Invited Perspective: The Promise of Fit-for-Purpose Systematic Evidence Maps for Supporting Regulatory Health Assessment

The U.S. Environmental Protection Agency (U.S. EPA) is charged with the Herculean task of critically assessing the safety of tens of thousands of chemicals. New methods that support improved efficiency and effectiveness of risk assessments, including the systematic evidence map described by Carlson et al.¹ in this issue, are greatly needed.

The application of systematic review (SR) methods to the field of environmental health began in earnest nearly a decade ago with the development and publication of applicable methods.^2–4 Today the approach has evolved to include systematic evidence maps (SEMs), which provide access to study data extracted from a large body of evidence to inform SR, risk assessment, and other chemical management workflows.⁵

SEMs are extremely useful for assessing large chemical classes, such as per- and polyfluoroalkyl substances ($\text{PFAS}$), for which the scientific evidence base is poorly characterized. $\text{PFAS}$ are widely used in consumer and industrial products; they are persistent and mobile, and thus ubiquitous in the environment; and several have been demonstrated to be harmful to humans and wildlife.^6–11 $\text{PFAS}$ have been detected in the bodies of nearly every person tested, in the United States and worldwide.^12,13 With more than $\mathrm{12,000}\text{\hspace{0.17em}PFAS}$ identified to date,¹⁴ the time it would take to assess them individually would lead to unnecessary delays in regulating these chemicals when so many people are already at risk. We and others have called for management of $\text{PFAS}$ as a single class.^15,16 Until that happens, the U.S. EPA can be commended for its efforts to assess large groups of $\text{PFAS}$ such as those evaluated by Carlson et al. in their SEM of approximately $150\text{\hspace{0.17em}PFAS}$ ($\text{PFAS-}150$).¹

$\text{PFAS-}150$ follows many of the best practices for conducting SR and SEM, including a clear statement of objectives, a comprehensive literature search that interrogates diverse data repositories (including gray literature), and a structured format for organizing extracted study details.^5,17 Therefore, we believe the data extracted for display in this SEM, which is a very resource-intensive endeavor, should be used in future health assessments of the included $\text{PFAS}$.

Carlson et al.¹ used machine learning tools to greatly improve the efficiency of the SEM workflow, including SWIFT-Active Screener, which iteratively prioritizes titles and abstracts for manual screening. The use of SWIFT-Active Screener for this purpose has been externally validated.¹⁸ The authors also used evidence stream filters in the related tool, SWIFT-Review, to prioritize studies most applicable to human health risk assessment. Although this tool appears valuable for reducing the screening burden, the validity and reliability of the tagging against manual review needs to be assessed.¹⁹ In the meantime, other tools such as DistillerSR’s “Check for Screening Errors” are available to confirm there were no “false excludes.” As new machine learning and artificial intelligence tools are developed to support the emerging field of SR and SEM, we believe it is imperative they be evaluated for accuracy and consistency.

The scope of an SEM is determined and defined by the Population, Exposure, Comparison, and Outcome (PECO) statement. We recently released the $\text{PFAS-Tox}$ Database, an SEM of 29 $\text{PFAS}$ available at https://pfastoxdatabase.org/.^20,21 Our SEM covers a similar time period, but our $\text{PECO}$ statement differs significantly from the one used for $\text{PFAS-}150$. This resulted in different results for the $\text{eight PFAS}$ in common between the two SEMs (Table 1). For example, whereas we identified 54 animal studies for $\text{PFUnDA}$, Carlson et al. identified only 2. The $\text{PECO}$ statement guiding development of the $\text{PFAS-Tox}$ Database was intentionally very broad, because our goal was to present the entirety of the peer reviewed health and toxicological literature for the included $\text{PFAS}$. In comparison, Carlson et al. used a narrower $\text{PECO}$ statement to guide their work, with their goal being a fit-for-purpose SEM that informs the type of human health assessment work that the U.S. EPA routinely conducts.

Table 1.

Comparison of the number of animal studies identified for $\text{eight PFAS}$ reviewed in both the $\text{PFAS-Tox}$ Database^20,21 and $\text{PFAS-}150$.¹

$\text{PFAS}$ name	$\text{CASRN}$	Number of studies
		$\text{PFAS-Tox}$ Database	U.S. EPA $\text{PFAS-}150$	In common
6:2 FTS	27619-97-2	6	6	1
$\text{ADONA}$	13252-14-7	2	3	0
$\text{PFHpS}$	375-92-8	7	0	0
$\text{PFPeA}$	2706-90-3	11	1	1
$\text{PFHpA}$	375-85-9	22	0	0
$\text{PFUnDA}$	2058-94-8	54	2	2
$\text{PFTrDA}$	72629-94-8	36	2	0
$\text{PFTeDA}$	376-06-7	22	2	0

Note: 6:2 FTS, fluorotelomer sulfonic acid; ADONA, 3H-Perfluoro-3-[(3-methoxy-propoxy)propanoic acid]; CASRN, Chemical Abstracts Service Registry Number; PFAS, per- and polyfluoroalkyl substances; PFHpA, perfluoroheptanoic acid; PFHpS, perfluoroheptanesulfonic acid; PFPeA, perfluoropentanoic acid; PFTeDA, perfluorotetradecanoic acid, PFTrDA, perfluorotridecanoic acid; PFUnDA, perfluoroundecanoic acid.

Table 1 shows the number of studies for the eight $\text{PFAS}$ included in both SEMs. Differences were primarily due to the inclusion in our SEM of wildlife observational studies, experimental studies in nonrodent species, and all routes of exposure. Additionally, $\text{PFAS-}150$ included nonpeer-reviewed gray literature not in our SEM, such as industry studies available in the U.S. EPA’s Health and Environmental Research Online database or the European Chemicals Agency’s Registration, Evaluation, Authorization and Restriction of Chemicals database.

A conclusion of our SEM was that there were more studies available than we anticipated for several $\text{PFAS}$. Carlson et al. concluded that many of the $\text{PFAS}$ they assessed were data poor.¹ Considering both SEMs, this comparison makes clear that there are many available studies on some $\text{PFAS}$ that would not be used by the U.S. EPA in a regulatory context. However, it is our hope that such studies would be included in a meaningful way in the body of evidence that the U.S. EPA uses to inform health assessments.

Though Carlson et al. concluded that many of the $\text{PFAS-}150$ chemicals were data poor, it is important to remember that lack of research does not mean lack of biological effect. SEMs such as $\text{PFAS-}150$ and the $\text{PFAS-Tox}$ Database can help inform future read-across efforts in which conclusions about data-poor chemicals are derived from evidence from data-rich chemicals. To this point, it is noteworthy that several $\text{PFAS}$ recently reviewed or currently under review at the U.S. EPA were not included in the $\text{PFAS-}150\text{\hspace{0.17em}SEM}$ ($\text{GenX}$, $\text{PFBA}$, $\text{PFHxA}$, $\text{PFOA}$, $\text{PFNA}$, $\text{PFDA}$, $\text{PFBS}$, $\text{PFHxS}$, $\text{PFOS}$). Swift efforts should be made to add these $\text{PFAS}$ to the SEM to better inform read across for $\text{PFAS}$ and support evaluation of the entire class.

A primary goal of SEMs in environmental health is to gather large amounts of data in one place to support deeper scientific analyses and decision-making that protects public health and the environment. Keeping SEMs up to date is challenging, especially for $\text{PFAS}$, because the body of literature is growing rapidly. We encourage the U.S. EPA to continue populating $\text{PFAS-}150$ with current evidence. We applaud the U.S. EPA authors for their efforts to support the field in this way and look forward to future SEMs and appropriate regulatory action on other chemicals of concern.

Refers to https://doi.org/10.1289/EHP10343