EADB: an estrogenic activity database for assessing potential endocrine activity

A genetically encoded fluorescent whole-cell biosensor for real-time detecting estrogenic activities in water samples

Real-time monitoring of estrogenic activity in the aquatic environment is a challenging task. Current biosensors face difficulties due to their limited response speed and environmental tolerance, especially for detecting wastewater, the major source of estrogenic compounds in aquatic environments. To address these difficulties, this study developed a single fluorescent protein (FP) -based whole-cell bacterial biosensor named ER-Light, which was achieved by inserting the sensing domain of the estrogen receptor (ER) into the FP Citrine and expressing it in the periplasm of Escherichia coli. As designed, ER-Light enables the detection of net estrogenic activity in mixtures, represented by estradiol equivalent concentration (EEQ). ER-Light detects EEQ in 40 s with a detection limit of 4.55 × 10-7 μM and a maximum working range of 1.1 × 10-4 μM, demonstrating sufficient response speed, sensitivity, and working range. In addition, the ER-Light can survive and tolerate wastewater effluent. Satisfactory recoveries (91.0 % to 102.1 %) eliminated concerns about the matrix effect of wastewater. EEQs (Not detected-2.9 ×10-5 µM) measured by ER-Light from the effluent of 9 wastewater treatment plants validate its practicality in detecting wastewater. This is the first attempt to integrate ER into FP-based biosensors for environment monitoring. Our findings provide valuable design rules for real-time detection of bioactivity effects in the environment, contributing to the safeguarding of ecological and human health.

Melatonin's protective role against Bisphenol F and S-induced skeletal damage: A morphometric and histological study in rat

This study aimed to evaluate the potential effects of Bisphenol F and S exposure on the skeletal structures of Sprague-Dawley rats. Given the increasing concern about the potential endocrine-disrupting effects of Bisphenol analogs on bone health, this research sought to elucidate their impact in conjunction with Melatonin. Using 80 male Sprague Dawley rats, bones were subjected to a 3-point bending test to assess mechanical properties, and histopathological evaluation was conducted after fixation and decalcification. Statistical analysis was performed using SPSS. The results of the mechanical tests revealed significant differences in deformation and elastic modulus values between groups treated with Bisphenol F+Melatonin and Bisphenol S+Melatonin compared to the control groups. However, the histological images showed no significant differences between the groups. In the discussion, it was noted that the injection of Bisphenol F and Melatonin together increased bone hardness, suggesting that Bisphenol F and Bisphenol S may mitigate the negative effects of melatonin on bone. We attributed the absence of histological differences to the male gender of the studied rats and previous exposure considerations. This study shows that Melatonin can reduce Bisphenol F and Bisphenol S' rapid adjustment effects and increase bone elasticity. The side effects of Bisphenol F and S, as well as the prophylactic effects of Melatonin, can be observed and improved by carefully adjusting the duration, dose, and gender selection.

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.

Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals

The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effective, suffer from being costly and time-consuming. In response to this challenge, new alternative methodologies, notably machine learning and deep learning techniques, have attracted attention for their potential in developing carcinogenicity prediction models. This article reviews the progress in predicting carcinogenicity using various machine learning and deep learning algorithms. A comparative analysis on these developed models reveals that support vector machine, random forest, and ensemble learning are commonly preferred for their robustness and effectiveness in predicting chemical carcinogenicity. Conversely, models based on deep learning algorithms, such as feedforward neural network, convolutional neural network, graph convolutional neural network, capsule neural network, and hybrid neural networks, exhibit promising capabilities but are limited by the size of available carcinogenicity datasets. This review provides a comprehensive analysis of current machine learning and deep learning models for carcinogenicity prediction, underscoring the importance of high-quality and large datasets. These observations are anticipated to catalyze future advancements in developing effective and generalizable machine learning and deep learning models for predicting chemical carcinogenicity.

Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study

BACKGROUND

Cardiotoxicity is a major cause of drug withdrawal. The hERG channel, regulating ion flow, is pivotal for heart and nervous system function. Its blockade is a concern in drug development. Predicting hERG blockade is essential for identifying cardiac safety issues. Various QSAR models exist, but their performance varies. Ongoing improvements show promise, necessitating continued efforts to enhance accuracy using emerging deep learning algorithms in predicting potential hERG blockade.

STUDY DESIGN AND METHOD

Using a large training dataset, six individual QSAR models were developed. Additionally, three ensemble models were constructed. All models were evaluated using 10-fold cross-validations and two external datasets.

RESULTS

The 10-fold cross-validations resulted in Mathews correlation coefficient (MCC) values from 0.682 to 0.730, surpassing the best-reported model on the same dataset (0.689). External validations yielded MCC values from 0.520 to 0.715 for the first dataset, exceeding those of previously reported models (0-0.599). For the second dataset, MCC values fell between 0.025 and 0.215, aligning with those of reported models (0.112-0.220).

CONCLUSIONS

The developed models can assist the pharmaceutical industry and regulatory agencies in predicting hERG blockage activity, thereby enhancing safety assessments and reducing the risk of adverse cardiac events associated with new drug candidates.

Computational framework for identifying and evaluating mutagenic and xenoestrogenic potential of food additives

Food additives are chemicals incorporated in food to enhance its flavor, color and prevent spoilage. Some of these are associated with substantial health hazards, including developmental disorders, increase cancer risk, and hormone disruption. Hence, this study aimed to comprehend the in-silico toxicology framework for evaluating mutagenic and xenoestrogenic potential of food additives and their association with breast cancer. A total of 2885 food additives were screened for toxicity based on Threshold of Toxicological Concern (TTC), mutagenicity endpoint prediction, and mutagenic structural alerts/toxicophores identification. Ten food additives were identified as having mutagenic potential based on toxicity screening. Furthermore, Protein-Protein Interaction (PPI) analysis identified ESR1, as a key hub gene in breast cancer. KEGG pathway analysis verified that ESR1 plays a significant role in breast cancer pathogenesis. Additionally, competitive interaction studies of the predicted potential mutagenic food additives with the estrogen receptor-α were evaluated at agonist and antagonist binding sites. Indole, Dichloromethane, Trichloroethylene, Quinoline, 6-methyl quinoline, Ethyl nitrite, and 4-methyl quinoline could act as agonists, and Paraldehyde, Azodicarbonamide, and 2-acetylfuranmay as antagonists. The systematic risk assessment framework reported in this study enables the exploration of mutagenic and xenoestrogenic potential associated with food additives for hazard identification and management.

Changes in the European Union definition for endocrine disruptors: how many molecules remain a cause for concern? The example of crop protection products used in agriculture in France in the six last decades

Background

The endocrine-disrupting effects of phytopharmaceutical active substances (PAS) on human health are a public health concern. The CIPATOX-PE database, created in 2018, listed the PAS authorized in France between 1961 and 2014 presenting endocrine-disrupting effects for humans according to data from official international organizations. Since the creation of CIPATOX-PE, European regulations have changed, and new initiatives identifying substances with endocrine-disrupting effects have been implemented and new PAS have been licensed.

Objectives

The study aimed to update the CIPATOX-PE database by considering new 2018 European endocrine-disrupting effect identification criteria as well as the new PAS authorized on the market in France since 2015.

Methods

The endocrine-disrupting effect assessment of PAS from five international governmental and non-governmental initiatives was reviewed, and levels of evidence were retained by these initiatives for eighteen endocrine target organs.

Results

The synthesis of the identified endocrine-disrupting effects allowed to assign an endocrine-disrupting effect level of concern for 241 PAS among 980 authorized in France between 1961 and 2021. Thus, according to the updated CIPATOX-PE data, 44 PAS (18.3%) had an endocrine-disrupting effect classified as "high concern," 133 PAS (55.2%) "concern," and 64 PAS (26.6%) "unknown effect" in the current state of knowledge. In the study, 42 PAS with an endocrine-disrupting effect of "high concern" are similarly classified in CIPATOX-PE-2018 and 2021, and 2 new PAS were identified as having an endocrine-disrupting effect of "high concern" in the update, and both were previously classified with an endocrine-disrupting effect of "concern" in CIPATOX-PE-2018. Finally, a PAS was identified as having an endocrine-disrupting effect of "high concern" in CIPATOX-PE-2018 but is now classified as a PAS not investigated for endocrine-disrupting effects in CIPATOX-PE-2021. The endocrine target organs associated with the largest number of PAS with an endocrine-disrupting effect of "high concern" is the reproductive system with 31 PAS. This is followed by the thyroid with 25 PAS and the hypothalamic-pituitary axis (excluding the gonadotropic axis) with 5 PAS.

Discussion

The proposed endocrine-disrupting effect indicator, which is not a regulatory classification, can be used as an epidemiological tool for occupational risks and surveillance.

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

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.

Novel machine learning models to predict endocrine disruption activity for high-throughput chemical screening

An area of ongoing concern in toxicology and chemical risk assessment is endocrine disrupting chemicals (EDCs). However, thousands of legacy chemicals lack the toxicity testing required to assess their respective EDC potential, and this is where computational toxicology can play a crucial role. The US (United States) Environmental Protection Agency (EPA) has run two programs, the Collaborative Estrogen Receptor Activity Project (CERAPP) and the Collaborative Modeling Project for Receptor Activity (CoMPARA) which aim to predict estrogen and androgen activity, respectively. The US EPA solicited research groups from around the world to provide endocrine receptor activity Qualitative (or Quantitative) Structure Activity Relationship ([Q]SAR) models and then combined them to create consensus models for different toxicity endpoints. Random Forest (RF) models were developed to cover a broader range of substances with high predictive capabilities using large datasets from CERAPP and CoMPARA for estrogen and androgen activity, respectively. By utilizing simple descriptors from open-source software and large training datasets, RF models were created to expand the domain of applicability for predicting endocrine disrupting activity and help in the screening and prioritization of extensive chemical inventories. In addition, RFs were trained to conservatively predict the activity, meaning models are more likely to make false-positive predictions to minimize the number of False Negatives. This work presents twelve binary and multi-class RF models to predict binding, agonism, and antagonism for estrogen and androgen receptors. The RF models were found to have high predictive capabilities compared to other in silico modes, with some models reaching balanced accuracies of 93% while having coverage of 89%. These models are intended to be incorporated into evolving priority-setting workflows and integrated strategies to support the screening and selection of chemicals for further testing and assessment by identifying potential endocrine-disrupting substances.

ER/AR Multi-Conformational Docking Server: A Tool for Discovering and Studying Estrogen and Androgen Receptor Modulators

The prediction of the estrogen receptor (ER) and androgen receptor (AR) activity of a compound is quite important to avoid the environmental exposures of endocrine-disrupting chemicals. The Estrogen and Androgen Receptor Database (EARDB, http://eardb.schanglab.org.cn/) provides a unique collection of reported ERα, ERβ, or AR protein structures and known small molecule modulators. With the user-uploaded query molecules, molecular docking based on multi-conformations of a single target will be performed. Moreover, the 2D similarity search against known modulators is also provided. Molecules predicted with a low binding energy or high similarity to known ERα, ERβ, or AR modulators may be potential endocrine-disrupting chemicals or new modulators. The server provides a tool to predict the endocrine activity for compounds of interests, benefiting for the ER and AR drug design and endocrine-disrupting chemical identification.

Machine Learning Models for Predicting Cytotoxicity of Nanomaterials

The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.

Elucidation of Agonist and Antagonist Dynamic Binding Patterns in ER-α by Integration of Molecular Docking, Molecular Dynamics Simulations and Quantum Mechanical Calculations

Estrogen receptor alpha (ERα) is a ligand-dependent transcriptional factor in the nuclear receptor superfamily. Many structures of ERα bound with agonists and antagonists have been determined. However, the dynamic binding patterns of agonists and antagonists in the binding site of ERα remains unclear. Therefore, we performed molecular docking, molecular dynamics (MD) simulations, and quantum mechanical calculations to elucidate agonist and antagonist dynamic binding patterns in ERα. 17β-estradiol (E2) and 4-hydroxytamoxifen (OHT) were docked in the ligand binding pockets of the agonist and antagonist bound ERα. The best complex conformations from molecular docking were subjected to 100 nanosecond MD simulations. Hierarchical clustering was conducted to group the structures in the trajectory from MD simulations. The representative structure from each cluster was selected to calculate the binding interaction energy value for elucidation of the dynamic binding patterns of agonists and antagonists in the binding site of ERα. The binding interaction energy analysis revealed that OHT binds ERα more tightly in the antagonist conformer, while E2 prefers the agonist conformer. The results may help identify ERα antagonists as drug candidates and facilitate risk assessment of chemicals through ER-mediated responses.

The Effect of 42-Day Exposure to a Low Deoxynivalenol Dose on the Immunohistochemical Expression of Intestinal ERs and the Activation of CYP1A1 and GSTP1 Genes in the Large Intestine of Pre-pubertal Gilts

Deoxynivalenol (DON) is a mycotoxin that contaminates various plant materials. Exposure to DON can disrupt hormonal homeostasis, decrease body weight gains and modulate the immune system in pigs. It can also cause diarrhea, vomiting, leukocytosis, hemorrhaging or even death. Prolonged exposure to low doses of DON can have serious health implications in mammals. This is the first in vivo study to show that per os administration of low DON doses probably contributes to specific dysfunctions in steroidogenesis processes by inducing the immunohistochemical expression of estrogen receptors alpha (ERα) in the entire gastrointestinal tract in strongly stained cells (3 points) and estrogen receptors beta (ERβ), but only in both investigated segments of the duodenum in pre-pubertal gilts. Therefore, the aim of this study was to determine whether a NOAEL dose of DON (12 μg DON/kg BW) administered per os over a period of 42 days induces changes in the immunohistochemical expression of ER in different intestinal segments and the transcriptional activation of CYP1A1 and GSTP1 genes in the large intestine of pre-pubertal gilts. This is the first report to demonstrate the expression of ER, in particular ERβ, with the associated consequences. The expression of ER was accompanied by considerable variations in the activation of CYP1A1 and GSTP1 genes, but it supported the maintenance of a stable consensus between the degree of mycotoxin exposure and the detoxifying effect in pre-pubertal gilts.

Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials

Nanomaterials have drawn increasing attention due to their tunable and enhanced physicochemical and biological performance compared to their conventional bulk materials. Owing to the rapid expansion of the nano-industry, large amounts of data regarding the synthesis, physicochemical properties, and bioactivities of nanomaterials have been generated. These data are a great asset to the scientific community. However, the data are on diverse aspects of nanomaterials and in different sources and formats. To help utilize these data, various databases on specific information of nanomaterials such as physicochemical characterization, biomedicine, and nano-safety have been developed and made available online. Understanding the structure, function, and available data in these databases is needed for scientists to select appropriate databases and retrieve specific information for research on nanomaterials. However, to our knowledge, there is no study to systematically compare these databases to facilitate their utilization in the field of nanomaterials. Therefore, we reviewed and compared eight widely used databases of nanomaterials, aiming to provide the nanoscience community with valuable information about the specific content and function of these databases. We also discuss the pros and cons of these databases, thus enabling more efficient and convenient utilization.

Developing QSAR Models with Defined Applicability Domains on PPARγ Binding Affinity Using Large Data Sets and Machine Learning Algorithms

Chemicals may cause adverse effects on human health through binding to peroxisome proliferator-activated receptor γ (PPARγ). Hence, binding affinity is useful for evaluating chemicals with potential endocrine-disrupting effects. Quantitative structure-activity relationship (QSAR) regression models with defined applicability domains (ADs) are important to enable efficient screening of chemicals with PPARγ binding activity. However, lack of large data sets hindered the development of QSAR models. In this study, based on PPARγ binding affinity data sets curated from various sources, 30 QSAR models were developed using molecular fingerprints, two-dimensional descriptors, and five machine learning algorithms. Structure-activity landscapes (SALs) of the training compounds were described by network-like similarity graphs (NSGs). Based on the NSGs, local discontinuity scores were calculated and found to be positively correlated with the cross-validation absolute prediction errors of the models using the different training sets, descriptors, and algorithms. Moreover, innovative ADs were defined based on pairwise similarities between compounds and were found to outperform some conventional ADs. The curated data sets and developed regression models could be useful for evaluating PPARγ-involved adverse effects of chemicals. The SAL analysis and the innovative ADs could facilitate understanding of prediction results from QSAR models.

Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors

This paper aims to identify structural motifs within a molecule that contribute the most toward a chemical being an endocrine disruptor. We have developed a deep neural network-based toolkit toward this aim. The trained model can virtually assess a synthetic chemical's potential to be an endocrine disruptor using machine-readable molecular representation, simplified molecular input line entry system (SMILES). Our proposed toolkit is a multilabel or multioutput classification model that combines both convolution and long short-term memory (LSTM) architectures. The toolkit leverages the advantages of an active learning-based framework that combines multiple sources of data. Class activation maps (CAMs) generated from the feature-extraction layers can identify the structural alerts and the chemical environment that determines the specificity of the structural alerts.

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches

As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mixture that alters function(s) of the endocrine system and consequently causes adverse health effects in an intact organism, its progeny, or (sub)populations. Traditional experimental testing regimens to identify toxicants that induce endocrine disruption can be expensive and time-consuming. Computational modeling has emerged as a promising and cost-effective alternative method for screening and prioritizing potentially endocrine-active compounds. The efficient identification of suitable chemical descriptors and machine-learning algorithms, including deep learning, is a considerable challenge for computational toxicology studies. Here, we sought to apply classic machine-learning algorithms and deep-learning approaches to a panel of over 7500 compounds tested against 18 Toxicity Forecaster assays related to nuclear estrogen receptor (ERα and ERβ) activity. Three binary fingerprints (Extended Connectivity FingerPrints, Functional Connectivity FingerPrints, and Molecular ACCess System) were used as chemical descriptors in this study. Each descriptor was combined with four machine-learning and two deep- learning (normal and multitask neural networks) approaches to construct models for all 18 ER assays. The resulting model performance was evaluated using the area under the receiver- operating curve (AUC) values obtained from a fivefold cross-validation procedure. The results showed that individual models have AUC values that range from 0.56 to 0.86. External validation was conducted using two additional sets of compounds (n = 592 and n = 966) with established interactions with nuclear ER demonstrated through experimentation. An agonist, antagonist, or binding score was determined for each compound by averaging its predicted probabilities in relevant assay models as an external validation, yielding AUC values ranging from 0.63 to 0.91. The results suggest that multitask neural networks offer advantages when modeling mechanistically related endpoints. Consensus predictions based on the average values of individual models remain the best modeling strategy for computational toxicity evaluations.

Integrating endocrine-related health effects into comparative human toxicity characterization

Endocrine-disrupting chemicals have the ability to interfere with and alter functions of the hormone system, leading to adverse effects on reproduction, growth and development. Despite growing concerns over their now ubiquitous presence in the environment, endocrine-related human health effects remain largely outside of comparative human toxicity characterization frameworks as applied for example in life cycle impact assessments. In this paper, we propose a new methodological framework to consistently integrate endocrine-related health effects into comparative human toxicity characterization. We present two quantitative and operational approaches for extrapolating towards a common point of departure from both in vivo and dosimetry-adjusted in vitro endocrine-related effect data and deriving effect factors as well as corresponding characterization factors for endocrine-active/endocrine-disrupting chemicals. Following the proposed approaches, we calculated effect factors for 323 chemicals, reflecting their endocrine potency, and related characterization factors for 157 chemicals, expressing their relative endocrine-related human toxicity potential. Developed effect and characterization factors are ready for use in the context of chemical prioritization and substitution as well as life cycle impact assessment and other comparative assessment frameworks. Endocrine-related effect factors were found comparable to existing effect factors for cancer and non-cancer effects, indicating that (1) the chemicals' endocrine potency is not necessarily higher or lower than other effect potencies and (2) using dosimetry-adjusted effect data to derive effect factors does not consistently overestimate the effect of potential endocrine disruptors. Calculated characterization factors span over 8-11 orders of magnitude for different substances and emission compartments and are dominated by the range in endocrine potencies.

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.

Paraben Toxicology

Parabens now being formally declared as the American Contact Dermatitis Society (non)allergen of the year, the allergologic concerns regarding parabens raised during the past century are no longer a significant issue. The more recent toxicological concerns regarding parabens are more imposing, stemming from the gravity of the noncutaneous adverse health effects for which they have been scrutinized for the past 20 years. These include endocrine activity, carcinogenesis, infertility, spermatogenesis, adipogenesis, perinatal exposure impact, and nonallergologic cutaneous, psychologic, and ecologic effects. To assert that parabens are safe for use as currently used in the cosmetics, food, and pharmaceutical industries, all toxicological end points must be addressed. We seek to achieve perspective through this exercise: perspective for the professional assessing systemic risk of parabens by all routes of exposure. The data reviewed in this article strive to provide a balanced perspective for the consumer hopefully to allay concerns regarding the safety of parabens and facilitate an informed decision-making process. Based on currently available scientific information, claims that parabens are involved in the genesis or propagation of these controversial and important health problems are premature. Haste to remove parabens from consumer products could result in their substitution with alternative, less proven, and potentially unsafe alternatives, especially given the compelling data supporting the lack of significant dermal toxicity of this important group of preservatives.

Predicting estrogen receptor activation by a group of substituted phenols: An integrated approach to testing and assessment case study

Traditional approaches for chemical risk assessment cannot keep pace with the number of substances requiring assessment. Thus, in a global effort to expedite and modernize chemical risk assessment, New Approach Methodologies (NAMs) are being explored and developed. Included in this effort is the OECD Integrated Approaches for Testing and Assessment (IATA) program, which provides a forum for OECD member countries to develop and present case studies illustrating the application of NAM in various risk assessment contexts. Here, we present an IATA case study for the prediction of estrogenic potential of three target phenols: 4-tert-butylphenol, 2,4-di-tert-butylphenol and octabenzone. Key features of this IATA include the use of two computational approaches for analogue selection for read-across, data collected from traditional and NAM sources, and a workflow to generate predictions regarding the targets' ability to bind the estrogen receptor (ER). Endocrine disruption can occur when a chemical substance mimics the activity of natural estrogen by binding to the ER and, if potency and exposure are sufficient, alters the function of the endocrine system to cause adverse effects. The data indicated that of the three target substances that were considered herein, 4-tert-butylphenol is a potential endocrine disruptor. Further, this IATA illustrates that the NAM approach explored is health protective when compared to in vivo endpoints traditionally used for human health risk assessment.

Computational prediction models for assessing endocrine disrupting potential of chemicals

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

Endocrine Disruptors of the Bisphenol and Paraben Families and Bone Metabolism

After menopause, when estrogen levels decrease, there is room for the activity of anthropogenic substances with estrogenic properties – endocrine disruptors (EDs) – that can interfere with bone remodeling and changes in calcium-phosphate metabolism. Selected unconjugated EDs of the bisphenol group – BPA, BPS, BPF, BPAF, and the paraben family – methyl-, ethyl-, propyl-, butyl-, and benzyl-parabens – were measured by high performance liquid chromatography-tandem mass spectrometry in the plasma of 24 postmenopausal women. Parameters of calcium-phosphate metabolism and bone mineral density were assessed. Osteoporosis was classified in 14 women, and 10 women were put into the control group. The impact of EDs on calcium-phosphate metabolism was evaluated by multiple linear regressions. In women with osteoporosis, concentrations of BPA ranged from the lower limit of quantification (LLOQ) – 104 pg/ml and methyl paraben (MP) from LLOQ – 1120 pg/ml. The alternative bisphenols BPS, BPF and BPAF were all under the LLOQ. Except for MP, no further parabens were detected in the majority of samples. The multiple linear regression model found a positive association of BPA (β=0.07, p<0.05) on calcium (Ca) concentrations. Furthermore, MP (β=-0.232, p<0.05) was negatively associated with C-terminal telopeptide. These preliminary results suggest that these EDs may have effects on calcium-phosphate metabolism.

Structural Changes Due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations

When a small molecule binds to the androgen receptor (AR), a conformational change can occur which impacts subsequent binding of co-regulator proteins and DNA. In order to accurately study this mechanism, the scientific community needs a crystal structure of the Wild type AR (WT-AR) ligand binding domain, bound with antagonist. To address this open need, we leveraged molecular docking and molecular dynamics (MD) simulations to construct a structure of the WT-AR ligand binding domain bound with antagonist bicalutamide. The structure of mutant AR (Mut-AR) bound with this same antagonist informed this study. After molecular docking analysis pinpointed the suitable binding orientation of a ligand in AR, the model was further optimized through 1 μs of MD simulations. Using this approach, three molecular systems were studied: (1) WT-AR bound with agonist R1881, (2) WT-AR bound with antagonist bicalutamide, and (3) Mut-AR bound with bicalutamide. Our structures were very similar to the experimentally determined structures of both WT-AR with R1881 and Mut-AR with bicalutamide, demonstrating the trustworthiness of this approach. In our model, when WT-AR is bound with bicalutamide, Val716/Lys720/Gln733, or Met734/Gln738/Glu897 move and thus disturb the positive and negative charge clumps of the AF2 site. This disruption of the AF2 site is key for understanding the impact of antagonist binding on subsequent co-regulator binding. In conclusion, the antagonist induced structural changes in WT-AR detailed in this study will enable further AR research and will facilitate AR targeting drug discovery.

Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents

The detrimental health effects associated with tobacco use constitute a major public health concern. The addiction associated with nicotine found in tobacco products has led to difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the targets of nicotine and are responsible for addiction to tobacco products. However, it is unknown if the other >8000 tobacco constituents are addictive. Since it is time-consuming and costly to experimentally assess addictive potential of such larger number of chemicals, computationally predicting human nAChRs binding is important for in silico evaluation of addiction potential of tobacco constituents and needs structures of human nAChRs. Therefore, we constructed three-dimensional structures of the ligand binding domain of human nAChR α7 subtype and then developed a predictive model based on the constructed structures to predict human nAChR α7 binding activity of tobacco constituents. The predictive model correctly predicted 11 out of 12 test compounds to be binders of nAChR α7. The model is a useful tool for high-throughput screening of potential addictive tobacco constituents. These results could inform regulatory science research by providing a new validated predictive tool using cutting-edge computational methodology to high-throughput screen tobacco additives and constituents for their binding interaction with the human α7 nicotinic receptor. The tool represents a prediction model capable of screening thousands of chemicals found in tobacco products for addiction potential, which improves the understanding of the potential effects of additives.

Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor

Endocrine disrupting effect has become a central point of concern, and various biological mechanisms involve in the disruption of endocrine system. Recently, we have explored the mechanism of disrupting hormonal transport protein, through the binding affinity of sex hormone-binding globulin in different fish species. This study, serving as a companion article, focused on the mechanism of activating/inhibiting hormone receptor, by investigating the binding interaction of chemicals with the estrogen receptor (ER) of different fish species. We collected the relative binding affinity (RBA) of chemicals with 17β-estradiol binding to the ER of eight fish species. With this parameter as the endpoints, quantitative structure-activity relationship (QSAR) models were established using DRAGON descriptors. Statistical results indicated that the developed models had satisfactory goodness of fit, robustness and predictive ability. The Euclidean distance and Williams plot verified that these models had wide application domains, which covered a large number of structurally diverse chemicals. Based on the screened descriptors, we proposed an appropriate mechanism interpretation for the binding potency. Additionally, even though the same chemical had different affinities for ER from different fish species, the affinity of ER exhibited a high correlation for fish species within the same Order (i.e., Salmoniformes, Cypriniformes, Perciformes), which consistent with that in our previous study. Hence, when performing the endocrine disrupting effect assessment, the species diversity should be taken into account, but maybe the fish species in the same Order can be grouped together.

Endocrine Disrupting Chemicals Mediated through Binding Androgen Receptor Are Associated with Diabetes Mellitus

Endocrine disrupting chemicals (EDCs) can mimic natural hormone to interact with receptors in the endocrine system and thus disrupt the functions of the endocrine system, raising concerns on the public health. In addition to disruption of the endocrine system, some EDCs have been found associated with many diseases such as breast cancer, prostate cancer, infertility, asthma, stroke, Alzheimer’s disease, obesity, and diabetes mellitus. EDCs that binding androgen receptor have been reported associated with diabetes mellitus in in vitro, animal, and clinical studies. In this review, we summarize the structural basis and interactions between androgen receptor and EDCs as well as the associations of various types of diabetes mellitus with the EDCs mediated through androgen receptor binding. We also discuss the perspective research for further understanding the impact and mechanisms of EDCs on the risk of diabetes mellitus.

A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols

Read-across is an important data gap filling technique used within category and analog approaches for regulatory hazard identification and risk assessment. Although much technical guidance is available that describes how to develop category/analog approaches, practical principles to evaluate and substantiate analog validity (suitability) are still lacking. This case study uses hindered phenols as an example chemical class to determine: (1) the capability of three structure fingerprint/descriptor methods (PubChem, ToxPrints and MoSS MCSS) to identify analogs for read-across to predict Estrogen Receptor (ER) binding activity and, (2) the utility of data confidence measures, physicochemical properties, and chemical R-group properties as filters to improve ER binding predictions. The training dataset comprised 462 hindered phenols and 257 non- hindered phenols. For each chemical of interest (target), source analogs were identified from two datasets (hindered and non-hindered phenols) that had been characterized by a fingerprint/descriptor method and by two cut-offs: (1) minimum similarity distance (range: 0.1 - 0.9) and, (2) N closest analogs (range: 1 - 10). Analogs were then filtered using: (1) physicochemical properties of the phenol (termed global filtering) and, (2) physicochemical properties of the R-groups neighboring the active hydroxyl group (termed local filtering). A read-across prediction was made for each target chemical on the basis of a majority vote of the N closest analogs. The results demonstrate that: (1) concordance in ER activity increases with structural similarity, regardless of the structure fingerprint/descriptor method, (2) increased data confidence significantly improves read-across predictions, and (3) filtering analogs using global and local properties can help identify more suitable analogs. This case study illustrates that the quality of the underlying experimental data and use of endpoint relevant chemical descriptors to evaluate source analogs are critical to achieving robust read-across predictions.

Development of estrogen receptor beta binding prediction model using large sets of chemicals

We developed an ER_β binding prediction model to facilitate identification of chemicals specifically bind ER_β or ER_α together with our previously developed ER_α binding model. Decision Forest was used to train ER_β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER_β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER_β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER_β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER_α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER_β or ER_α.

Molecular dynamics simulations and applications in computational toxicology and nanotoxicology

Nanotoxicology studies toxicity of nanomaterials and has been widely applied in biomedical researches to explore toxicity of various biological systems. Investigating biological systems through in vivo and in vitro methods is expensive and time taking. Therefore, computational toxicology, a multi-discipline field that utilizes computational power and algorithms to examine toxicology of biological systems, has gained attractions to scientists. Molecular dynamics (MD) simulations of biomolecules such as proteins and DNA are popular for understanding of interactions between biological systems and chemicals in computational toxicology. In this paper, we review MD simulation methods, protocol for running MD simulations and their applications in studies of toxicity and nanotechnology. We also briefly summarize some popular software tools for execution of MD simulations.

Editor's Highlight: Development of an In vitro Assay Measuring Uterine-Specific Estrogenic Responses for Use in Chemical Safety Assessment

A toxicity pathway approach was taken to develop an in vitro assay using human uterine epithelial adenocarcinoma (Ishikawa) cells as a replacement for measuring an in vivo uterotrophic response to estrogens. The Ishikawa cell was determined to be fit for the purpose of recapitulating in vivo uterine response by verifying fidelity of the biological pathway components and the dose-response predictions to women of child-bearing age. Expression of the suite of estrogen receptors that control uterine proliferation (ERα66, ERα46, ERα36, ERβ, G-protein coupled estrogen receptor (GPER)) were confirmed across passages and treatment conditions. Phenotypic responses to ethinyl estradiol (EE) from transcriptional activation of ER-mediated genes, to ALP enzyme induction and cellular proliferation occurred at concentrations consistent with estrogenic activity in adult women (low picomolar). To confirm utility of this model to predict concentration-response for uterine proliferation with xenobiotics, we tested the concentration-response for compounds with known uterine estrogenic activity in humans and compared the results to assays from the ToxCast and Tox21 suite of estrogen assays. The Ishikawa proliferation assay was consistent with in vivo responses and was a more sensitive measure of uterine response. Because this assay was constructed by first mapping the key molecular events for cellular response, and then ensuring that the assay incorporated these events, the resulting cellular assay should be a reliable tool for identifying estrogenic compounds and may provide improved quantitation of chemical concentration response for in vitro-based safety assessments.

Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products

Sunscreen products are predominantly regulated as over-the-counter (OTC) drugs by the US FDA. The "active" ingredients function as ultraviolet filters. Once a sunscreen product is generally recognized as safe and effective (GRASE) via an OTC drug review process, new formulations using these ingredients do not require FDA review and approval, however, the majority of ingredients have never been tested to uncover any potential endocrine activity and their ability to interact with the estrogen receptor (ER) is unknown, despite the fact that this is a very extensively studied target related to endocrine activity. Consequently, we have developed an in silico model to prioritize single ingredient estrogen receptor activity for use when actual animal data are inadequate, equivocal, or absent. It relies on consensus modeling to qualitatively and quantitatively predict ER binding activity. As proof of concept, the model was applied to ingredients commonly used in sunscreen products worldwide and a few reference chemicals. Of the 32 chemicals with unknown ER binding activity that were evaluated, seven were predicted to be active estrogenic compounds. Five of the seven were confirmed by the published data. Further experimental data is needed to confirm the other two predictions.

Phytoestrogens and Mycoestrogens Induce Signature Structure Dynamics Changes on Estrogen Receptor α

Endocrine disrupters include a broad spectrum of chemicals such as industrial chemicals, natural estrogens and androgens, synthetic estrogens and androgens. Phytoestrogens are widely present in diet and food supplements; mycoestrogens are frequently found in grains. As human beings and animals are commonly exposed to phytoestrogens and mycoestrogens in diet and environment, it is important to understand the potential beneficial or hazardous effects of estrogenic compounds. Many bioassays have been established to study the binding of estrogenic compounds with estrogen receptor (ER) and provided rich data in the literature. However, limited assays can offer structure information with regard to the ligand/ER complex. Our current study surveys the global structure dynamics changes for ERα ligand binding domain (LBD) when phytoestrogens and mycoestrogens bind. The assay is based on the structure dynamics information probed by hydrogen deuterium exchange mass spectrometry and offers a unique viewpoint to elucidate the mechanism how phytoestrogens and mycoestrogens interact with estrogen receptor. The cluster analysis based on the hydrogen deuterium exchange (HDX) assay data reveals a unique pattern when phytoestrogens and mycoestrogens bind with ERα LBD compared to that of estradiol and synthetic estrogen modulators. Our study highlights that structure dynamics could play an important role in the structure function relationship when endocrine disrupters interact with estrogen receptors.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.

Development of classification model and QSAR model for predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding globulin

Disturbing the transport process is a crucial pathway for endocrine disrupting chemicals (EDCs) to disrupt endocrine function. However, this mechanism has not gotten enough attention, compared with that of hormone receptors and synthetase up to now, especially for the sex hormone transport process. In this study, we selected sex hormone-binding globulin (SHBG) and EDCs as a model system and the relative competing potency of a chemical with testosterone binding to SHBG (log RBA) as the endpoints, to develop classification models and quantitative structure-activity relationship (QSAR) models. With the classification model, a satisfactory model with nR09, nR10 and RDF155v as the most relevant variables was screened. Statistic results indicated that the model had the sensitivity, specificity, accuracy of 86.4%, 80.0%, 84.4% and 85.7%, 87.5%, 86.2% for the training set and validation set, respectively, highlighting a high classification performance of the model. With the QSAR model, a satisfactory model with statistical parameters, specifically, an adjusted determination coefficient (Radj(2)) of 0.810, a root mean square error (RMSE) of 0.616, a leave-one-out cross-validation squared correlation coefficient (QLOO(2)) of 0.777, a bootstrap method (QBOOT(2)) of 0.756, an external validation coefficient (Qext(2)) of 0.544 and a RMSEext of 0.859, were obtained, which implied satisfactory goodness of fit, robustness and predictive ability. The applicability domain of the current model covers a large number of structurally diverse chemicals, especially a few classes of nonsteroidal compounds.

A Demonstration of the Uncertainty in Predicting the Estrogenic Activity of Individual Chemicals and Mixtures From an In Vitro Estrogen Receptor Transcriptional Activation Assay (T47D-KBluc) to the In Vivo Uterotrophic Assay Using Oral Exposure

In vitro estrogen receptor assays are valuable tools for identifying environmental samples and chemicals that display estrogenic activity. However, in vitro potency cannot necessarily be extrapolated to estimates of in vivo potency because in vitro assays are currently unable to fully account for absorption, distribution, metabolism, and excretion. To explore this issue, we calculated relative potency factors (RPF), using 17α-ethinyl estradiol (EE2) as the reference compound, for several chemicals and mixtures in the T47D-KBluc estrogen receptor transactivation assay. In vitro RPFs were used to predict rat oral uterotrophic assay responses for these chemicals and mixtures. EE2, 17β-estradiol (E2), benzyl-butyl phthalate (BBP), bisphenol-A (BPA), bisphenol-AF (BPAF), bisphenol-C (BPC), bisphenol-S (BPS), and methoxychlor (MET) were tested individually, while BPS + MET, BPAF + MET, and BPAF + BPC + BPS + EE2 + MET were tested as equipotent mixtures. In vivo ED50 values for BPA, BPAF, and BPC were accurately predicted using in vitro data; however, E2 was less potent than predicted, BBP was a false positive, and BPS and MET were 76.6 and 368.3-fold more active in vivo than predicted from the in vitro potency, respectively. Further, mixture ED50 values were more accurately predicted by the dose addition model using individual chemical in vivo uterotrophic data (0.7-1.5-fold difference from observed) than in vitro data (1.4-86.8-fold). Overall, these data illustrate the potential for both underestimating and overestimating in vivo potency from predictions made with in vitro data for compounds that undergo substantial disposition following oral administration. Accounting for aspects of toxicokinetics, notably metabolism, in in vitro models will be necessary for accurate in vitro-to-in vivo extrapolations.

Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A

Bisphenol A (BPA) is a ubiquitous compound used in polymer manufacturing for a wide array of applications; however, increasing evidence has shown that BPA causes significant endocrine disruption and this has raised public concerns over safety and exposure limits. The use of renewable materials as polymer feedstocks provides an opportunity to develop replacement compounds for BPA that are sustainable and exhibit unique properties due to their diverse structures. As new bio-based materials are developed and tested, it is important to consider the impacts of both monomers and polymers on human health. Molecular docking simulations using the Estrogenic Activity Database in conjunction with the decision forest were performed as part of a two-tier in silico model to predict the activity of 29 bio-based platform chemicals in the estrogen receptor-α (ERα). Fifteen of the candidates were predicted as ER binders and fifteen as non-binders. Gaining insight into the estrogenic activity of the bio-based BPA replacements aids in the sustainable development of new polymeric materials.

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

BACKGROUND

Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program.

OBJECTIVES

We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing.

METHODS

CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies.

RESULTS

Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing.

CONCLUSION

This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points.

CITATION

Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016.

CERAPP

Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023-1033; http://dx.doi.org/10.1289/ehp.1510267.

Structures of androgen receptor bound with ligands: advancing understanding of biological functions and drug discovery

INTRODUCTION

Androgen receptor (AR) is a ligand-dependent transcription factor and a member of the nuclear receptor superfamily. It plays a vital role in male sexual development and regulates gene expression in various tissues, including prostate. Androgens are compounds that exert their biological effects via interaction with AR. Binding of androgens to AR initiates conformational changes in AR that affect binding of co-regulator proteins and DNA. AR agonists and antagonists are widely used in a variety of clinical applications (i.e. hypogonadism and prostate cancer therapy).

AREAS COVERED

This review provides a close look at structures of AR-ligand complexes and mutations in the receptor that have been revealed, discusses current challenges in the field, and sheds light on future directions.

EXPERT OPINION

AR is one of the primary targets for the treatment of prostate cancer, as AR antagonists inhibit prostate cancer growth. However, these drugs are not effective for long-term treatment and lead to castration-resistant prostate cancer. The structures of AR-ligand complexes are an invaluable scientific asset that enhances our understanding of biological functions and mechanisms of androgenic and anti-androgenic chemicals as well as promotes the discovery of superior drug candidates.

Editor's Highlight: Analysis of the Effects of Cell Stress and Cytotoxicity on In Vitro Assay Activity Across a Diverse Chemical and Assay Space

Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Here, responses of 1060 chemicals including pharmaceuticals, natural products, pesticidals, consumer, and industrial chemicals across a battery of 815 in vitro assay endpoints from 7 high-throughput assay technology platforms were analyzed in order to distinguish between these types of activities. Both cell-based and cell-free assays showed a rapid increase in the frequency of responses at concentrations where cell stress/cytotoxicity responses were observed in cell-based assays. Chemicals that were positive on at least 2 viability/cytotoxicity assays within the concentration range tested (typically up to 100 μM) activated a median of 12% of assay endpoints whereas those that were not cytotoxic in this concentration range activated 1.3% of the assays endpoints. The results suggest that activity can be broadly divided into: (1) specific biomolecular interactions against one or more targets (eg, receptors or enzymes) at concentrations below which overt cytotoxicity-associated activity is observed; and (2) activity associated with cell stress or cytotoxicity, which may result from triggering specific cell stress pathways, chemical reactivity, physico-chemical disruption of proteins or membranes, or broad low-affinity non-covalent interactions. Chemicals showing a greater number of specific biomolecular interactions are generally designed to be bioactive (pharmaceuticals or pesticidal active ingredients), whereas intentional food-use chemicals tended to show the fewest specific interactions. The analyses presented here provide context for use of these data in ongoing studies to predict in vivo toxicity from chemicals lacking extensive hazard assessment.

Applying network analysis and Nebula (neighbor-edges based and unbiased leverage algorithm) to ToxCast data

BACKGROUND

ToxCast data have been used to develop models for predicting in vivo toxicity. To predict the in vivo toxicity of a new chemical using a ToxCast data based model, its ToxCast bioactivity data are needed but not normally available. The capability of predicting ToxCast bioactivity data is necessary to fully utilize ToxCast data in the risk assessment of chemicals.

OBJECTIVES

We aimed to understand and elucidate the relationships between the chemicals and bioactivity data of the assays in ToxCast and to develop a network analysis based method for predicting ToxCast bioactivity data.

METHODS

We conducted modularity analysis on a quantitative network constructed from ToxCast data to explore the relationships between the assays and chemicals. We further developed Nebula (neighbor-edges based and unbiased leverage algorithm) for predicting ToxCast bioactivity data.

RESULTS

Modularity analysis on the network constructed from ToxCast data yielded seven modules. Assays and chemicals in the seven modules were distinct. Leave-one-out cross-validation yielded a Q(2) of 0.5416, indicating ToxCast bioactivity data can be predicted by Nebula. Prediction domain analysis showed some types of ToxCast assay data could be more reliably predicted by Nebula than others.

CONCLUSIONS

Network analysis is a promising approach to understand ToxCast data. Nebula is an effective algorithm for predicting ToxCast bioactivity data, helping fully utilize ToxCast data in the risk assessment of chemicals.

Pathway Analysis Revealed Potential Diverse Health Impacts of Flavonoids that Bind Estrogen Receptors

Flavonoids are frequently used as dietary supplements in the absence of research evidence regarding health benefits or toxicity. Furthermore, ingested doses could far exceed those received from diet in the course of normal living. Some flavonoids exhibit binding to estrogen receptors (ERs) with consequential vigilance by regulatory authorities at the U.S. EPA and FDA. Regulatory authorities must consider both beneficial claims and potential adverse effects, warranting the increases in research that has spanned almost two decades. Here, we report pathway enrichment of 14 targets from the Comparative Toxicogenomics Database (CTD) and the Herbal Ingredients’ Targets (HIT) database for 22 flavonoids that bind ERs. The selected flavonoids are confirmed ER binders from our earlier studies, and were here found in mainly involved in three types of biological processes, ER regulation, estrogen metabolism and synthesis, and apoptosis. Besides cancers, we conjecture that the flavonoids may affect several diseases via apoptosis pathways. Diseases such as amyotrophic lateral sclerosis, viral myocarditis and non-alcoholic fatty liver disease could be implicated. More generally, apoptosis processes may be importantly evolved biological functions of flavonoids that bind ERs and high dose ingestion of those flavonoids could adversely disrupt the cellular apoptosis process.

A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals

Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.

Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering

In this study, hierarchical clustering classification models were developed to predict in vitro and in vivo oestrogen receptor (ER) activity. Classification models were developed for binding, agonist, and antagonist in vitro ER activity and for mouse in vivo uterotrophic ER binding. In vitro classification models yielded balanced accuracies ranging from 0.65 to 0.85 for the external prediction set. In vivo ER classification models yielded balanced accuracies ranging from 0.72 to 0.83. If used as additional biological descriptors for in vivo models, in vitro scores were found to increase the prediction accuracy of in vivo ER models. If in vitro activity was used directly as a surrogate for in vivo activity, the results were poor (balanced accuracy ranged from 0.49 to 0.72). Under-sampling negative compounds in the training set was found to increase the coverage (fraction of chemicals which can be predicted) and increase prediction sensitivity.

QSAR Models at the US FDA/NCTR

Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products.

Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets

Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration's Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency's ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼ 70-89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals.

Estrogenic activity data extraction and in silico prediction show the endocrine disruption potential of bisphenol A replacement compounds

Bisphenol A (BPA) replacement compounds are released to the environment and cause widespread human exposure. However, a lack of thorough safety evaluations on the BPA replacement compounds has raised public concerns. We assessed the endocrine disruption potential of BPA replacement compounds in the market to assist their safety evaluations. A literature search was conducted to ascertain the BPA replacement compounds in use. Available experimental estrogenic activity data of these compounds were extracted from the Estrogenic Activity Database (EADB) to assess their estrogenic potential. An in silico model was developed to predict the estrogenic activity of compounds lacking experimental data. Molecular dynamics (MD) simulations were performed to understand the mechanisms by which the estrogenic compounds bind to and activate the estrogen receptor (ER). Forty-five BPA replacement compounds were identified in the literature. Seven were more estrogenic and five less estrogenic than BPA, while six were nonestrogenic in EADB. A two-tier in silico model was developed based on molecular docking to predict the estrogenic activity of the 27 compounds lacking data. Eleven were predicted as ER binders and 16 as nonbinders. MD simulations revealed hydrophobic contacts and hydrogen bonds as the main interactions between ER and the estrogenic compounds.