Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor

Predicting estrogen receptor agonists from plastic additives across various aquatic-related species using machine learning and AlphaFold2

The absence of effective public databases greatly limits high-throughput prediction of hormonal effects mediated by nuclear receptors in aquatic organisms. In this study, we developed novel strategies for multi-species screening of estrogen receptor (ER) agonists in plastic additives using AlphaFold2. Firstly, Deep Forest (DF), artificial neural network (ANN) and conventional machine learning (ML) models were utilized to screen ERα agonists. The DF models using RDKit.Chem.Descriptors and MorganFingerprint achieved a sensitivity = 0.96, specificity > 0.99, and an F1 score > 0.95, identifying 42 plastic additives as ERα agonists. Subsequently, ERα structures for Danio rerio (Dr), Oryzias melastigma (Om), Delphinus delphis (Dd), Physeter catodon (Pc), Mytilus edulis (Me), Xenopus tropicalis (Xt), Nipponia nippon (Nn), and Aptenodytes forsteri (Af) were constructed using AlphaFold2. Except for Me ERα, most species shared two common key amino acid residues responsible for ERα activity: arginine 85 and glutamic acid 44 (aligned serial numbers in the LBD). However, aquatic-related species exhibited other three additional key residues: glycine 212, leucine 216 and phenylalanine 95 (aligned serial numbers in the LBD). The number of compounds with docking energy < -9 kcal/mol for Dr, Om, Dd, Pc, Me, Xt, Nn, and Af were 4, 8, 4, 12, 10, 13, 7, and 9, respectively. The docking energy of estrone in all species was < -9 kcal/mol, while that of bisphenol P varied greatly among different species. The combined application of ML and AlphaFold enables high-throughput evaluation of the ecotoxicity posed by emerging pollutants across multiple aquatic-related species.

Look What You Made Me Do: Discerning Feature for Classification of Endocrine-Disrupting Chemical Binding to Steroid Hormone Receptors

Exposure to metabolism-disrupting chemicals, which are a specific type of endocrine-disrupting chemical (EDC), is linked to metabolic problems such as dyslipidemia, insulin resistance, and hepatic steatosis. Steroid hormone receptors (SHRs) within the nuclear receptor superfamily are well-known targets for EDCs in reproductive tissues and, to a lesser extent, in liver. In this study, we investigated how five well-established SHR ligands and eight EDCs including pesticides, plasticizers, pharmaceuticals, flame retardants, industrial chemicals, and their metabolites affect estrogen (ERα in reproductive tissues) and glucocorticoid (GR in liver) receptors. We investigated the utility of structural molecular modeling to classify EDC binding to ERα and GR. To this end, we modeled a set of EDC binding to ER and GR using unbiased all-atom long-time scale molecular dynamics (MD) simulations and compared them against known established SHR agonists and antagonists. We systematically evaluated MD-derived variables such as protein-ligand interactions and binding energy, folding secondary structure elements, distances, and angles as relevant parameters. Our findings suggest that the well-established H12 folding and conformational angles can be discerning features for binding of EDCs to SHRs. Although SHR activation often involves changes in H12 folding and geometry, GR displayed less flexibility in this region, suggesting that protein-ligand interaction and binding energy are more relevant for its classification. We show that MD simulations combined with experimental assays can be a useful tool for studying novel EDCs by providing relevant structural features for their classification.

Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques

Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overdose contributing to the opioid crisis in the United States. Therefore, understanding the structure-activity relationship between MOR and its ligands is essential for predicting MOR binding of chemicals, which could assist in the development of non-addictive or less-addictive opioid analgesics. This study aimed to develop machine learning and deep learning models for predicting MOR binding activity of chemicals. Chemicals with MOR binding activity data were first curated from public databases and the literature. Molecular descriptors of the curated chemicals were calculated using software Mold2. The chemicals were then split into training and external validation datasets. Random forest, k-nearest neighbors, support vector machine, multi-layer perceptron, and long short-term memory models were developed and evaluated using 5-fold cross-validations and external validations, resulting in Matthews correlation coefficients of 0.528-0.654 and 0.408, respectively. Furthermore, prediction confidence and applicability domain analyses highlighted their importance to the models' applicability. Our results suggest that the developed models could be useful for identifying MOR binders, potentially aiding in the development of non-addictive or less-addictive drugs targeting MOR.

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.

QSAR: Using the Past to Study the Present

Quantitative structure-activity relationships (QSAR) is a method for predicting the physical and biological properties of small molecules; it is in use in industry and public services. However, as any scientific method, it is challenged by more and more requests, especially considering its possible role in assessing the safety of new chemicals. To answer the question whether QSAR, by exploiting available knowledge, can build new knowledge, the chapter reviews QSAR methods in search of a QSAR epistemology. QSAR stands on tree pillars, i.e., biological data, chemical knowledge, and modeling algorithms. Usually the biological data, resulting from good experimental practice, are taken as a true picture of the world; chemical knowledge has scientific bases; so if a QSAR model is not working, blame modeling. The role of modeling in developing scientific theories, and in producing knowledge, is so analyzed. QSAR is a mature technology and is part of a large body of in silico methods and other computational methods. The active debate about the acceptability of the QSAR models, about the way to communicate them, and the explanation to provide accompanies the development of today QSAR models. An example about predicting possible endocrine-disrupting chemicals (EDC) shows the many faces of modern QSAR methods.

In silico analysis of endocrine-disrupting potential of triclosan, bisphenol A, and their analogs and derivatives

Owning to the increasing body of evidence about the ubiquitous exposure to endocrine disruptors (EDCs), particularly bisphenol A (BPA), and associated health effects, BPA has been gradually substituted with insufficiently tested structural analogs. The unmanaged excessive use of antimicrobial agents such as triclosan (TCS) during the COVID-19 outbreak has also raised concerns about its possible interferences with hormonal functions. The similarity of BPA and estradiol, as well as TCS and non-steroidal estrogens, imply that endocrine-disrupting properties of their analogs could be predicted based on the chemical structure. Hence, this study aimed to evaluate the endocrine-disrupting potential of BPA substitutes as well as TCS derivatives and degradation/biotransformation metabolites, in comparison to BPA and TCS based on their molecular properties, computational predictions of pharmacokinetics and binding affinities to nuclear receptors. Based on the obtained results several under-researched BPA analogs exhibited higher binding affinities for nuclear receptors than BPA. Notable analogs included compounds detected in receipts (DD-70, BTUM-70, TGSA, and BisOPP-A), along with a flame retardant, BDP. The possible health hazards linked to exposure to TCS and its mono-hydroxylated metabolites were also found. Further research is needed in order to elucidate the health impacts of these compounds and promote better regulation practices.

Development of a comprehensive open access "molecules with androgenic activity resource (MAAR)" to facilitate risk assessment of chemicals

The increasing prevalence of endocrine-disrupting chemicals (EDCs) and their potential adverse effects on human health underscore the necessity for robust tools to assess and manage associated risks. The androgen receptor (AR) is a critical component of the endocrine system, playing a pivotal role in mediating the biological effects of androgens, which are male sex hormones. Exposure to androgen-disrupting chemicals during critical periods of development, such as fetal development or puberty, may result in adverse effects on reproductive health, including altered sexual differentiation, impaired fertility, and an increased risk of reproductive disorders. Therefore, androgenic activity data is critical for chemical risk assessment. A large amount of androgenic data has been generated using various experimental protocols. Moreover, the data are reported in different formats and in diverse sources. To facilitate utilization of androgenic activity data in chemical risk assessment, the Molecules with Androgenic Activity Resource (MAAR) was developed. MAAR is the first open-access platform designed to streamline and enhance the risk assessment of chemicals with androgenic activity. MAAR's development involved the integration of diverse data sources, including data from public databases and mining literature, to establish a reliable and versatile repository. The platform employs a user-friendly interface, enabling efficient navigation and extraction of pertinent information. MAAR is poised to advance chemical risk assessment by offering unprecedented access to information crucial for evaluating the androgenic potential of a wide array of chemicals. The open-access nature of MAAR promotes transparency and collaboration, fostering a collective effort to address the challenges posed by androgenic EDCs.

Characteristics and partitions of traditional and emerging organophosphate esters in soil and groundwater based on machine learning

Organophosphate esters (OPEs) pose hazards to both humans and the environment. This study applied target screening to analyze the concentrations and detection frequencies of OPEs in the soil and groundwater of representative contaminated sites in the Pearl River Delta. The clusters and correlation characteristics of OPEs in soil and groundwater were calculated by self-organizing map (SOM). The risk assessment and partitions of OPEs in industrial park soil and groundwater were conducted. The results revealed that 14 out of 23 types of OPEs were detected. The total concentrations (Σ23OPEs) ranged from 1.931 to 743.571 ng/L in the groundwater, and 0.218 to 79.578 ng/g in the soil, the former showed highly soluble OPEs with high detection frequencies and concentrations, whereas the latter exhibited the opposite trend. SOM analysis revealed that the distribution of OPEs in the soil differed significantly from that in the groundwater. In the industrial park, OPEs posed acceptable risks in both the soil and groundwater. The soil could be categorized into Zone I and II, and the groundwater into Zone I, II, and III, with corresponding management recommendations. Applying SOM to analyze the characteristics and partitions of OPEs may provide references for other new pollutants and contaminated sites.

Knowledge-based machine learning for predicting and understanding the androgen receptor (AR)-mediated reproductive toxicity in zebrafish

Traditional methods for identifying endocrine-disrupting chemicals (EDCs) that activate androgen receptors (AR) are costly, time-consuming, and low-throughput. This study developed a knowledge-based deep neural network model (AR-DNN) to predict AR-mediated adverse outcomes on female zebrafish fertility. This model started with chemical fingerprints as the input layer and was implemented through a five-layer virtual AR-induced adverse outcome pathway (AOP). Results indicated that the AR-DNN effectively and accurately screens new reproductive toxicants (AUC = 0.94, accuracy = 0.85), providing potential toxicity pathways. Furthermore, 1477 and 2448 chemicals that could lead to infertility were identified in the plastic additives list (PLASTICMAP, n = 7112) and the Inventory of Existing Chemical Substances in China (IECSC, n = 17741), respectively. Colourants containing steroid-like structures are the major active plastic additives that might lower female zebrafish fertility through AR binding, DNA binding, and transcriptional activation. While active IECSC chemicals primarily have the same fragments, such as benzonitrile, nitrobenzene, and quinolone. The predicted toxicity pathways were consistent with existing fish evidence, demonstrating the model's applicability. This knowledge-based approach offers a promising computational toxicology strategy for predicting and characterising the endocrine-disrupting effects and toxic mechanisms of organic chemicals, potentially leading to more efficient and cost-effective screening of EDCs.

Prediction of Endocrine-Disrupting Chemicals Related to Estrogen, Androgen, and Thyroid Hormone (EAT) Modalities Using Transcriptomics Data and Machine Learning

Endocrine-disrupting chemicals (EDCs) are chemicals that can interfere with homeostatic processes. They are a major concern for public health, and they can cause adverse long-term effects such as cancer, intellectual impairment, obesity, diabetes, and male infertility. The endocrine system is a complex machinery, with the estrogen (E), androgen (A), and thyroid hormone (T) modes of action being of major importance. In this context, the availability of in silico models for the rapid detection of hazardous chemicals is an effective contribution to toxicological assessments. We developed Qualitative Gene expression Activity Relationship (QGexAR) models to predict the propensities of chemically induced disruption of EAT modalities. We gathered gene expression profiles from the LINCS database tested on two cell lines, i.e., MCF7 (breast cancer) and A549 (adenocarcinomic human alveolar basal epithelial). We optimized our prediction protocol by testing different feature selection methods and classification algorithms, including CATBoost, XGBoost, Random Forest, SVM, Logistic regression, AutoKeras, TPOT, and deep learning models. For each EAT endpoint, the final prediction was made according to a consensus prediction as a function of the best model obtained for each cell line. With the available data, we were able to develop a predictive model for estrogen receptor and androgen receptor binding and thyroid hormone receptor antagonistic effects with a consensus balanced accuracy on a validation set ranging from 0.725 to 0.840. The importance of each predictive feature was further assessed to identify known genes and suggest new genes potentially involved in the mechanisms of action of EAT perturbation.

Screening Organic Components and Toxicogenic Structures from Regional Fine Particulate Matters Responsible for Myocardial Fibrosis in Male Mice

Numerous studies indicate that fine particulate matters (PM2.5) and its organic components are urgent risk factors for cardiovascular diseases (CVDs). Combining toxicological experiments, effect-directed analyses, and nontarget identification, this study aims to explore whether PM2.5 exposure in coal-combustion areas induces myocardial fibrosis and how to identify the effective organic components and their toxic structures to support regional risk control. First, we constructed an animal model of real-world PM2.5 exposure during the heating season and found that the exposure impaired cardiac systolic function and caused myocardial fibrosis, with chemokine Ccl2-mediated inflammatory response being the key cause of collagen deposition. Then, using the molecular event as target coupled with two-stage chromatographic isolation and mass spectrometry analyses, we identified a total of 171 suspect organic compounds in the PM2.5 samples. Finally, using hierarchical characteristic fragment analysis, we predicted that 40 of them belonged to active compounds with 6 alert structures, including neopentane, butyldimethylamine, 4-ethylphenol, hexanal, decane, and dimethylaniline. These findings provide evidence for risk management and prevention of CVDs in polluted areas.

Recognizing high-priority disinfection byproducts based on experimental and predicted endocrine disrupting data: Virtual screening and in vitro study

So far, about 130 disinfection by-products (DBPs) and several DBPs-groups have had their potential endocrine-disrupting effects tested on some endocrine endpoints. However, it is still not clear which specific DBPs, DBPs-groups/subgroups may be the most toxic substances or groups/subgroups for any given endocrine endpoint. In this study, we attempt to address this issue. First, a list of relevant DBPs was updated, and 1187 DBPs belonging to 4 main-groups (aliphatic, aromatic, alicyclic, heterocyclic) and 84 subgroups were described. Then, the high-priority endocrine endpoints, DBPs-groups/subgroups, and specific DBPs were determined from 18 endpoints, 4 main-groups, 84 subgroups, and 1187 specific DBPs by a virtual-screening method. The results demonstrate that most of DBPs could not disturb the endocrine endpoints in question because the proportion of active compounds associated with the endocrine endpoints ranged from 0 (human thyroid receptor beta) to 32% (human transthyretin (hTTR)). All the endpoints with a proportion of active compounds greater than 10% belonged to the thyroid system, highlighting that the potential disrupting effects of DBPs on the thyroid system should be given more attention. The aromatic and alicyclic DBPs may have higher priority than that of aliphatic and heterocyclic DBPs by considering the activity rate and potential for disrupting effects. There were 2 (halophenols and estrogen DBPs), 12, and 24 subgroups that belonged to high, moderate, and low priority classes, respectively. For individual DBPs, there were 23 (2%), 193 (16%), and 971 (82%) DBPs belonging to the high, moderate, and low priority groups, respectively. Lastly, the hTTR binding affinity of 4 DBPs was determined by an in vitro assay and all the tested DBPs exhibited dose-dependent binding potency with hTTR, which was consistent with the predicted result. Thus, more efforts should be performed to reveal the potential endocrine disruption of those high research-priority main-groups, subgroups, and individual DBPs.

Exposure to UV-B filter octylmethoxycinnamate and human health effects: Focus on endocrine disruptor actions

Human skin is the first line of photoprotection against UV radiation. However, despite having its defence mechanisms, the photoprotection that the skin exerts is not enough. To protect human skin, the inclusion of UV filters in the cosmetic industry has grown significantly as a photoprotection strategy. Octylmethoxycinnamate, also designated by octinoxate, or 2-ethylhexyl-4-methoxycinnamate (CAS number: 5466-77-3) is one of the most widely used UV-B filter in the cosmetic industry. The toxic effects of OMC have alarmed the public, but there is still no consensus in the scientific community about its use. This article aims to provide an overview of the UV filters' photoprotection, emphasizing the OMC and the possible negative effects it may have on the public health. Moreover, the current legislation will be addressed. In summary, the recommendations should be rethought to assess their risk-benefit, since the existing literature warns us to endocrine-disrupting effects of OMC. Further studies should be focus on the toxicity of OMC alone, in mixture and should consider its degradation products, to improve the knowledge of its risk assessment as EDC.

PM2.5 exposure contributes to anxiety and depression-like behaviors via phenyl-containing compounds interfering with dopamine receptor

As a global problem, fine particulate matter (PM2.5) really needs local fixes. Considering the increasing epidemiological relevance to anxiety and depression but inconsistent toxicological results, the most important question is to clarify whether and how PM2.5 causally contributes to these mental disorders and which components are the most dangerous for crucial mitigation in a particular place. In the present study, we chronically subjected male mice to a real-world PM2.5 exposure system throughout the winter heating period in a coal combustion area and revealed that PM2.5 caused anxiety and depression-like behaviors in adults such as restricted activity, diminished exploratory interest, enhanced repetitive stereotypy, and elevated acquired immobility, through behavioral tests including open field, elevated plus maze, marble-burying, and forced swimming tests. Importantly, we found that dopamine signaling was perturbed using mRNA transcriptional profile and bioinformatics analysis, with Drd1 as a potential target. Subsequently, we developed the Drd1 expression-directed multifraction isolating and nontarget identifying framework and identified a total of 209 compounds in PM2.5 organic extracts capable of reducing Drd1 expression. Furthermore, by applying hierarchical characteristic fragment analysis and molecular docking and dynamics simulation, we clarified that phenyl-containing compounds competitively bound to DRD1 and interfered with dopamine signaling, thereby contributing to mental disorders. Taken together, this work provides experimental evidence for researchers and clinicians to identify hazardous factors in PM2.5 and prevent adverse health outcomes and for local governments and municipalities to control source emissions for diminishing specific disease burdens.

VenomPred 2.0: A Novel In Silico Platform for an Extended and Human Interpretable Toxicological Profiling of Small Molecules

The application of artificial intelligence and machine learning (ML) methods is becoming increasingly popular in computational toxicology and drug design; it is considered as a promising solution for assessing the safety profile of compounds, particularly in lead optimization and ADMET studies, and to meet the principles of the 3Rs, which calls for the replacement, reduction, and refinement of animal testing. In this context, we herein present the development of VenomPred 2.0 (http://www.mmvsl.it/wp/venompred2/), the new and improved version of our free of charge web tool for toxicological predictions, which now represents a powerful web-based platform for multifaceted and human-interpretable in silico toxicity profiling of chemicals. VenomPred 2.0 presents an extended set of toxicity endpoints (androgenicity, skin irritation, eye irritation, and acute oral toxicity, in addition to the already available carcinogenicity, mutagenicity, hepatotoxicity, and estrogenicity) that can be evaluated through an exhaustive consensus prediction strategy based on multiple ML models. Moreover, we also implemented a new utility based on the Shapley Additive exPlanations (SHAP) method that allows human interpretable toxicological profiling of small molecules, highlighting the features that strongly contribute to the toxicological predictions in order to derive structural toxicophores.

Pyrene and its derivatives increase lung adverse effects by activating aryl hydrocarbon receptor transcription

Derivatives of polycyclic aromatic hydrocarbons (PAHs) pose significant threat to environment and human health due to their widespread and potential hazards. However, adverse effects and action mechanisms of PAH derivatives on human health have not been attempted yet. Herein, we chose pyrene and its derivatives (1-hydroxypyrene, 1-nitropyrene, and 1-methylpyrene) to investigate adverse effect mechanism to human lungs using in vitro and in vivo methods. Results showed that pyrene derivatives have higher lung health risks than original pyrene. They can activate AhR, subsequently affecting expression of downstream target genes CYP1A1 and CYP1B1. The binding energies of pyrene and its derivatives ranged from -16.07 to -27.25 kcal/mol by molecular dynamics simulations, implying that pyrene and its derivatives acted as agonists of AhR and increased adverse effects on lungs. Specifically, 1-nitropyrene exhibited stabler binding conformation and stronger AhR expression. In addition, sensitivity of pyrene and its derivatives to AhR activation was attributed to type and number of key amino acids in AhR, that is, pyrene (Leu293), 1-nitropyrene (Cys333, Met348, and Val381), 1-hydroxypyrene (Leu293 and Phe287), and 1-methylpyrene (Met348). In summary, we provide a universal approach for understanding action mechanisms of PAH derivatives on human health, and their adverse effects should be taken seriously.

Interpretable attention-based multi-encoder transformer based QSPR model for assessing toxicity and environmental impact of chemicals

The rising demand from consumer goods and pharmaceutical industry is driving a fast expansion of newly developed chemicals. The conventional toxicity testing of unknown chemicals is expensive, time-consuming, and raises ethical concerns. The quantitative structure-property relationship (QSPR) is an efficient computational method because it saves time, resources, and animal experimentation. Advances in machine learning have improved chemical analysis in QSPR studies, but the real-world application of machine learning-based QSPR studies was limited by the unexplainable 'black box' feature of the machine learnings. In this study, multi-encoder structure-to-toxicity (S2T)-transformer based QSPR model was developed to estimate the properties of polychlorinated biphenyls (PCBs) and endocrine disrupting chemicals (EDCs). Simplified molecular input line entry systems (SMILES) and molecular descriptors calculated by the Dragon 6 software, were simultaneously considered as input of QSPR model. Furthermore, an attention-based framework is proposed to describe the relationship between the molecular structure and toxicity of hazardous chemicals. The S2T-transformer model achieved the highest R2 scores of 0.918, 0.856, and 0.907 for logarithm of octanol-water partition coefficient (Log KOW), octanol-air partition coefficient (Log KOA), and bioconcentration factor (Log BCF) estimation of PCBs, respectively. Moreover, the attention weights were able to properly interpret the lateral (meta, para) chlorination associated with PCBs toxicity and environmental impact.

Effect of bisphenol A on the neurological system: a review update

Bisphenol A (BPA) is an endocrine-disrupting chemical (EDC) and one of the most produced synthetic compounds worldwide. BPA can be found in epoxy resins and polycarbonate plastics, which are frequently used in food storage and baby bottles. However, BPA can bind mainly to estrogen receptors, interfering with various neurologic functions, its use is a topic of significant concern. Nonetheless, the neurotoxicity of BPA has not been fully understood despite numerous investigations on its disruptive effects. Therefore, this review aims to highlight the most recent studies on the implications of BPA on the neurologic system. Our findings suggest that BPA exposure impairs various structural and molecular brain changes, promoting oxidative stress, changing expression levels of several crucial genes and proteins, destructive effects on neurotransmitters, excitotoxicity and neuroinflammation, damaged blood-brain barrier function, neuronal damage, apoptosis effects, disruption of intracellular Ca2+ homeostasis, increase in reactive oxygen species, promoted apoptosis and intracellular lactate dehydrogenase release, a decrease of axon length, microglial DNA damage, astrogliosis, and significantly reduced myelination. Moreover, BPA exposure increases the risk of developing neurologic diseases, including neurovascular (e.g. stroke) and neurodegenerative (e.g. Alzheimer's and Parkinson's) diseases. Furthermore, epidemiological studies showed that the adverse effects of BPA on neurodevelopment in children contributed to the emergence of serious neurological diseases like attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), depression, emotional problems, anxiety, and cognitive disorders. In summary, BPA exposure compromises human health, promoting the development and progression of neurologic disorders. More research is required to fully understand how BPA-induced neurotoxicity affects human health.

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

The role of estrogen receptors (ERs) in breast cancer is of great importance in both clinical practice and scientific exploration. However, around 15-30% of those affected do not see benefits from the usual treatments owing to the innate resistance mechanisms, while 30-40% will gain resistance through treatments. In order to address this problem and facilitate community-wide efforts, machine learning (ML)-based approaches are considered one of the most cost-effective and large-scale identification methods. Herein, we propose a new SMILES-based stacked approach, termed StackER, for the accelerated and efficient identification of ERα and ERβ inhibitors. In StackER, we first established an up-to-date dataset consisting of 1,996 and 1,207 compounds for ERα and ERβ, respectively. Using the up-to-date dataset, StackER explored a wide range of different SMILES-based feature descriptors and ML algorithms in order to generate probabilistic features (PFs). Finally, the selected PFs derived from the two-step feature selection strategy were used for the development of an efficient stacked model. Both cross-validation and independent tests showed that StackER surpassed several conventional ML classifiers and the existing method in precisely predicting ERα and ERβ inhibitors. Remarkably, StackER achieved MCC values of 0.829-0.847 and 0.712-0.786 in terms of the cross-validation and independent tests, respectively, which were 5.92-8.29 and 1.59-3.45% higher than the existing method. In addition, StackER was applied to determine useful features for being ERα and ERβ inhibitors and identify FDA-approved drugs as potential ERα inhibitors in efforts to facilitate drug repurposing. This innovative stacked method is anticipated to facilitate community-wide efforts in efficiently narrowing down ER inhibitor screening.

EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

A Graph Neural Network Model with a Transparent Decision-Making Process Defines the Applicability Domain for Environmental Estrogen Screening

The application of deep learning (DL) models for screening environmental estrogens (EEs) for the sound management of chemicals has garnered significant attention. However, the currently available DL model for screening EEs lacks both a transparent decision-making process and effective applicability domain (AD) characterization, making the reliability of its prediction results uncertain and limiting its practical applications. To address this issue, a graph neural network (GNN) model was developed to screen EEs, achieving accuracy rates of 88.9% and 92.5% on the internal and external test sets, respectively. The decision-making process of the GNN model was explored through the network-like similarity graphs (NSGs) based on the model features (FT). We discovered that the accuracy of the predictions is dependent on the feature distribution of compounds in NSGs. An AD characterization method called ADFT was proposed, which excludes predictions falling outside of the model's prediction range, leading to a 15% improvement in the F1 score of the GNN model. The GNN model with the AD method may serve as an efficient tool for screening EEs, identifying 800 potential EEs in the Inventory of Existing Chemical Substances of China. Additionally, this study offers new insights into comprehending the decision-making process of DL models.

Effect-Targeted Mapping of Potential Estrogenic Agonists and Antagonists in Wastewater via a Conformation-Specific Reporter-Mediated Biosensor

Wastewater treatment plants (WWTPs) are regarded as the main sources of estrogens that reach the aquatic environment. Hence, continuous monitoring of potential estrogenic-active compounds by a biosensor is an appealing approach. However, existing biosensors cannot simultaneously distinguish and quantify estrogenic agonists and antagonists. To overcome the challenge, we developed an estrogen receptor-based biosensor that selectively screened estrogenic agonists and antagonists by introducing rationally designed agonist/antagonist conformation-specific reporters. The double functional conformation-specific reporters consist of a Cy5.5-labeled streptavidin moiety and a peptide moiety, serving as signal recognition and signal transduction elements. In addition, the conformation recognition mechanism was further validated at the molecular level through molecular docking. Based on the two-step "turn-off" strategy, the biosensor exhibited remarkable sensitivity, detecting 17β-estradiol-binding activity equivalent (E2-BAE) at 7 ng/L and 4-hydroxytamoxifen-binding activity equivalent (4-OHT-BAE) at 91 ng/L. To validate its practicality, the biosensor was employed in a case study involving wastewater samples from two full-scale WWTPs across different treatment stages to map their estrogenic agonist and antagonist binding activities. Comparison with the yeast two-hybrid bioassay showed a strong liner relationship (r2 = 0.991, p < 0.0001), indicating the excellent accuracy and reliability of this technology in real applications.

4-Methylbenzylidene camphor triggers estrogenic effects via the brain-liver-gonad axis in zebrafish larvae

4-Methylbenzylidene camphor (4-MBC), an emerging contaminant, is a widely-used ultraviolet (UV) filter incorporated into cosmetics because it protects the skin from UV rays and counters photo-oxidation. Despite the well-established estrogenic activity of 4-MBC, the link between this activity and its effects on neurobehavior and the liver remains unknown. Thus, we exposed zebrafish larvae to environmentally relevant concentrations of 4-MBC with 1.39, 4.17, 12.5 and 15.4 μg/mL from 3 to 5 days postfertilization. We found that 4-MBC produced an estrogenic effect by intensifying fluorescence in the transgenic zebrafish, which was counteracted by co-exposure with estrogen receptor antagonist. 4-MBC-upregulated estrogen receptor alpha (erα) mRNA, and an interaction between 4-MBC and ERα suggested ERα's involvement in the 4-MBC-induced estrogenic activity. RNA sequencing unearthed 4-MBC-triggered responses in estrogen stimulus and lipid metabolism. Additionally, 4-MBC-induced hypoactivity and behavioral phenotypes were dependent on the estrogen receptor (ER) pathway. This may have been associated with the disruption of acetylcholinesterase and acetylcholine activities. As a result, 4-MBC increased vitellogenin expression and caused lipid accumulation in the liver of zebrafish larvae. Collectively, this is the first study to report 4-MBC-caused estrogenic effects through the brain-liver-gonad axis. It provides novel insight into how 4-MBC perturbs the brain and liver development.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Data driven toxicity assessment of organic chemicals against Gammarus species using QSAR approach

Nowadays, organic chemicals play an essential role in almost all walks of life and have become indispensable to modern society. However, the continually synthesized chemicals and the numerous potential adverse endpoints against living organisms increasingly promote the regulators regarding the computational approach as a crucial supplement and an alternative to the traditional animal tests in chemical risk assessment. In this present research, we evaluated the ecotoxicity of chemicals against four typical Gammarus species, which constituted a critical element in detritus cycle and also the recommended species for water monitoring. We first screened the molecular descriptors based on the Genetic Algorithm and then developed the Quantitative Structure-Activity Relationship models using the Multiple Linear Regression method. The statistical results from various validation metrics suggested that the obtained models were internally robust and externally predictive. The application domain analysis based on the leverage approach and standardized residual method demonstrated the broad application range of each model. The interpretation of molecular descriptors in each model suggested that the chemicals with higher polarity and hydrophilicity tend to be less toxic, whereas the lipophilic moieties would enhance the chemical toxicity. Meanwhile, the other selected descriptors, such as Chi-cluster, heterocyclic, and distance matrix descriptors, manifested that the chemical toxicity was also affected by molecular branching, connectivity, electrotopological state, and other various properties. In summary, the present work proposed well-performed QSAR models and clarified the possible toxic mechanism of chemicals against Gammarus species. The obtained models could help predict the toxicity data and conduct a preliminary risk assessment, thus guiding the subsequent animal tests and reducing the assessment cost.

QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application

Bioconcentration factor (BCF) is one of the important parameters for developing human health ambient water quality criteria (HHAWQC) for chemical pollutants. Traditional experimental method to obtain BCF is time-consuming and costly. Therefore, prediction of BCF by modeling has attracted much attention. QSAR (Quantitative Structure-Activity Relationship) model based on molecular descriptor is often used to predict BCF, however, in order to improve the accuracy of prediction, previous models are only applicable for prediction for a single category of substance and a single species, and cannot meet the needs of BCF prediction of pollutants lacing toxicity data. In this study, optimized 17 traditional molecular descriptor and five kinds of bioactivity descriptor were selected from more than 200 molecular descriptor and 25 kinds of biological activity descriptors. A QSAR-QSIIR (Quantitative Structure In vitro-In vivo Relationship) model suitable for multiple chemical substances and whole species is constructed by using optimized 4-MLP machine learning algorithm with selected molecular and bioactivity descriptors. The constructed model significantly improves the prediction accuracy of BCF. The R² of verification set and test set are 0.8575 and 0.7924, respectively, and the difference between predicted BCF and measured BCF is mostly less than 1.5 times. Then, BCF of BTEX in Chinese common aquatic products is predicted using the constructed QSAR-QSIIR model, and the HHAWQC of BTEX in China are derived using the predicted BCF, which provides a valuable reference for establishment of China's BTEX water quality standards.

Predicting Endocrine Disruption Using Conformal Prediction - A Prioritization Strategy to Identify Hazardous Chemicals with Confidence

Receptor-mediated molecular initiating events (MIEs) and their relevance in endocrine activity (EA) have been highlighted in literature. More than 15 receptors have been associated with neurodevelopmental adversity and metabolic disruption. MIEs describe chemical interactions with defined biological outcomes, a relationship that could be described with quantitative structure-activity relationship (QSAR) models. QSAR uncertainty can be assessed using the conformal prediction (CP) framework, which provides similarity (i.e., nonconformity) scores relative to the defined classes per prediction. CP calibration can indirectly mitigate data imbalance during model development, and the nonconformity scores serve as intrinsic measures of chemical applicability domain assessment during screening. The focus of this work was to propose an in silico predictive strategy for EA. First, 23 QSAR models for MIEs associated with EA were developed using high-throughput data for 14 receptors. To handle the data imbalance, five protocols were compared, and CP provided the most balanced class definition. Second, the developed QSAR models were applied to a large data set (∼55,000 chemicals), comprising chemicals representative of potential risk for human exposure. Using CP, it was possible to assess the uncertainty of the screening results and identify model strengths and out of domain chemicals. Last, two clustering methods, t-distributed stochastic neighbor embedding and Tanimoto similarity, were used to identify compounds with potential EA using known endocrine disruptors as reference. The cluster overlap between methods produced 23 chemicals with suspected or demonstrated EA potential. The presented models could be utilized for first-tier screening and identification of compounds with potential biological activity across the studied MIEs.

Identification of Glucocorticoid Receptor Antagonistic Activities and Responsible Compounds in House Dust: Bioaccessibility Should Not Be Ignored

More and more contaminants in dust have been found to be glucocorticoid receptor (GR) disrupting chemicals. However, little is known about the related potency and responsible toxicants, especially for the main bioaccessible ones in dust. An effect-directed analysis (EDA)-based workflow was developed, including solvent-based exhaustive extraction/tenax-assisted bioaccessible extraction (TBE), high-throughput bioassays, suspect and non-target analysis, as well as in silico candidate selection, for a more realistic identification of responsible contaminants in dust. None of the 39 dust samples from 23 cities in China exhibited GR agonistic activity, while GR antagonistic potencies were detected in 34.8% of samples, being significantly different from the high detection frequency of GR agonistic activities in other environmental media. The GR antagonistic potencies of the dust samples were all reduced after bioaccessible extraction. The mean bioaccessibility of GR antagonistic potency compared with the related exhaustive extracts was 36.8%, and the lowest value was 9%. By using in silico candidate selection, greater than 99% candidate chemical structures which were found by a non-target screening strategy were removed. Di-n-butyl phthalate (DnBP), diisobutyl phthalate (DiBP), and nicotine (NIC) were responsible for the activities of the exhaustive extracts of dust, contributing up to 91% potencies. DiBP and DnBP were also responsible for the bioaccessible activities, contributing up to 79% potencies. However, the contribution from NIC decreased significantly and can be ignored because of its low bioaccessibility. This study suggests that the improved workflow combining extraction, reporter gene bioassays, suspect and non-target analysis, as well as in silico candidate selection is useful for EDA analysis in dust samples. In addition, exhaustive extraction may overestimate the risk of contaminants, while bioaccessibility evaluation based on bioaccessible extraction is essential in both effect evaluation and toxicant identification.

Quick and efficient quantitative predictions of androgen receptor binding affinity for screening Endocrine Disruptor Chemicals using 2D-QSAR and Chemical Read-Across

Endocrine Disruptor Chemicals are synthetic or natural molecules in the environment that promote adverse modifications of endogenous hormone regulation in humans and/or in animals. In the present research, we have applied two-dimensional quantitative structure-activity relationship (2D-QSAR) modeling to analyze the structural features of these chemicals responsible for binding to the androgen receptors (logRBA) in rats. We have collected the receptor binding data from the EDKB database (https://www.fda.gov/science-research/endocrine-disruptor-knowledge-base/accessing-edkb-database) and then employed the DTC-QSAR tool, available from https://dtclab.webs.com/software-tools, for dataset division, feature selection, and model development. The final partial least squares model was evaluated using various stringent validation criteria. From the model, we interpreted that hydrophobicity, steroidal nucleus, bulkiness and a hydrogen bond donor at an appropriate position contribute to the receptor binding affinity, while presence of electron rich features like aromaticity and polar groups decrease the receptor binding affinity. Additionally we have also performed chemical Read-Across predictions using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home, and the results for the external validation metrics were found to be better than the QSAR-derived predictions. The best quality of external predictions emerged from the q-RASAR approach which combines both read-across and QSAR. To explore the essential features responsible for the receptor binding, pharmacophore mapping, molecular docking along with molecular dynamics simulation were also performed, and the results are in accordance with the QSAR/q-RASAR findings.

UV-B filter octylmethoxycinnamate-induced vascular endothelial disruption on rat aorta: In silico and in vitro approach

Throughout human life, an extensive and varied range of emerging environmental contaminants, called endocrine disruptors (EDCs), cause adverse health effects, including in the cardiovascular (CV) system. Cardiovascular diseases (CVD) are worryingly one of the leading causes of all mortality and mobility worldwide. The UV-B filter octylmethoxycinnamate (also designated octinoxate, or ethylhexyl methoxycinnamate (CAS number: 5466-77-3)) is an EDC widely present in all personal care products. However, to date, there are no studies evaluating the OMC-induced effects on vasculature using animal models to improve human cardiovascular health. This work analysed the effects of OMC on rat aorta vasculature and explored the modes of action implicated in these effects. Our results indicated that OMC relaxes the rat aorta by endothelium-dependent mechanisms through the signaling pathways of cyclic nucleotides and by endothelium-independent mechanisms involving inhibition of L-Type voltage-operated Ca²⁺ channels (L-Type VOCC). Overall, OMC toxicity on rat aorta may produce hypotension via vasodilation due to excessive NO release and blockade of L-Type VOCC. Moreover, the OMC-induced endothelial dysfunction may also occur by promoting the endothelial release of endothelin-1. Therefore, our findings demonstrate that exposure to OMC alters the reactivity of the rat aorta and highlight that long-term OMC exposure may increase the risk of human CV diseases.

Predicting bioconcentration factor and estrogen receptor bioactivity of bisphenol a and its analogues in adult zebrafish by directed message passing neural networks

The bioconcentration factor (BCF) is a key parameter for bioavailability assessment of environmental pollutants in regulatory frameworks. The comparative toxicology and mechanism of action of congeners are also of concern. However, there are limitations to acquire them by conducting field and laboratory experiments while machinelearning is emerging as a promising predictive tool to fill the gap. In this study, the Direct Message Passing Neural Network (DMPNN) was applied to predict logBCFs of bisphenol A (BPA) and its four analogues (bisphenol AF (BPAF), bisphenol B (BPB), bisphenol F (BPF) and bisphenol S (BPS)). For the test set, the Pearson correlation coefficient (PCC) and mean square error (MSE) were 0.85 and 0.52 respectively, suggesting a good predictive performance. The predicted logBCFs values by the DMPNN ranging from 0.35 (BPS) to 2.14 (BPAF) coincided well with those by the classical EPI Suite (BCFBAF model). Besides, estrogen receptor α (ERα) bioactivity of these bisphenols was also predicted well by the DMPNN, with a probability of 97.0 % (BPB) to 99.7 % (BPAF), which was validated by the extent of vitellogenin (VTG) induction in male zebrafish as a biomarker except BPS. Thus, with little need for expert knowledge, DMPNN is confirmed to be a useful tool to accurately predict logBCF and screen for estrogenic activity from molecular structures. Moreover, a gender difference was noted in the changes of three endpoints (logBCF, ER binding affinity and VTG levels), the rank order of which was BPAF > BPB > BPA > BPF > BPS consistently, and abnormal amino acid metabolism is featured as an omics signature of abnormal hormone protein expression.

Identifying the acute toxicity of contaminated sediments using machine learning models

Ecological risk assessment of contaminated sediment has become a fundamental component of water quality management programs, supporting decision-making for management actions or prompting additional investigations. In this study, we proposed a machine learning (ML)-based approach to assess the ecological risk of contaminated sediment as an alternative to existing index-based methods and costly toxicity testing. The performance of three widely used index-based methods (the pollution load index, potential ecological risk index, and mean probable effect concentration) and three ML algorithms (random forest, support vector machine, and extreme gradient boosting [XGB]) were compared in their prediction of sediment toxicity using 327 nationwide data sets from Korea consisting of 14 sediment quality parameters and sediment toxicity testing data. We also compared the performances of classifiers and regressors in predicting the toxicity for each of RF, SVM, and XGB algorithms. For all algorithms, the classifiers poorly classified toxic and non-toxic samples due to limited information on the sediment composition and the small training dataset. The regressors with a given classification threshold provided better classification, with the XGB regressor outperforming the other models in the classification. A permutation feature importance analysis revealed that Cr, Cu, Pb, and Zn were major contributors to toxicity prediction. The ML-based approach has the potential to be even more useful in the future with the expected increase in available sediment data.

Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials

Metal-organic frameworks (MOFs), a class of porous nanomaterials, have been widely used in gas adsorption-based applications due to their high porosities and chemical tunability. To facilitate the discovery of high-performance MOFs for different applications, a variety of machine learning models have been developed to predict the gas adsorption capacities of MOFs. Most of the predictive models are developed using traditional machine learning algorithms. However, the continuously increasing sizes of MOF datasets and the complicated relationships between MOFs and their gas adsorption capacities make deep learning a suitable candidate to handle such big data with increased computational power and accuracy. In this study, we developed models for predicting gas adsorption capacities of MOFs using two deep learning algorithms, multilayer perceptron (MLP) and long short-term memory (LSTM) networks, with a hypothetical set of about 130,000 structures of MOFs with methane and carbon dioxide adsorption data at different pressures. The models were evaluated using 10 iterations of 10-fold cross validations and 100 holdout validations. The MLP and LSTM models performed similarly with high prediction accuracy. The models for predicting gas adsorption at a higher pressure outperformed the models for predicting gas adsorption at a lower pressure. The deep learning models are more accurate than the random forest models reported in the literature, especially for predicting gas adsorption capacities at low pressures. Our results demonstrated that deep learning algorithms have a great potential to generate models that can accurately predict the gas adsorption capacities of MOFs.

Molecular consequences of the exposure to toxic substances for the endocrine system of females

Endocrine-disrupting chemicals (EDCs) are common in the environment and in everyday products such as cosmetics, plastic food packaging, and medicines. These substances are toxic in small doses (even in the order of micrograms) and enter the body through the skin, digestive or respiratory system. Numerous studies confirm the negative impact of EDCs on living organisms. They disrupt endocrine functions, contributing to the development of neoplastic and neurological diseases, as well as problems with the circulatory system and reproduction. EDCs affect humans and animals by modulating epigenetic processes that can lead to disturbances in gene expression or failure and even death. They also affect steroid hormones by binding to their receptors as well as interfering with synthesis and secretion of hormones. Prenatal exposure may be related to the impact of EDCs on offspring, resulting in effects of these substances on the ovaries and leading to the reduction of fertility through disturbances in the function of steroid receptors or problems with steroidogenesis and gametogenesis. Current literature indicates the need to continue research on the effects of EDCs on the female reproductive system. The aim of this review was to identify the effects of endocrine-disrupting chemicals on the female reproductive system and their genetic effects based on recent literature.

The strategy for estrogen receptor mediated-risk assessment in environmental water: A combination of species sensitivity distributions and in silico approaches

Risk assessment for molecular toxicity endpoints of environmental matrices may be a pressing issue. Here, we combined chemical analysis with species sensitivity distributions (SSD) and in silico docking for multi-species estrogen receptor mediated-risk assessment in water from Dongjiang River, China. The water contains high levels of phenolic endocrine-disrupting chemicals (PEDCs) and phthalic acid esters (PAEs). The concentration of ∑₄PEDCs and ∑₆PAEs ranged from 2202 to 3404 ng/L and 834-4368 ng/L, with an average of 3241 and 2215 ng/L, respectively. The SSD approach showed that 4-NP, BPA, E2 of PEDCs, and DBP, DOP, and DEHP could severely threaten the aquatic ecosystems, while most other target compounds posed low-to-medium risks. Moreover, binding affinities from molecular docking among PEDCs, PAEs, and estrogen receptors (ERα, Erβ, and GPER) were applied as toxic equivalency factors. Estrogen receptor-mediated risk suggested that PEDCs were the main contributors, containing 53.37-69.79% of total risk. They potentially pose more severe estrogen-receptor toxicity to zebrafish, turtles, and frogs. ERβ was the major contributor, followed by ERα and GPER. This study is the first attempt to assess the estrogen receptor-mediated risk of river water in multiple aquatic organisms. The in silico simulation approach could complement toxic effect evaluations in molecular endpoints.

Development, Validation, and Application of a Human Reproductive Toxicity Prediction Model Based on Adverse Outcome Pathway

A growing number of environmental contaminants have been proved to have reproductive toxicity to males and females. However, the unclear toxicological mechanism of reproductive toxicants limits the development of virtual screening methods. By consolidating androgen (AR)-/estrogen receptors (ERs)-mediated adverse outcome pathways (AOPs) with more than 8000 chemical substances, we uncovered relationships between chemical features, a series of pathway-related effects, and reproductive apical outcomes─changes in sex organ weights. An AOP-based computational model named RepTox was developed and evaluated to predict and characterize chemicals' reproductive toxicity for males and females. Results showed that RepTox has three outstanding advantages. (I) Compared with the traditional models (37 and 81% accuracy, respectively), AOP significantly improved the predictive robustness of RepTox (96.3% accuracy). (II) Compared with the application domain (AD) of models based on small in vivo datasets, AOP expanded the ADs of RepTox by 1.65-fold for male and 3.77-fold for female, respectively. (III) RepTox implied that hydrophobicity, cyclopentanol substructure, and several topological indices (e.g., hydrogen-bond acceptors) were important, unbiased features associated with reproductive toxicants. Finally, RepTox was applied to the inventory of existing chemical substances of China and identified 2100 and 7281 potential toxicants to the male and female reproductive systems, respectively.

Endocrine-Disrupting Effects of Bisphenol A on the Cardiovascular System: A Review

Currently, the plastic monomer and plasticizer bisphenol A (BPA) is one of the most widely used chemicals. BPA is present in polycarbonate plastics and epoxy resins, commonly used in food storage and industrial or medical products. However, the use of this synthetic compound is a growing concern, as BPA is an endocrine-disrupting compound and can bind mainly to estrogen receptors, interfering with different functions at the cardiovascular level. Several studies have investigated the disruptive effects of BPA; however, its cardiotoxicity remains unclear. Therefore, this review’s purpose is to address the most recent studies on the implications of BPA on the cardiovascular system. Our findings suggest that BPA impairs cardiac excitability through intracellular mechanisms, involving the inhibition of the main ion channels, changes in Ca²⁺ handling, the induction of oxidative stress, and epigenetic modifications. Our data support that BPA exposure increases the risk of developing cardiovascular diseases (CVDs) including atherosclerosis and its risk factors such as hypertension and diabetes. Furthermore, BPA exposure is also particularly harmful in pregnancy, promoting the development of hypertensive disorders during pregnancy. In summary, BPA exposure compromises human health, promoting the development and progression of CVDs and risk factors. Further studies are needed to clarify the human health effects of BPA-induced cardiotoxicity.

Bisphenol A Analogs Induce Cellular Dysfunction in Human Trophoblast Cells in a Thyroid Hormone Receptor-Dependent Manner: In Silico and In Vitro Analyses

Bisphenol A (BPA) and its analogs are frequently detected in human daily necessities and environmental media. Placental thyroid hormone plays an important role in fetal development. Herein, we followed the adverse outcome pathway (AOP) to explore the toxic mechanisms of BPA and its analogs toward placental thyroid hormone receptor (TR). First, the TOX21 database was used, and the interactions between BPA analogs and the ligand-binding domains (LBDs) of two subtypes of TR (TRα and TRβ) were subjected to in silico screening using molecular docking (MD) and molecular dynamics simulation (MDS). Fluorescence spectra and circular dichroism (CD) showed that BPA and its analogs interfere with TRs as a molecular initiation event (MIE), including static fluorescence quenching and secondary structural content changes in TR-LBDs. Key events (KEs) of the AOP, including the toxicity induced in placental chorionic trophoblast cells (HTR-8/SVneo) by an inverted U-shaped dose effect and changes in ROS levels, were tested in vitro. BPA, BPB, and BPAF significantly changed the expression level of TRβ, and only BPAF significantly downregulated the expression level of TRα. In conclusion, our study contributes to the health risk assessment of BPA and its analogs regarding placental adverse outcomes (AOs).

Facile one-step strategy for the formation of BiOIO3/[Bi6O6(OH)3](NO3)3·1.5H2O heterojunction to enhancing photocatalytic activity

The heterojunction photocatalyst, BiOIO₃/[Bi₆O₆(OH)₃](NO₃)₃·1.5H₂O (BiOIO₃/BBN), was successfully synthesized by a simple one-step hydrothermal method. The results showed that under UV light irradiation, the formation of a heterojunction could greatly enhance the photocatalytic efficiency of the prepared catalyst for bisphenol A (BPA). The BiOIO₃/BBN heterostructure had the best reaction rate constant, which was 81.82 times, 1.52 times, and 43.40 times improvement of TiO₂, BiOIO₃, and BBN respectively. Through the free radical capture experiments and electron spin resonance spectroscopy, it was conducted that ¹O₂, h⁺, e^-, •OH and •O₂^- were reactive species in the process of photocatalytic degradation of BPA. The photocatalytic mechanism was further investigated and confirmed that the BiOIO₃/BBN heterojunction could improve the separation and transfer of photo-generated carriers, thereby greatly enhancing the catalytic efficiency. The degradation products of BPA were detected by HPLC-MS, and the degradation reaction pathway was deduced.

The VEGAHUB Platform: The Philosophy and the Tools

VEGAHUB (www.vegahub.eu) is a repository of freely available, downloadable tools based on computational toxicology methodologies. The main software tool available in VEGAHUB is VEGA QSAR software encoding more than 90 quantitative structure-activity relationship (QSAR) models for tens of endpoints for human toxicology, ecotoxicology, environmental, physico-chemical and toxicokinetic properties. However, beyond VEGA QSAR, VEGAHUB offers several other tools. Here, we present these resources, the possibilities to fully exploit them and the ways in which to integrate results provided by different VEGAHUB tools. Read-across and weight-of-evidence represent a major advantage of VEGAHUB. Integration between hazard and exposure is provided within innovative tools, which are specific for well-defined scenarios, such as those for cosmetic products. Prioritisation can be achieved by integrating results from 48 models. Finally, we highlight how some tools may not only fit predefined endpoints but also could be applied to general problems and research applications in the QSAR field. A couple of examples are provided, in which a critical assessment of the predictions and the documentation associated with the prediction are considered, in order to properly assess the quality of the results. These results may be associated with different levels of uncertainty or even be conflicting.

Graphene oxide enhanced the endocrine disrupting effects of bisphenol A in adult male zebrafish: Integrated deep learning and metabolomics studies

In our previous studies, it was found that graphene oxide (GO) reduced the endocrine disruption of bisphenol A (BPA) in zebrafish embryo and larvae, but through different mechanisms. In this study, adult male zebrafish were selected to further understand the interactions between GO and BPA considering that adult zebrafish have different uptake pathways and metabolism from embryo and larvae. BPA was predicted to bind with the estrogen receptor α (ERα) with a probability of 98.1% by training a directed-message passing deep neural network model, and was confirmed by molecular docking analysis. The results were in accordance with the significantly increased vitellogenin (VTG) and estradiol (E2) levels, while decreased testosterone (T) and follicle-stimulating hormone (FSH) levels in the adult male zebrafish after 7 d exposure to 500 μg/L BPA. Compared to BPA single exposure group, the presence of GO led to significantly lower T and FSH levels and fewer spermatozoa, indicating that GO enhanced the endocrine disruption effects of BPA in the adult zebrafish. Metabolomics analysis revealed that 5 μg/L BPA could elicit changes in the metabolome, and the responses were correlated with BPA concentrations. Metabolic pathway analysis revealed more disturbance was caused by the mixture of GO and BPA compared to BPA alone, including three additional pathways and stronger perturbations on carbohydrate, lipid, and amino acid metabolism, fortifying that GO exaggerated the toxic effects of BPA. This was opposite to the depression effect observed in zebrafish embryo and larvae, magnifying that the joint effects of exposure to nanomaterials and endocrine disrupting chemicals are relevant to the life stages of organisms.

Allosteric binding on nuclear receptors: Insights on screening of non-competitive endocrine-disrupting chemicals

Endocrine-disrupting chemicals (EDCs) can compete with endogenous hormones and bind to the orthosteric site of nuclear receptors (NRs), affecting normal endocrine system function and causing severe symptoms. Recently, a series of pharmaceuticals and personal care products (PPCPs) have been discovered to bind to the allosteric sites of NRs and induce similar effects. However, it remains unclear how diverse EDCs work in this new way. Therefore, we have systematically summarized the allosteric sites and underlying mechanisms based on existing studies, mainly regarding drugs belonging to the PPCP class. Advanced methods, classified as structural biology, biochemistry and computational simulation, together with their advantages and hurdles for allosteric site recognition and mechanism insight have also been described. Furthermore, we have highlighted two available strategies for virtual screening of numerous EDCs, relying on the structural features of allosteric sites and lead compounds, respectively. We aim to provide reliable theoretical and technical support for a broader view of various allosteric interactions between EDCs and NRs, and to drive high-throughput and accurate screening of potential EDCs with non-competitive effects.

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

Ecological and human health risks of manure-borne steroid estrogens: A 20-year global synthesis study

Estrone (E1), 17α-estradiol (17α-E2), 17β-estradiol (17β-E2), and estriol (E3) are persistent in livestock manure and present serious pollution concerns because they can trigger endocrine disruption at part-per-trillion levels. This study conducted a global analysis of estrogen occurrence in manure using all literature data over the past 20 years. Besides, predicted environmental concentration (PEC) in soil and water was estimated using fate models, and risk/harm quotient (RQ/HQ) methods were applied to screen risks on children as well as on sensitive aquatic and soil species. The estradiol equivalent values ranged from 6.6 to 4.78 × 10⁴ ng/g and 12.4 to 9.46 × 10⁴ ng/L in the solid and liquid fraction. The estrogenic potency ranking in both fractions were 17β-E2> E1>17α-E2>E3. RQs of measured environmental concentration in the liquid fraction pose medium (E3) to high risk (E1, 17α-E2 & 17β-E2) to fish but are lower than risks posed by xenoestrogens. However, the RQ of PECs on both soil organisms and aquatic species were insignificant (RQ < 0.01), and HQs of contaminated water and soil ingestion were within acceptable limits. Nevertheless, meticulous toxicity studies are still required to confirm (or deny) the findings because endocrine disruption potency from mixtures of these classes of compounds cannot be ignored.

Structures of Endocrine-Disrupting Chemicals Correlate with the Activation of 12 Classic Nuclear Receptors

Endocrine-disrupting chemicals (EDCs) can inadvertently interact with 12 classic nuclear receptors (NRs) that disrupt the endocrine system and cause adverse effects. There is no widely accepted understanding about what structural features make thousands of EDCs able to activate different NRs as well as how these structural features exert their functions and induce different outcomes at the cellular level. This paper applies the hierarchical characteristic fragment methodology and high-throughput screening molecular docking to comprehensively explore the structural and functional features of EDCs for the 12 NRs based on more than 7000 chemicals from curated datasets. EDCs share three levels of key fragments. The primary and secondary fragments are associated with the binding of EDCs to four groups of receptors: steroidal nuclear receptors (SNRs, including androgen, estrogen, glucocorticoid, mineralocorticoid, and progesterone), retinoic acid receptors, thyroid hormone receptors, and vitamin D receptors. The tertiary fragments determine the activity type by interacting with two key locations in the ligand-binding domains of NRs (N-H5-H3-C and N-H7-H11-C for SNRs and N-H5-H5'-H2'-H3-C and N-H6'-H11-C for non-SNRs). The resulting compiled structural fragments of EDCs together with elucidated compound NR binding modes provide a framework for understanding the interactions between EDCs and NRs, facilitating faster and more accurate screening of EDCs for multiple NRs in the future.

Machine Learning: New Ideas and Tools in Environmental Science and Engineering

The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have not been widely utilized by researchers in ESE. This feature explores the potential of ML to revolutionize data analysis and modeling in the ESE field, and covers the essential knowledge needed for such applications. First, we use five examples to illustrate how ML addresses complex ESE problems. We then summarize four major types of applications of ML in ESE: making predictions; extracting feature importance; detecting anomalies; and discovering new materials or chemicals. Next, we introduce the essential knowledge required and current shortcomings in ML applications in ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper model interpretation, and sound applicability analysis. Finally, we discuss challenges and future opportunities in the application of ML tools in ESE to highlight the potential of ML in this field.

Brightened body coloration in female guppies (Poecilia reticulata) serves as an in vivo biomarker for environmental androgens: The example of 17β-trenbolone

In vivo testing systems for environmental androgens are scarce. The aim of this study was to evaluate the potential of male-specific brightened body coloration in female guppies (Poecilia reticulata) to serve as an in vivo biomarker of environmental androgens using 17β-trenbolone as an example. The high bioaccumulation of 17β-trenbolone in the skin of female guppies suggests that it is a potential target tissue of environmental androgens. The coloration index, pigment cell ultrastructure, pigment levels, sexual attractiveness, and reproductive capability of female guppies were analyzed following 28 days of exposure to 20 ng/L, 200 ng/L, and 2000 ng/L 17β-trenbolone. Increases in the coloration index caused by 17β-trenbolone exposure were attributable to increased pteridine and melanin levels. Decreases in the sexual attractiveness, number of offspring, and survival rate of offspring suggested that the changes in body coloration translated into adverse outcomes. Finally, mRNA sequencing indicated that 17β-trenbolone increased pteridine levels by activating genomic effects of androgen receptor on xanthine dehydrogenase and increased melanin levels by exerting non-genomic effects targeting microphthalmia-associated transcription factor, tyrosinase, and tyrosinase-related protein 1 that were mediated by mitogen-activated protein kinase and calcium signaling pathways. We have derived a robust adverse outcome pathway of environmental androgens, and our findings suggest that indicators at different biological levels related to brightened body coloration in female guppies can serve as less-invasive or noninvasive in vivo biomarkers of short-term exposure to environmental androgens.

Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods

The estrogen receptors α (ERα) are transcription factors involved in several physiological processes belonging to the nuclear receptors (NRs) protein family. Besides the endogenous ligands, several other chemicals are able to bind to those receptors. Among them are endocrine disrupting chemicals (EDCs) that can trigger toxicological pathways. Many studies have focused on predicting EDCs based on their ability to bind NRs; mainly, estrogen receptors (ER), thyroid hormones receptors (TR), androgen receptors (AR), glucocorticoid receptors (GR), and peroxisome proliferator-activated receptors gamma (PPARγ). In this work, we suggest a pipeline designed for the prediction of ERα binding activity. The flagged compounds can be further explored using experimental techniques to assess their potential to be EDCs. The pipeline is a combination of structure based (docking and pharmacophore models) and ligand based (pharmacophore models) methods. The models have been constructed using the Environmental Protection Agency (EPA) data encompassing a large number of structurally diverse compounds. A validation step was then achieved using two external databases: the NR-DBIND (Nuclear Receptors DataBase Including Negative Data) and the EADB (Estrogenic Activity DataBase). Different combination protocols were explored. Results showed that the combination of models performed better than each model taken individually. The consensus protocol that reached values of 0.81 and 0.54 for sensitivity and specificity, respectively, was the best suited for our toxicological study. Insights and recommendations were drawn to alleviate the screening quality of other projects focusing on ERα binding predictions.

Enhanced Disrupting Effect of Benzophenone-1 Chlorination Byproducts to the Androgen Receptor: Cell-Based Assays and Gaussian Accelerated Molecular Dynamics Simulations

Benzophenone-1 (BP-1), one of the commonly used ultraviolet filters, has caused increasing public concern due to frequently detected residues in environmental and recreational waters. Its susceptibility to residual chlorine and the potential to subsequently trigger endocrine disruption remain unknown. We herein investigated the chlorination of BP-1 in swimming pool water and evaluated the endocrine disruption toward the human androgen receptor (AR). The structures of monochlorinated (P1) and dichlorinated (P2) products were separated and characterized by mass spectrometry and ¹H-¹H NMR correlation spectroscopy. P1 and P2 exhibited significantly higher antiandrogenic activity in yeast two-hybrid assays (EC₅₀, 6.13 μM and 9.30 μM) than did BP-1 (12.89 μM). Our 350 ns Gaussian accelerated molecular dynamics simulations showed the protein dynamics in a long-time scale equilibrium, and further energy calculations revealed that although increased hydrophobic interactions are primarily responsible for enhanced binding affinities between chlorinated products and the AR ligand binding domain, the second chloride in P2 still hinders the complex motion because of the solvation penalty. The mixture of BP-1-P1-P2 elicited additive antiandrogenic activity, well fitted by the concentration addition model. P1 and P2 at 1 μM consequently downregulated the mRNA expression of AR-regulated genes, NKX3.1 and KLK3, by 1.7-9.1-fold in androgen-activated LNCaP cells. Because chlorination of BP-1 occurs naturally by residual chlorine in aquatic environments, our results regarding enhanced antiandrogenic activity and disturbed AR signaling provided evidence linking the use of personal care products with potential health risks.

Update on the Health Effects of Bisphenol A: Overwhelming Evidence of Harm

In 1997, the first in vivo bisphenol A (BPA) study by endocrinologists reported that feeding BPA to pregnant mice induced adverse reproductive effects in male offspring at the low dose of 2 µg/kg/day. Since then, thousands of studies have reported adverse effects in animals administered low doses of BPA. Despite more than 100 epidemiological studies suggesting associations between BPA and disease/dysfunction also reported in animal studies, regulatory agencies continue to assert that BPA exposures are safe. To address this disagreement, the CLARITY-BPA study was designed to evaluate traditional endpoints of toxicity and modern hypothesis-driven, disease-relevant outcomes in the same set of animals. A wide range of adverse effects was reported in both the toxicity and the mechanistic endpoints at the lowest dose tested (2.5 µg/kg/day), leading independent experts to call for the lowest observed adverse effect level (LOAEL) to be dropped 20 000-fold from the current outdated LOAEL of 50 000 µg/kg/day. Despite criticism by members of the Endocrine Society that the Food and Drug Administration (FDA)'s assumptions violate basic principles of endocrinology, the FDA rejected all low-dose data as not biologically plausible. Their decisions rely on 4 incorrect assumptions: dose responses must be monotonic, there exists a threshold below which there are no effects, both sexes must respond similarly, and only toxicological guideline studies are valid. This review details more than 20 years of BPA studies and addresses the divide that exists between regulatory approaches and endocrine science. Ultimately, CLARITY-BPA has shed light on why traditional methods of evaluating toxicity are insufficient to evaluate endocrine disrupting chemicals.