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

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.

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.

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.