Expanding the applicability domain of QSPRs for predicting water solubility and vapor pressure of PFAS

Identification of new PFAS for severe interference with thyroid hormone transport: A combined in vitro/silico approach

A tiered in vitro/in silico approach was developed to screen 12,654 per- and polyfluoroalkyl substances (PFAS) for their potential to disrupt the thyroid hormone transport. Initially, a set of 45 PFAS was tested using TTR-TRβ-CALUX bioassay, which was subsequently employed to develop a classification model, distinguishing active and inactive PFAS. The model fulfills all good practices for QSAR model validation and can predict whether a given PFAS can disrupt plasma transport of the thyroid hormone (T4). Subsequently, active compounds were used to develop two regression approaches: (i) multiple linear regression MLR, and (ii) second approach aimed at identifying multiple valid QSAR models based on different data-splitting strategies. Finally, a comprehensive virtual screening of a large PFAS dataset was conducted to assess their potency in disrupting thyroid hormone transport. The predictions indicated that more than 7500 compounds were active with over 100 PFAS potentially causing even greater adverse effects than PFOA. These findings highlight the critical role of integrating New Approach Methodologies (NAM)-based in vitro toxicity testing with multifaceted molecular modeling in assessing the risks associated with PFAS contamination in environmental matrices.

PFAS removal via adsorption: A synergistic review on advances of experimental and computational approaches

Per- and polyfluoroalkyl substances (PFAS), commonly known as "forever chemicals", have become a major focus of current research due to their toxicity and persistence in the environment. These synthetic compounds are notoriously difficult to degrade, accumulating in water systems and posing long-term health and environmental risks. Adsorption is one of the most investigated technologies for PFAS removal. This review comprehensively reviewed the PFAS adsorption process, focusing not only on the adsorption itself, but also on the behavior of PFAS in the aquatic environment prior to adsorption because these behaviors directly affect PFAS adsorption. Significantly, this review summarized in detail the advances made in PFAS adsorption from the computational approach and emphasized the importance of integrated experimental and computational studies in gaining molecular-level understanding on the adsorption mechanisms of PFAS. Toward the end, the review identified several critical research gaps and suggested key interdisciplinary research needs for further advancing our understanding on PFAS adsorption.

Health Impacts of Per- and Polyfluoroalkyl Substances (PFASs): A Comprehensive Review

Per- and polyfluoroalkyl substances (PFASs) are among the persistent organic pollutants characterized by their persistence in the environment, high mobility, and adverse impact not only on the ecosystem but also on human health. The biggest challenges in human biomonitoring are the low concentrations of PFASs in biological matrices and the presence of matrix interferents in samples. The combination of liquid chromatography with tandem mass spectrometry (LC-MS/MS) and solid-phase extraction (SPE) as a sample preparation technique appears to be the most suitable solution for achieving the desired selectivity and sensitivity in PFAS determination. The aim of this review is to describe possible sources of PFASs, their presence in various human matrices, analytical methods for determining PFASs in different biological matrices using various pretreatment techniques for complex samples, as well as adverse health risks associated with PFAS exposure. The most studied PFASs include PFOA and PFOS, which are most frequently detected in matrices such as plasma, serum, and breast milk. The average concentrations of PFOA range from 1.0 to 2.6 ng.mL-1 in plasma, 1.9 to 2.4 ng.mL-1 in serum, and 0.4 to 3.1 ng.mL-1 in breast milk. For PFOS, the average concentrations were 2.0-4.0 ng.mL-1, 3.7-4.6 ng.mL-1, and 3.6-4.8 ng.mL-1 for plasma, serum, and breast milk, respectively. The most significant health effects associated with exposure to long-chain PFASs (such as PFOA and PFOS) include lipid disorders, hypertension, diabetes mellitus, thyroid disorders, infertility, cancer, obesity, autism, neurodevelopmental issues, cardiovascular diseases, and kidney and liver disorders. It is of utmost importance to monitor PFAS exposure, predict their toxicity, and develop effective strategies to mitigate their potential effects on human health.

Effect of physicochemical parameters on the occurrence of per- and polyfluoroalkyl substances (PFAS) in aquatic environment

Perfluoroalkyl substances (PFAS) and their distribution in aquatic environments have been studied extensively, but more information is needed to link these occurrences to their physicochemical characteristics. Understanding how these parameters influence PFAS can help predict their fate, mobility, and occurrences in water. This study reviewed the influence of physicochemical parameters on the occurrences of PFAS in aquatic environment using the relevant keywords to retrieve articles from databases spanning mostly between 2017 and 2024. The result suggests that high pH, turbidity, and dissolved oxygen, give high concentration of PFAS, while high electrical conductivity, temperature and salinity give low PFAS concentration in the water. Therefore, monitoring and safeguarding the aquatic bodies for human and environmental safety is imperative. Future studies should include the effects of the physicochemical properties on PFAS occurrences in the natural environment and focus on an organism's distinctive characteristics to comprehend the bioaccumulation and biomagnification of PFAS in them and environmental matrices.

Discovery of perfluoroalkyl sulfonyl quaternary ammonium substances in the environment and their environmental behaviors

A variety of per- and polyfluoroalkyl substances (PFASs) have been released into the environment via wastewater treatment plant (WWTP) effluent, with current target and nontarget analytical methods typically focusing on negatively ionized PFASs while largely overlooking positively ionized ones. In this study, five cationic PFASs, perfluoroalkyl sulfonyl quaternary ammonium substances (PFAQASs), were first identified in surface water impacted by the WWTP effluent, applying a metabolomics-based nontarget analysis method. Environmental behaviors of identified novel PFAQASs were further investigated. In surface water, sediment, and fish (Coilia mystus) samples collected from the Yangtze River, 8:3 PFAQA was consistently the predominant PFAQASs, with the mean concentrations of 90 ng/L (< LOD-558 ng/L), 92 ng/g dw (< LOD-421 ng/g dw), and 2.3 ng/g ww (< LOD-4.6 ng/g ww), respectively. This study highlights the necessity to discover other cationic PFASs in the environment. Among PFAQASs, 8:4 PFAQA (4.2, range 3.4 - 4.6) had the highest mean sediment-water partitioning coefficient (log Koc), followed by 8:3 PFAQA (3.9, 2.8 - 4.5) and 6:3 PFAQA (3.7, 3.3 - 4.1). The log Koc of PFAQASs showed a general increase trend with the increasing carbon chain length. Mean bioaccumulation factor (BAF) values of PFAQASs calculated in the collected fish from the Yangtze River ranged from 1.9 ± 0.32 (4:3 PFAQA) to 2.9 ± 0.34 (8:4 PFAQA). The mean BAF values of PFAQASs generally increased with the carbon chain length. Further studies are warranted to elucidate the environmental fate, potential toxicity, and human exposure implications for these identified novel PFASs.

Predicting bioconcentration factors (BCFs) for per- and polyfluoroalkyl substances (PFAS)

The bioconcentration factor (BCF) is an important parameter that gives information regarding the ability of a contaminant to be taken up by organisms from the water. Per- and polyfluoroalkyl substances (PFAS) are widespread in the environment, causing concern regarding their impact on human health. Due to the lack of available bioaccumulation data for most compounds in the PFAS group, we developed a quantitative structure-property relationship (QSPR) model to predict the log BCF for fish (taxonomic class Teleostei), based on experimental data available for the most studied 33 representatives of this group of compounds. Furthermore, we implemented the developed model to predict log BCF for an external dataset of 2209 PFAS. Consequently, 1045 PFAS were found not to be bioaccumulative, 208 were classified as bioaccumulative, and 956 were predicted to be very bioaccumulative. Finally, we obtained the high correlation (R2 = 0.844) between the log BCFs obtained in laboratory and field studies for 13 PFAS. In silico analyses indicate that PFAS bioconcentration depends on the size (chain length - number of CF2 groups in alkyl tail/chain) of a molecule, as well as on the atomic distribution properties. In general, long-chain PFAS - above 8 and 6 carbon atoms for perfluorinated carboxylic acids (PFCAs)and perfluorinated sulfonic acids (PFSAs), respectively - tend to bioconcentrate more compared to the short-chain ones. In conclusion, predicting BCF on fish is possible for a wide range of fluorinated compounds, which can be further used for estimating PFAS behavior in the environment.

Prediction of critical micelle concentration for per- and polyfluoroalkyl substances

In this study, we focus on the development of Quantitative Structure-Property Relationship (QSPR) models to predict the critical micelle concentration (CMC) for per- and polyfluoroalkyl substances (PFASs). Experimental CMC values for both fluorinated and non-fluorinated compounds were meticulously compiled from existing literature sources. Our approach involved constructing two distinct types of models based on Support Vector Machine (SVM) algorithms applied to the dataset. Type (I) models were trained exclusively on CMC values for fluorinated compounds, while Type (II) models were developed utilizing the entire dataset, incorporating both fluorinated and non-fluorinated compounds. Comparative analyses were conducted against reference data, as well as between the two model types. Encouragingly, both types of models exhibited robust predictive capabilities and demonstrated high reliability. Subsequently, the model having the broadest applicability domain was selected to complement the existing experimental data, thereby enhancing our understanding of PFAS behaviour.

Environmental impact of PFAS: Filling data gaps using theoretical quantum chemistry and QSPR modeling

Per- and polyfluorinated alkyl substances (PFAS), known for their widespread environmental presence and slow degradation, pose significant concerns. Of the approximately 10,000 known PFAS, only a few have undergone comprehensive testing, resulting in limited experimental data. In this study, we employed a combination of physics-based methods and data-driven models to address gaps in PFAS bioaccumulation potential. Using the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, we predicted n-octanol/water partition coefficients (logKOW), crucial for PFAS bioaccumulation. Our developed Quantitative Structure-Property Relationship (QSPR) model exhibited high accuracy (R2 = 0.95, RMSEC = 0.75) and strong predictive ability (Q2LOO = 0.93, RMSECV = 0.83). Leveraging the extensive NORMAN, we predicted logKOW for over 4,000 compounds, identifying 244 outliers out of 4519. Further categorizing the database into eight Organisation for Economic Co-operation and Development (OECD) categories, we confirmed fluorine atoms role in enhanced bioaccumulation. Utilizing predicted logKOW, water solubility logSW, and vapor pressure logVP values, we calculated additional physicochemical properties that are responsible for the transport and dispersion of PFAS in the environment. Parameters such as Henry's Law (kH), air-water partition coefficient (KAW), octanol-air coefficient (KOA), and soil adsorption coefficient (KOC) exhibited favorable correlations with literature data (R2 > 0.66). Our study successfully filled data gaps, contributing to the understanding of ubiquitous PFAS in the environment and estimating missing physicochemical data for these compounds.