United States
Environmental Protection
IhI K mAgency
EPA-815-R-24-003
FINAL
Framework for Estimating Noncancer Health Risks
Associated with Mixtures of Per- and Poly fluoroalkyl
Substances (PFAS)
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Framework for Estimating Noncancer Health Risks Associated with Mixtures
of Per- and Polyfluoroalkyl Substances (PFAS)
Prepared by:
U.S. Environmental Protection Agency
Office of Water & Office of Research and Development
Washington, DC
EPA Document Number: EPA-815-R-24-003
April 2024
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Notices
This document has been reviewed in accordance with U.S. Environmental Protection Agency
policy and approved for publication.
This document provides a framework for estimating noncancer human health risks associated
with mixtures of per- and polyfluoroalkyl substances (PFAS), based on longstanding EPA
mixtures guidelines. This document is not a regulation and does not impose legally binding
requirements on the EPA, states, Tribes, or the regulated community, and might not apply to a
particular situation based on the circumstances. The extent of the utility of this document for a
particular programmatic application will need to be assessed on a case-by-case basis within each
specific decision context under applicable statutory and regulatory authority. The framework
included in this document does not supersede previously published EPA guidelines on mixtures
(e.g., USEPA, 1986, 2000b) or EPA approaches used to assess cumulative risks of contaminants
including chemical mixtures under various environmental statutes (e.g., Federal Insecticide,
Fungicide, and Rodenticide Act; Food Quality Protection Act; Comprehensive Environmental
Response, Compensation, and Liability Act). The EPA may change certain aspects of this
document in the future based on evolving availability of information relevant to human health
risk assessment and increasing confidence in New Approach Methods.
Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
Dedication
This document is dedicated to the memory of Dr. Jane Ellen Simmons and Mr. Jeffrey Swartout.
Jane Ellen and Jeff were both dedicated civil servants in the EPA's Office of Research and
Development for more than 30 years where they conducted rigorous chemical mixtures research
and championed cumulative risk assessment approaches for exposure to multiple stressors. Their
contributions to the field live on in this framework document.
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Authors, Contributors, and Reviewers
Prepared by
Justin Conley, PhD - EPA Office of Research and Development (ORD)
Colleen Flaherty, MS (co-lead) - EPA Office of Water (OW)
Earl Gray, PhD - ORD
Brittany Jacobs, PhD - OW
Jason C. Lambert, PhD, DABT (co-lead) - ORD
Alex Lan, MPH - OW
Casey Lindberg, PhD - OW
Kathleen Raffaele, PhD (Retired) - EPA Office of Land and Emergency Management
Contributors
Carlye Austin, PhD, DABT - OW
Kelly Cunningham, MS - OW
Hannah Hoi singer, MPH - OW
Amanda Jarvis, MS - OW
James R. Justice, MS - OW
Internal Technical Reviewers
Andrew Kraft, PhD - ORD
Allison Phillips, PhD - ORD
Glenn Rice, ScD - ORD
Jane Ellen Simmons, PhD - ORD
Executive Direction
Elizabeth Behl (Retired) - OW
Eric Burneson, P.E. - OW
Santhini Ramasamy, PhD - ORD
Jamie Strong, PhD - ORD
Russell Thomas, PhD - ORD
Tim Watkins, PhD - ORD
Formatting By
Tetra Tech, Inc.
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Contents
EXECUTIVE SUMMARY 1
1.0 Introduction and Background 5
1.1 Purpose 5
1.2 The EPA Science Advisory Board Review 6
1.3. Public Review 8
1.4 Background on PFAS 8
1.5 Occurrence of PFAS Mixtures 12
1.6 Evidence of PFAS Exposure in Humans 15
1.7 Brief Summary of State, National, and International Approaches to Address
PFAS Mixtures in Water 16
1.8 Overview of Proposed Framework for Estimating Noncancer Health Risks for
PFAS Mixtures 19
2.0 Background on EPA Mixtures Additivity Guidelines 22
2.1 Component-Based Mixtures Assessment Methods 23
2.1.1 Application of Dose Addition as the EPA's Default Approach 24
3.0 Dose Additivity for PFAS 26
3.1 Overview of Assessment Approaches for Chemical Mixtures 26
3.2 Examples of Chemical Classes and Toxicological Pathways Utilizing Mixture
Assessment Approaches 27
3.2.1 Dioxin-Like Chemicals and Aryl Hydrocarbon Receptor Pathway
Toxicity Equivalence Factors 27
3.2.2 Pyrethroids/Pyrethrins - Central Nervous System and Behavior 28
3.2.3 Organophosphates - Lethality, Central Nervous System, and Behavior 29
3.2.4 Estrogen Agonists - Mixture Effects on the Female Reproductive Tract 29
3.2.5 Phthalates in utero - Mixture Effects on the Female Reproductive Tract 29
3.2.6 Antiandrogens - Male Reproductive Tract Development 30
3.3 Systematic Reviews of Mixtures Toxicity: Quantification of Deviations from
Dose Additivity 32
3.3.1 Deviation from Additivity 33
3.4 PFAS Dose Additivity 33
4.0 Introduction to Estimating Noncancer PFAS Mixture Hazard or Risk 39
4.1 Whole Mixtures Approach 39
4.2 Data-Driven Component-Based Mixtures Approaches for PFAS 39
4.2.1 Conceptual Framework of the Approach 41
4.2.2 Introduction to a Hypothetical Example with Five PFAS 48
5.0 Hazard Index Approach 57
5.1 Background on the HI Approach 57
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5.2 Illustrative Example Application of the General HI to a Hypothetical Mixture of
Five PFAS 61
5.2.1 General HI Step 1: Assemble/derive component chemical health effects-
based values (e.g., Chronic oral RfDs) 62
5.2.2 General HI Step 2: Assemble/derive health-based media concentrations
(HBWC) 65
5.2.3 General HI Step 3: Select exposure estimates (measured water
concentrations) 68
5.2.4 General HI Step 4: Calculate PFAS mixture potency (component HQs
and overall HI) 68
5.2.5 General HI Step 5: Interpret the PFAS mixture HI 69
5.3 Illustrative Example Application of the Target-Organ-Specific Hazard Index to a
Hypothetical Mixture of Five PFAS 69
5.3.1 TOSHI Step 1: Assemble/derive component health effects endpoints
(RfDs or target-organ toxicity doses) 69
5.3.2 TOSHI Step 2: Assemble/derive health-based media concentrations
(HBWC) 70
5.3.3 TOSHI Step 3: Select exposure estimates (measured water
concentrations) 71
5.3.4 TOSHI Step 4: Calculate PFAS mixture potency (component HQs and
overall TOSHI) 71
5.3.5 TOSHI Step 5: Interpret the PFAS mixture HI 72
5.4 Advantages and Challenges of the General HI and TOSHI Approaches 72
6.0 Relative Potency Factor Approach 75
6.1 Background on RPF Approach 75
6.2 Illustrative Example Application of RPF to a Hypothetical Mixture of Five PFAS .. 77
6.2.1 RPF Step 1: Assemble/Derive component health effects endpoints (select
Index Chemicals, PODheds) 78
6.2.2 RPF Step 2: Assemble/derive health-based media concentrations
(HBWCs for the Index Chemicals) 79
6.2.3 RPF Step 3: Select exposure estimates (measured water concentrations) 79
6.2.4 RPF Step 4: Calculate PFAS mixture potency (RPFs and ICECs for each
effect domain) 79
6.2.5 RPF Step 5: Compare PFAS mixture potency (Total ICECmix) to an
existing health-based value (HBWC) 84
6.3 Advantages and Challenges of the Relative Potency Factor Approach 85
7.0 Mixture-BMD Approach 88
7.1 Background on the Mixture-BMD Approach 88
7.2 Illustrative Example Application of the Mixture Benchmark Dose Approach to a
Hypothetical Mixture of Five PFAS 92
7.2.1 Mixture BMD Step 1: Assemble/derive component health effects
endpoints (BMDx) 92
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7.2.2 Mixture BMD Step 2: Assemble/derive health-based media
concentrations (HBWC) 93
7.2.3 Mixture BMD Step 3: Select exposure estimates (measured water
concentrations) 93
7.2.4 Mixture BMD Step 4: Calculate PFAS mixture potency (Mixture BMD
HBWC) 93
7.2.5 Mixture BMD Step 5: Compare PFAS mixture potency (total PFAS
mixture concentration) to health-based value (Mixture BMD HBWC) 95
7.3 Advantages and Challenges of the Mixture BMD Approach 95
8.0 Comparison of Component-Based Approaches 97
References 99
Figures
Figure 2-1. Flow chart for evaluating chemical mixtures using component-based additive
methods 24
Figure 3-1. AOP network for chemicals that disrupt AR-mediated cellular signaling leading
to adverse effects on the development of male reproductive tract resulting from
in utero exposure 31
Figure 4-1. Framework for data-driven application of component-based assessment
approaches for mixtures of PFAS based on dose additivity 40
Figure 4-2. Example literature inventory heatmap for epidemiological or traditional
experimental animal studies for five PFAS currently under development/review
in the EPA/ORD's IRIS program (heat map circa 2018) 43
Figure 4-3. Example plot illustrating in vitro cell bioactivity expressed in AEDs 45
Figure 4-4. Hypothetical PFAS-specific literature search string applied to toxicity
information databases such as PubMed, Web of Science, Toxline, and TSCATS 50
Figure 4-5. Hypothetical PECO criteria and considerations used to determine study
relevance in the systematic review and evaluation of a literature inventory for
chemicals such as PFAS 51
Figure 4-6. Example literature screening logic flow for hypothetical PFAS using an EPA
systematic review approach 52
Figure 4-7. Example exposure-response arrays for the hypothetical example PFAS 1-3
identified as having existing human health risk assessment values for one or
more exposure durations 54
Figure 4-8. Evidence synthesis and integration across three target health effects domains
for a mixture of five hypothetical PFAS 55
Figure 5-1. General steps to derive bioactivity-based RfV using bioactivity data in human
or animal tissue/cells 64
Figure 6-1. General cell signaling pathways associated with PFAS-induced liver injury 80
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Figure 6-2. Example hypothetical process for integrating NAM-based RPFs and ICECs into
mixtures assessment 83
Figure 7-1. Example comparison of observed data with model predictions using two dose
addition-based mixture models and an RA model for a binary mixture study
(adapted from Gray et al., 2022) 90
Tables
Table 1-1. Two primary categories of PFAS 10
Table 1-2. Groups, structural traits, and examples of perfluoroalkyl acids (PFAAs),
including perfluoroalkylether acids 11
Table 1-3. Characterization system of short-chain and long-chain PFAAs 12
Table 1-4. U.S. and international approaches to addressing the combined toxicity of
multiple PFAS in drinking water or groundwater 17
Table 4-1. Hypothetical drinking water concentrations for five hypothetical PFAS 49
Table 4-2. Hypothetical analytical quantitation limits for drinking water for five
hypothetical PFAS 49
Table 4-3. Example data array to inform decisions in Steps 2 and 3 of the framework
approach for component-based mixtures assessment of PFAS 56
Table 5-1. EPA and ATSDR peer-reviewed human health assessments containing
noncancer toxicity values (RfDs or MRLs) for PFAS that are final or under
development 59
Table 5-2. Calculation of estimated clearance values for PFAS 4 in female rats and humans 64
Table 5-3. Summary of PODheds and RfDs for hypothetical PFAS in a mixture 65
Table 5-4. EPA exposure factors for drinking water intake 67
Table 5-5. Calculation of hypothetical HBWCs for example PFAS in a mixture 68
Table 5-6. Calculation of individual component HQs for the hypothetical PFAS mixture 69
Table 5-7. Hypothetical TTDs for the hypothetical mixture PFAS; the bolded numbers
represent the overall RfD for each respective PFAS 70
Table 5-8. Calculation of hypothetical developmental effect-specific HBWCs for
hypothetical PFAS in a mixture using TTDs 71
Table 5-9. Calculation of hypothetical individual component HQs specifically for
developmental effects associated with the hypothetical PFAS mixture 71
Table 6-1. Summary of hypothetical PODheds for three selected health effect domains for a
mixture of five hypothetical PFAS 78
Table 6-2. Example liver effect RPFs and ICECs for a hypothetical mixture of five PFAS 81
Table 6-3. Hypothetical example thyroid effect RPFs and ICECs for a hypothetical mixture
of five PFAS 84
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Table 6-4. Hypothetical example developmental effect RPFs and ICECs for a hypothetical
mixture of five PFAS 84
Table 7-1. "Best model" based on AIC values 90
Table 7-2. Summary of hypothetical PODheds for three selected health effect domains for a
mixture of five hypothetical PFAS 93
Table 7-3. M-BMD Approach: Hypothetical Water Sample and Hypothetical M-BMDs 94
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Abbreviations and Acronyms
6:2 FTS 6:2 fluorotelomer sulfonic acid
ADONA 4,8-dioxa-3H-perfluorononanoic acid
AED administered equivalent dose
AhR aryl hydrocarbon receptor
AIC Akaike Information Criteria
AOF adsorbable organofluorine
AOP adverse outcome pathway
AR androgen receptor
ATSDR Agency for Toxic Substances and Disease Registry
AUC area under the concentration vs. time curve
BBP butyl benzyl phthalate
BMD benchmark dose
BMDL lower statistical bound on a BMD
BMR benchmark response
CF carbon-fluorine
CAR constitutive androstane receptor
CAS Chemical Abstracts Service
CCL 5 fifth Contaminant Candidate List
CERCLA Comprehensive Environmental Response, Compensation, and Liability Act
CPSC CHAP Consumer Product Safety Commission Chronic Hazard Advisory Panel
DA dose additivity
DAF dosimetric adjustment factor
DBP di-n-butyl phthalate
DEHP di(2-ethylhexyl)phthalate
DIBP diisobutyl phthalate
DLC dioxin-like chemical
DWI drinking water intake
DWI-BW body weight-based drinking water intake
E duration-relevant exposure
ECx effect concentration
EDx effective dose in x percent of test animals
EOF extractable organofluorine
EPA U.S. Environmental Protection Agency
EU European Union
FT4 free serum thyroxine
FQPA Food Quality Protection Act
GenX chemicals hexafluoropropylene oxide (HFPO) dimer acid and HFPO dimer acid
GD
ammonium salt
gestational day
Vlll
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HBWC health-based water concentration
HED human equivalent dose
HepaRG epoxide hydrolase endpoint in liver
HFPO hexafluoropropylene oxide
HFPO-DA hexafluoropropylene oxide dimer acid
HI hazard index
HQ hazard quotient
HQ-115 lithium bis[(trifluoro-methyl)sulfonyl]azanide
IA integrated addition
IC index chemical
ICEC index chemical equivalent concentration
IC EC mix total mixture index chemical equivalent concentration
ICECnam new approach methodology (NAM)-based ICEC
ICED index chemical equivalent dose
IRIS Integrated Risk Information System
IVIVE in vitro-to-in vivo extrapolation
ke elimination rate constant
KE key event
L liter
LOAEL lowest-observed-adverse-effect level
M-BMD mixture benchmark dose
MCL Maximum Contaminant Level
MCLG Maximum Contaminant Level Goal
mg/kg/day milligrams per kilogram per day
MIE molecular initiating event
MOA mode of action
MRL minimal risk level
NAM new approach methodology(ies)
NAS National Academy of Sciences
NBP2 Nafion byproduct 2
ng/g nanograms per gram
ng/kg/week nanograms per kilogram per week
ng/L nanograms per liter
NHANES National Health and Nutrition Examination Survey
NOAEL no-ob served-adverse-effect level
NRC National Research Council
NTP National Toxicology Program
OECD Organisation for Economic Co-operation and Development
OP organophosphate
ORD Office of Research and Development
osRfV organ-specific reference value
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PCB
polychlorinated biphenyl
PCDD
polychlorinated dibenzo-p-dioxin
PCDF
polychlorinated dibenzofuran
PECO
Population, Exposure, Comparator, and Outcome
PFAA
perfluoroalkyl acid
PFAS
per- and polyfluoroalkyl substances
PFBA
perfluorobutanoic acid
PFBS
perfluorobutanesulfonic acid
PFCA
perfluoroalkyl carboxylic acid
PFDA
perfluorodecanoic acid
PFDoDA
perfluorododecanoic acid
PFDS
perfluorodecanesulfonate
PFECHS
perfluoroethylcyclohexane sulfonate
PFHpA
perfluoroheptanoic acid
PFHpS
perfluoroheptanesulfonic acid
PFHxA
perfluorohexanoic acid
PFHxS
perfluorohexanesulfonic acid
PFNA
perfluorononanoic acid
PFNS
perfluorononanesulfonic acid
PFOA
perfluorooctanoic acid
PFOS
perfluorooctanesulfonic acid
PFOSA
perfluorooctane sulfonamide
PFPA
perfluoroalkyl phosphonic acids
PFPeA
perfluoropentanoic acid
PFPeS
perfluoropentanesulfonic acid
PFPIA
perfluoroalkyl phosphinic acid
PFPrA
perfluoropropanoic acid
PFPS
perfluoropropane sulfonic acid
PFSA
perfluoroalkane sulfonic acid
PFSIA
perfluoroalkane sulfinic acid
PFTA
perfluorotetradecanoic acid
PFTrDA
perfluorotridecanoic acid
PFUnA
perfluoroundecanoic acid
PND
postnatal day
POD
point of departure
PODhed
human-equivalent point of departure
PPARa
peroxisome proliferator-activated receptor alpha
PPARy
peroxisome proliferator-activated receptor gamma
PPRTV
Provisional Peer-Reviewed Toxicity Value
ppt
parts per trillion
PWS
public water system
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RA
response addition
RfD
reference dose
RfV
reference value
RPF
relative potency factor
RPFnam
new approach methodology (NAM)-based RPF
RSC
relative source contribution
rTK
reverse toxicokinetic
SAB
Science Advisory Board
SD
standard deviation
T3
triiodothyronine
14
serum thyroxine
TT4
total serum thyroxine
TCDD
2,3,7,8-tetrachlorodibenzo-p-dioxin
TD
toxicodynamic
TEF
toxicity equivalence factor
TEQ
toxic equivalent
TK
toxicokinetic
TOSHI
target-organ-specific hazard index
TOSHIdev
target-organ-specific hazard index for developmental effects
TSCA
Toxic Substances Control Act
TSCATS
Toxic Substances Control Act Test Submissions
TTD
target-organ toxicity dose
UC MR
Unregulated Contaminant Monitoring Rule
UF
uncertainty factor
UFa
interspecies UF
UFc
composite UF
UFd
database UF
UFh
human interindividual variability UF
UFl
LOAEL-to-NOAEL uncertainty factor
UFs
extrapolation from subchronic to a chronic exposure duration UF
Yd
volume of distribution
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EXECUTIVE SUMMARY
The U.S. Environmental Protection Agency is releasing the final Framework for Estimating
Noncancer Health Risks Associated with Mixtures of Per- and Polyfluoroalkyl Substances
(PFAS) ("PFAS Mixtures Framework" or "framework"). This document is designed to
communicate and illustrate the practical application of existing EPA chemical mixtures
assessment approaches and methods to assess noncancer human health hazards and risks
associated with exposure to two or more PFAS co-occurring in environmental media, using
hypothetical drinking water examples. In November 2021, the EPA released a draft version of
this document for Science Advisory Board (SAB) review, and in March 2023, this document
underwent public comment as part of the proposed National Primary Drinking Water Regulation
for PFAS (USEPA, 2023b). The EPA has considered the SAB and public comments and revised
the document accordingly.
In a chemical mixtures risk assessment context, while it would be optimal to leverage whole-
mixture hazard and dose-response data, such data are extremely rare, particularly at component-
chemical proportions and concentrations consistent with environmentally occurring mixtures. As
such, mixtures risk assessment commonly relies upon the integration of available toxicity
information for the individual component chemicals that co-occur in environmental media.
This PFAS Mixtures Framework describes flexible, data-driven approaches that facilitate
practical component chemical-based mixtures evaluation of two or more PFAS based on dose
additivity. Dose additivity (described in detail in Section 3.0) means that the combined effect of
the component chemicals in the mixture is equal to the sum of the individual doses or
concentrations scaled for potency. Several perfluoroalkyl acid species (PFAAs) of PFAS tested
to date have been shown to elicit the same or similar profiles of adverse effects in several organs
and systems (ATSDR, 2021; EFSA, 2018, 2020; USEPA, 2021a, 2021b). Studies with PFAS and
other classes of chemicals (e.g., phthalates, polycyclic aromatic hydrocarbons, etc.) support the
EPA's health-protective conclusion that chemicals that elicit similar adverse health effects
following individual exposure will act in a dose-additive manner when present in a mixture
(unless data demonstrate otherwise). Although similarities among some PFAS have been shown
at the level of molecular and cellular perturbations, no conserved modes of action (MO As) have
been identified across PFAS for noncancer health effects assessed thus far. As such, in this
framework, the evaluation of toxicological similarity among component PFAS in a mixture is
proposed at the level of adverse health outcome. This concept and proposed application of dose
additivity for PFAS mixtures assessment are consistent with the EPA's mixtures guidelines
(USEPA, 1986, 2000b) and the EPA Risk Assessment Forum's Advances in Dose Addition for
Chemical Mixtures: A White Paper (USEPA, 2023h).
Descriptions of dose additivity-based approaches such as the hazard index (HI), relative potency
factor (RPF), and mixture benchmark dose (M-BMD) are presented here to demonstrate potential
application to PFAS mixtures, but they are not intended to provide a comprehensive treatise on
the methods themselves; EPA chemical mixtures guidelines (USEPA, 1986, 2000b) and the EPA
Risk Assessment Forum's Advances in Dose Addition for Chemical Mixtures: A White Paper
(USEPA, 2023h) exist for such a purpose. The EPA's mixture assessment concepts and
associated illustrative examples presented in this framework may inform PFAS evaluation(s) by
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federal, state, and Tribal partners, as well as public health experts, drinking water utility
personnel, and other stakeholders.
PFAS are a large and structurally diverse family of compounds used in myriad commercial
applications due to their unique physicochemical properties. Although PFAS have been
manufactured and used broadly in commerce since the 1940s, particular concern over potential
adverse effects on human health grew in the early 2000s with the discovery of perfluorooctanoic
acid (PFOA) and perfluorooctanesulfonic acid (PFOS) in human blood. Since then, hundreds of
PFAS have been identified in environmental media, including water, soil, and air.
Many PFAS and/or their precursors or degradants are environmentally persistent,
bioaccumulative, and have long half-lives in humans, particularly the longer-chain perfluoroalkyl
carboxylic acid (PFCA) and perfluoroalkane sulfonic acid (PFSA) species such as PFOA and
PFOS, respectively. PFCAs/PFSAs with shorter carbon chain lengths, such as
perfluorobutanesulfonic acid (PFBS) and hexafluoropropylene oxide dimer acid (HFPO-DA)
(also known as GenX Chemicals1), were developed and integrated into various consumer
products and industrial applications because they have the desired performance properties and
characteristics associated with this class of compounds but are more rapidly eliminated from the
human body than PFOA and PFOS. The range of PFAS encountered in environmental media is
often a diverse milieu of linear, branched, cyclic, and/or aromatic parent species, metabolites,
and/or abiotic degradants, leading to significant potential for PFAS mixture exposures in aquatic,
terrestrial, and human populations.
As of April 2024, final EPA human health assessments are available for PFBS (USEPA, 2021a),
HFPO-DA (USEPA, 2021b), perfluorobutanoic acid (PFBA; USEPA, 2022e), perfluorohexanoic
acid (PFHxA; USEPA, 2023c), PFOA (USEPA, 2024a), PFOS (USEPA, 2024b),
perfluoropropanoic acid (PFPrA; USEPA, 2023d), and lithium
bis[(trifluoromethyl)sulfonyl]azanide (HQ-115) (USEPA, 2023e). In addition, theEPA's
Integrated Risk Information System (IRIS) program is developing PFAS human health
assessments for perfluorohexanesulfonic acid (PFHxS), perfluorononanoic acid (PFNA), and
perfluorodecanoic acid (PFDA), which are expected to be completed in 2024. In May 2021, the
Agency for Toxic Substances and Disease Registry (ATSDR) published a Toxicological Profile
for Perfluoroalkyls that included quantitative minimal risk levels (MRLs) for PFAS, including
PFOA, PFOS, PFHxS, and PFNA (ATSDR, 2021).
A significant challenge in evaluating PFAS is the lack of hazard and dose-response data suitable
for human health risk assessment for the large majority of individual PFAS. In response to the
critical need, the EPA and the National Institute of Environmental Health Sciences are actively
engaged in research and testing to help address data gaps for a broad landscape of PFAS
(approximately 150 structures at the time of the drafting of this document). Examples of this
coordination include publishing systematic evidence maps for hundreds of PFAS (e.g., Carlson
et al., 2022), generating new hazard and dose-response data (e.g., new approach methodologies
or NAMs), applying read-across tools, and developing the EPA Transcriptomic Assessment
Product, which entails the derivation of toxicity reference values using transcriptomic pathway-
1 The EPA notes that the chemical HFPO-DA is used in a processing aid technology developed by DuPont to make
fluoropolymers without using PFOA. The chemicals associated with this process are commonly known as GenX Chemicals, and
the term is often used interchangeably for HFPO-DA along with its salts.
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based data from 5-day in vivo rat studies (see: https://www.epa.gov/chemical~research/epa~
transcriptomic-assessment-product-etap-and-value-information-voi-case-studvY Until results
from ongoing research and testing efforts are available, the evaluation of potential toxicity/risk
associated with PFAS mixtures is primarily limited to existing hazard and dose-response data
under the purview of human health assessments by federal, state, and/or international entities.
This framework describes component-based mixture assessment methods that can be used to
assess noncancer human health hazards and risks associated with exposure to PFAS mixtures. It
is not the intent of the framework to ignore potential carcinogenic effects associated with PFAS
exposure(s); however, at present, few PFAS have information available to evaluate potential
carcinogenic effects via any route of exposure. Should such information become available for an
increasing number of PFAS in the future, the EPA would consider approaches for addressing
joint carcinogenic effects. The EPA's National PFAS Testing Strategy: Identification of
Candidate Per- andPolyfluoroalkyl Substances (PFAS) for Testing (USEPA, 202le) is
underway to develop and issue test orders on data-poor2 PFAS. Testing requirements encompass
physicochemical properties, environmental fate and transport, and human health hazards,
including mechanistic information (e.g., genotoxicity).
It is anticipated that real-world practical application of the approaches communicated and
demonstrated in this framework may entail collecting, evaluating, and integrating diverse hazard
and dose-response information. For example, only a small fraction of the thousands of PFAS
have existent human health noncancer toxicity reference values, dozens more PFAS have
gradations of traditional in vivo bioassay data available, and dozens more have data only from
NAM assays/platforms (e.g., in vitro cell bioactivity). As such, to facilitate the use of potentially
disparate sources of PFAS toxicity information in a mixtures assessment context, the application
of the component-based methods presented in this framework is demonstrated using a
hypothetical example mixture of five PFAS:
PFAS 1 = comprehensively studied, most potent for effect(s), and has formal noncancer
human health assessment value(s) (i.e., reference dose [RfD]) and a health-based water
concentration (HBWC) available;
PFAS 2 = well-studied, second-most potent for effect(s) among PFAS 1-3, and has formal
noncancer human health assessment value(s) and HBWC available;
PFAS 3 = studied, least potent for effect(s) among PFAS 1-3, and has formal noncancer
human health assessment value(s) and HBWC available;
PFAS 4 = in vivo animal toxicity data available but no formal human health assessment
and no HBWC; and
PFAS 5 = data-poor; no in vivo animal toxicity data or human data available.
The hypothetical PFAS mixture is purposefully designed to demonstrate how this framework
allows for flexible integration of information derived from health assessment data sources (e.g.,
federal, state, international), available human and/or experimental animal hazard and dose-
2 In this framework document, "data-poor" refers to the lack or absence of hazard and dose-response data traditionally used to
support noncancer and/or cancer human health assessment (e.g., chronic oral exposure studies in humans and/or animals).
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response data (that have not yet been formally evaluated in an assessment product), and
information from NAMs. Opportunities for integrating additional PFAS into the context of a
mixture assessment are expected to evolve over time and will depend on the decision context and
availability of hazard and dose-response data from traditional and/or NAM-based assays and in
silico platforms.
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1.0 Introduction and Background
1.1 Purpose
Per- and polyfluoroalkyl substances (PFAS) are an urgent public health and environmental issue
facing communities across the United States. In April 2021, Administrator Michael Regan
established the Environmental Protection Agency's Council on PFAS and charged the Council to
develop a whole-of-EPA strategy to protect public health and the environment from the impacts
of PFAS. In October 2021, the EPA released the PFAS Strategic Roadmap3 (the Roadmap),
which lays out the EPA's approach to tackling PFAS and sets timelines by which the agency
plans to take concrete actions to deliver results for the American people. The Roadmap is built
on a number of key principles, including considering the lifecycle of PFAS, getting upstream of
the problem, holding polluters accountable, ensuring science-based decision-making, and
prioritizing the protection of disadvantaged communities. In November 2022, the EPA released
EPA's PFAS Strategic Roadmap: A Year of Progress, which underscores key actions taken by
the agency during the first year of implementing the Roadmap (USEPA, 2022f).
Recognizing that PFAS tend to occur in mixtures in environmental media (see Section 1.5), the
EPA has developed this data-driven framework for assessing the noncancer human health risks
associated with oral exposures to mixtures of PFAS. The approaches presented in this document
are based on longstanding EPA guidelines related to human health risk assessment for mixtures
(USEPA, 1986, 1991, 2000b). Although the framework and illustrative hypothetical examples
contained within focus on PFAS in drinking water, the framework itself is not limited to specific
media and may be useful for understanding the potential noncancer health effects of PFAS
mixtures under various authorities or decision contexts.
The approaches presented here are not intended to be used to assign groups or subclasses or
otherwise classify PFAS (instead, see the EPA National PFAS Testing Strategy: Identification of
Candidate Per- and Polyfluoroalkyl Substances (PFAS) for Testing for categorization efforts;
USEPA, 2021e). Rather, the framework is designed for the practical application of the EPA's
mixtures assessment approaches and methods to gain insight into the potential joint toxicity
associated with mixtures of PFAS. The mixtures assessment concepts and associated illustrative
examples presented in this framework may inform PFAS evaluation(s) by federal, state, and
Tribal partners, as well as public health experts, drinking water utility personnel, and other
stakeholders interested in assessing the potential noncancer human health risks associated with
exposure to PFAS mixtures.
The framework and hypothetical examples presented here are intended to demonstrate data-
driven application of EPA component-based mixture assessment methods based on gradations of
data availability and completeness anticipated to occur in real-world scenarios for PFAS.
Although the examples provided are focused on drinking water, the approaches described in this
framework could also be applied to other environmental media with oral4 exposure routes (e.g.,
soil, fish/shellfish, food). Due to the constantly evolving science related to PFAS, the approaches
3 https://www.epa.gov/pfas/pfas-strategic-roadmap-epas-commitments-action-2021-2024
4 In general, the component-based approaches presented in this document may also be applicable in assessing health risks
associated with inhalation exposures to PFAS mixtures. However, the dosimetry differences across categories of (volatile/semi-
volatile) PFAS gases/vapors would need to be considered in such an assessment. Data regarding the volatilization and toxicity of
inhaled PFAS are generally limited.
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presented herein have the flexibility to consider information as it becomes available, including
forthcoming EPA human health assessments, assessments from other sources (e.g., federal, state,
international), available hazard and dose-response data in the public domain, and information
from high(er)-throughput bioassays and other new approach methodologies (NAMs), including
data submitted to the agency under the Toxic Substances Control Act (TSCA).
Experimental evidence supports dose-additive effects from combined exposure to multiple
PFAS. Dose additivity, described in detail in Section 3.0, means that the combined effect of the
component chemicals in the mixture is equal to the sum of the individual doses or concentrations
scaled for potency. Several perfluoroalkyl acid species (PFAAs) of PFAS tested to date,
including perfluorohexanesulfonic acid (PFHxS), perfluorononanoic acid (PFNA),
hexafluoropropylene oxide dimer acid (HFPO-DA), and perfluorobutanesulfonic acid (PFBS),
have been shown to elicit the same or similar profiles of adverse effects in mammalian biological
systems including effects on thyroid hormone levels, lipid synthesis and metabolism,
development, immune system function, and liver function (ATSDR, 2021; EFSA, 2018, 2020;
USEPA, 2021a, 2021b). An increasing body of evidence also shows similarities in molecular and
cellular perturbations (e.g., common receptor binding/activation) across some PFAS; however,
no conserved noncancer or cancer mode(s) of action (MOA(s)) have been identified to date.
The framework is not a regulation and does not impose legally binding requirements on the EPA,
states, Tribes, or the regulated community, and might not apply to a particular situation based on
the circumstances.
1.2 The EPA Science Advisory Board Review
In November 2021, the EPA released the Draft Framework for Estimating Noncancer Health
Risks Associated with Mixtures of PFAS ("draft framework;" U SEP A, 2021 d) for the Science
Advisory Board (SAB) review. The SAB held public meetings on December 16, 2021;
January 4, 6, and 7, 2022; and July 20, 2022, to discuss the draft framework and three other
technical documents supporting the EPA's development of a National Primary Drinking Water
Regulation for PFAS under the Safe Drinking Water Act. The EPA sought SAB comment on
whether the draft framework and illustrative examples provided within were scientifically
supported, clearly described, and informative for assessing potential health risk(s) associated
with exposure to mixtures of PFAS. The EPA asked specific charge questions on PFAS dose
additivity and three component-based approaches: hazard index (HI), relative potency factor
(RPF), and mixture benchmark dose (M-BMD). A draft of the written SAB recommendations
was published on April 1, 2022, and the EPA received the final report from the SAB on
August 22, 2022 (SAB, 2022).
The EPA received a generally favorable review from SAB (SAB, 2022) for its development of
component-based mixture assessment approaches that rely on a health-protective conclusion of
dose additivity based on the same or similar adverse health outcome(s) instead of a shared MOA
to evaluate risks from exposure to PFAS mixtures in drinking water and other environmental
media. The EPA responded to the SAB's consensus advice in the development of this final
Framework for Estimating Noncancer Health Risks Associated with Mixtures of Per- and
Polyfluoroalkyl Substances (PFAS). The SAB's overarching consensus recommendations and the
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EPA's responses are summarized below. To view the EPA's complete responses to SAB
comments on the draft framework, please see USEPA (2023a).
• "The SAB supports dose additivity based on a common outcome, instead of a common
mode of action as a health protective default assumption and does not propose another
default approach. However, EPA should more thoroughly and clearly present the
uncertainties associated with this approach along with information supporting this
approach" (SAB, 2022).
o The EPA has added text in Section 3.0 (Dose Additivity for PFAS) to address the
SAB's comments related to uncertainties associated with dose additivity as the
default assumption for assessment of PFAS mixtures. The EPA has added further
discussion on deviations from dose additivity, such as synergy or antagonism, but
available evidence suggests that dose additivity should be considered the default
model.
• "The SAB expressed concern regarding the requirement for "external peer review" of
toxicity values developed by states and recommends that this phrase in the draft
framework be broadened to recommend the need for scientific input and review in
general" (SAB, 2022).
o In response to this point of clarification, the EPA has removed the text related to
external peer review. The text now reads, "If de novo derivation of toxicity values
is necessary, it is recommended that experts in hazard identification and dose-
response assessment be consulted for scientific input and review, and the
associated uncertainties (e.g., data gaps) be transparently characterized."
• "EPA should consider using a menu-based framework to support selection of fit-for-
purpose approaches, rather than a tiered approach as described in the draft Mixtures
document. Tiered approaches that require increasingly complex information before
reaching a final decision point can be extremely challenging for data-poor chemicals such
as PFAS" (SAB, 2022).
o In response to this and other SAB comments, the EPA has eliminated the tiered
approach and restructured the framework as a data-driven, flexible approach to
facilitate PFAS mixtures assessment in various decision contexts (e.g., at a
contaminated site, water system, etc.) (see Section 4.2 and Figure 4-1). With "fit-
for-purpose assessment" in mind, the EPA has included a discussion of key steps
in the framework, including problem formulation and scoping, assembling
information, evaluating data objectives, considering the data landscape to select
component-based mixture assessment approach(es), and implementing
component-based mixture assessment approach(es) (see Section 4.2.1).
• "EPA should provide clarification regarding the conceptual similarities and differences
between the target-organ-specific hazard index (TOSHI) approach, the relative potency
factor (RPF) approach, and the mixture benchmark dose (BMD) approach, since all are
based on health effect-specific values (i.e., Reference Values [RfVs] or RPFs) for the
individual PFAS in the PFAS mixture. More discussion and comparison of approaches,
as well as when they converge, is needed. For instance, given the mathematical
correspondence between the RPF and mixture BMD approaches, EPA should consider
revising the discussion of these two approaches to present them as essentially the same
(or highlighting any essential differences), and perhaps also merging them into a single
section" (SAB, 2022).
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o The EPA has added a section (Section 8.0) that describes similarities and
differences among the different component-based mixtures assessment
approaches. In addition, the EPA has revised the framework to use the same
hypothetical example mixture of five PFAS (ranging from data-poor to well-
studied) for all the illustrative examples so that the user can better understand
similarities/differences among the approaches.
• "For both the RPF and mixture BMD approach, EPA's approach would be strengthened
by using PODs from animal studies that are based on human equivalent doses (HEDs)
rather than administered doses. The SAB found it difficult to envision situations in which
the mixture BMD was advantageous; therefore, EPA should provide additional
information on how the proposed Mixtures BMD approach will be applied in practice"
(SAB, 2022).
o Text has been added in several places to indicate that it is optimal to calculate and
use HEDs rather than oral-administered doses in test animals when possible. This
includes additional text that walks the reader through the EPA's logic flow for
cross-species scaling (see new Subsection 5.2.1). Regarding the M-BMD
approach, text has been added to better articulate when this specific approach is
more appropriate (e.g., component chemical data that indicate common health
outcome but with non-similarly shaped dose-response functions). Further,
Subsection 7.3 has been revised to reiterate the conditions that warrant
consideration of this specific component-based mixtures approach (as opposed to
the RPF approach).
1.3. Public Review
On March 14, 2023, the EPA released the draft framework for public comment (revised in
response to the SAB review, as summarized in Section 1.2) as part of the proposed National
Primary Drinking Water Regulation for six PFAS (USEPA, 2023b). The public comment period
ended on May 30, 2023. The public docket can be accessed at www.regulations.gov under
Docket ID: EPA-HQ-OW-2022-0114. The EPA has developed responses to public comments to
support the final National Primary Drinking Water Regulation, including responses to comments
on PFAS dose additivity and regulation of PFAS mixtures in drinking water using an HI
approach (USEPA, 2024d).
1.4 Background on PFAS
PFAS are a large group of structurally diverse anthropogenic chemicals that include
perfluorooctanoic acid (PFOA), perfluorooctanesulfonic acid (PFOS), and thousands of other
fully or partially fluorinated chemicals. There is no consensus definition of PFAS as a class of
chemicals (OSTP, 2023). Based on three related structural definitions associated with the EPA's
identification of PFAS to be included in the fifth Contaminant Candidate List (CCL 5; see
below), the universe of environmentally relevant PFAS, including parent chemicals, metabolites,
and degradants, is approximately 15,000 compounds.5 The Organisation for Economic Co-
operation and Development (OECD) New Comprehensive Global Database of Per- and
Polyfluoroalkyl Substances (PFASs) includes over 4,700 PFAS (OECD, 2018). Comparatively,
5 See the EPA List of PFAS Structures: lit tps://comptox. epa.gov/dasliboaiil/clieniical-lists/PFASSTRIJCT
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the EPA has identified more than 1,300 PFAS on the TSCA Inventory, of which more than 600
are considered "active" in U.S. commerce.
PFAS have been manufactured and used in a wide variety of industries worldwide, including in
the United States, since the 1940s. The chemical structures and physicochemical properties of
some PFAS enable them to repel water and oil, remain chemically and thermally stable, and
exhibit surfactant properties; these properties confer utility in commercial and industrial
applications but are also, in part, what make some PFAS persistent in the human body and the
environment (Calafat et al., 2007, 2019). In general, PFAAs studied to date have strong, stable
carbon-fluorine (CF) bonds, making them resistant to hydrolysis, photolysis, microbial
degradation, and metabolism (Ahrens, 2011; Beach et al., 2006; Buck et al., 2011; Evich et al.,
2022). Conversely, the larger PFAS universe is more structurally and physicochemically diverse
and includes categories of substances that may be more or less stable, persistent, and/or
bioaccumulative compared to PFAAs studied thus far (see the EPA's National PFAS Testing
Strategy: Identification of Candidate Per- and Polyfluoroalkyl Substances (PFAS) for Testings
USEPA, 2021e). Due to their widespread use, physicochemical properties, persistence, and
bioaccumulation potential, many PFAS co-occur in exposure media (e.g., indoor air/house dust,
water, ice, sediment) and in tissues and blood of aquatic and terrestrial organisms and humans.
There are many families or subclasses of PFAS, and each contains many individual structural
homologues and can exist as either branched-chain or straight-chain isomers (Buck et al., 2011;
USEPA, 2021c). These PFAS families can be divided into two primary categories: nonpolymers
and polymers. Nonpolymer PFAS include perfluoroalkyl and polyfluoroalkyl substances and
encompasses parent structures, precursors, and some environmental degradation and
transformation products. Polymer PFAS include fluoropolymers, perfluoropolyethers, and side-
chain fluorinated polymers (Table 1-1). Several U.S. federal, state, and industry stakeholders and
European entities have posited various definitions of what constitutes a PFAS. The OECD-led
"Reconciling Terminology of the Universe of Per- and Polyfluoroalkyl Substances:
Recommendations and Practical Guidance" workgroup provided an updated definition of PFAS
(OECD, 2021), originally posited in part by Buck et al. (2011), as follows: "PFASs are defined
as fluorinated substances that contain at least one fully fluorinated methyl or methylene carbon
atom (without any H/Cl/Br/I atom attached to it), i.e. with a few noted exceptions, any chemical
with at least a perfluorinated methyl group (-CF3) or a perfluorinated methylene group (-CF2-)
is a PFAS." It is not within the scope of this framework to compare and contrast the various
definitions or the nuances associated with defining or scoping PFAS; rather, the reader is
referred to OECD (2021) for review. However, for the purposes of development of the EPA's
CCL 5, the structural definition of PFAS includes chemicals that have at least one of the
following three structures:
1. R-(CF2)-CF(R')R", where both the CF2 and CF moieties are saturated carbons, and
none of the R groups can be hydrogen.
2. R-CF20CF2-R', where both the CF2 moieties are saturated carbons, and none of the R
groups can be hydrogen.
3. CF3C(CF3)RR', where all the carbons are saturated, and none of the R groups can be
hydrogen.
It should also be noted that what defines or constitutes a PFAS may change or evolve over time
and under different purviews (e.g., federal, state, international).
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Table 1-1. Two primary categories of PFAS.a
PFAS nonpolymers
Perfluoroalkyl acids
Polyfluoroalkyl acids
PFAS polymers
Fluoropolymers
Side-chain fluorinated
polymers
Structural elements
Compounds in which all
carbon-hydrogen bonds,
except those on the functional
group, are replaced with
carbon-fluorine bonds
Compounds in which carbon-
hydrogen bonds on at least one
carbon (but not all) are
replaced with carbon-fluorine
bonds
Structural elements
Carbon-only polymer
backbone with fluorines
directly attached
Nonfluorinated polymer
backbone with fluorinated side
chains with variable
composition
Example PFAS families
Perfluoroalkyl carboxylic and
sulfonic acids (e.g., PFOA,
PFOS), perfluoroalkyl
phosphonic and phosphinic
acids, perfluoroalkylether
carboxylic and sulfonic acids
polyfluoroalkyl carboxylic
acids, polyfluoroalkylether
carboxylic and sulfonic acids
Example PFAS families
polytetrafluoroethylene,
polyvinylidene fluoride,
fluorinated ethylene
propylene, perfluoroalkoxy
polymer
F-(CmF2mO-)nCF3, where
the CmF2mO represents -
CF20, -CF2CF20, and/or -
CF(CF3)CF20 distributed
randomly along polymer
backbone
n: 1 or n:2 fluorotelomer-based
acrylates, urethanes, oxetanes,
or silicones; perfluoroalkyl
fluorides; perfluoroalkane
sulfonyl fluorides
Polymeric perfluoropolyethers
Carbon and oxygen polymer
backbone with fluorines
directly attached to carbon
Note:
a Amalgamation of information from Figure 9 in OECD (2021) and Buck et al. (2011).
PFOA and PFOS are PFAAs in the nonpolymer PFAS category and are among the most studied
PFAS in terms of human health toxicity and biomonitoring (see USEPA, 2024a, 2024b; Podder
et al., 2021). The PFAA family includes perfluoroalkyl carboxylic, phosphonic, and phosphinic
acids and perfluoroalkane sulfonic and sulfinic acids (Table 1-2). Many PFAA are highly
persistent and are frequently found in the environment (Ahrens, 2011; Brendel et al., 2018;
Wang et al., 2017). Although the EPA defines, specifically for purposes under the purview of
TSCA, long-chain perfluoroalkyl carboxylate substances as having perfluorinated carbon chain
lengths equal to or greater than seven carbons and less than or equal to 20 carbons (85 Federal
Register [FR] 45109, July 27, 2020), a more comprehensive delineation of what constitutes
short-chain vs. long-chain PFAAs is provided by the OECD (OECD, 2021). Specifically, the
OECD established long-chain perfluoroalkyl carboxylic acids (PFCAs) as those species with
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eight or more carbons (seven or more carbons are perfluorinated) and short-chain PFCAs as
those with seven or fewer carbons (six or fewer carbons are perfluorinated). Conversely, long-
chain perfluoroalkane sulfonic acids (PFSAs) are identified as those species with six or more
carbons (six or more carbons are perfluorinated), and short-chain PFSAs are identified as those
with five or fewer carbons (five or fewer carbons are perfluorinated) (see Table 1-3).
Table 1-2. Groups, structural traits, and examples of perfluoroalkyl acids (PFAAs),
including perfluoroalkylether acids."
Group
Functional group
Examples
Perfluoroalkyl carboxylic
acids (PFCAs)
-COOH
Perfluorooctanoic acid (PFOA),
C7F15COOH
Perfluoroalkane sulfonic acids
(PFSAs)
-S03H
Perfluorooctane sulfonic acid
(PFOS), C8F17S03H
Perfluoroalkyl phosphonic
acids (PFPAs)
-P03H2
Perfluorooctyl phosphonic acid
(C8-PFPA)
Perfluoroalkyl phosphinic
acids (PFPIAs)
-P02H
Bis(perfluorooctyl) phosphinic
acid (C8/C8-PFPIA)
Perfluoroalkylether
carboxylates (PFECAs)
-0C2F40CF2C00H
Perfluoro-2-methyl-3-oxahexanoic
acid (GenX chemicals), 4,8-Dioxa-
3H-perfluorononanoic acid
(ADONA)
Perfluoroalkylether sulfonic
acids (PFESAs)
-0CF2CF2S03H
Nafion byproduct 2 (NBP2)
Perfluoroalkyl dicarboxylic
acids (PFdiCAs)
HOOC-CnF2n-COOH
Perfluoro-1,10-decanedicarboxylic
acid, Perfluorosebacic acid
Perfluoroalkane disulfonic
acids (PfdiSAs)
H03 S-CnF2n-S03H
Perfluoroalkane sulfinic acids
(PFSIAs)
-S02H
Perfluorooctane sulfinic acid
Note:
a Modified from Figure 9 in OECD (2021).
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Table 1-3. Characterization system of short-chain and long-chain PFAAs.3
Total # of carbons
3
4
5
6
7
8
9
10
# of fluorinated
carbons
2
3
4
5
6
7
8
9
PFCAs
Short-chain PFCAs
Long-chain PFCAs
PFPrA
PFBA
PFPeA
PFHxA
PFHpA
PFOA
PFNA
PFDA
# of fluorinated
carbons
3
4
5
6
7
8
9
10
PFSAs
PFPS
PFBS
PFPeS
PFHxS
PFHpS
PFOS
PFNS
PFDS
Short-chain PFSAs
Long-chain PFSAs
Notes:
PFPrA = perfluoropropanoic acid; PFBA = perfluorobutanoic acid; PFPeA = perfluoropentanoic acid;
PFHxA = perfluorohexanoic acid; PFHpA = periluoroheptanoic acid; PFOA = periluorooctanoic acid;
PFNA = perfluorononanoic acid; PFDA = periluorodecanoic acid; PFPS = perfluoropropane sulfonic acid;
PFBS = periluorobutanesulfonic acid; PFPeS = periluoropentaiiesulfonic acid; PFELxS = perfluorohexaiiesulfonic acid;
PFHpS = periluoroheptaiiesulfonic acid; PFOS = periluorooctaiiesulfonic acid; PFNS = perfluorononanesulfonic acid;
PFDS = perfluorodecanesulfonate.
For brevity, Table 1-3 only includes PFAAs of 3-10 carbons; the long-chain class of PFCAs and PFSAs can be expanded
considerably.
a Modification of Table 2-2 in ITRC (2022).
Although many PFAS are manufactured in various salt forms (e.g., potassium [K+] PFBS), they
typically fully dissociate to their protonated acid and/or anionic forms depending on their acid
strength (pKa value) in aqueous environmental media, soils or sediments, and the human body.
Importantly, the protonated and anionic forms may have different physicochemical and
environmental fate and transport properties. It should also be noted that the structural diversity of
PFAS goes beyond the PFCAs and PFSAs indicated in Table 1-3. There are branched, cyclic,
aromatic, and multi-component (e.g., polymers) structures that have been or are currently
classified as PFAS. However, in general, the linear PFCAs and PFSAs have been the most
studied PFAS to date and have been the primary focus of formal human health risk assessment
activities in the federal and state sectors.
1.5 Occurrence of PFAS Mixtures
Improved analytical monitoring and detection methods have enabled detection of the co-
occurrence of multiple PFAS in drinking water, ambient surface waters, aquatic organisms,
biosolids (sewage sludge), and other environmental media.6 PFOA and PFOS have historically
been target analytes, but recent water monitoring studies have begun to focus on additional
PFAS via advanced analytical instrumentation/methods and nontargeted analysis (De Silva et al.,
2020; McCord and Strynar, 2019; McCord et al., 2020). The proposed framework for estimating
the likelihood of noncancer human health risks associated with oral exposure to mixtures of
0 For a more detailed discussion of the occurrence of PFOA, PFOS, and other PFAS in potential human exposure sources, see the
relative source contribution (RSC) sections in USEPA (2024f, 2024g).
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PFAS (described in Section 4) is flexible to accommodate information for any PFAS mixture of
interest, provided sufficient hazard and dose-response information is available.
The EPA uses the Unregulated Contaminant Monitoring Rule (UCMR) to collect occurrence
data nationwide for contaminants suspected to be present in drinking water. Between 2013 and
2015, the EPA's third UCMR (UCMR 3) required all large public water systems (PWSs) (each
serving more than 10,000 people) and a statistically selected, representative national sample of
800 small PWSs (each serving 10,000 people or fewer) to monitor for 30 unregulated
contaminants in drinking water, including six PFAS: PFOS, PFOA, PFNA, PFHxS, PFBS, and
perfluoroheptanoic acid (PFHpA). UCMR 3 data demonstrated that two or more of those six
PFAS co-occurred in 48% (285 / 598) of sampling events with PFAS detected, and PFOA and
PFOS co-occurred in 27% (164 / 598) of sampling events with two or more PFAS detected
(Guelfo and Adamson, 2018; USEPA, 2019b). The EPA found that 4% of PWSs reported results
for which one or more of these six PFAS were measured at or above their respective minimum
reporting levels (USEPA, 2019b).7 Under UCMR 5 (2023 to 2025), PWSs will monitor for
29 PFAS. A small subset of UCMR 5 data (24% of the total results that the EPA expects to
receive) was released to the public in early 2024. Preliminary sampling results from UCMR 5 are
available in the PFAS Occurrence and Contaminant Background Support Document for the
Final PFAS NPDWR (USEPA, 2024c).
Outside of the UCMR data collection, many states have undertaken individual efforts to monitor
for PFAS in both source and finished drinking water. These results show that occurrence in
multiple geographic locations is consistent with what was observed during UCMR 3 monitoring,
as well as the occurrence and co-occurrence of other PFAS not included in the UCMR 3.
Additionally, these results show that PFAS are very likely to co-occur as mixtures in the
environment. These data suggest that PWSs with high concentrations of one PFAS are likely to
have high concentrations of other PFAS and that there is notable co-occurrence at elevated
concentrations (Cadwallader et al., 2022; USEPA, 2024c).
PFAS mixtures have also been reported in U.S. ambient surface waters and aquatic biota
(Ahrens, 2011; Benskin et al., 2012; Burkhard, 2021; McCord and Strynar, 2019; Nakayama et
al., 2007; Remucal, 2019; Zareitalabad et al., 2013). Most environmental monitoring of PFAS in
surface waters has focused on sites of historical manufacturing and known contamination (3M
Company, 2000; Boulanger et al., 2004; Cochran, 2015; Hansen et al., 2002; Jarvis et al., 2021;
Konwick et al., 2008; Nakayama et al., 2007). Simcik and Dorweiler (2005) consistently
detected both PFOA and PFHpA in all 12 surface waters sampled across the U.S. Midwest and
PFOS in all but two locations. Sinclair and Kannan (2006) detected PFOA and PFOS in all
effluent-dominated samples collected across New York State; Sinclair and Kannan (2006) also
detected PFHxS, but PFBS and perfluorooctane sulfonamide (PFOSA) were below detection
limits in all samples. De Silva et al. (2011) detected PFOS and additional PFAS (i.e.,
perfluoropentanoic acid [PFPeA] [C5], perfluorohexanoic acid [PFHxA] [C6], PFHpA [C7], and
PFOA [C8]) co-occurring as mixtures in all surface water samples (n = 32) collected across the
five Laurentian Great Lakes. Other PFAS, including PFNA (C9), perfluorodecanoic acid (PFDA)
(C10), perfluoroundecanoic acid (PFUnA) (Cll), PFBS (C4), PFHxS (C6),
7 The 4% figure is based on 198 PWSs reporting measurable PFAS results for one or more sampling events from one or more of
their sampling locations. Those 198 PWSs serve an estimated total population of approximately 16 million (USEPA, 2019b).
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perfluoroethylcyclohexane sulfonate (PFECHS) (C8), and perfluoromethylcyclohexane sulfonate
(C7), were also quantified in at least 20 of the 32 samples collected from the Great Lakes.
PFAS mixtures in the environment can be linked to the direct application of manufactured
products that contain a specific mixture of PFAS. For example, Anderson et al. (2016) quantified
PFAS in ambient surface waters across 10 U.S. Air Force bases where there were known
historical uses of aqueous film-forming foam, which is used in firefighting and training activities
and can contain hundreds of polyfluoroalkyl precursors (Ruyle et al., 2021). PFOA and PFOS
largely co-occurred and were detected in 88% and 96% of samples, respectively. Anderson et al.
(2016) also detected PFBA, PFBS, PFPeA, PFHxA, PFHxS, and PFHpA in > 80% of samples.
Environmental monitoring of PFAS in aquatic biota has primarily focused on fish. Generally,
PFCAs are less bioaccumulative than PFSAs in aquatic systems, with longer-chain PFAS being
more bioaccumulative than short-chain PFAS (Burkhard, 2021; Conder et al., 2008; Kannan et
al., 2005). Within the United States, PFAS in aquatic biota have been measured in major rivers,
in the Laurentian Great Lakes region, in several estuaries, and in targeted studies of sites of
known contamination (e.g., industrial). A recent national probabilistic survey by the EPA
measured up to 33 PFAS in fish samples collected in 2013-2014 and 2018-2019 from hundreds
of river sites across the U.S. One or more PFAS were detected in 99.1% of fish fillet samples
collected in 2013-2014 and in 95.2% of samples collected in 2018-2019. For both sampling
periods, detection frequency was dominated by PFOS (91%-99%), PFUnA (85%-88%), PFDA
(84%-88%), and PFDoA (69%-70%) (Stahl et al., 2023). De Silva et al. (2011) measured PFAS
from lake trout (Salvelinus namaycush) samples collected in 2001 from each of the Great Lakes.
Eight different PFAS (i.e., PFNA, PFDA, PFUnA, PFDoDA, perfluorotridecanoic acid
(PFTrDA), perfluorotetradecanoic acid (PFTA), PFHxS, and PFOS) were detected in tissues of
lake trout from across all the Great Lakes, with PFOA, PFECHS, and perfluorodecanesulfonic
acid (PFDS) also being detected in lake trout from Lake Ontario (De Silva et al., 2011). Sedlak et
al. (2017) measured PFAS in composite samples containing yellowfin gobies (Acanthogobius
flavimanus), chameleon/cheekspot gobies (Tridentiger trigonocephalus/Ilypnus gilberti),
northern anchovy (Engraulis mordax), shiner surfperch (Cymatogaster aggregata), and staghorn
sculpin (Leptocottus armatus) that were collected from the San Francisco Bay estuary. PFOS and
PFOSA were detected in nearly all composite samples and at relatively high concentrations
(geometric mean PFOS concentration of 3.9 nanograms (ng) per gram (g); geometric mean
PFOSA concentration of 3.2 nanograms per gram [ng/g]). Other longer-chain PFAS, including
PFNA, PFDA, PFUnA, and perfluorododecanoic acid (PFDoDA), were also frequently detected
in the fish composite samples but at relatively low concentrations (geometric mean
concentrations < 2.4 ng/g). Shorter-chain PFAS, including PFBS, PFBA, PFHxA, and PFHpA,
were not detected in any of the fish composite samples. Houde et al. (2006) measured whole-
body PFAS in six fish species in Charleston Harbor, South Carolina, and in five fish species in
Sarasota Bay, Florida. Charleston Harbor (the more developed of the two sites) had higher
overall PFAS concentrations. PFOA, PFOS, PFNA, PFDA, PFUnA, PFDoDA, PFHxS, and
PFOSA were all commonly detected in tissues of the six fish species from Charleston Harbor.
PFOS and PFDoDA were the only two PFAS detected at elevated concentrations in the fish from
Sarasota Bay (Houde et al., 2006). A study in New Jersey found co-occurrence of PFAS in
ambient water, sediment, and fish at sites with historical and current industrial activities
(Goodrow et al., 2020). Fish tissue concentrations of PFOS were generally higher than other
PFAS and high enough in nearly all fish species to trigger fish consumption advisories.
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Within the United States, PFAS occurrence in invertebrate tissues, such as shellfish, has not been
as extensively monitored as PFAS occurrence in fish. Kannan et al. (2005) measured PFAS in
several species, including zebra mussels, from two rivers in southern Michigan (Raisin River, St.
Claire River) and one in northern Indiana (Calumet River). Overall, PFAS concentrations in
zebra mussels were lower than in fish. Nevertheless, PFOS and PFOSA were both detected in
zebra mussels in the Raisin River (PFOS concentration = 3.1 ng/g wet weight; PFOSA
concentration = 2.7 ng/g wet weight). Interestingly, PFOA was not detected in zebra mussel
tissues even though it was detected in elevated concentrations in the Raisin River water column
(PFOA water concentration = 14.7 nanograms/liter [ng/L]), suggesting that chemical-specific
considerations (e.g., carbon chain length, functional group differences) affect bioaccumulation
dynamics in aquatic organisms and resultant human exposures to PFAS mixtures via ingestion of
fish and shellfish (Kannan et al., 2005).
1.6 Evidence of PFAS Exposure in Humans
Humans can be exposed to PFAS through a variety of sources, including food packaged in
PFAS-containing materials, processed with equipment that uses PFAS, or grown or raised in
PF AS-contaminated soil or water (including livestock and seafood); commercial household
products, including stain- and water-repellent fabrics, nonstick products, polishes, waxes, paints,
and cleaning products; the fire suppressant, aqueous film-forming foam; production facilities or
industries that use PFAS; and drinking water, where these chemicals have contaminated water
supplies. Although humans may be exposed to PFAS via dermal and inhalation routes, the
primary focus of this document is the oral route of exposure, including via drinking water, food,
fish/shellfish, and incidental soil/dust ingestion (Egeghy and Lorber, 2010; Lorber and Egeghy,
2011; Poothong et al., 2020).
The Centers for Disease Control and Prevention's National Health and Nutrition Examination
Survey (NHANES) has measured blood serum concentrations of several PFAS in the general
U.S. population since 1999 (CDC, 2023). Results from this nationally representative
biomonitoring study in which data were gathered from 1999-2000 through 2017-2018
documented measurable serum levels of PFOS, PFOA, PFHxS, and PFNA in more than 95% of
participants, indicating widespread exposure to these PFAS in the U.S. population. PFOA and
PFOS have been detected in up to 98% of serum samples collected in biomonitoring studies that
are representative of the U.S. general population; however, from 1999 to 2018, blood levels of
PFOA and PFOS declined by > 70% and > 85%, respectively, presumably due to restrictions on
commercial use of PFOA and PFOS in the United States. Under the EPA's PFOA Stewardship
Program, the eight major companies of the perfluoropolymer/fluorotelomer industry agreed to
voluntarily reduce facility emissions and product content of PFOA, precursor chemicals that can
break down to PFOA, and related higher homologue chemicals, including PFNA and longer-
chain PFCAs, by 95% on a global basis by no later than 2010 and to eliminate these substances
in products by 2015 (USEPA, 2021c). However, since the voluntary phase-out of these longer-
chain PFAS in the United States, manufacturers have been shifting to shorter-chain and
alternative forms of PFAS, such as HFPO-DA. The most recent available NHANES survey
(2017-2018) measured ADONA, HFPO-DA, perfluoroheptanesulfonic acid (PFHpS), 9-
chlorohexadecafluoro-3-oxanonane-l-sulfonate, and PFHxA in blood and found that PFHpS was
detected in 78% of samples and 9-chlorohexadecafluoro-3-oxanonane-l-sulfonate was detected
in 12%) of samples (the others were not detected or found in less than 1% of samples). The 2015-
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2016 NHANES survey detected some other PFAS in more than 30% of samples, including
PFDA, PFUA, and 2-(N-methylperfluoroctanesulfonamido)acetic acid (Me-PFOSA-AcOH).
Studies of residents in locations of suspected PFAS contamination show higher serum levels of
PFAS compared to the general U.S. population reported by NHANES (ATSDR, 2022; Table 17-
6 in ITRC, 2022; Kotlarz et al., 2020; Yu et al., 2020). Compared to PFOA and PFOS, there is
less publicly available information on the occurrence and health effects of replacements for
PFOA and PFOS and other members of the carboxylic acid and sulfonate PFAS families.
1.7 Brief Summary of State, National, and International Approaches to Address
PFAS Mixtures in Water
In 2016, the EPA finalized drinking water Health Advisories of 70 parts per trillion (ppt or ng/L)
for PFOA and PFOS, both individually and when present as a mixture (USEPA, 2016a, 2016b),
because the reference doses (RfDs) were based on developmental effects and numerically
identical. Subsequently, some states developed state-specific cleanup levels or drinking water or
groundwater guidelines, advisories, or standards for PFOA, PFOS, and other PFAS. In some
cases, the state values are the same as the EPA's 2016 drinking water Health Advisory; in other
cases, states developed different values (see examples in Table 1-4).
In June 2022, the EPA issued interim updated drinking water Health Advisories for PFOA and
PFOS and final Health Advisories for HFPO-DA and PFBS (USEPA, 2022a, 2022b, 2022c,
2022d). The EPA's interim updated Health Advisories for PFOA and PFOS are 0.004 ng/L and
0.02 ng/L, respectively, and the final Health Advisories for HFPO-DA and PFBS are 10 ng/L
and 2,000 ng/L, respectively. Each of the health advisory documents provides an example of
how to use the HI approach (see Section 5.0) to assess the potential noncancer human health
risks of exposure to a mixture of PFAS (USEPA, 2022a, 2022b, 2022c, 2022d) consistent with
the approach presented in this framework.
In March 2023, the EPA proposed and requested comment on a National Primary Drinking
Water Regulation that included an HI public health goal (i.e., Maximum Contaminant Level
Goal [MCLG]) and enforceable level (i.e., Maximum Contaminant Level [MCL]) to protect
public health from exposure to mixtures of any combination of two or more of PFHxS, PFNA,
HFPO-DA, and/or PFBS, four PFAS that individually can affect similar health
endpoints/outcomes and co-occur in drinking water (USEPA, 2023b). After consideration of
prior peer-review advice and public comment, and consistent with the provisions set forth under
the Safe Drinking Water Act, the EPA finalized an HI MCLG and MCL for mixtures of these
four PFAS. In consideration of their known toxic effects, dose additivity health concerns, and
occurrence and likely co-occurrence in drinking water, the EPA finalized an HI of 1 (unitless) as
the MCLG and MCL for any mixture containing two or more of PFHxS, PFNA, HFPO-DA, and
PFBS (USEPA, 2024e).
International approaches to addressing multiple PFAS in drinking water have resulted in a range
of proposed and promulgated standards and guideline values, as well as a variety of grouping
methods (Table 1-4). Canada proposed a drinking water objective of 30 ng/L as a summed total
of all PFAS measured in drinking water (using EPA Method 533 or EPA Method 537.1 or both).
Australia has established a combined level of 70 ppt for PFOS and PFHxS, based on the
assumption that PFHxS is similar in toxicity to PFOS (i.e., PFOS tolerable daily intake also
applies to PFHxS). Several countries have expanded the combined toxicity approach to include a
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variety of other PFAS. For instance, Denmark has set a limit of 100 ppt to account for any
combination of the following: C4-C10 PFCAs, PFBS, PFHxS, PFOS, PFOSA, and
6:2 fluorotelomer sulfonic acid (6:2 FTS). Sweden has adopted the same approach, not including
PFOSA, and set a maximum limit of 90 ppt. In both Denmark and Sweden, it is assumed that
these PFAS are similar in toxicity to PFOS. Most recently, the European Union (EU) adopted a
level of 100 ppt for the sum of 20 PFAS, including C4-C13 PFSAs and C4-C13 PFCAs and a
level of 500 ppt for all PFAS, as measured by extractable organofluorine (EOF) or adsorbable
organofluorine (AOF) (Cousins et al., 2020; EU, 2020). Further, Sweden and the Netherlands
have evaluated the potential human health risk(s) associated with mixtures of PFAS using
component-based methods consistent with the HI or RPF approaches presented in this
framework (Borg et al., 2013; RIVM, 2018). Although not specifically related to drinking water,
the European Food Safety Authority has also taken PFAS mixture toxicity into consideration in
its development of a Tolerable Weekly Intake for the sum of PFOA, PFNA, PFHxS, and PFOS
(4.4 nanograms per kilogram per week [ng/kg/week]) (EFSA, 2020).
Table 1-4. U.S. and international approaches to addressing the combined toxicity of
multiple PFAS in drinking water or groundwatera'b (only combined PFAS approaches are
presented).
Entity
Date
Cone. (ng/L)
Sum of PFAS
Background
EPA (USEPA,
2024
10 PFHxS
HI MCLG and
Final PFAS National Primary
2024e, 2022a,
10 PFNA
MCL = 1 for any
Drinking Water Regulation
2022b, 2022c,
10 HFPO-DA
combination of
Rulemaking.
2022, 2016a,
2000 PFBS
two or more of
2016b)
four PFAS
2022
0.004 PFOA
Example HI for
0.02 PFOS
four PFAS
Interim Updated Drinking Water
10 HFPO-DA
Health Advisories (HAs) for
2000 PFBS
PFOA and PFOS; Final HAs for
PFBS and HFPO-DA.
2016
70
PFOA and PFOS
Drinking Water Health Advisory.
Alaska (USA)
2019
70
PFOA and PFOS
Application of the EPA 2016
(Alaska DEC,
Health Advisory.
2019)
Colorado (USA) 2020
70
PFOA and PFOS
Application of the EPA 2016
(CDPHE, 2020)
Health Advisory.
Delaware
2018
17°
PFOA and PFOS
Based on the sum of
(USA) (DE
approximately 50% of each
DHHS, 2021)
individual MCLd
Florida (USA)
2019
70
PFOA and PFOS
Application of the EPA 2016
(Florida Health,
Health Advisory.
2020)
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Entity
Date Cone. (ng/L) Sum of PFAS Background
Maine (USA)
2021
20
PFOA, PFOS,
Based on similarities in chemical
(Maine DEP,
PFNA, PFHxS,
structure and toxicities of four
2021)
PFHpA, and
PFDA
PFAS to PFOS and PFOA. Same
approach as the EPA 2016 Health
Advisory but includes an
additional uncertainty factor.
Massachusetts
2019
20
PFOA, PFOS,
Based on similarities in chemical
(USA) (Mass
PFNA, PFHxS,
structure and toxicities of four
DEP, 2019)
PFHpA, and
PFDA
PFAS to PFOS and PFOA. Same
approach as the EPA 2016 Health
Advisory but includes an
additional uncertainty factor.
Montana (USA)
2019
70
PFOA and PFOS
Application of the EPA 2016
(MT DEQ,
2021)
Health Advisory.
Ohio (USA)
2019
70
PFOA and PFOS
Application of the EPA 2016
(Ohio EPA,
2019)
Health Advisory.
Rhode Island
2019
70
PFOA and PFOS
Application of the EPA 2016
(USA)
(RIDEM, 2017)
Health Advisory.
Vermont (USA)
2019
20
PFOA, PFOS,
PFHxS, PFHpA, and PFNA are
(Levine, 2018;
VT DEC, 2021)
PFNA, PFHxS,
and PFHpA
considered sufficiently similar to
PFOA and PFOS. Difference from
the EPA 2016 Health Advisory is
due to Vermont's calculation being
based on infant consumption rates.
Wisconsin
2022
70
PFOA and PFOS
Application of the EPA 2016
(USA) (WI
DHS, 2022)
Health Advisory.
European Union 2020
(EU, 2020)
100
500
100 ng/L for sum
of 20 PFAS (C4-
C13 PFSAs and
"PFAS Total" proposed to be
enforced through measurement of
EOF/AOF once validated or
C4-C13 PFCAs)
500 ng/L for
"PFAS Total" -
the total of all
100 ppt for the sum of 20 PFAS
considered to be a concern for
drinking water (implementation
January 12, 2023).
PFAS
Denmark
2015
100
C4-C10 PFCAs,
Assumes all 12 PFAS are similarly
(Danish
PFBS, PFHxS,
toxic as PFOS. Rationale: PFOS is
Environmental
PFOS, PFOSA,
the most toxic and toxicity data on
Protection
and 6:2 FTS
PFAS other than PFOS and PFOA
Agency, 2015)
are limited.
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Date Cone. (ng/L) Sum of PFAS Background
Entity
Sweden 2014 90
(Swedish Food
Agency, 2021)
Australia 2017 70
(Australian
Government
Department of
Health, 2019)
Canada (Health 2023 30
Canada, 2023)
C4-C10 PFCAs,
PFBS, PFHxS,
PFOS, and
6:2 FTS
PFOS and PFHxS
combined, if both
present
Assumes all 11 PFAS are similarly
toxic as PFOS. Rationale: PFOS is
the most toxic and toxicity data on
PFAS other than PFOS and PFOA
are limited.
Assumes PFHxS is similarly toxic
as PFOS. Rationale: PFOS is the
most toxic and toxicity data on
PFAS other than PFOS and PFOA
are limited.
Total all PFAS Technology-based,
measured using
EPA Method 533,
537.1 or both
Notes:
a Modified from Cousins et al. (2020).
b As of July 2021, several states have passed or proposed compound-specific MCLs or Health Advisories (e.g., California,
Illinois, Michigan, Minnesota, New Jersey, New York, Pennsylvania, Texas, and Washington). Some states have applied the
EPA's Health Advisory to interpret narrative water quality standards under the Clean Water Act (e.g., Colorado, Montana).
Only approaches using the sum of PFAS parameters are presented in this table.
c Proposed level based on the Delaware PFOA and PFOS MCL Implementation Plan.
d Based on a PFOA MCL of 21 ppt and PFOS MCL of 14 ppt.
1.8 Overview of Proposed Framework for Estimating Noncancer Health Risks for
PFAS Mixtures
This document describes a framework of component-based options with different levels of data
requirements and objectives for estimating the noncancer human health risks associated with
exposure to mixtures of PFAS based on longstanding EPA chemical mixtures guidelines. To
address concerns over health risks from multichemical exposures, the EPA issued the Guidelines
for the Health Risk Assessment of Chemical Mixtures in 1986 (USEPA, 1986). The 1986
guidelines were followed in 2000 by the Supplementary Guidance for Conducting Health Risk
Assessment of Chemical Mixtures (USEPA, 2000b). These documents define a chemical mixture
as "any combination of two or more chemical substances, regardless of source or of spatial or
temporal proximity, that can influence the risk of chemical toxicity in the target population"
(USEPA, 1986, 2000b); this definition is used in this framework document.
Several laws direct the EPA to address health risks posed by exposures to chemical mixtures,
including the Comprehensive Environmental Response, Compensation, and Liability Act
(CERCLA) of 1980, the Superfund Amendments and Reauthorization Act of 1986, and
amendments in 2002 (CERCLA, 2002; SARA, 2002) (commonly referred to as Superfund); the
Clean Air Act Amendments of 1990 (CAA, 1990); the Safe Drinking Water Act Amendments of
1996 (SDWA, 1996); and the Food Quality Protection Act (FQPA) of 1996 (FQPA, 1996). Both
the 1986 Guidelines for the Health Risk Assessment of Chemical Mixtures (USEPA, 1986) and
the 2000 Supplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures
(USEPA, 2000b) were developed, in part, to be responsive to these laws. When developing
assessment information for exposures to chemical mixtures, risk assessors and risk managers in
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the EPA's programs currently implement environmental laws through regulations that rely on the
methods articulated in these two chemical mixtures guidelines documents. This framework does
not supersede previously published EPA guidelines on mixtures or longstanding EPA approaches
used to assess health risks of contaminants, including chemical mixtures under various
environmental statutes (e.g., Federal Insecticide, Fungicide, and Rodenticide Act; FQPA;
CERCLA).
The objective of this document is to provide a flexible, data-driven framework that facilitates
practical component-based mixtures evaluation of two or more PFAS based on dose additivity.
All approaches presented involve integrating dose-response metrics that have been scaled based
on the potency of each PFAS in the mixture. Three approaches are presented:
1) The general HI (based on overall RfDs [or similar noncancer toxicity value such as an
ATSDR minimal risk level (MRL)] irrespective of similarity in target organ or system)
and TOSHI (based on RfDs in same target organ) for each component chemical provide
an indication of noncancer risk associated with exposure to a PFAS mixture of concern
(Section 5).
2) The RPF approach provides a mixture toxicity estimate by scaling the potency of
component chemicals, for a common health effect, relative to a well-characterized
member of the mixture, referred to as the index chemical (IC) (Section 6).
3) The M-BMD approach uses a dose additivity (DA) model-based equation (similar to the
Berenbaum equation; Section 4.2.6 in USEPA, 2000b) to calculate a BMD (e.g.,
BMDx-hed) for the mixture (Section 7).
The HI facilitates the estimation of potential combined toxicity associated with the co-occurrence
of chemicals in environmental media (e.g., water, soil) (USEPA, 1991, 2000b). The RPF method
is more data-intensive than the HI approach in that the mixture component chemicals typically
must meet two requirements: (1) there are data to demonstrate or suggest that component
chemicals share either a similar toxicological MOA8 or have a conserved toxicological target
(i.e., share a common apical endpoint/effect); and (2) the dose-response functions for the effect
of concern exhibit similar shape and slope over the exposure ranges most relevant to the decision
context (USEPA, 2000b). The RPF method is illustrated in Section 6 using the same target
organs/systems, including liver, thyroid, and developmental. An MOA for a given toxic effect is
a detailed description of the source-to-outcome pathway, including the key molecular/cellular or
organellar events, leading to a defined health effect or syndrome of effects (e.g.,
"developmental" can be a collection of related outcomes). In general, a health effect or outcome
is the terminus of one or more operant MOA(s). In addition to the HI and RPF methods, the
assumption of similarity in MOA or toxicological target is also inherent when applying the M-
BMD approach; however, in contrast to the RPF method there is no necessity or assumption of
similar dose-response functions (i.e., same/similar shape or slope) across component chemicals.
This approach provides predictions of a mixture effect even if the slopes of the dose-response
curves differ among the chemicals (Section 7). Considering that PFAS are an emerging chemical
class of interest for toxicological evaluations and human health risk assessment, data pertaining
8 Mode of action is a sequence of key events and processes, starting with interaction between an agent and a cell, proceeding
through operational and anatomical changes, and resulting in a noncancer effect or cancer formation (modification of footnote 2
in USEPA, 2005).
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to biological pathway perturbations are limited or not available for many PFAS; so, while there
is an evolving landscape of evidence demonstrating shared molecular and cellular effects by
some PFAS, no conserved noncancer or cancer MOA(s) have been identified across PFAS to
date. As such, this framework focuses the biological level of organization for evaluation of
potential dose additivity on similarity of toxicological endpoint/effect/adverse outcome rather
than similarity in MO A, which is consistent with EPA chemical mixtures guidelines (USEPA,
1986, 1991, 2000b), the EPA Risk Assessment Forum's Advances in Dose Addition for Chemical
Mixtures: A White Paper (USEPA, 2023h), and expert opinion from the National Academy of
Sciences, National Research Council (NRC, 2008).
Recognizing the evolving and dynamic nature of PFAS science, the component-based mixtures
assessment approaches described herein are flexible to allow for consideration of new or
evolving dose-response data and toxicity assessments as they become available. Additionally,
because publicly available traditional (e.g., in vivo mammalian) toxicity studies are limited to
only a small fraction of the -15,000 PFAS estimated at the time of this writing, this framework
also provides suggestions for practical integration of validated NAMs such as toxicogenomics
(e.g., in vitro cell bioactivity) and in silico platforms (e.g., structure-activity, read-across) into the
HI, RPF, and M-BMD approaches. The illustrative examples in Sections 5, 6, and 7 are intended
to demonstrate the application of dose-additivity-based component chemical mixture approaches
using hypothetical human health-relevant toxicity and exposure information.
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2.0 Background on EPA Mixtures Additivity Guidelines
Exposure to mixtures of environmental chemicals occurs in human populations through
ingestion, inhalation, and/or dermal contact with contaminated media (e.g., water, air, food). It
should be noted that a "mixture" of chemicals may be a function of both co-occurrence in
exposure media and/or internal bioaccumulation and persistence in biological matrices. In
recognition of the need for methods and approaches that inform the evaluation of potential health
risks associated with chemical mixtures, the EPA developed the 1986 Guidelines for the Health
Risk Assessment of Chemical Mixtures and, subsequently, the 2000 Supplementary Guidance for
Conducting Health Risk Assessment of Chemical Mixtures (USEPA, 1986, 2000b). In those
guideline documents, the EPA proposed a hierarchy of mixtures approaches where the preferred
approach is to evaluate health risk using hazard and dose-response data for a specific whole
mixture of concern or, alternatively, a sufficiently similar mixture. However, whole-mixture data
are rare; there are often many chemical combinations and proportions in the environment (e.g.,
parent chemicals, metabolites, and/or abiotic degradants), introducing a level of complexity that
is difficult to evaluate and characterize.
Further, most controlled experimental toxicity data derive from single-chemical exposures or, at
best, small mixtures (i.e., a limited number of component chemicals at fixed proportions/ratios).
As such, the EPA also developed multiple component chemical-based mixtures assessment
approaches. Component-based methods are used more frequently than whole-mixture methods.
These component-based methods are based on assumptions about how the chemicals behave
biologically when co-occurring. Although observed toxicity could be related to direct chemical-
to-chemical interaction(s), the manner in which co-occurring chemicals induce toxicity in a
coordinated or independent way is the basis for the concept of "additivity." The basic tenets of
the EPA mixtures additivity theory and practice are:
• Additivity-based methods are used to estimate the probability or magnitude of a given
health outcome (e.g., incidence and/or severity, or change in magnitude, of a noncancer
target organ effect) associated with exposure to mixtures of two or more component
chemicals. In the 1986 and 2000 EPA mixtures guidelines documents, the development
of component-based mixture approaches was informed by two main concepts, simple
similar action and simple independent action, as described by Bliss (1939) and Finney
(1971).
• Simple similar action applies to mixture component chemicals that cause a common
health effect via toxicologically similar pathway(s). Under simple similar action (i.e.,
DA), the evidence associated with toxic responses to mixture component chemicals
demonstrates or suggests coordinated (i.e., same/similar) pathway events. DA is generally
applied when mixture chemicals are assumed to act through simple similar action.
• Simple independent action applies to mixture component chemicals that cause a common
health effect via toxicologically independent pathways. Under simple independent action
(i.e., response addition [RA]), the evidence associated with toxic responses to different
mixture component chemicals demonstrates or suggests independent pathway events. RA
is generally applied when mixture chemicals are assumed to act through simple
independent action.
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2.1 Component-Based Mixtures Assessment Methods
Component-based methods that the EPA has developed for evaluating potential additivity of
dose, response, or both are shown in Figure 2-1. Based primarily on similarity in toxicity
endpoint/health effect of PFAS, this framework document focuses on the use of dose-additive,
component-based methods (left side of Figure 2-1; shaded box), specifically the HI (Section 5),
RPF (Section 6), and M-BMD (Section 7) approaches. The methods, although all based on dose
addition, involve different assumptions, data requirements and objectives for evaluating the joint
toxicity of component chemicals in a mixture. Each method is introduced and detailed in
Sections 5-7 and includes a demonstration of the application using a hypothetical five-
component PFAS mixture. Specifically, to facilitate the use of potentially disparate sources and
types of PFAS information in a mixture context, the data-driven application of the component-
based methods presented in this framework document is demonstrated using a hypothetical
example mixture of five PFAS:
PFAS 1 = comprehensively studied, most potent for effect(s), and has formal noncancer
human health assessment value(s) and an HBWC available;
PFAS 2 = well-studied, second-most potent for effect(s) among PFAS 1-3, and has
formal noncancer human health assessment value(s) and HBWC available;
PFAS 3 = studied, least potent for effect(s) among PFAS 1-3, and has formal noncancer
human health assessment value(s) and HBWC available;
PFAS 4 = in vivo animal toxicity data available but no formal human health assessment
and no HBWC; and
PFAS 5 = data-poor; no in vivo animal toxicity data or human data available.
This hypothetical PFAS mixture is purposefully designed to demonstrate how the framework
allows for the flexible integration of information derived from diverse data types and sources.
Opportunities for integrating PFAS into a mixture assessment are expected to evolve over time
and will depend on the decision context and availability of hazard and dose-response data from
traditional and/or NAM-based assays and/or in silico platforms.
An important property of DA-based methods is that they can aid in the indication or estimation
of the effects of a mixture even when all the individual component chemical exposures are at or
below their individual no-observed-adverse-effect levels (NOAELs; i.e., "something from
nothing"). For example, in the hypothetical PFAS mixture applications (Sections 5-7), the HI in
general indicates a point (i.e., 1) above which a hazard might be anticipated for the mixture, or
below which adverse effects are not expected (see Section 5). In the RPF approach, the sum of
the scaled IC9 equivalent doses/concentrations across component chemicals is compared to the
equivalent threshold dose of the IC (Jonker et al., 1996; Silva et al., 2002). In the context of
water-specific application, a mixture IC equivalent dose/concentration may be compared to a
health-based water concentration (HBWC) for the IC to indicate if the selected health effect may
or may not be expected (see Section 6). Finally, no IC is required in the M-BMD approach, and
9 An IC is that mixture component that is typically the most toxicologically well-studied. The qualitative and quantitative hazard
and dose-response data for an IC serve as an index or anchor against which all other components are compared. IC equivalent
doses/concentrations represent scaled dose(s) of mixture components, based on potency for a given toxicity endpoint/health
effect, in a corresponding dose of the IC.
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Notes:
Modification of Figure 4-3b (USEPA, 2007).
BMD = benchmark dose; HI = hazard index; HQ = hazard quotient; MOE = margin of exposure; RPF = relative potency factor;
TOSHt = target organ-specific hazard index.
Component-based methods selection is based on the relevant evidence supporting toxicological similarity (DA) or toxieological
independence (RA or effect summation). Integrated addition methods are reserved for mixtures of component chemicals that
demonstrate a profile of both toxicological similarity and independence.
Figure 2-1. Flow chart for evaluating chemical mixtures using component-based additive
methods.
dose-response shape(s) and slope(s) do not have to be similar among components. The
aggregated M-BMD is converted to a noncancer RfV and corresponding HBVVC and then
compared directly to the total measured mixture PFAS concentration to indicate if the selected
health effect may or may not be expected following exposure to the combinations and
proportions for that specific mixture (see Section 7).
2.1.1 Application of Dose Addition as the EPA's Default Approach
Several in vivo studies have examined predicted mixture responses based on dose-addition
models for specific groups of chemicals (e.g., Altenburger et al., 2000; Crofton et al., 2005;
Gennings et al., 2004; Hass et al., 2017; Howdeshell et al., 2015; Kortenkamp and Haas, 2009;
Moser et al., 2005, 2012; Mwanza et al., 2012; Rider et al., 2008, 2009, 2010; USEPA, 2007;
Walker et al., 2005), focusing primarily on whether experimentally observed toxicity is
consistent with modeled predictions of dose additivity. Many of these studies examined groups
of chemicals that are thought to target the same biological signal transduction pathways (Moser
et al,, 2012; Mwanza et al., 2012; Walker et al., 2005), while others have examined chemicals
thought to target disparate pathways that lead to the same health outcome (NRC, 2008; Rider et
al., 2009; Van Der Yen et al., 2022). In general, the results of such studies listed here, and many
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others, support the continued application of DA as the EPA's default component-based mixture
assessment approach. Further discussion and examples of the basis for the use of dose additivity
for component-based evaluation of PFAS mixtures are provided in Section 3.
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3.0 Dose Additivity for PFAS
This section presents a review of in vivo chemical mixture studies for different biological
pathways that provide information on how mixtures of chemicals with similar and dissimilar
molecular and cellular perturbations interact. Section 3.2 discusses the evidence demonstrating
that mixtures of chemicals disrupting common pathway events typically produce dose-additive
alterations. In in vivo studies that rigorously tested accuracy for DA, Integrated Addition (IA),
and RA model predictions for mixtures with components that disrupted common pathway events,
DA models provided predictions that were better than or equal to IA and RA predictions of the
observed mixture effects (Section 3.2). Consistent with the conclusions of the National Academy
of Sciences (NAS) (NRC, 2008), Boobis et al. (2011) and Martin et al. (2021) found that
published studies in the literature (Section 3.2) support DA as the default model for estimating
mixture effects, even when the mixtures included chemicals with diverse biological signal
transduction pathways (but common target organs/effects). Further, these two large systematic
reviews of the literature on chemical mixtures found little evidence for deviations from dose
additivity, such as synergy or antagonism (Boobis et al., 2011; Martin et al., 2021). For example,
Martin et al. (2021), following a review of more than 1,200 mixture studies (selected
from > 10,000 reports), concluded that there was little evidence for synergy or antagonism
among chemicals in mixtures and that DA should be considered as the default model. Taken
together, this supports the health-protective conclusion that a mixture of chemicals with similar
apical effect profiles should be assumed to also act in a dose-additive manner unless data
demonstrate otherwise. Further, experimental data demonstrate that PFOS, PFOA, and other
PFAS disrupt signaling in multiple biological pathways, resulting in common adverse effects on
several of the same biological systems and functions, including thyroid hormone signaling, lipid
synthesis and metabolism, developmental toxicity, and immune and liver function, and are
reviewed in Section 3.4. Finally, Section 3.4 summarizes several EPA Office of Research and
Development (ORD) PFAS developmental toxicity mixture studies that provide robust evidence
that PFAS behave in a dose-additive manner.
3.1 Overview of Assessment Approaches for Chemical Mixtures
Over 30 years ago, scientists developed quantitative dose metrics and methods to assess the joint
toxicity of mixtures of large classes of chemicals that disrupt a common biological pathway
(NATO, 1988). For example, toxicity equivalence factors (TEFs) were initially developed in the
mid-1980s for hundreds of dioxin-like polychlorinated biphenyls (PCBs), polychlorinated
dibenzofurans (PCDFs), and polychlorinated dibenzo-p-dioxins (PCDDs) based on their potency
relative to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). Many of the lessons learned from
assessing the effects of mixtures of dioxin-like chemicals (DLCs) are also generally applicable to
assessing the effects of PFAS mixtures. Since that time, TEF-like approaches (e.g., RPFs) have
been used to evaluate mixtures of other chemical classes. However, the evolving picture since
early applications of RPFs or TEFs is that some chemicals, regardless of similarities or
dissimilarities in molecular initiating events (MIEs) or early key events (KEs), produce mixture
effects on common apical endpoints that generally are well predicted using DA models. This has,
in part, been explained by the concept of pathway convergence; that is, across mixture
component chemicals, some pathway perturbations may qualitatively look dissimilar at the level
of MIEs or early KEs but ultimately converge upon shared or common events nearer to the
terminus of a pathway leading to health effects (for further details, see the EPA Risk Assessment
Forum's Advances in Dose Addition for Chemical Mixtures: A White Paper [USEPA, 2023h]).
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The general applicability of DA models is based on reviews of studies specifically designed to
evaluate how well different mixture models predict how chemicals in a mixture interact to
produce effects. Studies evaluating mixture models and effects typically include an evaluation of
individual chemical dose-response curves and apply this information to different statistical
models. In general, systematic reviews have noted that many mixture studies do not include
information conducive for evaluating the utility of different mixture models. The data from
several studies (reviewed in Section 3.2) indicate that chemicals that produce common adverse
effects will typically interact in a dose-additive manner when they occur together in a mixture.
Thus, the effects of any combination of co-occurring chemicals can be predicted when sufficient
chemical dose-response data are available for all the individual components within an
environmentally relevant mixture. For example, the Consumer Product Safety Commission
Chronic Hazard Advisory Panel (CPSC CHAP) on Phthalates used DA models to predict the
hazard posed by mixtures of phthalates to pregnant women and children. In its assessment,
phthalate mixture exposures from NHANES data were used to predict individual hazard scores
for each person and then determine the percentage of people who exceeded a point-of-departure
(POD) (CHAP, 2014).
In the absence of an adequate in vivo database to evaluate mixture models, it should be assumed
that any mixture acts in a dose-additive manner if the individual mixture components produce
common effects. This approach was fully endorsed by NRC (2008) and Martin et al. (2021).
3.2 Examples of Chemical Classes and Toxicological Pathways Utilizing Mixture
Assessment Approaches
3.2.1 Dioxin-Like Chemicals and AryS Hydrocarbon Receptor Pathway Toxicity
Equivalence Factors
In 2010, the EPA published guidelines for using TEFs for human health risk assessment of
DLCs, which produce many of their adverse effects by acting as aryl hydrocarbon receptor
(AhR) agonists (USEPA, 2010). It should be noted that the TEF approach is a specialized
application under the RPF umbrella that is only applicable when all mixture components induce
an effect via an identical MIE/MOA (e.g., AhR agonism). DLCs such as PCBs, PCDFs, and
PCDDs have been identified as AhR agonists. As such, the EPA and the World Health
Organization have recommended the use of the TEF methodology to evaluate risks associated
with exposure to mixtures of TCDD and DLCs for human health (USEPA, 1987, 1989, 2003)
and ecological risk assessments (USEPA, 2008). TEFs can be calculated for each DLC based on
dietary dose or internal whole-body toxic equivalent concentrations.
The joint toxicity of a DLC mixture is based on toxic equivalents (TEQs), which are toxicity-
weighted masses of mixtures of PCDDs, PCDFs, and PCBs. The TEQ for each chemical in the
mixture is calculated by multiplying each TEF by the corresponding chemical concentration in
the mixture. The individual TEQs are then summed to calculate the TEQ of the mixture. The
TEQ provides toxicity information about the mixture of chemicals and is more meaningful than
reporting the total mass of DLCs in grams.
This approach assumes:
• Chemical mixture components interact in a dose-additive manner;
• They all act via a common AhR-mediated pathway, among other pathways;
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• Synergistic and antagonistic interactions are uncommon within the group (Safe, 1994);
and
• TEFs and TEQs based on AhR agonism are not necessarily predictive of chemical
potency for effects mediated by other receptors or pathways.
The EPA's TEFs have undergone several revisions (Van den Berg et al., 2006). In 2010, the EPA
published guidelines for the use of TEFs for human health risk assessment of DLCs (USEPA,
2010). Although the AhR is present in all classes of vertebrates, vertebrate species vary greatly
in their sensitivity to environmental TEQ levels. Sensitive species include terns and cormorants
(bill deformities), herons (embryo mortality), and mink (lethality and reproductive failure)
(Beckett et al., 2008; Restum et al., 1998), for example. Adverse effects also occur in frogs
(amphibians) (Gutleb et al., 2000), fish (Monosson, 2000), and snapping turtles (reptiles)
(Bishop et al., 1998; Gale et al., 2002). The EPA stated that the TEQ methodology was
appropriate for evaluating risks to fish, birds, and mammals associated with AhR agonists
(USEPA, 2008).
Studies of AhR agonists in various species indicate:
• Species and tissues differ in sensitivity to the effects of a DLC mixture; and
• Even though the AhR pathway is conserved, the adverse outcomes can vary greatly from
species to species.
One common effect of DLCs is a reduction in serum thyroxine (T4). Crofton et al. (2005)
conducted a mixture study of 18 thyroid-disrupting DLCs consisting of 12 PCBs, 4 PCDFs, and
2 PCDDs at 6 dilutions of the highest dose, which contained effective dose (ED30) concentrations
of each chemical in the high dose. This mixture reduced serum T4 in a dose-related manner. The
reduction in T4 was dose additive in the low dose range of interest, but the observed reduction in
T4 at the high dose (46% reduced) exceeded DA predictions (28% reduced) by about 18%. In a
review of the literature on the effects of mixtures on the thyroid axis, Crofton (2008) concluded,
"To date, the limited data from thyroid disrupting chemical mixture studies suggest that DA is
reasonably accurate in predicting the effects on serum T4 concentrations."
3.2.2 Pyrethroids/Pyrethrins - Centra! Nervous System and Behavior
Pyrethrins and pyrethroids share the ability to interact with voltage-gated sodium channels,
ultimately leading to neurotoxicity. Wolansky et al. (2009) administered a single oral gavage
dose of a mixture of 11 pyrethroid pesticides to adult male rats using a fixed-ratio dilution design
at eight dose levels and measured locomotor activity on the day of dosing. The reduction in
exploratory activity by the mixture was accurately predicted by DA modeling.
The EPA has determined that naturally occurring pyrethrins and synthetic pyrethroid pesticides
form a common mechanism group. This common mechanism grouping is based on (1) shared
structural characteristics; (2) a shared ability to interact with voltage-gated sodium channels,
resulting in disruption of membrane excitability in the nervous system; and (3) neurotoxicity
characterized by two different toxicity syndromes. In 2011, after establishing a common
mechanism grouping for the pyrethroids and pyrethrins, the EPA conducted a cumulative risk
assessment using an RPF approach (USEPA, 201 la).
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3.2.3 Organophosphates - Lethality, Centra! Nervous System, and Behavior
In the late 1950s, Murphy and Dubois (1957) reported that O-ethyl O-p-nitrophenyl
phenylphosphonothioate potentiated the lethality of malathion when the two chemicals were
administered simultaneously. Subsequently, all organophosphate (OP) pesticides in use were
evaluated in binary mixture studies to determine if deviation from additivity (e.g., synergism or
antagonism) was a common outcome among this class of insecticides (reviewed by Moser et al.,
2005; Padilla, 2006). An examination of the interactions of 43 pairs of OP insecticides revealed
that four pairs showed greater-than-additive effects on lethality (Dubois, 1961). Moser et al.
(2005, 2006) reported a range of responses with mixtures of four or five OPs. The ratios of the
predicted-to-observed ED20S and ED50S of the mixtures indicated that several effects displayed
greater-than-additive effects (ratios = 1.2 to 2.6), a few were less than additive (ratio = 0.5 to
0.9), and most were dose additive (ratio = 1).
In 1999, the EPA determined that the OPs form a common mechanism group based on their
shared ability to bind to and phosphorylate the enzyme acetylcholinesterase (AchE), leading to
the accumulation of acetylcholine and, ultimately, cholinergic neurotoxicity (USEPA, 1999). As
such, the cumulative risk to OPs has been assessed using AchE inhibition as the effect on which
dose-response data are integrated under the assumption of dose addition. The most recent OP
cumulative assessment was conducted in 2006 and employed an RPF approach (USEPA, 2006).
Further, in 2018, ATSDR concluded that the "default assumption of dose-additive joint action at
shared targets of toxicity (i.e., effects on neurological endpoints) be used for screening level
assessments of the potential adverse health outcome from concurrent oral exposure to mixtures
of pyrethroids, organophosphorus, and carbamate insecticides" (ATSDR, 2018).
3.2.4 Estrogen Agonists - Mixture Effects on the Female Reproductive Tract
Scientists have examined the effects of mixtures of estrogenic chemicals in the female rat using a
uterotrophic assay, an EPA Endocrine Disruptor Screening Program Test Guideline that is a
sensitive in vivo test for estrogenicity (USEPA, 2009). In this assay, immature or adult
ovariectomized female rats are typically exposed to test chemicals for 3-4 days, after which
uterine weights are measured. Exposures can be oral or through subcutaneous injection. Tinwell
and Ashby (2004) exposed immature female rats for 3 days to several known xenoestrogens,
either individually or as mixtures. In a reanalysis of the data, predictions of a DA model for a
binary mixture of bisphenol A and genistein were consistent with the observed effects of the
mixture, with an average deviation of observed results vs. the DA model of 4%. Similarly,
Conley et al. (2016) found that the effects of mixtures of bisphenol S + methoxychlor, bisphenol
AF + methoxychlor, and bisphenol F + bisphenol S + methoxychlor + bisphenol C + ethinyl
estradiol, administered orally to female rats, produced effects that were comparable to
predictions using DA models. Because the chemicals all stimulate uterine growth via a common
estrogen receptor alpha pathway and produce a common effect, Conley et al. (2016) determined
that DA was the most appropriate model for mixtures of these estrogenic compounds.
3.2.5 Phthaiates in utero - Mixture Effects on the Female Reproductive Tract
Hannas et al. (2013) reported that administration of a mixture of five phthaiates (> 520 mg total
phthalate) to pregnant rats from gestational days (GDs) 8 to 13 induced reproductive tract
malformations in female rat offspring. These malformations included complete to partial uterine
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agenesis and agenesis of the lower vagina, an effect similar to a human congenital condition
known as the Mayer-Rokitansky-Kiister-Hauser syndrome that occurs in about 1 in 4,500 female
newborns. The phthalate mixture was a fixed-ratio dilution and contained five phthalates that do
not produce malformations in either female or male offspring when administered individually at
the doses used in the mixture. These malformations have been seen in dibutyl (500 milligrams
per kilogram per day [mg/kg/day]) and diethylhexyl (750 mg/kg/day) phthalate studies at a low
incidence and at high doses but were not seen in similar studies with the other three phthalates.
Although there was not enough individual phthalate data to compare DA and RA prediction
models, it is clear these effects exceed RA (i.e., 0 + 0 + 0 + 0 + 0 = 75% for uterine agenesis)
and is an example of "something from nothing" (Silva et al., 2002).
3.2.6 Antiandrogens - Male Reproductive Tract Development
Historically, it has been hypothesized that mixtures of chemicals with dissimilar MIEs would
interact in an RA or IA manner. However, this conclusion is not currently supported by a large
body of literature on the effects of chemical mixtures and was rejected by the NRC (2008).
Studies on the effects of mixture exposures on male reproductive development provide one of the
larger databases supporting the use of DA as the default model. These studies include chemical
mixtures with common MIEs and those with multiple MIEs that converge on a common KE in
multiple adverse outcome pathways (AOPs) in an AOP network. These studies focus on
chemicals that disrupt androgen signaling in utero during the critical period of mammalian
sexual differentiation. For over 20 years, scientists have examined the in utero effects of
mixtures of chemicals that disrupt androgen signaling on the male reproductive tract (e.g., Gray
et al, 2001; reviewed by Haas et al., 2007; Howdeshell et al., 2017; Metzdorff et al., 2007).
These studies include defined binary or multi-chemical fixed-ratio dilution mixtures and were
designed to compare the observed effects to DA, RA, and IA model predictions. The number of
chemicals used in these studies range from 2 to 18, administered at a range of doses, enabling
one to discriminate additive from antagonistic or synergistic interactions. In all these studies, the
DA model predicted the effects of the mixture on the male reproductive tract more accurately
than IA or RA. Likewise, Metzdorff et al. (2007) concluded that the "Effects of a mixture of
similarly acting anti-androgens can be predicted fairly accurately based on the potency of the
individual mixture components by using the DA concept. Exposure to anti-androgens, which
individually appear to exert only small effects, may induce marked responses in concert with,
possibly unrecognized, similarly acting chemicals."
In addition, two recent studies were designed to specifically address a gap in the literature
identified by the CPSC CHAP (Lioy et al., 2015). At the time of its review, no published studies
addressed whether or not phthalate mixtures exhibited mixture effects when administered at
levels below the lowest-observed-adverse-effect levels (LOAELs) of each individual chemical.
In the first study, a mixture of 18 administered chemicals induced effects at dose levels about 80-
fold below each chemical's individual LOAEL (Conley et al., 2018). These 18 chemicals disrupt
androgen signaling via five different MIEs (Figure 3-1) and multiple AOPs that converge on
common KEs, resulting in common adverse reproductive effects in male rat offspring.
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Toxicol Sci, Volume 164, Issue 1, July 2018, Pages 166-178. https://doi.org/10.1Q93/toxsci/kfv069
The content of this slide may be subject to copyright: please see the slide notes for details.
OXPORD
UNIVERSITY PRESS
Notes-
Adapted from Conley et al. (2018).
The bold outlined KE indicates the critical node that links the various MIEs to the downstream adverse outcomes.
DHT = dihydrotestosterone; AR = androgen receptor, CYP = cytochrome P450; HMG-CoA = 3-hydroxy-3-methyl-glytaryl
coenzyme A.
Figure 3-1. AOP network for chemicals that disrupt AR-mediated cellular signaling
leading to adverse effects on the development of male reproductive tract resulting from in
utero exposure.
In the second study (Conley et al., 2021a), 15 chemicals (acting via at least three MIEs)
demasculinized male rat offspring at dose levels 2- to 4-fold lower than the individual no-
observed-effect levels for each chemical, and the DA models were always as good or better than
RA or IA models in estimating mixture effect. For example, 60% of male offspring were found
to have penile malformations that resulted in infertility; DA accurately predicted this effect,
whereas IA and RA predicted that none of the males would be malformed. This is not a unique
observation; rather, it is a typical finding with male reproductive tract malformations.
Recently, Gray et al. (2022) demonstrated that the in utero effects of a PFAS pesticide,
pyrifluquinazon (contains a heptafluoroisopropyl side chain; see
https://comptox.epa.gOv/dashboard/chemical/details/DTXSID6058057) combined with the di-
ortho phthalate ester dibutyl phthalate produced dose-additive effects that were more accurately
predicted with DA models that did not require parallel slopes than with RA models for multiple
male reproductive abnormalities.
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All these endocrine-active chemicals act via AOPs that converge on a common KE in an AOP
network (Figure 3-1) that regulates the sequence of molecular events in cells involved in the
development of androgen-dependent tissues. Each of the identified chemicals/classes reduces the
number of androgen receptor (AR) dimers, AR/AR, activated by an androgen agonist. AR
antagonists, like vinclozolin or procymidone, accomplish abrogation of androgen-dependent
signaling by blocking androgens from binding to Ars, and the PFAS pesticide pyrifluquinazon
has been hypothesized to act by enhancing AR degradation (Gray et al., 2019; Yasunaga et al.,
2013). Chemicals like the phthalates di-n-butyl phthalate (DBP), di(2-ethylhexyl)phthalate
(DEHP), dipentyl phthalate (DpeP), butyl benzyl phthalate (BBP), and diisobutyl phthalate
(DIBP) reduce the levels of androgens available to respondent cell populations (Hannas et al.,
2011; Howdeshell et al., 2008; Furr et al., 2014). In contrast, chemicals like finasteride inhibit
the enzyme in tissues that converts testosterone to dihydrotestosterone (a more active androgen
with a higher affinity for the AR) (Clark et al., 1990).
The result of these related androgen-disrupting AOPs is that fewer activated AR/AR
heterodimers bind the promoter region on the DNA of androgen-regulated genes, androgen-
dependent mRNA and protein synthesis levels are reduced, and growth and differentiation of
androgen-dependent tissues in the fetus is inhibited. As a result, male offspring display agenesis
or hypoplasia or malformations in androgen-dependent tissues. In summary, an examination of
KEs disrupted in androgen signaling pathways by chemicals such as those identified in Figure 3-
1, at the cellular-molecular level, explains why one should expect the mixtures to behave in a
dose-additive manner.
In summary, an examination of the literature on the effects of mixtures on male reproductive
tract development demonstrates that common effects are most accurately modeled by DA; the
chemicals discussed above acted in a dose-additive manner even when including chemicals with
different MIEs, and IA and RA models consistently underestimated the hazard of a mixture of
chemicals acting on common molecular or cellular (more downstream) events resulting in a
common profile of apical effects.
3.3 Systematic Reviews of Mixtures Toxicity: Quantification of Deviations from
Dose Additivity
Boobis et al. (2011) examined the literature from 1990 to 2008 that discussed synergy in
mammalian test systems with an emphasis on "low dose" studies. Of the 90 papers identified,
43 papers had original data from which synergy could be examined, and only 11 studies reported
the magnitude of the difference between the dose-additive estimates of toxicity with the observed
results. Of these 11 studies, six reported magnitudes of synergy that were generally less than 2-
fold with a maximum value of 3.5-fold. As a result, the authors concluded that deviations from
DA at low doses were not common.
The issue of the occurrence of greater-than-DA (sometimes referred to as synergistic) vs. DA or
less-than-DA (sometimes referred to as antagonistic) interactions was recently reassessed by
Martin et al. (2021). The authors conducted a systematic literature review and quantitative
reappraisal of 10 years of a broad range of mixture studies published between 2007 and 2017.
Martin et al. (2021) identified 1,220 mixture studies, -65% of which did not incorporate more
than 2 component chemicals. They reported that "relatively few claims of synergistic or
antagonist effects stood up to scrutiny in terms of deviations from expected additivity that exceed
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the boundaries of acceptable between-study variability" and that the observed effects were not
more than 2-fold greater than the predicted effects of the mixture based on an assumption of DA.
3.3.1 Deviation from Additivity
Although the literature indicates that significant deviations from dose additivity are not common
among mixtures containing chemicals that disrupt common targets via common AOPs or AOP
networks, greater-than-additive (i.e., synergism) and less-than-additive (i.e., antagonism)
interactions may occur with co-exposure to chemicals that affect different target organs or
different, unrelated AOPs. There are several examples of chemical interactions that deviate from
DA in which one chemical has the capability to alter the metabolism of the other chemical(s).
For example, 20 years of research have identified at least 85 drugs whose metabolism is inhibited
by a chemical in grapefruit, potentially resulting in serious side effects (Bailey, 2013).
Furanocoumarins in grapefruit bind to the active site on the CYP3 A4 enzyme, causing
irreversible inactivation that prolongs the half-life and AUC (the area under the concentration vs.
time curve) of some drugs, like some statins, for example.
The effects of metabolic alterations of chemical toxicity are not limited to drug-drug interactions.
Hodgson (2012) published a comprehensive review of the effects of metabolism on the toxicity
of a large number of pesticides and also described the metabolic mechanisms of chemical
activation and/or inactivation.
In addition to alterations in metabolic activity leading to synergistic or antagonistic dose-
response among chemicals in mixture, there are other examples of deviations from DA that do
not include interactions across different levels of biological organization but instead entail
physical-chemical interactions. For example, although melamine and its derivatives, including
cyanuric acid, individually present low toxicity, together the compounds can physically interact
to lead to the formation of cyanurate crystals in nephrons, causing kidney effects and kidney
failure in mammals. Such impacts have been observed in cats and dogs from adulteration of pet
food (Jacob et al., 2011), as well as in infants and young children in China from contaminated
infant formula and related dairy products (WHO, 2008).
3.4 PFAS Dose Additivity
PFAA, such as PFOA and PFOS, with linear or branched alkyl or alkyl ether chains and sulfonic
or carboxylic acid functional groups, as well as PFAA precursors, while not necessarily
toxicologically identical, do elicit similar toxicological effects across different levels of
biological organization, tissues/organs, life stages, and species (ATSDR, 2021; EFSA et al.,
2018, 2020). As described above (Section 3.2), precedents of prior research conducted on
mixtures of various chemical classes with common pathway perturbations or KEs (for those
chemicals with established MO As) and adverse outcomes, support the use of dose-additive
models for estimating mixture-based risks, even in instances where chemicals with disparate
MIEs were included. Thus, in the absence of identification and characterization of MOA(s) for
most PFAS, it is considered a health-protective conclusion that PFAS that can be demonstrated
to share one or more molecular/cellular pathway events and/or adverse health outcomes will
produce dose-additive effects from co-exposure. The EPA's SAB supported this approach in its
review of a draft version of this document (SAB, 2022). PFOA and PFOS have historically been
the most studied and well-characterized PFAS, but recent work has also provided supportive
evidence of similar effects of other PFAAs, including ether-linked structures. Below is a brief
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overview of similarities and differences in MIEs, intermediate pathway events, and adverse
outcomes that have been reported for those PFAS studied to date and experimental evidence that
supports dose-additive effects from combined exposure to multiple PFAS. This overview
highlights study results from, among others, the National Institute of Environmental Health
Sciences' National Toxicology Program (NTP) 28-day repeat dose guideline toxicity studies of
perfluoroalkyl carboxylates (PFHxA, PFOA, PFNA, and PFDA) (NTP, 2019a) and
perfluoroalkyl sulfonates (PFBS, PFHxS, and PFOS) (NTP, 2019b). The NTP studies provide
high-quality side-by-side comparisons of multiple PFAS from experiments conducted by a single
lab with rigorous exposure characterization and multiple effects/endpoints spanning MIEs,
intermediate pathway events, and adverse outcomes. More comprehensive reviews of PFAS
toxicity endpoints in experimental animal studies and observational human studies can be found
elsewhere (e.g., AT SDR, 2021; EFSA et al., 2018, 2020).
Mechanistically, in vitro and in vivo studies have demonstrated the activation of multiple nuclear
receptors associated with exposure to many structurally diverse PFAS, indicating several
potential MIEs for PFAS-relevant toxicity pathways. The most commonly reported MIE
associated with many PFAS is the activation of peroxisome proliferator-activated receptor alpha
(PPARa) based on in vitro binding and transcriptional activation assays (Behr et al., 2020; Evans
et al., 2022; Ishibashi et al., 2019; Nielsen et al., 2022; Takacs and Abbott, 2007; Vanden Heuvel
et al., 2006; Wolf et al., 2012), in vitro upregulation of PPARa target genes (Bjork et al., 2011),
and in vivo tissue-specific upregulation of PPARa target genes (Bjork et al., 2008; Rosen et al.,
2007). All PFAA carboxylates and sulfonates included in the NTP 28-day studies displayed
upregulation of the PPARa target genes Acoxl and Cyp4al in male and female rat livers (NTP,
2019a, 2019b). PPARa is a highly conserved transcription factor that regulates pleiotropic
effects on mammalian energy homeostasis and lipid metabolism, among others. Similarly,
multiple PFAS have been shown to activate peroxisome proliferator-activated receptor gamma
(PPARy) in vitro (Evans et al., 2022; Houck et al., 2021; Vanden Heuvel et al., 2006) and
upregulate PPARy target genes in vitro (Marques et al., 2022) and in vivo (Rosen et al., 2017).
PPARy is also a highly conserved transcription factor that regulates multiple physiological
processes, including adipogenesis and glucose metabolism. Further, in vivo studies of tissue-
specific gene expression patterns have also demonstrated the activation of the constitutive
androstane receptor (CAR) for both PFOA and PFOS, among other PFAS, due to the
upregulation of CAR-dependent genes (Rosen et al., 2017). All PFAA carboxylates and
sulfonates that were tested in the NTP 28-day studies and displayed upregulation of the PPARa
target genes also displayed upregulation of the CAR-inducible genes Cyp2bl and Cyp2b2 in
adult male and female rat livers (NTP, 2019a, 2019b). Additional in vitro data indicate potential
involvement from several other nuclear receptors following PFAS exposures including estrogen
receptor alpha (Evans et al., 2022; Houck et al., 2021, Kjeldsen and Bonefeld-Jorgensen, 2013),
pregnane X receptor (Bjork et al., 2011; Houck et al., 2021), farnesoid X receptor (Bjork et al.,
2011), and liver X receptor (Bjork et al., 2011, Houck et al., 2021). Multiple PFAAs, including
PFOA and PFOS, activate multiple nuclear receptors and gene transcription pathways, which is a
data-driven basis for positing shared or overlapping pathway perturbations across PFAAs.
In addition to the cell- and/or tissue-specific gene expression changes described above, multiple
pathway events or markers of toxicity downstream of the above-mentioned potential MIEs are
also shared between PFOA, PFOS, and other PFAAs. In both rodent and nonhuman primate
studies, serum lipids (cholesterol, triglycerides) are altered, and markers of liver injury or
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dysfunction (ALT, AST, and/or ALP) are consistently elevated in a dose-responsive manner
(ATSDR, 2021; EFSA et al., 2018, 2020). Specifically, theNTP 28-day studies reported reduced
serum cholesterol, triglycerides, and globulin and elevated serum ALT, AST (males only), ALP,
and bile acids in rats following exposure to PFHxA, PFOA, PFNA, PFDA, PFBS, or PFOS
(NTP, 2019a, 2019b). Further, circulating thyroid hormone concentrations were reduced
following oral PFAA exposure, with all compounds tested in the NTP 28-day studies being
associated with decreased serum total and free thyroxine (T4) (NTP, 2019a, 2019b). Ether-linked
PFAAs have also been shown to reduce circulating thyroid hormone concentrations (Conley et
al., 2019, 2022a). In combination with a common profile of nuclear receptor activity and gene
expression, there is a pronounced similarity in the landscape of serum clinical chemistry and
thyroid hormone-based markers of altered physiology for PFOA, PFOS, and several other
studied PFAS. Identification and characterization of PFAS-relevant pathway events as formal
Kes in established MOA(s) is currently an area of high research activity, as additional
associations across different levels of biological organization with the reported MIEs continue to
be investigated across various life stages and species.
Similar adverse health outcomes at the organ and whole animal levels have been described for
PFAAs, including PFOA and PFOS. Developmental exposure studies with PFOA, PFOS, PFNA,
HFPO-DA and Nafion byproduct 2 (NBP2; an emerging polyfluoroethersulfonic acid compound
recently detected in human serum) in rats and/or mice have reported consistent effects on pups
including reduced offspring survival/viability and reduced offspring body weight (Abbott et al.,
2007, 2009; Blake et al., 2020; Butenhoff et al., 2004; Conley et al., 2021b, 2022a; Das et al.,
2015; Lau et al., 2003, 2006; Luebker et al., 2005a, 2005b; Thibodeaux et al., 2003). PFAS
studied by NTP (2019a, 2019b) have been observed to increase rat liver weights and produce
hepatocyte hypertrophy. PFOA and PFOS, and potentially other PFAS, have also been shown to
produce functional immunotoxicity (i.e., reduced antibody response) in animal studies (NTP,
2016). Taken together, there are numerous adverse effects that occur in laboratory animals that
are shared across PFAS such as PFOA, PFOS and other PFAAs. These adverse effects are
consistent with the molecular and cellular pathway perturbations highlighted above. However, it
is important to recognize that while there are the same/similar qualitative effect profiles across
many PFAS (e.g., liver injury, decreased thyroid hormones), there are quantitative oral potency
differences in reported effects, and not all effects appear to be shared across all PFAS, or even
across all PFAAs, at the dose levels reported in published studies. For example, PFHxS exposure
did not result in changes in serum ALT in male or female rats at any dose in the NTP 28-day
studies, while all other PFAAs tested increased serum ALT levels, indicative of hepatocellular
injury.
The specific molecular mechanisms or precise MO As for a given adverse health outcome may be
disparate across some PFAS. For example, studies utilizing transgenic mice with PPARa
deletion have demonstrated that some effects, such as survival of neonates following in utero
PFOA exposure, were dependent on PPARa involvement, while this effect appeared independent
of PPARa for PFOS (Abbott et al., 2007, 2009). It is important to note in those studies that fetal
mortality (as opposed to neonatal mortality) was independent of the PPARa genotype for PFOA.
The relevance of rodent PPARa-based effects has been debated in the toxicology literature for
decades, largely as it relates to hepatocarcinogenesis, yet the pharmacological utility of PPARa
modulation is widely accepted and exploited in the development of therapeutics. Further, studies
of PPARa knockout mice have also demonstrated that many liver effects are PPARa
35
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independent for PFOA and PFOS (Abbott et al., 2007, 2009). Although there is potential for
disparate MIEs, and more importantly the lack of formal MOA(s), in PFAS-related adverse
health outcomes, it is a reasonable health-protective conclusion that effects shared across PFAS
in a given mixture will be dose additive.
Some studies have evaluated other models for quantifying potential joint toxicity (e.g., dose
addition, synergism, antagonism) associated with combined exposure to PFAS in experimental
systems either in vitro or in vivo. In vitro studies have directly assessed the mixture-based effects
of combined PFAS exposures by comparing observed experimental data with model-based
predictions. For example, Wolf et al. (2014) evaluated in vitro PPARa activation and observed
joint effects of combined exposure to binary combinations of PFOA and PFOS, PFNA, PFHxA,
and PFHxS that were consistent with dose additivity in the lower dose ranges, but the authors
reported slightly greater than additive effects at higher mixture doses. In contrast, Carr et al.
(2013) reported slightly less than additive responses for in vitro PPARa activation of binary
mixtures of PFAAs, including PFOA, PFNA, PFOS, and PFHxS. Further, Ojo et al. (2022)
reported both synergistic and antagonistic effects compared to a dose-addition model for binary,
ternary, and multicomponent mixtures of PFAAs for cytotoxicity in HepG2 cells. Nielsen et al.
(2022) demonstrated the utility of generalized dose addition for PPARa activation of PFAA
mixtures in an in vitro system with variable efficacy across compounds; however, there are no
studies indicating this model variation on simple dose addition is applicable for prediction of in
vivo mixture effects. Most recently, Addicks et al. (2023) conducted in vitro exposures of
primary human liver spheroids to seven different PFAS mixtures, including combinations of
PFBA, PFPeA, PFHxA, PFHpA, PFOA, PFNA, PFDA, PFUnA, PFBS, PFHxS, PFOS, PFOS A,
6-2 FtS, and 8-2 FtS. Liver spheroids were evaluated for mRNA transcriptomic points of
departure, and all mixtures produced effects that were within 2- to 3-fold of mixture effects
predicted using dose addition. Similarly, as described above, systematic reviews of chemical
mixture studies across various compound classes indicate that departures from dose additivity are
uncommon and rarely exceed minor deviations (~2-fold) from predictions based on dose
additivity (Martin et al., 2021). Recent PFAS mixture studies in zebrafish reported interactions
for combinations of PFOA and PFOS, but departures from additive models were also minor
(Ding et al., 2013). Menger et al. (2020) reported zebrafish behavioral effects from a PFAS
mixture that were less than individual PFAS; however, an evaluation of chemical dose-response
and comparison to mixture models was not conducted. Regarding zebrafish PFAS effects, it is
notable that fish PPARy has a relatively low sequence homology to that of mammalian PPARy
(Zhao et al., 2015), and the potent PPARy agonist rosiglitazone activates this rat, mouse, and
human receptor in vitro but not in three species of fish or the clawed frog (Medvedev et al.,
2020). The interactions described in the literature thus far for combined in vitro exposure to
PFAAs demonstrate results that are either consistent with or have relatively minor deviations
from predictions based on dose-additive models.
Mammalian in vivo toxicity studies evaluating exposure to multiple PFAS are more limited, but
recent studies indicate that exposure to a mixture of PFOA, PFOS, and PFHxS in mice (Marques
et al., 2021), a mixture of PFOA, PFOS, PFNA, PFHxS, and HFPO-DA in mice (Roth et al.,
2021), or a mixture of PFBA, PFPeA, PFHxA, PFHpA, PFOA, PFNA, PFDA, PFBS, PFHxS,
and PFOS in rabbits (Crute et al., 2022) produced numerous significant health effects compared
to control animals, which were consistent with the spectrum of individual PFAS effects
described above (e.g., liver injury; thyroid hormone alterations). However, these studies did not
36
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include individual PFAS dose-response data or conduct any mixture model-based analyses, so it
is impossible to ascertain if the mixtures behaved in a DA or RA manner or if interactions
occurred (i.e., deviations from DA).
Recent and ongoing work at the EPA includes developmental toxicity studies of PFAS mixtures
in rats. One study recently investigated in vivo effects in maternal rats and offspring from
combined exposure to PFOA and PFOS during gestation and early lactation (Conley et al.,
2022b). The study included a series of experiments designed to characterize dose-response
curves across multiple endpoints for PFOA and PFOS individually, followed by a mixture study
of the two chemicals combined. The mixture experiment was designed to test for shifts in the
PFOA dose-response curves from combined exposure to a fixed dose of PFOS, compare DA and
RA model predictions, and conduct an RPF analysis to evaluate mixture effects. Exposure to
binary combinations of PFOA and PFOS significantly shifted the PFOA dose-response curves
left towards elicitation of effects at lower doses compared to PFOA-only exposure. This clearly
indicated mixture effects for a range of endpoints, including decreased pup survival, maternal
and pup body weight, pup serum triiodothyronine (T3) and glucose, and increased maternal
kidney weight, maternal and pup liver weight, and pup bile acids, BUN, and bilirubin. Maternal
kidneys and maternal and pup livers in the mixture study also displayed a range of treatment-
related histopathological lesions. For nearly all endpoints amenable to mixture model analyses,
the DA equation produced equivalent or better estimates of observed data than RA. Similarly, for
nearly all maternal and neonatal endpoints modeled, the RPF approach produced accurate
estimates of dose-additive mixture effects. Only maternal body weight at term and gestational
weight gain demonstrated departures from dose additivity, and these effects were slightly less
than additive. This work is ongoing, with multiple KE analyses still to be conducted on samples
collected during the studies. However, results thus far support the hypothesis of joint toxicity on
shared endpoints from PFOA and PFOS co-exposure and dose additivity as a reasonable
conclusion for predicting mixture effects of co-occurring PFAS.
A second PFAS mixture study by Conley et al. (2023) reported data on a mixture of PFOS,
HFPO-DA, and NBP2 (an emerging polyfluoroethersulfonic acid compound recently detected in
human serum [Kotlarz et al., 2020]). Multiple endpoints, including maternal serum cholesterol,
triglyceride, and thyroid hormone concentrations (total T3 and T4), pup birthweight and body
weight at 2 days of age, and pup mortality, all conformed to dose additivity. Similar to the
PFOA + PFOS mixture above, effects on maternal weight gain were slightly less than additive,
and no endpoints demonstrated synergy. Further, the mixture shifted the dose-response curves
for increased maternal and pup liver weights towards effects at lower doses when comparing the
HFPO-DA responses in the mixture to the dose responses from HFPO-DA exposure alone.
Finally, preliminary data from a third in vivo study of a mixture of six PFAS (PFOA, HFPO-DA,
PFMOAA, PFOS, PFHxS, and NBP2) further indicate dose-additive effects on maternal and
neonatal endpoints including pup body weight and pup survival. The published and preliminary
results discussed above provide robust evidence of combined toxicity of PFOA, PFOS, and other
PFAS on multiple maternal and developmental endpoints and the greater accuracy of DA for
predicting mixture effects in vivo than RA. Similarly, the joint toxicity of a mixture of PFOS and
PFOA on Japanese quail chick 10-day survival is accurately predicted by DA but not RA (Gray
et al., 2023).
37
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In summary, PFAS data reported in the literature support an assumption of similarity in toxicity
profiles for several health effect domains (for review, see Carlson et al., 2022). Importantly,
study results reported in this section across multiple chemical classes, biological effects, and
study designs clearly support a dose-additive mixture assessment approach. Most notably, recent
efforts to characterize in vivo mixture effects from combined exposure to multiple PFAS provide
key supportive evidence that co-exposure produces dose-additive effects on several endpoints
within the range of "same/similar" endpoints shared across the spectrum of PFAS effects.
Further, the National Academies of Sciences, Engineering, and Medicine (NASEM, 2022)
recently recommended clinicians apply an additive approach for evaluating patient levels of
PFAS currently measured in NHANES. The EPA will continue to review how mixtures of PFAS
and other chemicals interact. Dose additivity is proposed as the "default" model for PFAS
mixtures assessment, and other models will be evaluated when data empirically support or
demonstrate significant deviations from dose additivity.
38
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4.0 Introduction to Estimating Noncancer PFAS Mixture Hazard or
Risk
4.1 Whole Mixtures Approach
The preferred hazard and dose-response knowledge base for any mixture of environmental
chemicals would be derived from exposure to a whole mixture of concern. However, the
exponential diversity of chemicals such as PFAS co-occurring in different component
combinations and proportions makes whole mixture evaluations difficult and complex. That is, in
the environment, due to differing fate and transport properties of chemicals, biotic (metabolism)
and abiotic (degradation) processes, pH, ultraviolet radiation, media temperature, and so on,
components commonly co-occur in an array of parent species, metabolites, and/or degradants,
making characterization of any given mixture complicated. In controlled experimental study
designs, whole mixtures can be assembled with defined component membership and proportions.
However, the relevance of toxicity associated with exposure to a defined mixture in a laboratory
setting may not be translatable to environmental mixtures of different component combinations
and proportions across time and space in environmental media. In the context of PFAS,
increasing environmental evidence (e.g., ambient and drinking water, fish, air, and soil sampling
results) suggests that the complexities briefly summarized above with regard to the diversity of
chemicals co-occurring in different component proportions make evaluating each unique whole
mixture of PFAS intractable, which is why component-based mixture approaches are considered
particularly useful and appropriate for addressing human exposure(s) to mixtures of PFAS (see
Sections 5-7).
The EPA's Supplemental Guidance for Conducting Health Risk Assessment of Chemical
Mixtures (USEPA, 2000b) indicates that there may be opportunities to infer hazard and dose-
response for a mixture of concern from a "sufficiently similar mixture." A mixture is considered
sufficiently similar to a mixture of concern when the components and respective proportions
exist in approximately the same pattern. There are clearly gradations of expert judgment
involved in determining what constitutes a sufficiently similar mixture, but determinations
should be based on a comparison of similarities or differences in the components' chemical fate
and transport in the environment, persistence, bioaccumulative potential, kinetics, and toxicity
profiles. If no significant qualitative differences are identified in a systematic comparison of
mixtures of chemicals, the hazard and dose-response information associated with the sufficiently
similar mixture could be used as a surrogate for the mixture of concern. However, as with a
whole mixture of concern, information pertaining to a sufficiently similar mixture is rare. The
whole mixture options are most appropriate for localized, site-specific assessments with stable
mixtures (i.e., low/no temporal and spatial variability) and should be considered before moving
to a component-based mixtures approach.
4.2 Data-Driven Component-Based Mixtures Approaches for PFAS
As a result of both the complexities associated with the characterization and evaluation of whole
mixtures (see Section 4.1 above) and the reality that most toxicological information derives from
exposure-response studies of individual chemicals, component-based mixtures risk assessment is
particularly relevant (Figure 4-1). In addition, although the methodological approaches and
associated illustrative examples in this framework are targeted at application to water, the
concepts may facilitate the evaluation of PFAS mixtures in other exposure media as well (e.g.,
39
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Problem formulation and scoping
-
f
Assemble PFAS mixture component chemical toxicity information*
Do components have
existent oral toxicity
values (e.g., RfD)?
V_
No
Do components have
available in vivo
hazard and dose-
response data?
No
Do components have
available NAM data
to inform dose-
response evaluation?
No
Flag mixture
component(s) for
further analysis
and data needs
Yes
Yes
Are component
toxicity values based
on same effect?
Yes
Does component
data support de novo
derivation of toxicity
values?
Yes(a)
Yes (b)
No
Are component dose-
response functions
similar (e.g., shape,
slope) for same or
No
Are dose-response
data amenable to
benchmark dose
No
Yes
N- - ™
Yes
Yes
c
Hazard Index
(see Section 5.2)
v >
Target-Organ Specific
Hazard Index
(see Section 5.3)
Relative Potency Factors
and ICECs (see Section 6)
V J
r
Mixture BMD
(see Section 7)
*ln a data-driven mixtures assessment approach, available hazard and dose-response data may support evaluation using
more than one method in step 4; the practitioner should consider confidence and data quality objectives across the
methods based upon the data landscape assembled for a given mixture ofPFAS.
(RfD = Reference Dose; NAM = New Approach Methodology; ICEC = Index Chemical Equivalent Concentration; Mixture
BMD = Mixture Benchmark Dose)
Figure 4-1. Framework for data-driven application of component-based assessment approaches for mixtures ofPFAS based
on dose additivity.
40
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soil, air). As outlined in earlier sections of this framework, while EPA component-based methods
and approaches are available for an evaluation of mixtures of chemicals under different
assumptions of additivity (USEPA, 2000b), the currently available hazard evidence on PFAS,
and several other classes of environmental chemicals, support an assumption of dose additivity
(see Section 3). The HI and RPF are two component-based mixture approaches based on dose
additivity that are well validated, supported by peer-reviewed guidelines, and actively used by
the EPA. These two approaches are discussed below and include illustrative examples based on a
hypothetical five-component mixture of PFAS (see Sections 5 and 6). An alternative M-BMD
approach, generally based on the Berenbaum equation (see Section 4.2.6 in the EPA mixtures
guidelines [USEPA, 2000b]), is also a dose-additive approach that is described and illustrated
(see Section 7). The primary difference between the RPF and M-BMD approaches is that RPF
assumes component chemical dose-response curves are similarly shaped, while the M-BMD
approach is more applicable for mixture component chemicals with dissimilar dose-response
curve shapes/slopes. It should be noted that others have recently demonstrated the application of
the HI and RPF approaches in the evaluation of PFAS (Bil et al., 2021, 2022; Mumtaz et al.,
2021), lending confidence to the use of this framework document in guiding formal component-
based assessment of PFAS mixtures. The M-BMD approach was indirectly described within the
context of a mixture RfD in the EPA's mixtures guidelines (USEPA, 2000b) and by the National
Research Council (NRC) (NRC, 2008); further, laboratory studies have provided empirical
evidence in support of this approach (Gray et al., 2022; see example in Section 7).
4.2.1 Conceptual Framework of the Approach
A pragmatic data-driven approach to the application of component-based evaluation of mixtures
of PFAS with variable hazard and dose-response databases is presented in Figure 4-1. The
general steps of the component-based approach, as shown in Figure 4-1, are:
Problem formulation and scoping. Problem formulation is the part of the risk assessment
framework that articulates the purpose of the assessment and defines the problem (e.g., PFAS
source and occurrence, fate and transport, populations/subpopulations potentially at risk, health
endpoints). Problem formulation also typically includes developing a conceptual model and
analysis plan and engaging with potentially affected stakeholders (e.g., states and Tribes, risk
managers, affected community) to discuss foreseeable science and implementation issues.
Step 1: Assemble information.
Step 1 of the data-driven mixture assessment approach is to identify the available hazard and
dose-response information for: (1) a whole mixture of the PFAS of potential concern at
component proportions consistent with the environmental sampling data, (2) a sufficiently
similar mixture, and/or (3) data for the individual component PFAS. If toxicity data for the
whole mixture itself or a sufficiently similar mixture are not available or are insufficient, then a
structured search, collection, and assembly of all available toxicity data for mixture component
PFAS is conducted. Although the optimal approach would be to use formal systematic literature
search and review principles as set forth by the EPA (please see the Integrated Risk Information
System [IRIS] systematic review protocol for PFBA, PFHxA, PFHxS, PFNA, and PFDA as an
example10), the user of this framework may employ a structured literature search approach of
their choosing so long as the underpinning decisions resulting in the literature inventory and data
10 https://cfpub.epa.gov/ncea/iris draf'ts/recordi splay. cfm?deid=345065
41
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landscape used in steps 2-4 of the framework approach are transparent. It should be noted that
while this step is primarily intended for the identification and collation of human
epidemiological and/or traditional experimental animal toxicity data, it is ideal to also assemble
information such as toxicokinetic (TK) parameters (e.g., clearance, plasma/serum half-life,
volume of distribution), mechanistic pathway data, empirical (or predicted) physicochemical
properties and, if available, validated NAM data such as cell bioactivity, high(er)-throughput
transcriptomics, and/or structure-activity/read-across.
Step 2: Evaluate data objectives.
Once collected, curated, and arrayed, the user should be able to evaluate the following primary
data objectives: (a) Existence of human health risk assessment values (e.g., EPA RfD; ATSDR
MRL); duration-specific values (e.g., subchronic [note: ATSDR refers to this duration as
"intermediate"], or chronic RfVs) may be available from various sources and should be
assembled and incorporated into the data-driven mixture assessment approach(es) as deemed
appropriate by the user; it should be noted that human health assessment values may be available
from different sources utilizing different levels of analytical rigor and/or peer-review. The
practitioner is advised to consider the confidence associated with noncancer assessment values
located across mixture components and integrate accordingly into subsequent steps of this
workflow; and (b) Development of health effect domain profiles (see example literature
inventory in Figure 4-2 excerpted from the IRIS systematic review protocol for PFBA, PFHxA,
PFHxS, PFNA, and PFDA10), and associated dose-response information sorted based on
exposure duration (e.g., acute or short[er]-term, longer-term [i.e., subchronic, chronic];
developmental/reproductive), across component PFAS supported by the assembled (i.e., human
epidemiological and/or experimental animal) toxicity data from Step 1.
Users of this framework may find that many component PFAS of interest are data-poor (i.e., no
traditional human health assessment relevant epidemiological or experimental animal study data
are available). In such cases, NAM platforms or assays might provide opportunities to inform
dose response for PFAS mixture components. For example, read-across is a NAM approach that
could potentially be leveraged to identify surrogate dose-response metrics (e.g., POD, ECx, ICx)
for integration into the component-based mixtures assessment approaches presented in the
subsections below. Analog-based read-across, in general, is a process in which chemicals (i.e.,
analogs) with relatively replete toxicity databases are compared to a data-poor target chemical
across similarity domains including structural, physicochemical, TK, and/or toxicodynamic (TD)
similarity (Lizarraga et al., 2023; Wang et al., 2012; Wu et al., 2010). Based on weight-of-
evidence for similarity between a data-poor target chemical and candidate analogs, hazard and
dose-response data (e.g., PODs) are then adopted from a selected (single-best) analog as a
surrogate for the target chemical. This read-across approach might facilitate incorporation of
data-poor PFAS into the component-based methods presented in this framework, as surrogate
PFAS data that inform similarity of toxic endpoint/health effect and dose-response could
potentially: (a) be used in the derivation of a noncancer RfD (using uncertainty factors
appropriate for the data-poor target chemical) and subsequent calculation of a hazard quotient
(HQ); (b) be used in the calculation of RPF(s); or (c) the surrogate health-effect dose-response
data could undergo BMD modeling and be included in the calculation of an overall M-BMD.
The EPA's ORD has been employing expert-driven analog-based read-across in the evaluation of
data-poor chemicals for over a decade; for an illustrative example human health assessment
application of the approach, please see Appendix A of the Provisional Peer-Reviewed Toxicity
42
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PFAS currently under development/review in the EPA/ORD's IRIS program (heat map circa 2018). Health effects are based
on groupings from the IRIS website (https://ordspub.epa.gov/ords/eims/eimscomm.getfile7p download id=542033).
43
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Value (PPRTV) document for 2,3-toluenediamine at
https://cfpub.epa.gov/ncea/pprtv/recordisplay.cfm?deid=352932 and/or for perylene at
http s: //cfpub. epa. gov/ncea/ pprtv/recordi splay, cfm ? c
Another opportunity for integration of NAMs into the proposed mixture approaches involves cell
bioactivity (e.g., ToxCast/Tox21), including pathway-based transcriptomic and metabolomic
data from experimental animals and/or in vitro cell cultures. For over a decade, the EPA and the
National Institute of Environmental Health Sciences have invested significant resources into
high(er)-throughput assay development and application to hundreds of chemicals (Richard et al.,
2016; Kavlock et al., 2012; Dix et al., 2007). The cell bioactivity assays are primarily targeted at
nuclear receptor activity but also include several other assays that inform cell viability, enzyme
activity, DNA reactivity, cell transport, and macromolecular/cellular dysfunction. The bioactivity
information is quality assured, assembled, curated, and presented in a manner that is intended to
facilitate incorporation into risk-based decision contexts such as human health assessment (see
example bioactivity plot in Figure 4-3). However, inherent complexities and challenges are
associated with study designs and data interpretation using NAM assays/platforms, such as in
vitro cell culture. For example, there is no a priori assumption that molecular/cellular
perturbations observed in cells in vitro have any direct qualitative relationship to phenotypic
health effect(s). Further, considerations such as metabolic capacity, shorter-term exposure
durations (e.g., hours to days), and specificity or sensitivity of in vitro effect(s), and how these
factors influence precision, accuracy and/or reproducibility of quantitative concentration-
response relationship within an assay, within a lab, across labs, etc., continue to present
challenges for integration into decision-making foci.
Recent investigation has demonstrated that quantitative data (e.g., PODs) from in vitro cell
bioactivity and corresponding traditional in vivo toxicity assay-based PODs differ by
approximately 100-fold (median of range); however, the NAM-based PODs were found to be
lower than in vivo PODs for 89% of chemicals evaluated (Paul-Friedman et al., 2020). Likewise,
over the past decade, systematic comparisons of pathway-based (e.g., Gene Ontology or "GO"11)
transcriptomic PODs to phenotypic health outcome PODs has illustrated that for most chemicals
evaluated to date, the dose-response relationship between genotype and phenotype for toxic
effects is typically within an order of magnitude (Johnson et al., 2020; Thomas et al., 2011,
2013). For many chemicals, bioactivity assays may also provide information on the potential to
disrupt specific MIEs and KEs of known or postulated MO As or AOPs and could potentially
inform the relevance of specific pathways to humans. While in vitro assays can be informative,
they are not without limitations. For example, to use the cell bioactivity data in the component-
based mixture approaches discussed in this framework, the in vitro concentration-based metrics
should be first converted to administered equivalent doses (AEDs) in humans. Converting in
vitro bioactivity concentrations to estimated human in vivo doses (i.e., AEDs) requires the
application of in vitro-to-in vivo extrapolation (IVIVE) and reverse toxicokinetics (rTK), which
introduces additional uncertainty and might not be possible for many data-poor PFAS. In
addition, several in vitro cell-based assays to date employ truncated nuclear receptors with only
the ligand binding domain, and, as a consequence, the transcriptional events that follow binding
may not be fully representative of quantitative chemical potencies compared to that seen with
11 http://geneontology.org/docs/ontology-documentation/
44
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channel 1
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Figure 4-3. Example plot illustrating in vitro cell bioactivity expressed in AEDs.
An AED is an estimated oral exposure dose that results in an internal steady-
state concentration consistent with the in vitro concentration associated with a
biological perturbation or activity. The example shows the distribution of
AEDs; the vertical orange dashed line indicates the 5th percentile on the
bioactivity landscape. The green dashed line corresponds to an upper bound on
the estimated general population-based median exposure (generated from the
EPA's Systematic Empirical Evaluation of Models (SEEM3):
https://www.epa.gov/cheinical-research/computational-toxicology-communities-
practice-svsteinatic-empirical-evaluation).
45
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full-length receptors in native in vivo systems. Further, for such approaches to gain widespread
regulatory acceptance, it will be important to demonstrate that the NAMs under consideration are
reproducible, robust, and can be transferred to other laboratories and produce results that are
relevant to in vivo adverse effects.
Step 3: Consider data landscape to select component-based approach(es).
Considering the data landscape across mixture component PFAS in Step 2 will help the user
ascertain which option(s) in Step 4 is the most supportable. For example, dose-response data for
PFAS mixture components may apply to more than one option in Step 4; however,
characteristics of the data (e.g., shape or slope of dose-response between components) may not
be similar, leading the user to select a M-BMD approach over the RPF approach. Another
example entails dose-response data for components that do not indicate "same" health outcomes
or MO A; in this case, it may be more practicable to use the available data in a general HI
approach (assuming human health assessment values already exist or dose-response data support
de novo derivation of assessment values). When using any of the component-based mixture
approaches, it is optimal to calculate and use HEDs (for PODs, Edx, etc.) rather than orally
administered doses in test animals where and when possible. The user is not precluded from
applying available hazard and dose-response data across the entirety of the options in Step 4
when/where supported; however, study datatypes, design (e.g., exposure duration, route), and
confidence will likely dictate the optimal selection of component-based approach for PFAS on a
case-by-case basis. In most cases, a user would likely select only one of the approaches based on
the data available for the PFAS components in a mixture of concern (e.g., if toxicity values and
HBWCs are available or can be calculated, then the HI approach is appropriate). If, instead,
toxicity values are not available or cannot be readily derived, then the RPF or M-BMD approach
may be more suitable depending on characteristics and confidence in available dose-response
data across mixture components.
Step 4: Perform component-based approach(es).
a. HI and TOSHI. For component PFAS that have human health risk assessment value(s),
the user in Step 3 should have determined if the critical effect(s) for two or more PFAS
fit into the same health effect domain (e.g., liver, thyroid, developmental). If not, then
those PFAS are entered into a general HI approach (Section 5). For many PFAS, no
formal health assessment exists; however, human epidemiological and/or experimental
animal hazard and dose-response data may be available in the public domain. Should
such data be available, the user has the option of performing de novo derivation of
duration-specific noncancer toxicity values using assessment guidance and practices
accepted under their specific purview. Again, if the de novo-derived values are not based
on the same/similar effect, then the general HI approach is recommended. In brief, the HI
approach entails the use of duration-relevant exposure (E) and toxicity values (e.g., RfV)
for each component PFAS in a simple ratio (E/RfV) to calculate an HQ. The general HI
involves the use of RfVs for each PFAS mixture component irrespective of the health
outcome domain. Because each mixture component HQ is calculated using a
corresponding RfV (protective of all effects), the mixture HI may represent a health-
protective indicator of potential mixture risk. The component PFAS HQs are then
summed to generate a mixture HI (see Equation 5-1). A mixture HI exceeding one (1)
indicates potential concern for health risk(s) associated with a given environmental media
46
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or site. The HI provides an indication of: (1) risk associated with the overall PFAS
mixture; and (2) potential driver PFAS (i.e., those PFAS with high(er) HQs). Conversely,
those PFAS with low(er) HQs (e.g., < 0.0X) might be deprioritized as they may not have
a significant impact(s) on overall mixture risk at the specific media concentrations
identified. It should be noted that a user of this approach should consider the potential
exposure (e.g., water concentration), the potency for toxic effect (e.g., low(er) or high(er)
RfVs, PODs), the duration associated with exposure and toxicity, and qualitative and
quantitative uncertainty (i.e., totality of UF application) for each PFAS mixture
component.
If RfVs across PFAS mixture components are based on the same effect, then a TOSHI is
recommended (see Section 5.3). The TOSHI approach is exactly as the name suggests;
that is, it entails calculating component chemical HQs and corresponding mixture His for
specific target-organ effects/endpoints using only those mixture components with a
reference value for the specified effect. As such, while more consistent with the concept
of toxicological similarity, a potential limitation of the TOSHI approach might entail the
loss or exclusion of one or more mixture components for which a toxicity value for the
specific health effect does not exist. A further advancement under the TOSHI is the
development of target-organ toxicity doses (TTDs) (note: some TTDs could also be the
overall RfD for a given PFAS). In practice, it is recommended to calculate TTDs for all
health outcomes associated with component PFAS, where/when hazard and dose-
response data support. This may facilitate the calculation of TOSHIs for more mixture
component PFAS across more health outcomes, thus enriching the evaluation of PFAS
co-occurring in a given environmental medium.
If data are available that indicate and support deviations from dose additivity (e.g.,
synergy or antagonism), an interactions-based HI may be employed (see USEPA, 2000b).
However, in this framework, based on dose additivity, only the HI and TOSHI are
included. Specifically, data to inform deviations from dose additivity (e.g., interactions
such as synergism or antagonism) are virtually nonexistent for PFAS co-occurring in the
mixture; as such, an interactions-based HI is not feasible at present.
b. RPF. In contrast to the HI, the RPF approach provides a PFAS mixture risk estimate for a
selected health effect when specific media concentrations of the component PFAS are
available (see Section 6). In the RPF approach, potency for an effect across each
component PFAS is scaled to a selected IC for critical health effect domains of concern.
In the illustrative example in Section 6.2, application of the RPF method is demonstrated
for liver, thyroid, and developmental effects associated with the hypothetical five-
component PFAS mixture. These three health effect domains were selected primarily
because: (1) the effects are common across several PFAS assessed by the EPA (and
ATSDR) thus far; and (2) each hypothetical example PFAS has differing levels of hazard
and dose-response data available across the three health effect domains, to best illustrate
demonstration of the RPF methodology. In practice, for application in a water context,
each respective PFAS RPF is multiplied by its corresponding specific media
concentration (e.g., water concentration), resulting in an Index Chemical Equivalent
Concentration (ICEC). The ICECs across PFAS mixture components are summed to
generate an overall mixture ICEC (see Equation 6-2), which is effectively a total
47
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concentration of the IC, for each health effect domain. In traditional EPA mixtures risk
assessment practice, the mixture ICEC is then mapped to the dose-response function of
the IC to arrive at a "mixture response." In this framework, in the context of water, the
mixture ICEC (i.e., the total dose of IC) is compared directly to an HBWC (e.g., Health
Advisory, MCLG) based on the relevant health effect domain (e.g., liver, thyroid,
developmental) for the IC. If the mixture ICEC for one or more of the selected effect
domains exceeds the corresponding IC HBWC then there may be cause for concern for
the mixture at the reported/measured component water concentrations. Conversely, health
risk is not anticipated if the mixture ICECs for all effect domains are below the
corresponding IC HBWC. Additionally, component PFAS with large(r) RPFs and
corresponding ICECs should be flagged regardless of whether the total mixture ICEC is
above or below an IC HBWC.
c. M-BMD. An additional option entails the calculation of an M-BMD (see Section 7) and
is applicable even when PFAS in the mixture have dissimilarly shaped dose-response
curves for the same or similar effect. In contrast to the RPF approach, there is no need for
identification of mixture ICs, calculation of RPFs or ICECs, or existence of HBWCs. The
final determination of risk is based on a comparison of the observed total mixture water
concentration with an HBWC derived from the most sensitive effect-based M-BMD.
Similar to the RPF approach, dose-response data across one or more health effect
domains for each PFAS in the mixture are needed to determine the corresponding dose at
the benchmark response (BMR) for each PFAS component (i.e., each component PFAS
BMD). Then, the individual component chemical BMDs are scaled based on their
proportion in the mixture and added using a simple dose-addition-based equation to
arrive at a total M-BMD. The M-BMD approach does not require that component
chemicals meet an assumption of similarly shaped dose-response functions (i.e., slopes).
The resulting M-BMD (i.e., mixture POD) could be converted into a mixture RfD, using
expert judgment in UF application, and subsequently incorporated into the calculation of
a corresponding mixture-specific HBWC (e.g., Health Advisory or MCLG). However, it
is cautioned that such values would be specific to a given mixture of PFAS at defined
component proportions (e.g., individual PFAS water concentrations). Consistent with the
RPF approach, Section 7.2 presents an illustrative example for liver, thyroid, and
developmental effects associated with the hypothetical five component PFAS mixture. If
the total measured PFAS mixture water concentration exceeds the mixture-specific
HBWC, derived using the mixture toxicity value based on the M-BMD (and appropriate
composite UF application), for one or more effect domains, then there may be cause for
concern for the mixture at the reported/measured component water concentrations. If the
total mixture concentration is below all mixture-specific HBWCs calculated, then a
health risk is not anticipated.
4.2.2 Introduction to a Hypothetical Example with Five PFAS
In the following sections, the HI (Section 5), RPF (Section 6), and M-BMD (Section 7)
approaches will be detailed and accompanied by a demonstration of practical application to a
hypothetical five-component mixture of PFAS. As a reminder, PFAS 1-5 are:
PFAS 1 = comprehensively studied, most potent for effect(s) among PFAS 1-3, and has
formal noncancer human health assessment value(s) and an HBWC available;
48
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PFAS 2 = well-studied, second most potent for effect(s) among PFAS 1-3, and has
formal noncancer human health assessment value(s) and HBWC available;
PFAS 3 = studied, least potent for effect(s) among PFAS 1-3, and has formal noncancer
human health assessment value(s) and HBWC available;
PFAS 4 = in vivo animal toxicity data available but no formal human health assessment
and no HBWC; and
PFAS 5 = data-poor; no in vivo animal toxicity data or human available.
To help introduce the illustrative case study, the hypothetical drinking water scenario is as
follows: Periodic analysis of drinking water samples obtained at the tap across a community
revealed the presence of five PFAS, referred to as PFAS 1-5 (median concentrations shown in
Table 4-1), above the hypothetical analytical quantitation limits12 (Table 4-2).
Problem formulation and scoping. For simplicity, the problem formulation is scoped to "What
are the potential (noncancer) public health risks associated with exposure to the mixture of
PFAS 1-5 in drinking water for the community?" A formal problem formulation and scoping
exercise might include identifying population/community exposure details (e.g., distribution of
sexes, ages, exposure frequency, exposure duration), seasonal variations in PFAS 1-5 levels in
the drinking water, groundwater/surface water PFAS 1-5 concentrations, density of wells in the
community, and other modifying circumstances or factors.
Table 4-1. Hypothetical drinking water concentrations for five hypothetical PFAS.
PFAS exposure estimates (measured in drinking water) (ng/L)
PFAS1 PFAS2 PFAS3 PFAS4 PFAS5
Median 4~8 55 172 58 120
Note:
The values represent the median of a distribution of sampling data collected across a community over time.
Table 4-2. Hypothetical analytical quantitation limits for drinking water for five
hypothetical PFAS.
PFAS analytical quantitation limits (ng/L)
PFAS 1 PFAS 2 PFAS 3 PFAS 4
PFAS 5
Analytical limit
3.0 5.0 5.0 4.0
5.0
Step 1: Assemble information.
The structured literature search for the hypothetical mixture of PFAS 1-5 included
comprehensive Boolean search strings applied across information databases such as PubMed,
12 Analytical quantitation limits for chemicals are generally defined as the lowest detectable concentration of an analyte where the
accuracy achieves the objectives of the intended purpose. For example, the EPA's UCMR Program, the agency establishes
"minimum reporting levels" to ensure consistency in the quality of the information reported to the agency. Under UCMR 5, an
analytical quantitation limit is the minimum quantitation level that, with 95% confidence, can be achieved by capable analysts at
75% or more of the laboratories using a specified analytical method.
49
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Web of Science, Toxline, and the Toxic Substances Control Act Test Submissions (TSCATS)
(Figure 4-4). Please note that the specific PFAS names, synonyms, and Chemical Abstracts
Service (CAS) registry numbers in Figure 4-4 are for illustrative purposes only. In an
application, the search string(s) would need to be scoped and developed to optimize the literature
search for component PFAS on a case-by-case basis.
The assembled literature inventory was then screened at the title and abstract level to determine
preliminary relevance to informing human health risk assessment using defined Population,
Exposure, Comparator, and Outcome (PECO) elements, as illustrated in Figure 4-5. Again,
specific details provided in Figure 4-5 are for illustrative purposes only; mention of PFAS other
than the hypothetical PFAS 1-5 should not be construed as the basis of the illustrative PFAS
mixture example in subsequent sections.
* PubMed (National Library of Medicine)
• Web of Science [Thomson Reuters]
• Toxline f National Library of Medicine!
Search
Search strategy
Dates of search
PubMed
Search
terms
375-22-4(rn] OR "Heptafluoro-l-butanoic acid"(tw] OR "Heptafluorobutanoic
acid"[tw] OR "Heptafluorobutyric acid"(tw] OR "Kyselina
heptafluormaselna"[tw] OR "Perfluorobutanoic acid"[tw] OR
"Perfluorobutyric acid"Itw] OR "Perfluoropropanecarboxylic acid"[tw) OR
"2,2,3,3,4,4,4-heptafluoro-Butanoic acid"[tw] OR "Butanoic acid,
2,2,3,3,4(4,4-heptafluoro-"[tw] OR "Butanoic acid, heptafluoro-"[tw] OR
"Perfluoro-n-butanoic acid"[tw] OR "Perfluorobutanoate"[tw] OR
"2,2,3,3,4,4,4-Heptafluorobutanoic acid"[tw] OR "Butyric acid,
heptafluoro-"[tw] OR "Fluorad FC 23"[tw] OR "H 0024"[tw] OR "NSC 820"[tw]
OR ((PFBA[tw] OR "FC 23"[tw] OR HFBA[tw]} AND (fluorocarbori*[tw] OR
fluorotelomer*[tw] OR polyfluoro'Itw] OR perfluoro-*[tw] OR
perfluoroa*[tw] OR perfluorob*[tw] OR perfluoroc*[tw] OR perfluorod*[tw]
OR perfluoroe*[tw] OR perfluoroh*[tw] OR perfluoron*[tw] OR
perfluoroo*[tw] OR perfluorop*[tw] OR perfluoros*[tw] OR perfluorou*[tw]
OR perfluorinated[tw] OR fluorinated[tw] OR PFAS[tw] OR PFOS[tw] OR
PFOA[tw]))
No date
limit
Figure 4-4. Hypothetical PFAS-specific literature search string applied to toxicity
information databases such as PubMed, Web of Science, Toxline, and TSCATS.
50
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PECO element
Description
population
Human: Anv population and lifestage (occupational or general population. including children and other
sensitive populations).
Animal: Nonhuman mammalian animal species (whole organism) of anv lifestage (including fetal, earlv
postnatal, adolescents and adults).
Exposures
Relevant forms:
[chemical X] (CAS number)
Other forms of [chemical X] that readily dissociate (e.g., list any salts, etc.)
Metabolites of interest, including metabolites used to estimate exposures to [chemical X]
Occupations that may be considered surrogates of exposure
Human: Anv exposure to [chemical Xl Tvia [oral or inhalationl routetsl if applicablel. Studies will also be
Included if biomarkers of exposure are evaluated (e.g., measured chemical or metabolite levels in tissues or
bodily fluids) but the exposure route is unclear or likely from multiple routes. Other exposure routes, such
as those that are clearly dermal, will be tracked during title and abstract screening and tagged as
"potentially relevant supplemental material."
Animal: Anv exposure to fchemical XI via Toral or inhalationl routeM of >1 day duration, or anv duration
assessing exposure during reproduction or development. Studies involving exposures to mixtures will be
included only if they include an experimental arm with exposure to [chemical X) alone. Other exposure
routes, including [dermal or injection], will be tracked during title and abstract as "potentially relevant
supplemental material."
Comparators
Human: A comparison or referent population exposed to lower levels (or no exposure/exposure below
detection limits), or exposure for shorter periods of time, or cases versus controls, or a repeated measures
design. However, worker surveillance studies are considered to meet PECO criteria even if no statistical
analyses using a referent group Is presented. Case reports or case series of > 3 people will be considered to
meet PECO criteria, while case reports describing findings in 1-3 people will be tracked as "potentially
relevant supplemental material."
Animal: A concurrent control group exposed to vehicle-onlv treatment and/or untreated control (control
could be a baseline measurement, e.g., acute toxicity studies of mortality, or a repeated measure design).
Outcomes
All health outcomes (cancer and noncancer). In general, endpoints related to clinical diagnostic criteria,
disease outcomes, biochemical, histopathological examination, or other apical/phenotypic outcomes are
considered to meet PECO criteria.
Figure 4-5. Hypothetical PECO criteria and considerations used to determine study
relevance in the systematic review and evaluation of a literature inventory for chemicals
such as PFAS.
Following the removal of duplicate references and systematic screening of the initial inventory
using the defined PECO, nonrelevant studies/reports were excluded, and the remaining
references were full-text screened. The full-text screening resulted in three buckets of references:
(1) studies or reports meeting PECO; (2) studies or reports tagged as supplemental (i.e., do not
meet PECO criteria but are potentially relevant to the specific aims of the assessment); and
(3) studies or reports that upon further review were excluded as not PECO-relevant (bottom of
Figure 4-6).
51
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Literature Searches (through 2018)
PubMed
(n = 1,111)
WOS
(n = 707)
Toxline
(n = 862)
TSCATS
(n * 57)
Other
ATSDR assessment (n = 36)
Submitted to EPA (n = 0)
Non-English or non-peer
reviewed [n = 37)
NTP (2018) report (n ¦ 1)
/
Title & Abstract St
(1,202 records after dupl
FULL TEXT SCRE
Full-Text Scree
(n = 403)
Studies Meeting PEG
• Human health effects st>
¦ Animal health effect stu
¦ Genotoxicity studies (n =
¦ PBPK models
Tagged as Supplemental (n= 70)
• mechanistic or MOA (n = 26), ADME (n = 32),
qualitative exposure only (n = 12), mixture-only (n
= 0), non-PECO route of exposure (n = 0), case
report or case study (n = 0)
Figure 4-6. Example literature screening logic flow for hypothetical PFAS using an EPA
systematic review approach. The figure depicts example PECO-dependent development of
evidence bases to support human health assessment application(s). Note; Only four
hypothetical PFAS (i.e., PFAS 1-4) are represented, as PFAS 5 is data-poor.
Step 2: Evaluate data objectives.
For the hypothetical example, the systematic literature search and screening resulted in studies
meeting the PECO criteria for only PFAS 1-4. PFAS 5 was identified as data-poor (no in vivo
animal toxicity data or human data available) and further interrogated in the EPA's
Computational Toxicology Dashboard ( tps://comptox.epa.gov/dashboard/) for the presence of
alternative toxicity testing data (e.g., in vitro cell bioactivity assay data). At the conclusion of the
data gathering exercise for the five PFAS in Step 1 of the framework approach, and once all the
52
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hazard and dose-response studies and data were assembled and evaluated, it was determined
under Step 2 that: (1) there are no whole mixture/sufficiently similar mixture studies available
for the combination of PFAS of concern; (2) PFAS 1-3 have existing human health assessment
values for the oral route of exposure (Figure 4-7); (3) PFAS 4 has existing hazard and potentially
useful dose-response data but no known health assessment value(s); and (4) PFAS 5, although
data-poor, has predicted physicochemical property and empirical in vitro cell bioactivity data
(identified from the systematic literature search/publications tagged as supplemental and/or
searching of the EPA's Computational Toxicology Dashboard). In addition, across the five
hypothetical PFAS, different levels/types of TK data were identified. Specifically, clearance13
values for experimental rats and humans were located, for example, for PFAS 1-3. No clearance
or volume of distribution (Vd)14 values were identified for PFAS 4; only serum half-life data
were harvested where available. For PFAS 5, only rat serum half-life data were located from
publications tagged as supplemental during the literature search. This type of data is important
because it may inform data-driven cross-species extrapolation of TKs between experimental
animals and humans (i.e., kinetic adjustment of PODs and corresponding reduction of the
animal-to-human uncertainty factor [UFa]).
For brevity, in the evaluation of the hypothetical five-component PFAS mixture, the health effect
domains are truncated to three targets: liver, thyroid, and developmental. Across these three
target health effect domains, the studies meeting the PECO criteria were subjected to systematic
review principles and practice (e.g., risk of bias analysis; evidence integration) across the
available data sources/streams (Figure 4-8).
Step 3: Consider data landscape to select a component-based approach(es).
As illustrated in the hypothetical evidence heat map in Figure 4-8, PFAS 1, 2 and 4 have
traditional experimental animal assay hazard and dose-response data available for component-
based mixture assessment of liver effect(s); for thyroid effect(s), PFAS 1-3; and for
developmental effect(s), PFAS 1-4. The landscape of duration-relevant dose-response data (e.g.,
chronic) is then interrogated for identification of the single best study/dataset for a critical effect
to represent the PFAS in a given effect domain (Table 4-3). "Single best" study/dataset will be
subjective and user-dependent; however, considerations such as the methodological strengths of
the study(ies) (e.g., power of study/high N per treatment group, comprehensiveness and
transparency of toxicity evaluation; statistics), effect level identification (e.g., are both a LOAEL
and NOAEL identifiable?), and amenability to BMD modeling are just a few factors for which a
study might be selected. For PFAS with an existing health assessment (e.g., in the hypothetical
example, PFAS 1-3), the publishing authors have already made such decisions. In practice, if the
user of this framework deviates from use of existing health assessment dose-response metrics, a
clear rationale must be provided in the mixtures assessment. The dose-response data/metrics
(e.g., PODs, dose-response curves) selected across component PFAS should be clearly presented.
It should be noted that for NAM-based data (such as in vitro cell bioactivity in the hypothetical
13 Clearance represents the combined intrinsic ability of organs and tissues to remove chemical(s) from the plasma and is
commonly expressed in units of volume/time-body mass (e.g., L/day-kg body weight); it is typically calculated as the product of
the elimination rate constant (ke) x volume of distribution (Vd). An elimination rate constant represents the fraction of chemical
eliminated from the body per unit of time, commonly expressed in units of hour(s) or day(s).
14 The Vd represents the degree to which a chemical is distributed in body tissues. For example, chemicals highly bound to
plasma proteins and not broadly distributed in tissues have a low Vd; conversely, chemicals with low affinity for plasma proteins
typically have a high Vd and distribute broadly across tissues/compartments. Vd is commonly expressed in units of volume/body
mass (e.g., L/kg).
53
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PFAS mixture example), the "POD" should be clearly described consistent with the datastream
from which it was derived and expressed in terms of an HED. This facilitates comparisons to
other PODs (i.e., human epidemiological or experimental animal-based) for other mixture
components.
Figure 4-7. Example exposure-response arrays for the hypothetical example PFAS 1-3
identified as having existing human health risk assessment values for one or more exposure
durations.
54
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(A) Evidence Factors
Considered1
Consistency
Dose-response
Magnitude & precision
Coherence
Mechanistic evidence on
biological plausibility
(B)
Evidence Synthesis
Judgments
Consistent among studies
with minimal bias &
sensitive analyses,
additional support
Less consistent or low
confidence evidence, no
additional support
Adapted from Hill's considerations for causality (Hill,
1965). Analysis of each factor considers confidence in
individual studies (risk of bias, degree of sensitivity,
sources of susceptibility). For details and additional
context, see the EPA's ORD Staff Handbook for Developing
IRIS Assessments (USEPA, 2022g).
Robust
l
Strongest evidence,
Moderate
little or some
uncertainty
Slight
_|
Indeterminate
Compelling
evidence of no
effect
Inconsistent or little
confidence in
evidence, greater
uncertainty
1
Evidence Integration
Judgments
Evidence
demonstrates
Evidence likely
indicates
Evidence
suggests
Evidence
inadequate
Strong evidence
of no effect
°!
o era
e;
Effect Domain
PFAS 1
PFAS 2
PFAS 3
PFAS 4
PFAS 5
Liver
++
+++
—
++
*
Thyroid
++
++
+++
—
—
Developmental
+++
++
++
+++
—
Figure 4-8. Evidence synthesis and integration across three target health effects domains for
a mixture of five hypothetical PFAS. (A) Figure depicting evidence synthesis and integration
considerations and judgments, based on USEPA (2022g). (B) The heat map indicates the
strength of evidence supporting an effect of each hypothetical PFAS in each of three target
health effects domains. (+++) evidence likely indicates an effect; (++) evidence suggests an
effect; (—) evidence is inadequate to determine an effect. * Although PFAS 5 has no
applicable human epidemiological or traditional experimental animal assay data available,
in vitro cell bioactivity data are available from assays performed predominately in
hepatocyte cell lines.
55
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Table 4-3. Example data array to inform decisions in Steps 2 and 3 of the framework
approach for component-based mixtures assessment of PFAS.
Existent HHRA value
(mg/kg-day}
Existent dose-response data for
critical effect from principal study
Existent NAM data
PFAS 1
RfD:3 E-8
PODhed: 0.00001
UFC: 300
Critical effect: Delayed
growth and development in
offspring (ABC et al. 2022)
S-D rat; single generation
repro/dev study;
Daily gavage GD 1-20
(ABC etal. 2022)
K
1 '411 i ¦ -i.
Ojm (rnp%g-d|
PFAS 2
RfD: 1 E-5
PODHED: 0.0013
UFC: 100
Critical effect: Liver necrosis
(DEF et al. 2022)
S-D rat; 2-year bioassay;
DW ad libitum
(DEF et al. 2022) *!-
¦ V
Com
Bioactivity profile in ToxCast;
Biological perturbation at AED
(2.8 E-4 mg/kg-day) = -J/
epoxide hydrolase in HepaRG
cells; "t" oxidative stress
PFAS 3
RfD:7 E-4
PODhed: 0.21
UFC: 300
Critical effect: Decreased
thyroid hormones (T4/T3)
(GHI et al. 2022)
C57BL6 mouse;
90-day gavage;
(GHI etal. 2022)
•i ° n. a.
Odm i fT>>Vy-d|
PFAS 4
F344 Rat; two generation
repro/dev study;
Multiple dev outcomes
in offspring;
Feed ad libitum
(JKL etal. 2022)
' ^ ' TU
Dm* l TQ*g
-------
5.0 Hazard Index Approach
5.1 Background on the HI Approach
The HI is the EPA's most commonly used component-based mixture risk assessment method.
Because the HI employs a population-level human exposure and human health assessment value,
such as an oral RfD, this ratio indicates potential health risk(s). The HI is based on an
assumption of DA among the mixture components (USEPA, 2000b; Svendsgaard and Hertzberg,
1994). In the HI approach, an HQ is calculated as the ratio of human exposure I to a health-based
RfV for each mixture component chemical (USEPA, 1986). The HI is unitless, so in the HI
formula, E and the RfV must be in the same units (Equation 5-1). For example, if E is the oral
intake rate (mg/kg-day), the RfV could be the RfD, which has the same units. Alternatively, the
exposure metric can be a media-specific metric such as water concentration, and the toxicity
value is best represented as a duration-specific HBWC such as an EPA lifetime drinking water
Health Advisory (e.g., USEPA, 2022a, 2022b) or MCLG, or a similar value (e.g., developed by a
state). In this case, the HQ is calculated as the ratio of water concentration (in mass/volume) to
an HBWC (also in mass/volume). The component chemical HQs are then summed across the
mixture to yield the HI, as illustrated in Equation 5-1.
/=1 /=1 ' (Eqn. 5-1)
Where:
HI = Hazard Index
HQ; = Hazard Quotient for chemical i
E; = Exposure, i.e., dose (mg/kg-day) or media concentration, such as in drinking
water (ng/L), for chemical i
RfVi = Reference value (e.g., oral RfD or MRL (mg/kg-day), or corresponding health-
based, media-specific value; e.g., such as an HBWC, for example, a drinking
water Health Advisory or MCLG for chemical i (ng/L)
Because the numerator of each component chemical HQ is the estimated population-level human
exposure, the noncancer health RfVs used in the denominator must be based on human toxicity.
These RfVs are derived either directly from human study-based PODs (or measured or modeled
EDx from exposure-response data in a cohort or population) or as human-equivalent PODs
converted from experimental animal studies (e.g., conversion of a rodent POD to an HED
(PODhed) using cross-species TK-based modeling or allometric body-weight scaling).
The HI approach in practical application may be subdivided into a "general" HI and a "target-
organ-specific" HI (i.e., TOSHI). In either case, following the logic flow in Figure 4-1 to the
general HI or the TOSHI, they are both applied under an assumption of dose additivity. In the
general HI, the RfV for each mixture component chemical is used in the calculation of an HQ,
irrespective of the effect on which each component RfV is based (e.g., RfD for mixture
chemical 1 may be based on liver effect, for chemical 2 thyroid effect, and chemical 3
developmental effect). The resultant HI is generally a health-protective indicator because the
most sensitive health effects are often used as the basis for each respective chemical RfV and
57
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corresponding HQ. Conversely, the TOSHI entails derivation of HQs for each mixture
component chemical based on a toxicity value for the "same" effect, which may or may not be
the most sensitive or potent effect across the landscape of identified hazards. For example, in the
case of a liver-specific HI, for some mixture components, liver effect(s) may indeed be the basis
for the RfD, whereas for other components, the liver might be among the least sensitive of
effects.
In some cases, the liver may not be identified as a hazard for a given component chemical; for
example, the available toxicity data may be insufficient or lacking to support the derivation of a
toxicity value. To use this TOSHI approach more fully, organ-specific reference values (osRfVs)
or target-organ toxicity doses (TTDs) are needed (note: these are the same type of noncancer
values, just with different naming conventions) for each mixture component of potential concern.
Under the TOSHI approach, for chemicals lacking hazard and dose-response data from
traditional or NAM-based data streams for the selected health effect, it may not be possible to
determine their potential contribution to joint toxicity of the mixture, which might result in an
underestimation of the overall mixture risk.
An HI greater than one (1) is generally regarded as an indicator of potential adverse health risks
associated with exposure to the mixture. An HI less than or equal to 1 is generally regarded as
having no appreciable risk (recall that an RfV, such as an oral RfD, represents an estimate at
which no appreciable risk of deleterious effects exists), typically requiring no further analysis
(USEPA, 1986, 1991, 2000b). However, in some circumstances, the user may want to consider
an HI less than 1, for example, for screening when multiple contaminants of concern are present
at a site or one or more are present in multiple exposure media. In the case of PFAS, final peer-
reviewed toxicity assessments are only available for a small proportion of the approximately
15,000 environmentally relevant PFAS (e.g., see the summary of the EPA and ATSDR PFAS
assessments in Table 5-1). The EPA's primary source of peer-reviewed human health toxicity
assessments is its IRIS program, but in some cases (e.g., when no IRIS assessment exists or there
is a more current assessment from another authoritative source), the agency relies on assessments
from other EPA program offices, and other state, national, and international programs. U.S.
federal human health assessments, such as EPA's IRIS16, PPRTV17, the EPA Office of Water
toxicity assessments18, TSCA risk evaluations19, and ATSDR's ToxProfiles20, undergo rigorous
peer and public review processes (note: PPRTV assessments do not include public review); as a
result, they are considered to be of high scientific quality. The chronic RfDs for PFOA (USEPA,
2024a), PFOS (USEPA, 2024b), PFHxA (USEPA, 2023c), PFPrA (USEPA, 2023d), HQ-115
(USEPA, 2023 e), PFBA (USEPA, 2022e), PFBS (USEPA, 2021a), and HFPO-DA (USEPA,
2021b) represent the only final EPA toxicity values for PFAS available at the time of drafting of
this document. Several more PFAS assessments are under development in the EPA/ORD (e.g.,
PFHxS, PFNA, and PFDA; see Table 5-1 below) that can be considered in the future. Also, the
use of this approach could consider other PFAS toxicity values (e.g., ATSDR MRLs).
16 https://www.epa.gov/iris
17 https://www. epa. go v/pprtv
18 e-g-j https://www.epa.gov/systeiii/fLles/documents/2021-10/genx-chemicals-toxicity-assessment tech-edited oct-21-508.pdf
19 https://www.epa.gov/assessing-and-managing-chemicals-under-tsca/risk-evaluations-existing-chemicals-under-tsca
20 https://www.atsdr.cdc.gov/toxprofiledocs/iiidex.litiiil
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Table 5-1. EPA and ATSDR peer-reviewed human health assessments containing
noncancer toxicity values (RfDs or MRLs) for PFAS that are final or under development.
ATSDR intermediate oral
Chemical
EPA chronic oral RfD
MRLa
PFOA
Final 2024 RfD = 3 x 10"8
Final 2021 MRL = 3 x 10"6
mg/kg/day (USEPA, 2024a)
mg/kg/day (ATSDR, 2021)
PFOS
Final 2024 RfD = 1 x 10"7
Final 2021 MRL = 2 x 10"6
mg/kg/day (USEPA, 2024b)
mg/kg/day (ATSDR, 2021)
PFNA
Under development in the EPA
Final 2021 MRL = 3 x 10"6
IRIS program
mg/kg/day (ATSDR, 2021)
PFDA
Draft 2023 RfD = 4 x 1010
mg/kg/day (USEPA, 2023f)
N/A
PFBA
Final 2022 RfD = 1 x 10"3
mg/kg/day (USEPA, 2022e)
N/A
PFBS
Final 2021 RfD = 3 x 10"4
mg/kg/day (USEPA, 2021a)
N/A
PFHxA
Final 2023 RfD = 5 x 10"4
mg/kg/day (USEPA, 2023c)
N/A
PFHxS
Draft 2023 RfD = 4 x 1010
Final 2021 MRL = 2 x 10"5
mg/kg/day (USEPA, 2023g)
mg/kg/day (ATSDR, 2021)
HFPO-DA
Final 2021 RfD = 3 x 10"6
mg/kg/day (USEPA, 2021b)
N/A
PFPrA
Final 2023 RfD = 5 x 10"4
mg/kg/day (USEPA, 2023d)
N/A
HQ-115b
Final 2023 RfD = 3 x 10"4
mg/kg/day (USEPA, 2023e)
N/A
Notes: N/A = Not available.
aNote that MRLs and RfDs are not necessarily equivalent (e.g., intermediate duration MRL vs. chronic duration RfD; the EPA
and ATSDR may apply different uncertainty/modifying factors) and are developed for different purposes.
bHQ-l 15 is the trade name for lithium bis[(trifluoromethyl)sulfonyl]azanide (CASRN 90076-65-6).
Some state health agencies publish toxicological assessments for PFAS that could potentially be
used in HI calculations. For example, the Minnesota Department of Health publishes
Toxicological Summaries that include the assessment of available toxicological information and
subsequent development of oral toxicity values if adequate data are available (MN DOH, 2021).
It should be noted that state or other (e.g., international) assessments may have varying levels of
peer and public review and may reflect different risk assessment practices or policy choices as
compared to the EPA or ATSDR assessments.
There may be scenarios where a final peer-reviewed toxicity assessment for one or more
component chemicals in a mixture is not available. In these cases, evaluating available hazard
and dose-response information for PFAS in the mixture may be necessary under an HI approach.
For instance, there may be a need to develop toxicity value(s) to estimate potential risks
associated with site-specific/localized contamination from a PFAS mixture with a component(s)
that may not be relevant to other areas, sites, or exposure sources, and/or has not been prioritized
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for assessment at the federal level. In such cases, the user of this framework might need to
develop a targeted, fit-for-purpose assessment, if possible (i.e., based on the availability of
hazard and dose-response data, resources, and expertise). Excluding component PFAS that lack
off-the-shelf toxicity values from further analysis could result in an underestimation of the
potential health risk(s) of the mixture. If de novo derivation of toxicity values is necessary, it is
recommended that experts in hazard identification and dose-response assessment be consulted
for scientific input and review, and the associated uncertainties (e.g., data gaps) be transparently
characterized. The EPA has published several peer-reviewed documents that may assist in efforts
to derive chronic (or subchronic) oral RfDs for chemicals with no available peer-reviewed
toxicological assessment (for more information, see the EPA's Human Health Risk Assessment
website at https://www.epa.eov/risk/human-health-risk-assessment).
To date, the majority of environmental chemicals, including PFAS, are data-poor, having no
known or available information to inform hazard or dose-response in a screening/prioritization or
assessment context. Considering that the number of legacy and new(er) chemicals present in
commerce and the environment is in the tens of thousands, the generation of traditional animal
toxicity data to support hazard identification and dose-response assessment would take decades
and extraordinary numbers of animals and fiscal resources to complete. As human populations
and biota are currently exposed to mixtures of chemicals such as PFAS, it is critical to identify
methods, approaches, and platforms that can provide some reasonable context for potential
human health hazard(s) and associated dose-response/potency for effects associated with
exposure to multiples of PFAS (i.e., two or more co-occurring PFAS). A diverse set of resources
has been developed over the past 15 + years that entails, in general, high(er)-throughput assays in
cell culture (or cell-free) systems (e.g., transcriptomics; macromolecular/cellular bioactivity), in
silico computational prediction models, alternative animal species (e.g., zebrafish), and refined
short-term laboratory rodent assays and databases and platforms to collate and deliver such data
to end-users. These methods, assays, and platforms are collectively referred to as NAMs. In the
absence of traditional experimental animal bioassay and human epidemiological information,
NAMs could potentially play a pivotal and transformational role in human health risk
assessment, particularly in evaluating hazard and dose-response of PFAS that co-occur in
mixtures.
Individually or in concert, NAMs such as in vitro cell bioactivity and in silico platforms (e.g.,
quantitative structure-activity models) might inform the identification or prediction of data that
can be used in PFAS-specific hazard and dose-response assessment. For example, in vitro
concentration-bioactivity data from resources such as ToxCast and Tox21 can be transformed
into an estimated human in vivo exposure-response using IVIVE and rTK (Rotroff et al., 2010;
Wambaugh et al., 2015; Wetmore et al., 2012, 2014). These administered human-equivalent dose
datasets could potentially then be used to identify PODs (e.g., BMDs, NOAELs, LOAELs) and,
with an expert-driven application of appropriate UFs, be leveraged into the derivation of
corresponding noncancer toxicity values. These NAM-based toxicity values could then be
converted into corresponding HBWCs and used, with exposure data, to calculate HQs for data-
poor PFAS. Alternatively, particularly for RPF application, NAM-based dose-response data
expressed in HEDs could be leveraged to calculate/model BMD values (e.g., BMDx-aed) for
expertly selected bioactivity (e.g., same/similar transcriptional pathway(s) and/or cellular
bioactivity) to compare to that of a more data-informed member of the mixture with a similar
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bioactivity profile (i.e., mixture IC). This might facilitate the derivation of RPFs for data-poor
mixture components.
A critical consideration in using NAM-based hazard and concentration/dose-response data is
recognizing that for some platforms or bioassays, perturbations of underlying biological
pathways may not be readily identifiable as being directly related to specific apical toxic effects
or even the hazard domain of interest. That is, chemical exposures may elicit a myriad of
perturbations or responses at the molecular, macromolecular, or cellular level, with some
alterations being critical or key to eliciting an apical toxic effect level response, whereas many
other alterations may seemingly have no relationship to toxic effect(s) (e.g., general stress,
housekeeping). However, the dose-response relationship associated with non-apical perturbations
or effects (e.g., cell-based bioactivity) may be considered in a health effect agnostic context.
Specifically, although there may not be clear qualitative linkages between non-apical biological
perturbations and a specific, apical tissue- or organ-level effect, corresponding dose-response
relationships for biological perturbations have been shown to provide a quantitative
approximation for dose-response (e.g., POD) associated with traditional apical effects that are
protective for the majority of chemicals evaluated (Paul-Friedman et al., 2020; Johnson et al.,
2020; Thomas et al., 2011, 2013). The implication for use of NAM data such as in vivo or in
vitro cell-based bioactivity or transcriptomics, for example, is that pathway- or cell function-
based response levels (e.g., effect concentration 50 [ECso], inhibitory concentration 50 [IC50], or
other biologically supported response levels of interest), could potentially be leveraged and
applied in the mixture component approaches proposed in this chapter if the accuracy of the
predicted PODs can demonstrated (e.g., HI, RPF, M-BMD).
In summary, considering the lengthy and resource-intensive processes and study protocols (e.g.,
OECD Test Guidelines-type studies) typically involved in generating traditional repeat-dose
bioassay data for human health assessment of chemicals, incorporating NAMs could potentially
serve an important role for PFAS screening and assessment, including in a mixture context. It is
recognized that the practical application of NAMs in an assessment, whether for a single
chemical or mixtures of chemicals, would be dependent on whether the results provide
information that fits a decision context or purpose, and this may not be intuitive. It is
recommended that experts in NAM data interpretation be consulted for potential integration into
mixtures screening/assessment to contextualize the applicability of results appropriately and that
they transparently communicate uncertainties associated with a given platform or assay output(s)
in human health assessment.
5.2 Illustrative Example Application of the General HI to a Hypothetical Mixture
of Five PFAS
As mentioned previously, final human health assessments with chronic oral RfDs exist for
hypothetical PFAS 1-3. Based on the RfDs for PFAS 1-3 (see Table 4-3), PFAS 1 is a
comprehensively studied chemical that is most potent for effect(s); PFAS 2 is also well-studied
but is less potent than PFAS 1 for effect(s); and PFAS 3 has been studied and is even less potent
than PFAS 1 or 2. PFAS 4 has experimental animal toxicity data available but no formal human
health assessment. Finally, PFAS 5 is data-poor and was identified as having only bioactivity
data available under Step 2 of the framework approach to inform hazard and dose-response (see
Table 4-3). As PFAS 1-3 have existing human health assessment values, integration into the HI
approach is simplified. However, for both PFAS 4 and 5, integration would necessitate de novo
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calculation of noncancer health RfVs to develop component HQs and an overall PFAS mixture
HI (Equation 5-1). For the purposes of the defined illustrative example for the hypothetical five-
component PFAS mixture, this process is as follows:
5.2.1 General HI Step 1: Assemble/derive component chemical health effects-based
values (e.g., Chronic oral RfDs)
PFAS 1-3: Upon review of the available information harvested in the literature search in Step 1
of the framework approach, formal human health assessments containing oral RfDs were
identified (see Table 4-3). However, the critical effect on which each corresponding RfD was
derived is in different effect domains: PFAS 1 critical effect = developmental effect in offspring;
PFAS 2 critical effect = liver effect in adults; and PFAS 3 critical effect = thyroid hormone effect
in adult females (in a repro/developmental life stage). As such, applying the general HI is
optimal in this scenario and will entail using the overall RfD (or ATSDR MRL), regardless of
the underlying critical health effect. If a subchronic RfD or an MRL is only available for an
intermediate duration (akin to subchronic duration for EPA purposes), the user may consider the
available evidence base. Additional uncertainty (e.g., subchronic-to-chronic duration) may be
considered for extrapolation to a corresponding chronic duration value, unless
subchronic/intermediate duration is the target.
PFAS 4: No federal, state, or other assessments with an RfV are available, but traditional hazard
and dose-response (e.g., traditional experimental animal study) data were judged adequate to
support derivation. Systematic review and evaluation of the animal study data led to the
identification of a single best study (e.g., hypothetical 2-Gen repro/dev rat study; see Table 4-3)
and multiple developmental health outcomes as candidate critical effects such as delayed growth
and development at postnatal day 1 (PND 1) and decreased neonatal viability and thyroid
hormone levels at PND 4. Thus, the user may choose to calculate an RfV using appropriate dose-
response metrics (i.e., PODhed) and the application of UFs. Appropriate characterization of
hazard conclusions and qualitative and quantitative confidence and uncertainty(ies) in de novo
derivation of RfVs for PFAS in this category is imperative. For the specific hypothetical PFAS
example, the dose-response data associated with delayed growth and development in PND 1 rat
offspring provided the most robust endpoint and confidence in dose-response for PFAS 4.
Following BMD modeling (as per the EPA BMD guidelines [USEPA, 2012]), a lower statistical
bound on a BMD (BMDL) for developmental effects of 1.06 mg/kg-day was calculated and used
as the POD.
As the candidate POD for RfD derivation is identified from rats, available TK data for PFAS 4 in
rats and humans should be considered for a data-informed adjustment approach for cross-species
extrapolation (i.e., estimating the dosimetric adjustment factor [DAF]; Equation 5-2). In
Recommended Use of Body Weight4 as the Default Method in Derivation of the Oral Reference
Dose (USEPA, 201 lb), the EPA endorses a hierarchy of approaches to derive human equivalent
oral exposures using data from laboratory animal species, with the preferred approach being
physiologically based TK modeling. Other approaches might include using chemical-specific
information without a complete physiologically based TK model. In the absence of chemical-
specific models or data to inform the derivation of human equivalent oral exposures, the EPA
endorses BW3/4 as a default to extrapolate toxicologically equivalent doses of orally administered
agents from laboratory animals to humans to derive an RfD under certain exposure conditions. In
this illustrative hypothetical mixture example, it was determined that: (1) clearance values for
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experimental animals and humans were available and included in the dosimetric adjustment of
PODs used in the derivation of noncancer human health assessment values for PFAS 1-3, and
(2) kinetic data for PFAS 4 are sufficient to support a data-informed dosimetric adjustment of the
rat POD. Briefly, while specific TK data needed to estimate clearance or volume of distribution
in rodents or humans for PFAS 4 were not available, clearance values for humans and rats could
be estimated, under the assumption that the volume of distribution in human females is equal to
female adult rats (i.e., the PFAS-exposed unit leading to effects in PND 1 offspring), as follows:
Clearance = elimination rate constant (ke) x volume of distribution (Vd)
Where ke= (ln2 / plasma half-life) = (0.693 / plasma half-life), and Vd is assumed equivalent
between female rats and humans.
Having made this assumption, the ratio of clearance values (Table 5-2) in human females to that
in female rats, CLh:CLa, can be used to calculate the DAF, and the resulting HED can be
calculated using Equation 5-2 as follows:
HED = POD X ^ (Eqn. 5-2)
cla
Where:
POD = the rat BMDLisd of 1.06 mg/kg-day
DAF = CLh/ CLa
CLa = 0.021 L/day-kg (female adult rat; the effect in offspring is a function of maternal
intake)
CLh = 0.000028 L/day-kg
The application of the hypothetical DAF to the rat POD results in a PODhed of 0.0011 mg/kg-
day. This PODhed was then divided by a hypothetical composite UF of 100, resulting in an RfD
for PFAS 4 = PODhed / UF = 0.0011 mg/kg-day / 100 = 1 E-5 mg/kg-day.
PFAS 5: Because no final federal, state, or other RfD or MRL, or traditional hazard and dose-
response data are available, NAM data streams could be surveyed and leveraged for PFAS
information that might facilitate the development of a POD and, potentially, derivation of a
NAM-based RfV using the application of UFs consistent with the data scenario (Judson et al.,
2011; Parish et al., 2020). It is recommended that NAM data be systematically evaluated for
suitability in supporting the derivation of RfVs using accepted approaches and practice.
Unfortunately, no formal EPA technical guidelines currently exist to guide the approach for the
use of NAM-based PODs in quantitative human health risk assessment applications. However,
for the purposes of demonstrating the potential application of NAM data (e.g., in vitro cell
bioactivity) in the hypothetical PFAS mixture evaluation, the general process within the context
of this framework approach is as shown in Figure 5-1.
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Table 5-2. Calculation of estimated clearance values for PFAS 4 in female rats and humans.
PFAS 4
Plasma half-life (hr)
Elimination rate
constant (hr"1)
Volume of Distribution
(L/day)*
Estimated Clearance
(L/day-kg)
Female rats
33.6
0.021
1.0
0.021
Humans
24,528
0.000028
1.0
0.000028
Note:
* The value of 1.0 was used for volume of distribution (Vd) strictly for the purpose of calculating an estimated clearance value;
the Vd of 1.0 is not based on empirical evidence for PFAS 4.
Bioactivity
data in
human or
animal
tissue/cells
Convert in vitro
concentrations to
in vivo AEDs in
human using
IVIVE/rTK
Perform dose-
response analyses
on AED data (e.g.,
benchmark dose)
to identify POD
Apply
uncertainty
factors to human
PODs to derive a
bioactivity-based
RfV
Figure 5-1. General steps to derive bioactivity-based RfV using bioactivity data in human or
animal tissue/cells.
The detailed steps and mechanics of the bioactivity > IVIVE/rTK > AED process outlined in
Figure 5-1 are beyond the scope of this framework document; the reader is referred instead to
(Paul-Friedman et al., 2020; Wambaugh et al., 2018; Wetmore et al., 2012, 2014, 2015) for
better context for the conversion of in vitro cell-based exposure concentrations to approximately
equivalent human external exposure doses using IVIVE and rTK. In this hypothetical example,
the AED identified as a NAM-based human POD for PFAS 5 is a BMD modeled off of the
AED-based dose-response data for the decreased epoxide hydrolase endpoint in liver (HepaRG)
cells; in practice, there are no a priori data-driven or default BMRs suggested for NAM data.
BMR identification will be assay/platform/data-specific and should be contextualized by expert-
driven analysis of the available data. As such, due to this flexibility in NAM-based data
application, in the hypothetical PFAS mixture example, the BMDx-aed = 0.004 mg/kg-day. This
human equivalent POD was then divided by a hypothetical composite UF of 100. The resulting
RfD for PFAS 5 = 0.004 mg/kg-day / 100 = 4 E-5 mg/kg-day. Appropriate characterization and
denoting of confidence and qualitative and quantitative uncertainty(ies) in the NAM data
leveraged in POD identification and corresponding RfVs derived for PFAS in this category is
imperative. Consultation with experts in the field of NAM data interpretation and risk assessment
application is recommended for data-poor PFAS.
Summary of RfDs
In summary, as shown in Table 5-3, RfDs for PFAS 1-5 range from 10"4 to 10"8 mg/kg-day,
with PFAS 1 being the most potent overall. Note that PFAS 4 and 5 have similar RfDs despite
their different data limitations.
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Table 5-3. Summary of PODheds and RfDs for hypothetical PFAS in a mixture.
Liver
Thyroid
Develop-
mental
PODhed
PODhed
PODhed
RfD
(mg/kg-day)
(mg/kg-day)
(mg/kg-day)
(mg/kg-day)
Basis
PFAS 1
0.044
0.24
0.00001
3 E-8
formal toxicity
(BMDLx-hed)
(BMDL y-hed)
(BMDL z-hed)
assessment
PFAS 2
0.0013
0.23
0.0051
1 E-5
formal toxicity
(BMDLx-hed)
(BMDL y-hed)
(BMDL z-hed)
assessment
PFAS 3
N/A
0.21
(BMDL y-hed)
2.1
(BMDL z-hed)
7E-4
formal toxicity
assessment
PFAS 4
50
(BMDLx-hed)
N/A
0.0011
(BMDL z-hed)
1 E-5
high quality in
vivo data
PFAS 5
0.004
(BMDx-aed)3
N/A
N/A
4 E-5
bioactivity-
based
Note:
Bold values indicate the lowest (most-sensitive) POD for the corresponding RiD derivation.
a Represents the NAM-based POD for in vitro cell bioactivity (e.g., for PFAS 5 = j epoxide hydrolase activity).
5.2.2 General HI Step 2: Assemble/derive health-based media concentrations (HBWC)
Depending on the problem formulation, the user can either use the oral RfVs calculated for
mixture components or leverage such values in the calculation of media-specific values, such as
HBWCs for drinking water. Care should be taken to ensure that all HBWCs are applicable to the
same exposure duration. In the following examples, the HBWCs are derived using chronic oral
RfDs and, thus, are considered health protective values over a lifetime of exposure.
How to Calculate an HBWC for Drinking Water
The following equation is used to derive a noncancer HBWC. A noncancer HBWC, such as a
lifetime HA or MCLG, is designed to be protective of noncancer effects over a lifetime of
exposure, including for sensitive populations and life stages, and is typically based on data from
chronic experimental animal toxicity and/or human epidemiological studies. The calculation of
an HBWC includes an oral RfV such as an RfD (or chronic MRL or duration relevant user-
provided value), body weight-based drinking water intake (DWI-BW), and a relative source
contribution (RSC) factor as presented in Equation 5-3.
Noncancer HBWC = (RfD/(DWI-BW)) * RSC (Eqn. 5-3)
Where:
RfD = chronic reference dose—an estimate (with uncertainty spanning perhaps an order
of magnitude) of a daily oral exposure of the human population to a substance that is
likely to be without an appreciable risk of deleterious effects during a lifetime (see HI
Step 1 above).
DWI-BW = the 90th percentile drinking water intake (DWI) for the selected population
or life stage, adjusted for body weight (BW), in units of liters of water consumed per
kilogram body weight per day (L/kg bw-day). The DWI-BW considers both direct and
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indirect consumption of drinking water (indirect water consumption encompasses water
added in the preparation of foods or beverages, such as tea or coffee).
RSC = relative source contribution—the percentage of the total exposure attributed to
drinking water sources (USEPA, 2000a), with the remainder allocated to all other routes
or sources.
When developing HBWCs, the goal is to protect all ages of the general population, including
potentially sensitive populations or life stages such as children. The approach to select the DWI-
BW and RSC for the HBWC includes a step to identify sensitive population(s) or life stage(s)
(i.e., populations or life stages that may be more susceptible or sensitive to a chemical exposure)
by considering the available data for the contaminant. Although data gaps can make it difficult to
identify the most sensitive population (e.g., not all windows or life stages of exposure or health
outcomes may have been assessed in available studies), the critical effect and POD that form the
basis for the RfD can provide some information about sensitive populations because the critical
effect is typically observed at the lowest tested dose among the available data. Evaluation of the
critical study, including the exposure interval, may identify a particularly sensitive population or
life stage (e.g., pregnant women, infants, lactating women). In such cases, the user can select the
corresponding DWI-BW for that sensitive population or life stage from the Exposure Factors
Handbook (USEPA, 2019a) to derive the HBWC. In practice, when multiple populations or life
stages are identified based on the critical study design and critical effect or other health effects
data (from animal or human studies), the EPA selects the population or life stage with the
greatest DWI-BW because it is the most health protective. This approach ensures that all
populations and life stages are protected at the HBWC, and in the case of the HI approach, that
each component HQ and the overall HI is protective of all populations and life stages. In the
absence of information indicating a sensitive population or life stage (e.g., nondevelopmental
critical effect as for PFAS 2 and 3 or NAM-based RfD as for PFAS 5), the DWI-BW
corresponding to all ages of the general population may be selected (Table 5-4).
Table 5-4 shows the EPA exposure factors for DWI for some sensitive populations and life
stages. Other populations or life stages may also be considered depending on the available
information regarding study design and sensitivity to health effects after exposure to a
contaminant.
To account for potential aggregate risk from exposures and exposure pathways other than oral
ingestion of drinking water, the EPA applies an RSC when calculating HBWCs to ensure that
total human exposure to a contaminant does not exceed the daily exposure associated with the
RfD. When data are available for multiple sensitive populations or life stages, the most health-
protective RSC is selected. The RSC represents the proportion of an individual's total exposure
to a contaminant attributed to drinking water ingestion (directly or indirectly in beverages like
coffee, tea, or soup, as well as from transfer to dietary items prepared with drinking water)
relative to other exposure pathways. The remainder of the exposure equal to the RfD is allocated
to other potential exposure sources (USEPA, 2000a). The purpose of the RSC is to ensure that
the level of a contaminant (e.g., HBWC), when combined with other identified potential sources
of exposure for the population of concern, will not result in exposures that exceed the RfD
(USEPA, 2000a).
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Table 5-4. EPA exposure factors for drinking water intake.
Population or life stage
DWI-BW
(L/kg bw-
day)
Description of exposure
metric
Source
General population
0.0338
90th percentile direct and
indirect consumption of
community water, consumer-
only two-day average, all
ages.
2019 Exposure
Factors Handbook
Chapter 3, Table 3-
21, NHANES 2005-
2010 (USEPA,
2019a)
Infants
0.143
90th percentile direct and
indirect consumption of
community water, consumer-
only two-day average, birth
to < 1 year.
2019 Exposure
Factors Handbook
Chapter 3, Table 3-
21, NHANES 2005-
2010 (USEPA,
2019a)
Children
0.0343
90th percentile direct and
indirect consumption of
community water, consumer-
only two-day average, birth
to < 21 years.
2019 Exposure
Factors Handbook
Chapter 3, Table 3-
21, NHANES 2005-
2010 (USEPA,
2019a)a
Pregnant women
0.0333
90th percentile direct and
indirect consumption of
community water, consumer-
only two-day average.
2019 Exposure
Factors Handbook
Chapter 3, Table 3-
63, NHANES 2005-
2010 (USEPA,
2019a)
Women of childbearing
age
0.0354
90th percentile direct and
indirect consumption of
community water, consumer-
only two-day average, 13
to < 50 years.
2019 Exposure
Factors Handbook
Chapter 3, Table 3-
63, NHANES 2005-
2010 (USEPA,
2019a)
Lactating women
0.0469
90th percentile direct and
indirect consumption of
community water, consumer-
only two-day average.
2019 Exposure
Factors Handbook
Chapter 3, Table 3-
63, NHANES 2005-
2010b (USEPA,
2019a)
Notes:
CSFII = continuing survey of food intake by individuals; L/kg bw-day = liter per kilogram body weight per day.
aDWI-BWs are based on NHANES 2005-2010 data, also reported in the Exposure Factors Handbook. DWI-BWs for this
population or life stage were calculated using the EPA's Food Commodity Intake Database, Commodity Consumption
Calculator (https://fcid.foodrisk.org/percentiles).
bEstimates are less statistically reliable based on guidance published inNCHS (1993).
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To determine the RSC, the EPA follows the Exposure Decision Tree for Defining Proposed RfD
(or POD/UF) Apportionment in the EPA's Methodology for Deriving Ambient Water Quality
Criteria for the Protection of Human Health (USEPA, 2000a). The EPA considers whether there
are significant known or potential uses/sources other than drinking water, the adequacy of data
and strength of evidence available for each relevant exposure medium and pathway, and whether
adequate information on each source is available to quantitatively characterize the exposure
profile. The RSC is developed to reflect the exposure to the general population or a sensitive
population within the general population exposure.
Per the EPA's methods, in the absence of adequate data to quantitatively characterize exposure
to a contaminant, the EPA typically recommends an RSC of 20%. When scientific data
demonstrating that sources and routes of exposure other than drinking water are not anticipated
for a specific pollutant, the RSC can be raised as high as 80% based on the available data,
thereby allocating the remaining 20% to other potential exposure sources (USEPA, 2000a). For
the illustrative hypothetical PFAS mixture, an RSC of 0.2 (i.e., 20%) is selected as no
information was identified to suggest a higher value. The calculation of HBWCs for PFAS 1-5 is
presented in Table 5-5.
5.2.3 General HI Step 3: Select exposure estimates (measured water concentrations)
Select appropriate exposure estimates consistent with the problem formulation. Specifically, the
user may choose to calculate or use exposure estimates for the oral route in general (i.e., total
intake in mg/kg-day) or media-specific concentrations. In the hypothetical PFAS mixture
example, "exposure" is represented by the drinking water monitoring data in Table 4-1.
5.2.4 General HI Step 4: Calculate PFAS mixture potency (component HQs and overall
HI)
Using the median of the drinking water monitoring data (see Table 4-1) and the calculated
HBWCs for PFAS 1-5 (see Table 5-5), individual component HQs are derived as shown in
Table 5-6. Component HQs are expressed to two decimal places (hundredths place) and then
summed across the PFAS mixture to yield the HI. The HI is rounded to one (1) significant digit.
Table 5-5. Calculation of hypothetical HBWCs for example PFAS in a mixture.
Chemical
Oral reference
dose (mg/kg-day)
DWI-BW
(L/kg-day)
RSC
HBWC (ng/L)
PFAS 1
3 E-8
0.0354
0.2
0.2
PFAS 2
1 E-5
0.0338
0.2
60
PFAS 3
7E-4
0.0338
0.2
4,000
PFAS 4
1 E-5
0.0469
0.2
40
PFAS 5
4 E-5
0.0338
0.2
200
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Table 5-6. Calculation of individual component HQs for the hypothetical PFAS mixture.
Hypothetical drinking
water exposure
estimate (ng/L)
Hypothetical HBWC
(ng/L)
Hypothetical general
HQ
PFAS 1
4.8
0.2
24.00
PFAS 2
55
60
0.92
PFAS 3
172
4,000
0.04
PFAS 4
58
40
1.45
PFAS 5
120
200
0.60
Mixture general HI
27.01 (rounded to 30)
Note:
HQ is the DW exposure estimate / HBWC; HI is the sum of individual HQs.
5.2.5 General HI Step 5: Interpret the PFAS mixture HI
The HI (30) in the hypothetical example is significantly greater than 1, indicating potential health
risks resulting from exposure to the mixture of PFAS at the measured drinking water
concentrations. Further, as illustrated by the individual component HQs, PFAS 1 and 4 are risk
drivers of the mixture HI with individual HQs greater than 1; PFAS 2 and 5 also appear to be
contributors with HQs of 0.92 and 0.60, respectively. Assessment of PFAS 2 and 5 in isolation
(individually) would indicate no/low health risk (i.e., individual HQs < 1.00), but the assessment
of the binary mixture of PFAS 2 and 5 would indicate appreciable risk (HI = 1.52, rounded to 2).
Conversely, with an HQ of 0.04, PFAS 3 is less influential than the other mixture components. In
this hypothetical scenario, clearly PFAS 1 and 4, and potentially PFAS 2 and 5, might be
prioritized for remediation activity(ies).
It should be noted that in the example PFAS mixture, the hypothetical HBWC for PFAS 1
(0.2 ng/L) is lower than its corresponding hypothetical drinking water analytical quantitation
limit of 3 ng/L (see Table 4-2) by over an order of magnitude. In such cases, any detectable level
(i.e., of PFAS 1) will result in an HI greater than 1 for the whole mixture.
5.3 Illustrative Example Application of the Target-Organ-Specific Hazard Index to
a Hypothetical Mixture of Five PFAS
5.3.1 TOSHI Step 1: Assemble/derive component health effects endpoints (RfDs or
target-organ toxicity doses)
Application of the TOSHI is essentially identical to the steps for the general HI. The critical
nuance is that the use of human health/toxicity values across mixture components is
effect/endpoint-specific. For some PFAS, this might be the overall RfD or MRL; for other PFAS,
this may involve TTDs (i.e., an RfD for a specific health effect that may differ from the overall
RfD for a given component chemical). In the TOSHI approach, there is a greater likelihood that
TTDs have not been derived for effects other than the critical effect that underpins the derivation
of an overall RfD for a given PFAS, although in some federal and state purviews, this practice is
changing. In those instances where only an overall RfD (or ATSDR MRL) has been derived,
TTDs could potentially be derived de novo for other health effect domains but should be
accomplished with transparent characterization of qualitative and quantitative uncertainties
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associated with hazard and dose-response data on a case-by-case basis. TTDs are derived
identically to RfDs; however, there may be differing circumstances to consider such as type of
POD (e.g., BMD vs. NOAEL or LOAEL), cross-species TK dosimetric adjustment (e.g., RfD
may have been derived from a POD based on an adjustment of rat kinetics to human kinetics,
whereas a TTD for the same chemical might be mouse to human resulting in a different
PODhed), and/or different qualitative and quantitative uncertainties. In practice, human health
assessment applications, including mixtures assessment, may be more robust if TTDs are derived
across all health outcome domains that are supported by evidence. For the purposes of the
illustrative hypothetical PFAS mixture example, the calculation of TTDs is limited to the three
selected health effect domains listed in Table 5-7. Several more TTDs could potentially be
derived based on the availability of data and confidence in the evidence conclusions.
5.3.2 TOSHI Step 2: Assemble/derive health-based media concentrations (HBWC)
To calculate HBWCs for the TOSHI, the TTDs for a specific effect domain across mixture
components are used in the calculation of HQs and a TOSHI. For example, a TOSHI for
developmental effects (TOSHIdev) for the hypothetical PFAS mixture can be calculated using
the developmental TTDs, appropriate DWI-BWs, and RSCs (Table 5-8). Each DWI-BW is the
90th percentile direct and indirect consumption of community water, consumer-only two-day
average and was selected based on sensitive populations or life stages as identified by evaluating
of each of the critical studies, including the exposure intervals. For this hypothetical example, the
DWI-BW for PFAS 1, 2, and 3 is for women of childbearing age (13 to < 50 years), and the
DWI-BW for PFAS 4 is for lactating women (see Table 5-4).
Table 5-7. Hypothetical TTDs for the hypothetical component PFAS; the bolded numbers
represent the overall RfD for each respective PFAS.
Target Organ Toxicity Doses (mg/kg-day)
Effect domain PFAS 1
PFAS 2
PFAS 3
PFAS 4
PFAS 5
Liver
7 E-6
1 E-5
-
5 E-5
4 E-5*
Thyroid
2 E-6
4 E-4
7 E-4
-
-
Developmental
3 E-8
9 E-3
2 E-3
1 E-5
-
Note:
* TTDnam based on in vitro perturbation indicative of oxidative stress in liver cells.
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Table 5-8. Calculation of hypothetical developmental effect-specific HBWCs for
hypothetical PFAS in a mixture using TTDs.
Chemical
Target-organ
toxicity dose
(mg/kg-day)
DWI-BW
(L/kg-day)
RSC
TOSHIdev HBWC
(ng/L)
PFAS 1
3E-8
0.0354
0.2
0.2
PFAS 2
9E-3
0.0354
0.2
50,000
PFAS 3
2E-3
0.0354
0.2
10,000
PFAS 4
1 E-5
0.0469
0.2
40
PFAS 5
—
N/A
N/A
ND
Notes:
N/A = not applicable; ND = not determined.
Bolded numbers indicate that the TTD for developmental effects is the overall RiD for that PFAS.
5.3.3 TOSH! Step 3: Select exposure estimates (measured water concentrations)
Select appropriate exposure estimates consistent with the problem formulation. Specifically, the
user may choose to calculate or use exposure estimates for the oral route in general (i.e., total
intake in mg/kg-day) or media-specific concentrations. In the hypothetical PFAS mixture
example, "exposure" is represented by the drinking water monitoring data in Table 4-1.
5.3.4 TOSHI Step 4: Calculate PFAS mixture potency (component HQs and overall
TOSHI)
Using the median of the drinking water monitoring data (see Table 4-1) and the calculated
HBWCs for PFAS 1-4 derived from TTDs for the developmental effect domain (see Table 5-8),
individual component HQs are derived as shown in Table 5-9.
Table 5-9. Calculation of hypothetical individual component HQs specifically for
developmental effects associated with the hypothetical PFAS mixture.
Chemical
Hypothetical drinking
water exposure
estimate (ng/L)
Hypothetical
TOSHIdev HBWC
(ng/L)
Hypothetical
TOSHIdev HQ
PFAS 1
4.8
0.2
24.00
PFAS 2
55
50,000
0.0011
PFAS 3
172
10,000
0.02
PFAS 4
58
40
1.45
PFAS 5
120
ND
ND
Mixture TOSHIdev
25.47 (rounded to 30)
Notes:
HQ is the DW exposure estimate / HBWC; HI is the sum of individual HQs; ND = not determined.
The HBWCs in this TOSHI application are derived from TTDs for the developmental effect domain.
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5.3.5 TOSHI Step 5: Interpret the PFAS mixture HI
The TOSHIdev of 30 in the hypothetical example indicates concern for developmental effects
associated with exposure to the hypothetical PFAS mixture at the measured drinking water
concentrations (see Table 4-1). While this example application shows that use of TTDs did not
meaningfully diminish indication of health risk associated with the mixture (compared to a
general HI approach), the individual HQs clearly demonstrate drivers (PFAS 1 and 4) and
relative inerts (PFAS 2 and 3) for developmental health outcomes. The converse is possible
dependent on the TTDs for different health outcomes, and differing PFAS concentrations in
environmental media.
5.4 Advantages and Challenges of the General HI and TOSHI Approaches
The general HI approach provides an indication of the joint toxicity associated with co-
occurrence of PFAS in environmental media, such as drinking water. One advantage of the HI
formula in risk communication is that the interpretation of the results is relatively
straightforward. The simplicity of the method is in taking a ratio of the exposure to hazard to
indicate potential concern for a mixture of PFAS and providing an alert to specific PFAS that
may be potential drivers in risk to human health (i.e., those PFAS for which the HQs have
greater contribution to an HI > 1, relative to other PFAS mixture components).
Another advantage is that the "hazard" does not necessarily have to be the same for general HI
(e.g., all liver or all kidney effects). Specifically, the general HI approach can be used where the
individual HQ calculated for each mixture component PFAS is based on the most well-
characterized, and often the most sensitive, toxic effect and corresponding noncancer RfV (e.g.,
oral RfD). As such, a general HI will typically represent the most health-protective indicator of
mixture risk, as each component HQ is based on each mixture component's overall RfV.
Alternatively, in a TOSHI, toxicity values are aggregated by the "same" target organ
endpoint/effect, and HQ (and HI) values are developed for each effect domain independently
(e.g., liver-specific HI, thyroid-specific HI). Although more closely aligned to the concept of
DA, the disadvantage of a TOSHI is that it can only be performed for those PFAS for which a
health effect-specific RfD (e.g., TTD) is calculated. For example, for some PFAS, a given health
effect might be poorly characterized or not studied at all, or, as a function of dose, may be one of
the less(er) potent effects in the profile of toxicity. Another limitation is that so many PFAS lack
human epidemiological or experimental animal hazard and dose-response information across a
broad(er) health effect range, thus limiting the potential scope or landscape of derived TTD
values. As with the general HI, a TOSHI approach might benefit from consideration of NAM
data and approaches that can inform organ/tissue-specific dose-response.
The HI is an indication of appreciable risk, not an estimate of the concentration of the mixture in
water that may result in adverse health outcomes after a specific period of exposure.
Comparisons of HI estimates across different exposure routes/scenarios (e.g., oral versus
inhalation; comparing drinking water His to soil ingestion His) can be misleading and
challenging to interpret. Because the HI is based on DA, it implies that if two exposure scenarios
involve the same chemicals and their HI values are the same, then with other factors being equal
(e.g., exposure frequency and duration, similar health endpoints, and similar life stage), the two
exposure scenarios could be judged to have the same potential for causing toxic effects. That
interpretation has the strongest scientific foundation when there are only minor differences in the
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component exposures (thus, same exposure route, same chemicals, and similar exposure duration
for specific receptors) between the two scenarios. In addition, the magnitude of an HI, TOSHI, or
an individual component HQ, should not be directly interpreted as a quantitative estimate of
increased level of concern. For example, a mixture HI of 20 is not necessarily of 10-fold greater
concern than a mixture HI of 2. The practical interpretation is that both mixture His would
indicate an appreciable risk of health effects in exposed populations.
Another challenge of the application of the general HI and TOSHI approaches to specific media
such as water is that it requires derivation of a health-based, media-specific concentration like a
drinking water Health Advisory or MCLG, in addition to the underlying oral RfV (e.g., RfD,
TTD). Development of these values typically requires significant expertise and resources often
on a longer timeframe (i.e., years). In addition, while a formal hierarchy of preferred human
health reference/toxicity values is not being proposed in this framework, there is a recognized
gradation of confidence across possible PFAS values that might exist or could be derived.
Specifically, it would clearly be preferable to use RfVs obtained from assessment sources that
use comprehensive and transparent systematic approaches and standardized protocols. The level
of confidence or certainty in such values would be greater and associated with lower levels of
qualitative and quantitative uncertainty than other values.
What might be perceived as a challenge for PFAS human health assessment in general could be
an opportunity to advance risk assessment science and practice. Specifically, in the case of
NAMs, dose-response metrics obtained from bioactivity-based assays/platforms (or read-across)
may be assigned some level of a priori uncertainty simply because of a lack of confidence by
end-users in the interpretation and risk assessment application of such data and outputs. As
mentioned previously in this framework, NAMs may represent the only opportunity to integrate
a data-poor PFAS into mixtures assessment. Further, while the integration of NAMs into
applications such as mixtures risk assessment was demonstrated in the hypothetical example
using a POD from a specific assay type {in vitro cell bioactivity), available NAM data could be
leveraged from a diverse assay or platform portfolio. For example, transcriptomic data from
whole animals or cells in vitro using platforms such as BioSpyder (i.e., TempO-Seq; see
https://www.biospyder.com/). microarrays, and/or RT-PCR may represent additional
opportunities to integrate validated methods and data into assessment application.
A potential future improvement using NAMs such as cell bioactivity (including transcriptomics)
may be the categorical integration of qualitative and quantitative information from across
platforms to develop more comprehensive NAM-based hazard determinations and identification
of candidate PODs (consensus lower bound BMD, cross-NAM platform mean, etc.). The end-
user of this framework, in consultation with experts/practitioners in NAM development and
application, would be advised to leverage NAMs when and where possible while always
characterizing and transparently communicating qualitative and quantitative uncertainty(ies)
along the continuum from data generation and fit-for-purpose application (Parish et al., 2020) to
POD identification, RfV derivation, and subsequent HQ and HI calculations. The disadvantage
of not using NAM data and approaches when applicable to a given PFAS mixture is that data-
poor PFAS would not be accounted for in the HI, thus potentially underestimating mixture
hazard.
In summary, in scenarios where a diverse amalgamation of different types of RfVs (i.e., deriving
from different assessment sources and/or data types) are used in the calculation of HQs and His,
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the respective confidence and qualitative uncertainty characterizations for each PFAS need to be
transparently communicated in overall mixture hazard interpretations.
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6.0 Relative Potency Factor Approach
6.1 Background on RPF Approach
RPF approaches comprise another basic dose-addition method used most commonly by the EPA
in mixtures assessment. There are two key types of the RPF approach: (1) the general RPF
approach that has been applied to pesticides, disinfection by-products (Simmons et al., 2004),
and a few other chemical groups such as polycyclic aromatic hydrocarbons and (2) the TEF
approach that was originally developed for mixtures of dioxins and DLCs. The TEF approach is
considered a special case of the RPF approach wherein mixture components are known to act via
an identical MOA (e.g., dioxins and DLCs and AhR activation).
For chemicals demonstrated to act via a similar MOA, or in the case of this framework, those
shown to induce the same/similar health effect (see Section 3 for discussion and justification), an
RPF represents the relative difference in potency between a mixture IC and other members of the
mixture. The IC does not necessarily have to be the most potent member of a given mixture.
Rather, an IC is typically selected because it has the highest quality and most robust
toxicological database and is considered to be most representative of the type of toxicity caused
by the mixture components (USEPA, 1986, 2000b). The role of the IC in the RPF approach is to
serve as the point of reference for standardizing the common toxicity (i.e., scaling the potencies)
of all component chemicals in the analysis. The most important consideration in selecting one
mixture component over another as an IC is that high-quality dose-response data are available
(e.g., for the common toxic effect/species/sex) for the exposure route, duration, and pathways of
interest.
Further, the IC must have dose-response data for the dose range of interest; chemicals with steep
slopes that cause an effect and/or induce significant toxicity at all doses tested are not ideal for
IC selection. In most cases, the identification of a single best mixture component IC will be
evident. However, in the event that two or more mixture components are identified as candidate
ICs, the user must judge which candidate is most representative of the mixture, or subgroupings
within a mixture, and has the most robust toxicity database. It should be noted that the selection
of an IC can be duration-, exposure route-, and/or health outcome-specific. That is, in practical
application, it is possible that different mixture components may be optimal ICs under different
scenarios. For example, mixture components A and B may both be identified as candidate Ics in
general; however, candidate A may be selected as the IC if it has a more robust evidence base for
a specific application of interest (e.g., oral/sub chronic duration). In the RPF approach, the
assumption under dose additivity is that the toxicity of each mixture component chemical
induces effects via a similar pathway of biological perturbation and can operationally be
considered a fixed concentration or dilution of the IC (USEPA, 2000b). Mathematically, when
using response-specific doses, the RPF is the ratio of the IC to that of each individual mixture
component chemical (j) at a common point on the corresponding dose-response curves (e.g.,
human equivalent LOAELs, BMDs, or EDx). Ideally, the dose-response functions used to
calculate RPFs across mixture components would be approximately the same in exposure
duration and study design (e.g., sex, species, life stage).
Further, considering the known differences in TK characteristics across PFAS (e.g., internal
plasma half-life) between rodents, nonhuman primates, and humans, it is advisable to convert
experimental animal dose-response data to human equivalents where possible before calculating
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RPFs. Lastly, of the options for dose-response metrics to use in calculating RPFs across
component PFAS, BMDs (e.g., the central tendency estimate) would be optimal. BMDs
incorporate the totality of a given dose-response and facilitate the identification of a dose at a
predefined BMR level (e.g., 0.5 standard deviation (SD) or 1 SD over control; 10% change in
some effect/endpoint). BMD modeling would optimize the comparison of "same" as a function
of dose across component PFAS for a given health effect or endpoint. It is recognized that dose-
response data for chemicals are sometimes not amenable to BMD modeling. Isoeffective human
equivalent LOAELs or EDx values are suitable alternatives. No matter which dose-response
metric is used, the RPF for the IC is always one. The potency ratio can be calculated for each
mixture component chemical (j) as the ratio of the effect doses as shown in Equation 6-1:
RPFj = *j&L (Eqn. 6-1)
where IC refers to the index chemical.
For example, if mixture component chemical 2 is twice as potent as the IC, its LOAEL, BMDx,
or EDx will be half as large, and the calculated RPF would be a 2. Conversely, if mixture
component chemical 2 is half as potent as the IC, its LOAEL, BMDx, or EDx will be twice as
large, and the RPF would be 0.5. In practice, the EPA determines a single RPF for the response
range or dose range of interest. When data are available, RPFs can potentially be determined for
more than one health effect domain and/or exposure scenario (e.g., developmental versus thyroid
toxicity, shorter-term vs. chronic exposure, oral vs. inhalation exposure). As illustrated in the
RPF examples in the next section, that flexibility or scenario specificity is an advantage of the
general RPF approach. Once RPFs are calculated for each mixture component chemical using a
common metric in Equation 6-1, ICECs are then calculated by multiplying each respective RPF,
by the corresponding component chemical's concentration (d7), as shown in Equation 6-2:
ICECmix = YJj=i dj * RPFj (Eqn. 6-2)
The total mixture ICEC (ICECmix) is then obtained by taking the sum of the component
chemical ICECs (including that of the IC) (Equation 6-3). A numerical estimate of risk for
noncancer health effects associated with exposure to the mixture of concern is then obtained by
mapping the ICECmix onto the dose-response function for the IC. For example, if the IC's
dose-response model is denoted f(d), then the RPF-based response to the mixture is estimated as:
Jmix = f(JCECMIX) (Eqn. 6-3)
where the ICEC is derived from Equation 6-3. In the context of this PFAS mixture framework,
there are important modifications or adaptations of this approach to note that include: (1) use of
ICECs, which are water-specific, correlates to index chemical equivalent doses (ICEDs)
(USEPA, 2000b) and (2) using effect-specific HBWCs for the IC as a benchmark point to
compare a mixture ICEC to rather than directly mapping the mixture ICEC onto the IC dose-
response. This serves the purpose of providing the end-user a basic indication of "yes," there is
potential effect-specific risk associated with the mixture (e.g., ICECmix > IC HBWC), or "no,"
there is no anticipated effect-specific risk (e.g., ICECmix < IC HBWC), as well as the magnitude
of health effect concern and identification of potential component chemical drivers of an ICEC.
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The EPA's supplementary guidelines (USEPA, 2000b) state: "The common mode-of-action
assumption can be met using a surrogate of toxicological similarity, but for specific conditions
(endpoint, route, duration)." This suggests that although the common MOA metric for the
application of RPFs is optimal, there is flexibility in the level of biological organization at which
"similarity" can be determined among mixture components. To date, the EPA has developed
RPFs for only a few chemical groups, largely pesticides (organophosphorus pesticides, triazines,
N-methyl carbamates, chloroacetanilides, and pyrethrins/pyrethroids), which in each case were
based on MOA-level information (USEPA, 2018). However, MOA data are limited or not
available for many PFAS. As such, in the interim, when using the RPF approach, it is advisable
to focus the biological level of organization for component-based evaluation of potential
mixtures additivity for PFAS on similarity in toxicity endpoint/effect. Further, as empirically
demonstrated by Conley et al. (2022b, 2023), due to the potential variability of potency for
health effects across PFAS, RPFs can vary by more than an order of magnitude. Thus, where
possible, it is preferable for a given PFAS mixture to evaluate multiple common effect domains
or endpoints, where and when dose-response data are available, to identify the most sensitive
endpoint for evaluation of risk. Using the most sensitive endpoint(s) for the RPF analysis helps
to ensure that risks are not underestimated, and providing a landscape of candidate RPFs across
PFAS and health effects ensures transparent communication of mixtures risk assessment for
decision-making. This approach is taken in the illustrative RPF examples below; it is consistent
with previous NAS recommendations for evaluating chemicals that cause common adverse
health outcomes, presumably through diverse biological pathways (NRC, 2008).
6.2 Illustrative Example Application of RPF to a Hypothetical Mixture of Five
PFAS
The example application of the RPF approach incorporates hazard and dose-response
information for the hypothetical five PFAS mixture presented in the HI sections above.
However, in this context only dose-response data for like/similar health effect(s) are needed.
Recall that PFAS 1-3 have existing hazard and dose-response data that have been formally
evaluated for human health risk assessment purposes; these three PFAS also have existing
HBWCs. PFAS 4 has not undergone risk assessment but has existing experimental animal assay
data. Lastly, PFAS 5 is data-poor with only physicochemical, TK, and in vitro cell-based
bioactivity data. This hypothetical example focuses on the development of RPFs for liver,
thyroid, and developmental effects only (Figure 4-8), which have been reported as toxicity
targets of several compounds within the broader class of PFAS (ATSDR, 2021; EFSA et al.,
2020; Section 7.1 in ITRC, 2022; USEPA, 2021a, 2021b). The approach here is to use a
construct that allows for a combination of PFAS with a shared, common health outcome (e.g.,
delayed growth and development in offspring), as opposed to a stringent requirement of the same
MOA, to calculate RPFs across one or more health effect domains. Including multiple
effects/domains among the constellation of PFAS effects allows for evaluation of the potential
impact of differences in RPFs across PFAS in the mixture for those effects (e.g., the potency of
PFAS 1 relative to PFAS 2 may be different for effects on the liver as compared to effects on the
thyroid) (Mumtaz et al., 2021).
The intention is not necessarily to seek the most sensitive effects/domains; rather, it is to
optimize the identification of those effects shared among the PFAS in the assessed mixture.
However, for purposes of evaluating mixture risk using the RPF approach in a specific
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environmental medium (e.g., drinking water), it is critical to have an IC effect-specific value or
metric (e.g., an HBWC) so that the mixture ICEC can be compared to a benchmark point. For
PFAS, given the limited availability of hazard effect and dose-response data, if one seeks to
include several PFAS (i.e., beyond those few congeners with robust toxicity databases), the
approach may be limited to a single effect domain or only those endpoints for which reasonable
estimation of dose-response metrics (e.g., PODs, EDx) for "same/similar" is possible. However,
leveraging available NAM data, such as in vitro cell bioactivity, may provide opportunities to
integrate those PFAS with poor(er) hazard and dose-response databases.
6.2.1 RPF Step 1: Assemble/Derive component health effects endpoints (select Index
Chemicals, PODheds)
As PFAS 1-3 are toxicologically well-characterized and have existing HBWCs, all three are
identified as candidate Ics for the mixture. PFAS 4 is also reasonably well characterized
toxicologically and might be considered as a candidate IC in some RPF contexts; however, in a
drinking water-specific application, another key consideration for IC selection is the existence of
a quantitative benchmark such as an HBWC. This is necessary so that the ICEC for the mixture
(ICECmix) can be compared to the IC's corresponding HBWC to determine the potential for
health risk(s). As such, PFAS 1-3 are the only candidate ICs identified for the hypothetical five-
component mixture. Based on the strength of toxicological evidence (see Figure 4-8), not
necessarily the quantitative potency for effect, ICs were selected as follows: Liver IC = PFAS 2;
Thyroid IC = PFAS 3; and Developmental IC = PFAS 1.
The dose-response metrics for this RPF example are the same as those used above in the HI
example (see Table 5-3) with the addition of a NAM-based POD for PFAS 2 (the IC for the liver
effect domain). The PODheds for three effect domains used in the calculation of the effect-
specific RPFs and corresponding ICECs are presented below (Table 6-1).
Table 6-1. Summary of hypothetical PODheds for three selected health effect domains for a
mixture of five hypothetical PFAS.
Liver PODhed
(mg/kg-day)
Thyroid PODhed
(mg/kg-day)
Developmental PODhed
(mg/kg-day)
PFAS 1
0.044 (BMDLx-hed)
0.24 (BMDLy-hed)
0.00001 (BMDL^hed)
PFAS 2
0.0013 (BMDLx-
hed); 0.0052 (BMDx-
AED)a
0.23 (BMDLy-hed)
0.005 I(BMDLz-hed)
PFAS 3
N/A
0.21 (BMDLy-hed)
2.1 (BMDLz-hed)
PFAS 4
50 (BMDLx-hed)
N/A
0.0011 (BMDLz-hed)
PFAS 5
0.004 (BMDx-aed)"
N/A
N/A
Notes:
Bold indicates lowest POD for the corresponding RiD derivation.
a Represents the NAM-based POD for same in vitro cell bioactivity event between PFAS 2 and PFAS 5 (e.g., j epoxide
hydrolase activity).
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6.2.2 RPF Step 2: Assemble/derive health-based media concentrations (HBWCs for the
Index Chemicals)
For this illustrative RPF example, the hypothetical HBWCs are the same as those used in the
General HI example (see Table 5-5). Specifically, the PFAS 1 HBWC is 0.2 ng/L (IC for
developmental effects), PFAS 2 HBWC is 60 ng/L (IC for liver effects), and PFAS 3 HBWC is
4,000 ng/L (IC for thyroid effects).
6.2.3 RPF Step 3: Select exposure estimates (measured water concentrations)
Select appropriate exposure estimates consistent with the problem formulation. Specifically, the
user may choose to calculate or use exposure estimates for the oral route in general (i.e., total
intake in mg/kg-day) or media-specific concentrations. In the hypothetical PFAS mixture
example, "exposure" is represented by the drinking water monitoring data in Table 4-1.
6.2.4 RPF Step 4: Calculate PFAS mixture potency (RPFs and ICECsfor each effect
domain)
Liver: Available traditional animal assay data indicate liver effects for PFAS 1, 2, and 4. PFAS 5
only has bioactivity data; however, the molecular and cellular perturbations were observed
primarily in hepatocyte cell cultures (e.g., HepaRG). As such, there is increased confidence in
the opportunity to integrate NAM-based information into the RPF approach specifically for the
liver effect domain. Across the landscape of experimental rodent studies that inform liver
toxicity for hypothetical PFAS 1, 2 and 4, several effects were noted after oral exposures such as
increased absolute and relative organ weights, increased incidence of macro- and microvesicular
steatosis (i.e., lipid accumulation in hepatocytes), histopathological evidence of focal
hepatocellular necrosis, and increased serum ALT, AST, and ALP, indicative of hepatocyte or
biliary epithelium injury, respectively. In addition, in vitro cell bioactivity data for PFAS 2 and 5
indicate increased pro-oxidation/oxidative stress, mitochondrial stress, and altered lipid
homeostasis in the lower tested concentration range. Many of these observed cellular effects are
considered Kes in signal transduction pathways leading to liver tissue alteration and injury
(Figure 6-1). Of the effects observed in experimental rodents across PFAS 1, 2, and 4,
histopathological evidence of a significantly increased incidence of hepatocellular death was
common across studies. In this hypothetical example, the identified common liver effect was the
effect used as the basis for deriving an oral RfD and corresponding HBWC for PFAS 2 (e.g., this
liver RfD was interpreted with the highest confidence across PFAS 1, 2, and 4). As such,
increased incidence of hepatocellular death is identified as the common effect for the liver
domain for PFAS 1, 2, and 4. The liver effect-specific RPFs are calculated by dividing the
selected liver effect PODhed for the IC PFAS 2 by the PODhed for PFAS 1 and 4 for the same
effect (Table 6-2). Each RPF is multiplied by the corresponding chemical-specific measured
water concentration to derive a PFAS 2 ICEC (Table 6-2). The example Mixture Total
PFAS 2 ICECmix is then compared to the HBWC for PFAS 2, which is based on the effect of
increased incidence of hepatocellular death.
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Molecular Interaction
Cellular Effects
Organ Effects
Organism Effects
PFAS
exposure
Other receptor(s),
signaling pathways,
e.g. TiNFc./NFkB
CAR/PXR
activation
PPARa
activation
PPARy
activation
t XME
expression
/levels
Altered lipid
cholesterol,
metabolizing
enzyme,
levels/activity
»• Inflammation
1
Hepatocyte
damage, loss of
function
Reactive metabolite
production
J
Mitochondrial damage
1
ROS, T1 oxidative
stress
I
Altered lipid,
cholesterol, glucose
metabolism,
accumulation
Liver: fatty acid
accumulation
(steatosis),^ weight,
histopathology (e.g.
necrosis), ^ serum
enzymes
Liver disease (e.g.
NAFLD, fibrosis)
Figure 6-1. General cell signaling pathways associated with PFAS-induced liver injury. Figure sourced from Appendix A of
the EPA's 2021 Systematic Review Protocol for the PFBA, PFHxA, PFHxS, PFNA, and PFDA IRIS Assessments (USEPA,
2021f).
80
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Table 6-2. Example liver effect RPFs and ICECs for a hypothetical mixture of five PFAS.
Mixture
Hypothetical
PODhed (mg/kg-
day);
Increased incidence
of hepatocellular
Hypothetical
Hypothetical
exposure
estimate
Hypothetical
PFAS 2ICEC
component
death
example RPF
(ng/L)
(ng/L)
PFAS 1
0.044 (BMDLx-hed)
0.03
4.8
0.14
PFAS 2 (IC)
0.0013 (BMDLx-
hed); 0.0052
(BMDx-AED)a
1
55
55
PFAS 3
N/A
N/A
172
—
PFAS 4
50 (BMDLx-hed)
0.00003
58
0.0017
PFAS 5
0.004 (BMDx-aed)"
1.3 (RPFnam)15
120
39°
Mixture total PFAS 2 ICEC (ppt)
94
Notes:
a NAM-based BMD modeled from the AED-based dose-response for the selected bioactivity event (e.g., decreased epoxide
hydrolase activity, denoted as "hydrolase" in the example plots). This selected event is based on identifying the lowest (i.e.,
most sensitive) common bioactivity between the cell assay profiles for the IC (PFAS 2) and PFAS 5.
b RPFnam for PFAS 5 was calculated as the ratio of the BMDx-aed for PFAS 2 (IC) / BMDx-aed for PFAS 5 for the selected
bioactivity event; in this example application, 0.0052 mg/kg-day / 0.004 mg/kg-day =1.3.
c The ICEC for PFAS 5 was calculated by first deriving the ICECnam as follows: RPFnam x Exposure estimate for
PFAS 5 = 1.3 x 120 (ng/L) = 156 ng/L; the ICECnam was then converted to an ICEC by multiplying by the ratio of the
BMDLx-hed for PFAS 2 /BMDx-aed for PFAS 2 = 156 ng/L x (0.0013/0.0052) = 39 ng/L.
For PFAS 5, the dose-response data used in this hypothetical example RPF application, obtained
from IVIVE/rTK of the in vitro cell bioactivity data, is identified based on the lowest bioactivity
event(s)21 common with the IC; that is, noncancer bioactivity at the lower end of the distribution
for the IC is the driver for identification of "same" effect for the data-poor mixture component
PFAS. In a perfect scenario, the "same" bioactivity event(s) would be shared at the level of
NAM-based PODs between the IC and one or more data-poor components. However, while two
or more mixture components may share a qualitatively similar profile of biological perturbations,
the relative quantitative potency or dose-response at which various bioactivity events occur may
be diverse. For simplification of the hypothetical PFAS mixture application, the selected
bioactivity for the IC (PFAS 2) and PFAS 5 in HepaRG cells in vitro was identified as the same
event, decreased epoxide hydrolase activity, with a BMDx-aed of 0.0052 mg/kg-day and BMDx-
aed of 0.004 mg/kg-day, respectively (see Table 6-1). Importantly, epoxide hydrolases are a key
component in the metabolism and detoxification of xenobiotics, particularly structures with
reactive epoxide moieties; decreased hydrolase activity has been associated with increased
oxidative stress, cellular/tissue inflammation, and cell death.
This NAM-based approach aims to scale the potency of the selected bioactive event for the data-
poor chemical(s) to the same/similar bioactive event for the IC, where or when available data
allow. The NAM-based RPF (RPFnam) is calculated by taking the ratio of the BMDx-aed for the
21 The "lowest" bioactivity for noncancer application purposes should not be a potential carcinogenic event (e.g., mutagenicity or
clastogenicity).
81
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selected bioactivity event of the IC to the BMDx-aed for the same event associated with the data-
poor mixture component chemical (see Table 6-2). The resulting RPFnam represents the relative
potency between the data-poor PFAS (PFAS 5) and the IC (PFAS 2) for the selected bioactive
event. This RPFnam is then multiplied by the data-poor chemical (e.g., PFAS 5) exposure metric
(e.g., measured water concentration) to obtain a NAM-based ICEC (ICECnam); to convert the
ICECnam to a mixture ICEC that comports with the other traditional assay-based component
PFAS ICECs, the ICECnam is multiplied by the ratio of BMDx-hed for the critical effect of the
IC (in this example, the BMDx-hed for increased incidence of hepatocellular death) to the BMD
x-aed for the bioactive event of the IC. The resulting ICEC represents the estimated contribution
of PFAS 5 to the overall risk of the liver-specific effect; however, it is represented as a dose
scaled for potency, relative to the IC, across different levels of biological organization (i.e.,
PFAS 5 in vitro to PFAS 2 in vivo). This process is illustrated in Figure 6-2.
Thyroid: Available traditional animal assay data indicate thyroid effects for PFAS 1, 2, and 3.
PFAS 4 and 5 have no data available to support inclusion in the RPF analysis for this health
effect domain. Applying the same approach outlined for the liver, the selected common effect
was identified and hypothetically best represented the thyroid RfD for PFAS 3. Thus, the
hypothetical thyroid effect-specific RPFs are calculated by dividing the selected thyroid effect
PODhed for the IC PFAS 3 by the PODhed for PFAS 1 and 2 for the same effect (Table 6-3).
Each RPF is multiplied by the corresponding chemical-specific measured water concentration to
derive a PFAS 3 ICEC (Table 6-3). The example Mixture Total PFAS 3 ICEC is then compared
to the HBWC for PFAS 3, which is based on the effect of decreased total serum thyroxine (TT4)
and free serum thyroxine (FT4). The calculation of the thyroid-specific RPFs and corresponding
ICECs are presented in Table 6-3.
Developmental: Developmental effects associated with oral exposures to PFAS 1, 2, 3, or 4
were observed in rats and mice; the studies available were predominately single-generation
reproductive-developmental design however PFAS 4 also had a two-generation study in rats.
PFAS 5 had no studies/data to suggest effects in the developmental domain. Based on the
approaches above, the common effect was identified and hypothetically best represented by the
oral RfD from and corresponding HBWC for the IC, PFAS 1. As such, decreased body weight in
offspring was selected as the common developmental effect for the purposes of this RPF
illustrative example. The developmental effect-specific RPFs are calculated by dividing the
PODhed for the selected effect associated with the IC PFAS 1 by the PODhed for PFAS 2, 3, and
4 for the same effect (Table 6-4). Each RPF is multiplied by the corresponding chemical-specific
measured water concentration to derive a PFAS 1 ICEC (Table 6-4).
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Bioactivity for Index Chemical
Bioactivity for 'data-poor' chemical
* I
Of Dmir r*au -
, •«— |
I "»»* I
Mndmi
III >11 w 1
I 1
Asnwvifere# Ewfcltor*
Mixture Component
J Selected Bioactivity AED
IC Critical Effect
Index Chem (IC)
Modeled BMDx.^p m
Modeled BMDx.hed
Data-poor member
m
Modeled EMD*.^
-
RPF
NAM
Selected Bioactivity BMDy.&fn for IC
Selected Bioactivity BMDx.aed for data-poor component
ICECnam = RPFnamx Data-poor component exposure level (e.g., DW conc.)
ICEC - ICECnam x BMDx.hed for IC critical effect / Selected Bioactivity BMDx-aed for IC
Figure 6-2. Example hypothetical process for integrating NAM-based RPFs and ICECs
into mixtures assessment. Black boxes = "same" bioactive event for RPF approach.
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Table 6-3. Hypothetical example thyroid effect RPFs and ICECs for a hypothetical mixture
of five PFAS.
Mixture
component
Hypothetical
PODhed (mg/kg-
day);
Decreased TT4
and FT4
Hypothetical
Example exposure estimate
RPF (ng/L)
Hypothetical
PFAS 3ICEC
(ng/L)
PFAS 1
0.24 (BMDLy-hed)
0.9
4.8
4.3
PFAS 2
0.23 (BMDLy-hed)
0.9
55
50
PFAS 3 (IC)
0.21 (BMDLy-hed)
1
172
172
PFAS 4
N/A
N/A
58
—
PFAS 5
N/A
N/A
120
—
Mixture total PFAS 3 ICEC (ppt)
226 (230)
Table 6-4. Hypothetical example developmental effect RPFs and ICECs for a hypothetical
mixture of five PFAS.
Mixture
component
Hypothetical PODhed
(mg/kg-day);
Decreased Body
Weight in Offspring
Hypothetical
example RPF
Hypothetical
exposure
estimate
(ng/L)
Hypothetical
PFAS1ICEC
(ng/L)
PFAS 1 (IC)
0.000010 (BMDLz-
hed)
1
4.8
4.8
PFAS 2
0.0051 (BMDLz-hed)
0.002
55
0.11
PFAS 3
2.1 (BMDLz-hfd)
5E-6
172
0.00086
PFAS 4
0.0011 (BMDLz-hed)
0.009
58
0.52
PFAS 5
N/A
N/A
120
—
Mixture total PFAS 1 ICEC (ppt)
5.4
6.2.5 RPF Step 5: Compare PFAS mixture potency (Total ICECmix) to an existing health-
based value (HBWC)
In the liver-specific RPF application (see Table 6-2), the health risk(s) associated with the
mixture is represented by comparing the PFAS 2 ICECmix to the IC HBWC, which is based on
the specified effect for that hazard domain (e.g., for this example, increased incidence of
hepatocellular death). In this hypothetical example, the PFAS 2 ICECmix of 94 ppt exceeds the
PFAS 2 HBWC of 60 ppt, indicating the potential for risk of liver effects in individuals or
populations exposed to a mixture of the five PFAS at the hypothetical water exposure estimates
provided. Importantly, PFAS 2 and 5 appear to be drivers for the liver health risk associated with
the hypothetical mixture.
84
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In the thyroid-specific RPF application (see Table 6-3), the health risk(s) associated with the
mixture is represented by comparing the mixture total PFAS 3 ICECmix to the IC HBWC, which
is based on the specified effect (e.g., for this example, decreased TT4 and FT4). In this
hypothetical example, the PFAS 3 ICECmix of 226 ppt is far below the PFAS 3 HBWC of
4000 ppt, indicating no apparent risk of thyroid effects in exposed individuals or populations to a
mixture of the five PFAS at the hypothetical water exposure estimates provided.
In the developmental effect-specific RPF application (see Table 6-4), the health risk(s)
associated with the mixture is represented by comparing the mixture total PFAS 1 ICECmix to
the IC HBWC, which is based on the specified effect for hazard domain. In this hypothetical
example, the PFAS 1 ICECmix of 5.4 ppt exceeds the PFAS 1 HBWC of 0.2 ppt by over an order
of magnitude, indicating significant potential for health risks in developmental populations
exposed to a mixture of the five PFAS at the hypothetical water exposure estimates provided.
As illustrated in the RPF examples above, PFAS can have different potencies across health effect
domains. Due to the differences in TK and TD, PFAS may exhibit complex gradations of
potency for different effects, which will be reflected in the corresponding RPFs. Some PFAS
may be exquisitely potent for some effects and yet virtually inactive in others; however,
expanding the number of PFAS and the toxicity endpoint profiles across the structural landscape
will be key to illustrating such a diversity in relative potency(ies). Thus, calculating RPFs for as
many endpoints/effects as possible helps to ensure that subsequent PFAS risk management
strategies are health protective. In the example above, the risk would have been underestimated if
the RPF analysis was limited to liver and thyroid effects: developmental effects are the risk
driver in this scenario.
Further, another critical consideration illustrated in the RPF examples is the impact of
component chemical concentrations. That is, in practical field application, PFAS concentrations
in water, soil, or air may be drastically different depending on a number of factors (e.g., different
physicochemical and environmental fate and transport properties, proximity to PFAS
manufacturing or use locales, water sources [well water vs. finished drinking water], waste
handling, temporal and spatial variability). In application, transparent presentation and
communication of hazard and dose-response data sources, RPFs, media concentrations, ICECs,
and any associated uncertainties across as many health effect domains as is practicable is ideal
forRPF-based evaluation of PFAS mixtures. As mentioned previously, limitations for PFAS are
the availability of human health assessment grade toxicity data, and as with many environmental
mixture chemicals, dissimilarity in dose-response shapes and slopes; Section 7 offers an
alternative to the RPF approach in such a scenario.
6.3 Advantages and Challenges of the Relative Potency Factor Approach
An advantage of the RPF approach is that formal toxicity or RfV derivation is unnecessary for
the component chemicals. Rather, only effects/endpoints and associated dose-response metrics
(e.g., NOAEL, BMDx, EDx) are needed to perform the exercise. While it would be ideal to
conduct potency comparisons between mixture components for the same effect/endpoint using
the same dose metrics from the same study design/durations, calculation of RPFs across PFAS
may, in practical application, entail or necessitate the use of effect data deriving from diverse
study designs and exposure durations. As such, in some cases, there may be a need to normalize
or adjust available quantitative metrics such that potency comparisons are at comparable points
85
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on a given dose-response. For example, there might be a need to selectively apply UFs in the
RPF method, in particular, the LOAEL-to-NOAEL (UFl) and/or subchronic-to-chronic duration
(UFs) factors to convert quantitative metrics to NOAELs from an estimated chronic-duration
exposure. This flexibility is needed as, in some cases, effect data for mixture component PFAS
may come from a variety of study designs such as reproductive/developmental in mice (e.g.,
GDs 1-20), less than lifetime repeat-dose (e.g., 28- or 90-days) in rats, and/or 2-year bioassays in
rats. When sufficiently supported through evaluation of the available component-specific studies,
such adjustments can provide the opportunity for a more 1:1 comparison of potency for a given
effect (e.g., developmental body weight, increase in liver weight) among component PFAS. A
critical facet of this is being transparent about POD adjustments (i.e., purpose/rationale) when
applied.
RPFs were generally intended for use when mixture components are demonstrated to have
similar/same MOA. This commonly presents a problem as it pertains to the practical application
of RPF methodology in that a vast majority of environmental chemicals, including PFAS, have
limited-to-no MOA data available. The EPA mixtures guidelines provide flexibility in using data
from different levels of biological organization in dose additive approaches such as RPF. As
demonstrated in this framework document, this flexibility is an advantage in that there is a
greater probability of identifying effect/endpoint and associated dose-response data (e.g., effect-
specific PODs) for mixture components than for MOA-type data. However, as the data for PFAS
evolve, the toxicity profiles, including the number of effect types and granularity of biological
perturbations (e.g., potential KE data that inform proposed MOA(s)), may eventually support
MOA-based evaluations.
Another advantage is that the RPF method facilitates the calculation of an actual mixture toxicity
dose or concentration estimate, as opposed to the HI, which is considered an indicator of
potential health risk/toxicity. Although a given mixture ICEC is traditionally mapped to the IC's
effect-specific dose-response function to arrive at a corresponding "mixture response," an
advantage of the RPF approach is that the mixture ICEC may alternatively be used to inform
mixture risk in the context of the relationship to a media-specific health-based value (such as an
HBWC for an IC).
A clear challenge, not uniquely associated with the RPF approach, is the use of potentially
disparate hazard and dose-response data (both in terms of type and confidence) across mixture
components. The implicit assumption for dose-response data selection in the calculation of RPFs
in this framework is that the same dose-response data that underpinned the derivation of
corresponding RfVs (overall RfDs or TTDs) for use as input(s) for HQs and His would also be
leveraged in RPF and/or M-BMD approaches (see Sections 6 and 7). However, although ideal,
this is not an expressed requirement of the framework. The user should be afforded the flexibility
to make decisions regarding suitable dose-response selection for RPF calculations on a case-by-
case basis. Key to this flexibility is transparent characterization and communication of literature
searching strategy and review results, hazard data selection, dose-response evaluation (e.g.,
BMD preferred; effect levels such as LOAELs are acceptable but ideally evaluated at
isoeffective dose [EDx], which may not be practicable), and qualitative and quantitative
uncertainties or confidence in what could potentially be a diverse assembly of data/metrics to
support RPF application(s).
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Another challenge is that depending on data availability for the component PFAS, the effect
domains used for the RPF analysis may not be the overall most sensitive out of the total
constellation of common PFAS effects. In the hypothetical RPF examples shown above, the risk
is indicated based on the liver and developmental RPFs but not for the thyroid effect domain. To
use the RPF approach effectively, the user needs effect data for at least one common endpoint
among the effects for all component PFAS in the mixture. Ideally this would include the most
sensitive effect across PFAS in the mixture of interest in order to provide a conservative (health
protective) risk-based scenario.
An additional potential challenge that may present an opportunity to advance the science of
mixtures risk assessment is the use of NAM data. The constantly evolving information coming
from alternative toxicity testing assays and platforms is important to human health assessment of
environmental chemicals in general (not just for mixtures applications); however, there are
inherent challenges associated with the application to hazard identification and dose-response
assessment. In a PFAS mixtures assessment context, for some mixture component chemicals,
NAM data (e.g., read-across or cell-based bioactivity [such as ToxCast and/or Tox21]) might be
the only source(s) of evidence available to inform an RPF approach. The challenge might then be
identifying and assembling "same" or "similar" effect/endpoint data compared to other PFAS in
the mixture that have human epidemiological and/or experimental animal (i.e., apical
(phenotypic) effect level) bioassay data. While the RPF approach affords flexibility in the
selection of "effect" data, a key requirement is that the "effect" on which RPFs are based be the
same. For example, one component PFAS may have histopathological evidence of multi-focal
liver necrosis from in vivo repeat-dose rat studies, whereas another PFAS may have evidence of
cytochrome c release, mitochondrial damage, and cell death in in vitro rat hepatocyte cell culture
studies only. While in this hypothetical example NAM data clearly demonstrate hallmarks of
cellular demise typically associated with necrotic (and apoptotic) cell death, pathologically
consistent with cell death foci observed in whole rat liver, it may be difficult to make the case
that the in vitro-based concentration-response data (converted to an AED) is suitable for
traditional RPF calculation simply based on the interpretation of "same" effect. Further
investigation is needed to evaluate the qualitative and quantitative merits of applying hazard and
dose-response data from across different levels of biological organization in a component-based
mixtures assessment context. This is particularly true of NAMs where the possible lumping or
splitting of assay/data types to inform an integrated or more individualized interpretation of
hazard and dose-response for data-poor mixture components is in its infancy; case studies using
validated NAM assays and data are needed to help optimize application in mixtures risk
assessment.
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7.0 Mixture-BMD Approach
7.1 Background on the Mixture-BMD Approach
Given the broad range of PFAA congeners and structural diversity across the PFAS class, it is
likely that for some effects used for mixture assessment, the dose-response functions (i.e.,
slopes) will be dissimilar across component chemicals. Using an IC in the RPF approach
assumes component chemicals have similarly shaped dose-response slopes for the same health
effect (and/or MO A). In addition, the HI approach requires human health assessment values,
such as oral RfDs and individual HBWCs, because these metrics serve as the denominator in
determining if the exposure exceeds a level estimated to be acceptable for human intake. In some
cases, a PFAS mixture may contain component chemicals that do not have similarly shaped
dose-response curves or have available human health assessment values (e.g., RfDs). In these
cases, a third approach, called the M-BMD, can be used to estimate health risk(s) associated with
mixture exposure. This approach is described in the EPA's supplementary guidelines (2000b)
(Section 4.2.6) and NRC (2008) (Appendix C) and employs a DA model-based calculation of a
total M-BMD that corresponds to a defined BMR (e.g., BMDio) for a PFAS mixture. Similar to
the RPF approach, only effects/endpoints and associated dose-response metrics (e.g., BMDx) are
needed to perform the exercise. Further, the mixture evaluation is based on a similar
toxicological effect for component chemicals and the equation provided can be used to define a
response-equivalent single point estimate (e.g., BMDx) or derive a full dose-response curve for
the PFAS mixture of interest (i.e., by using multiple BMDx response levels for each compound
in the mixture).
Because RPFs are special applications of the DA concept, such approaches can be a
straightforward way of making quantitative assessments of the effects of chemicals, including
PFAS. However, application of the RPF concept requires similarly shaped dose-response curves
for all component chemicals for the given effect. When the response curves are dissimilar in
shape, RPFs will vary with the effect levels in the mixture and thus could not be considered a
"dilution" of the IC across the full dose-response range. For example, the RPF for a given PFAS
may be different in the low dose range vs. the middle or high dose range, depending on slope
differences with the IC. In this regard, published data (Hass et al., 2007; Howdeshell et al., 2008;
Metzdorff et al., 2007; and Rider et al., 2008) reveal that chemical dose-response curves for a
common effect can display very different slopes and shapes even across related structures within
a given class. In contrast to the RPF approach, other DA-based equations can be used for
quantitative evaluations of the effects of chemical mixtures when the slopes for a common effect
differ among chemicals in the mixture.
The following discussion compares the predictions of two DA models. One assumes that the
individual chemicals in the mixture have similarly shaped slopes, whereas the second DA model
(described by NRC [2008]) does not require similar slopes to yield accurate predictions. As an
example, these two models were recently used in a laboratory study to predict the full dose-
response curve of a mixture using the EDsos and slopes of each component chemical in the
mixture (Gray et al., 2022). The first model, similar to the RPF method (discussed above),
assumes that the chemicals in the mixture have similar dose-response slopes, indicating that the
relative potencies are constant across the entire dose-response curve. Equation 7-1 is an example
of such a model that uses an average slope value to calculate the joint toxicity of a mixture with
88
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the following equation (Olmstead and LeBlanc, 2005; Rider and LeBlanc, 2005; Rider et al.,
2008):
R
1
(Eqn. 7-1)
( " D Y
v z=i ED50i j
where R is the response to the mixture, A is the dose of chemical / in the mixture, HI)50, is the
dose of chemical / that causes a 50% response, and p is the average power (Hillslope) associated
with the chemicals.
Because the assumption of similar slopes is not always met, DA models, like the M-BMD
method described below, that do not require similarly shaped dose-response curves for the
chemicals in the mixture, may provide more accurate predictions of the mixture effects. Several
of these DA-based models have been previously described (for example, Altenburger et al.,
2000; Kortenkamp et al., 2007; Metzdorff et al., 2007; and NRC, 2008). The M-BMD equation
below (Equation 7-2) is an example of such a DA model that calculates a given effect level for
the total mixture (EDxmixture) where p, is the proportion of chemical i in the mixture and eDxi
denotes the dose producing the given level of response for the z'th chemical in the mixture:
When the slopes of the dose-response curves differ among chemicals in the mixture, the two DA
models (i.e., Equation 7-1 and Equation 7-2) can yield different dose-response predictions (see
Figure 7-1). Further, there is greater uncertainty in the accuracy of the DA mixture predictions
when the assumption of similar shape among slopes is violated (NRC, 2008). The following
example compares the accuracy of a DA model that assumes similar dose-response shapes
(Equation 7-1) with a DA model that does not (Equation 7-2), and these model predictions are
compared with a RA model. The data used in this example are from a published binary mixture
study, but the chemicals are not identified (Gray et al., 2022).
In this example, two chemicals were mixed using a fixed-ratio design. The top dose of this
mixture contained each chemical at a dose close to their respective EDsos, but they have different
dose-response shapes using nonlinear, four-parameter logistic regression (Chemical A
slope = 40, Chemical B slope = 5 using linear Y axis and loglO X axis). The mixture effect
described in Figure 7-1 is the percent reduction in reproductive organ weight, ranging from
0% reduction in the control (0 dose of the mixture) to complete agenesis (100% reduced).
eDxmixture = (pi/eDxi + P2/ eDxi)
(Eqn. 7-2)
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Reproductive Organ Weight Reduction
100-
TJ
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In contrast to the above example of chemicals with disparate slopes, if the dose-response curves
of the component chemicals in a mixture have similar slope parameters from nonlinear
regression, then there would be little or no difference between the predictions of the two DA
models shown here. Hence, if sufficient dose-response information is available and the slopes are
not similar, then it is preferable to model the data with the M-BMD equation (Equation 7-2) that
does not assume similarly shaped curves, as stated by the NRC (2008).
Estimating PFAS mixture effects using the M-BMD method requires empirical data-driven or
reasonable estimation (e.g., read-across between structures) of common endpoints for all PFAS
in the mixture. Similar to the RPF approach above, ideally the dose-response functions used to
calculate effect endpoints (e.g., BMDx) across mixture components would be approximately the
same in exposure duration and study design (e.g., sex, species, life stage). Further, considering
the known differences in TK characteristics across PFAS (e.g., internal plasma half-life) between
rodents, nonhuman primates, and humans, it is strongly recommended to convert experimental
animal dose-response data to human equivalents (i.e., HEDs). Lastly, of the options for dose-
response metrics to use across component PFAS, risk assessment-based PODs (e.g., BMDLxhed)
would be optimal. BMDs incorporate the totality of a given dose-response and facilitate the
identification of a dose at a predefined BMR level (e.g., 0.5 SD or 1 SD over control;
10% change in the common effect/endpoint). BMD modeling would optimize the comparison of
"same" as a function of dose across component PFAS for a given health effect or endpoint and
identify a human health-relevant POD for M-BMD derivation. It is recognized that dose-
response data for chemicals is sometimes not amenable to BMD modeling.
Importantly, the response level (i.e., BMR) for the common endpoint should be the same for all
PFAS included in the calculation, for example, BMDLX for a common liver effect. In this case,
the equation will produce an equivalent response metric (i.e., BMDLX) for the total mixture with
the given proportions of component PFAS being evaluated. In the illustrative example below, the
BMDLs associated with hypothetical response levels (i.e., BMDLX, BMDLy, BMDLz) estimated
for each chemical in a mixture are used to determine an "M-BMD" that represents an equivalent
POD for the mixture that was identified for each component PFAS. The choice of BMRs is
based on expert judgment and data availability. As stated above, it is preferable to calculate the
M-BMDs using HED doses rather than oral mg/kg doses administered to test animals. The
equation explanation and example below will reference BMDL, as the model components using
Equation 7-3 (similar to Equation 7-2), where M-BMD is the total mixture dose in mg/kg/day, a*
are the fixed proportions of the component PFAS in the mixture, and BMD; is ith chemical BMD
(e.g., a BMDx).
The equation results in a single mixture-specific M-BMD for a given BMR that could then be
converted to an assessment value (e.g., oral RfD) and a corresponding HBWC for the mixture.
Then, the original observed PFAS mixture concentration is compared to the estimated M-BMD-
HBWC from the M-BMD equation. If the observed concentration is greater than the mixture-
based HBWC, there is potential for human health risk. If the observed concentration is below the
mixture-based HBWC, then the risk of health effects is not expected. Further, the calculation can
be repeated at multiple BMR levels to allow for the modeling of a full mixture dose-response
curve, if needed. Finally, similar to RPF, due to the potential for different effect domains to have
(Eqn. 7-3)
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variable potencies across PFAS within a given mixture, the DA model should be applied across
more than one effect domain for which data are available for each of the PFAS in the mixture to
identify the lowest mixture-specific endpoint, which indicates the most sensitive domain for the
mixture.
For consistency and comparison with the RPF illustrative example above, in the illustrative
example below, the effect levels for each chemical in the hypothetical mixture of five PFAS
from Tables 6-2, 6-3, and 6-4 are used to determine an "M-BMD." The previously described
(Section 6.2) hypothetical response-equivalent POD values serve as the denominator values in
Equation 7-3. The numerator values are the proportions of each component PFAS in the given
mixture on a concentration basis. The total M-BMD is the inverse of the sum of the proportion
divided by the POD for each PFAS in the mixture. The total M-BMDx represents an equivalent
BMDx as each of the individual chemical BMDs that were used in the calculations (i.e., if the
individual chemical data were BMDLio values, the DA calculation derives a BMDLio for the
mixture of PFAS with those specific proportions).
The M-BMD, which is in the same units as the component chemical BMDs (e.g., human
equivalent of oral dose in a rodent study such as mg/kg-day), can then be adjusted based on user-
defined extrapolation factors (e.g., application of dosimetric adjustment, RSC, UFs, and life
stage-specific drinking water consumption rates) to derive a unique HBWC for the total PFAS
mixture (as opposed to an IC-specific HBWC as in the RPF approach). In practice, the lowest
mixture-specific endpoint indicates the most sensitive effect domain for the mixture; this
endpoint can then be used to derive an equivalent M-BMD-HBWC and estimation of risk. The
derived M-BMD-HBWC can then be compared to the actual (measured) mixture concentration;
if the actual mixture concentration exceeds the M-BMD-HBWC, there is a risk of the specific
effect from exposure to that mixture at the measured concentrations.
7.2 Illustrative Example Application of the Mixture Benchmark Dose Approach
to a Hypothetical Mixture of Five PFAS
7.2.1 Mixture BMD Step 1: Assemble/derive component health effects endpoints
(BMDx)
Hypothetical data for the five PFAS in the illustrative example here are detailed in section 6.2
above (Note: Table 7-2 is a compilation of Hypothetical PODheds from Tables 6-2, 6-3, and 6-4
as used in the RPF example).
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Table 7-2. Summary of hypothetical PODheds for three selected health effect domains for a
mixture of five hypothetical PFAS.
Hypothetical liver
PODhed
(mg/kg-day)
Hypothetical thyroid
PODhed
(mg/kg-day)
Hypothetical
developmental PODhed
(mg/kg-day)
PFAS 1
0.044 (BMDLx-hed)
0.24 (BMDLy-hed)
0.00001 (BMDLz-hed)
PFAS 2
0.0013 (BMDLx-hed)
0.0052 (BMDx-aed)"
0.23 (BMDLy-hed)
0.0051 (BMDLz-hed)
PFAS 3
N/A
0.21 (BMDLy-hed)
2.1 (BMDLz-hfd)
PFAS 4
50 (BMDLx-hed)
N/A
0.0011 (BMDLz-hed)
PFAS 5
0.004 (BMDx-aed)"
0.001 (BMDLx-hed)13
N/A
N/A
Notes:
aNAM-based BMD modeled from the AED-based dose-response for the selected bioactivity event (e.g., decreased epoxide
hydrolase activity, denoted as "hydrolase" in the example plots). This selected event is based on identifying the lowest (i.e.,
most sensitive) common bioactivity between the cell assay profiles for the IC (PFAS 2) and PFAS 5.
b Hypothetical NAM-derived Liver POD based on first calculating NAM-based relative potency for PFAS 5 = 0.0052 BMDx-aed
/ 0.004 BMDx-aed =1.3 then estimating a PFAS 5 in vivo PODhed using the PODhed for PFAS 2 divided by the relative
potency of PFAS 5, 0.0013 BMDLx-hed / 1.3 = 0.001 BMDLx-hed.
7.2.2 Mixture BMD Step 2: Assemble/derive health-based media concentrations
(HBWC)
In the case of the M-BMD approach (unlike the HI/TO SHI and RPF approaches), there is no
need for pre-existing HBWC(s) because the goal of this approach is to develop a unique,
mixture-specific HBWC for comparison to the Mixture Total PFAS concentration. The
calculation of the M-BMD HBWC is shown in Section 7.2.4.
7.2.3 Mixture BMD Step 3: Select exposure estimates (measured water
concentrations)
Select appropriate exposure estimates consistent with the problem formulation. Specifically, the
user may choose to calculate or use exposure estimates for the oral route in general (i.e., total
intake in mg/kg-day) or media-specific concentrations. In the hypothetical PFAS mixture
example, "exposure" is represented by the drinking water monitoring data in Table 4-1.
7.2.4 Mixture BMD Step 4: Calculate PFAS mixture potency (Mixture BMD HBWC)
In this example, M-BMDs are calculated for three effect domains: Liver, Thyroid, and
Developmental (Table 7-3). Application of Equation 7-3 to the example water sample in Table 4-
1 is used to derive the M-BMD, as shown in Equation 7-4. This example is for the
developmental domain, as it was the lowest M-BMD of the three effect domains.
(Eqn. 7-4)
T-i n * r\ /V.4 a; \_1 ( 0 01 0.13 0.42 0.14 0.29\_1
Mixture BMD = (2/—i ) = ( 1 1 1 1 ) = 0.00087
V BMDiJ V0.00001 0.0051 2.1 0.0011 N/Aj
mg/kg-day
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Table 7-3. M-BMD Approach: Hypothetical Water Sample and Hypothetical M-BMDs.
Median
measured
water
concentration
(ng/L)
Mixing ratio
(proportion)
Liver
PODhed
(mg/kg/day)
Thyroid
PODhed
(mg/kg/day)
Develop-
mental
PODhed
(mg/kg/day)
PFAS 1
4.8
0.01
0.044
0.24
0.00001
PFAS 2
55
0.13
0.0013
0.23
0.0051
PFAS 3
172
0.42
N/A
0.21
2.1
PFAS 4
58
0.14
50
N/A
0.0011
PFAS 5
120
0.29
0.0022
N/A
N/A
Mixture total
409.8
1.0
M-BMD
calculation
0.0025
0.38
0.00087a
Notes:
N/A = data not available.
a The lowest M-BMD is converted to a mixture-HBWC using Eqn. 7-3 for comparison to the measured concentration (i.e.,
409.8 ng/L).
The developmental-effect produced the lowest M-BMD (i.e., 0.00087 mg/kg-day), representing
the most sensitive effect domain; this value is selected for the calculation of the M-BMD
HBWC. The developmental-based M-BMD is first converted to an RfD by applying UFs that are
consistent with the data being used. The selection of uncertainty factors will likely be different
across mixture component chemicals based on the available hazard and dose-response data. As
such, there is no standard application of quantitative uncertainty for a mixture of components,
although it is suggested that a user of this approach consider the composite uncertainty (UFc)
across the five areas used in EPA human health risk assessment practice: (1) Human
interindividual variability (UFh); (2) extrapolation from animal-to-human (UFa); (3) subchronic-
to-chronic duration extrapolation (UFs); (4) LOAEL-to-NOAEL extrapolation (UFl); and
(5) database uncertainty (UFd). In the specific context of the application of uncertainty to an
M-BMD, a reasonable health-protective approach is to apply factors consistent with the data
status of the most data-poor member of the mixture used in the hazard domain-specific
calculation of the M-BMD selected for use in deriving the RfD. In the example illustrated in
Equation 7-5, the POD from PFAS 4 is the most data-poor of the PFAS used to calculate the M-
BMD for developmental effects selected for the RfD. Hypothetically, the UFc for this PFAS 4
POD was 300. Thus:
RfD = m =
V UFC /
'0.00087^/dN
300
= 0.000003 mg/kg-day
(Eqn. 7-5)
An HBWC can then be derived using Equation 7-3. In the example shown in Equation 7-6, the
DWI-BW is for women of childbearing age (i.e., 90th percentile direct and indirect consumption
of community water, consumer-only two-day average, 13 to < 50 years), and the RSC is 20%
(0.2). The M-BMD HBWC is calculated as follows:
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/ Rfn \ /0.0000032i/d\
HBWC = ( * RSC = r-^— * 0.2 = 0.00002 mg/L = 20 ng/L (Eqn. 7-6)
VDWI-BWV ^ 0.0354-^/d J 5 5 V 4 '
7.2.5 Mixture BMD Step 5: Compare PFAS mixture potency (total PFAS mixture
concentration) to health-based value (Mixture BMD HBWC)
In the developmental effect-specific M-BMD application, the health risk(s) associated with the
mixture is represented by comparing the mixture's total PFAS concentration (409.8 ng/L) to the
M-BMD HBWC, which is based on the specified effect for hazard domain (e.g., for this
example, decreased body weight in offspring). In this hypothetical example, the mixture total
PFAS concentration of 409.8 ng/L exceeds the M-BMD HBWC 20 ng/L by over an order of
magnitude, indicating significant potential for health risks in developmental populations exposed
to a mixture of the five PFAS at the hypothetical water exposure estimates provided.
Although not shown in the above example, if the M-BMD was instead based on the liver effect
domain, the M-BMD HBWC would be 48 ng/L (hypothetically assuming the same UFc and
DWI-BW were used), below the measured PFAS concentration (409.8 ng/L), indicating there is
also potential for liver effects in populations exposed to the hypothetical mixture. Alternatively,
if the M-BMD was based on the thyroid effect domain (again assuming the same UFc and DWI-
BW were applied to the M-BMD), the resulting M-BMD HBWC would be 7,177 ng/L, well
above the measured total PFAS concentration (409.8 ng/L), indicating unlikely risk for thyroid
effects among the exposed population. In practice, the composite UFc for each health-effect
domain should be estimated based on the expert judgment of the most data-poor component
chemical used to derive the M-BMD. Further, depending on the specific shared health effect
within a given domain, the appropriate life stage DWI-BW should be used to convert the M-
BMD to an M-BMD-HBWC.
7.3 Advantages and Challenges of the Mixture BMD Approach
There are several advantages to the M-BMD approach. First, there is no a priori requirement for
having formal human health assessment values, such as oral RfDs or chemical-specific HBWCs,
for any of the individual PFAS in the mixture. The only data needs are response-equivalent effect
endpoints (e.g., BMDLs) for each PFAS in the mixture for the common endpoint(s) being
modeled. Another advantage is that it avoids any potential confusion that could arise from
putting the mixture POD in the units of a single chemical (i.e., the IC from the RPF approach).
Rather, the end result is a POD that is specific for the assortment and ratios of PFAS in the
mixture being evaluated. It is important to recognize that the DA model calculation of the M-
BMD is different for each PFAS mixture depending on (1) the specific PFAS in the mixture,
(2) the mixing ratio, and (3) the effect or endpoint being modeled. For example, one could expect
that a mixture of PFAS that has a greater concentration of a more potent compound and a lower
concentration of a less potent compound would have a lower (i.e., more potent) M-BMD than a
similar assortment of compounds that has a lower concentration of the more potent PFAS and a
greater concentration of the less potent PFAS. It is also advantageous that the M-BMD approach
does not actually require or assume that the component PFAS in a given mixture have similarly
shaped dose-response curves for each effect being evaluated (reviewed in NRC [2008]). Finally,
it is ideal to have well-resolved dose-response curves for each component PFAS in a mixture to
estimate equivalent BMRs (e.g., BMDX, PODx), and this is necessary if a goal is to model the
entire dose-response for the mixture. However, in the absence of such data, M-BMD modeling is
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also amenable to simple point estimates such as NOAELs, as long as they are toxicologically
similar across component chemicals (i.e., for the same endpoint, such as increased incidence of
hepatocellular death) but use of this type of point data would impede the modeling of the full
mixture dose-response curve if desired.
There are also several challenges with the M-BMD approach. Similar to the RPF approach, the
user needs effect data for at least one common endpoint from the constellation of PFAS effects
for all components of the mixture. Ideally, this would be for one of the most sensitive—if not the
most sensitive—effects across PFAS in the mixture of interest to provide a conservative
(protective) risk scenario. For some mixtures that contain less well-studied PFAS there may be
limited or no available dose-response data to derive component chemical BMDs to calculate the
M-BMD. Another limitation is the absence of standard guidelines for selecting uncertainty
factors for a mixture of components, as opposed to the procedures used to apply uncertainty
factors to individual chemicals in a risk assessment. The present document provides a
hypothetical example of using a composite UF for the mixture based on the composite UF of the
most data-poor component in the M-BMD calculation.
A limitation that is not unique to this specific approach is that PFAS mixtures may vary over
time in environmental media. As proportions of component PFAS change in the mixture, the
calculations would need to be recalculated as the composition of the mixture changed from site
to site or over time within the same site. However, the calculation can be readily and easily
repeated for different mixing ratios and mixture concentrations once the component chemical
effect endpoint values have been determined. Finally, for both the RPF and M-BMD approaches,
depending on data availability for the individual compounds, the effect domains modeled may
potentially not be the overall most sensitive out of the total constellation of common PFAS
effects (e.g., in reality, developmental effects may be the most sensitive and would produce the
lowest M-BMD, but data are only available for the component PFAS to calculate M-BMDs for
liver and thyroid effects). In this example, the M-BMD HBWC is based on developmental
effects because it is the most sensitive of the three assessed effect domains and, thus, is
protective of the other effects (i.e., liver and thyroid). As described in the previous section, if this
M-BMD analysis was instead based on the thyroid effect, the user would conclude that potential
risk is unlikely.
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8.0 Comparison of Component-Based Approaches
This framework document describes the conceptual bases and practical application of data-
driven options for estimating the noncancer health risks associated with human exposure to
mixtures of PFAS. The component-based options described are included in prior EPA mixtures
guidelines (USEPA, 1986, 2000b) and/or supported by NRC (2008). Although the approaches
and illustrative hypothetical examples are provided using drinking water as the exposure route,
the technical basis of each approach could be readily applied or adapted to other sources of oral
exposure (e.g., soil, fish/shellfish, foods). Each of the approaches included requires varying
levels of data input, has relatively subtle but substantive differences in assumptions, and
ultimately produces risk indications/estimations that may differ slightly based on those
assumptions. Importantly, the interpretations of health risks associated with mixtures of PFAS
will be highly dependent on the specific PFAS components within a given mixture and the
individual concentrations or proportions of each component. Given the significant lack of
toxicity data across the diverse structural landscape of compounds within the PFAS chemical
class, it is likely that many users of this framework will need to incorporate information from
NAMs such as in vitro cell bioactivity, toxicogenomic platforms, and/or structure-activity/read-
across to facilitate estimation of health risk for a PFAS mixture of interest; however, in vivo
animal or human toxicity data are strongly recommended where available.
Given the range of data-driven options presented in this framework, an important consideration
is under what circumstances the different options produce similar or dissimilar indications or
estimates of health risk(s). The primary basis for differing risk estimates relates to differences in
data input requirements, model assumptions, and final value derivation. Both the HI and TOSHI
approaches necessitate the availability (or de novo derivation) of health assessment values (e.g.,
oral RfDs) to calculate mixture component HQs. In contrast, the RPF and M-BMD approaches
target dose-response data for the same/similar effect, sans derivation of health assessment values,
to inform mixture risk estimates (e.g., concentrations or doses) for comparison to measured
media concentrations either for an anchor/IC (such as in the RPF) or across each mixture
component (such as in the M-BMD). The general HI approach allows for component PFAS in
the mixture to have different health effects or endpoints as the basis for the component chemical
RfVs (see Figure 4-1); thus, this approach is likely a more health-protective indicator of risk (i.e.,
produce a component HQ or mixture HI of > 1) since the representation of toxicity will likely be
the most sensitive, compared to the RPF and M-BMD approaches where similarity in toxicity
does not have to a priori be the most sensitive effect domain. In contrast, the TOSHI approach is
more targeted and assumes the component RfVs are based on the same organ or organ system.
This more narrowed focus is likely to produce a less health-protective indicator of risk than the
general HI (i.e., less likely than general HI to produce HQ > 1) because the range of potential
effects has been scoped to a specific target organ or organ system; for example, for some mixture
components, the effect domain identified for TOSHI application may be one of the less potent
across a profile of effects. This important nuance will be dependent on the availability of target
organ-specific RfVs, and case-by-case interpretations of "potency" for effect will be a function
of both dose-response (e.g., POD) and the uncertainty factor application. The user of the TOSHI
approach would be advised to also perform the general HI for the same mixture and compare the
His (and component HQs) across each approach. It should be noted that any component
chemical HQ or mixture HI > 1 indicates potential health risks; the magnitude of HI is not an
optimal comparator. On the other hand, the TOSHI and RPF approach will give essentially the
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same answer when the ratio of the POD values used to calculate the RPFs is equal to the ratio of
the endpoint-specific PODs used in the derivation of RfVs used to calculate the TOSHI. The
major difference between the RPF and M-BMD estimates is the RPF approach assumes similarly
shaped dose-response curves, whereas the M-BMD does not. If the mixture component
chemicals have similarly shaped dose-response curves for a common effect, the RPF and M-
BMD calculations produce nearly identical risk estimates for the same mixture. However, if the
mixture component chemicals display a range of dose-response curve shapes, then the
assumptions for application of the RPF method are violated and the M-BMD approach should be
used to produce a more accurate estimate of risk (NRC, 2008).
Another factor in the concordance (or not) of mixture risk estimates across the component-based
approaches presented are the factors used in deriving RfVs or HBWCs from hazard data across
components. The HI and TOSHI approaches are calculated based on an exposure metric divided
by the component chemical RfVs or HBWCs. The input data for calculating RfV and HBWC,
including the critical effect and POD (i.e., BMDLx or NOAEL or LOAEL), correction factors
(i.e., UFs, DAF), and exposure route adjustments (i.e., DWI, RSC) used for each component
PFAS will have impacts on the resulting risk estimates and should be carefully selected based on
available data for each component PFAS. Similarly, when conducting RPF or M-BMD
assessment, the PODs used across component PFAS in a mixture could potentially be derived
from dissimilar response metrics (i.e., LOAEL vs. BMDLx) and the resulting M-BMD-HBWC
or Total ICEC comparison will be somewhat dependent on the applicability of the correction and
route adjustment factors used for the M-BMD HBWC or Total ICEC derivation across all
component PFAS in the mixture. For example, the correction factors and route adjustment
factors may not be appropriate for all component PFAS; thus, the Total ICEC to IC RfV or
HBWC comparison or M-BMD HBWC to measured concentration may be affected. Therefore, it
is strongly encouraged to use comparable PODs across component PFAS where possible and
select adjustment factors given careful consideration of the components for the specific PFAS
mixture being evaluated. It will be key to transparently present and communicate the selection of
uncertainty factors or exposure route adjustment factors and associated rationale(s), such that
interpretations and conclusions of mixture risk are supportable.
A critical consideration for using the approaches in this framework document is that it may be
prudent to apply each approach to the same mixture where data are available. The purpose of the
comparison is not necessarily to determine which approach provides the most conservative
estimate of mixture risk but rather which reflects the greatest level of confidence in the data
underlying the component PFAS.
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