Mechanisms and Opportunities for Rational In Silico Design of Enzymes to Degrade Per- and Polyfluoroalkyl Substances (PFAS).

Per and polyfluoroalkyl substances (PFAS) present a unique challenge to remediation techniques because their strong carbon-fluorine bonds make them difficult to degrade. This review explores the use of in silico enzymatic design as a potential PFAS degradation technique. The scope of the enzymes included is based on currently known PFAS degradation techniques, including chemical redox systems that have been studied for perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) defluorination, such as those that incorporate hydrated electrons, sulfate, peroxide, and metal catalysts. Bioremediation techniques are also discussed, namely the laccase and horseradish peroxidase systems. The redox potential of known reactants and enzymatic radicals/metal-complexes are then considered and compared to potential enzymes for degrading PFAS. The molecular structure and reaction cycle of prospective enzymes are explored. Current knowledge and techniques of enzyme design, particularly radical-generating enzymes, and application are also discussed. Finally, potential routes for bioengineering enzymes to enable or enhance PFAS remediation are considered as well as the future outlook for computational exploration of enzymatic in situ bioremediation routes for these highly persistent and globally distributed contaminants.

Information Requirements under the Essential-Use Concept: PFAS Case Studies.

Per- and polyfluoroalkyl substances (PFAS) are a class of substances for which there are widespread concerns about their extreme persistence in combination with toxic effects. It has been argued that PFAS should only be employed in those uses that are necessary for health or safety or are critical for the functioning of society and where no alternatives are available ("essential-use concept"). Implementing the essential-use concept requires a sufficient understanding of the current uses of PFAS and of the availability, suitability, and hazardous properties of alternatives. To illustrate the information requirements under the essential-use concept, we investigate seven different PFAS uses, three in consumer products and four industrial applications. We investigate how much information is available on the types and functions of PFAS in these uses, how much information is available on alternatives, their performance and hazardous properties and, finally, whether this information is sufficient as a basis for deciding on the essentiality of a PFAS use. The results show (i) the uses of PFAS are highly diverse and information on alternatives is often limited or lacking; (ii) PFAS in consumer products often are relatively easy to replace; (iii) PFAS uses in industrial processes can be highly complex and a thorough evaluation of the technical function of each PFAS and of the suitability of alternatives is needed; (iv) more coordination among PFAS manufacturers, manufacturers of alternatives to PFAS, users of these materials, government authorities, and other stakeholders is needed to make the process of phasing out PFAS more transparent and coherent.

Bayesian Refinement of the Permeability-Limited Physiologically Based Pharmacokinetic Model for Perfluorooctanoic Acid in Male Rats.

Physiologically based pharmacokinetic (PBPK) modeling is a powerful technique to inform risk assessment of xenobiotic substances such as perfluorooctanoic acid (PFOA). In our previous study, a permeability-limited PBPK model was developed to simulate the toxicokinetics and tissue distribution of PFOA in male rats. However, due to limited information on some key model parameters (e.g., protein binding and active transport rates), the uncertainty of the permeability-limited PBPK model was quite high. To address this issue, a hierarchical Bayesian analysis with Markov chain Monte Carlo (MCMC) was applied to reduce the uncertainty of parameters and improve the performance of the PBPK model. With the optimized posterior parameters, the PBPK model was evaluated by comparing its prediction with experimental data from three different studies. The results show that the uncertainties of the posterior model parameters were reduced substantially. In addition, most of the PBPK model predictions were improved: with the posterior parameters, most of the predicted plasma toxicokinetics (e.g., half-life) and tissue distribution fell well within a factor of 2.0 of the experimental data. Finally, the Bayesian framework could provide insights into the molecular mechanisms driving PFOA toxicokinetics: PFOA-protein binding, membrane permeability, and active transport.

A Classification Model to Identify Direct-Acting Mutagenic Polycyclic Aromatic Hydrocarbon Transformation Products.

Polycyclic aromatic hydrocarbons (PAHs) are a complex group of environmental contaminants, many having long environmental half-lives. As these compounds degrade, the changes in their structure can result in a substantial increase in mutagenicity compared to the parent compound. Over time, each individual PAH can potentially degrade into several thousand unique transformation products, creating a complex, constantly evolving set of intermediates. Microbial degradation is the primary mechanism of their transformation and ultimate removal from the environment, and this process can result in mutagenic activation similar to the metabolic activation that can occur in multicellular organisms. The diversity of the potential intermediate structures in PAH-contaminated environments renders hazard assessment difficult for both remediation professionals and regulators. A mixture of structural and energetic descriptors has proven effective in existing studies for classifying which PAH transformation products will be mutagenic. However, most existing studies of environmental PAH mutagens primarily focus on nitrogenated derivatives, which are prevalent in the atmosphere and not as relevant in soil. Additionally, PAH products commonly found in the environment can range from as large as five rings to as small as a single ring, requiring a broadly inclusive methodology to comprehensively evaluate mutagenic potential. We developed a combination of supervised and unsupervised machine learning methods to predict environmentally induced PAH mutagenicity with improved performance over currently available tools. K-means clustering with principal component analysis allows us to identify molecular clusters that we hypothesize to have similar mechanisms of action. Recursive feature elimination identifies the most influential descriptors. The cluster-specific regression outperforms available classifiers in predicting direct-acting mutagens resulting from the microbial biodegradation of PAHs and provides direction for future studies evaluating the environmental hazards resulting from PAH biodegradation.

Addressing Urgent Questions for PFAS in the 21st Century.

Despite decades of research on per- and polyfluoroalkyl substances (PFAS), fundamental obstacles remain to addressing worldwide contamination by these chemicals and their associated impacts on environmental quality and health. Here, we propose six urgent questions relevant to science, technology, and policy that must be tackled to address the "PFAS problem": (1) What are the global production volumes of PFAS, and where are PFAS used? (2) Where are the unknown PFAS hotspots in the environment? (3) How can we make measuring PFAS globally accessible? (4) How can we safely manage PFAS-containing waste? (5) How do we understand and describe the health effects of PFAS exposure? (6) Who pays the costs of PFAS contamination? The importance of each question and barriers to progress are briefly described, and several potential paths forward are proposed. Given the diversity of PFAS and their uses, the extreme persistence of most PFAS, the striking ongoing lack of fundamental information, and the inequity of the health and environmental impacts from PFAS contamination, there is a need for scientific and regulatory communities to work together, with cooperation from PFAS-related industries, to fill in critical data gaps and protect human health and the environment.

Quantitative Chemical Proteomics Reveals Interspecies Variations on Binding Schemes of L-FABP with Perfluorooctanesulfonate.

Evaluating interspecies toxicity variation is a long-standing challenge for chemical hazard assessment. This study developed a quantitative interspecies thermal shift assay (QITSA) for in situ, quantitative, and modest-throughput investigation of chemical-protein interactions in cell and tissue samples across species. By using liver fatty acid binding protein (L-FABP) as a case study, the QITSA method was benchmarked with six per- and polyfluoroalkyl substances, and thermal shifts (ΔTm) were inversely related to their dissociation constants (R2 = 0.98). The QITSA can also distinguish binding modes of chemicals exemplified by palmitic acid. The QITSA was applied to determine the interactions between perfluorooctanesulfonate (PFOS) and L-FABP in liver cells or tissues from humans, mice, rats, and zebrafish. The largest thermal stability enhancement by PFOS was observed for human L-FABP followed by the mouse, rat, and zebrafish. While endogenous ligands were revealed to partially contribute to the large interspecies variation, recombinant proteins were employed to confirm the high binding affinity of PFOS to human L-FABP, compared to the rat and mouse. This study implemented an experimental strategy to characterize chemical-protein interactions across species, and future application of QITSA to other chemical contaminants is of great interest.

A pathway level analysis of PFAS exposure and risk of gestational diabetes mellitus.

Per- and polyfluoroalkyl substances (PFAS) have been found to be associated with gestational diabetes mellitus (GDM) development, a maternal health disorder in pregnancy with negative effects that can extend beyond pregnancy. Studies that report on this association are difficult to summarize due to weak associations and wide confidence intervals. One way to advance this field is to sharpen the biologic theory on a causal pathway behind this association, and to measure it directly by way of molecular biomarkers. The aim of this review is to summarize the literature that supports a novel pathway between PFAS exposure and GDM development. Epidemiological studies demonstrate a clear association of biomarkers of thyroid hormones and glucose metabolism with GDM development. We report biologic plausibility and epidemiologic evidence that PFAS dysregulation of maternal thyroid hormones and thyrotropin (TSH) may disrupt glucose homeostasis, increasing the risk of GDM. Overall, epidemiological studies demonstrate that PFAS were positively associated with TSH and negatively with triiodothyronine (T3) and thyroxine (T4). PFAS were generally positively associated with glucose and insulin levels in pregnancy. We propose dysregulation of thyroid function and glucose metabolism may be a critical and missing component in the accurate estimation of PFAS on the risk of GDM.

Network Analysis for Prioritizing Biodegradation Metabolites of Polycyclic Aromatic Hydrocarbons.

Polycyclic aromatic hydrocarbons (PAHs) are a diverse group of environmental contaminants released during the combustion of organic materials and the production and utilization of fossil fuels. Once released, PAHs deposit in soil and water bodies where they are subjected to environmental transport and transformations. As they degrade, intermediate transformation products may play an important role in their environmental impact. However, studying the effects of these degradation products has proven challenging because of the complexity, transience, and low concentration of many intermediates. Herein, a novel integration of a pathway prediction system and network theory was developed and applied to a set of four PAHs to demonstrate a possible solution to this challenge. Network analysis techniques were employed to refine the thousands of potential outputs and elucidate compounds of interest. Using these tools, we determined correlations between PAH degradation network data and intermediate metabolite structures, gaining information about the chemical characteristics of compounds based on their placement within the degradation network. Upon applying our developed filtering algorithm, we are able to predict up to 48% of the most common transformation products identified in a comprehensive empirical literature review. Additionally, our integrated approach uncovers potential metabolites which connect those found by past empirical studies but are currently undetected, thereby filling in the gaps of information in PAH degradation pathways.

Are Fluoropolymers Really of Low Concern for Human and Environmental Health and Separate from Other PFAS?

Fluoropolymers are a group of polymers within the class of per- and polyfluoroalkyl substances (PFAS). The objective of this analysis is to evaluate the evidence regarding the environmental and human health impacts of fluoropolymers throughout their life cycle(s). Production of some fluoropolymers is intimately linked to the use and emissions of legacy and novel PFAS as polymer processing aids. There are serious concerns regarding the toxicity and adverse effects of fluorinated processing aids on humans and the environment. A variety of other PFAS, including monomers and oligomers, are emitted during the production, processing, use, and end-of-life treatment of fluoropolymers. There are further concerns regarding the safe disposal of fluoropolymers and their associated products and articles at the end of their life cycle. While recycling and reuse of fluoropolymers is performed on some industrial waste, there are only limited options for their recycling from consumer articles. The evidence reviewed in this analysis does not find a scientific rationale for concluding that fluoropolymers are of low concern for environmental and human health. Given fluoropolymers' extreme persistence; emissions associated with their production, use, and disposal; and a high likelihood for human exposure to PFAS, their production and uses should be curtailed except in cases of essential uses.

Nontarget Screening of Per- and Polyfluoroalkyl Substances Binding to Human Liver Fatty Acid Binding Protein.

More than 1000 per- and polyfluoroalkyl substances (PFASs) have been discovered by nontarget analysis (NTA), but their prioritization for health concerns is challenging. We developed a method by incorporating size-exclusion column co-elution (SECC) and NTA, to screen PFASs binding to human liver fatty acid binding protein (hL-FABP). Of 74 PFASs assessed, 20 were identified as hL-FABP ligands in which eight of them have high binding affinities. Increased PFAS binding affinities correlate with stronger responses in electrospray ionization (ESI-) and longer retention times on a C18 column. This is well explained by a mechanistic model, which revealed that both polar and hydrophobic interactions are crucial for binding affinities. Encouraged by this, we then developed an SECC method to identify hL-FABP ligands, and all eight high-affinity ligands were selectively captured from 74 PFASs. The method was further applied to an aqueous film-forming foam (AFFF) product in which 31 new hL-FABP ligands were identified. Suspect and nontargeted screening revealed these ligands as analogues of perfluorosulfonic acids and homologues of alkyl ether sulfates (C8- and C10/EOn, C8H17(C2H4O)nSO4-, and C10H21(C2H4O)nSO4-). The SECC method was then applied to AFFF-contaminated surface waters. In addition to perfluorooctanesulfonic acid and perfluorohexanesulfonic acid, eight other AFFF chemicals were discovered as novel ligands, including four C14- and C15/EOn. This study implemented a high-throughput method to prioritize PFASs and revealed the existence of many previously unknown hL-FABP ligands.

Using Machine Learning to Classify Bioactivity for 3486 Per- and Polyfluoroalkyl Substances (PFASs) from the OECD List.

A recent OECD report estimated that more than 4000 per- and polyfluorinated alkyl substances (PFASs) have been produced and used in a broad range of industrial and consumer applications. However, little is known about the potential hazards (e.g., bioactivity, bioaccumulation, and toxicity) of most PFASs. Here, we built machine-learning-based quantitative structure-activity relationship (QSAR) models to predict the bioactivity of those PFASs. By examining a number of available molecular data sets, we constructed the first PFAS-specific database that contains the bioactivity information on 1012 PFASs for 26 bioassays. On the basis of the collected PFAS data set, we trained 5 different machine learning models that cover a variety of conventional models (e.g., random forest and multitask neural network (MNN)) and advanced graph-based models (e.g., graph convolutional network). Those models were evaluated based on the validation data set. Both MNN and graph-based models demonstrated the best performance. The average of the best area-under-the-curve score for each bioassay is 0.916. For predictions on the OECD list, most of the biologically active PFASs have perfluoroalkyl chain lengths less than 12 and are categorized into fluorotelomer-related compounds and perfluoroalkyl acids and their precursors.

Predicting Relative Protein Affinity of Novel Per- and Polyfluoroalkyl Substances (PFASs) by An Efficient Molecular Dynamics Approach.

With the phasing out of long-chain per- and polyfluoroalkyl substances (PFASs), production of a wide variety of alternative PFASs has increased to meet market demand. However, little is known about the bioaccumulation potential of these replacement compounds. Here, we developed a modeling workflow that combines molecular docking and molecular dynamics simulation techniques to estimate the relative binding affinity of a total of 15 legacy and replacement PFASs for human and rat liver-type fatty acid binding protein (hLFABP and rLFABP). The predicted results were compared with experimental data extracted from three different studies. There was good correlation between predicted free energies of binding and measured binding affinities, with correlation coefficients of 0.97, 0.79, and 0.96, respectively. With respect to replacement PFASs, our results suggest that EEA and ADONA are at least as strongly bound to rLFABP as perfluoroheptanoic acid (PFHpA), and as strongly bound to hLFABP as perfluorooctanoic acid (PFOA). For F-53 and F-53B, both have similar or stronger binding affinities than perfluorooctanesulfonate (PFOS). Given that interactions of PFASs with proteins (e.g., LFABPs) are important determinants of bioaccumulation potential in organisms, these alternatives could be as bioaccumulative as legacy PFASs, and are therefore not necessarily safer alternatives to long-chain PFASs.

Polybrominated Diphenyl Ether (PBDE) Accumulation in Farmed Salmon Evaluated Using a Dynamic Sea-Cage Production Model.

Food is an important source of human exposure to hazardous chemicals. Chemical concentration in a food item depends on local environmental contamination, production conditions, and, for animal-derived foods, on feed. Here, we investigate these influences on the accumulation of individual polybrominated diphenyl ether congeners (PBDEs) in farmed Atlantic salmon ( Salmo salar). We develop a dynamic model over a full sea-cage salmon production cycle. To assess the influence of metabolic debromination on PBDE congener profiles, in vitro measurements of debromination rates in fish liver cells were extrapolated to whole-body metabolic rate constants. Model results indicate that the dominant factors governing PBDE concentration in Atlantic salmon fillet are uptake via contaminated feed and fish growth, whereas the influence of metabolic debromination is minor. PBDE concentrations in fish feed depend on several factors, including the geographic origin of fish feed ingredients, which are produced and traded globally. Human exposure to PBDE via salmon consumption is less influenced by environmental concentrations at the location of salmon farming than by environmental concentrations influencing feed components. This dependence of PBDE concentrations in salmon on the origin and composition of feed reveals the complexity of predicting contaminant concentrations in globally traded food.

A Permeability-Limited Physiologically Based Pharmacokinetic (PBPK) Model for Perfluorooctanoic acid (PFOA) in Male Rats.

Physiologically based pharmacokinetic (PBPK) modeling is a powerful in silico tool that can be used to simulate the toxicokinetics and tissue distribution of xenobiotic substances, such as perfluorooctanoic acid (PFOA), in organisms. However, most existing PBPK models have been based on the flow-limited assumption and largely rely on in vivo data for parametrization. In this study, we propose a permeability-limited PBPK model to estimate the toxicokinetics and tissue distribution of PFOA in male rats. Our model considers the cellular uptake and efflux of PFOA via both passive diffusion and transport facilitated by various membrane transporters, association with serum albumin in circulatory and extracellular spaces, and association with intracellular proteins in liver and kidney. Model performance is assessed using seven experimental data sets extracted from three different studies. Comparing model predictions with these experimental data, our model successfully predicts the toxicokinetics and tissue distribution of PFOA in rats following exposure via both IV and oral routes. More importantly, rather than requiring in vivo data fitting, all PFOA-related parameters were obtained from in vitro assays. Our model thus provides an effective framework to test in vitro-in vivo extrapolation and holds great promise for predicting toxicokinetics of per- and polyfluorinated alkyl substances in humans.

Exploring the Use of Molecular Docking to Identify Bioaccumulative Perfluorinated Alkyl Acids (PFAAs).

Methods to predict the bioaccumulation potential of per- and polyfluorinated alkyl substances (PFAS) are sorely needed, given the proliferation of these substances and lack of data on their properties and behavior. Here, we test whether molecular docking, a technique where interactions between proteins and ligands are simulated to predict both bound conformation and interaction affinity, can be used to predict PFAS binding strength and biological half-life. We show that an easy-to-implement docking program, Autodock Vina, can successfully redock perfluorooctanesulfonate (PFOS) to human serum albumin with deviations smaller than 2 Å. Furthermore, predicted binding strengths largely fall within one standard deviation of measured values for perfluorinated alkyl acids (PFAAs). Correlations with half-lives suggest both membrane partitioning and protein interactions are important, and that serum albumin is only one of a number of proteins controlling the fate of these chemicals in organisms. However, few data are available for validation of our approach as a broad screening tool, and available data are highly variable. We therefore call for collection of new data, particularly including proteins other than serum albumin and substances beyond perfluorooctanoic acid (PFOA) and PFOS. The methods we discuss in this work can serve as a framework for guiding such data collection.

Bioaccumulation of perfluorinated alkyl acids: observations and models.

In this review, we consider the two prevailing hypotheses for the mechanisms that control the bioaccumulation of perfluorinated alkyl acids (PFAAs). The first assumes that partitioning to membrane phospholipids, which have a higher affinity for charged species than neutral storage lipids, can explain the high bioaccumulation potential of these compounds. The second assumes that interactions with proteins--including serum albumin, liver fatty acid binding proteins (L-FABP), and organic anion transporters--determine the distribution, accumulation and half-lives of PFAAs. We consider three unique phenomena to evaluate the two models: (1) observed patterns of tissue distribution in the laboratory and field, (2) the relationship between perfluorinated chain length and bioaccumulation, and (3) species- and gender-specific variation in elimination half-lives. Through investigation of these three characteristics of PFAA bioaccumulation, we show the strengths and weaknesses of the two modeling approaches. We conclude that the models need not be mutually exclusive, but that protein interactions are needed to explain some important features of PFAA bioaccumulation. Although open questions remain, further research should include perfluorinated alkyl substances (PFASs) beyond the long-chain PFAAs, as these substances are being phased out and replaced by a wide variety of PFASs with largely unknown properties and bioaccumulation behavior.

Socio-economic analysis for the authorisation of chemicals under REACH: a case of very high concern?

Under the European chemicals' legislation, REACH, substances that are identified to be of "very high concern" will de facto be removed from the market unless the European Commission grants authorisations permitting specific uses. Companies who apply for an authorisation without demonstrating "adequate control" of the risks have to show by means of a socio-economic analysis (SEA) that positive impacts of use outweigh negative impacts for human health and ecosystems. This paper identifies core challenges where further in-depth guidance is urgently required in order to ensure that a SEA can deliver meaningful results and that it can effectively support decision-making on authorisation. In particular, we emphasise the need (i) to better guide the selection of tools for impact assessment, (ii) to explicitly account for stock pollution effects in impact assessments for persistent and very persistent chemicals, (iii) to define suitable impact indicators for PBT/vPvB chemicals given the lack of reliable information about safe concentration levels, (iv) to guide how impacts can be transformed into values for decision-making, and (v) to provide a well-balanced discussion of discounting of long-term impacts of chemicals.

Evaluating contamination of seafood purchased from U.S. retail stores by persistent environmental pollutants, pesticides and veterinary drugs.

Studies have reported health risks associated with seafood contamination, but few data exist on levels in commercially available seafood in the US. To better understand, the magnitude of foodborne exposure and identify vulnerable populations in the US, we measured concentrations of veterinary drugs, persistent organic pollutants (POPs) (polycyclic aromatic hydrocarbons [PAHs], polybrominated diphenyl ethers [PBDEs] and polychlorinated biphenyls [PCBs]), and legacy and current-use pesticides in 46 seafood samples purchased from retail outlets. Measured levels were used to estimate risk based on available maximum residue limits (MRLs) and toxic equivalence (TEQ) factors for analytes. Only seventeen of the 445 analytes were detected, at low substance frequencies. However, half of the samples tested positive for one or more analyte, with total concentrations ranging from below the limit of detection (LOD) to as high as 156 µg/kg wet weight. Based on the risk assessment for individual pesticides and veterinary drugs, the hazard quotients (HQ) were all <1, indicating no risk. However, for the sum of PCB126 and PCB167, two dioxin-like PCBs detected in our samples, the TEQ was nearly two orders of magnitude higher than the WHO limits in one catfish sample. Moreover, vulnerable groups with higher rates of consumption of specific fish types may face higher risks.

Building Chemical Intuition about Physicochemical Properties of C8-Per-/Polyfluoroalkyl Carboxylic Acids through Computational Means.

We have predicted acid dissociation constants (pK a), octanol-water partition coefficients (K OW), and DMPC lipid membrane-water partition coefficients (K lipid-w) of 150 different eight-carbon-containing poly-/perfluoroalkyl carboxylic acids (C8-PFCAs) utilizing the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) theory. Different trends associated with functionalization, degree of fluorination, degree of saturation, degree of chlorination, and branching are discussed on the basis of the predicted values for the partition coefficients. In general, functionalization closest to the carboxylic headgroup had the greatest impact on the value of the predicted physicochemical properties.

Associations of Serum Perfluoroalkyl Substances and Placental Human Chorionic Gonadotropin in Early Pregnancy, Measured in the UPSIDE Study in Rochester, New York.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are widely detected in pregnant women and associated with adverse outcomes related to impaired placental function. Human chorionic gonadotropin (hCG) is a dimeric glycoprotein hormone that can indicate placental toxicity. OBJECTIVES: Our aim was to quantify the association of serum PFAS with placental hCG, measured as an intact molecule (hCG), as free alpha-(hCGα) and beta-subunits (hCGß), and as a hyperglycosylated form (h-hCG), and evaluate effect measure modification by social determinants and by fetal sex. METHODS: Data were collected from 326 pregnant women enrolled from 2015 to 2019 in the UPSIDE study in Rochester, New York. hCG forms were normalized for gestational age at the time of blood draw in the first trimester [multiple of the median (MoM)]. Seven PFAS were measured in second-trimester maternal serum. Multivariate imputation by chained equations and inverse probability weighting were used to evaluate robustness of linear associations. PFAS mixture effects were estimated by Bayesian kernel machine regression. RESULTS: Perfluorohexane sulfonic acid (PFHxS) [hCGß: 0.29 log MoM units per log PFHxS; 95% confidence interval (CI): 0.08, 0.51] and perfluorodecanoic acid (PFDA) (hCG: -0.09; 95% CI: -0.16, -0.02) were associated with hCG in the single chemical and mixture analyses. The PFAS mixture was negatively associated with hCGα and positively with hCGß. Subgroup analyses revealed that PFAS associations with hCG differed by maternal race/ethnicity and education. Perfluoropentanoic acid (PFPeA) was associated with hCGß only in Black participants (-0.23; 95% CI: -0.37, -0.09) and in participants with high school education or less (-0.14; 95% CI: -0.26, -0.02); conversely, perfluorononanoic acid (PFNA) was negatively associated with hCGα only in White participants (-0.15; 95% CI: -0.27, -0.03) and with hCGß only in participants with a college education or greater (-0.19; 95% CI: -0.36, -0.01). These findings were robust to testing for selection bias, confounding bias, and left truncation bias where PFAS detection frequency was <100%. Two associations were negative in male (and null in female) pregnancies: Perfluoroundecanoic acid (PFUnDA) with hCGα, and PFNA with h-hCG. CONCLUSIONS: Evidence was strongest for the association between PFHxS and PFDA with hCG in all participants and for PFPeA and PFNA within subgroups defined by social determinants and fetal sex. PFAS mixture associations with hCGα and hCGß differed, suggesting subunit-specific types of toxicity and/or regulation. Future studies will evaluate the biological, clinical and public health significance of these findings. https://doi.org/10.1289/EHP12950.