Investigation of the Binding Fraction of PFAS in Human Plasma and Underlying Mechanisms Based on Machine Learning and Molecular Dynamics Simulation

Explainable machine learning models enhance prediction of PFAS bioactivity using quantitative molecular surface analysis-derived representation

The extensive use of per- and polyfluoroalkyl substances (PFAS) in industrial and consumer products poses health risks due to their toxicity. Computational toxicology approaches, particularly quantitative structure-activity relationship (QSAR) models are essential for predicting PFAS bioactivity. However, established QSAR models including machine learning-based ones with traditional molecular descriptors such as constitutional, topological, and geometric descriptors, have limited predictive capability and interpretability. Herein, we proposed a novel machine learning approach that leverages quantitative molecular surface analysis (QMSA) of molecular electrostatic potential. Using QMSA descriptors, five machine learning models (e.g., random forest) achieved outstanding performance, with best accuracy of 0.950 ± 0.017, AUC-ROC of 0.938 ± 0.012, F1-score of 0.734 ± 0.024, and MCC of 0.684 ± 0.111 for five targets (tyrosyl-DNA phosphodiesterase 1 in the absence/presence of camptothecin, ATXN2 protein, transcription factor SMAD3, and transcription factor NRF2), which outperform previously reported models. SHAP analyses revealed that estimated density, molecular volume, positive surface area, and nonpolar surface area were the most important descriptors. These descriptors were deeply involved in PFAS binding to target proteins via non-covalent interactions as evidenced by molecular docking and molecular dynamics simulations. Our results demonstrated that QMSA descriptors-based machine learning models are capable of predicting PFAS toxicity with extraordinary performance and interpretability. This study provides a novel machine learning framework for the high-throughput and cost-effective screening of high-risk emerging PFAS in aquatic environments. By identifying the contaminants that should be prioritized for regulation and treatment among the growing number of PFAS, our work aids in water quality monitoring and risk assessment, and guides decision-making in aquatic environmental management. Furthermore, this work enhances our understanding of the molecular mechanisms involved in PFAS bioactivity.

Prediction of acute toxicity of organic contaminants to fish: Model development and a novel approach to identify reactive substructures

In this study, count-based Morgan fingerprints (CMF) were employed to represent the fundamental chemical structures of contaminants, and a neural network model (R² = 0.76) was developed to predict acute fish toxicity (AFT) of organic compounds. Models based on CMF consistently outperformed those based on binary Morgan fingerprints (BMF), likely due to the latter's inefficiency in describing homologous structures. The similarity of CMF was calculated using an improved method based on Tanimoto distance, which was used for calculation of dataset partition and application domain. The similarity-based dataset partitioning method ensured structural diversity within the training set and improved performance on the validation set, demonstrating its potential for toxicological structure analysis and priority screening. Toxic substructures identified by Shapley additive explanation (SHAP) method were substituted benzenes, long carbon chains, unsaturated carbons and halogen atoms. By incorporating Kow and monitoring shifts in feature importance, the influence of substructures on AFT was further delineated, revealing their roles in facilitating exposure (e.g.: long carbon chains) and reactive toxicity (e.g.: methyl). Additionally, we compared the toxicity of similar substructures and the same substructure in different chemical environments as well. To address SHAP's insensitivity to low-variance features, this study introduced a novel metric termed the toxicity index (TI), designed to pinpoint substructures that are present in minimal quantities yet potentially exhibit high toxicity. With TI, we identified several important substructures, such as parathion and polycyclic substituents. Finally, prevalent toxic substructures and potential highly toxic substances were identified in two external datasets.

Unraveling the Exposure Spectrum of PFAS in Fluorochemical Occupational Workers: Structural Diversity, Temporal Trends, and Risk Prioritization

Despite extensive poly/perfluoroalkyl substance (PFAS) discovery studies in various samples, the exposure spectrum in fluorochemical occupational workers remains largely unexplored. Here, serum samples from 28 workers at a fluorochemical facility were analyzed using nontarget techniques, identifying 64 PFAS classes, including 15 novel ones such as pentafluorosulfur ether-substituted perfluoroalkyl sulfonic acids, hydrogen-substituted perfluoroalkylamines, and perfluoroalkylsulfonyl protocatechualdehyde esters. Temporal trend analyses (2008-2018) revealed stable levels for most PFAS but an increase in perfluorobutanoic acid (PFBA) and perfluorohexanesulfonic acid (PFHxS), suggesting industrial shifts from long-chain PFAS to short-chain homologues in China since the early 2010s. Commonly reported structurally modified PFAS (e.g., hydrogen/carbonyl/chlorine substitution, ether insertion, and unsaturation) were likely historical byproducts of legacy PFAS production rather than intentionally manufactured alternatives. A Toxicological Priority Index-based risk assessment, integrating mobility, persistence, and bioaccumulation indices, identified perfluoroalkylamines, di(perfluoroakyl sulfonyl)imides, structurally modified perfluoroalkyl sulfonic acids/carboxylic acids, and perfluoroalkylsulfonamidoacetic acids as high-risk PFAS chemicals. Overall, structurally modified PFAS exhibited higher mobility but lower persistence and bioaccumulation than legacy PFAS, except for chlorinated variants, which showed increased bioaccumulation potential. This study highlights critical gaps in the spectrum of historically emitted PFAS and emphasizes the need for large-scale monitoring and extensive risk assessments to manage emerging PFAS.

Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment

Over the past decades, exposure to per- and polyfluoroalkyl substances (PFAS), a group of synthetic chemicals notorious for their environmental persistence, has been shown to pose increased health risks. Despite that some PFAS were reported to have endocrine-disrupting toxicity in previous studies, accurate prediction models based on deep learning and the underlying structural characteristics related to the effect of molecular fluorination remain limited. To address these issues, we proposed a stacking deep learning architecture, GXDNet, that integrates molecular descriptors and molecular graphs to predict the estrogen receptor α (ERα) activities of compounds, enhancing the generalization ability compared to previous models. Subsequently, we screened the ERα activity of 10,067 PFAS molecules using the GXDNet model and identified potential ERα binders. The representative PFAS molecules with the top docking scores showed that the introduction of fluorinated alkane chains significantly increased the binding affinities of parent molecules with ERα, suggesting that the combination of phenol structural fragments and fluorinated alkane chains has a synergistic effect in improving the binding capacity of the ligands to ERα. The binding modes, SHapley Additive Explanations analysis, and attention map emphasized the importance of π-π stacking and hydrogen bonding interactions with the phenol group, while the fluorinated alkane chain enhanced the interaction with the hydrophobic amino acids of the active pocket. Experimental validation using zebrafish models further confirmed the ERα activity of the representative PFAS molecules. Overall, the current computational workflow is beneficial for the toxicological screening of emerging PFAS and accelerating the development of eco-friendly PFAS molecules, thereby mitigating the environmental and health risks associated with PFAS exposure.

Data-Driven Insights into Resin Screening for Targeted Per- and Polyfluoroalkyl Substances Removal Using Machine Learning

In this study, we address the challenge of screening resins and optimizing operation conditions for the removal of 43 perfluoroalkyl and polyfluoroalkyl substances (PFASs), spanning both long- and short-chain fluorocarbon variants, across diverse water matrices, using machine learning (ML) models. We first develop ML models that can accurately predict removal efficiency of PFASs based on resin properties, operation conditions, and water matrix. The model performance is validated by using both a test set and our own experimental tests. The key features from resin properties, operation conditions, and water matrix influencing PFAS removal as well as their interaction effects are comprehensively investigated. We finally target long-chain (e.g., PFOS, PFOA) and short-chain PFASs (e.g., PFBS, GenX), using the developed ML models to inversely screen resins and determine the optimal operation conditions under a specified water matrix. Experimental tests demonstrated that our ML-guided approach achieves the desired removal efficiency (RE) for these PFASs, with RE values reaching 86.56% for PFBS and 83.73% for GenX, outperforming many reported resins. This work underscores the potential of ML methodologies in resin screening and operational optimization across diverse water matrices, enabling the efficient removal of structurally varied PFAS compounds.

Perfluorohexane sulfonate exposure caused multiple developmental abnormalities in early life of zebrafish

Perfluorohexane sulfonate (PFHxS) has been listed as a new persistent organic pollutant since 2022. Although the production and use of PFHxS are now restricted, it remains highly persistent in aquatic environments for decades. However, so far research about the toxic effects on early-life exposure of PFHxS and underlying mechanisms are still limited. In this study, we employed both wild type and specifically labeled transgenic zebrafish as model to investigate the developmental toxicity of PFHxS during early-stage exposure in zebrafish. A series of phenotypic and molecular indicators were analyzed at various time points between 24 h post-fertilization (hpf) and 7 days post-fertilization (dpf). Our data showed that the acute toxicity of PFHxS was much lower than PFOS, with a lethal concentration 50% of 508.11 ± 88.54 μM at 120 hpf. Low-dose PFHxS exposure significantly altered heart rates, blood flow, and swimming behavior in zebrafish larvae, suggesting potential cardiotoxicity and neurotoxicity of zebrafish. Data from transgenic zebrafish with specifically labeled hearts (CZ40) confirmed that PFHxS affects cardiovascular system development. PFHxS-induced changes in transgenic zebrafish with labeled liver and pancreas (CZ16) suggest that PFHxS may cause metabolic disorders and contribute to developmental defects. Gene expression analysis showed that PFHxS with potential estrogenic effect might also affect the gonadal development of zebrafish. Our study can offer an insight into the toxicity of PFHxS in aquatic environment and health risks of early-stage PFHxS exposure in humans.

Transfer Learning with a Graph Attention Network and Weighted Loss Function for Screening of Persistent, Bioaccumulative, Mobile, and Toxic Chemicals

In silico methods for screening hazardous chemicals are necessary for sound management. Persistent, bioaccumulative, mobile, and toxic (PBMT) chemicals persist in the environment and have high mobility in aquatic environments, posing risks to human and ecological health. However, lack of experimental data for the vast number of chemicals hinders identification of PBMT chemicals. Through an extensive search of measured chemical mobility data, as well as persistent, bioaccumulative, and toxic (PBT) chemical inventories, this study constructed comprehensive data sets on PBMT chemicals. To address the limited volume of the PBMT chemical data set, a transfer learning (TL) framework based on graph attention network (GAT) architecture was developed to construct models for screening PBMT chemicals, designating the PBT chemical inventories as source domains and the PBMT chemical data set as target domains. A weighted loss (LW) function was proposed and proved to mitigate the negative impact of imbalanced data on the model performance. Results indicate the TL-GAT models outperformed GAT models, along with large coverage of applicability domains and interpretability. The constructed models were employed to identify PBMT chemicals from inventories consisting of about 1 × 106 chemicals. The developed TL-GAT framework with the LW function holds broad applicability across diverse tasks, especially those involving small and imbalanced data sets.

Potential impact of organophosphate esters on thyroid eye disease based on machine learning and molecular docking

Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers in daily commodities and building materials. Some OPEs, acting as agonists of the thyroid-stimulating hormone receptor (TSHR), may contribute to the development of thyroid eye disease (TED). This study analyzes the serum and urine of patients and control groups, using machine learning and molecular docking to investigate the potential impact of OPEs on TED. Results indicate significantly higher concentrations of OPEs and di-OPEs of TED patients compared to controls (Mann-Whitney U test, p < 0.05). Aryl OPEs exhibit the strongest binding affinity with TSHR. We developed a predictive model for OPE-TSHR affinity to explore the impact of OPE structural features on TSHR activity and effectively capture the complex relationships between changes in OPE side chains and their effects on TSHR. Predictions from the USEPA's database indicate that 28 % of 1011 OPEs have a tendency to bind with TSHR. Furthermore, a high-accuracy classification model successfully identified key substructures associated with high affinity for TSHR. This study not only enhances our understanding of the complex relationship between the structural diversity of OPEs and their thyroid impact but also offers molecular design insights to prevent releasing OPEs with high thyroid harm potential into the environment.

Elucidating per- and polyfluoroalkyl substances (PFASs) soil-water partitioning behavior through explainable machine learning models

In this study, an optimized random forest (RF) model was employed to better understand the soil-water partitioning behavior of per- and polyfluoroalkyl substances (PFASs). The model demonstrated strong predictive performance, achieving an R2 of 0.93 and an RMSE of 0.86. Moreover, it required only 11 easily obtainable features, with molecular weight and soil pH being the predominant factors. Using three-dimensional interaction analyses identified specific conditions associated with varying soil-water partitioning coefficients (Kd). Results showed that soils with high organic carbon (OC) content, cation exchange capacity (CEC), and lower soil pH, especially when combined with PFASs of higher molecular weight, were linked to higher Kd values, indicating stronger adsorption. Conversely, low Kd values (< 2.8 L/kg) typically observed in soils with higher pH (8.0), but lower CEC (8 cmol+/kg), lesser OC content (1 %), and lighter molecular weight (380 g/mol), suggested weaker adsorption capacities and a heightened potential for environmental migration. Furthermore, the model was used to predict Kd values for 142 novel PFASs in diverse soil conditions. Our research provides essential insights into the factors governing PFASs partitioning in soil and highlights the significant role of machine learning models in enhancing the understanding of environmental distribution and migration of PFASs.

Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely used across a spectrum of industrial and consumer goods. Nonetheless, their persistent nature and tendency to accumulate in biological systems pose substantial environmental and health threats. Consequently, striking a balance between maximizing product efficiency and minimizing environmental and health risks by tailoring the molecular structure of PFAS has become a pivotal challenge in the fields of environmental chemistry and sustainable development. To address this issue, a computational workflow was proposed for designing an environmentally friendly PFAS by incorporating deep learning (DL) and molecular generative models. The hybrid DL architecture MolHGT+ based on heterogeneous graph neural network with transformer-like attention was applied to predict the surface tension, bioaccumulation, and hepatotoxicity of the molecules. Through virtual screening of the PFAS master database using MolHGT+, the findings indicate that incorporating the siloxane group and betaine fragment can effectively decrease both the bioaccumulation and hepatotoxicity of PFAS while preserving low surface tension. In addition, molecular generative models were employed to create a structurally diverse pool of novel PFASs with the aforementioned hit molecules serving as the initial template structures. Overall, our study presents a promising AI-driven method for advancing the development of environmentally friendly PFAS.

Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions

Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.

Unbound Fractions of PFAS in Human and Rodent Tissues: Rat Liver a Suitable Proxy for Evaluating Emerging PFAS?

Adverse health effects associated with exposures to perfluoroalkyl and polyfluoroalkyl substances (PFAS) are a concern for public health and are driven by their elimination half-lives and accumulation in specific tissues. However, data on PFAS binding in human tissues are limited. Accumulation of PFAS in human tissues has been linked to interactions with specific proteins and lipids in target organs. Additional data on PFAS binding and unbound fractions (funbound) in whole human tissues are urgently needed. Here, we address this gap by using rapid equilibrium dialysis to measure the binding and funbound of 16 PFAS with 3 to 13 perfluorinated carbon atoms (ηpfc = 3-13) and several functional headgroups in human liver, lung, kidney, heart, and brain tissue. We compare results to mouse (C57BL/6 and CD-1) and rat tissues. Results show that funbound decreases with increasing fluorinated carbon chain length and hydrophobicity. Among human tissues, PFAS binding was generally greatest in brain > liver ≈ kidneys ≈ heart > lungs. A correlation analysis among human and rodent tissues identified rat liver as a suitable surrogate for predicting funbound for PFAS in human tissues (R2 ≥ 0.98). The funbound data resulting from this work and the rat liver prediction method offer input parameters and tools for toxicokinetic models for legacy and emerging PFAS.

Accurate Prediction of Rat Acute Oral Toxicity and Reference Dose for Thousands of Polycyclic Aromatic Hydrocarbon Derivatives Based on Chemometric QSAR and Machine Learning

Acute oral toxicity is currently not available for most polycyclic aromatic hydrocarbons (PAHs), especially their derivatives, because it is cost-prohibitive to experimentally determine all of them. Here, quantitative structure-activity relationship (QSAR) models using machine learning (ML) for predicting the toxicity of PAH derivatives were developed, based on oral toxicity data points of 788 individual substances of rats. Both the individual ML algorithm gradient boosting regression trees (GBRT) and the stacking ML algorithm (extreme gradient boosting + GBRT + random forest regression) provided the best prediction results with satisfactory determination coefficients for both cross-validation and the test set. It was found that those PAH derivatives with fewer polar hydrogens, more large-sized atoms, more branches, and lower polarizability have higher toxicity. Software based on the optimal ML-QSAR model was successfully developed to expand the application potential of the developed model, obtaining reliable prediction of pLD50 values and reference doses for 6893 external PAH derivatives. Among these chemicals, 472 were identified as moderately or highly toxic; 10 out of them had clear environment detection or use records. The findings provide valuable insights into the toxicity of PAHs and their derivatives, offering a standard platform for effectively evaluating chemical toxicity using ML-QSAR models.

Comparative analysis of UV-initiated ARPs for degradation of the emerging substitute of perfluorinated compounds: Does defluorination mean the sole factor?

Due to the increasing attention for the residual of per- and polyfluorinated compounds in environmental water, Sodium p-Perfluorous Nonenoxybenzenesulfonate (OBS) have been considered as an alternative solution for perfluorooctane sulfonic acid (PFOS). However, recent detections of elevated OBS concentrations in oil fields and Frontal polymerization foams have raised environmental concerns leading to the decontamination exploration for this compound. In this study, three advanced reduction processes including UV-Sulfate (UV-SF), UV-Iodide (UV-KI) and UV-Nitrilotriacetic acid (UV-NTA) were selected to evaluate the removal for OBS. Results revealed that hydrated electrons (eaq-) dominated the degradation and defluorination of OBS. Remarkably, the UV-KI exhibited the highest removal rate (0.005 s-1) and defluorination efficiency (35 %) along with the highest concentration of eaq- (K = -4.651). Despite that nucleophilic attack from eaq- on sp2 carbon and H/F exchange were discovered as the general mechanism, high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (HPLC/Q-TOF-MS) analysis with density functional theory (DFT) calculations revealed the diversified products and routes. Intermediates with lowest fluorine content for UV-KI were identified, the presence nitrogen-containing intermediates were revealed in the UV-NTA. Notably, the nitrogen-containing intermediates displayed the enhanced toxicity, and the iodine poly-fluorinated intermediates could be a potential-threat compared to the superior defluorination performance for UV-KI.

Quinolone Antibiotics Inhibit the Rice Photosynthesis by Targeting Photosystem II Center Protein: Generational Differences and Mechanistic Insights

Soil antibiotic pollution profoundly influences plant growth and photosynthetic performance, yet the main disturbed processes and the underlying mechanisms remain elusive. This study explored the photosynthetic toxicity of quinolone antibiotics across three generations on rice plants and clarified the mechanisms through experimental and computational studies. Marked variations across antibiotic generations were noted in their impact on rice photosynthesis with the level of inhibition intensifying from the second to the fourth generation. Omics analyses consistently targeted the light reaction phase of photosynthesis as the primary process impacted, emphasizing the particular vulnerability of photosystem II (PS II) to the antibiotic stress, as manifested by significant interruptions in the photon-mediated electron transport and O2 production. PS II center D2 protein (psbD) was identified as the primary target of the tested antibiotics, with the fourth-generation quinolones displaying the highest binding affinity to psbD. A predictive machine learning method was constructed to pinpoint antibiotic substructures that conferred enhanced affinity. As antibiotic generations evolve, the positive contribution of the carbonyl and carboxyl groups on the 4-quinolone core ring in the affinity interaction gradually intensified. This research illuminates the photosynthetic toxicities of antibiotics across generations, offering insights for the risk assessment of antibiotics and highlighting their potential threats to carbon fixation of agroecosystems.

Towards process-based modelling and parameterisation of bioaccumulation in humans across PFAS congeners

Per- and polyfluoroalkyl substances (PFAS) are a large class of stable toxic chemicals which have ended up in the environment and in organisms in significant concentrations. Toxicokinetic models are needed to facilitate extrapolation of bioaccumulation data across PFAS congeners and species. For the present study, we carried out an inventory of accumulation processes specific for PFAS, deviating from traditional Persistent Organic Pollutants (POPs). In addition, we reviewed toxicokinetic models on PFAS reported in literature, classifying them according to the number of compartments distinguished as a one-compartment model (1-CM), two-compartment model (2- CM) or a multi-compartment model, (multi-CM) as well as the accumulation processes included and the parameters used. As the inventory showed that simple 1-CMs were lacking, we developed a generic 1-CM of ourselves to include PFAS specific processes and validated the model for legacy perfluoroalkyl acids. Predicted summed elimination constants were accurate for long carbon chains (>C6), indicating that the model properly represented toxicokinetic processes for most congeners. Results for urinary elimination rate constants were mixed, which might be caused by the exclusion of reabsorption processes (renal reabsorption, enterohepatic circulation). The 1-CM needs to be improved further in order to better predict individual elimination pathways. Besides that, more data on PFAS-transporter specific processes are needed to extrapolate across PFAS congeners and species.

Quantitative cross-species comparison of serum albumin binding of per- and polyfluoroalkyl substances from five structural classes

Per- and polyfluoroalkyl substances (PFAS) are a class of over 8000 chemicals, many of which are persistent, bioaccumulative, and toxic to humans, livestock, and wildlife. Serum protein binding affinity is instrumental in understanding PFAS toxicity, yet experimental binding data is limited to only a few PFAS congeners. Previously, we demonstrated the usefulness of a high-throughput, in vitro differential scanning fluorimetry assay for determination of relative binding affinities of human serum albumin for 24 PFAS congeners from 6 chemical classes. In the current study, we used this assay to comparatively examine differences in human, bovine, porcine, and rat serum albumin binding of 8 structurally informative PFAS congeners from 5 chemical classes. With the exception of the fluorotelomer alcohol 1H, 1H, 2H, 2H-perfluorooctanol (6:2 FTOH), each PFAS congener bound by human serum albumin was also bound by bovine, porcine, and rat serum albumin. The critical role of the charged functional headgroup in albumin binding was supported by the inability of albumin of each species tested to bind 6:2 FTOH. Significant interspecies differences in serum albumin binding affinities were identified for each of the bound PFAS congeners. Relative to human albumin, perfluoroalkyl carboxylic and sulfonic acids were bound with greater affinity by porcine and rat serum albumin, and the perfluoroalkyl ether acid congener bound with lower affinity to porcine and bovine serum albumin. These comparative affinity data for PFAS binding by serum albumin from human, experimental model, and livestock species reduce critical interspecies uncertainty and improve accuracy of predictive bioaccumulation and toxicity assessments for PFAS.

An artificial intelligence platform for automated PFAS subgroup classification: A discovery tool for PFAS screening

Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS-Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform's classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS. In addition, the platform incorporates deep unsupervised learning models to visualize the chemical space of PFAS by clustering similar structures and linking related classes. Through real-world use cases, we demonstrate that PFAS-Atlas can rapidly screen for relationships between chemical structure and persistence, bioaccumulation, or toxicity data for PFAS. The platform can also guide the planning of the PFAS testing strategy by showing which PFAS classes urgently require further attention. Ultimately, the release of PFAS-Atlas will benefit both the PFAS research and regulation communities.

Improving predictions and understanding of primary and ultimate biodegradation rates with machine learning models

This study aimed to develop machine learning based quantitative structure biodegradability relationship (QSBR) models for predicting primary and ultimate biodegradation rates of organic chemicals, which are essential parameters for environmental risk assessment. For this purpose, experimental primary and ultimate biodegradation rates of high consistency were compiled for 173 organic compounds. A significant number of descriptors were calculated with a collection of quantum/computational chemistry software and tools to achieve comprehensive representation and interpretability. Following a pre-screening process, multiple QSBR models were developed for both primary and ultimate endpoints using three algorithms: extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR). Furthermore, a unified QSBR model was constructed using the knowledge transfer technique and XGBoost. Results demonstrated that all QSBR models developed in this study had good performance. Particularly, SVM models exhibited high level of goodness of fit (coefficient of determination on the training set of 0.973 for primary and 0.980 for ultimate), robustness (leave-one-out cross-validated coefficient of 0.953 for primary and 0.967 for ultimate), and external predictive ability (external explained variance of 0.947 for primary and 0.958 for ultimate). The knowledge transfer technique enhanced model performance by learning from properties of two biodegradation endpoints. Williams plots were used to visualize the application domains of the models. Through SHapley Additive exPlanations (SHAP) analysis, this study identified key features affecting biodegradation rates. Notably, MDEO-12, APC2D1_C_O, and other features contributed to primary biodegradation, while AATS0v, AATS2v, and others inhibited it. For ultimate biodegradation, features like No. of Rotatable Bonds, APC2D1_C_O, and minHBa were contributors, while C1SP3, Halogen Ratio, GGI4, and others hindered the process. Also, the study quantified the contributions of each feature in predictions for individual chemicals. This research provides valuable tools for predicting both primary and ultimate biodegradation rates while offering insights into the mechanisms.

Quantitative Cross-Species Comparison of Serum Albumin Binding of Per- and Polyfluoroalkyl Substances from Five Structural Classes

Per- and polyfluoroalkyl substances (PFAS) are a class of over 8,000 chemicals that are persistent, bioaccumulative, and toxic to humans, livestock, and wildlife. Serum protein binding affinity is instrumental in understanding PFAS toxicity, yet experimental binding data is limited to only a few PFAS congeners. Previously, we demonstrated the usefulness of a high-throughput, in vitro differential scanning fluorimetry assay for determination of relative binding affinities of human serum albumin for 24 PFAS congeners from 6 chemical classes. In the current study, we used this differential scanning fluorimetry assay to comparatively examine differences in human, bovine, porcine, and rat serum albumin binding of 8 structurally informative PFAS congeners from 5 chemical classes. With the exception of the fluorotelomer alcohol 1H,1H,2H,2H-perfluorooctanol (6:2 FTOH), each PFAS congener bound by human serum albumin was also bound by bovine, porcine, and rat serum albumin. The critical role of the charged functional headgroup in albumin binding was supported by the inability of serum albumin of each species tested to bind 6:2 FTOH. Significant interspecies differences in serum albumin binding affinities were identified for each of the bound PFAS congeners. Relative to human albumin, perfluoroalkyl carboxylic and sulfonic acids were bound with greater affinity by porcine and rat serum albumin, and perfluoroalkyl ether congeners bound with lower affinity to porcine and bovine serum albumin. These comparative affinity data for PFAS binding by serum albumin from human, experimental model and livestock species reduce critical interspecies uncertainty and improve accuracy of predictive toxicity assessments for PFAS.

Humic acids alleviate aflatoxin B1-induced hepatic injury by reprogramming gut microbiota and absorbing toxin

Aflatoxin B1 (AFB1) is a hepatotoxic fungal metabolite that is widely present in food and can cause liver cancer. As a potential detoxifier, naturally occurring humic acids (HAs) may be able to reduce inflammation and restructure the gut microbiota composition; however, little is known about the mechanism of HAs detoxification as applied to liver cells. In this study, HAs treatment alleviated AFB1-induced liver cell swelling and the infiltration of inflammatory cells. HAs treatment also reinstated various enzyme levels in the liver disturbed by AFB1 and substantially alleviated AFB1-caused oxidative stress and inflammatory responses by enhancing immune functions in mice. Moreover, HAs increased the length of the small intestinal and villus height to restore intestinal permeability, which is impaired by AFB1. In addition, HAs reconstructed the gut microbiota, increasing the relative abundance of Desulfovibrio, Odoribacter, and Alistipes. In vitro and in vivo assays demonstrated that HAs could efficiently remove AFB1 by absorbing the toxin. Therefore, HAs treatment can ameliorate AFB1-induced hepatic injury by enhancing gut barrier function, regulating gut microbiota, and adsorbing toxin.