In the assigned reading for this unit, you learned about various chemicals that induce reproductive toxicity. Not limiting yourself to the chemicals that are mentioned in the assigned reading, identify a toxicant that causes reproductive toxicity. Develop a research paper that includes the following: 

1. background information on the toxicant, its use, and routes of exposure; 
2. the process by which this toxicant causes reproductive toxicity and the concentration of exposure; 
3. ways exposure to the toxicant might be limited, treated, and/or effects reversed; and 
4. recent research findings (within the last five years) on this toxicant. 

The research paper should be a minimum of three pages in length, not including the title and reference pages, and written in APA format with proper in-text citations and references. The paper should utilize at least three credible sources that include at least one peer reviewed journal article published within the last five years.

Identifying and Prioritizing Chemicals with Uncertain Burden of Exposure:
Opportunities for Biomonitoring and Health-Related Research
Edo D. Pellizzari,1 Tracey J. Woodruff,2 Rebecca R. Boyles,3 Kurunthachalam Kannan,4 Paloma I. Beamer,5 Jessie P. Buckley,6
Aolin Wang,2 Yeyi Zhu,7,8 and Deborah H. Bennett9 (Environmental influences on Child Health Outcomes)
1Fellow Program, RTI International, Research Triangle Park, North Carolina, USA
2Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San
Francisco, San Francisco, California, USA
3Bioinformatics and Data Science, RTI International, Research Triangle Park, North Carolina, USA
4Wadsworth Center, New York State Department of Health, Albany, New York, USA
5Department of Community, Environment and Policy, Zuckerman College of Public Health, University of Arizona, Tucson, Arizona, USA
6Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Heath, Johns Hopkins University,
Baltimore, Maryland, USA
7Northern California Division of Research, Kaiser Permanente, Oakland, California, USA
8Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
9Department of Public Health Sciences, University of California, Davis, Davis, California, USA

BACKGROUND: The National Institutes of Health’s Environmental influences on Child Health Outcomes (ECHO) initiative aims to understand the
impact of environmental factors on childhood disease. Over 40,000 chemicals are approved for commercial use. The challenge is to prioritize chemi-
cals for biomonitoring that may present health risk concerns.

OBJECTIVES: Our aim was to prioritize chemicals that may elicit child health effects of interest to ECHO but that have not been biomonitored nation-
wide and to identify gaps needing additional research.

METHODS: We searched databases and the literature for chemicals in environmental media and in consumer products that were potentially toxic. We
selected chemicals that were not measured in the National Health and Nutrition Examination Survey. From over 700 chemicals, we chose 155 chemi-
cals and created eight chemical panels. For each chemical, we compiled biomonitoring and toxicity data, U.S. Environmental Protection Agency ex-
posure predictions, and annual production usage. We also applied predictive modeling to estimate toxicity. Using these data, we recommended
chemicals either for biomonitoring, to be deferred pending additional data, or as low priority for biomonitoring.

RESULTS: For the 155 chemicals, 97 were measured in food or water, 67 in air or house dust, and 52 in biospecimens. We found in vivo endocrine, de-
velopmental, reproductive, and neurotoxic effects for 61, 74, 47, and 32 chemicals, respectively. Eighty-six had data from high-throughput in vitro
assays. Positive results for endocrine, developmental, neurotoxicity, and obesity were observed for 32, 11, 35, and 60 chemicals, respectively.
Predictive modeling results suggested 90% are toxicants. Biomarkers were reported for 76 chemicals. Thirty-six were recommended for biomonitor-
ing, 108 deferred pending additional research, and 11 as low priority for biomonitoring.
DISCUSSION: The 108 deferred chemicals included those lacking biomonitoring methods or toxicity data, representing an opportunity for future
research. Our evaluation was, in general, limited by the large number of unmeasured or untested chemicals. https://doi.org/10.1289/EHP5133

Introduction
Environmental chemical exposures can adversely impact child-
ren’s health (Bellinger 2013; Bose-O’Reilly et al. 2010; Grandjean
and Landrigan 2006; Grandjean et al. 2008; Jurewicz and Hanke
2011; Lanphear et al. 2005; Selevan et al. 2000; Sharpe and Irvine
2004; Weiss 2000; WHO 2010). Children are susceptible to envi-
ronmental influences during developmental periods, including in
utero exposures, due to rapidly and uniquely changing physiology,
behaviors, and exposures, including hand-to-mouth behaviors and
increased contact with the ground. Environmental exposures can
interact with other physiological and external stressors to modify
risks of adverse child health outcomes (Holmstrup et al. 2010).
Despite advances in children’s environmental health science, there
is still a paucity of data to inform intervention and prevention

activities. This is of particular concern given that the prevalence of
certain child diseases has increased over the last 10–30 y, including
adverse birth outcomes, neurodevelopmental delays and deficits,
respiratory effects, obesity, diabetes, and cancer (American
College of Obstetricians and Gynecologists Committee on Health
Care for Underserved Women et al. 2013; Diamanti-Kandarakis
et al. 2009; Reuben 2010). This is an underlying driver for the
National Institutes of Health (NIH)’s Environmental influences on
Child Health Outcomes (ECHO) initiative launched in 2016,
whose goal is to understand the environmental influences on child-
hood disease with a focus on pre-, peri-, and postnatal factors for
respiratory, endocrine, neurodevelopmental, and other outcomes.
ECHO consists of multiple cohorts of participating children, with
broad spatial coverage across the United States. Comprising
>50,000 children, it will capitalize on existing participant popula-
tions by supporting multiple, synergistic, and longitudinal studies
to investigate environmental exposures—including chemicals—
on child health and development.

One of the challenges in ECHO (https://echochildren.org/) is
to identify and evaluate health effects resulting from chemical
exposures. Although many of the ECHO cohorts plan to evaluate
exposures to about 200 chemicals—the same chemicals measured
in the National Health and Nutrition Examination Survey
(NHANES) (CDC 2018a, 2019b)—via biomonitoring, exposure
questionnaires, and geospatial mapping. A few examples of chemi-
cal panels currently being studied in ECHO include alternative
plasticizers (APs), environmental phenols (EPs), organophosphate
flame retardants (OPFRs), perfluoroalkyl substances (PFASs), and
pesticides (PEs). This still leaves thousands of chemical exposures

Address correspondence to Edo D. Pellizzari, P.O. Box 12194, RTI
International, Research Triangle Park, NC 27709 USA. Email: edp.Emeritus@
rti.org
Supplemental Material is available online (https://doi.org/10.1289/EHP5133).
The authors declare they have no actual or potential competing financial

interests.
Received 1 February 2019; Revised 13 November 2019; Accepted 19

November 2019; Published 18 December 2019.
Note to readers with disabilities: EHP strives to ensure that all journal

content is accessible to all readers. However, some figures and Supplemental
Material published in EHP articles may not conform to 508 standards due to
the complexity of the information being presented. If you need assistance
accessing journal content, please contact ehponline@niehs.nih.gov. Our staff
will work with you to assess and meet your accessibility needs within 3
working days.

Environmental Health Perspectives 126001-1 127(12) December 2019

A Section 508–conformant HTML version of this article
is available at https://doi.org/10.1289/EHP5133.Review

https://doi.org/10.1289/EHP5133

Home

mailto:edp.Emeritus@rti.org

mailto:edp.Emeritus@rti.org

https://doi.org/10.1289/EHP5133

http://ehp.niehs.nih.gov/accessibility/

mailto:ehponline@niehs.nih.gov

https://doi.org/10.1289/EHP5133

unconsidered. Tens of thousands of chemicals have been approved
for use in the United States (Board on Population Health and
Public Health Practice 2014). Further, nearly 8,000 chemicals are
manufactured or imported in high amounts (>11,000 kg=y) (U.S.
EPA 2016a), indicating that humans are likely exposed to many
more chemicals than are routinely measured via biomonitoring
(Wang et al. 2016). There is a paucity of information on biomoni-
toring of exposures in pregnant women, infants, and children that
limits our ability to evaluate the potential health impact of these
chemicals.

Exposure to chemicals primarily occurs through one or a
combination of three routes—inhalation, ingestion, and dermal
absorption—and by secondary mechanisms such as maternal trans-
fer in utero, breastfeeding, or intravenous injections (e.g., phthalates
as contaminants from medical devices and in drugs). Further, expo-
sures occur to mixtures of many chemicals, creating opportunities
for additive, synergistic, or antagonistic interactions (NRC 2007;
Rider et al. 2013). The large number of chemicals that are not cur-
rently evaluated for prenatal or childhood exposures creates a chal-
lenge for determining the best approach to prioritize chemicals for
biomonitoring and evaluation in ECHO.

In this paper, we collect and evaluate quantitative extant
data for chemicals that are not currently measured by ECHO or
NHANES (CDC 2019c). Our specific objectives were to a)
identify chemicals to which mothers and children are likely
exposed based on environmental and biomonitoring data and
chemicals associated with commercial/consumer products, b)
compile and use information on the health effects/toxicity rele-
vant to ECHO to prioritize the chemicals for study, and c) iden-
tify knowledge gaps needing further research. We limited our
scope to a segment of the universe of chemicals that have been
measured in environmental media from industrial processes or
have been identified as being in consumer products and have
the potential for health effects in pregnant women and in chil-
dren. Chemicals reported only as occupational exposures were
not considered.

Methods

Overview of Approach
The goal of ECHO is to study pre-, peri-, and postnatal factors
affecting upper and lower airway, respiratory, endocrine, neurode-
velopmental, and other outcomes. Our overall screening frame-
work integrated exposure- and toxicity-related data to identify and
prioritize chemicals (Figure 1) with this goal in mind. Chemicals
of interest were identified using a two-prong approach. First, we
sought chemicals in environmental media reported in government
databases (drinking water and food) and the literature (air, drinking
water, house dust, and food) that occurred at a quantifiable fre-
quency of ≥20% in samples [Figure 1 (environmental media list);
Excel Table S1]. We recorded the mean or median value for each
chemical by sample type that was published in an environmental
study or the highest value from multiple studies. For biospecimens,
we recorded the highest value at the 75th percentile from one or
more yearly reporting cycles in NHANES.

Second, we selected chemicals in the U.S. Environmental
Protection Agency (EPA) consumer product database (CPCat) that
had a potentially toxic structural moiety to construct the consumer
products list (Figure 1). After removing duplicates and inorganic
chemicals, metalloids, radionuclides, and particulate matter from
the environmental media and consumer products lists, the two lists
were combined [Figure 1 (C list)]. Inorganic chemicals and metal-
loids were excluded, given that they are currently being studied in
ECHO and have been measured in NHANES (CDC 2019c).

Subsequently, we sorted the chemicals on the C list (see
Excel Table S2) into five groups, setting aside from further con-
sideration those measured in NHANES [Figure 1 (Group I)],
and those considered as legacy chemicals (Group II). Legacy
chemicals—such as, heptachlor, p,p0-dichlorodiphenyltrichloro-
ethane (p,p0-DDT), and polychlorinated biphenyls—have been
extensively studied and have a clear health impact but with dra-
matically decreasing exposure over the last four to five decades.
From the remaining three groups (see Excel Tables S3–S5),
we selected a subset of chemicals to create the eight panels—
alternative flame retardants (AFRs), APs, aromatic amines
(AAs), EPs, OPFRs, PFASs, PEs, and quaternary ammonium
compounds (QACs) (Figure 1). Chemicals in the panels were
then prioritized. Chemicals recommended for biomonitoring
supplement those currently being measured in ECHO.

Search Strategy for Prevalent Chemicals
Government databases. We reviewed the chemicals reported in
several governmentdatabases for prevalence andlevels of chemicals
in environmental media and human biospecimens. Specifically, we
examined the U.S. EPA Six-Year Review 1, 2, and 3 Compliance
Monitoring Data (1993–1997, 1998–2005, and 2006–2011) for
water (U.S. EPA 2016b, 2016c, 2016d); the U.S. Department of
Agriculture (USDA) Pesticide Data Program, 2010–2011 Pilot
Study data containing PE residues occurring in food commodities
and drinking water (USDA 2017), and the U.S. Food and Drug
Administration (FDA) files containing results for PEs reported for
the periods 2004–2005 and 2006–2011 (FDA 2011).

We recorded chemicals found in environmental media with
the highest reported median or mean results from across multiple
studies and occurring at least at a detection frequency of 20% in
the sample type. The purpose for applying this threshold was to
find chemicals that were more likely to be detected in biospeci-
mens than if no detection threshold was used. We normalized the
data to a common set of concentration units by medium (e.g.,
food) and class of compounds (e.g., pesticides) when the reported
units (means or medians) differed in multiple studies. USDA data
for PEs in drinking water were only reported in ranges; thus,
chemicals with a detection frequency of ≥20% to 40% and >40%
were recorded. (A description of the search of the literature for
the levels found in human biospecimens for chemicals in panels
slated for prioritization is presented below, in the “Searching for
environmental and biomonitoring data” section.)

For chemicals measured in human serum and urine, we reviewed
the Updated Tables, March 2018 and January 2019, Volumes 1 and
2, published in the Fourth National Report on Human Exposure to
Environmental Chemicals, a Centers for Disease Control and
Prevention NHANES study (CDC 2018b, 2019a, 2019b). This
report contains nationally representative biomonitoring data for
survey periods 1999–2000 through 2015–2016, including pooled
samples.

We recorded the highest value for each chemical found across
the yearly reporting cycles at the 75th percentile in the age group
of 12–19 y from the NHANES database. In addition, we noted
nonmeasurable values at the 75th percentile interval.

We used the U.S. EPA’s CPCat Database and its search
algorithms to find chemicals that may lead to human exposure
and health-related consequences (U.S. EPA 2014a, 2014b).
This search ended in June 2017. We limited our search to a seg-
ment of the universe of chemicals in commerce by focusing on
a subset of consumer products. We chose product categories in
the CPCat database that included products that may expose ex-
pectant mothers, infants, or children to chemicals. There were
372 consumer product categories, and we chose 45 to include in
our search (see Table S1). Twenty-seven were categories that

Environmental Health Perspectives 126001-2 127(12) December 2019

contained formulated personal products, and the remaining 18
categories contained chemicals used as biocides or to control
pests or are constituents in household items. We screened chemi-
cals in these product categories to identify those that qualified as
potentially toxic. For the chemicals with structures in the 45 cate-
gories, we visually inspected each chemical structure for func-
tional moieties that have been empirically determined to elicit
toxic effects. These moieties generally follow the principle of
Chemical Structure—Toxicological Activity Relationships (Cronin
et al. 2003). The moieties were acyl halide, aldehyde, aliphatic or
aromatic N-nitroso, alkyl nitrite, aromatic mono- or dialkylamine,
aziridine, carbamate, epoxide, halogenated aliphatic or aromatic,
halogenated polycyclic aromatic hydrocarbon, heterocyclic aro-
matic hydrocarbon, hydroxyl amine, isocyanate, isothiocyanate,
lactone, phosphonic or sulfonic acid, polycyclic aromatic hydrocar-
bon (PAH), sultone, triazene, primary aromatic amine, thiocarba-
mate, thiocarbonyl, and a and b unsaturated carbonyl (Chen et al.
2013). We regard this approach as a screening method because it
only broadly differentiates the categories of toxicity. Furthermore,

it does not account for absorption, distribution, metabolism, and
excretion of the chemical in a living organism or for the minimal
dosage to affect toxicity—all factors that play a role in determining
toxic potency.

We only inspected chemicals with displayed structures in
CPCat (U.S. EPA 2014a, 2014b). Chemicals listed by name and
Chemical Abstracts Service Registry Number (CASRN) with no
accompanying structures were excluded because creating their
chemical structures would have been overly burdensome. The
resulting group of chemicals constituted the consumer products
list (Figure 1).

Published literature. Besides government reports, we searched
the literature for chemicals in environmental media with at least a
20% detection frequency and recorded the highest median or mean
concentration for chemicals reported in one or more studies in an
electronic database. We focused our search on environmental media
such as drinking water, food, indoor dust, and air that are closely
associated with a route of exposure—inhalation, ingestion, and der-
mal. Papers that represented U.S. nationwide, probability-based, or

Chemicals Selected
with “toxic” Moiety

EPA CPCat Database:
372 Product Categories;

~170K chemicals

C List

USDA, FDA, EPA,
NHANES Databases

and literature
Drinking water, air,
house dust, food,
biofluids

EM list
Chemicals Quantifiable
in > 20% of Samples

Duplicates Removed
Excluded inorganics

CP list

C List (932)*

Selected 45
Consumer
Product Categories

Selected 155
Chemicals – Created 8
Panels: AFRs (23), APs

(10), AAs (28), EPs
(16), OPFRs (11),

PFASs (8), PEs (43),
QACs (16)

Sorted into Chemical
Groups I-V

Remaining Chemicals (565)

GIII (260), GIV (293), GV (167)
Total chemicals (720)

GI, Measured Nation –
wide (NHANES)

GII, legacy chemicals

*(Number of chemicals) Chemical Prioritized as:
● recommended for biomonitoring
● deferred, insufficient data, or
● low priority for biomonitoring

Figure 1. Overview for identifying chemicals of interest in environmental media and consumer products. C list, combined list (EM plus CP lists); CP list, con-
sumer product list; CPCat, Consumer Product Categories; EM list, environmental list; EPA, U.S. Environmental Protection Agency; FDA, Food and Drug
Administration; GI, Group I chemicals with NHANES exposure data; GII, Group II Legacy chemicals with extensive environmental, exposure and health data;
GIII, Group III, chemicals with extensive environmental and no NHANES exposure data; GIV, Group IV chemicals with U.S. EPA exposure predictions and
limited environmental and exposure data; GV, Group V chemicals with no U.S. EPA exposure predictions and limited environmental and exposure data,
AFRs, alternative flame retardants, APs, alternative plasticizers, AAs, aromatic amines, EPs, environmental phenols, OPFRs, organophosphorus flame retard-
ants, PFASs, perfluoroalkyl substances, PEs, pesticides; NHANES, National Health and Nutrition Examination Survey; QACS, quaternary ammonium com-
pounds; USDA, U.S. Department of Agriculture. *, number of chemicals.

Environmental Health Perspectives 126001-3 127(12) December 2019

cohort studies that quantified hundreds to thousands of samples
were included while small studies with fewer than 25 observations
were excluded. Additional study criteria included papers published
(in English) in or after 1995.

We conducted an extensive search ending in December 2018
using Science and Technology Collection (advanced search mode),
Google Scholar, Scopus, and Web of Science. We used the search
terms listed in Table S2. Studies met eligibility criteria if the
reporting nationwide data were from the United States, Canada,
the European Union, Japan, or Australia. We excluded a) countries
with no, few, or unknown regulatory standards on chemical
releases to the environment; b) media from occupational environ-
ments; and c) publications not in English.

For the chemicals selected for prioritization (discussed below),
we will elaborate on additional literature searches in sections on
measured levels in human biospecimens, toxicity, and biomarkers.

Acquiring Exposure Predictions
We obtained the U.S. EPA exposure predictions and NHANES-
reported measured values for the youngest age group, 6–11 y
(no data were available for younger age groups), that were
available for each chemical on the C list (see Excel Table S2).
U.S. EPA exposure predictions were provided by the U.S. EPA
as an ExpoCast™ Excel file or retrieved from U.S. EPA’s
Chemistry Dashboard (U.S. EPA 2017; Wambaugh et al. 2014)
during the period from June to October 2017. The chemicals on
the C list were sorted into those with and without exposure
predictions.

Grouping Chemicals by Data Availability
We assigned each chemical on the C list to one of five groups
based on available information and according to the following
characteristics: a) Group I—chemicals with NHANES exposure
data and measured in environmental media; b) Group II—legacy
chemicals with extensive environmental, exposure and health
data; c) Group III—chemicals with extensive environmental and
no NHANES exposure data; d) Group IV—chemicals with U.S.
EPA exposure predictions and limited environmental and expo-
sure data; and e) Group V—chemicals with no U.S. EPA-
predicted exposures and limited environmental and exposure data
(Figure 1). A purpose of this grouping was to identify chemicals
reported in the NHANES and legacy chemicals (Groups I and II,
respectively) and, given that many of these chemicals were al-
ready being studied in ECHO, they were excluded from this pa-
per. Instead, we focused on the chemicals found in Groups III–V
(see Excel Tables S3–S5).

Creating Chemical Panels
To fulfill our primary goal of recommending priority chemicals
for biomonitoring in ECHO, we evaluated chemicals that have

been understudied with respect to human health effects. Because
Groups III–V contain a total of 719 chemicals, we limited our
prioritization to a manageable subset of potential candidates for
study in ECHO (Figure 1; Table 1).

Alternative flame retardants. Polybrominated diphenyl ethers
were used for decades as flame retardants but are gradually
being phased out of commerce and replaced by AFRs. AFRs
consist of a diverse array of bromine-, chlorine-, and nitrogen-
containing compounds. They are found in environmental media,
and with continual use, their levels in the environment will
likely increase.

Alternative plasticizers. Developed as substitutes for phtha-
lates, APs (e.g., terephthalate esters) have been introduced into
commerce (Nayebare et al. 2018). Phthalates, depending on their
structure, have been identified as reproductive and developmental
toxicants (Wu et al. 2013), whereas APs are not well studied
regarding their potential effects on human health. Like phthalates,
APs are not chemically bound to the polymer (Kastner et al.
2012) and can leach out of children’s and other consumer prod-
ucts, leading to exposure.

Aromatic amines. Predominantly used in dyes (e.g., Acid
Red, Red 9 and 22, and D&C Red 21, 21L, and 22) (Abe et al.
2016; Fautz et al. 2002) and pigments (including hair dyes,
mascara, tattoo ink, toners, paints) (Anezaki et al. 2015;
Clarke and Anliker 1980), polyurethane production, polymeric
resins, corrosion inhibitors, rubber vulcanization accelerators,
PEs, and pharmaceuticals (Ahlström et al. 2005; Anezaki et al.
2015; Trier et al. 2010; Weisz et al. 2004; Yavuz et al. 2016).
AAs have been reported in tobacco smoke; however, the AAs
listed in this panel were also found in consumer products that
are used by children and mothers. Thus, dermal contact from
the use of consumer products is an important route of exposure
for many AAs.

Environmental phenols. Depending on their structures, EPs
are used in consumer and household products and serve as plasti-
cizers, detergents, and preservatives (Cadogan and Howick, 2000;
U.S. EPA 2010). 3,30,5,50 Tetrabromobisphenol A is used both as a
plasticizer and flame retardant (Alaee et al. 2003). Research on EPs
that have been in the environment for decades—such as bisphenol
A, triclosan, parabens, and triclocarban—has shown the potential
for endocrine disruption (Cullinan et al. 2012; Koeppe et al. 2013;
Meeker 2012; Witorsch and Thomas 2010). We found several EPs
that have structures similar to bisphenol A and may have similar
endocrine toxicity.

Organophosphorus-based flame retardants. OPFRs are one
of several classes of AFRs and comprise at least two groups
(alkyl- and aryl-). The alkyl group has been in commerce for sev-
eral decades, whereas the aryl group is an emerging group
(Christia et al. 2018). Patent activity in the flame-retardant field
has proliferated (Weil 2005), and OPFRs are produced in high
volumes. In addition to serving as flame retardants, they are
applied to consumer products as plasticizers, stabilizers, lubricants,

Table 1. Number of chemicals evaluated in each panel.

Panel name Chemicals (n) Recommended for biomonitoring Deferred pending additional data Low priority for biomonitoring

Alternative flame retardants (AFRs) 23 4 16 3
Alternative plasticizers (APs) 10 2 5 3
Aromatic amines (AAs) 28 3 25 0
Environmental phenols (EPs) 16 6 9 1

Organophosphorus-based flame
retardants (OPFRs)

11 5 5 1

Perfluoroalkyl substances (PFASs) 8 4 4 0
Pesticides (PEs) 43 12 28 3

Quaternary ammonium compounds
(QACs)

16 0 16 0

Total 155 36 108 11

Environmental Health Perspectives 126001-4 127(12) December 2019

and antifoaming agents (Brandsma et al. 2014; Levchik and Weil
2006). They are incorporated after polymerization, and as a result
are not chemically bonded to the material, permitting their release
from products into the environment. They are used in electronic
equipment, furniture, and textiles (van der Veen and de Boer
2012).

Perfluoroalkyl substances. Comprising perfluoroalkyl and
polyfluoroalkyl substances, PFASs are widely used in the produc-
tion of Teflon® and related fluorinated polymers (Wang et al.
2013). They have been used to confer hydrophobicity, stain-
resistance to fabrics, and as fire-fighting foams. In addition, they
are designed with properties to reduce surface tension. They con-
centrate at the aqueous–lipid interface due to the lipophobicity of
fluorocarbons and the hydrophilicity of carboxyl and sulfonic
acid moieties (Ritscher et al. 2018). As such, PFASs are
extremely stable. Some tend to bioaccumulate and are stored in
tissues. After reviewing the environmental monitoring literature
and consumer products, we selected PFASs as panel category
because they are chemically related to perfluorooctane sulfonate
and perfluorooctanoic acid and have been shown to exhibit health
effects (DeWitt 2015).

Pesticides. PEs are extensively used to control fungal disease
and pests and for growth stimulation and vector disease control
such as lice treatment (Casida 2009). To account for infections
by numerous fungi, multiple applications of a spectrum of fungi-
cides are needed (Casida 2009). Thus, numerous chemicals are
applied to food commodities to cover the array of diseases. For
example, in 2015, the USDA monitored >470 PEs (USDA
2018). Similarly, the FDA monitors >700 PEs in a broad range
of food samples (FDA 2018).

Quaternary ammonium compounds. Used in fabric soften-
ers, antistatics, disinfectants, biocides, detergents, phase transfer
agents, and numerous personal care products such as hair care
products (Tsai and Ding 2004; Zhang et al. 2015), QACs are
released into the environment in effluents and sludge from sew-
age treatment plants (Zhang et al. 2015). Other sources contami-
nating the environment are effluents from hospitals, laundry
wastewater, and roof runoff (Zhang et al. 2015) and consumer
products. The sorption of QACs to media is faster than degrada-
tion. Therefore, QACs accumulate in the environment, especially
in anoxic/anaerobic compartments (Zhang et al. 2015). The pres-
ence of QACs in the environment is toxic to both aquatic and ter-
restrial organisms (Zhang et al. 2015). However, human exposure
and health effects remain mostly unknown.

Chemicals were selected for prioritization based on their
potential occurrence in media that may lead to human exposure
and health effects. A total of 155 chemicals were selected from
Groups III–V and assigned to the eight panels (Table 1). Even
though the AFR, AP, EP, OPFR, PFAS, and PE panels are the

same as those currently being evaluated in ECHO, the new chem-
icals selected are supplementary. The AAs and QACs are new
panels and have not been widely measured in environmental
media and human biospecimens.

Last, our strategy was to group chemicals in panels with
similar chemical properties, permitting the development of
multi-chemical analysis methods. This approach allows for an
economies-of-scale cost savings as compared with a method
that analyzes a single compound. Except for PEs, we assigned
all chemicals on the C list with common functional properties
to a chemical panel.

Because the environmental media list (see Excel Table S1) had
many PEs, we reduced the number to a manageable list for this pa-
per. Our selected PEs came primarily from the USDA’s database
of reported PE residues (see Excel Table S6) because this database
emphasizes food commodities highly consumed by infants and
children (USDA 2017). Of the 180 PEs measured in food and
drinking water, we found 156 had not been biomonitored in the
NHANES (see Excel Table S6). Of the 156 PEs, we selected 40
that represented those with higher prevalence and median concen-
trations in food (>50 ng=g) or drinking water (see Excel Table S6).
This selection included 12 PEs that exhibited overlapping toxicity
(U.S. EPA ToxCast™ Program results) and U.S. EPA exposure
predictions (Wetmore et al. 2012). Furthermore, we added three
PEs to this panel (acifluorfen, fluroxypr-meptyl, and isoxaben) that
were also reported with overlapping toxic activity and predicted
exposures (Wetmore et al. 2012), yielding a total of 43 pesticides
for prioritization.

Because PE exposure is episodic (i.e., primarily by ingestion),
we chose PEs that occur on several food commodities (FDA
2018; USDA 2018), thus likely increasing the frequency of expo-
sure episodes. In addition, children can have greater exposures
because of their smaller body size and select eating of certain
food groups (Bruckner 2000). Even though PEs associated with
fruits and vegetables grown domestically are seasonal, they also
occur on imported foods (USDA 2018). In addition, we chose
PEs that were found on domestic and on imported foods, increas-
ing the likelihood that exposure occurs year-round.

Prioritizing Candidate Chemicals for Biomonitoring
Figure 2 depicts the overall approach used for prioritizing candi-
date chemicals for biomonitoring in ECHO. We conducted litera-
ture searches (described below) to gather information that
answered three questions. If all three questions (exposure, toxic-
ity, biomarker) were answered affirmatively for a chemical,
then it was recommended for biomonitoring in ECHO. For cases
lacking enough information, we recommended deferring biomo-
nitoring pending additional data. Chemicals in the deferred group
were divided into four categories based on data available for

Figure 2. Overview for identifying candidate chemicals for biomonitoring. ECHO, Environmental Influences on Child Health Outcomes; HTP, high
throughput.

Environmental Health Perspectives 126001-5 127(12) December 2019

exposure, toxicity, and availability of a biomarker (Table 2).
These categories were created to elucidate research needed to fill
existing knowledge gaps concerning exposure, chemical toxicity,
or development of a biomarker.

The literature searches yielded a spectrum of information types
within and across chemicals. As such, we grouped each type of in-
formation into categories to indicate their relative importance as ei-
ther high, moderate, or low to help guide answering the three
questions and prioritizing the chemicals (Table 3). The intention of
this classification approach was to denote the relative certainty that
a) a chemical found in a human biofluid signifies exposure or in an
environmental medium can lead to human exposure, b) health
effects occur in humans or are suggested by in vivo or in vitro
results, and c) a biomarker is available to assess chemical exposure.
We used expert judgement guided by the following decision crite-
ria for each question:

1. Was the chemical (or its metabolite) found in quantifiable
levels in biospecimens (Figure 2)?
a. If a chemical was detected at >10% detection frequency
in human biospecimens, then it was further considered
for biomonitoring, pending Question 2 being satisfied.

b. If only measured in environmental media at ≥20%
detection frequency, which suggested a potential for ex-
posure, and the U.S. EPA exposure predictions were in
the 1:0 × 10−7 mg=kg body weight ðBWÞ per day range or
higher, then the chemical was further considered for bio-
monitoring, pending Questions 2 and 3 being satisfied.
If a U.S. EPA exposure prediction was not available,
then biomonitoring was recommended in samples from

nonoccupationally exposed U.S. subjects to establish the
frequency of detection in biospecimens, and Steps 1a or 1c
should be applied, based on the detection frequency found.

c. If there was limited chemical occurrence in biospeci-
mens (<10% detection frequency) or environmental media (<20% detection frequency), then it was assigned a low priority for biomonitoring at this time.

d. If there were no publications on biomonitoring or envi-
ronmental data, and U.S. production volume (PV) data
were available, then for PVs ≥450,000 kg=y it was fur-
ther considered for biomonitoring, pending Question 2
being satisfied and additional information on its preva-
lence in biospecimens for a nonoccupationally exposed
U.S. population becoming available.

2. Was there evidence that the chemical may cause health
effects of interest to ECHO?
a. Nonoccupational exposure to a chemical that yielded pos-
itive endocrine disruption or reproductive, developmen-
tal, or neurotoxicity results in an epidemiological study
was adequate evidence for considering biomonitoring
(also satisfies Questions 1 and 3).

b. In the absence of human data, in vivo animal (rat, mouse,
rabbit, dog) data were used to prioritize. When available, if
risk-based assessments using animal study data exhibited
observable effects at doses <100 mg=kg BW per day (or <30 lg=kg BW per day when applying a 3,000-uncertainty factor), then the chemicals were considered potentially toxic and further considered for biomonitoring, pending Questions 1 and 3 being satisfied. For example, for doses

Table 3. Information used for prioritizing chemicals for biomonitoring: grouped by subject and relative importance.

Subject Highly important Moderately important Low importance

Environmental media/
biomonitoring
measurements

� Biomonitoring or environmental media (air,
house dust, food, and drinking water) data;
with ≥20% detection frequency in environ-
mental media and ≥10% in biospecimens
reported by cohort or epidemiological studies
conducted in the United States, Canada,
European Union, Japan, and Australia

� Qualitative screening data for chemical
in dust. U.S. EPA exposure predictions
(ExpoCast)

� Production and usage statistics for
chemical in the United States

� Presence of chemical in consumer
products

� Qualitative screening studies for chemical in
biofluids

Health effects/toxicity � Federal review of pesticide’s toxicity, risk
assessments, state priority lists, or human
health studies

� In vivo animal toxicological studies � In vitro studies
� U.S. EPA-reported overlapping bioas-
say activity and predicted exposure

� HTP in vitro assay data
� Predictive modeling

Biomarkers � Specific parent or metabolite has been quanti-
fied in a biospecimen in cohort or epidemio-
logical study

� Chemical measured in limited, small
scale; method demonstration study in
humans or animals

� Biomarker available; however, it
may not be specific (i.e., metabo-
lite formed from multiple com-
pounds). In such cases, proxy
exposure methods may be
recommended

� Favorable toxicokinetic parameters of
parent or metabolite support potential
marker; however, needs validation

� Chemical quantified in occupational
studies where exposure levels are higher
than under environmental conditions

Table 2. Categories of additional data needed for deferred chemicals.

Category
Detected in

biospecimens?
Detected in

environmental media? Toxicity concern?
Biomarker
exists? Additional research needed

A Insufficient information Insufficient information Yes Yes Chemicals should be measured in biospecimens of
a nonoccupationally exposed population to deter-
mine if there is exposure.

B Insufficient information Yes Yes No No biomarker exists; develop one and test it in
nonoccupationally exposed human biospecimens
to confirm anticipated exposure.

C Insufficient information Insufficient information Yes No Needs data on exposure levels for nonoccupation-
ally exposed population and develop a biomarker.

D Insufficient information Insufficient information Insufficient information Yes/no Needs information on exposure, toxicity and per-
haps biomarker development.

Environmental Health Perspectives 126001-6 127(12) December 2019

of up to 1,000 mg=kg BW per day developmental effects
were not observed for metolachlor ethane sulfonic acid
(MESA; MDH 2018a) and metolachlor oxanilic acid
(MOXA; MDH 2018b). Thus, MESA and MOXA were
assigned a low priority for biomonitoring even though
they were detected at a frequency of >20% in groundwater
samples (Sutherland-Ashley et al. 2017).

c. In the absence of in vivo data, in vitro assay and in silico
predictive data were used to prioritize chemicals for
biomonitoring. If chemicals showed positive high-
throughput (HTP) in vitro assay and predictive model-
ing results for the same end point of toxicity, then they
were further considered for biomonitoring, pending
Questions 1 and 3 being satisfied. Predictive modeling
data alone were insufficient evidence, and the chemical
was deferred pending additional data.

d. If no data were available for in vivo studies, HTP in
vitro assays, and predictive modeling, then we deferred
the inclusion of a chemical in ECHO pending additional
toxicity data.

3. Was there a biomarker that may assess body burden of the
chemical?
a. If the chemical or its metabolite was measured in bio-
specimens, then this criterion was satisfied.

b. If the first two questions were affirmatively answered
and the use of a biomarker was not reported, we recom-
mended one be developed, if possible.

c. If a biomarker(s) was not specific or could not be devel-
oped, then we recommended the use of proxy measures
to estimate exposure.

If chemicals had <10% detection frequency in human biospe- cimens, <20% environmental levels, or low reported toxicity, they were assigned a low priority for ECHO.

Next, we searched the literature for data to answer the three
questions: a) Was the chemical (or its metabolite) found in
quantifiable levels in biospecimens? b) Was there evidence that
the chemical may cause health effects of interest to ECHO? c)
Was there a biomarker that may assess body burden to the
chemical (Figure 2)? We performed literature searches on mea-
surement levels in human biospecimens, levels in environmen-
tal media, toxicity, and biomarkers of exposure for the 155
chemicals.

Searching for Environmental and Biomonitoring Data
Because chemicals in Groups IV and V were from the consumer
product search, we sought biomonitoring and environmental media
data. We conducted literature searches ending in December 2018
for studies reporting chemical levels in environmental media and
human biomonitoring data and using Science and Technology
Collection (advanced search mode), Google Scholar, Scopus, Web
of Science, and PubMed for all document types. We limited our
search to studies published in or after 2000. We used a combination
of the strings of terms listed in Table S2. There were instances
where expert judgement was used, due to the complexity of chemi-
cal nomenclature and environmental media characteristics. Studies
met eligibility criteria if the reporting data were from the United
States, Canada, the European Union, Japan, or Australia. We
excluded countries with no, few, or unknown regulatory standards
on chemical releases to the environment. Publications not in
English were excluded.

Searching for Health Effects and Toxicity Data
For the 155 chemicals we searched for data on health effects in
humans and in vivo toxicity studies in animals using ToxNet

(International Toxicity Estimates for Risk; Developmental and
Reproductive Toxicology Database, https://toxnet.nlm.nih.gov/),
PubMed, Scopus, and Google Scholar for all document types.
We did not limit the period of years searched. We used a combi-
nation of the strings of terms listed in Table S2. In vivo studies
met the criteria if they were conducted in humans, rats, mice, rab-
bits, or dogs, and in vitro studies met the criteria if they were con-
ducted using tissues from those species.

In addition, we examined the following databases: the State
of California’s Chemicals Known to the State to Cause Cancer or
Reproductive Toxicity (OEHHA 2018); the State of Minnesota’s
Chemicals of High Concern List (Bell 2016); and the State of
Washington’s Chemicals of High Concern to Children Reporting
List (WADEC 2018). These databases list chemicals in alphabeti-
cal order, and the information of interest was obtained manually.

Searching for Biomarkers for Chemicals on Panels
We used PubChem and the literature search approach and key
words (see Table S2) for human biomonitoring of chemicals or
their metabolites as described above except that we included
papers from any country that reported on biomarkers of exposure.
Furthermore, we searched for papers that reported metabolism of
chemicals and excretion of metabolites in humans or animals.

Acquiring HTP in Vitro Assay Data
The goal of ECHO is to study pre-, peri-, and postnatal factors
affecting upper and lower airway and neurodevelopment out-
comes. For chemicals in Groups III–V (see Excel Tables S3–S5),
we utilized additional HTP in vitro assay results from ToxCast™,
and where possible, from scientific papers that merged results
from several assays, producing an overall score (Judson et al.
2015; Karmaus et al. 2016; Kleinstreuer et al. 2017). We focused
on results related to all neurological, endocrine, and obesity
processes.

Neurological assays. There were three calcium, one ligand,
three potassium, and one sodium ion-channel assays, all in the
ToxCast™ database and reported as NVS_IC (Sipes et al. 2013).
These assays were included directly and were evaluated based
on the hit call value being equal to 1. We also utilized an integrated
assay that measures neural network activity in vitro using micro-
electrode arrays (mwMEA). This assay has performed well when
run on known neuroactive compounds (Strickland et al. 2018;
Valdivia et al. 2014) and has been used to screen 1,055 chemicals
from the U.S. EPA’s Phase II ToxCast™ library with data from
the paper presented as binary MEA-Hits (Strickland et al. 2018).

Endocrine processes. The four main endocrine processes
evaluated were estrogen, androgen, thyroid, and steroidogenic.
Eighteen in vitro HTP assays were developed measuring estrogen
receptor binding, dimerization, chromatin binding, transcriptional
activation, and estrogen receptor-dependent cell proliferation. We
used the results from an algorithm that combined the assays into
a single interaction score [area under the curve (AUC) score],
providing a more robust measure of the potential for estrogenic
activity (Judson et al. 2015). Compounds with an AUC score as
either antagonist or agonist of >0:1 were considered. We used
the published results from an integrated model of 11 HTP in vitro
androgen receptor assays (Kleinstreuer et al. 2017). Ideally, four
thyroid processes should be considered, specifically: receptor ac-
tivity, thyroperoxidase inhibition, deiodinase inhibition, and so-
dium iodide symporter inhibition. We used results from the
Amplex UltraRed-thyroperoxidase (AUR-TPO) assay that cap-
tures multiple molecular-initiating events that converge on per-
turbed thyroid hormone homeostasis for those resulting in >20%
thyroperoxidase inhibition (Friedman et al. 2016). The results

Environmental Health Perspectives 126001-7 127(12) December 2019

https://toxnet.nlm.nih.gov/

from the thyroid receptor activity measured by a specific HTP
assay were used (Rotroff et al. 2013). We incorporated results
from a method that considered 10 steroid hormones—including
progestogens, glucocorticoids, androgens, and estrogens—using
an HTP assay with H295R human adrenocortical carcinoma cells
(Karmaus et al. 2016).

Obesity. Several biological processes have been associated
with diabetes and obesity (insulin sensitivity in peripheral tissue,
pancreatic islet and b-cell function, adipocyte differentiation, and
feeding behavior). HTP assay results relative to these processes
were used (Auerbach et al. 2016).

In Silico Predictive Modeling of Chemicals for Toxicity
To supplement gaps in our knowledge about potentially toxic
chemicals, we applied in silico models to predict toxicity for chem-
icals in Groups III–V. We used quantitative structure–activity rela-
tionship (QSAR) models on all chemicals in these groups to
predict developmental and reproductive toxicity and carcinogenic-
ity. Even though carcinogenicity is not being studied in ECHO, it
was included because a risk for carcinogenicity is likely to also
affect child health in other ways. We applied an in silico docking
model to predict endocrine disruption for only chemicals in the
eight panels.

Bioaccumulation factors were determined using the Toxicity
estimator Software (TEST) model (see below). These data were
used as a proxy for chemical persistence in tissues of organ-
isms. The Canonical Simplified Molecular Input Line Entry
Specification (SMILES) code for each chemical was submitted
to the models.

Endocrine disruption. We used the Endocrine Disruptome®
model, an open source, web-based prediction tool that uses mo-
lecular docking to predict the binding of compounds to 16 dif-
ferent human nuclear receptors (Kolšek et al. 2014). The
nuclear receptors were two androgen receptors [AR and AR an-
tagonist (an)]; four estrogen receptors (a and a an; b and b an);
two glucocorticoid receptors (GR and GR an); two liver X
receptors (a and b); four peroxisome proliferator-activated
receptors (a, b, c, and d); and two thyroid receptors (a and b).
We applied only the Endocrine Disruptome® model to the chem-
icals selected for prioritization, given that the model interface
did not permit submitting SMILES in batch form, making the
process burdensome.

TEST model. We used TEST (version 4.2), a U.S. EPA-
developed model that estimates the toxicity of chemicals using
QSAR methodologies (U.S. EPA 2016e). TEST has several mod-
ules; we used the Developmental Toxicity and Bioconcentration
Factor modules.

Virtual models for property evaluation of chemicals within a
global architecture. We used virtual models for property evalua-
tion of chemicals within a global architecture (VEGA), a consortium
of models based on QSAR methodologies (Benfenati et al. 2013).
Specifically, we employed the Developmental, Developmental/
Reproductive, Estrogen-binding, and Carcinogenicity modules to
assess the toxicity of each chemical.

CarcinoPred-EL. We used CarcinoPred-EL to predict carci-
nogenicity. This model consisted of three individual classification
models—Ensembles SVM, RF, and XGBoost—that use seven
types of molecular fingerprints and three machine-learning meth-
ods (Zhang et al. 2017). A score of 0–3 was recorded based on
the number of ensembles that gave positive predictions.

Output information from the QSAR models also provided the
level of reliability for its prediction, and this qualifier was
depicted in a heat map format. The Endocrine Disruptome®
model provided the binding intensity to each of 16 nuclear recep-
tors; this affinity measure was also displayed in a heat map form.

Results

Compiled List of Chemicals
We found >560 chemicals with a quantifiable frequency ≥20% in
samples of environmental media that were reported in government
databases and published literature (Figure 1; Excel Table S1). A
total of 180 PEs from the U.S. EPA, USDA, and FDA databases
met the quantifiable criteria frequency. Of the 180, 156 had not
been measured in the NHANES biomonitoring program (see
Excel Table S6). Forty were selected for prioritization in this pa-
per, which leaves another 116 pesticides that can be evaluated for
future biomonitoring.

To qualify a chemical for inclusion in our environmental media
data set, we set our detection frequency cut point at ≥20%. We eval-
uated this cut point with data we initially collected for chemicals in
environmental media that were also measured in biospecimens by
the NHANES (see Excel Table S1). There were 129 chemicals that
were detected in environmental media (ambient air, personal air,
indoor air, house dust, drinking water, or food). Of these 129 chemi-
cals 67% also had quantifiable levels in biospecimens. Grouping
these 129 chemicals as APs, EPs, OPFRs, PFASs, and PEs, we
found that PEs had the lowest percentage of quantifiable levels in
both environmental and biospecimens (38%). For other chemical
panels, these proportions were 66%, 83%, and 91% for PFASs, EPs,
and APs, respectively. Based on these observations, we believe that
a cut point of ≥20% was adequate for identifying chemicals with a
likelihood of ≥10% prevalence in biospecimens, the criterion we
used to include a chemical for biomonitoring.

For the 45 consumer product categories we selected, approxi-
mately 36% of the entries depicted chemical structures (see Table
S1). Chemicals with and without structures in CPCat contained
considerable redundancy (i.e., the same chemical appeared in mul-
tiple consumer products). In addition, consumer products con-
tained inorganics and water. Thus, the tallies in Table S1 include
this redundancy.

We found >500 chemicals in the consumer product survey
that met the potentially “toxic” chemical moiety criterion (con-
sumer products list). After combining this group with the >560
chemicals found in environmental media and removing dupli-
cates and only including organic chemicals, the result yielded
932 chemicals (Figure 1; Excel Table S2). There were 568 and
364 chemicals with and without U.S. EPA exposure predictions,
respectively (see Excel Table S2) (U.S. EPA 2017).

Chemical Groups and Panels
From the 932 chemicals that we started with, we were left with 720
chemicals in Groups III–V after removing legacy chemicals and those
biomonitored in NHANES (Figure 1). The number of chemicals in
Groups III, IV, and V was 260, 293 and 167, respectively. Chemicals
in Groups IV and V were from the consumer products list.

The amount of published data decreased from Groups III to V
(see Excel Tables S3–S5) for human biomonitoring, environmen-
tal media levels, and U.S. EPA exposure predictions. Sixty-five
percent of the chemicals in Group III had U.S. EPA exposure pre-
dictions (see Excel Table S3). All chemicals in Group IV (see
Excel Table S4)had U.S. EPA exposure predictions. Group V chem-
icals (see Excel Table S5) had no U.S. EPA exposure predictions.

The number of the chemicals prioritized in each panel is
given in Table 1. Except for AAs and QACs, all remaining panels
contain chemicals that would supplement the panels currently
being studied in ECHO. The AAs and QACs represent a new
class of chemicals that have not been previously included in chil-
dren health-related studies such as ECHO. The individual chemi-
cals are listed in Tables 4–6.

Environmental Health Perspectives 126001-8 127(12) December 2019

HTP in Vitro Assay Results
Of the chemicals not assigned to one of the eight panels, there were
80, 180, and 120 chemicals in Groups III, IV, V, respectively, that
were not tested in HTP in vitro assays (see Excel Table S7).
Testing results were available for 98, 33, and 0 compounds in
Groups III, IV, and V, respectively (see Excel Table S8).

A full complement of HTP in vitro assay results were not
available for 45% of the 155 chemicals selected for prioritization.
One or more HTP in vitro assay results were reported for 5 of 23
AFRs, 8 of 10 APs, 9 of 28 AAs, 9 of 16 EPs, 7 of 11 OPFRs, 1
of 8 PFASs, 39 of 43 PEs, and 8 of 16 QACs (see Excel Table
S9). Of the chemicals tested, 1 of 2 AFRs, 6 of 9 APs, 5 of 9
AAs, 8 of 9 EPs, 6 of 9 OPFRs, 30 of 35 PEs, and 3 of 4 QACs
were active in the obesity in vitro assays. Except for tris-(2-ethyl-
hexyl) trimellitate, which was active in the estrogen agonist, thy-
roperoxidase thyroid, and endocrine assays, the remaining APs
were not positive in the assays (see Excel Table S9). Of those
tested, 4 OPFRs and 10 PEs were active in the calcium ion-
channel assays, a test for neurotoxicity (see Excel Table S9). One
of 8 APs, 9 of 9 AAs, 6 of 7 EPs, 9 of 39 PEs, and 1 of 2 QACs
tested positive in the thyroperoxidase assay (see Excel Table S9).
Eighteen AFRs, 2 APs, 19 AAs, 7 EPs, 4 OPFRs, 7 PFAs, 7 PEs
and 8 QACs lacked HTP in vitro assaying and are candidates for
future testing (see Excel Table S9).

In Silico Predictive Toxicity Modeling Results
Approximately 96% of the nearly 700 chemicals in Groups III, IV,
and V exhibited positive results for developmental, reproductive,
or estrogen toxicities, or carcinogenicity (see Excel Table S10).
Fifty-eight percent of the chemicals were positive for multiple end
points of toxicity. The endocrine disruption prediction results were
observed for 97% of the chemicals in the panels (see Excel Table
S11). Strong binding affinities were observed for 4 AAs, 2 EPs, 1
OPFR, 6 PFASs, 11 PEs, and 1 QAC (see Excel Table S11).

For some chemicals, the QSAR in silico and docking models
did not converge to predict toxicity (see Excel Tables S10 and
S11). For 15 chemicals, the QSAR models predicted that the
chemical was inactive. However, the absence of predicted toxic-
ity does not necessarily indicate an absence of toxicity, given that
a model may not have enough reference compounds for making a
comparison. In addition, we are cognizant that models may yield
false-positive and false-negative predictions.

Literature Search Results
The literature searches yielded a broad spectrum of information
types within and across chemicals in our eight panels (see Excel
Table S12). We found variable amounts of information across the
parameters considered, with some chemicals having ample infor-
mation to prioritize them for biomonitoring, whereas published
data were sparse for others (see Excel Table S12). For example,
we did not find information on biomonitoring or environmental
media levels for 7 AFRs, 2 APs, 10 AAs, 1 OPFR, 2 PEs, and 8
QACs. Twelve AFRs, 2 APs, 10 AAs, 4 OPFRs, 3 PFASs, 2 PE,
and 12 QACs had no in vivo or in vitro toxicity data. Twenty-one
AFRs, 2 APs, 18 AAs, 6 EPs, 4 OPFRs, 7 PFASs, 5 PEs, and 7
QACs lacked HTP in vitro assay data. No published biomarkers
were found for 14 AFRs, 6 APs, 11 AAs, 5 EPs, 3 OPFRs, 21
PEs, and 16 QACs. This lack of information resulted in many
chemicals being deferred pending more data.

Prioritized Chemicals for Biomonitoring
We prioritized 155 compounds based on exposure, toxicity, and
biomarker(s) (see Tables S3–S5). Although we reviewed pub-
lished data available for exposure, toxicity, and biomarkers, we
did not review or comment on the quality of the studies.

Alternative flame retardants. We evaluated 23 AFRs for bio-
monitoring in ECHO (see Tables S3–S5). Biomonitoring meas-
urements were reported for 9 AFRs, and 19 have been measured
in environmental media (see Excel Table S12). In vivo and in
vitro endocrine disruption, developmental, reproductive, and neu-
rotoxicity studies have been reported for 14, 6, 2, and 2 AFRs,
respectively (see Excel Table S12). Our modeling results sug-
gested that some may be endocrine disruptors and developmental
toxicants (see Excel Table S12). Moreover, some of these chemi-
cals are persistent in the body—that is, they have a propensity to
bioaccumulate in tissues (see Excel Table S10). In adults, their
clearance rates vary from a few days to months (Covaci et al.
2011; Geyer et al. 2004; Trudel et al. 2011). Biomarkers for 11
AFRs were used in studies (see Excel Table S12).

We recommended 4 AFRs for biomonitoring in ECHO
(Table 4); 16 as deferred pending more research on exposure,
toxicity, or biomarker development (Table 5); and 3 as low pri-
ority for biomonitoring (Table 6). Of the 16 AFRs deferred
pending additional data, all were in Category D (Table 5).

Table 4. Chemicals recommended for biomonitoring.

Chemical panel Chemical name Chemical name

Alternative flame retardants (AFRs) Bis(2-ethylhexyl) tetrabromophthalate (BEH-TEBP) Hexabromobenzene (HBBz)
Hexabromocyclododecane (HBCD) Melamine

Alternative plasticizers (APs) Bis(2-ethylhexyl) adipate (DEHA) Bis(2-ethylhexyl)-1,4-benzenedicarboxylate
Aromatic amines (AAs) 2-Methoxyaniline (anisidine) 2-Methylaniline (also known as o-toluidine)

3,4-Dichloroaniline
Environmental phenols (EPs) Bisphenol A diglycidyl ether (BADGE) Bisphenol AF (BPAF)

Bisphenol B 3,30,5,50-Tetrabromobisphenol A (TBBP-A)
2,20,6,60-Tetrachlorobisphenol A (TraTBA) 4-n-Nonylphenol

Organophosphorus-based flame retardants
(OPFRs)

2,2-Bis(chloromethyl) propane-1,3-diyltetrakis(2-chloroethyl)
bisphosphate (V6)

2-Ethylhexyl diphenyl phosphate (EHDPP)

Bis(2-ethylhexyl) phosphate (BEHP) Tris(2-butoxyethyl) phosphate (TBOEP)
Tris(2-ethylhexyl) phosphate (TEHP)

Perfluoroalkyl substances (PFASs) Perfluorobutanoic acid (PFBA) Perfluorohexanoic acid (PFHxA)
Perfluoropentanoic acid (PFPeA) Perfluorotridecanoic acid (PFTrDA)

Pesticides (PEs) Azoxystrobin Benomyl
Captan Chlorpropham
Cyprodinil Dicloran
Glyphosate Iprodione
Metalaxyl Propiconazole
Pyrimethanil Tebuconazole

Environmental Health Perspectives 126001-9 127(12) December 2019

Table 5. Chemicals deferred pending additional data.

Category Chemical Panel Chemical Name Chemical Name

A: Enough concern for toxicity; a
biomarker exists; need to measure
chemicals in human biospecimens
of a non-occupationally exposed
population to determine if there is
exposure.

Aromatic amines (AAs) 2,4-Diaminotoluene 4,40-Diaminodiphenylmethane
Environmental phenols (EPs) 3,30,5-Trichlorobisphenol A (TrCBA)
Perfluoroalkyl Substances (PFASs) Perfluorooctadecanoic acid (PFODA)
Pesticides (PEs) Difenocoazole Metribuzin

Pyraclostrobin Tetraconazole
Triclopyr

B: Enough concern for toxicity;
exposure is likely prevalent based
on measured levels in food; no
biomarker, develop one and test it in
non-occupationally exposed human
biospecimens to confirm exposure.

Alternative plasticizers (APs) 2,2,4-Trimethyl 1,3-pentanediol
monoisobutyrate (TXIB)

Acetyl tributyl citrate (ATBC)

Tri-2-ethylhexyl trimellitate (TETM)
Aromatic amines (AAs) 2-Methoxy-5-methylaniline 4,40-Methylenebis(2-methylaniline)

4,40-Oxydianiline (ODA)
Environmental phenols (EPs) 2,6-Di-Tert-butylphenol Dibutylated hydroxytoluene (BHT)

4-Nonylphenol diethoxylate 4-Nonylphenol monoethoxylate
Phenol

Organo-phosphorus flame
retardants (OPFRs)

Triethyl phosphate (TEP) Tris(2,3-dichloropropyl) phosphate
(TDCnPP)

Pesticides (PEs) Boscalid Carbendazim (MBC)
Dimethomorph Diphenylamine
Fenbuconazole Fludioxonil
Thiabendazole (TBZ) Triflumizole

Quaternary ammonium compounds
(QACs)

Benzylhexadecyldimethylammonium
chloride (BAC C16)

Didecyldimethylammonium chloride
(DDMAC)

N, N-Dimethyl-N-benzyl-N-octadecy-
lammonium chloride (BAC C18)

C: Enough concern for toxicity;
insufficient environmental measures
to determine if exposure is likely; no
biomarker, develop one and test it in
non-occupationally exposed
population to determine if there is
exposure.

Alternative plasticizers (APs) o-Toluene sulfonamide (OTSA)
Aromatic amines (AAs) 2-Amino-5-azotoluene 2,3-Dichloroaniline

2,5-Dichloroaniline 2-Nitro-1,4-phenylenediamine
(2NPPD)

Pesticides (PEs) Acetochlor ethane sulfonic acid (ESA) Acifluorfen
Alachlor ethane sulfonic acid Alachlor oxanilic acid (OA)
Piperonyl butoxide Quinclorac
Spiroxamine

Quaternary ammonium compounds
(QACs)

1-(Benzyl)quinolinium chloride

D: Need more information on toxicity,
may or may not have enough
information on exposure and
biomarkers

Alternative flame retardants (AFRs) 1,2-Bis(2,4,6-tribromophenoxy)
ethane (TBE)

2-Bromoallyl 2,4,6-tribromophenyl
ether (BATE)

alpha-Tetrabromoethylcyclohexane
(α-DBE-DBCH)

beta-Tetrabromoethylcyclohexane
(β-DBE-DBCH)

Dibromostyrene (DBS) Decabromobiphenyl ethane (DBDPE)
Dimethyl hydrogen phosphite (DHP) Dimethyl propyl phosphonate

(DMPP)
Dimethyl N-methylolphosphonopro-
pionamide (DNMPP)

Ethylene bis(tetrabromo) phthalmide
(ETBP)

Pentabromobenzene (PBBz) Pentabromoethylbenzene (PBEB)
Pentabromotoluene (PBT) Tetrabromo-o-chlorotoluene (TBCT)
Tetrabromophthalic anhydride
(TBPA)

Tetrabromophthalate diol (TBPD)

Alternative plasticizers Dioctyl terephthalate (DOTP)
Aromatic amines (AAs) 1,2,4-Benzenetriamine, N0-phenyl 1,3-Benzodioxol-5-amine

2,4,6-Tribromoaniline (TBA) 2,6-Toluenediamine
2-Aminotoluene-5-
methylbenzenesulphonic acid
(PTMS/PTMSA)

2-Biphenylamine (2-aminobiphenyl)

2-Bromo-4,6-dinitroaniline 2-Chloro-1,4-diaminobenzene sulfate
2-Chloro-4,6-dinitroaniline 2-Naphthylamine
3-Nitroaniline 4,40-Methylenebis(2-chloroaniline)

(MOCA)
4-Chloro-2-nitroaniline Aniline
N,N,4-Trimethylaniline p-Chloroaniline

Environmental phenols (EPs) 4-Nonylphenol ethoxycarboxylate 4-Octylphenol diethoxylate
4-Octylphenol monoethoxylate

Organophosphorus-based flame
retardants (OPFRs)

Diquanidine hydrogen phosphate
(DHP)

Tris(2-chloro-iso-propyl) phosphate
(TCIPP)

Tris-(tribromoneopentyl) phosphate
(TTBNPP)

Perfluoroalkyl substances (PFASs) Perfluoroheptane sulfonic acid
(PFHpS)

Perfluorohexadecanoic acid
(PFHxDA)

Perfluoropentane sulfonic acid
(PFPeS)

Pesticides (PEs) 2-Hydroxyatrazine Fenamidone
Fenhexamid Fluroxypyr-meptyl
Isoxaben Metolachlor
Prometon Tetrahydrophthalimide (THPI)

Environmental Health Perspectives 126001-10 127(12) December 2019

Alternative plasticizers. Ten APs were evaluated as candidates
for biomonitoring in ECHO (see Tables S3–S5). Biomonitoring
measurements for 3 APs were reported, whereas 8 were measured
in environmental media (see Excel Table S12). In vivo and in vitro
endocrine disruption, developmental, reproductive and neurotox-
icity was reported for 1, 4, 4, and 1 APs, respectively (see Excel
Table S12). None of those tested activated in vitro neurotoxicity
assays (see Excel Table S12). Six compounds were active in HTP
in vitro assays for obesity (see Excel Table S12). The QSAR mod-
eling results for each AP suggested 5 were developmental and 3
reproductive toxicants (see Excel Table S12). They have a low bio-
accumulation factor (see Excel Table S10) and adult urinary elimi-
nation rates of hours to a few days. Biomarkers for 4 APs were
used in biomonitoring studies (see Excel Table S12). Biomarkers
for 4 APs were used in studies (see Excel Table S12).

We recommended two APs for biomonitoring in ECHO
(Table 4), five as deferred pending more research (Table 5), and
three as low priorities (Table 6). Of the 5 APs deferred pending
additional data, 3, 1, and 1 APs were in Categories B, C, and D,
respectively (Table 5).

Aromatic amines. Of the 28 AAs we evaluated, environmental
or biomonitoring information was available for 16 compounds (see
Excel Table S12 and Tables S3–S5). Biomonitoring results for AAs
were reported for 7 chemicals, whereas 15 were measured in envi-
ronmental media (see Excel Table S12). In vivo and in vitro effects
for endocrine disruption, developmental, reproductive, neurotoxic-
ity, and carcinogenicity were reported for 1, 6, 1, and 14 AAs,
respectively (see Excel Table S12). A major toxic effect of AAs was
carcinogenicity (see Excel Table S12). We found occupational ex-
posure studies for several AAs that linked to cancer (mostly bladder
and methemoglobinemia) in highly exposed workers. For com-
pounds that had undergone HTP in vitro assay testing, 2 were neuro-
toxic, 2 were endocrine disruptors, and 5 affected obesity processes
(see Excel Table S12). The predictive QSAR modeling results for
AAs also suggested that 4 were endocrine disruptors, 13 develop-
mental toxicants, and 1 a reproductive toxicant (see Excel Table
S12). Most of them are high-PV chemicals (U.S. EPA 2016a). They
vary in bioaccumulation factors from 10 to 1,000s (see Excel Table
S10), with urinary elimination rates on the order of hours to a few

days. Biomarkers for 15 AAs were reported (see Excel Table S12).
Biomarkers for 18 APs were used in studies (see Excel Table S12).

We recommended 3 AAs for biomonitoring in ECHO (Table 4),
25 deferred pending more research (Table 5), and none as low prior-
ity for biomonitoring. Of the 25 AAs deferred pending addtional
data, 2, 3, 4, and 16 AAs were in Categories A, B, C, and D, respec-
tively (Table 5).

Environmental phenols. We evaluated 16 EPs as candidates
for biomonitoring in ECHO (see Tables S3–S5). Biomonitoring
measurements for 11 EPs were reported, whereas 15 were meas-
ured in environmental media (see Excel Table S12). We identified
EPs occurring in environmental media that have limited research
on early life exposure and health effects. In vivo and in vitro toxic-
ity studies for endocrine disruption, developmental, reproductive,
and neurotoxicity were published for 13, 9, 5, and 6 EPs, respec-
tively (see Excel Table S12). Predictive modeling results for each
EP also suggested that 2 were endocrine disruptors (see Excel
Table S11), 13 were developmental toxicants, and 6 were repro-
ductive toxicants (see Excel Table S12). Their bioaccumulation
factors vary from 100 to 1,000s (see Excel Table S10), and urine
elimination rates range from days to weeks. Biomarkers for 11 EPs
were measured in biospecimens (see Excel Table S12).

We recommended six EPs for biomonitoring in ECHO
(Table 4), nine as deferred pending more research (Table 5),
and one as low priority for biomonitoring (Table 6). Of the 9
EPs deferred pending additional data, 1, 5, and 3 EPs were in
Categories A, B, and D, respectively (Table 5).

Organophosphorus flame retardants. Eleven OPFRs were
evaluated as candidates for biomonitoring in ECHO (see Tables
S3–S5). Biomonitoring measurements for 8 OPFRs were reported,
whereas 9 were measured in environmental media (see Excel
Table S12). In vivo and HTP in vitro endocrine disruption, devel-
opmental, reproductive, and neurotoxicity studies were reported
for 2, 6, 5, and 3 OPFRs, respectively (see Excel Table S12).
Predictive modeling results for OPFRs also suggested that some
were endocrine disruptors (see Excel Table S11) and developmen-
tal toxicants (see Excel Table S12). Relative to brominated AFRs,
OPFRs have lower bioaccumulation factors of 10 to 100 (see Excel
Table S10) and urinary elimination rates of hours to a few days

Table 6. Chemicals with low priority for biomonitoring in ECHO.

Chemical panel Chemical name Chemical name

Alternative flame retardant (AFRs) 2,3,5,6-Tetrabromo-p-xylene (pTBX) 1,2-Bis(2,4,6-tribromophenoxy) ethane (BTBPE)
2,3-Dibromopropyl 2,4,6-tribromophenyl ether (TBP-DBPE)

Alternative plasticizers (APs) Di-butyl adipate (DBA) Di-butyl sebacate (DBS)
Dioctytl succinate (DOS)

Environmental phenols (EPs) 4-Methyl phenol (p-cresol)
Organophosphorus-based flame
retardants (OPFRs)

Tris (2,3-dibromopropyl) phosphate

Pesticides (PEs) Imazapyr Metolachlor ethane sulfonic acid (MESA)
Metolachlor oxanilic acid (MOXA)

Table 5. (Continued.)

Category Chemical Panel Chemical Name Chemical Name
Quaternary ammonium compounds
(QACs)

3-Methylbenzethonium chloride Behentrimonium methosulfate

Benzyldimethyldodecylammonium
chloride (BAC C12)

Benzyldimethyl[2-[2-[[4-(1, 1, 3,
3-tetramethylbutyl)-m-tolyl]oxy]
ethoxy]ethyl]ammonium chloride

Dimethyldiallylammonium chloride
(DADMAC)

Dodecyldimethyl(4-ethylbenzyl)
ammonium chloride

N,N-Trimethyloctadecan-1-aminium
chloride (ATMAC C18)

Octyl decyl dimethyl aminium
chloride

Quaternium-15 Quaternium-24
Quaternium-52 Tetradonium bromide

Environmental Health Perspectives 126001-11 127(12) December 2019

(Lynn et al. 1981; Nomeir et al. 1981). Biomarkers for 8 OPFRs
were applied in biomonitoring studies (see Excel Table S12).

We recommended five OPFRs for biomonitoring in ECHO
(Table 4), five as deferred pending more research (Table 5), and
one as a low priority for biomonitoring (Table 6). Of the 9
OPFRs deferred pending additional data, 2 and 3 OPFRs were in
Categories B and D, respectively (Table 5).

Perfluoroalkyl substances. We evaluated eight PFASs as
candidates for biomonitoring (see Tables S3–S5). Biomonitoring
measurements for seven PFASs was reported, whereas nine were
measured in environmental media (see Excel Table S12). Health
effects for several PFASs were reported; however, we identified a
few additional PFASs occurring in environmental media that
have been understudied regarding exposure and health effects in
children. In fact, >4,000 perfluoroalkyl and polyfluoroalkyl sub-
stances were reported to be in commerce, and only a small frac-
tion of them were studied for their occurrence, toxicity, and
exposures (Ritscher et al. 2018).

In vivo and HTP in vitro toxicity studies for endocrine disrup-
tion, developmental, reproductive, and neurotoxicity were reported
for four, four, one, and one PFASs, respectively (see Excel Table
S12). Predictive modeling results for PFASs also suggested that
six were endocrine disruptors (see Excel Table S11) and one was a
reproductive toxicant (see Excel Table S12). The bioaccumulation
factors for PFASs are chain-length dependent (see Excel Table
S10). Likewise, their urine elimination rates are lower (days to
months) for the short-chain lengths as compared with long-chain
(years) PFASs (Olsen et al. 2007). Biomarkers for eight PFASs
were measured in biospecimens.

We recommended four PFASs for biomonitoring in ECHO
(Table 4) and four as deferred pending more research (Table 5).
Of the 4 PFASs deferred pending additional data, 1 and 3 PFASs
were in Categories A and D, respectively (Table 5).

Pesticides. Forty-three PEs (24 fungicides, 3 insecticides, and
16 herbicides) were evaluated for biomonitoring in ECHO (see
Tables S3–S5). The same PE was often detected on different food
commodities (USDA 2018). For example, azoxystrobin was
detected in 16 different food commodities and tebuconazole in 11
different foods. Biomonitoring measurements for 10 PEs were
reported, whereas 38 and 14 were measured in foods and air/house
dust, respectively (see Excel Table S12). Results for in vivo devel-
opmental and reproductive toxicity were reported for 41 PEs.
Thirty and 20 PEs exhibited activity in HTP in vitro obesity and
neurotoxicity assays, respectively (see Excel Table S12). Predictive
modeling results for PEs also suggested that 11, 22, and 11 exhib-
ited strong binding to endocrine nuclear receptors and developmen-
tal and reproductive toxicity, respectively (see Excel Tables S11
and S12). The fungicides and herbicides have a low propensity to
accumulate in biological tissues (see Excel Table S10), and their
estimated biological half-lives are <24 h. Biomarkers for 21 PEs were measured in biospecimens (see Excel Table S12).

We recommended 12 PEs (10 fungicides and 2 herbicides) for
biomonitoring in ECHO (Table 4), 28 deferred pending more
research (Table 5), and 3 as low priority for biomonitoring (Table 6).
Of the 28 PEs deferred pending additional data, 5, 8, 7, and 8 PEs
were in Categories A, B, C, and D,respectively (Table 5).

Quaternary ammonium compounds. We evaluated 16 QACs
(see Tables S3–S5). The 3 most frequently detected QACs in
natural environments were alkyltrimethyl ammonium com-
pounds (ATMACs) (C12–C18), benzylalkyldimethyl ammo-
nium compounds (BACs) (C12–C18), and dialkyldimethyl
ammonium compounds (DADMACs) (C8–C18) (Zhang et al.
2015). We did not include all the individual ATMACs, BACs,
and DADMACs in the current study because they were not pres-
ent in consumer product categories that were surveyed.

Six QACs were measured in food or drinking water, and der-
mal exposure was reported for 2. We deferred 16 QACs pending
additional data on biological concentrations in biospecimens,
environmental media, toxicity, and biomarkers (Table 5).

Deferred and Low-Priority Chemicals
For 108 chemicals, there was insufficient information regarding
exposure, toxicity, or availability of a biomarker to recommend
for biomonitoring (Table 5). The data gaps for each of these 108
chemicals (see Tables S4 and S5 for more detail) serve as oppor-
tunities for future research. Forty-five chemicals in Categories
A–C lack exposure data (Table 5). Thirty-eight compounds lack
a developed biomarker. Sixty-three chemicals in Category D
have insufficient toxicity data and, in some cases, insufficient ex-
posure prevalence and/or biomarker information (Table 5).

We recommended 11 chemicals as a low priority for biomoni-
toring (Table 6). These chemicals either have been measured but
not detected in environmental media or have low toxicity effects
as tested in animals or in HTP in vitro tests.

Discussion

Data Gaps, Opportunities for Research, and Limitations
There are about 8,000 chemicals that are manufactured or
imported in high volumes in the United States (U.S. EPA 2016a).
In this effort, we identified 720 chemicals as candidates for inclu-
sion in ECHO. We selected 155 for prioritization based on inclu-
sion in one of eight chemical panels, and then found that only 36
had enough data for consideration. Thus, for chemicals that did
not make our prioritization due to lack of data, there is a large op-
portunity to expand our ability to measure and evaluate chemicals
to which the public is likely exposed. These opportunities include
performing exposure measurements, developing methods for bio-
monitoring, and toxicity testing of chemicals.

Our selection approach was limited to evaluating chemicals
that had structures displayed in the CPCat database (see Table S1)
and possessed a toxic moiety, an empirically determined character-
istic that is subject to false negatives. In addition, because only
chemicals with displayed structures were visually inspected,
approximately 64% of the chemicals (see Table S1) were excluded
from the selection process. Thus, our approach addressed only a
small segment of the universe of chemicals in consumer products
that may have some type of childhood toxicity. In addition, there
were additional product categories in the U.S. EPA CPCat database
that we did not select for screening. These product categories may
also contain chemicals that have end points of toxicity important to
ECHO. Finally, CPCat is far from comprehensive due to the lack
of federal laws and regulations requiring full disclosure of chemi-
cals used in consumer products.

Our strategy was to group chemicals into panels based on their
similar chemical properties and uses in commerce. The composi-
tion of these panels will likely be dynamic over time because expo-
sure to some may decrease, chemical toxicity may be discovered
for others, and some may be deleted from the lists and new ones
added. Nevertheless, a common factor is the methodology used for
their analyses. Because single-chemical analysis methods are pro-
hibitively expensive to implement, biomonitoring methods are
developed to provide analysis for a suite of chemicals to achieve
scales of economy while conserving human biospecimens. Thus,
we selected chemicals from the 720 candidates with common ana-
lytical properties to create chemical panels amenable to a single
extraction step to yield multi-chemical analysis.

We confined the ranking of chemicals to eight panels: AFRs,
APs, AAs, EPs, OPFRs, PFASs, PEs, and QACs for biomonitoring

Environmental Health Perspectives 126001-12 127(12) December 2019

in ECHO. Many chemicals that we have prioritized for biomonitor-
ing add to the AFRs, EPs, OPFRs, PFASs, and PEs panels cur-
rently being studied by ECHO. The AAs and QACS are new
classes of chemicals that have not been previously examined in a
nationwide study. Over a dozen additional chemical panels remain
for future prioritization.

Six of the eight panel categories are currently being studied in
ECHO. The analytical methodology for current chemicals in a
panel is likely applicable to new supplemental chemicals (Table 4),
thus reducing costs for method development and for performing
measurements in large populations Because the chemicals selected
have available methods for measuring biomarkers, analytical stand-
ards are available for quantitative analysis, which are required for
measurement.

We followed an evidence-driven approach for prioritizing
chemicals using published data on exposure, health effects, and
availability of biomarkers. Our evaluation was conservative,
given that we required a fairly high level of data to be recom-
mended for analysis in ECHO. For most of the chemicals we
evaluated, there were insufficient data. This resulted in a rela-
tively small portion of the chemicals evaluated being recom-
mended as our highest priority for study in ECHO. For example,
several compounds we evaluated were known or possible human
carcinogens (e.g., AAs such as 2-naphthylamine, 4,40-methylene-
bis(2-chloroaniline), aniline, and p-chloroaniline), but they have
not been recommended based on the lack of in vivo or HTP in
vitro data for endocrine, reproductive, developmental, or neuro-
toxicity effects. Future use of our methodology could consider a
wider range of toxicity end points to qualify for high priority.

Humans are often more sensitive than not at lower chemical
doses compared with animals (NRC 2000; National Academies
of Sciences, Engineering, and Medicine 2017) and children are
more sensitive than adults (Bruckner 2000). Thus, our prioritiza-
tion may be conservative when relying on risk assessments
derived from animal toxicity data and studies based on adults.
Given the high number of chemicals and the modest resources of
ECHO, we used rigorous prioritization criteria to select a reason-
able number of chemicals that could be studied with available
resources.

Taking into consideration that environmental contamination
and exposure as well as knowledge about toxicity likely will
change over time, intermittent reassessment of a chemical’s pri-
ority for biomonitoring is recommended. Chemical exposure and
usage should be periodically assessed to determine whether expo-
sure levels or uses have increased. For this reason, prioritization
should be considered a dynamic process.

For chemicals in the deferred categories B–D, to our knowl-
edge, there are no known biomarkers or they have not yet been
developed (Table 5). We recommend developing biomarkers and
obtaining additional toxicity data for the deferred chemicals, which

includes most of the QACs and APs, AAs, EPs, OPFRs, and PEs,
given that there was some suggestion for both toxicity and expo-
sure (Table 5). To develop methods for their analysis, we recom-
mend toxicokinetic research to determine whether the parent
compound or its metabolite, if any, is best suited for biomonitoring.
If standards do not exist for the parent compound or metabolite,
then they need to be developed and may involve custom synthesis.
Once a method is developed, then round-robin interlaboratory stud-
ies should be performed to establish method and laboratory per-
formance. Because there can be a substantial cost associated with
method development, combining chemicals with similar properties
to produce a multi-chemical analysis method is preferred.

We used QSAR in silico predictions only to screen for toxic-
ity because it can be prone to false-positive and false-negative
predictions. To substantiate positive in silico prediction results,
we sought corresponding positive results from HTP in vitro
assays (Table 7) for all compounds that had HTP assay results.
The results for 11 chemicals agreed between the two methods.
However, the results for 16 compounds differed between the two
methods. False-negative results can occur, given that their predic-
tions depend on the availability of model reference compounds in
the database with similar chemical features. For in vitro assays,
parameters such as temperature control, pH, osmotic pressure,
solubility, and volatility properties can cause errors in the results
obtained (Saeidnia et al. 2013).

The in silico prediction models, including the Chen model
(Chen et al. 2013), do not discern toxic potency. An absence of
toxicokinetic factors in in silico and in vitro procedures may lead
to misclassification of the results because they do not incorporate
absorption, distribution, metabolism, and excretion by a living or-
ganism or the minimum dosing level needed to elicit an effect in
their predictions. These models may underpredict toxicity because
they do not account for genetic variation or susceptibility. Thus,
caution was exercised when making interpretations based on these
approaches. We recommend that in vivo, HTP in vitro, and in silico
modeling results be compared to assess the frequency of false-
positive and false-negative results. Thus, for prioritization we
relied on additional evidence of toxicity, such as in vivo animal tox-
icity data and HTP in vitro assay results.

We found many of the remaining 565 chemicals (Figure 1;
Table 8) were positive in the in silico prediction models (see
Excel Table S8) but have not been tested in HTP in vitro assays.
We recommend HTP testing (Table 8) to fill in knowledge gaps
for these chemicals. We note that some chemicals in Table 8
have been studied in vivo, whereas others are in queue to be
tested in the National Toxicology Program (NTP 2019) or by
HTP in vitro assays. Additional HTP in vitro assay results
coupled with in vivo data would permit verification of the predic-
tive modeling results. These added toxicity data would allow pri-
oritization of additional chemicals for biomonitoring in ECHO.

Table 7. Comparison of results for in vitro HTP assays and prediction models.

Comparison of in vitro and model results Chemical panel Chemical name

Positive in in vitro assays and in prediction
models

Phthalate, alternative plasticizers,
and metabolites

4-n-Octylphenol; di-n-butyl phthalate (DBP); di-n-hexyl phthalate
(DnHP)

Polyaromatic hydrocarbons Benzo[b]fluoranthene
Amines 3,30-Dimethylbenzidine; 4,40-methylenebis(N,N-dimethyaniline);

4-aminoazobenzene; benzidine
Miscellaneous Benzophenone-2; C.I. Solvent Yellow 14; phenolphthalein

Positive in in vitro assays and negative in
prediction model

Miscellaneous 1-Naphthol (carbaryl MTB); biphenyl; ethoxyquin; benz(a)anthracene;
2-ethyl-1-hexanol

Positive in in vitro assays and in prediction
models

Environmental haloacids Dichloroacetic acid; trichloroacetic acid
Phthalate, alternative plasticizers,
and metabolites

Di-n-pentyl phthalate; di-n-propyl phthalate

Volatile organic compounds 1,2,3-Trichloropropane; 1,2,4-trimethylbenzene; 1-butanol; 2-methyl-
1-butanol; 3-methyl-1-butanol; alpha-pinene; isobutanol

Environmental Health Perspectives 126001-13 127(12) December 2019

Toxicokinetic Considerations That Modulate Chemical
Levels and Health Effects
There is a growing body of animal and in vitro data that suggest ex-
ogenous chemical exposures, endogenous physiological changes,
and genetic regulation may together heighten susceptibility to
some chemicals and increase pregnant women’s health risks
(Varshavsky et al. 2019). Furthermore, the susceptibility of infants
and children to chemicals may vary considerably, depending on
factors such as the age of the child because changes occur in organ
size, structure, and function from infancy through puberty
(Bruckner 2000), all affecting the toxicokinetics and toxicodynam-
ics of chemicals. Thus, there may be windows of vulnerability
from infancy to adulthood that were not accounted for in the animal
toxicity data that we used for prioritizing chemicals.

Toxicokinetics and elimination rates discussed earlier for
chemicals in the panels generally pertain to adults. However, toxi-
cokinetics can differ between children and adults due to physiolog-
ical differences, immaturity of enzyme systems, and clearance
mechanisms (Ginsberg et al. 2002). Ginsberg et al. (2002) esti-
mated clearance rates of chemicals for six age groupings by com-
paring toxicokinetics parameters between children and adults
using published data. Their results suggest that from premature to
2 years of age, a child has lower clearance rates for certain struc-
tures than an adult, comparable clearance rates at 2 years of age,
and then a higher clearance rate up to 12 years of age. Finally, there
are some toxicant elimination rate data for animal (Hurst et al.
1998; Lin et al. 2013; Loccisano et al. 2012) and human (Burd et al.
2012) fetuses that suggest their rates are slower than infants.

Young infants and toddlers also have less lipophilic proteins
in their blood, and could have less likelihood of protein binding,
leaving more lipophilic chemicals free in the blood and able to
diffuse and accumulate in adipose tissue (Beamer et al. 2012).
This phenomenon may partly account for the differences in clear-
ance rates of chemicals between infants, children and adults.

Detection of chemicals in children may be enhanced by the
slower clearance rate and the reduced protein binding that
occurs up to 2 years of age; however, their detection frequency
and levels thereafter may be diminished because of higher
clearance rates and if exposure is episodic. We have recom-
mended biomonitoring for chemicals measured in urine (those
that are generally rapidly metabolized) and blood (those with
slower clearance); however, newer methods, such as hair analy-
sis, which represent a less invasive method that can also reflect
temporal exposures (LeBeau et al. 2011) may not be viable for
all populations of interest because of cultural beliefs (e.g.,
Native Americans).

The predicted bioaccumulation factors were based on lipophi-
licity of chemicals. We used these factors to approximate the accu-
mulation of chemicals in tissues, thus the chemical’s retention. In
general, a chemical that persists in the body for a week to years is
suitable for measuring in serum, plasma, or tissues. However, non-
persistent chemicals with short half-lives (e.g., hours) are generally
measured in urine if the route of exposure is primarily episodic and
infrequent (e.g., ingestion of a contaminated food commodity).
Measurement of chemicals with short half-lives in serum may be
suitable if continuous or near-continuous exposure occurs that
results in a steady-state or near steady-state serum level.

This work is intended to encourage the scientific community
to study the chemicals identified here, specifically to improve our
understanding of the potential health consequences for pregnant
women, infants, and young children from exposures to chemicals
found in environmental media. Five hundred and sixty-five chem-
icals remain as candidates for prioritization and are opportunities
for future research.

Acknowledgments
We thank our Environmental influences on Child Health

Outcomes (ECHO) colleagues; the medical, nursing, and

Table 8. Chemicals positive in prediction models and not tested in HTP in vitro assays.

Chemical class Chemical name

Aldehydes 3-(4-tert-Butylphenyl)-2-methyl propanal; acrolein; formaldehyde; piperonal
Dyes HC Yellow no. 10; C.I. Pigment Red 122; C.I. Pigment Red 2; C.I. Pigment Yellow 74; C.I. Solvent Yellow 6
Environmental haloacids Dibromoacetic acid, monobromoacetic acid; monochloroacetic acid
Isocyanates 4,40-Diphenylmethane diisocyanate; 4-chlorophenyl isocyanate; allyl isothiocyanate; methylene bis(thiocyanate);

toluene 2,6-diisocyanate; toluene-2,4-diisocyanate
Pesticides Benoxacor; bromoxynil; dichlorophen; trichlorfon; voriconazole; etidronic acid; flucytoxine; fluridone; folpet;

leptophos; lythidathion; metronidazole; nitroxoline; parthenolide; piperazine; 1,4-dimethylpiperazine
Phenolic compounds 1,2,4-Benzenetriol; 1,2-benzenediol, 4-(phenylazo)-; 2,4-dihydroxybenzophenone; 2,4-dinitrophenol; 2-amino-5-

nitrophenol; 2-methyl-1,3-benzenediol; 2,3,4,5-tetrabromo-6-methylphenol; 3-hydroxycarbofuran; 3,4,5-tri-
chlorophenol; 4-amino-3-fluoro-phenol; 4-aminophenol; 5-aminno-2-methylphenol; 2-methoxy-4-nitro-; phenol,
octylphenol diethoxylate

Polyaromatic hydrocarbons Benzo[ghi]perylene; coronene; cyclopenta(c,d)pyrene; perylene
Polybrominated diphenyl ethers 2,20,4,50-Tetrabromodiphenyl ether (PBDE 49); 2,20,3,30,4,40,5,60-Octa-bromodiphenyl ether (PBDE 196);

2,20,3,4,40,5,50,6-Octa-bromodiphenyl ether (PBDE 203); heptabromodiphenyl ether
Pyrrolidone and hydantoin compounds 1-(Hydroxymethyl)-5,5-dimethylhydantoin; 1,2-dichloro-5-ethyl-5-methylhydantoin; 1,3-dibromo-5,5-dimethyl-

hydantoin; 1,3-dichloro-5,5-dimethylhydantoin; 1,3-dimethylol-5,5-dimethylhydantoin; 1-bromo-3-chloro-5,5-
dimethylhydantoin; 1-vinyl-2-pyrrolidone, 2-pyrrolidinone; 3-bromo-1-chloro-5,5-dimethylhydantoin; 5,5-
dimethylhydantoin

Volatile organic compounds 1,2-Epoxybutane; 1,4-dioxane; 2-hexanone; 3-buten-2-one; 3-methylfuran; alpha-methyl styrene; beta-pinene;
diisopropyl ether; hexane; isopropyl benzene; styrene oxide; tert-amyl methyl ether; vinyl chloride

Halogenated containing compounds 1,1,3,3-Tetrachloropropanone; 3,3-dichloropropenoic acid; allyl pentabromophenyl ether; chlorobenzophenone;
chlorendic acid; chloroprene; chlorophene; hexachlorophene

Nitrogen-containing compounds 1,3,5-Triazine-1,3,5(2h,4h,6h)-triethanol; 1h-1,2,4-triazole; 1h-benzo[de]isoquinoline-1,3(2h)-dione; 1-(2,4-dia-
mino-5-methylphenoxy)ethan-1-ol; 1,4-bis(butylamino)anthracene-9,10-dione; 2-butanone oxime; 2,6-diamino-
3-((pyridine-3-yl)azo)pyridine; 2-amino-2-methylpropanol; 2-amino-5-; 3,5-dinitrotoluene; cis-4-cyclohexene-
1,2-dicarboxamide; diethanol amine; indole-3-butyric acid; n-(2-hydroxyethyl)acetamide; niacinamide

Other compounds 1,4-Benzodioxin; 10-acetonaphthone; 2-ethylhexyl acrylate; 3-methyl-4-phenyl-; 5,6,7,8-tetrahydroquinoxaline;
6-methylcoumarin; beta-propiolactone; butyl glycidyl ether; clioquinol; coumarin; coumatetralyl; furan; glycolic
acid; methyl trans-styryl ketone; octyl methoxycinnamate; oxalic acid; phenyl glycidyl ether; saccharin; sesa-
mol; tris(4-methylphenyl) phosphate

Environmental Health Perspectives 126001-14 127(12) December 2019

program staff; as well as the children and families participating in
the ECHO cohorts. We also thank D. Balshaw [National Institute
of Environmental Health Sciences (NIEHS)], T. Fennell (RTI
International), E. Guallar (Johns Hopkins University), and R. Miller
(University of Rochester) for their insightful discussions. We
are indebted to A. Williams (U.S. EPA) for providing the
ExpoCast™ file that contained invaluable predicted exposure
data and R. Judson (U.S. EPA) for providing high-throughput
in vitro assay data for our paper. We thank J. Wambaugh (U.S.
EPA) and B. Wetmore (U.S. EPA) for providing guidance and
sources of information. We are grateful to the anonymous peer
reviewers for their constructive insights.

Research reported in this publication was supported by the
ECHO program, Office of The Director, National Institutes of
Health, under awards U2COD023375 (Coordinating Center),
U24OD023382 (Data Analysis Center), 5U24OD023382-02 (E.D.P.,
R.R.B., J.P.B.), 5UG3OD023365-02 (D.H.B.), 5UG3OD023282
(P.I.B.), UG3OD023289 (Y.Z.), U2CES026542-01 (K.K.), and
5UG3OD023272-02, NIEHS P01ES022841, U.S. EPA RD
83,543,301, and NIEH R01ES02705 (A.W., T.J.W.). The content
is solely the responsibility of the authors and does not necessarily
represent the official views of the National Institutes of Health, or
the institutions with which the authors are affiliated.

References
Abe Y, Yamaguchi M, Mutsuga M, Akiyama H, Kawamura Y. 2016. Survey of pri-

mary aromatic amines and colorants in polyurethane, nylon and textile toys.
Shokuhin Eiseigaku Zasshi 57(2):23–31, PMID: 27211915, https://doi.org/10.3358/
shokueishi.57.23.

Ahlström LH, Raab J, Mathiasson L. 2005. Application of standard addition method-
ology for the determination of banned azo dyes in different leather types. Anal
Chim Acta 552(1–2):76–80, https://doi.org/10.1016/j.aca.2005.07.048.

Alaee M, Arias P, Sjödin A, Bergman Å. 2003. An overview of commercially used
brominated flame retardants, their applications, their use patterns in different
countries/regions and possible modes of release. Environ Int 29(6):683–689,
PMID: 12850087, https://doi.org/10.1016/S0160-4120(03)00121-1.

American College of Obstetricians and Gynecologists Committee on Health Care for
Underserved Women, American Society for Reproductive Medicine Practice
Committee, University of California San Francisco Program on Reproductive
Health and the Environment. 2013. Exposure to toxic environmental agents.
Committee Opinion No. 575. Washington, DC: American College of Obstetricians
and Gynecologists. https://www.acog.org/Resources-And-Publications/Committee-
Opinions/Committee-on-Health-Care-for-Underserved-Women/Exposure-to-Toxic-
Environmental-Agents [accessed 26 November 2019].

Anezaki K, Kannan N, Nakano T. 2015. Polychlorinated biphenyl contamination of paints
containing polycyclic- and naphthol AS-type pigments. Environ Sci Pollut Res Int
22(19):14478–14488, PMID: 24809497, https://doi.org/10.1007/s11356-014-2985-6.

Auerbach S, Filer D, Reif D, Walker V, Holloway AC, Schlezinger J. 2016. Prioritizing
environmental chemicals for obesity and diabetes outcomes research: a screen-
ing approach using ToxCast™ high-throughput data. Environ Health Perspect
124(8):1141–1154, PMID: 26978842, https://doi.org/10.1289/ehp.1510456.

Beamer PI, Canales RA, Ferguson AC, Leckie JO, Bradman A. 2012. Relative pesti-
cide and exposure route contribution to aggregate and cumulative dose in
young farmworker children. Int J Environ Res Public Health 9(1):73–96, PMID:
22470279, https://doi.org/10.3390/ijerph9010073.

Bell D. 2016. 2016 Update: Minnesota Chemicals of High Concern List. St. Paul,
MN: Minnesota Division of Environmental Health. https://www.health.state.mn.
us/communities/environment/childenvhealth/docs/report2016 [accessed 26
November 2019].

Bellinger DC. 2013. Prenatal exposures to environmental chemicals and children’s
neurodevelopment: an update. Saf Health Work 4(1):1–11, PMID: 23515885,
https://doi.org/10.5491/SHAW.2013.4.1.1.

Benfenati E, Manganaro A, Gini G. 2013. VEGA-QSAR: AI inside a platform for predictive
toxicology. CEUR Workshop Proc 1107:21–28. https://pdfs.semanticscholar.org/
2785/e310a83fbc61c8cc018ef941e68175758573 [accessed 2 December 2019].

Board on Population Health and Public Health Practice. 2014. Identifying and
Reducing Environmental Health Risks of Chemicals in Our Society: Workshop
Summary. Washington, DC: National Academies Press.

Bose-O’Reilly S, McCarty KM, Steckling N, Lettmeier B. 2010. Mercury exposure
and children’s health. Curr Probl Pediatr Adolesc Health Care 40(8):186–215,
PMID: 20816346, https://doi.org/10.1016/j.cppeds.2010.07.002.

Brandsma SH, de Boer J, van Velzen MJ, Leonards PE. 2014. Organophosphorus
flame retardants (PFRs) and plasticizers in house and car dust and the influ-
ence of electronic equipment. Chemosphere 116:3–9, PMID: 24703013,
https://doi.org/10.1016/j.chemosphere.2014.02.036.

Bruckner JV. 2000. Differences in sensitivity of children and adults to chemical tox-
icity: the NAS panel report. Regul Toxicol Pharmacol 31(3):280–285, PMID:
10915586, https://doi.org/10.1006/rtph.2000.1393.

Burd L, Blair J, Dropps K. 2012. Prenatal alcohol exposure, blood alcohol con-
centrations and alcohol elimination rates for the mother, fetus and new-
born. J Perinatol 32(9):652–659, PMID: 22595965, https://doi.org/10.1038/jp.
2012.57.

Cadogan DF, Howick CJ. 2000. Plasticizers. In: Kirk-Othmer Encyclopedia of Chemical
Technology. Kirk RE, Othmar DF, eds. New York, NY: John Wiley & Sons.

Casida JE. 2009. Pest toxicology: the primary mechanisms of pesticide action. Chem
Res Toxicol 22(4):609–619, PMID: 19284791, https://doi.org/10.1021/tx8004949.

CDC (Center for Disease Control and Prevention). 2018a. Fourth National Report on
Human Exposure to Environmental Chemicals, Volumes 1 and 3. https://www.
cdc.gov/exposurereport/ [accessed 16 April 2018].

CDC. 2018b. Fourth National Report on Human Exposure to Environmental Chemicals,
Updated Tables, March 2018. https://www.cdc.gov/exposurereport/ [accessed 19
March 2018].

CDC. 2019a. Fourth National Report on Human Exposure to Environmental Chemicals,
Updated Tables, March 2019, Volume 1. https://www.cdc.gov/exposurereport/
[accessed 26 November 2019].

CDC. 2019b. Fourth National Report on Human Exposure to Environmental Chemicals,
Updated Tables, March 2019, Volume 2. https://www.cdc.gov/exposurereport/
[accessed 26 November 2019].

CDC. 2019c. Updated Tables, January 2019: Fourth National Report on Human
Exposure to Environmental Chemicals. https://www.cdc.gov/exposurereport/
[accessed 13 August 2018].

Chen L, Lu J, Zhang J, Feng KR, Zheng MY, Cai YD. 2013. Predicting chemical toxic-
ity effects based on chemical-chemical interactions. PLoS One 8(2):e56517,
PMID: 23457578, https://doi.org/10.1371/journal.pone.0056517.

Christia C, Poma G, Besis A, Samara C, Covaci A. 2018. Legacy and emerging organo-
phosphomicronrus flame retardants in car dust from Greece: implications for
human exposure. Chemosphere 196:231–239, PMID: 29304461, https://doi.org/10.
1016/j.chemosphere.2017.12.132.

Clarke EA, Anliker R. 1980. Organic dyes and pigments. In: Handbook of Environmental
Chemistry, Vol. 3. Barceló D, Aboul-Kassim TAT, Bahnemann DW, Beek B,
Bosland MC, Boule P, et al. eds. Berlin, Germany: Springer Berlin.

Covaci A, Harrad S, Abdallah MA-E, Ali N, Law RJ, Herzke D, et al. 2011. Novel bro-
minated flame retardants: a review of their analysis, environmental fate and
behaviour. Environ Int 37(2):532–556, PMID: 21168217, https://doi.org/10.1016/j.
envint.2010.11.007.

Cronin MT, Jaworska JS, Walker JD, Comber MH, Watts CD, Worth AP, et al. 2003.
Use of QSARs in international decision-making frameworks to predict health
effects of chemical substances. Environ Health Perspect 111(10):1391–1401,
PMID: 12896862, https://doi.org/10.1289/ehp.5760.

Cullinan MP, Palmer JE, Carle AD, West MJ, Seymour GJ. 2012. Long term use of
triclosan toothpaste and thyroid function. Sci Total Environ 416:75–79, PMID:
22197412, https://doi.org/10.1016/j.scitotenv.2011.11.063.

DeWitt JC, ed. 2015. Toxicological Effects of Perfluoroalkyl and Polyfluoroalkyl
Substances. Cham Switzerland: Springer Nature Switzerland AG.

Diamanti-Kandarakis E, Bourguignon J-P, Giudice LC, Hauser R, Prins GS, Soto
AM, et al. 2009. Endocrine-disrupting chemicals: an Endocrine Society scien-
tific statement. Endocr Rev 30(4):293–342, PMID: 19502515, https://doi.org/10.
1210/er.2009-0002.

Fautz R, Fuchs A, van der Walle H, Henny V, Smits L. 2002. Hair dye-sensitized hair-
dressers: the cross-reaction pattern with new generation hair dyes. Contact
Dermatitis 46(6):319–324, PMID: 12190619, https://doi.org/10.1034/j.1600-0536.
2002.460601.x.

FDA (U.S. Food and Drug Administration). 2011. Analytical results for the total diet study.
https://www.fda.gov/Food/FoodScienceResearch/TotalDietStudy/ucm184293.htm
[accessed 26 November 2019].

FDA. 2018. Pesticide residue monitoring program questions and answers. https://
www.fda.gov/Food/FoodborneIllnessContaminants/Pesticides/ucm583711.htm
[accessed 10 December 2018].

Friedman KP, Watt ED, Hornung MW, Hedge JM, Judson RS, Crofton KM, et al.
2016. Tiered high-throughput screening approach to identify thyroperoxidase
inhibitors within the ToxCast phase II and II chemical libraries. Toxicol Sci
151(1):160–180, PMID: 26884060, https://doi.org/10.1093/toxsci/kfw034.

Geyer HJ, Schramm KW, Feicht EA, Fried KW, Henkelmann B, Lenoir D, et al.
2004. Terminal elimination half-lives of the brominated flame retardants
TBBPA, HBCD, and lower brominated PBDEs in humans. In: Proceedings,
Organohalogen Compounds. 24th International Symposium on Halogenated
Environmental Organic Pollutants and POPs (DIOXIN 2004). 6–10 September

Environmental Health Perspectives 126001-15 127(12) December 2019

https://www.ncbi.nlm.nih.gov/pubmed/27211915

https://doi.org/10.3358/shokueishi.57.23

https://doi.org/10.3358/shokueishi.57.23

https://doi.org/10.1016/j.aca.2005.07.048

https://www.ncbi.nlm.nih.gov/pubmed/12850087

https://doi.org/10.1016/S0160-4120(03)00121-1

https://www.acog.org/Resources-And-Publications/Committee-Opinions/Committee-on-Health-Care-for-Underserved-Women/Exposure-to-Toxic-Environmental-Agents

https://www.acog.org/Resources-And-Publications/Committee-Opinions/Committee-on-Health-Care-for-Underserved-Women/Exposure-to-Toxic-Environmental-Agents

https://www.acog.org/Resources-And-Publications/Committee-Opinions/Committee-on-Health-Care-for-Underserved-Women/Exposure-to-Toxic-Environmental-Agents

https://www.ncbi.nlm.nih.gov/pubmed/24809497

https://doi.org/10.1007/s11356-014-2985-6

https://www.ncbi.nlm.nih.gov/pubmed/26978842

https://doi.org/10.1289/ehp.1510456

https://www.ncbi.nlm.nih.gov/pubmed/22470279

https://doi.org/10.3390/ijerph9010073

https://www.health.state.mn.us/communities/environment/childenvhealth/docs/report2016

https://www.health.state.mn.us/communities/environment/childenvhealth/docs/report2016

https://www.ncbi.nlm.nih.gov/pubmed/23515885

https://doi.org/10.5491/SHAW.2013.4.1.1

https://pdfs.semanticscholar.org/2785/e310a83fbc61c8cc018ef941e68175758573

https://pdfs.semanticscholar.org/2785/e310a83fbc61c8cc018ef941e68175758573

https://www.ncbi.nlm.nih.gov/pubmed/20816346

https://doi.org/10.1016/j.cppeds.2010.07.002

https://www.ncbi.nlm.nih.gov/pubmed/24703013

https://doi.org/10.1016/j.chemosphere.2014.02.036

https://www.ncbi.nlm.nih.gov/pubmed/10915586

https://doi.org/10.1006/rtph.2000.1393

https://www.ncbi.nlm.nih.gov/pubmed/22595965

https://doi.org/10.1038/jp.2012.57

https://doi.org/10.1038/jp.2012.57

https://www.ncbi.nlm.nih.gov/pubmed/19284791

https://doi.org/10.1021/tx8004949

https://www.cdc.gov/exposurereport/

https://www.cdc.gov/exposurereport/

https://www.cdc.gov/exposurereport/

https://www.cdc.gov/exposurereport/

https://www.cdc.gov/exposurereport/

https://www.cdc.gov/exposurereport/

https://www.ncbi.nlm.nih.gov/pubmed/23457578

https://doi.org/10.1371/journal.pone.0056517

https://www.ncbi.nlm.nih.gov/pubmed/29304461

https://doi.org/10.1016/j.chemosphere.2017.12.132

https://doi.org/10.1016/j.chemosphere.2017.12.132

https://www.ncbi.nlm.nih.gov/pubmed/21168217

https://doi.org/10.1016/j.envint.2010.11.007

https://doi.org/10.1016/j.envint.2010.11.007

https://www.ncbi.nlm.nih.gov/pubmed/12896862

https://doi.org/10.1289/ehp.5760

https://www.ncbi.nlm.nih.gov/pubmed/22197412

https://doi.org/10.1016/j.scitotenv.2011.11.063

https://www.ncbi.nlm.nih.gov/pubmed/19502515

https://doi.org/10.1210/er.2009-0002

https://doi.org/10.1210/er.2009-0002

https://www.ncbi.nlm.nih.gov/pubmed/12190619

https://doi.org/10.1034/j.1600-0536.2002.460601.x

https://doi.org/10.1034/j.1600-0536.2002.460601.x

https://www.fda.gov/Food/FoodScienceResearch/TotalDietStudy/ucm184293.htm

https://www.fda.gov/Food/FoodborneIllnessContaminants/Pesticides/ucm583711.htm

https://www.fda.gov/Food/FoodborneIllnessContaminants/Pesticides/ucm583711.htm

https://www.ncbi.nlm.nih.gov/pubmed/26884060

https://doi.org/10.1093/toxsci/kfw034

2004. Berlin, Germany: Bundesmin. fuer Umwelt, Naturschutz und Reaktorsi-
cherheit, Bonn. 66:3820–3825.

Ginsberg G, Hattis D, Sonawane B, Russ A, Banati P, Kozlak M, et al. 2002.
Evaluation of child/adult pharmacokinetic differences from a database derived
from the therapeutic drug literature. Toxicol Sci 66(2):185–200, PMID: 11896285,
https://doi.org/10.1093/toxsci/66.2.185.

Grandjean P, Bellinger D, Bergman Å, Cordier S, Davey-Smith G, Eskenazi B, et al.
2008. The Faroes statement: human health effects of developmental exposure
to chemicals in our environment. Basic Clin Pharmacol Toxicol 102(2):73–75,
PMID: 18226057, https://doi.org/10.1111/j.1742-7843.2007.00114.x.

Grandjean P, Landrigan PJ. 2006. Developmental neurotoxicity of industrial chemi-
cals. Lancet 368(9553):2167–2178, PMID: 17174709, https://doi.org/10.1016/
S0140-6736(06)69665-7.

Holmstrup M, Bindesbøl A-M, Oostingh GJ, Duschl A, Scheil V, Köhler H-R, et al.
2010. Interactions between effects of environmental chemicals and natural
stressors: a review. Sci Total Environ 408(18):3746–3762, PMID: 19922980,
https://doi.org/10.1016/j.scitotenv.2009.10.067.

Hurst CH, Abbott BD, DeVito MJ, Birnbaum LS. 1998. 2,3,7,8-Tetrachlorodibenzo-p-
dioxin in pregnant Long Evans rats: disposition to maternal and embryo/fetal tis-
sues. Toxicol Sci 45(2):129–136, PMID: 9848119, https://doi.org/10.1006/toxs.1998.
2520.

Judson RS, Magpantay FM, Chickarmane V, Haskell C, Tania N, Taylor J, et al.
2015. Integrated model of chemical perturbations of a biological pathway using
18 in vitro high-throughput screening assays for the estrogen receptor. Toxicol
Sci 148(1):137–154, PMID: 26272952, https://doi.org/10.1093/toxsci/kfv168.

Jurewicz J, Hanke W. 2011. Exposure to phthalates: reproductive outcome and chil-
dren health. A review of epidemiological studies. Int J Occup Med Environ Health
24(2):115–141, PMID: 21594692, https://doi.org/10.2478/s13382-011-0022-2.

Karmaus AL, Toole CM, Filer DL, Lewis KC, Martin MT. 2016. High-throughput screen-
ing of chemical effects on steroidogenesis using H295R human adrenocortical
carcinoma cells. Toxicol Sci 150(2):323–332, PMID: 26781511, https://doi.org/10.
1093/toxsci/kfw002.

Kastner J, Cooper DG, Marić M, Dodd P, Yargeau V. 2012. Aqueous leaching of di-
2-ethylhexyl phthalate and “green” plasticizers from poly(vinyl chloride). Sci
Total Environ 432:357–364, PMID: 22750182, https://doi.org/10.1016/j.scitotenv.
2012.06.014.

Kleinstreuer NC, Ceger P, Watt ED, Martin M, Houck K, Browne P, et al. 2017.
Development and validation of a computational model for androgen receptor
activity. Chem Res Toxicol 30(4):946–964, PMID: 27933809, https://doi.org/10.
1021/acs.chemrestox.6b00347.

Koeppe ES, Ferguson KK, Colacino JA, Meeker JD. 2013. Relationship between uri-
nary triclosan and paraben concentrations and serum thyroid measures in
NHANES 2007–2008. Sci Total Environ 445–446:299–305, PMID: 23340023,
https://doi.org/10.1016/j.scitotenv.2012.12.052.

Kolšek K, Mavri J, Sollner Dolenc M, Gobec S, Turk S. 2014. Endocrine disruptome
—an open source prediction tool for assessing endocrine disruption potential
through nuclear receptor binding. J Chem Inf Model 54(4):1254–1267, PMID:
24628082, https://doi.org/10.1021/ci400649p.

Lanphear BP, Hornung R, Khoury J, Yolton K, Baghurst P, Bellinger DC, et al. 2005.
Low-level environmental lead exposure and children’s intellectual function: an
international pooled analysis. Environ Health Perspect 113(7):894–899, PMID:
16002379, https://doi.org/10.1289/ehp.7688.

LeBeau MA, Montgomery MA, Brewer JD. 2011. The role of variations in growth
rate and sample collection on interpreting results of segmental analyses of
hair. Forensic Sci Int 210(1–3):110–116, PMID: 21382678, https://doi.org/10.1016/
j.forsciint.2011.02.015.

Levchik SV, Weil ED. 2006. A review of recent progress in phosphorus-based flame
retardants. J Fire Sci 24(5):345–364, https://doi.org/10.1177/0734904106068426.

Lin Z, Fisher JW, Wang R, Ross MK, Filipov NM. 2013. Estimation of placental and
lactational transfer and tissue distribution of atrazine and its main metabolites
in rodent dams, fetuses, and neonates with physiologically based pharmacoki-
netic modeling. Toxicol Appl Pharmacol 273(1):140–158, PMID: 23958493,
https://doi.org/10.1016/j.taap.2013.08.010.

Loccisano AE, Campbell JL Jr, Butenhoff JL, Andersen ME, Clewell HJ III. 2012.
Evaluation of placental and lactational pharmacokinetics of PFOA and PFOS in
the pregnant, lactating, fetal and neonatal rat using a physiologically based
pharmacokinetic model. Reprod Toxicol 33(4):468–490, PMID: 21872655,
https://doi.org/10.1016/j.reprotox.2011.07.003.

Lynn RK, Wong K, Garvie-Gould C, Kennish JM. 1981. Disposition of the flame re-
tardant, tris (1,3-dichloro-2-propyl) phosphate, in the rat. Drug Metab Dispos
9(5):434–441, PMID: 6117442.

Meeker JD. 2012. Exposure to environmental endocrine disruptors and child develop-
ment. Arch Pediatr Adolesc Med 166(10):952–958, PMID: 22664748, https://doi.org/
10.1001/archpediatrics.2012.241.

MDH (Minnesota Department of Health). 2018a. Toxicological summary for:
Metolachlor ESA. St. Paul, MN: Minnesota Department of Health. https://

www.health.state.mn.us/communities/environment/risk/docs/guidance/gw/
metolachloresasumm [accessed 2 December 2019].

MDH. 2018b. Toxicological summary for: Metolachlor OXA. St. Paul, MN: Minnesota
Department of Health. https://www.health.state.mn.us/communities/environment/
risk/docs/guidance/gw/metolachloroxasumm [accessed 2 June 2018].

National Academies of Sciences, Engineering, and Medicine. 2017. Application of
Systematic Review Methods in an Overall Strategy for Evaluating Low-Dose
Toxicity from Endocrine Active Chemicals. Washington DC: National Academies
Press.

Nayebare SR, Karthikraj R, Kannan K. 2018. Analysis of terephthalate metabolites
in human urine by high-performance liquid chromatography-tandem mass
spectrometry (HPLC-MS/MS). J Chromatogr B Analyt Technol Biomed Life Sci
1092:473–479, PMID: 30008303, https://doi.org/10.1016/j.jchromb.2018.06.044.

Nomeir A, Kato S, Matthews H. 1981. The metabolism and disposition of tris (1,3-
dichloro-2-propyl) phosphate (fyrol FR-2) in the rat. Toxicol Appl Pharmacol
57(3):401–413, PMID: 7222047, https://doi.org/10.1016/0041-008x(81)90238-6.

NRC (National Research Council). 2000. Scientific Frontiers in Developmental
Toxicology and Risk Assessment. Washington DC: National Academies Press.

NRC. 2007. Toxicity Testing in the 21st Century: A Vision and a Strategy.
Washington DC: National Academies Press.

NTP (National Toxicology Program). 2019. Management Status Report. Report
6010. Research Triangle Park, NC: National Institute of Environmental Health
Sciences. https://ntp.niehs.nih.gov/testing/types/cartox/msr/msr [accessed
7 January 2019].

OEHHA (Office of Environmental Health Hazard Assessment). 2018. The Proposition 65
List. Sacramento, CA: OEHHA. https://oehha.ca.gov/proposition-65/proposition-65-
list [accessed 13 August 2018].

Olsen GW, Burris JM, Ehresman DJ, Froehlich JW, Seacat AM, Butenhoff JL, et al.
2007. Half-life of serum elimination of perfluorooctanesulfonate, perfluorohexa-
nesulfonate, and perfluorooctanoate in retired fluorochemical production work-
ers. Environ Health Perspect 115(9):1298–1305, PMID: 17805419, https://doi.org/
10.1289/ehp.10009.

Reuben SH. 2010. Reducing Environmental Cancer Risk: What We Can Do Now:
President’s Cancer Panel 2008–2009 Annual Report. Bethesda, MD: President’s
Cancer Panel.

Rider CV, Carlin DJ, DeVito MJ, Thompson CL, Walker NJ. 2013. Mixtures research
at NIEHS: an evolving program. Toxicology 313(2–3):94–102, PMID: 23146757,
https://doi.org/10.1016/j.tox.2012.10.017.

Ritscher A, Wang Z, Scheringer M, Boucher JM, Ahrens L, Berger U, et al. 2018.
Zürich statement on future actions on per-and polyfluoroalkyl substances
(PFASs). Environ Health Perspect 126(8):84502, PMID: 30235423, https://doi.org/
10.1289/EHP4158.

Rotroff DM, Dix DJ, Houck KA, Knudsen TB, Martin MT, McLaurin KW, et al. 2013.
Using in vitro high throughput screening assays to identify potential
endocrine-disrupting chemicals. Environ Health Perspect 121(1):7–14, PMID:
23052129, https://doi.org/10.1289/ehp.1205065.

Saeidnia S, Manayi A, Abdollahi M. 2013. The pros and cons of the in-silico
pharmaco-toxicology in drug discovery and development. Int J Pharmacol
9(3):176–181, https://doi.org/10.3923/ijp.2013.176.181.

Selevan SG, Kimmel CA, Mendola P. 2000. Identifying critical windows of exposure
for children’s health. Environ Health Perspect 108(suppl 3):451–455, PMID:
10852844, https://doi.org/10.1289/ehp.00108s3451.

Sharpe RM, Irvine DS. 2004. How strong is the evidence of a link between envi-
ronmental chemicals and adverse effects on human reproductive health?
BMJ 328(7437):447–451, PMID: 14976101, https://doi.org/10.1136/bmj.328.
7437.447.

Sipes NS, Martin MT, Kothiya P, Reif DM, Judson RS, Richard AM, et al. 2013. Profiling
976 ToxCast chemicals across 331 enzymatic and receptor signaling assays. Chem
Res Toxicol 26(6):878–895, PMID: 23611293, https://doi.org/10.1021/tx400021f.

Strickland JD, Martin MT, Richard AM, Houck KA, Shafer TJ. 2018. Screening the
ToxCast phase II libraries for alterations in network function using cortical neu-
rons grown on multi-well microelectrode array (mwMEA) plates. Arch Toxicol
92(1):487–500, PMID: 28766123, https://doi.org/10.1007/s00204-017-2035-5.

Sutherland-Ashley K, Eya B, Lim L. 2017. Metolachlor and metolachlor degradates ethane-
sulfonic acid and oxanilic acid in groundwater. Sacramento, CA: California
Environmental Protection Agency. https://oehha.ca.gov/media/downloads/pesticides/
report/metolachlor05312017 [accessed 26 November 2019].

Trier X, Okholm B, Foverskov A, Binderup ML, Petersen JH. 2010. Primary aromatic
amines (PAAs) in black nylon and other food-contact materials, 2004–2009.
Food Addit Contam Part A 27(9):1325–1335, PMID: 20640962, https://doi.org/10.
1080/19440049.2010.487500.

Trudel D, Scheringer M, von Goetz N, Hungerbühler K. 2011. Total consumer expo-
sure to polybrominated diphenyl ethers in North America and Europe. Environ
Sci Technol 45(6):2391–2397, PMID: 21348481, https://doi.org/10.1021/es1035046.

Tsai P-C, Ding W-H. 2004. Determination of alkyltrimethylammonium surfactants in hair
conditioners and fabric softeners by gas chromatography–mass spectrometry with

Environmental Health Perspectives 126001-16 127(12) December 2019

https://www.ncbi.nlm.nih.gov/pubmed/11896285

https://doi.org/10.1093/toxsci/66.2.185

https://www.ncbi.nlm.nih.gov/pubmed/18226057

https://doi.org/10.1111/j.1742-7843.2007.00114.x

https://www.ncbi.nlm.nih.gov/pubmed/17174709

https://doi.org/10.1016/S0140-6736(06)69665-7

https://doi.org/10.1016/S0140-6736(06)69665-7

https://www.ncbi.nlm.nih.gov/pubmed/19922980

https://doi.org/10.1016/j.scitotenv.2009.10.067

https://www.ncbi.nlm.nih.gov/pubmed/9848119

https://doi.org/10.1006/toxs.1998.2520

https://doi.org/10.1006/toxs.1998.2520

https://www.ncbi.nlm.nih.gov/pubmed/26272952

https://doi.org/10.1093/toxsci/kfv168

https://www.ncbi.nlm.nih.gov/pubmed/21594692

https://doi.org/10.2478/s13382-011-0022-2

https://www.ncbi.nlm.nih.gov/pubmed/26781511

https://doi.org/10.1093/toxsci/kfw002

https://doi.org/10.1093/toxsci/kfw002

https://www.ncbi.nlm.nih.gov/pubmed/22750182

https://doi.org/10.1016/j.scitotenv.2012.06.014

https://doi.org/10.1016/j.scitotenv.2012.06.014

https://www.ncbi.nlm.nih.gov/pubmed/27933809

https://doi.org/10.1021/acs.chemrestox.6b00347

https://doi.org/10.1021/acs.chemrestox.6b00347

https://www.ncbi.nlm.nih.gov/pubmed/23340023

https://doi.org/10.1016/j.scitotenv.2012.12.052

https://www.ncbi.nlm.nih.gov/pubmed/24628082

https://doi.org/10.1021/ci400649p

https://www.ncbi.nlm.nih.gov/pubmed/16002379

https://doi.org/10.1289/ehp.7688

https://www.ncbi.nlm.nih.gov/pubmed/21382678

https://doi.org/10.1016/j.forsciint.2011.02.015

https://doi.org/10.1016/j.forsciint.2011.02.015

https://doi.org/10.1177/0734904106068426

https://www.ncbi.nlm.nih.gov/pubmed/23958493

https://doi.org/10.1016/j.taap.2013.08.010

https://www.ncbi.nlm.nih.gov/pubmed/21872655

https://doi.org/10.1016/j.reprotox.2011.07.003

https://www.ncbi.nlm.nih.gov/pubmed/6117442

https://www.ncbi.nlm.nih.gov/pubmed/22664748

https://doi.org/10.1001/archpediatrics.2012.241

https://doi.org/10.1001/archpediatrics.2012.241

https://www.health.state.mn.us/communities/environment/risk/docs/guidance/gw/metolachloresasumm

https://www.health.state.mn.us/communities/environment/risk/docs/guidance/gw/metolachloresasumm

https://www.health.state.mn.us/communities/environment/risk/docs/guidance/gw/metolachloresasumm

https://www.health.state.mn.us/communities/environment/risk/docs/guidance/gw/metolachloroxasumm

https://www.health.state.mn.us/communities/environment/risk/docs/guidance/gw/metolachloroxasumm

https://www.ncbi.nlm.nih.gov/pubmed/30008303

https://doi.org/10.1016/j.jchromb.2018.06.044

https://www.ncbi.nlm.nih.gov/pubmed/7222047

https://doi.org/10.1016/0041-008x(81)90238-6

https://ntp.niehs.nih.gov/testing/types/cartox/msr/msr

https://oehha.ca.gov/proposition-65/proposition-65-list

https://oehha.ca.gov/proposition-65/proposition-65-list

https://www.ncbi.nlm.nih.gov/pubmed/17805419

https://doi.org/10.1289/ehp.10009

https://doi.org/10.1289/ehp.10009

https://www.ncbi.nlm.nih.gov/pubmed/23146757

https://doi.org/10.1016/j.tox.2012.10.017

https://www.ncbi.nlm.nih.gov/pubmed/30235423

https://doi.org/10.1289/EHP4158

https://doi.org/10.1289/EHP4158

https://www.ncbi.nlm.nih.gov/pubmed/23052129

https://doi.org/10.1289/ehp.1205065

https://doi.org/10.3923/ijp.2013.176.181

https://www.ncbi.nlm.nih.gov/pubmed/10852844

https://doi.org/10.1289/ehp.00108s3451

https://www.ncbi.nlm.nih.gov/pubmed/14976101

https://doi.org/10.1136/bmj.328.7437.447

https://doi.org/10.1136/bmj.328.7437.447

https://www.ncbi.nlm.nih.gov/pubmed/23611293

https://doi.org/10.1021/tx400021f

https://www.ncbi.nlm.nih.gov/pubmed/28766123

https://doi.org/10.1007/s00204-017-2035-5

https://oehha.ca.gov/media/downloads/pesticides/report/metolachlor05312017

https://oehha.ca.gov/media/downloads/pesticides/report/metolachlor05312017

https://www.ncbi.nlm.nih.gov/pubmed/20640962

https://doi.org/10.1080/19440049.2010.487500

https://doi.org/10.1080/19440049.2010.487500

https://www.ncbi.nlm.nih.gov/pubmed/21348481

https://doi.org/10.1021/es1035046

electron-impact and chemical ionization. J Chromatogr A 1027(1–2):103–108, PMID:
14971489, https://doi.org/10.1016/j.chroma.2003.10.047.

U.S. EPA (Environmental Protection Agency). 2010. Nonylphenol (NP) and Nonylphenol
Ethoxylates (NPEs) Action Plan. RIN 2070-ZA09. Washington, DC: U.S. EPA. https://
www.epa.gov/sites/production/files/2015-09/documents/rin2070-za09_np-npes_
action_plan_final_2010-08-09 [accessed 26 November 2019].

U.S. EPA. 2014a. Exploring consumer exposure pathways and patterns of use
for chemicals in the environment. Toxicol Rep 2:228–237, PMID: 28962356,
https://doi.org/10.1016/j.toxrep.2014.12.009.

U.S. EPA. 2014b. CPCat: Chemical and product categories. Research Triangle
Park, NC: U.S. EPA. https://actor.epa.gov/cpcat/faces/search.xhtml;jsessionid=
48EE234E7C47ACAAED15B813D9884EB1 [accessed 13 August 2017].

U.S. EPA. 2016a. 2016 Chemical data reporting results. Washington, DC: U.S. EPA.
https://www.epa.gov/chemical-data-reporting [accessed 1 June 2018].

U.S. EPA. 2016b. Six-year review 1 contaminant occurrence data (1993–1997).
Washington, DC: U.S. https://www.epa.gov/dwsixyearreview/six-year-review-
1-contaminant-occurrence-data-1993-1997 [accessed 26 November 2019].

U.S. EPA. 2016c. Six-year review 2 contaminant occurrence data (1998–2005).
Washington, DC: U.S. https://www.epa.gov/dwsixyearreview/six-year-review-
2-contaminant-occurrence-data-1998-2005 [accessed 26 November 2019].

U.S. EPA. 2016d. Six-year review 3 compliance monitoring data (2005–2011).
Washington, DC: U.S. EPA. https://www.epa.gov/dwsixyearreview/six-year-
review-3-compliance-monitoring-data-2006-2011 [accessed 26 November 2019].

U.S. EPA. 2016e. User’s Guide for T.E.S.T. (version 4.2) (Toxicity Estimation Software
Tool) a program to estimate toxicity from molecular structure. EPA/600/R-16/058.
Cincinnati, OH: U.S. EPA. https://www.epa.gov/chemical-research/users-guide-
test-version-42-toxicity-estimation-software-tool-program-estimate [accessed 26
November 2019].

U.S. EPA. 2017. Chemistry dashboard. Office of Research and Development, National
Center for Computational Toxicology. Research Triangle Park, NC: U.S. EPA.
https://comptox.epa.gov/dashboard/ [accessed 19 March 2018].

USDA (U.S. Department of Agriculture). 2017. Pesticide Data Program: databases
and annual summary reports. Washington, DC: USDA. https://www.ams.usda.
gov/datasets/pdp [accessed 16 April 2017].

USDA. 2018. PDP databases and annual summaries. Washington, DC: USDA. https://
www.ams.usda.gov/datasets/pdp/pdpdata [accessed 16 April 2017].

ValdiviaP,MartinM,LeFewWR,RossJ,HouckKA,ShaferTJ.2014.Multi-wellmicroelec-
trode array recordings detect neuroactivity of ToxCast compounds. Neurotoxicology
44:204–217,PMID:24997244,https://doi.org/10.1016/j.neuro.2014.06.012.

van der Veen I, de Boer J. 2012. Phosphorus flame retardants: properties, production,
environmental occurrence, toxicity and analysis. Chemosphere 88(10):1119–1153,
PMID: 22537891, https://doi.org/10.1016/j.chemosphere.2012.03.067.

Varshavsky J, Smith A, Wang A, Hom E, Izano M, Huang H, et al. 2019. Heightened
susceptibility: a review of how pregnancy and chemical exposures influence
maternal health. Reprod Toxicol, PMID: 31055053, https://doi.org/10.1016/j.
reprotox.2019.04.004.

WADEC (Washington State Department of Ecology). 2018. Chemicals of high
concern to children reporting list. Pullman, WA: WADEC. https://ecology.wa.
gov/Regulations-Permits/Reporting-requirements/Reporting-for-Childrens-Safe-
Products-Act/Chemicals-of-high-concern-to-children [accessed 13 August
2018].

Wambaugh JF, Wang A, Dionisio KL, Frame A, Egeghy P, Judson R, et al. 2014. High
throughput heuristics for prioritizing human exposure to environmental chemi-
cals. Environ Sci Technol 48(21):12760–12767, PMID: 25343693, https://doi.org/10.
1021/es503583j.

Wang A, Padula A, Sirota M, Woodruff TJ. 2016. Environmental influences on
reproductive health: the importance of chemical exposures. Fertil Steril
106(4):905–929, PMID: 27513554, https://doi.org/10.1016/j.fertnstert.2016.07.
1076.

Wang Z, Cousins IT, Scheringer M, Hungerbühler K. 2013. Fluorinated alternatives
to long-chain perfluoroalkyl carboxylic acids (PFCAs), perfluoroalkane sulfonic
acids (PFSAs) and their potential precursors. Environ Int 60:242–248, PMID:
24660230, https://doi.org/10.1016/j.envint.2013.08.021.

Weil E. 2005. Patent activity in the flame retardant field. In: Proceedings of the
16th Annual Conference on Recent Advances in Flame Retardancy of
Polymeric Materials 2005. Lewin M, ed. 22–25 May 2005. Stamford, CT:
Business Communications Corp., 313–321.

Weiss B. 2000. Vulnerability of children and the developing brain to neurotoxic hazards.
Environ Health Perspect 108(suppl 3):375–381, PMID: 10852831, https://doi.org/10.
1289/ehp.00108s3375.

Weisz A, Andrzejewski D, Rasooly IR. 2004. Determination of 2,4,6-tribromoani-
line in the color additives D&C Red Nos. 21 and 22 (Eosin Y) using solid-
phase microextraction and gas chromatography-mass spectrometry. J
Chromatogr A 1057(1–2):185–191, PMID: 15584238, https://doi.org/10.1016/j.
chroma.2004.09.065.

Wetmore BA, Wambaugh JF, Ferguson SS, Sochaski MA, Rotroff DM, Freeman K,
et al. 2012. Integration of dosimetry, exposure, and high-throughput screening
data in chemical toxicity assessment. Toxicol Sci 125(1):157–174, PMID:
21948869, https://doi.org/10.1093/toxsci/kfr254.

WHO (World Health Organization). 2010. Persistent Organic Pollutants: Impact on Child
Health. https://apps.who.int/iris/bitstream/handle/10665/44525/9789241501101_eng.
pdf [accessed 26 November 2019].

Witorsch RJ, Thomas JA. 2010. Personal care products and endocrine disruption:
a critical review of the literature. Crit Rev Toxicol 40(Suppl 3):1–30, PMID:
20932229, https://doi.org/10.3109/10408444.2010.515563.

Wu S, Fisher J, Naciff J, Laufersweiler M, Lester C, Daston G, et al. 2013. Framework
for identifying chemicals with structural features associated with the potential to
act as developmental or reproductive toxicants. Chem Res Toxicol 26(12):1840–
1861, PMID: 24206190, https://doi.org/10.1021/tx400226u.

Yavuz O, Valzacchi S, Hoekstra E, Simoneau C. 2016. Determination of primary aro-
matic amines in cold water extract of coloured paper napkin samples by liquid
chromatography-tandem mass spectrometry. Food Addit Contam Part A Chem
Anal Control Expo Risk Assess 33(6):1072–1079, PMID: 27146949, https://doi.org/
10.1080/19440049.2016.1184493.

Zhang C, Cui F, Zeng G-M, Jiang M, Yang Z-Z, Yu Z-G, et al. 2015. Quaternary ammo-
nium compounds (QACs): a review on occurrence, fate and toxicity in the envi-
ronment. Sci Total Environ 518–519:352–362, PMID: 25770948, https://doi.org/10.
1016/j.scitotenv.2015.03.007.

Zhang L, Ai H, Chen W, Yin Z, Hu H, Zhu J, et al. 2017. CarcinoPred-EL: novel models
for predicting the carcinogenicity of chemicals using molecular fingerprints and
ensemble learning methods. Sci Rep 7(1):2118, PMID: 28522849, https://doi.org/
10.1038/s41598-017-02365-0.

Environmental Health Perspectives 126001-17 127(12) December 2019

https://www.ncbi.nlm.nih.gov/pubmed/14971489

https://doi.org/10.1016/j.chroma.2003.10.047

https://www.epa.gov/sites/production/files/2015-09/documents/rin2070-za09_np-npes_action_plan_final_2010-08-09

https://www.epa.gov/sites/production/files/2015-09/documents/rin2070-za09_np-npes_action_plan_final_2010-08-09

https://www.epa.gov/sites/production/files/2015-09/documents/rin2070-za09_np-npes_action_plan_final_2010-08-09

https://www.ncbi.nlm.nih.gov/pubmed/28962356

https://doi.org/10.1016/j.toxrep.2014.12.009

https://actor.epa.gov/cpcat/faces/search.xhtml;jsessionid=48EE234E7C47ACAAED15B813D9884EB1

https://actor.epa.gov/cpcat/faces/search.xhtml;jsessionid=48EE234E7C47ACAAED15B813D9884EB1

https://www.epa.gov/chemical-data-reporting

https://www.epa.gov/dwsixyearreview/six-year-review-1-contaminant-occurrence-data-1993-1997

https://www.epa.gov/dwsixyearreview/six-year-review-1-contaminant-occurrence-data-1993-1997

https://www.epa.gov/dwsixyearreview/six-year-review-2-contaminant-occurrence-data-1998-2005

https://www.epa.gov/dwsixyearreview/six-year-review-2-contaminant-occurrence-data-1998-2005

https://www.epa.gov/dwsixyearreview/six-year-review-3-compliance-monitoring-data-2006-2011

https://www.epa.gov/dwsixyearreview/six-year-review-3-compliance-monitoring-data-2006-2011

https://www.epa.gov/chemical-research/users-guide-test-version-42-toxicity-estimation-software-tool-program-estimate

https://www.epa.gov/chemical-research/users-guide-test-version-42-toxicity-estimation-software-tool-program-estimate

https://comptox.epa.gov/dashboard/

https://www.ams.usda.gov/datasets/pdp

https://www.ams.usda.gov/datasets/pdp

https://www.ams.usda.gov/datasets/pdp/pdpdata

https://www.ams.usda.gov/datasets/pdp/pdpdata

https://www.ncbi.nlm.nih.gov/pubmed/24997244

https://doi.org/10.1016/j.neuro.2014.06.012

https://www.ncbi.nlm.nih.gov/pubmed/22537891

https://doi.org/10.1016/j.chemosphere.2012.03.067

https://www.ncbi.nlm.nih.gov/pubmed/31055053

https://doi.org/10.1016/j.reprotox.2019.04.004

https://doi.org/10.1016/j.reprotox.2019.04.004

https://ecology.wa.gov/Regulations-Permits/Reporting-requirements/Reporting-for-Childrens-Safe-Products-Act/Chemicals-of-high-concern-to-children

https://ecology.wa.gov/Regulations-Permits/Reporting-requirements/Reporting-for-Childrens-Safe-Products-Act/Chemicals-of-high-concern-to-children

https://ecology.wa.gov/Regulations-Permits/Reporting-requirements/Reporting-for-Childrens-Safe-Products-Act/Chemicals-of-high-concern-to-children

https://www.ncbi.nlm.nih.gov/pubmed/25343693

https://doi.org/10.1021/es503583j

https://doi.org/10.1021/es503583j

https://www.ncbi.nlm.nih.gov/pubmed/27513554

https://doi.org/10.1016/j.fertnstert.2016.07.1076

https://doi.org/10.1016/j.fertnstert.2016.07.1076

https://www.ncbi.nlm.nih.gov/pubmed/24660230

https://doi.org/10.1016/j.envint.2013.08.021

https://www.ncbi.nlm.nih.gov/pubmed/10852831

https://doi.org/10.1289/ehp.00108s3375

https://doi.org/10.1289/ehp.00108s3375

https://www.ncbi.nlm.nih.gov/pubmed/15584238

https://doi.org/10.1016/j.chroma.2004.09.065

https://doi.org/10.1016/j.chroma.2004.09.065

https://www.ncbi.nlm.nih.gov/pubmed/21948869

https://doi.org/10.1093/toxsci/kfr254

https://apps.who.int/iris/bitstream/handle/10665/44525/9789241501101_eng

https://apps.who.int/iris/bitstream/handle/10665/44525/9789241501101_eng

https://www.ncbi.nlm.nih.gov/pubmed/20932229

https://doi.org/10.3109/10408444.2010.515563

https://www.ncbi.nlm.nih.gov/pubmed/24206190

https://doi.org/10.1021/tx400226u

https://www.ncbi.nlm.nih.gov/pubmed/27146949

https://doi.org/10.1080/19440049.2016.1184493

https://doi.org/10.1080/19440049.2016.1184493

https://www.ncbi.nlm.nih.gov/pubmed/25770948

https://doi.org/10.1016/j.scitotenv.2015.03.007

https://doi.org/10.1016/j.scitotenv.2015.03.007

https://www.ncbi.nlm.nih.gov/pubmed/28522849

https://doi.org/10.1038/s41598-017-02365-0

https://doi.org/10.1038/s41598-017-02365-0

Copyright of Environmental Health Perspectives is the property of Superintendent of
Documents and its content may not be copied or emailed to multiple sites or posted to a
listserv without the copyright holder’s express written permission. However, users may print,
download, or email articles for individual use.