Maternal-fetal transfer rates of PCBs, OCPs, PBDEs, and dioxin-like compounds predicted through quantitative structure-activity relationship modeling

Developmental polychlorinated biphenyl (PCB) exposure impacts on voiding physiology persist into adulthood and influence sensitivity to bladder stimuli in mice

Polychlorinated biphenyls (PCBs) are toxicants present in the environment, foodstuff, animal and human tissues. PCBs are linked to numerous adverse health effects; however, impacts of developmental PCB exposure on lower urinary tract function are a comparatively newer area of interest. We have previously found developmental exposure (in utero and lactational) to a human-relevant PCB mixture in mice leads to sex- and dose- dependent changes to urinary voiding physiology at 6 weeks of age. This study expands upon previous findings to investigate if developmental PCB-induced urinary voiding phenotypes persist or shift as mice age to 12 weeks of age. Urinary voiding physiology testing through void spot assays, uroflowmetry, and cystometry demonstrated several sex- and dose- dependent effects of PCB exposure at 12 weeks of age. Further, patterns of dysfunction were either maintained, newly acquired, or reversed compared to those from younger adult mice in a previous study. Here, developmental PCB exposure decreased number of small urine spots in adult male and female mice in a dose dependent manner, and female mice had more frequent voiding events assessed by anesthetized cystometry. Mice also had PCB dose-dependent changes to urinary voiding physiology when challenged with intravesical capsaicin infusion to target transient receptor potential cation channel subfamily V member 1 (TRPV1)-mediated pathways. PCBs either blocked or exacerbated capsaicin induced responses depending on the endpoint examined, suggesting this pathway may play a role in PCB-dependent changes in voiding. PCBs also had subtle impacts on prostate wet weight, with high PCB doses reducing tissue mass compared to low PCB doses, while none differed from vehicle. This study demonstrates developmental exposure to PCBs continues to impact lower urinary tract function in adulthood to at least 12 weeks of age both during homeostatic conditions and upon challenge of capsaicin. Better understanding of how early life stressors like PCBs contribute to aging-associated voiding dysfunction are imperative as these findings may help mitigate risk or improve treatment strategies for patients suffering from lower urinary tract symptoms.

Risk of autism spectrum disorder at 18 months of age is associated with prenatal level of polychlorinated biphenyls exposure in a Japanese birth cohort

Prenatal exposure to polychlorinated biphenyls (PCBs) has a detrimental effect on early cognitive development. Based on these observations, some researchers suggested that prenatal exposure to PCB may be an environmental cause of autism spectrum disorder (ASD). To investigate the potential link between prenatal exposure to PCB, we analyzed the link between the level of prenatal PCB exposure and ASD risk evaluated at 18 months of age and behavioral problems at 5 years old based on longitudinal birth cohort data collected in urban areas in Japan based on the data from 115 mother-infant pairs. Logistic regression analysis revealed a significant association between ASD risk at 18 months of age and the factor scores of the principal components (PCB PCs) obtained by compressing the exposure level to PCB congeners. There was no reliable relationship between PCB PCs and problematic behaviors at 5 years of age. Furthermore, machine learning-based analysis showed the possibility that, when the information of the pattern of infants' spontaneous bodily motion, a potential marker of ASD risk, was used as the predictors together, prenatal PCB exposure levels predict ASD risk at 18 months of age. Together, these findings support the view that prenatal exposure to PCBs is associated with the later emergence of autistic behaviors and indicate the predictability of ASD risk based on the information available at the neonatal stage.

Organic Sunscreens and Their Products of Degradation in Biotic and Abiotic Conditions-In Silico Studies of Drug-Likeness and Human Placental Transport

A total of 16 organic sunscreens and over 160 products of their degradation in biotic and abiotic conditions were investigated in the context of their safety during pregnancy. Drug-likeness and the ability of the studied compounds to be absorbed from the gastrointestinal tract and cross the human placenta were predicted in silico using the SwissADME software (for drug-likeness and oral absorption) and multiple linear regression and "ARKA" models (for placenta permeability expressed as fetus-to-mother blood concentration in the state of equilibrium), with the latter outperforming the MLR models. It was established that most of the studied compounds can be absorbed from the gastrointestinal tract. The drug-likeness of the studied compounds (expressed as a binary descriptor, Lipinski) is closely related to their ability to cross the placenta (most likely by a passive diffusion mechanism). The organic sunscreens and their degradation products are likely to cross the placenta, except for very bulky and highly lipophilic 1,3,5-triazine derivatives; an avobenzone degradation product, 1,2-bis(4-tert-butylphenyl)ethane-1,2-dione; diethylamino hydroxybenzoyl hexyl benzoate; and dimerization products of sunscreens from the 4-methoxycinnamate group.

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.

A chirality-sensitive approach to predict chemical transfer across the human placental barrier

The placenta is a membrane that separates the fetus from the maternal circulation, and in addition to protecting the fetus, plays a key role in fetal growth and development. With increasing drug use in pregnancy, it is imperative that reliable models of estimating placental permeability and safety be established. In vitro methods and animal models such as rodent placenta are limited in application since the species-specific nature of the placenta prevents meaningful extrapolations to humans. In this regard, in silico approaches such as quantitative structure-property relationships (QSPRs) are useful alternatives. However, despite evidence that drug transport across the placenta is stereoselective (i.e., governed by the spatial arrangement of the atoms in a molecule), many QSPR models for placental transfer have been built using 2D descriptors that do not account for chirality and stereochemistry. In this study, we apply a chirality-sensitive and proven QSPR methodology titled "EigenValue ANalySis" (EVANS) to build QSPR models for placental transfer. We deploy EVANS along with robust machine learning algorithms to build (i) regression models on a dataset of environmental chemicals (dataset PD I) followed by (ii) classification models on a set of drug-like compounds (dataset PD II). The best models were found to achieve state-of-the-art performance, with the support vector machine algorithm returning rtrain2=0.85,rtest2=0.75 for PD I, and the logistic regression algorithm giving accuracy 0.88 and F1 score 0.93 for PD II. The best models were interpreted with the Shapley Additive Explanations paradigm, and it was found that autocorrelation descriptors are crucial for modelling placental permeability. In conclusion, we demonstrate the need of a chirality-sensitive approach for modelling placental transfer of chemicals, and present two predictive QSPR models that may reliably be used for prediction of placental transfer.

Machine learning models based on residue interaction network for ABCG2 transportable compounds recognition

As the one of the most important protein of placental transport of environmental substances, the identification of ABCG2 transport molecules is the key step for assessing the risk of placental exposure to environmental chemicals. Here, residue interaction network (RIN) was used to explore the difference of ABCG2 binding conformations between transportable and non-transportable compounds. The RIN were treated as a kind of special quantitative data of protein conformation, which not only reflected the changes of single amino acid conformation in protein, but also indicated the changes of distance and action type between amino acids. Based on the quantitative RIN, four machine learning algorithms were applied to establish the classification and recognition model for 1100 compounds with transported by ABCG2 potential. The random forest (RF) models constructed with RIN presented the best and satisfied predictive ability with an accuracy of training set of 0.97 and the test set of 0.96 respectively. In conclusion, the construction of residue interaction network provided a new perspective for the quantitative characterization of protein conformation and the establishment of prediction models for transporter molecular recognition. The ABCG2 transport molecular recognition model based on residue interaction network provides a possible way for screening environmental chemistry transported through placenta.

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.

Contribution of Reliable Chromatographic Data in QSAR for Modelling Bisphenol Transport across the Human Placenta Barrier

Regulatory measures and public concerns regarding bisphenol A (BPA) have led to its replacement by structural analogues, such as BPAF, BPAP, BPB, BPF, BPP, BPS, and BPZ. However, these alternatives are under surveillance for potential endocrine disruption, particularly during the critical period of fetal development. Despite their structural analogies, these BPs differ greatly in their placental transport efficiency. For predicting the fetal exposure of this important class of emerging contaminants, quantitative structure-activity relationship (QSAR) studies were developed to model and predict the placental clearance indices (CI). The most usual input parameters were molecular descriptors obtained by modelling, but for bisphenols (BPs) with structural similarities or heteroatoms such as sulfur, these descriptors do not contrast greatly. This study evaluated and compared the capacity of QSAR models based either on molecular or chromatographic descriptors or a combination of both to predict the placental passage of BPs. These chromatographic descriptors include both the retention mechanism and the peak shape on columns that reflect specific molecular interactions between solute and stationary and mobile phases and are characteristic of the molecular structure of BPs. The chromatographic peak shape such as the asymmetry and tailing factors had more influence on predicting the placental passage than the usual retention parameters. Furthermore, the QSAR model, having the best prediction capacity, was obtained with the chromatographic descriptors alone and met the criteria of internal and cross validation. These QSAR models are crucial for predicting the fetal exposure of this important class of emerging contaminants.

Human-Based New Approach Methodologies in Developmental Toxicity Testing: A Step Ahead from the State of the Art with a Feto-Placental Organ-on-Chip Platform

Developmental toxicity testing urgently requires the implementation of human-relevant new approach methodologies (NAMs) that better recapitulate the peculiar nature of human physiology during pregnancy, especially the placenta and the maternal/fetal interface, which represent a key stage for human lifelong health. Fit-for-purpose NAMs for the placental-fetal interface are desirable to improve the biological knowledge of environmental exposure at the molecular level and to reduce the high cost, time and ethical impact of animal studies. This article reviews the state of the art on the available in vitro (placental, fetal and amniotic cell-based systems) and in silico NAMs of human relevance for developmental toxicity testing purposes; in addition, we considered available Adverse Outcome Pathways related to developmental toxicity. The OECD TG 414 for the identification and assessment of deleterious effects of prenatal exposure to chemicals on developing organisms will be discussed to delineate the regulatory context and to better debate what is missing and needed in the context of the Developmental Origins of Health and Disease hypothesis to significantly improve this sector. Starting from this analysis, the development of a novel human feto-placental organ-on-chip platform will be introduced as an innovative future alternative tool for developmental toxicity testing, considering possible implementation and validation strategies to overcome the limitation of the current animal studies and NAMs available in regulatory toxicology and in the biomedical field.

Gestational PCB52 exposure induces hepatotoxicity and intestinal injury by activating inflammation in dam and offspring mice: A maternal and progeny study

Although Polychlorinated biphenyl (PCB) levels are decreased in the environment, the adverse effects of gestational exposure on the mother and offspring cannot be ignored due to the vulnerability of the fetus. In the present study, pregnant Balb/c mice were administered PCB52 (1 mg/kg BW/day) or corn oil vehicle by gavage until parturition. In the dams, PCB52 caused histopathological changes in the liver, higher serum levels of aminotransferase and alanine aminotransferase, and activated apoptosis and autophagy, suggesting hepatotoxicity. Overexpressed indicators of TLR4 pathway were observed in the liver of PCB52-exposed dams, indicated hepatic inflammation. Moreover, PCB52 exposure weakened the intestinal barrier and triggered inflammatory response, which might contribute to the hepatic inflammation by gut-liver axis. In the pups, prenatal PCB52 exposure affected the sex ratio at birth and reduced birth length and weights. Similar to the dams, prenatal PCB52 exposure induced hepatotoxicity in the pups without gender difference. Consistent with the alteration of gut microbiota, intestinal inflammation was confirmed, accompanying the disruption in the intestinal barrier and the activation of apoptosis and autophagy in the PCB52-exposed pups. Intestinal injury might be responsible for hepatotoxicity at least in part. Taken together, these findings suggested that gestational PCB52 exposure induced hepatic and intestinal injury in both maternal and offspring mice by arousing inflammation.

A Critical Review on Transplacental Transfer of Per- and Polyfluoroalkyl Substances: Prenatal Exposure Levels, Characteristics, and Mechanisms

Prenatal exposure to perfluoroalkyl and polyfluoroalkyl substances (PFASs) has aroused public concerns as it can pose multiple health threats to pregnant women and cause adverse birth outcomes for fetuses. In previous studies, the prenatal exposure levels and transplacental transfer efficiencies (TTE) of PFASs have been reported and discussed. Specifically, the binding affinities between PFASs and some transporters were determined, demonstrating that the TTE values of PFASs are highly dependent on their binding behaviors. To summarize primary findings of previous studies and propose potential guidance for future research, this article provides a systematic overview on levels and characteristics of prenatal exposure to PFASs worldwide, summarizes relationships between TTE values and structures of PFASs, and discusses possible transplacental transfer mechanisms, especially for the combination between PFASs and transporters. Given the critical roles of transporters in the transplacental transfer of PFASs, we conducted molecular docking to further clarify the binding behaviors between PFASs and the selected transporters. We proposed that the machine learning can be a superior method to predict and reveal behaviors and mechanisms of the transplacental transfer of PFASs. In total, this is the first review providing a comprehensive overview on the prenatal exposure levels and transplacental transfer mechanisms of PFASs.

The Role of the 3Rs for Understanding and Modeling the Human Placenta

Modeling the physiology of the human placenta is still a challenge, despite the great number of scientific advancements made in the field. Animal models cannot fully replicate the structure and function of the human placenta and pose ethical and financial hurdles. In addition, increasingly stricter animal welfare legislation worldwide is incentivizing the use of 3R (reduction, refinement, replacement) practices. What efforts have been made to develop alternative models for the placenta so far? How effective are they? How can we improve them to make them more predictive of human pathophysiology? To address these questions, this review aims at presenting and discussing the current models used to study phenomena at the placenta level: in vivo, ex vivo, in vitro and in silico. We describe the main achievements and opportunities for improvement of each type of model and critically assess their individual and collective impact on the pursuit of predictive studies of the placenta in line with the 3Rs and European legislation.

Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens

In this review, applications of various molecular modelling methods in the study of estrogens and xenoestrogens are summarized. Selected biomolecules that are the most commonly chosen as molecular modelling objects in this field are presented. In most of the reviewed works, ligand docking using solely force field methods was performed, employing various molecular targets involved in metabolism and action of estrogens. Other molecular modelling methods such as molecular dynamics and combined quantum mechanics with molecular mechanics have also been successfully used to predict the properties of estrogens and xenoestrogens. Among published works, a great number also focused on the application of different types of quantitative structure-activity relationship (QSAR) analyses to examine estrogen's structures and activities. Although the interactions between estrogens and xenoestrogens with various proteins are the most commonly studied, other aspects such as penetration of estrogens through lipid bilayers or their ability to adsorb on different materials are also explored using theoretical calculations. Apart from molecular mechanics and statistical methods, quantum mechanics calculations are also employed in the studies of estrogens and xenoestrogens. Their applications include computation of spectroscopic properties, both vibrational and Nuclear Magnetic Resonance (NMR), and also in quantum molecular dynamics simulations and crystal structure prediction. The main aim of this review is to present the great potential and versatility of various molecular modelling methods in the studies on estrogens and xenoestrogens.

Polychlorinated biphenyls influence on autism spectrum disorder risk in the MARBLES cohort

BACKGROUND

Autism spectrum disorder (ASD) is suspected to have environmental and genetic contributions. Polychlorinated biphenyls (PCBs) are environmental risk factors of interest due to their potential as neurodevelopmental toxicants and environmental persistence despite a US production ban in the 1970s.

METHODS

Participants were mother-child pairs from MARBLES, a high-risk pregnancy cohort that enrolls families who have one child diagnosed with ASD and are planning to have another child. PCB concentrations were measured in maternal blood at each trimester of pregnancy using gas chromatography coupled with triple quadruple mass spectrometry. Concentrations were summed into total PCB and two categories based on function/mechanisms of action: dioxin-like (DL), and ryanodine receptor (RyR)-activating PCBs. Multinomial logistic regression assessed risk of clinical outcome classification of ASD and non-typical development (Non-TD) compared to typically developing (TD) in the children at 3 years old.

RESULTS

A total of 104 mother-child pairs were included. There were no significant associations for total PCB; however, there were borderline significant associations between DL-PCBs and decreased risk for Non-TD outcome classification (adjusted OR: 0.41 (95% CI 0.15-1.14)) and between RyR-activating PCBs and increased risk for ASD outcome classification (adjusted OR: 2.63 (95% CI 0.87-7.97)).

CONCLUSION

This study does not provide strong supporting evidence that PCBs are risk factors for ASD or Non-TD. However, these analyses suggest the need to explore more deeply into subsets of PCBs as risk factors based on their function and structure in larger cohort studies where non-monotonic dose-response patterns can be better evaluated.

Quantitative structure-activity relationship for the partition coefficient of hydrophobic compounds between silicone oil and air

The silicon oil-air partition coefficients (K_SiO/A) of hydrophobic compounds are vital parameters for applying silicone oil as non-aqueous-phase liquid in partitioning bioreactors. Due to the limited number of K_SiO/A values determined by experiment for hydrophobic compounds, there is an urgent need to model the K_SiO/A values for unknown chemicals. In the present study, we developed a universal quantitative structure-activity relationship (QSAR) model using a sequential approach with macro-constitutional and micromolecular descriptors for silicone oil-air partition coefficients (K_SiO/A) of hydrophobic compounds with large structural variance. The geometry optimization and vibrational frequencies of each chemical were calculated using the hybrid density functional theory at the B3LYP/6-311G** level. Several quantum chemical parameters that reflect various intermolecular interactions as well as hydrophobicity were selected to develop QSAR model. The result indicates that a regression model derived from logK_SiO/A, the number of non-hydrogen atoms (#nonHatoms) and energy gap of E_LUMO and E_HOMO (E_LUMO-E_HOMO) could explain the partitioning mechanism of hydrophobic compounds between silicone oil and air. The correlation coefficient R² of the model is 0.922, and the internal and external validation coefficient, Q²_LOO and Q²_ext , are 0.91 and 0.89 respectively, implying that the model has satisfactory goodness-of-fit, robustness, and predictive ability and thus provides a robust predictive tool to estimate the logK_SiO/A values for chemicals in application domain. The applicability domain of the model was visualized by the Williams plot.

Fetal exposure markers of dioxins and dioxin-like PCBs

Fetal exposure to polychlorinated biphenyls (PCBs), polychlorinated-p-dibenzodioxins (PCDDs), and polychlorinated dibenzofurans (PCDFs) have been associated with a number of adverse health outcomes. Although the placenta acts as a barrier between the mother and the fetus, these contaminants transfer through the placenta exposing the fetus. Several studies have investigated placental transfer, but few have assessed the co-variation among these contaminants. Maternal blood, cord blood, and cord tissue were collected from 41 Japanese mother-infant pairs and analyzed for dioxin-like PCBs and PCDD/Fs. Hierarchical cluster analysis followed by principal component analysis were used to assess the co-variation. Two stable clusters of dioxin-like PCBs were found in maternal and cord blood. One cluster of low/medium chlorinated dioxin-like PCBs was present in all three matrices with 2,3',4,4',5-PeCB(#118) and 3,3',4,4',5-PeCB(#126) explaining the majority of the clusters' variances. Medium/high chlorinated dioxin-like PCBs clustered in maternal blood and cord blood but not in cord tissue. 2,3,4,4',5-PeCB(#114) and 2,3,3',4,4',5,5'-HpCB(#189) explained the majority of the clusters' variances. There was a substantial correlation between the sum of dioxin-like PCBs and total PCDD/F in all three matrices. The sum of the four suggested PCBs plus 3,3',4,4'-TeCB(#77) correlated well with total PCDD/F in all three matrices. Apart from the dioxin-like PCBs, little co-variation existed among the studied contaminants. The five PCBs can be used as fetal exposure markers for dioxin and dioxin-like PCBs in maternal and cord blood respectively. In cord tissue, more higher chlorinated dioxin-like PCBs need to be measured as well.

Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels

To develop a new ensemble learning method and construct highly predictive regression models in chemoinformatics and chemometrics, applicability domains (ADs) are introduced into the ensemble learning process of prediction. When estimating values of an objective variable using subregression models, only the submodels with ADs that cover a query sample, i.e., the sample is inside the model's AD, are used. By constructing submodels and changing a list of selected explanatory variables, the union of the submodels' ADs, which defines the overall AD, becomes large, and the prediction performance is enhanced for diverse compounds. By analyzing a quantitative structure-activity relationship data set and a quantitative structure-property relationship data set, it is confirmed that the ADs can be enlarged and the estimation performance of regression models is improved compared with traditional methods.

Logistic Regression of Ligands of Chemotaxis Receptors Offers Clues about Their Recognition by Bacteria

Because of relative simplicity of signal transduction pathway, bacterial chemotaxis sensory systems have been expected to be applied to biosensor. Tar and Tsr receptors mediate chemotaxis of Escherichia coli and have been studied extensively as models of chemoreception by bacterial two-transmembrane receptors. Such studies are typically conducted using two canonical ligands: l-aspartate for Tar and l-serine for Tsr. However, Tar and Tsr also recognize various analogs of aspartate and serine; it remains unknown whether the mechanism by which the canonical ligands are recognized is also common to the analogs. Moreover, in terms of engineering, it is important to know a single species of receptor can recognize various ligands to utilize bacterial receptor as the sensor for wide range of substances. To answer these questions, we tried to extract the features that are common to the recognition of the different analogs by constructing classification models based on machine-learning. We computed 20 physicochemical parameters for each of 38 well-known attractants that act as chemoreception ligands, and 15 known non-attractants. The classification models were generated by utilizing one or more of the seven physicochemical properties as descriptors. From the classification models, we identified the most effective physicochemical parameter for classification: the minimum electron potential. This descriptor that occurred repeatedly in classification models with the highest accuracies, This descriptor used alone could accurately classify 42/53 of compounds. Among the 11 misclassified compounds, eight contained two carboxyl groups, which is analogous to the structure of characteristic of aspartate analog. When considered separately, 16 of the 17 aspartate analogs could be classified accurately based on the distance between their two carboxyl groups. As shown in these results, we succeed to predict the ligands for bacterial chemoreceptors using only a few descriptors; single descriptor for single receptor. This result might be due to the relatively simple topology of bacterial two-transmembrane receptors compared to the G-protein-coupled receptors of seven-transmembrane receptors. Moreover, this distance between carboxyl groups correlated with the receptor binding affinity of the aspartate analogs. In view of this correlation, we propose a common mechanism underlying ligand recognition by Tar of compounds with two carboxyl groups.