM. (2020). An Automated Framework for Compiling and Integrating Chemical Hazard
Data. Clean Technologies and Environmental Policy 22(2):441-58.
49
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For each associated sub-domain (skin sensitization, skin irritation, eye irritation) a
chemical substance could have one or more hazard determinations from different sources. The
evaluation method used for combining these values into an overall metric for each sub-domain is
shown in Figure 11. Briefly, the evaluation was based on the source of the information. Similar
to the approach used by the Clean Production Action's GreenScreen List Translator112, this
evaluation method selects the highest value (highest hazard level) from the most authoritative
source as the final output. In the method that was presently implemented (Figure 11), authoritative
sources take precedence over screening sources, which take precedence over QSAR models.
Within each of those three levels, the source that produces the highest value takes precedence.
Using this method, an overall value across all sources was determined for each sub-domain. Then,
the most conservative of the three sub-domain values was used as the final value for the skin/eye
domain.
Table 9a. Criteria for Skin Sensitization Sub-Domain Metric
Metric
Description
Classification
GHS Code
ECHA (eChemPortal)
0
No data
No Data Available
—
—
1
Low
Not Likely to be
Sensitizing
Not Classified
Not sensitizing, Not
Classified
2
Moderate
Low to Moderate
Frequency of
Sensitization
Category IB, Moderate
Sensitizer, Mild
Sensitizer, Weak
Sensitizer
3
High
High Frequency of
Sensitization
H317, SkinSensl,
Sah/Sh
Category 1A,
Sensitizing
Table 9b. Criteria for Skin Irritation Sub-Domain Metric
Metric
Description
Classification
GHS Code
ECHA (eChemPortal)
0
No data
No Data Available
—
—
1
Low
Studies Indicate No
Significant Irritation
Not Classified
Not Irritating, Not
Classified
2
Moderate
Moderate or Mild
Irritation
H316, 6.3B
Category 3, Moderately
Irritating, Mildly
Irritating, Slightly
Irritating
3
High
Severe Irritation
H315, Skinlrr2,
6.3A
Category 2, Highly
Irritating, Irritating
112 Clean Production Action (2018). Greenscreen for Safer Chemicals Hazard Assessment Guidance version 1.4.
https://greenscreenchemicals.org/method/full-greenscreen-method
50
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4
Very High
Corrosive
H314, 8.2A, 8.2B,
8.2C
Category 1, Corrosive
Table 9c. Criteria for Eye Irritation Sub-Domain Metric
Metric
Description
Classification
GHS Code
ECHA (eChemPortal)
0
No data
No Data Available
—
—
1
Low
Studies Indicate No
Significant Irritation
Not Classified
Not Irritating, Not
Classified
2
Moderate
Moderate or Mild
Irritation
H320
Category 2B,
Moderately Irritating,
Mildly Irritating,
Slightly Irritating
3
High
Severe Irritation
H319, Category
6.4A
Category 2A, Severely
Irritating, Highly
Irritating, Irritating
4
Very High
Corrosive or Irritation
Persists for > 21 days
H314, H318,
Category 8.3A
Category 1, Corrosive,
Serious Eye Damage
IG Flag: No information for sub-domain
Limitations and Longer-term Options
A limitation of the skin sensitization and skin/eye irritation evaluation was the availability
of data sources, particularly a lack of large databases of validated non-animal data. Thus, the data
sources that were used have some limitations. While there are non-animal test methods to assess
both skin sensitization and skin/eye irritation and corrosion, the data for large numbers of chemical
substances remains limited. Moreover, the ability of alternative eye irritation assessment methods
to discriminate between GHS categories remains a limitation. The source of the ECHA data in
eChemPortal is industry-submitted REACH registration dossiers. REACH requires that at least
5% of the registration dossiers of each tonnage band of chemical substances must be checked for
compliance with legal requirements for chemical substance identity descriptions and safety
information113. Because up to 95% of REACH registration dossiers may not be checked for
compliance, the quality assurance of the data from these dossiers is limited. The majority of
chemical substances (60-80%) had information from only the ECHA REACH dossiers or a GHS
source but not both. For chemical substances that had data available from the REACH dossiers
and another source, the results from the REACH dossiers often were consistent with the results
from the other source. However, there were instances in which a chemical substance had a value
of VH (4) based on the GHS classification data, but the ECHA data indicated a value of L (1). The
113 ECHA, REACH compliance checks: https://echa.europa.eu/regulations/reach/evaluation/compliance-checks
51
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GHS classifications were generally more severe on average than those from ECHA, which might
be partially attributable to GHS classification being more likely to be conducted when there is
prior cause for concern. The GHS categorization of "Not Classified", which indicates that a
chemical substance does not meet the requirements to be classified as hazardous under the GHS,
was reported by Japan's National Institute of Technology and Evaluation (NITE) but was not
reported by the other sources of GHS data. An additional limitation relates to the lack of reporting
of GHS classifications for chemicals which do not meet the classification criteria. For this reason,
lack of a classification is ambiguous, meaning either no data or not meeting criteria. For the
purpose of the PICS approach, "not classifiable" or "classification not possible" were interpreted
as no data (or insufficient data or ambiguous). In contrast, we interpreted "not classified" as not
meeting GHS criteria for being classified as hazardous. Further in-depth review would be
considered part of the expert review for compounds of interest that may follow the application of
the PICS approach, which would involve evaluating the mapping of summary sentences from
REACH dossiers. The method of determining an overall value for each endpoint by selecting the
most hazardous value from the most authoritative source reduces the influence of differences in
data quality between different data sources, but the inability to check all primary sources for data
quality remains a limitation. Potential longer-term improvements include additional quality
assurance checks as well as the inclusion of additional data sources if such sources become
available. Furthermore, new QSAR models are being developed to fill in missing data for these
endpoints.
5.3 Scientific Domain Metric Calculation
The overall scientific domain output is calculated by summing the metrics from each of the
7 domains. Any individual domain metric of zero is given a value of 1, which helps to normalize
the values across chemical substances and domains. The lack of data is captured in the IAM and
is visualized in Figure 14 based on the size of the point representing the chemical. The maximum
possible output is 27 and the minimum output is 7. The summed results are scaled to values from
0 to 100 to match the IAM. Therefore, the minimum value of 7 is converted to zero, with the
maximum value of 27 equal to 100.
5.4. Information Availability Metric
The second dimension of the PICS approach is a metric that represents the information
available for use in any future chemical substance risk evaluation. Under TSCA, there is no
minimum data requirement necessary to perform a chemical substance risk evaluation, as decisions
about what would be considered a sufficient amount of hazard or exposure data are typically
context specific and would require expert judgment to determine. While this would be possible
during the formal prioritization and risk evaluation processes, expert judgment is not part of the
52
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automated approach described here. The IAM is designed to automatically evaluate chemical
substances based on the number and type of studies available to inform this analysis. To partially
address the context-specific aspect of the data, this metric includes a relatively simple set of four
modifying criteria of potentially relevant exposure, human health and ecological toxicity
information. The criteria include a combination of primary use as a chemical substance
intermediate, environmental half-life, water solubility, molecular weight, whether the chemical
substance is an exempt polymer and whether the chemical substance has been assessed for human
risk by an authoritative source (Figure 12). Following application of the criteria, the IAM is
calculated as a function of information in the associated lists. Missing information is flagged for
potential future information gathering, but these IG flags do not directly impact the IAM and only
identify specific information gaps. The IAM is calculated by giving a chemical substance one point
for having experimental data in each of the domains corresponding to the appropriate box in Figure
12. This metric does not take into account the quality or quantity of studies for each chemical.
Predicted data is also not incorporated into this metric with the exception of the SEEM3 exposure
model.
If the chemical substance has an authoritative human risk assessment from one of these
specific sources (IRIS, EFSA, ATSDR, SIDs, OPPT, OPP), it is given a point for each of the 8
human information availability study types (mammalian values plus carcinogenicity, genotoxicity
and skin and eye). The output is then calculated by dividing by the total number of domains for
the appropriate box and multiplying by 100. A detailed scheme for the calculation is given in
Appendix G, Figure G-l.
53
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TSCA Active Inventory
I
Modifying Criteria
Potentially Relevant Studies:
• Acute Mammalian Toxicity%
• Repeat-dose Mammalian Toxicity
(subchronic or chronic)%
• Developmental Toxicity%
• Reproductive Toxicity%
• Genotoxicity%
• Carcinogenicity%
• Skin Sensitization or Eye
Corrosivity%
• Acute Aquatic Ecotoxicity#
• Chronic Aquatic Ecotoxicity#
• Exposure
X
Chemical Intermediate AND
Short Environmental Half-Life
(Hours)
Potentially Relevant Studies:
• Acute Mammalian Toxicity%
• Repeat-dose Mammalian Toxicity
(subchronic or chronic)%
• Developmental Toxicity%
• Reproductive Toxicity%
• Genotoxicity%
• Carcinogenicity0/*)
• Skin Sensitization or Eye
Corrosivity%
• Acute Aquatic Ecotoxicity#
• Exposure
Low Water Solubility
(< 0.1 mg/L)*
Potentially Relevant Studies:
• Acute Mammalian Toxicity%
• Repeat-dose Mammalian Toxicity
(subchronic or chronic)%
• Developmental Toxicity%
• Reproductive Toxicity%
• Genotoxicity%
• Carcinogenicity%
• Skin Sensitization or Eye
Corrosivity%
• Exposure
MW> 1000 OR
Exempt Polymers
Potentially Relevant Studies:
• Skin Sensitization or Eye
Corrosivity%
• Exposure
Information Availability Metric = f (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
Figure 12. Flow chart for determining the IAM for each chemical substance based on a small number of
physicochemical and use criteria for identifying potentially relevant human health and ecological toxicity
information. Modifying criteria as shown here are used to inform the set of potentially relevant exposure,
human health and ecological toxicity information for specific types of chemical substances.
5.5 Results of the Proof-of-Concept Analysis
Overall Evaluation
The number of chemical substances in the current non-confidential TSCA active inventory
is 33,364; however, only 14,017 of these are unique organic chemical substances with defined
structures (Table 10). The majority of chemical substances in the inventory are mixtures of varying
complexity. Chemical substances that have some in vivo mammalian and ecotoxicological data
constitute 11-13% of the overall inventory and 3% have experimental cancer data. The data
included in the PICS approach is public and excludes industry submitted CBI studies. The PICS
approach also does not include data extracted from the literature beyond what is included in the
Type 1 data sources currently being utilized.
54
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Table 10. Number of chemical substances in the non-confidential active TSCA
inventory with specific types of experimental data.
Experimental Information
Number of
Chemical
substances
Percentage
Human Exposure
14,477
43
Mammalian Repeat Dose Toxicity
4,109
12
Ecological Toxicity (Acute)
3,963
12
Ecological Toxicity (Repeat Dose)
3,466
10
Carcinogenicity
765
2.3
Genotoxicity
3,027
9.1
Skin Sensitization and Skin/Eye Irritation
8,689
26
Total TSCA Active Inventory1
33,364
—
lrThe Non-confidential TSCA Active inventory contains 33,364 chemical substances but the
study only included the subset that can be mapped to the DSSTox database.
As noted earlier, the POC238 was also selected to test the PICS approach using a subset of the
TSCA active inventory and spanned a range of potential concern and information availability (Fig.
13).
>
o _
O
c
CD
—
3
cr
o
CD
L_
C\l "
Ll_
POC: Scientific Domain
i
20
-r~
40
-r~
60
-r~
80
Scaled value
100
POC: Information Availability
o
00
>
o
09
c
CD
o
3
cr
CD
LL
20
o
20
-r~
40
-1—
60
Scaled value
-r~
80
100
Figure 13. Distributions of the scaled SDM andlAM for the POC238 subset. The x-axis displays the scaled
value and the y-axis shows the frequency of that value in the subset. Similar histograms for the TSCA active
inventory can be found in Appendix F.
55
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Proof of Concept
o
L_
o
to
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o
Q
o
i*—
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o
V
~T~
0
20
40
60
80
100
Information Availability Metric
Figure 14. Plot of the Information Availability vs. Scientific Domain Metrics for the POC238 set of
chemical substances. Each dot represents one chemical substance, with the size of the dot representing the
number of domains with data for the specific chemical. The red dots represent the first ten TSCA Work
Plan chemical substances selected for risk evaluation in 2016 (TSCA 10). The green dots represent the
TSCA Work Plan chemical substances from the 2014 update (TSCA 90). The red triangles represent the
high priority chemical substances and the yellow triangles represent the low priority chemical substances
released in March 2019. Positions of points are staggered for ease of visualization. A similar graph for the
entire non-confidential TSCA active inventory is found in Appendix F.
The two-dimensional representation of the SDM and IAMs can be summarized for the
POC238 (Figure 14; results used to inform this figure can be found in Appendix E). There is an
association between IAM and SDM (i.e., more information tends to produce a higher value). This
may be a result of potential testing or publication bias. Chemical substances that are expected to
show or have previously shown indications of potential hazard will lead to more data being
generated, while those that are not expected to show high hazard are less likely to be tested.
Additionally, there is a publication bias towards positive results as most peer-reviewed
publications do not describe negative results. Further, a lack of available data does not indicate a
lack of toxicity.
56
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Using the recently released TSCA high and low priority chemical substance candidates
selected by EPA114, the high priority candidates generally having higher metrics than the low
priority candidates when analyzed by the PICS approach (Fig 14). In part, these candidates were
selected by expert reviewers examining both publicly available and CBI data for each chemical
substance using a systematic review process115, which takes into consideration study quality and
consistency in the database. Further, this review would take into consideration various policy
aspects and scientific judgment that are not part of the automated PICS approach. Discrepancies
between the conclusions of expert reviewers and the results of the PICS approach may be related
to the different data sources used, but also may be related to the more in-depth review of the studies
used as a basis for the candidate selection. For example, in some cases, the conservative decisions
used in the genotoxicity domain of the PICS approach (i.e., assigning a positive genotoxicity score
in the presence of one positive study regardless of the results of other studies) may give a chemical
substance a higher domain metric than a weight of evidence analysis as the latter would take into
account the full dataset.
114 https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/list-chemical-undergoing-prioritization
115 https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/application-SYStematic-review-tsca-risk-
evaluations
57
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POC
Human Hazard : acute | |
Human Hazard: subchronic | |
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 : repeat dose plant | |
Ecological Hazard : acute vertebrate | |
Ecological Hazard : acute invertebrate | |
Ecological Hazard : acute plant | |
Genotoxicity : Only predicted genetox data | |
Genotoxicity : No genetox data or predictions | [
Cancer: No cancer data [ |
Sensitization/1 rritation : skin irritation | |
Sensitization/1 rritation : eye irritation | ~]
Sensitization/lrritation : skin sensitization | |
Susceptible Population : No exposure predictions | |
Bioaccumulation : No BAF data or models | |
Bioaccumulation : BAF medium confidence | |
Bioaccumulation : BAF low confidence [ |
I 1 1 1 1 1
0.0 0.2 0.4 0.6 0.8 1.0
Fraction of POC with IG Flag
Figure 15. Plot of the frequency distribution of IG flags for each SDMs for the POC238 set of chemical
substances. IG flags are designed to highlight data types used in specific SDMs as well as possible data
gaps. IG = information gathering.
In addition to the TSCA high and low priority candidates, the POC238 also includes a
selection of chemical substances from other lists that had a prior expectation of higher or lower
potential concern. For instance, the chemical substances from the 2014 TSCA Work Plan116 are
generally expected to be of higher than average concern with an existing authoritative hazard
assessment. The chemical substances on the SCIL list or chemical substances that are intentional
food ingredients are expected to be of lower than average concern. Figure 16 summarizes the
metric distributions for selected chemical substance lists from across the full TSCA active
inventory. From this plot we can see that the SDM values are largely consistent with expectations.
The TSCA high priority candidates and the 2014 TSCA Work Plan chemical substances are
relatively high while the TSCA low priority candidates, SCIL, and food ingredients are relatively
low. The median scientific domain metr6ic for the full TSCA active inventory is very low, but this
generally reflects the overall low information availability. Comparatively low information
116 https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/tsca-work-plan-chemicals
58
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availability is also seen in the SCIL and food ingredient lists. The POC238 list is enriched in the
high priority regulatory chemical substances, and the remaining chemical substances were largely
selected because of knowledge of some toxicological concern. As a result, the POC238 has a
distribution similar to the high concern lists and is not reflective of the overall TSCA active
inventory.
O - ^TSCA High
O TSCA 90
# TSCA POC
ATSCA Low
0 § — O Food Ingredients
£ 0 SCIL
CD o SCIL Full Green
^ O TSCA Active
s= O .
—O
0 20 40 60 80 100
Information Availability Metric
Figure 16. Plot showing distributions of metric scores for selected chemical substance lists. For each list,
the point shows the median scientific domain and IAMs. The whiskers span 90% of the distributions. Data
here are taken from the lists across the non-confidential TSCA active inventory. TSCA High = high priority
candidates; TSCA 90 = chemical substances from 2014 TSCA Work Plan; TSCA POC = 238 chemical
substances from TSCA POC; TSCA Low = low priority candidates; Food Ingredients = chemical
substances from the FDA food ingredients list; SCIL = Safer Choice Ingredients List; SCIL Full Green =
SCIL labeled low concern based on experimental and modeled data; TSCA Active = nonconfidential TSCA
active inventory.
To illustrate how the process works for individual chemical substances, we show
information for two chemical substances, one with a relatively high value for the SDM (benzene,
68) and one with a relatively low value (3-methoxybutyl acetate, 14.7). Both chemical substances
have relatively high values for the IAM (benzene = 93% and 3-methoxybutyl acetate = 67%).
Table 11 shows values for individual domains. Following the overall metric scores are the
59
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information gathering flags that indicate the types of in vivo data that are lacking and information
about the BAF values used. There were no IG flags for the other domains. Next are the seven
individual domain metrics, whose sum could range from 7 to 27. As described above, the overall
score is equal to 100 x (sum-7)/(27-7). Benzene has a 2.5 (out of 4) for the human hazard-to-
exposure ratio metric, based on the HER value. 3-Methoxybutyl acetate has a 2.3 of out 4 for the
human hazard-to-exposure ratio metric.
The human health repeat dose POD for benzene is 0.015 mg/kg-bw/day, which is the
chronic NOAEL from an authoritative human hazard assessment (ATSDR / CDC). The
corresponding value for 3-methoxybutyl acetate is 100 mg/kg-bw/day, which is a NOAEL from
an ECHA repeat dose study in guinea pigs. For the ecological hazard metric, the minimum PODs
for the two chemical substances are very similar (0.71 and 0.49 mg/L), which leads to the same
ecological hazard metric value. The minimum ecological POD for benzene is 0.49 mg/L, derived
from an acute study (96 hours) with POD of a 4.9 mg/L (divided by 10 in our process) in sockeye
salmon [ECHA / eChemPortal117 For 3-methoxybutyl acetate, the minimum ecological POD is
7.1 mg/L in an acute zebrafish study from ECHA / eChemPortal. This POD was then divided by
10 using the acute-to-repeat dose factor. In the OCSPP evaluation of this chemical, the zebrafish
study was disregarded because it did not meet the minimum quality criteria. OCSPP identified a
repeat-dose study with a NOAEL of 74 mg/L from a source not included in the current Type 1 data
sources.
Benzene has a maximum value for the cancer metric because it is classified as a Group 1
human carcinogen by IARC, while 3-methoxybutyl acetate had no cancer-related information. For
genotoxicity, benzene is classified as genotoxic and 3-methoxybutyl acetate is classified as non-
genotoxic, based on a single negative Ames assay. Benzene has 27 total assays in the genotoxicity
database, including 2 positive Ames tests, leading to the positive overall classification. However,
there are also 3 negative and 2 ambiguous Ames results. Both chemical substances had elevated
values for the susceptible population metric (4 for benzene, 3 for 3-methoxybutyl acetate),
indicating that there is a high probability that children may be exposed to these chemical
substances. Benzene has a moderate metric for persistence / bioaccumulation based on a
persistence score from EpiSuite of 2.4, while 3-methoxylbutyl acetate has a low metric for
persistence/bioaccumulation based on low persistence and bioaccumulation values. Benzene has a
sensitization / irritation score of 3 based on High scores for both skin and eye irritation. These are
based on GHS classifications, which were consistent between ECHA, New Zealand, Canada,
Malaysia and Japan. 3-Methoxybutyl acetate has low scores for skin and eye irritation.
117 Black JA, Birge WJ, McDonnell WE, Westennan AG, Ramey BA, Bruser DM. (1982). The Aquatic Toxicity of
Organic Compounds to Embryo-Larval Stages of Fish and Amphibians. Research Report No. 133, Water Resources
Research Institute, University of Kentucky, Lexington, KY.
60
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Table 11. Results for benzene and 3-methoxybutyl acetate.
CASRN
4435-53-4
71-43-2
Name
3-Methoxybutyl acetate
Benzene
Scientific Domain Metric
14.6
67.9
Information Availability Metric
67
93
IG flag human hazard
Mammalian in vivo hazard
data missing: subchronic,
chronic
Mammalian in vivo hazard data
missing: developmental
IG flag ecological hazard
Eco in vivo hazard data
missing: acute plant, repeat
dose invertebrate, repeat dose
vertebrate
Eco in vivo hazard data missing:
acute plant
IG flag BAF
BAF medium confidence
(modeled value)
BAF medium confidence (modeled
value)
Human hazard-to-exposure
ratio metric
2.3
2.5
Ecological hazard metric
1.8
1.8
Carcinogenicity metric
0 (no data)
4
Genotoxicity metric
1
4
Susceptible population metric
2
4
Persistence bioaccumulation
metric
1
2
Sensitization / irritation metric
1
3
HER repeat dose
1,325,3000
909,925
POD in vivo oral repeat dose
100 mg/kg-day
0.015 mg/kg-day
Human exposure (SEEM3)
0.0000075 mg/kg-day
0.0000013 mg/kg-day
Ecological min POD
0.71 mg/L1
0.49 mg/L
Bioaccumulation EpiSuite
1.3
8.9
Bioaccumulation OPERA
2.4
7.1
Persistence EpiSuite
3.0
2.4
Persistence OPERA
4.6
10.3
Genotoxicity call
non-genotoxic
genotoxic
Carcinogenicity call
Group I: carcinogenic to humans
Skin sensitization metric
L
Eye irritation metric
L
H
Skin irritation metric
L
H
Volatile
No
Yes
Water soluble
Yes
Yes
1 The minimum ecological POD is 7.1 mg/L from an acute toxicity study, which was then converted to a
value of 0.71 using the acute-to-repeat dose factor. OCSPP did not use this value in their evaluation
because the study did not meet their minimum quality criteria. IG = information gathering flag.
61
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Each of the workflows also includes the use of IG flags to identify missing experimental
information, even in the case where predicted values could be used (Figure 15). For example,
there are a large number of study types that may be used in calculating the human hazard-to-
exposure domain metric. However, even if one or more such values are available, the schema
depicted in Figure 12 includes flags for study types that are missing. For instance, Figure 15 shows
that a large fraction (0.85) of POC238 chemical substances are missing neurotoxicity data,
although most had at least one other acceptable mammalian study to be used in calculating an HER
value. Similarly, for the ecological hazard domain, this figure shows that that most of the POC
were lacking an acute plant study, although in most cases there was still at least one in vivo study
that could be used to provide an ecotoxicological POD.
5.6 Overall Limitations and Long-term Options
The PICS approach described here was designed under consideration for use in support of
TSCA. However, this approach was designed to be adaptable to other decision contexts. The main
limitations in adapting the PICS approach is the availability of state of the science methods and
access to curated datasets. Addressing these areas would allow for the incorporation of other
specific endpoints (e.g., reproductive or developmental toxicity), pathways (e.g., estrogenic), and
additional data sources. As the science progresses, changes to this approach could also address
applicability to data poor compounds by increasing focus on the use of NAMs to help fill specific
data gaps. Future advances across the scientific domains, including the development and
incorporation of additional NAMs (e.g., in chemico and alternative species models) which could
also aid in incorporating specific chemical classes that are not easily addressed with the methods
in the current PICS approach (e.g., volatile chemicals). The adaptability of the approach also
applies to how the impact of specific domains may be adjusted. Depending on the decision context,
the user may want to weight some scientific domains or data sources differently than we have done
in this proof-of-concept case study in order to focus on scientific endpoints of specific concern for
that decision context. Alternatively, the decisionmaker may want to incorporate additional IG flags
to highlight aspects important to that decision context (e.g., IG flag for GHS classification for
carcinogenicity). Longer term efforts could also help to address how this work could be applied to
mixtures, although more research is needed to determine how best to address this issue. As noted
earlier in the document, a limitation for this case study is the focus of the susceptible exposure
domain only on children's exposure. However, if data sources are available, they could be
incorporated to include additional populations (e.g., workers, elderly) as appropriate for future
applications. As with the hazard domains, as the research and data evolve, additional populations
can be incorporated as appropriate for the decision context.
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6. Summary
Historical approaches that search, compile, and manually evaluate relevant information
would be very time and resource intensive to implement for all -33,000 chemical substances in
the non-confidential, active TSCA inventory. The EPA developed the PICS approach to integrate
information from a variety of sources to better understand the landscape of publicly available
information for these chemical substances. The PICS approach uses a large information
management and technology infrastructure to synthesize traditional and NAM information in key
scientific domains including human health hazard-to-exposure ratio, ecological hazard,
carcinogenicity, genotoxicity, exposure to susceptible populations, persistence/bioaccumulation,
and skin sensitization and skin/eye irritation. The output is a display of the chemical substances
from the TSCA active inventory that reflects the overall degree of potential concern related to
human health and the environment and the relative coverage of potentially relevant human health
and ecological toxicity and exposure information. Behind this visual display is a quantitative
summary of the individual domain metrics. This information could aid in determining the level of
effort and resources that may be needed to evaluate specific chemical substances together with
flags to identify potential information needs.
A proof-of-concept case study was performed by applying the PICS approach to a subset
of the TSCA active inventory. The design of the scientific domain workflows was an iterative
process using the results for chemical substances of known/expected hazard or exposure. For
example, the results of the analyses for chemical substances with previous genotoxicity
assessments helped to refine the genotoxicity domain workflow and determine where and why the
workflow may vary from past assessments. The results of this case study showed that the overall
SDM was generally correlated with the IAM, suggesting potential testing or reporting bias.
However, the PICS approach was able to segregate the recently released TSCA high- and low
priority candidate chemical substances, with some differences related to important aspects of
expert review. Expert review would include data and study quality analysis, which may lead to
removal of some studies or endpoints included in the PICS approach. Further, expert review would
include a weight-of-evidence analysis and take into account the breadth of the available data unlike
the PICS approach that focuses on selecting the more conservative result in order to limit the
number of false negatives.
Apart from the TSCA high and low priority candidate chemical substances, most of the
remaining chemical substances from the 2014 TSCA Work Plan were juxtaposed with TSCA high
priority candidates. The chemical substances from the 2014 TSCA Work Plan were expected to
have both a high SDM and IAM due to the rigorous selection process lead up to the Work Plan.
However, a small subset had limited information availability suggesting that the Type 1 data
sources may not capture all of the information sources utilized in the selection process. The
63
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POC238 also included chemical substances from the SCIL and intentional food ingredients lists.
The PICS approach generally resulted in these chemical substances having a lower SDM, with
some exceptions related to the conservative approach that may be addressed during a more
systematic review (e.g., study quality). However, the chemical substances from the SCIL and
intentional food ingredients lists also had lower than expected IAMs suggesting either missing
information sources or the information collected on these chemicals may be targeted towards the
specific uses and exposures.
As described above, the PICS approach has caveats and limitations. To accelerate the process
of integrating publicly available data for a large number of chemical substances, the evaluation of
the Scientific Domain and IAMs are performed using an automated process that may not account
for all potential exceptions or contexts that may occur for a specific chemical substance or
chemical substance group. The PICS approach relies on a large database of chemical substance
properties, hazard, exposure, persistence, and bioaccumulation information that have been
integrated from multiple publicly available sources and models. As the databases and
methodologies are updated, the PICS approach can be applied again to update the results based on
the latest available information. Although efforts have been taken to ensure the accuracy of the
information, the database may contain errors propagated from the source databases. The cleaning
and curation of the information will be an ongoing process and require significant resource
investment to iteratively improve and develop new systems that avoid regeneration of legacy data.
In many cases, data used in this analysis were not able to be verified back to primary source
information. Data points that were verified from authoritative secondary sources were flagged with
an information gathering flag and individual study quality was not considered. The quality control
effort relied on the acceptance of data and information from authoritative sources. Finally, the
domain workflows were designed to select the more conservative options unless otherwise stated;
this likely results in a higher incidence of false positives, activities reported at lower doses, and
exposures reported at higher doses. This was done to create the most comprehensive group of
potential candidates for prioritization with the potential false positives identified in the subsequent
expert review phase.
7. Conclusion
The EPA has developed the PICS approach to integrate information from a variety of
sources to better understand the landscape of publicly available information for these chemical
substances. The automated approach provides a systematic and reproducible process for
integrating available information and identifying potential information gaps. Overtime, the PICS
approach will increase efficiency and workload management by focusing expert review on
substances that may have a greater potential for selection as high- or low priority potential
64
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candidates. The domain-specific workflows embedded in the approach can be adapted to scientific
advances or the availability of new information to create a flexible and sustainable process. The
proof-of-concept study suggests that the PICS approach can help inform chemical prioritization,
identify possible data gaps which can inform data needs, and provide other information related to
the EPA's TSCA program. The PICS approach is designed in discrete domains and utilizes well-
documented data sources which allows flexibility for future adaptation and customization as
needed to meet program requirements or needs that might be different to those of the TSCA. In
addition, the approach may also be useful in identifying common data gaps across large groups of
chemicals, which could facilitate research efficiencies.
65
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Appendix A. Proof-of-Concept (POC) Subset of the Non-confidential
TSCA Active Inventory
CASRN
DTXSID
PREFERRED NAME
156-60-5
DTXSID7024031
(E)-1,2-Dichloroethylene
79-33-4
DTXSID6034689
(S)-2-Hydroxypropionic acid
54464-59-4
DTXSID5052200
l-(l,2,3,4,5,6,7,8-Octahydro-2,3,5,5-tetramethyl-2-
naphthyl)ethan-1 -one
68155-66-8
DTXSID9052397
l-(l,2,3,5,6,7,8,8a-Octahydro-2,3,8,8-tetramethyl-2-
naphthyl)ethan-1 -one
68155-67-9
DTXSID6041923
l-(2,3,8,8-Tetramethyl-l,2,3,4,6,7,8,8a-
octahydronaphthalen-2-yl)ethanone
79-34-5
DTXSID7021318
1,1,2,2-Tetrachloroethane
79-00-5
DTXSID5021380
1,1,2-Trichloroethane
75-34-3
DTXSID 102043 7
1,1 -Dichloroethane
1163-19-5
DTXSID9020376
l,l'-Oxybis[2,3,4,5,6-pentabromobenzene]
110-98-5
DTXSID7026863
1,1 '-Oxybis-2-propanol
96-18-4
DTXSID9021390
1,2,3-Trichloropropane
120-82-1
DTXSID0021965
1,2,4-Trichlorobenzene
3194-55-6
DTXSID4027527
1,2,5,6,9,10-Hexabromocyclododecane
106-93-4
DTXSID3 020415
1,2-Dibromoethane
95-50-1
DTXSID602043 0
1,2-Dichlorobenzene
107-06-2
DTXSID602043 8
1,2-Dichloroethane
78-87-5
DTXSID0020448
1,2-Dichloropropane
6920-22-5
DTXSID40863959
1,2-Hexanediol
57-55-6
DTXSID0021206
1,2-Propylene glycol
106-99-0
DTXSID3 020203
1,3-Butadiene
99-65-0
DTXSID9024065
1,3-Dinitrobenzene
102-06-7
DTXSID3025178
1,3-Diphenylguanidine
66
-------
106-46-7
DTXSID1020431
1,4-Dichlorobenzene
123-91-1
DTXSID4020533
1,4-Dioxane
106-94-5
DTXSID6021874
1-Bromopropane
71-36-3
DTXSID 1021740
1-Butanol
88-73-3
DTXSID0020280
1 -Chloro-2-nitrobenzene
100-00-5
DTXSID5020281
1 -Chloro-4-nitrobenzene
661-19-8
DTXSID4027286
1-Docosanol
629-96-9
DTXSID0027272
1-Eicosanol
36653-82-4
DTXSID4027991
1-Hexadecanol
112-92-5
DTXSID802693 5
1-Octadecanol
111-87-5
DTXSID7021940
1-Octanol
118-96-7
DTXSID7024372
2,4,6-Trinitrotoluene
732-26-3
DTXSID2021311
2,4,6-T ri s(tert-butyl)phenol
51-28-5
DTXSID0020523
2,4-Dinitrophenol
121-14-2
DTXSID0020529
2,4-Dinitrotoluene
108-31-6
DTXSID7024166
2,5-Furandione
96-29-7
DTXSID 1021821
2-Butanone oxime
110-44-1
DTXSID3021277
2E,4E-Hexadienoic acid
183658-27-7
DTXSID9052686
2-Ethylhexyl 2,3,4,5-tetrabromobenzoate
149-30-4
DTXSID 1020807
2-Mercaptob enzothi azol e
109-86-4
DTXSID5024182
2-Methoxyethanol
78-83-1
DTXSID0021759
2-Methy 1 -1 -prop anol
534-52-1
DTXSID 1022053
2-Methyl-4,6-dinitrophenol
55583-69-2
DTXSID70873187
2-Methylallyl alcohol ethoxylate
88-74-4
DTXSID 1025 726
2-Nitroaniline
79-94-7
DTXSID 1026081
3,3',5,5'-Tetrabromobisphenol A
91-94-1
DTXSID6020432
3,3' -Di chl orob enzi dine
67
-------
612-83-9
DTXSID102043 3
3,3'-Dichlorobenzidine dihydrochlonde
591-35-5
DTXSID2025006
3,5 -Di chl orophenol
4435-53-4
DTXSID2052106
3-Methoxybutyl acetate
99-08-1
DTXSID5021831
3-Nitrotoluene
140-66-9
DTXSID90223 60
4-( 1,1,3,3 -Tetramethylbutyl)phenol
17540-75-9
DTXSID8029315
4-(Butan-2-yl)-2,6-di-tert-butylphenol
16090-02-1
DTXSID0027777
4,4'-Bis(2-morpholino-4-anilino-s-triazinyl-6-
amino)stilbene-2,2'-disulfonic acid disodium salt
101-14-4
DTXSID5020865
4,4'-Methylenebis(2-chloroaniline)
80-51-3
DTXSID7026499
4,4'-Oxybis(benzenesulfohydrazide)
101-80-4
DTXSID0021094
4,4'-Oxydianiline
136-85-6
DTXSID 103 8743
5 -Methyl -1 H-b enzotri azol e
51-52-5
DTXSID5021209
6-Propyl-2-thiouracil
75-07-0
DTXSID5039224
Acetaldehyde
103-90-2
DTXSID2020006
Acetaminophen
79-06-1
DTXSID5020027
Aery 1 amide
79-10-7
DTXSID003 9229
Acrylic acid
107-13-1
DTXSID5020029
Acrylonitrile
3825-26-1
DTXSID8037708
Ammonium perfluorooctanoate
62-53-3
DTXSID8020090
Aniline
NOCAS_8724
14
DTXSID30872414
Antimony & Antimony Compounds
NOCAS_8724
15
DTXSID90872415
Arsenic & Arsenic Compounds
137-66-6
DTXSID3 041611
Ascorbyl palmitate
50-78-2
DTXSID5020108
Aspirin
1912-24-9
DTXSID9020112
Atrazine
25057-89-0
DTXSID0023 901
Bentazone
68
-------
1302-78-9
DTXSID6030782
Bentonite
71-43-2
DTXSID3 03 9242
Benzene
65-85-0
DTXSID6020143
Benzoic acid
119-61-9
DTXSID0021961
Benzophenone
85-68-7
DTXSID3 020205
Benzyl butyl phthalate
26040-51-7
DTXSID7027887
Bis(2-ethylhexyl) tetrabromophthalate
103-23-1
DTXSID0020606
Bis(2-ethylhexyl)hexanedioate
80-05-7
DTXSID7020182
Bisphenol A
75-25-2
DTXSID1021374
Bromoform
128-37-0
DTXSID2020216
Butylated hydroxytoluene
17852-99-2
DTXSID2066270
C.I. Pigment Red 52, calcium salt (1:1)
5567-15-7
DTXSID1021453
C.I. Pigment Yellow 83
7440-43-9
DTXSID1023 940
Cadmium
NOCAS_8724
17
DTXSID10872417
Cadmium & Cadmium Compounds
58-08-2
DTXSID0020232
Caffeine
62-54-4
DTXSID0020234
Calcium acetate
299-28-5
DTXSID2029618
Calcium D-gluconate
105-60-2
DTXSID4020240
Caprolactam
10605-21-7
DTXSID4024729
Carbendazim
56-23-5
DTXSID8020250
Carbon tetrachloride
513-77-9
DTXSID 1029623
Carbonic acid, barium salt (1:1)
1698-60-8
DTXSID3034872
Chloridazon
108-90-7
DTXSID4020298
Chlorobenzene
143-28-2
DTXSID0022010
cis-Oleyl alcohol
77-92-9
DTXSID3020332
Citric acid
1702-17-6
DTXSID9029221
Clopyralid
69
-------
NOCAS_8724
19
DTXSID30872419
Cobalt & Cobalt Compounds
7646-79-9
DTXSID9040180
Cobalt chloride
64-86-8
DTXSID5024845
Colchicine
8001-58-9
DTXSID2023987
Creosote
420-04-2
DTXSID9034490
Cyanamide
NOCAS_8724
20
DTXSID40872420
Cyanide salts
1222-05-5
DTXSID8027373
Cyclopenta[g]-2-benzopyran, 1,3,4,6,7,8-hexahydro-
4,6,6,7,8,8-hexamethyl-
134-62-3
DTXSID2021995
DEET
50-02-2
DTXSID3 0203 84
Dexamethasone
81-13-0
DTXSID3 022906
Dexpanthenol
50-70-4
DTXSID5023588
D-Glucitol
526-95-4
DTXSID8027169
D-Gluconic acid
50-99-7
DTXSID7022910
D-Glucose
117-81-7
DTXSID5020607
Di(2-ethylhexyl) phthalate
131-17-9
DTXSID7020392
Diallyl phthalate
109-43-3
DTXSID1041847
Dibutyl decanedioate
84-74-2
DTXSID2021781
Dibutyl phthalate
75-09-2
DTXSID0020868
Dichloromethane
62-73-7
DTXSID5020449
Dichlorvos
99-30-9
DTXSID2020426
Dicloran
84-61-7
DTXSID5025021
Dicyclohexyl phthalate
105-53-3
DTXSID7021863
Diethyl propanedioate
111-77-3
DTXSID3025049
Diethylene glycol monomethyl ether
35367-38-5
DTXSID 1024049
Diflubenzuron
84-69-5
DTXSID9022522
Diisobutyl phthalate
70
-------
26761-40-0
DTXSID4025082
Diisodecyl phthalate
28553-12-0
DTXSID4022521
Diisononyl phthalate
60-51-5
DTXSID7020479
Dimethoate
108-59-8
DTXSID4029145
Dimethyl malonate
108-01-0
DTXSID2020505
Dimethylaminoethanol
117-84-0
DTXSID1021956
Di-n-octyl phthalate
25265-71-8
DTXSID0027856
Dipropylene glycol
88917-22-0
DTXSID4029062
Dipropyleneglycol methyl ether acetate
330-54-1
DTXSID0020446
Diuron
69-65-8
DTXSID 102323 5
D-Mannitol
112-85-6
DTXSID3 026930
Docosanoic acid
577-11-7
DTXSID8022959
Docusate sodium
9004-32-4
DTXSID2020555
Edifas B
759-94-4
DTXSID 1024091
EPTC
64-17-5
DTXSID9020584
Ethanol
91-53-2
DTXSID9020582
Ethoxyquin
141-78-6
DTXSID 1022001
Ethyl acetate
100-41-4
DTXSID3020596
Ethylbenzene
107-21-1
DTXSID8020597
Ethylene glycol
110-71-4
DTXSID0025286
Ethylene glycol dimethyl ether
75-21-8
DTXSID0020600
Ethylene oxide
60168-88-9
DTXSID2032390
Fenarimol
50-00-0
DTXSID7020637
Formaldehyde
446-72-0
DTXSID5022308
Geni stein
106-24-1
DTXSID8026727
Geraniol
90-80-2
DTXSID0026549
Gluconolactone
111-30-8
DTXSID6025355
Glutaraldehyde
71
-------
25637-99-4
DTXSID8025383
Hexabromocyclododecane
87-68-3
DTXSID7020683
Hexachloro-1,3 -butadiene
118-74-1
DTXSID2020682
Hexachl orob enzene
51235-04-2
DTXSID4024145
Hexazinone
123-31-9
DTXSID7020716
Hydroquinone
54464-57-2
DTXSID7031290
Isocyclemone E
97-54-1
DTXSID7022413
Isoeugenol
78-79-5
DTXSID2020761
Isoprene
1332-58-7
DTXSID6049640
Kaolin
50-21-5
DTXSID7023192
Lactic acid
63-42-3
DTXSID2023193
Lactose
52-90-4
DTXSID8022876
L-Cysteine
NOCAS_8724
21
DTXSID00872421
Lead & Lead Compounds
63-68-3
DTXSID5040548
L-Methionine
NOCAS_8724
22
DTXSID60872422
Long-chain chlorinated paraffins (CI8-20)
6915-15-7
DTXSID0027640
Malic acid
NOCAS_8724
23
DTXSID20872423
Medium-chain chlorinated paraffins (CI4-17)
7487-94-7
DTXSID5020811
Mercuric chloride
67-56-1
DTXSID2021731
Methanol
625-45-6
DTXSID1031591
Methoxyacetic acid
74-83-9
DTXSID8020832
Methyl bromide
9004-67-5
DTXSID 103 6919
Methyl cellulose
99-76-3
DTXSID4022529
Methylparaben
NOCAS_8724
24
DTXSID80872424
Molybdenum & Molybdenum Compounds
31138-65-5
DTXSID7027966
Monosodium D-glucoheptonate
72
-------
108-38-3
DTXSID6026298
m-Xylene
99-97-8
DTXSID0021832
N,N,4-Trimethylaniline
91-20-3
DTXSID8020913
Naphthalene
NOCAS_8724
25
DTXSID40872425
Nickel & Nickel Compounds
872-50-4
DTXSID6020856
N-Methyl -2-py rroli done
86-30-6
DTXSID6021030
N-Nitrosodiphenylamine
25154-52-3
DTXSID3021857
n-Nonylphenol
95-48-7
DTXSID8021808
o-Cresol
1843-05-6
DTXSID9027441
Octabenzone
556-67-2
DTXSID7027205
Octamethylcyclotetrasiloxane
124-07-2
DTXSID3021645
Octanoic acid
112-80-1
DTXSID1025809
Oleic acid
95-47-6
DTXSID3021807
o-Xylene
133-49-3
DTXSID3044540
P ent achl orob enzenethiol
87-86-5
DTXSID7021106
Pentachlorophenol
3296-90-0
DTXSID9020164
Pentaerythritol dibromide
375-73-5
DTXSID5030030
Perfluorobutanesulfonic acid
335-76-2
DTXSID3031860
Perfluorodecanoic acid
335-67-1
DTXSID8031865
Perfluorooctanoic acid
108-95-2
DTXSID5021124
Phenol
85-44-9
DTXSID2021159
Phthalic anhydride
1918-02-1
DTXSID 1021160
Pi cl oram
81-33-4
DTXSID9052555
Pigment Violet 29
6528-34-3
DTXSID0052336
Pigment Yellow 65
298-14-6
DTXSID0021177
Potassium bicarbonate
299-27-4
DTXSID7029617
Potassium D-gluconate
29420-49-3
DTXSID3037707
Potassium perfluorobutanesulfonate
73
-------
106-42-3
DTXSID2021868
p-Xylene
108-46-3
DTXSID2021238
Resorcinol
68-26-8
DTXSID3023556
Retinol
127-47-9
DTXSID6021240
Retinol acetate
90-02-8
DTXSID1021792
Salicylaldehyde
122-34-9
DTXSID4021268
Simazine
497-19-8
DTXSID 1029621
Sodium carbonate
4418-26-2
DTXSID7026029
Sodium dehydroacetate
527-07-1
DTXSID7027170
Sodium D-gluconate
7632-00-0
DTXSID0020941
Sodium nitrite
1344-09-8
DTXSID9029647
Sodium silicate
10102-17-7
DTXSID6044197
Sodium thiosulfate, pentahydrate
111-01-3
DTXSID0046513
Squalane
100-42-5
DTXSID2021284
Styrene
57-50-1
DTXSID2021288
Sucrose
994-05-8
DTXSID8024521
tert-Amyl methyl ether
127-18-4
DTXSID2021319
T etrachl oroethy 1 ene
58-55-9
DTXSID5021336
Theophylline
62-55-5
DTXSID9021340
Thioacetamide
137-26-8
DTXSID5021332
Thiram
7772-99-8
DTXSID8021351
Tin(II) chloride
126-73-8
DTXSID3021986
Tributyl phosphate
1461-22-9
DTXSID3 027403
Tributyltin chloride
79-01-6
DTXSID0021383
T ri chl oroethy 1 ene
101-20-2
DTXSID4026214
Triclocarban
55335-06-3
DTXSID0032497
Triclopyr
77-93-0
DTXSID0040701
Tri ethyl citrate
74
-------
2451-62-9
DTXSID4026262
Triglycidyl isocyanurate
115-86-6
DTXSID1021952
Triphenyl phosphate
68937-41-7
DTXSID4028880
Triphenyl phosphates isopropylated
24800-44-0
DTXSID7027837
Tripropylene glycol
55934-93-5
DTXSID8042503
Tripropylene glycol butyl ether
115-96-8
DTXSID5021411
Tris(2-chloroethyl) phosphate
75-01-4
DTXSID8021434
Vinyl chloride
75
-------
Appendix B. Detailed Information on Data Sources used in the PICS
Approach
76
-------
Sources
Human Hazard
Carcinogenicity
Genotoxicitv
¦a
U
CS
N
CS
X
'5
o
"o
w
-
Persistence and Bioaccumulation
Skin Sensitization and Skin/Eve
Exnosure
Suscentible Ponulations
Description
Reference
URL
Compiled POI
"oxicity Values and Cancer Classifications
Alaska
Departme
nt of
Environm
ental
Conservati
on
*
Cancer slope factors
and unit risk compiled
by State of Alaska
NA
https://dec.alaska.sov/spar/csp/
gui dance/cl eanupl evel s. p df
Agency
for Toxic
Substance
s and
Diseases
Registry
(ATSDR)
*
NOAEL values derived
from CDC / ATSDR
risk assessments
NA
httDs://www.atsdr.cdc.gov/mrl
s/mrllist.asp
77
-------
California
Environm
ental
Protection
Agency,
Office of
Environm
ental
Health
Hazard
Assessme
nt (OEHH
A)
*
Cancer slope factors
and unit risk compiled
by State of California
NA
httD s: //oehha. ca. sov/chemi cal s
California
Environm
ental
Protection
Agency
*
Cancer classifications
from California EPA
NA
https://oehha.ca.sov/propositio
n-65/DroDOsition-65-list
COSMOS
*
*
*
Data compiled by the
COSMOS project, a
collaboration between
the US FDA and
Cosmetics Europe using
integrated in silico
models for the
prediction of human
repeated dose toxicity
of COSMetics to
Optimize Safety.
NA
http://www.cosmostox.eu/what
/COSMOSdb/
78
-------
Departme
nt of
Energy
(DOE)
Wildlife
Benchmar
ks
*
*
PODs from ecological
risk assessments
performed by DOE on
both mammalian and
aquatic species
Sample, B.E., Opresko,
D M., Suter, G.W. (1996)
Toxicological Benchmarks
for Wildlife: 1996 Revision.
Springfield, VA: National
Technical Information
Service, U.S. Department of
Commerce
httDs://rais.ornl,«ov/documents
/tm86r3.pdf
European
Chemicals
Agency
(ECHA)
eChemPor
tal
*
*
*
*
Data compiled by
ECHA and made
available through
eChemPortal
NA
httDs://www.echemDortal.ors/e
chemportal/index. action
ECHA
(IUCLID)
*
*
Data compiled by
ECHA (European
Chemicals Agency) and
made available via an
IUCLID data file
NA
httDs://echa.euroDa.eu/informat
i on-on-chemi cal s/resi stered-
sub stances
European
Union
Reference
Lab orator
y for
Alternativ
es to
Animal
Testing
(ECVAM
EURL)
Genotoxic
ity and
Carcinoge
*
Genotoxicity data
compiled by EURL
ECCVAM
NA
https ://data. europa. eu/euodp/da
ta/dataset/irc-eurl-ecvam-
senotoxicitv-carcinosenicitv-
ames
79
-------
nicity
Consolidat
ed
Database
European
Food
Safety
Authority
(EFSA)
*
*
POD values compiled
by EFSA (European
Food Safety Agency)
NA
http s://zenodo. ore/record/1252
752#.W-WNeDNReHs
EPA
Health
Effects
Assessme
nt
Summary
Tables
(HEAST)
*
*
POD values compiled
by EPA HEAST
NA
httDs://eDa-
heast. ornl. sov/heast. php
EPA High
Productio
n Volume
Informatio
n System
(HP VIS)
*
*
POD values compiled
by EPA OPPT High
Production Volume
Information System
NA
Data was initially fully public,
but access is now restricted.
Data used here is a download
from HP VIS from 2015
EPA
Office of
Pesticide
Programs
(OPP)
Assessme
nts
*
Cancer slope factors
and classifications
compiled by EPA OPP
NA
httDs://iasDub.eDa.sov/aDex/Des
ticides/f?p=HHBP:home
80
-------
EPA
Office of
Pollution
Prevention
and
Toxics
(OPPT)
*
POD values from EPA
OPPT Risk Assessment
documents
NA
Data extracted from pdf files
provided by OPPT, which
should reflect data from
ChemView
https://chemview.epa.gov/che
mview
Health
Assessme
nt
Workspac
e
Collaborat
ive
(HAWC)
*
*
POD values compiled
from public HAWC
projects
NA
http s: //hawcproi ect. ore/
Health
Canada
*
Cancer slope factors,
unit risk and
classifications compiled
by Health Canada
NA
http://publications.sc.ca/collect
ions/collection 2012/sc-
hc/H128-l-l 1-638-ens.odf
Hazard
Evaluation
Support
System
(HESS)
*
POD values compiled
by HESS Japan (Hazard
Evaluation Support
System Integrated
Platform, National
Institute of Technology
and Evaluation)
NA
http://www.nite.so.ip/en/chem/
qsar/hess-e.html
Internatio
nal
Agency
for
Research
on Cancer
(IARC)
*
Cancer classifications
derived by IARC
NA
https://monosraphs.iarc.fr/list-
of-classifications-volumes/
81
-------
Integrated
Risk
Informatio
n System
(IRIS)
*
*
POD values, cancer
slope factors, unit risk
and cancer
classifications from
EPA IRIS
NA
httDs://cfDub.eDa.sov/ncea/iris
drafts/simple list.cfm
National
Institute
for
Occupatio
nal Safety
and
Health
(NIOSH)
*
Cancer classifications
compiled by NIOSH
(CDC, National
Institute for
Occupational Safety
and Health)
NA
https://www.cdc.sov/niosh/top
ics/cancer/nDOtocca.html
National
Toxicolog
y Program
(NTP)
Report on
Carcinoge
ns (ROC)
*
Cancer classifications
compiled by NTP
(National Toxicology
Program)
NA
https://ntp.niehs.nih.sov/pubhe
alth/roc/index-1 ,html#tocl
EPA
Provisiona
1 Peer-
Review
Toxicity
Values
(PPRTV)
[NCEA
database]
*
POD Values from EPA
PPRTV documents,
provided by EPA
NCEA
NA
data provided by NCEA as an
MS Access database
EPA
Provisiona
1 Peer-
Review
*
*
POD Values from EPA
PPRTV documents,
cancer classifications
NA
httD s: //hhDDrtv. ornl. sov/
82
-------
Toxicity
Values
(PPRTV)
[ORNL
database]
extracted from ORNL
PPRTV web site
Data compilations
EPA
Chemical
Data
Reporting
(CDR)
*
*
TSCA Chemical data
reporting rule (CDR)
production volume
information
NA
httDs://www.eDa.sov/chemical-
data-reportins/2016-chemical-
data-reoortin "-results
EPA
Chemical
And
Product
Categories
(CPCat)
*
Chemical and product
categories
Dioniso et al. Exploring
consumer exposure pathways
and patterns of use for
chemicals in the
environment, Tox. Reports
vol 2, pp 28-237(2015)
https://actor.epa.sov/cpcat/face
s/home.xhtml
EPA
Chemical
and
Products
Database
(CPDat)
*
Chemical and product
database
Dionisio KL, Phillips K,
Price PS, Grulke CM,
Williams A, Biryol D, Hong
T,
Isaacs KK. The Chemical
and Products Database, a
resource for exposure-
relevant
data on chemicals in
consumer products. Sci Data.
2018 Jul 10;5:180125. doi:
10.1038/sdata.2018.125.
PubMed PMID: 29989593;
PubMed Central PMC ID:
PMC6038847
https://www.epa.sov/chemical-
research/chemical-and-
products-database-cpdat
83
-------
EPA
ECOTOXi
cology
knowledg
ebase
(ECOTO
X)
*
*
EPA ECOTOX
database. Data imported
to ToxValDB
NA
httos ://cfoub ,eoa. sov/ ecotox/
EPA
Toxicity
Forecaster
(ToxCast)
v3.0
*
High-throughput data
for a variety of high
level cellular responses.
Judson et al. In vitro
Screening of Environmental
Chemicals for Targeted
Testing Prioritization: The
ToxCast Project,
Environmental Health
Perspectives, volume 118, p
485
https://fisshare.com/articles/To
xCast Database invitroDB /6
062623/2
EPA
Toxicity
Reference
Database
(ToxRefD
B)
*
POD values from EPA
ToxRefDB
Watford, S., A. Adrian, J.
Wignall, J. Brown, AND M.
Martin. ToxRefDB 2.0:
Improvements in Capturing
Qualitative and Quantitative
Data from in vivo Toxicity
Studies (SOT). Presented at
SOT Annual Meeting,
Baltimore, MD, March 12 -
16, 2017.
https://doi.org/10.23645/epac
omptox. 5178622
https://epa.fisshare.com/article
s/Animal Toxicitv Studies Ef
fects and Endpoints Toxicitv
Reference Database -
ToxRefDB files /6062545
Istituto
Superiore
di Sanita
Chemical
Toxicity
database
*
Database of
genotoxicity data
compiled by Istituto
Superiore di Sanita
(ISS), Italy
Begnini et al. "a novel
approach: chemical relational
databases,and the role of the
ISScaN database
on assessing chemical
carcinogenicity", Ann 1st
http://old.iss.it/publ/anna/2008/
1/44148.odf
84
-------
on long-
term
carcinoge
nicity
bioassay
on rodents
(rat and
mouse)
(IS SCAN)
super sAnlta 2008 Vol. 44,
no. 1: 48-56
Toxicolog
y Data
Network
(TOXNET
)
*
Genetoxi city data
downloaded from
National Library of
Medicine (NLM)
TOXNET
NA
http s: //toxnet. nlm. nih. now! new
toxnet/genet ox. htm
Prediction
Models
EPA Tool
for High-
Throughp
ut
Toxicokin
etics
(HTTK)
*
High-throughput
toxicokinetic data and
models used to predict
in vivo administered
equivalent doses from
in vitro bioactive
concentrations
Pearce et al. "httk: R Package
for High-Throughput
Toxicokinetics", J Stat
Softw. 2017 Jul 17; 79(4): 1-
26.
doi: 10.1863 7/jss.v079.i04
https://cran.r-
proj ect.org/web/packages/httk/
index.html (data are in the
RData within the package)
EPA
Exposure
Forecaster
(ExpoCast
) Systemic
Empirical
Evaluation
of Models
3
(SEEM3)
*
Systematic Empirical
Evaluation of Models
(SEEM) framework
includes calibration and
evaluation of the
models using chemical
concentrations found in
blood and urine samples
from the National
Health and Nutrition
Examination Study.
Ring et al., "Consensus
Modeling of Median
Chemical Intake for the U.S.
Population Based on
Predictions of Exposure
Pathways", Environ. Sci.
Technol., 2019, 53 (2), pp
719-732
DOI:
10.1021/acs.est.8b04056
httD s : // Dub s. acs. or s/doi/10.102
l/acs.est.8b04056
85
-------
Ecological
Structure
Activity
Relationsh
ip
Prediction
Model
(ECOSAR
)
*
SAR model to predict
aquatic PODs
NA
httDs://www.eDa.sov/tsca-
screeni n "-tool s/ecol oyi cal -
structure-activitv-
relationships-ecosar-
Dredictive-model
EPI Suite
*
QSAR software to
estimate
physicochemical and
fate and transport
properties
https://www.epa.sov/tsca-
screening-tools/download-epi-
suitetm-estimation-prosram-
interface-v411
EPA
Toxicity
Estimation
Software
Tool
(TEST)
*
*
Skin and Eye irritation
and sensitization data
derived from GHS
documents, and
genotoxicity QSAR
model
Vegosen and Martin, "An
automated framework for
compiling and integrating
chemical hazard data", Clean
Tech. Environ. Policy, 2020,
22, pp. 441-458
DOI 10.1007/sl 0098-019-
01795-w
httDs://www.eDa.sov/chemical-
research/toxicitv-estimation-
software-tool-test
OPEn
structure-
activity/pr
operty
Relationsh
ip App
(OPERA)
*
OPEn structure-
activity/property
Relationship App for
predicting
physicochemical and
environmental fate
properties
Mansouri et al."OPERA
models for predicting
physicochemical properties
and environmental fate
endpoints", Journal of
Cheminformatics201810:10
httDs://icheminf.biomedcentral.
com/articles/10.1186/s 13321 -
018-0263-1
ToxTree
*
Toxic Hazard
Estimation by decision
tree approach used to
predict threshold of
Patlewicz, G., et al. (2008).
"An evaluation of the
implementation of the
Cramer classification scheme
httD: //toxtree. sourcefor se. net/
86
-------
toxicological concern
(TTC)
in the Toxtree software."
SAR QSAR Environ Res
19(5-6): 495-524.
World
Health
Organizati
on (WHO)
Internatio
nal
Programm
e on
Chemical
Safety
(IPCS)
*
Acute toxicity values
from WHO Pesticides
Classification
See URL
http://www.who.int/ipcs/public
ations/pesticides hazard 2009.
pdf
Publications
Arnot and
Gob as
(2006)
*
BAF and BCF values
compiled from
experimental studies
Arnot and Gobas, "A review
of bioconcentration factor
(BCF) and bioaccumulation
factor (BAF) assessments for
organic chemicals in aquatic
organisms", Environmental
Reviews, 2006, 14(4): 257-
297,
https ://doi. org/ 10.113 9/a06-
005
httDs://www.nrcresearchDress.c
om / doi/ 10.113 9/a06-
005#.XL90kehKe2w
Chiu et al.
(2018)
*
POD values compiled
by Chiu et al.
Chiu W, et al. "Beyond the
RfD: Broad Application of a
Probabilistic Approach to
Improve Chemical Dose-
Response Assessments for
Noncancer Effects", Env.
httos://doi.ore/10.1289/EHP33
M
87
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Health. Persp. Vol 126
(2018)
Wignall et
al. (2014)
*
BMD values compiled
or derived by Wignall et
al.
Wignall J. et al.
"Standardizing Benchmark
Dose Calculations to
Improve Science-Based
Decisions in Human Health
Assessments", Env. Health
Persp. Vol. 22 p 499 (2014)
httDs://ehD.niehs.nih. now! doi/1
0.1289/eho. 1307539
88
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Appendix C. Quality Assurance Recommendations to Efficiently
Review Datasets to Support Candidate Chemical Identification for
TSCA
Overview
This appendix describes the approaches that ORD implemented to ensure a high standard
of review of compiled publicly available information from Type 1 data sources on the proof-of-
concept 238 chemical substances (POC238). The data curation effort reviewed chemical,
toxicological, and exposure data to confirm accuracy. Specific details about the review processes
for each data domain are described below.
The QC review approaches were developed in a "learn-by-doing" pilot study using the
POC238. The pilot study developed methods for data aggregation, curation, and evaluation, as
well as recommendations to efficiently review large Type 1 datasets. The TSCA data curation team
consisted of scientists from ORD organized into workgroups based on expertise and given data for
review.
Procedures for QC review
Chemical data were sorted into six data domains: human hazard, exposure, genotoxicity,
ecological hazard, skin sensitization and skin/eye irritation, and bioaccumulation. Data on the
chemicals were collected from Type 1 data sources [as defined in A Working Approach for
Identifying Potential Candidate Chemicals for Prioritization119,; see Appendix B for Data Source
list]. Type 1 data sources are publicly available and readily searchable, enabling data extraction in
a structured form. To review these data, workgroups were organized according to scientific
expertise. Each workgroup established a process for reviewing their data domain and these
processes are summarized below.
No study quality considerations were evaluated during QC review. Reviewers did not
perform a critical analysis of experimental design, statistical analyses, or data interpretation.
Rather, reviewers compared the collected Type 1 data to primary and secondary sources. A primary
source was defined as the study, report, or manufacturer report with health safety data. A secondary
source was defined as a database or source that provided data aggregated from multiple primary
sources.
Reviewers flagged data that could not be confirmed to the primary source, even if the
aggregated data matched the secondary source. However, certain secondary sources, such as the
118 https://www.epa.gov/sites/production/files/2018-09/documents/preprioritization white paper 9272018.pdf
89
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ECOTOX Knowledgebase119, the Integrated Risk Information System (IRIS)120, or exposure
data121 have existing QC processes or peer review processes. For these select databases,
confirmation to secondary source was sufficient to pass QC review. Reviewers recorded reasons
for QC flags and developed QC metrics for data errors. "QC flag" measured the percentage of data
without confirmation to the primary source. "Error rate" determined how often a secondary source
incorrectly reported a value from a primary source.
The workgroups collected metrics on the data sources. The QC flag metric reported the
percentage of data points that could not be confirmed to a primary source, for example, in instances
where the primary source was not available. QC flag metric was calculated by dividing the number
of data points that were flagged by the total number of data points reviewed. Error rate measured
how often a secondary database did not match the primary source, that is when both the primary
source and secondary source are available, but the values do not match. Error rate was calculated
by dividing the number of data points that did not match the primary source by the number of data
points that have both a primary and secondary source.
Human Hazard Domain Workgroup Review Approach
Human hazard data consisted of in vivo data aggregated from publicly available databases.
Data were provided in a spreadsheet for the workgroup to review. A single scientist was assigned
to review an individual data source. The human hazard workgroup QC review focused on a subset
of the aggregated Type 1 data: chemical identifier, route of exposure, study duration, and point-
of-departure (POD) data.
Each reviewer compared the chemical identifier, route of exposure, study duration, and
POD data to the secondary source - i.e., the Type 1 source from which data were extracted. In
addition, each reviewer attempted to link data points to a primary source - i.e., the original
reference. If data could not be confirmed to a primary reference, the data was flagged. However,
the workgroup recognized that certain secondary sources, such as IRIS3, had existing QC
processes. For these databases, the secondary source was sufficient to pass QC review.
Study quality considerations were not evaluated during QC review. Reviewers did not
review experimental design, statistical analyses, or data interpretation. Rather QC review was
limited to comparing the data with primary and secondary sources. The QC review scope was
limited for three reasons. First, the study quality would be evaluated during the expert review for
candidate selection. However, if a reviewer noted a potential study quality issue, the issue could
119 https ://cfpub .epa. gov/ecotox/
1211 https://www.epa.gov/iris
121 https://www.epa.gov/chemical-researcli/rapid-chemical-exposure-and-dose-research
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be recorded for consideration during the subsequent expert review. Secondly, systematic data
quality review requires a minimum of two reviewers for each data point. The human hazard
workgroup did not have sufficient personnel to have two reviewers per data point within the
accelerated timeline of the pilot study. Lastly, determining a POD is often a fit-for-purpose process
that may differ between academic, industrial, and regulatory groups. Harmonizing POD selection
across studies was beyond the scope of the human hazard workgroup.
Exposure Domain Workgroup Review Approach
Two datasets were reviewed by the exposure workgroup: exposure model data (including
model parameters and outputs) and selected parameters related to susceptible population exposure.
Exposure model data estimated potential human exposures via chemical use parameters from
publicly available data sources (Wambaugh et al., 2014122, Ring et al., 2018123). Susceptible
population data was limited to potential exposure in children. Susceptible population data
consisted of chemical occurrence in the following media: consumer products with which children
either directly (children's products) or indirectly (other household or personal care products) come
into contact, flooring product, house dust, breast milk, foods or food-contact materials, or far-field
sources. Chemical occurrence data of compounds in house dust124 or breast milk125 were collected
from two primary references. Occurrence data for the remainder of the media were collected from
three different secondary data sources: Chemical Data Reporting (CDR)126, Chemical and
Products Database (CPDat)127, and Chemical Product Categories Database (CPCat)128. These
secondary sources aggregate government and/or manufacturer reported information on chemical
presence in various types of consumer products or industrial processes. The exposure data and
susceptible life stage data were provided in separate spreadsheets for the workgroup to review. All
data were reviewed by at least two workgroup members.
122 Wambaugh JF, Wang A, Dionisio KL, Frame A, Egeghy P, Judson R, Setzer RW. (2014). High throughput
heuristics for prioritizing human exposure to enviromnental chemicals. Enviromnental Science and Technology
48(21): 12760-7.
123 Ring CL, Arnot J, Bennett DH, Egeghy P, Fantke P, Huang L, Isaacs KK, Jolliet O, Phillips K, Price PS, Shin
HM, Westgate JN, Setzer RW, Wambaugh JF. (2018). Consensus Modeling of Median Chemical Intake forthe U.S.
Population Based on Predictions of Exposure Pathways. Enviromnental Science and Technology. 53(2):719-732.
124 Mitro SD, Dodson RE, Singla V, Adamkiewicz G, Elmi AF, Tilly MK, and Zota AR. (2016). Consumer product
chemicals in indoor dust: A quantitative meta-analysis of U.S. studies. Enviromnental Science and Technology 50:
13611-11.
125 Lehmann GM, LaKind JS, Davis MH, Hines EP, Marchitti SA, Alcala C, and Lorber C. (2018). Enviromnental
chemicals in breast milk and formula: Exposure and risk assessment implications. Environmental Health
Perspectives, 126: 96001.
126 https://www.epa.gov/chemical-data-reporting/2016-chemical-data-reporting-results
127 https://comptox.epa.gov/dashboard/downloads. CPDATdownload
128 https://comptox.epa.gov/dashboard/downloads. CPCAT ARCHIVE
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For the exposure model data, the workgroup focused on a subset of chemical use
parameters (pesticide active, pesticide inert, production volume) and estimated exposure outputs
from the exposure model median. These values were checked for accuracy against the Wambaugh
et al. (2014)6 and Ring et al. (2018)7 references. The workgroup did not do a QC review of all
model inputs. Rather, the workgroup focused their review on the 3 of the 5 most predictive
heuristics in Wambaugh et al. 20146. The heuristics that were not reviewed were the industrial
and/or consumer use of a chemical. The workgroup did not re-run the exposure models to confirm
outputs, but instead focused on accurate transposition from the source material.
Susceptible population occurrence data were confirmed by reviewing either the primary
sources for house dust8 or breast milk9 occurrence or the primary sources cited in the secondary
sources (i.e., reported information of chemicals contained in consumer products or used in
industrial processes in primary references found in secondary sources). When primary sources
were derived from secondary sources, the workgroup reviewed chemicals reported in consumer
products related to child use, flooring, food or food-contact material, or far-field exposure sources
until a primary source was found that met QC requirements. OCSPP uses presence/absence in
children's products is used as an indicator for potential susceptible population exposure during
candidate identification. As this metric is binary (i.e., yes/no) rather than weighted (i.e., occurrence
in 10 products versus present only in 1 product), the workgroup focused QC review on (a) ease of
record access and (b) confirming occurrence in each medium related to susceptible population
exposure. House dust and breast milk were confirmed by checking to respective primary sources.
However, for all other media, CDR10 records were most readily accessible and reviewable in an
automated fashion. In addition, CDR10 records are manufacturer reported under TSCA, so these
records will likely be relevant for candidate chemical identification. For these reasons, CDR10
records were reviewed first, and if CDR10 records passed QC review, then available CPDat11 or
CPCat12 records were not reviewed. CPDat11 records were reviewed if no CDR10 data was
available or if CDR9 records did not pass QC, as CPDat11 records link a chemical directly to
manufacturer-reported information of compounds in a consumer product. Finally, if neither CDR10
nor CPDat11 records were available or did not pass QC review, CPCat12 data records were
reviewed.
As most data for this domain consisted of manufacturer-reported data, no study quality
considerations were evaluated during the QC review. The reviewers did not analyze model design,
statistical analyses, or data interpretation. Rather the workgroup focused spreadsheet data accuracy
relative to primary and secondary sources.
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Genotoxicity Domain Workgroup Review Approach
Genotoxicity data was aggregated from publicly available databases and provided in a
spreadsheet for the workgroup to review. A single reviewer was assigned to group of chemicals
and asked to review the genotoxicity data for those chemicals. The genotoxicity workgroup
focused their QC review on a subset of the aggregated Type 1 data: mutagenicity data and
clastogenicity data. The workgroup evaluated data in the standard bacterial mutation assays (the
Salmonella and E. coli WP2 strains), as well as three main assays for chromosomal mutation (in
vitro chromosome aberration assay, mouse bone-marrow micronucleus assay, and the mouse
lymphoma Tk+/- assay). These data were selected based on a comparison of the predictivity of
combination of genotoxicity assays to the Salmonella (Ames) mutagenicity assay alone129. Using
a database of >10,000 compounds, genotoxicity data from two bacterial strains (TA98 and TA100
of Salmonella) identified 93% of the mutagens. When chromosomal mutation assay data were
included, 99% of the mutagens were identified. These findings suggest that bacterial and
chromosomal mutation data are sufficient for evaluating genotoxicity potential. Each reviewer
confirmed selected data back to the secondary source - i.e., the database from which spreadsheet
data were extracted. In addition, each reviewer tried to confirm data back to the primary source -
i.e., the original reference cited in the secondary source. If data could not be confirmed to a primary
reference, the data was flagged.
The genotoxicity workgroup categorized chemicals as "genotoxic," "non-genotoxic," or
"inconclusive" based on preliminary review of available genotoxicity data. This categorization
was not intended to represent a final determination on the genotoxicity of these chemicals. For
example, chemical substances with at least one positive genotoxic assay were categorized as
genotoxic, with the understanding that further evaluation of study quality and design may lead to
a different determination.
Bioaccumulation Subdomain Workgroup Review Approach
Bioaccumulation data were aggregated from public databases and provided in a
spreadsheet for review. All data were reviewed by the workgroup members. The QC review
focused on a subset of the bioaccumulation chemical data: bioconcentration factor (BCF) and
bioaccumulation factor (BAF).
Chemical data consisted of two categories: experimental data and modeled data. Although
more limited, experimental data were given priority. Reviewers attempted to trace experimental
data back to a primary source - i.e., the original reference with measured values. If the reviewers
129 Williams RV, DM DeMarini, LF Stankowski Jr, PA Escobar, E Zeiger, J Howe, R Elespuru, KP Cross. (2019).
Are all bacterial strains required by OECD mutagenicity test guideline TG471 needed? Mutation Research
848:503081.
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could not confirm the data to a primary source, the data was flagged. The workgroup was only
able to confirm a small percentage of the data to a secondary source (i.e., the database from which
spreadsheet data were extracted) because secondary source no longer existed or contained
proprietary data. The QC review pilot study was limited to publicly available data.
For the predicted BCF and BAF values, the workgroup only reviewed publicly available
models. Models were re-run and compared with the spreadsheet for accuracy. Where the models
produced different values, the values were actively corrected. The workgroup did not QC review
the model inputs. For some data, underlying model inputs and predicted values had published QC
review processes. Other model outputs did not provide information on model inputs or QC review.
The workgroup flagged models that did not have a publicly accessible QC process as a decision
point for further consideration.
Ecological Hazard Domain Workgroup Review Approach
Ecological hazard data consisted of data extracted from two Type 1 sources - the US EPA
ECOTOX Knowledgebase2 and the European Chemicals Agency (ECHA) database130. These data
were provided in a spreadsheet for the workgroup to review. A single reviewer was assigned to a
group of chemicals and asked to review the ecological hazard data for those chemicals. The
ecotoxicology workgroup reviewed a large amount of data across a variety of species.
Each reviewer was instructed to confirm the selected data back to the secondary source -
i.e., the database from which data on the spreadsheet were extracted. Each reviewer also tried to
trace the data point back to a primary source - i.e., the original reference cited in the secondary
source. If data could not be confirmed to a primary reference, the data was flagged. However, the
ECOTOX Knowledgebase2 has a robust QC process ensuring data are verified using reliable
source and reflect what was reported in the publication. Once quality assurance steps have been
completed, the data are released to the ECOTOX Knowledgebase2. Therefore, confirming data to
the ECOTOX Knowledgebase2 (i.e., secondary source) was equivalent to a primary source.
No study quality considerations were evaluated during the QC review. The workgroup did
not review experimental design, statistical analyses, or data interpretation. Rather, the ecological
hazard workgroup focused only on the data accuracy relative to the primary and secondary sources.
Skin Sensitization and Skin/Eye Irritation Workgroup Review Approach
Skin sensitization and skin/eye irritation data were collected from publicly available
sources and aggregated for review. Information standardized with the Global Harmonized System
1311 https://echa.europa.eu/
94
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of Classification and Labeling of Chemicals (GHS)131 was extracted. The GHS classification codes
— including hazard codes or H-codes; numerical hazard categories; signal word codes; and
classification, labeling, and packaging (CLP) hazard class — were included and used for
designating the chemicals as sensitizing or irritating. A single reviewer was assigned to review the
skin and eye irritation data.
A reviewer was asked to verify data in the resulting spreadsheet for quality control of the
programmatic data collection process. The reviewer performed an automated check of the data to
ensure that transposition of secondary source data was correct by comparing numbers in the
spreadsheet to the secondary source material. The reviewer then manually reviewed 10% of the
data back to a primary source. This limited manual review was necessary owing to time and
resource issues.
Summary
In summary, the POC238 chemical substances were reviewed for transcription from
primary and secondary sources into the database used for the PICS approach. This review did not
take into account study quality or data validity, as that was determined to be part of an expert
review process separate from this effort. For the PICs approach, data were deemed 'acceptable' if
confirmed to a secondary source; primary source confirmation was not required. The case study
developed methods for data aggregation, curation, and evaluation, as well as QA recommendations
to efficiently review Type 1 datasets. The case highlighted various challenges in data quality and
availability of primary sources in addition to the changing landscape of online resources worldwide
and urgent need for specialized curation/quality resources, data quality tool(s), common data
dictionary, process to store the documents for data provenance and quality flags for various data
usage. These lessons learned have informed the QC process moving forward, storage and linking
of secondary or primary sources to data records wherever available for data provenance, inclusion
of data audit capability, development of quality flags to enable fit-for-purpose data aggregation,
and the development of a QC tool for future use with large datasets as part of continuous curation
effort.
131 https://www.unece.org/trans/danger/robli/ghs/ghs welcome e.html
95
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Appendix D. Definition of Exposure Pathways for Calculating the
Susceptible Population Domain Metric.
Exposure Source
Definition
Data Source
Primary
Contributors to
Increased
Children's
Exposure
versus the
General
Population
Exposure
Differential
Metric
Consumer Sources
-Children's
Products
Known occurrence (via
reporting or measurement) in
consumer products used more
commonly (or exclusively) by
children (e.g., arts and crafts
formulations, baby
preparations, car seats and
other gear, toys, and marketed
to children such as children's
sunscreens). Known
occurrence in products to
which infants or children have
closer contact than adults due
to behavior (i.e., carpet,
flooring)
CPDat: reported presence of
chemical in a product used
primarily by children; CPCat:
chemicals directly reported or
measured in children's products;
chemicals reported or detected in
flooring (e.g., carpet, carpet
padding); 2016 CDR
Consumer/Commercial Use
Information: flag for use in
children's products
Increased
prevalence of
use by children
versus adults;
closer proximity
of source to
children
compared to
adults
4
Breast Milk or
Formula
Chemical detected in breast
milk or formula
Lehmann GM, LaKind JS, Davis
MH, Hines EP, Marchitti SA,
Alcala C, and Lorber C. (2018)
Environmental chemicals in breast
milk and formula: Exposure and
risk assessment implications.
Environ Health Perspect, 126:
96001
Source unique to
children
4
Dust
Measured in residential house
dust in at least two studies in
published meta-analysis.
Mitro SD, Dodson RE, Singla V,
Adamkiewicz G, Elmi AF, Tilly
MK, and ZotaAR. (2016).
Consumer product chemicals in
indoor dust: A quantitative meta-
analysis of U.S. studies. Environ
Sci Technol, 50: 13611-11.
Increased
contact by
children;
increased hand-
to-mouth
behaviors (and
thus chemical
ingestion);
closer proximity
of source to
children (e.g.,
within children's
breathing zone)
3
Consumer Sources
- Flooring and
Related Products
Known occurrence (via
reporting or measurement) in
flooring or floor coverings, or
in products used on these items
(such as cleaners)
CPDat: reported presence of
chemical in a flooring related
product, CPCat: presence in
consumer product categories
associated with flooring or related
products; 2016 CDR
Consumer/Commercial Use
Information: reported in product
categories associated with flooring
Increased
contact by
children;
Increased hand-
to-mouth
behaviors (and
thus chemical
ingestion);
closer proximity
of source to
children (e.g.,
3
96
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within children's
breathing zone)
Consumer Sources
- Other Products
(General
Population)
Known occurrence (via
reporting or measurement) in
consumer products not
captured elsewhere that may be
either used by children or
transferred to children by
adults
CPDat: reported presence in
consumer products; CPCat:
presence in general consumer
product categories; 2016 CDR
Consumer/Commercial Use
Information: reported as having
"Consumer" use or "Both"
(consumer and commercial)
Increased hand-
to-mouth
behaviors (and
thus chemical
ingestion);
increased
inhalation rates
of children
versus adults
2
Dietary Sources
Chemicals in food packaging
(indirect food additives), direct
food additives, agricultural
chemicals, chemicals measured
in drinking water
CPCat: presence in related
categories; 2016 CDR
Consumer/Commercial Use
Information: presence in food-
related product categories
Increased food
consumption per
unit body weight
by children
1
Far-field Sources
Industrial pollutants
(which may be released the
environment and result in
ultimate exposures via contact
with contaminated media)
CPCat: presence in industrial use
categories; 2016 CDR Industrial
Process and Use Information:
chemical was reported
Increased hand-
to-mouth
behaviors (and
thus chemical
ingestion);
increased
inhalation rates
of children
versus adults
1
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Appendix E. Public Information Curation and Synthesis (PICS) Output for Proof-of-Concept (POC)
Subset of the Non-confidential TSCA Active Inventory
Attached is the output results for the proof-of-concept subset used to inform the Public Information Curation and Synthesis (PICS)
Approach. This data can also be viewed at https://ccte-tscapoc.epa.gov. Results were determined as described in the text and displayed
visually in Figure 14 of the report.
Order
Column
Description
1
DTXSID
DSSTox generic substance ID
2
CASRN
Chemical Abstracts Registry Number
3
Name
Chemical Name
4
TSCA Active
Is the chemical in the TSCA Active Inventory?
5
TSCA 90
Is the chemical in the 2014 update to the TSCA Workplan?
6
TSCA POC
Is the chemical in the TSCA POC (this list)?
7
TSCA 10
Is the chemical in the 2016 Work Plan Chemicals?
8
SCIL
Is the chemical in the EPA Safer Chemicals Ingredients List (SCIL)?
9
SCIL Green Circle
Is the chemical in the SCIL Green Circle List?
10
SCIL Half Green Circle
Is the chemical in the SCIL Half Green Circle List?
11
SCIL Yellow Triangle
Is the chemical in the SCIL Yellow Triangle List?
12
SCOGS GRAS
Is the chemical in the FDA Generally Regarded as Safe (GRAS) List?
13
Intentional Food Ingredient
Is the chemical in the FDA Substances Added to Food Inventory?
14
IRIS
Is the chemical in the EPA IRIS Inventory?
15
PPRTV
Is the chemical in the EPA PPRTV Inventory?
16
SIDS
Is the chemical in the OECD Screening Information Data Set List?
17
TSCA Low Priority
Is the chemical in the High Priority TSCA list?
18
TSCA High Priority
Is the chemical in the Low Priority TSCA list?
19
Scientific Domain Metric
The value of the scientific domain metric (range from 0-100)
20
Information Availability Metric
The value of the information availability metric (range from 0-100)
98
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21
Public Risk Assessment Noncancer
If there is a public non-cancer risk assessment, lists whether there is an
RfD and/or RfC available
22
Public Risk Assessment Cancer
If there is a public cancer risk assessment, lists whether there is an slope
factor and/or unit risk available
23
IG Flag (human hazard)
Information gathering flags for human hazard
24
IG Flag (ecological hazard)
Information gathering flags for ecological hazard
25
IG Flag (genotoxicity)
Information gathering flags for genotoxicity
26
IG Flag (cancer)
Information gathering flags for cancer
27
IG Flag (childrens exposure)
Information gathering flags for children's exposure
28
IG Flag (persistence / bioaccumulation)
Information gathering flags for persistence / bioaccumulation
29
IG Flag (sensitization / irritation)
Information gathering flags for sensitization / irritation
30
Score human hazard to exposure ratio
Score for human hazard to exposure ratio (range of 0-4)
31
Score ecological hazard
Score for ecological hazard (range of 0-4)
32
Score cancer
Score for cancer (range of 0-4)
33
Score genotoxicity
Score for genotoxicity (range of 0-4)
34
Score children
Score for children's exposure (range of 0-4)
35
Score persistence / bioaccumulation
Score for persistence / bioaccumulation (range of 0-4)
36
Score sensitization irritation
Score for sensitization / irritation (range of 0-4)
37
Human hazrd to exposure ratio (repeat dose)
Minimum repeat-dose mammalian POD / exposure estimate
38
POD mammalian in vivo oral repeat dose (mg/kg/day)
Minimum repeat-dose mammalian POD
39
Estimated human exposure (mg/kg/day)
Human exposure estimate from the SEEM3 model
40
Minimum aquatic ecological POD (mg/L)
Minimum aquatic ecological POD (experimental repeat dose or acute or
predicted)
41
Ecological aquatic POD in vivo acute (mg/L)
Minimum aquatic ecological POD in vivo, acute
42
Ecological aquatic POD in vivo repeat dose (mg/L)
Minimum aquatic ecological POD in vivo, repeat dose
43
Data available (mammalian acute)
Is there an experimental mammalian POD for an acute toxicity studv?
44
Data available (mammalian subchronic)
Is there an experimental mammalian POD for a subchronic toxicity study?
45
Data available (mammalian chronic)
Is there an experimental mammalian POD for a chronic toxicity studv?
46
Data available (mammalian repeat dose)
Is there an experimental mammalian POD for a repeat dose toxicity study?
47
Data available (mammalian developmental)
Is there an experimental mammalian POD for a developmental toxicity
study?
99
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48
Data available (mammalian reproductive)
Is there an experimental mammalian POD for a reproductive toxicity
study?
49
Data available (mammalian neurotoxicity)
Is there an experimental mammalian POD for a neurotoxicity studv?
50
Data available (ecological acute plant)
Is there an experimental ecological POD for an acute plant study?
51
Data available (ecological acute invertebrate)
Is there an experimental ecological POD for an acute invertebrate studv?
52
Data available (ecological acute vertebrate)
Is there an experimental ecological POD for an acute vertebrate study?
53
Data available (ecological repeat dose plant)
Is there an experimental ecological POD for a repeat dose plant study?
54
Data available (ecological repeat dose invertebrate)
Is there an experimental ecological POD for a repeat dose invertebrate
study?
55
Data available (ecological repeat dose vertebrate)
Is there an experimental ecological POD for a repeat dose vertebrate
study?
100
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Appendix F. Comparison of Individual Scientific Domain Metrics for
the POC238 and Non-confidential TSCA Active Inventory
Below are the results for the full PICS approach and the individual metrics for the POC238
subset as compared to the non-confidential TSCA active inventory. As expected, these
comparisons show that the POC238 subset is more data-rich than the full non-confidential TSCA
active inventory. This level of detail could allow the decision-maker to examine if any specific
endpoint is potentially of more concern than any other and to explore potential data gaps for the
chemical group of interest.
Proof of Concept
TSCA Active Inventory
O
"(D
C
CT3
E
o
o
o
o
o H
o
CO
o
CD
O
c
g>
'o
CO o
Osl
POC not in TSCA 10/90
TSCA 10
TSCA 90
Low Priority
High Priority
V
20
40
60
80
Information Availability Metric
03
£
o
o
C
CD
O
cn
100
o
o
o
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o _
CD
O
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o
C\J
o POC not in TSCA 10/90
• TSCA 10
o TSCA 90
v Low Priority
a High Priority
o High MW Polymers (exempt)
¦ Other TSCA
t t t" t t |'! °i:' t i !¦
t t "t if' i 5 ? 4 V' x «
20
40
60
80
Information Availability Metric
100
Figure F-l. Plot of the Information Availability vs. Scientific Domain Metrics for the POC238 set of
chemical substances (left) and non-confidential TSCA active inventory (right). Each dot represents one
chemical substance, with the size of the dot representing the number of domains with data for the specific
chemical. The red dots represent the first ten TSCA Work Plan chemical substances selected for risk
evaluation in 2016 (TSCA 10). The green dots represent the TSCA Work Plan chemical substances from
the 2014 update (TSCA 90). The red triangles represent the high priority chemical substances and the
yellow triangles represent the low priority chemical substances released in March 2019. The blue dots
represent the high molecular weight compounds and exempt polymers and the small black dots represent
the remaining compounds in the inventory. Positions of points are staggered for ease of visualization.
101
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POC: Scientific Domain
o
c
CD
O"
,
o
c
0
3
cr
100
o
o
o H
o
o -1
-r~
20
—r~
40
—r~
60
-r~
80
100
Scaled value
Scaled value
Figure F-2 - Distributions of the scaled SDM and IAM for the POC238 subset and non-confidential TSCA
active inventory. The x-axis displays the scaled value and the y-axis shows the frequency of that value in
the subset.
POC: Carcinogenicity
tf)
03
U
E
-------
-S ®
CO
CJ
E
(D O
sz ^r
o
POC: Susceptible Population
POC: Persistence and Bioaccumulation
CO
ro
o
E
0 o _
jz
o
0 12 3 4
POC: Skin Sensitization and Skin/Eye Irritation
2
metric
03
u
E
0
JZ
o
o
CD
O
CN
0 12 3
metric
POC: Human Hazard-to-Exposure Ratio
0 12 3 4
Figure F-3 - Distributions of the scaled SDM for
axis displays the scaled value and the y-axis shows
POC: Ecological Hazard
O
C\J -i
T—
—
o
r
o _
CD
CO
ZJ
cr
CD
L_
LL
o _
0 12 3 4
individual domains for the POC238 subset. The x-
frequency of that value in the subset.
103
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Appendix G. Information Availability Metric Calculation
Figure G-l. Flow chart explaining the Information Availability Metric (IAM) calculation used in the
PICS approach.
Available data
categories
Modifying Criteria
I.Mammalian Acute
2.One of (mammalian
subchronic, mammalian
repeat dose, mammalian
chronic)
3.Mammalian reproductive
4.Mammalian developmental
5.Mammalian neurotoxicity
6.Mammalian cancer
7.Mammalian genotoxicity
8.Skin Sensitization or eye
corrosivity
9.Exposure
10.Eco aquatic plant acute
II.Eco aquatic invertebrate
acute
12.Eco aquatic vertebrate
acute
13.Eco aquatic plant repeat
dose
14.Eco aquatic invertebrate
repeat dose
15.Eco aquatic vertebrate
repeat dose
None
Is there a high-
quality public
risk assessment
(cancer or non-
cancer)?
Is this a
chemical
intermediate
AND a short
environmental
half-life
(hours)?
Is this a
chemical with
low water
solubility
(< 0.1 mg/L)*?
Is this a
chemical with
MW > 1000
OR an
exempt
polymer?
Add 1 for
categories 1-
15 with
available data
Add 8 for the
assumption that all
mammalian data is
available (1-8 on list
of data categories)
plus 1 for categories
9-15 with available
data
Add 1 for
categories 1-9
with available
data
Add 1 for
categories 1-8
with available
data
Add 1 for
categories 8
and 9 with
available data
Divide by the
denominator
(15)
Divide by the
denominator
(15)
Divide by the
denominator
(9)
Divide by the
denominator
(8)
Divide by the
denominator
(2)
Scale to percent.
IAM
104
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