Modeling the transplacental transfer of small molecules using machine learning: a case study on per- and polyfluorinated substances (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.

Insights into PFAS environmental fate through computational chemistry: A review

Per- and polyfluoroalkyl substances (PFAS) are widely used chemicals that exhibit exceptional chemical and thermal stability. However, their resistance to degradation has led to their widespread environmental contamination. PFAS also negatively affect the environment and other organisms, highlighting the need for effective remediation methods to mitigate their presence and prevent further contamination. Computational chemistry methods, such as Density Functional Theory (DFT) and Molecular Dynamics (MD) offer valuable tools for studying PFAS and simulating their interactions with other molecules. This review explores how computational chemistry methods contribute to understanding and tackling PFAS in the environment. PFAS have been extensively studied using DFT and MD, each method offering unique advantages and computational limitations. MD simulates large macromolecules systems however it lacks the ability model chemical reactions, while DFT provides molecular insights however at a high computational cost. The integration of DFT with MD shows promise in predicting PFAS behavior in different environments. This work summarizes reported studies on PFAS compounds, focusing on adsorption, destruction, and bioaccumulation, highlighting contributions of computational methods while discussing the need for continued research. The findings emphasize the importance of computational chemistry in addressing PFAS contamination, guiding risk assessments, and informing future research and innovations in this field.

A Data-Driven Computational Framework for Assessing the Risk of Placental Exposure to Environmental Chemicals

A computational framework based on placental gene networks was proposed in this work to improve the accuracy of the placental exposure risk assessment of environmental compounds. The framework quantitatively characterizes the ability of compounds to cross the placental barrier by systematically considering the interaction and pathway-level information on multiple placental transporters. As a result, probability scores were generated for 307 compounds crossing the placental barrier based on this framework. These scores were then used to categorize the compounds into different levels of transplacental transport range, creating a gradient partition. These probability scores not only facilitated a more intuitive understanding of a compound's ability to cross the placental barrier but also provided valuable information for predicting potential placental disruptors. Compounds with probability scores greater than 90% were considered to have significant transplacental transport potential, whereas those with probability scores less than 80% were classified as unlikely to cross the placental barrier. Furthermore, external validation set results showed that the probability score could accurately predict the compounds known to cross the placental barrier. In conclusion, the computational framework proposed in this study enhances the intuitive understanding of the ability of compounds to cross the placental barrier and opens up new avenues for assessing the placental exposure risk of compounds.

PFAS alters placental arterial vasculature in term human placentae: A prospective pregnancy cohort study

INTRODUCTION

Perfluoroalkyl substances (PFAS) are synthetic chemicals used in industrial and consumer goods that are widely detected in human populations and are associated with adverse health outcomes, including perinatal health risks and child health. One mechanism of influence may be the impact of PFAS exposure on placental structure and function.

OBJECTIVES

The objective of this study is to investigate the relationship between maternal prenatal exposure to PFAS and measures of placental vascularization, and to assess whether changes in vascularization play a role in mediating the impact of PFAS on birth outcomes.

METHODS

Using data from a prospective cohort study, we examined associations between second trimester PFAS (individually and as mixtures using Bayesian kernel machine regression) and placental arterial vasculature in term placentae (N = 158); secondarily we evaluated the degree to which alterations in placental arterial vasculature explained associations between PFAS exposure and birth outcomes. Placental arterial vasculature features were collected from arterial tracings of each placental image.

RESULTS

In both linear regression and mixture models, natural log-transformed perfluorooctanoic acid concentrations were negatively associated with surface vasculature, indexed by the mean distance from arterial end point to perimeter (β = -0.23, 95% CI: -0.41, -0.041); additionally, maximum arterial tortuosity was negatively associated with placental weight (β = -0.19, 95% CI: -0.34, -0.051). There were no reliable differences in effect by fetal sex.

DISCUSSION

The findings provide some of the first evidence of PFAS exposure shaping a key measure of placental vascular function, which may underlie the impact of PFAS on perinatal and child health risks.

Perfluorooctanoic acid induces transcriptomic alterations in second trimester human cytotrophoblasts

Poly- and perfluroroalkylated substances (PFAS) are a major class of surfactants used in industry applications and consumer products. Despite efforts to reduce the usage of PFAS due to their environmental persistence, compounds such as perfluorooctanoic acid (PFOA) are widely detected in human blood and tissue. Although growing evidence supports that prenatal exposures to PFOA and other PFAS are linked to adverse pregnancy outcomes, the target organs and pathways remain unclear. Recent investigations in mouse and human cell lines suggest that PFAS may impact the placenta and impair trophoblast function. In this study, we investigated the effects of PFOA on cytotoxicity and the transcriptome in cultured second trimester human cytotrophoblasts (CTBs). We show that PFOA significantly reduces viability and induces cell death at 24 h, in a concentration-dependent manner. At subcytotoxic concentrations, PFOA impacted expression of hundreds of genes, including several molecules (CRH, IFIT1, and TNFSF10) linked with lipid metabolism and innate immune response pathways. Furthermore, in silico analyses suggested that regulatory factors such as peroxisome proliferator-activated receptor-mediated pathways may be especially important in response to PFOA. In summary, this study provides evidence that PFOA alters primary human CTB viability and gene pathways that could contribute to placental dysfunction and disease.

Impact of effluent organic matter on perfluoroalkyl acid removal from wastewater effluent by granular activated carbon and alternative adsorbents

Occurrence of perfluoroalkyl acids (PFAAs) in wastewater effluent coupled with increasingly stringent regulations has increased the need for more effective sorption-based PFAA treatment approaches. This study investigated the impact of ozone (O3)- biologically active filtration (BAF) as integral components of non-reverse osmosis (RO)-based potable reuse treatment trains and as a potential pretreatment option to improve adsorptive PFAA removal from wastewater effluent by nonselective (e.g., granular activated carbon (GAC) and selective (e.g., anionic exchange resins (AER) and surface-modified clay (SMC)) adsorbents. For nonselective GAC, O3 and BAF resulted in similar PFAA removal improvements, while BAF alone performed better than O3 for AER and SMC. O3-BAF in tandem resulted in the highest PFAA removal performance improvement among pretreatments investigated for selective and nonselective adsorbents. Side by side evaluation of the dissolved organic carbon (DOC) breakthrough curves and size exclusion chromatography (SEC) for each pretreatment scenario suggested that despite the higher affinity of selective adsorbents towards PFAAs, the competition between PFAA and effluent organic matter (EfOM) (molecular weights (MWs): 100-1000 Da) negatively impacts the performance of these adsorbents. The SEC results also demonstrated that transformation of hydrophobic EfOM to more hydrophilic molecules during O3 and biotransformation of EfOM during BAF were the dominant mechanisms responsible for alleviating the competition between PFAA and EfOM, resulting in PFAA removal improvement.

A Review on Per- and Polyfluoroalkyl Substances in Pregnant Women: Maternal Exposure, Placental Transfer, and Relevant Model Simulation

Per- and polyfluoroalkyl substances (PFASs) are important and ubiquitous environmental contaminants worldwide. These novel contaminants can enter human bodies via various pathways, subsequently posing risks to the ecosystem and human health. The exposure of pregnant women to PFASs might pose risks to the health of mothers and the growth and development of fetuses. However, little information is available about the placental transfer of PFASs from mothers to fetuses and the related mechanisms through model simulation. In the present study, based upon a review of previously published literature, we initially summarized the exposure pathways of PFASs in pregnant women, factors affecting the efficiency of placental transfer, and mechanisms associated with placental transfer; outlined simulation analysis approaches using molecular docking and machine learning to reveal the mechanisms of placental transfer; and finally highlighted future research emphases that need to be focused on. Consequently, it was notable that the binding of PFASs to proteins during placental transfer could be simulated by molecular docking and that the placental transfer efficiency of PFASs could also be predicted by machine learning. Therefore, future research on the maternal-fetal transfer mechanisms of PFASs with the benefit of simulation analysis approaches is warranted to provide a scientific basis for the health effects of PFASs on newborns.

Exposure forecasting - ExpoCast - for data-poor chemicals in commerce and the environment

Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.