Versatility or promiscuity: the estrogen receptors, control of ligand selectivity and an update on subtype selective ligands

Dysregulation of deubiquitination in breast cancer

Breast cancer (BC) is a highly frequent malignant tumor that poses a serious threat to women's health and has different molecular subtypes, histological subtypes, and biological features, which act by activating oncogenic factors and suppressing cancer inhibitors. The ubiquitin-proteasome system (UPS) is the main process contributing to protein degradation, and deubiquitinases (DUBs) are reverse enzymes that counteract this process. There is growing evidence that dysregulation of DUBs is involved in the occurrence of BC. Herein, we review recent research findings in BC-associated DUBs, describe their nature, classification, and functions, and discuss the potential mechanisms of DUB-related dysregulation in BC. Furthermore, we present the successful treatment of malignant cancer with DUB inhibitors, as well as analyzing the status of targeting aberrant DUBs in BC.

Increased Endocrine Activity of Xenobiotic Chemicals as Mediated by Metabolic Activation

The US Environmental Protection Agency (USEPA) is faced with long lists of chemicals that require hazard assessment. The present study is part of a larger effort to develop in vitro assays and quantitative structure-activity relationships applicable to untested chemicals on USEPA inventories through study of estrogen receptor (ER) binding and estrogen-mediated gene expression in fish. The present effort investigates metabolic activation of chemicals resulting in increased estrogenicity. Phenolphthalin (PLIN) was shown not to bind rainbow trout (Oncorhynchus mykiss) ER (rtER) in a competitive binding assay, but vitellogenin (Vtg) expression was induced in trout liver slices exposed to 10-4 and 10-3.7 M PLIN. Phenolphthalein (PLEIN), a metabolite of PLIN, was subsequently determined to be formed when slices were exposed to PLIN. It binds rtER with a relative binding affinity to 17β-estradiol of 0.020%. Slices exposed to PLEIN expressed Vtg messenger RNA (mRNA) at 10-4.3 , 10-4 , and 10-3.7 M, with no detectable PLIN present. Thus, Vtg expression noted in PLIN slice exposures was explained by metabolism to PLEIN in trout liver slices. A second model chemical, 4,4'-methylenedianiline (MDA), was not shown to bind rtER but did induce Vtg mRNA production in tissue slices at 10-4.3 , 10-4 , and 10-3.7 M in amounts nearly equal to reference estradiol induction, thus indicating metabolic activation of MDA. A series of experiments were performed to identify a potential metabolite responsible for the observed increase in activity. Potential metabolites hydroxylamine-MDA, nitroso-MDA, azo-MDA, and azoxy-MDA were not observed. However, acetylated MDA was observed and tested in both ER-binding and tissue slice Vtg induction assays. Environ Toxicol Chem 2023;42:2747-2757. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Preclinical Development of Brain Permeable ERβ Agonist for the Treatment of Glioblastoma

Glioblastoma (GBM) is the most prevalent and aggressive type of adult brain tumors with low 5-year overall survival rates. Epidemiologic data suggest that estrogen may decrease brain tumor growth, and estrogen receptor beta (ERβ) has been demonstrated to exert antitumor functions in GBM. The lack of potent, selective, and brain permeable ERβ agonist to promote its antitumor action is limiting the therapeutic promise of ERβ. In this study, we discovered that Indanone and tetralone-keto or hydroxyl oximes are a new class of ERβ agonists. Because of its high activity in ERβ reporter assays, specific binding to ERβ in polar screen assays, and potent growth inhibitory activity in GBM cells, CIDD-0149897 was discovered as a possible hit by screening a library of compounds. CIDD-0149897 is more selective for ERβ than ERα (40-fold). Treatment with CIDD-0149897 markedly reduced GBM cell viability with an IC50 of ∼7 to 15 μmol/L, while having little to no effect on ERβ-KO cells and normal human astrocytes. Further, CIDD-0149897 treatment enhanced expression of known ERβ target genes and promoted apoptosis in established and patient-derived GSC models. Pharmacokinetic studies confirmed that CIDD-0149897 has systemic exposure, and good bioavailability in the brain. Mice tolerated daily intraperitoneal treatment of CIDD-0149897 (50 mg/kg) with a 7-day repeat dosage with no toxicity. In addition, CIDD-0149897 treatment significantly decreased tumor growth in U251 xenograft model and extended the survival of orthotopic GBM tumor-bearing mice. Collectively, these findings pointed to CIDD-0149897 as a new class of ERβ agonist, offering patients with GBM a potential means of improving survival.

Estrogen signaling via estrogen receptor alpha and its implications for neurodegeneration associated with Alzheimer's disease in aging women

Estrogen receptor alpha (ERα) is a transcription factor activated by estrogenic hormones to regulate gene expression in certain organs, including the brain. In the brain, estrogen signaling pathways are central for maintaining cognitive functions. Herein, we review the neuroprotective effects of estrogens mediated by ERα. The estrogen/ERα pathways are affected by the reduction of estrogens in menopause, and this event may be a risk factor for neurodegeneration associated with Alzheimer's disease in women. Thus, developing a better understanding of estrogen/ERα signaling may be critical for defining new biomarkers and potential therapeutic targets for Alzheimer's disease in women.

Human Estrogen Receptor Alpha Antagonists, Part 3: 3-D Pharmacophore and 3-D QSAR Guided Brefeldin A Hit-to-Lead Optimization toward New Breast Cancer Suppressants

The estrogen receptor α (ERα) is an important biological target mediating 17β-estradiol driven breast cancer (BC) development. Aiming to develop innovative drugs against BC, either wild-type or mutated ligand-ERα complexes were used as source data to build structure-based 3-D pharmacophore and 3-D QSAR models, afterward used as tools for the virtual screening of National Cancer Institute datasets and hit-to-lead optimization. The procedure identified Brefeldin A (BFA) as hit, then structurally optimized toward twelve new derivatives whose anticancer activity was confirmed both in vitro and in vivo. Compounds as SERMs showed picomolar to low nanomolar potencies against ERα and were then investigated as antiproliferative agents against BC cell lines, as stimulators of p53 expression, as well as BC cell cycle arrest agents. Most active leads were finally profiled upon administration to female Wistar rats with pre-induced BC, after which 3DPQ-12, 3DPQ-3, 3DPQ-9, 3DPQ-4, 3DPQ-2, and 3DPQ-1 represent potential candidates for BC therapy.

Human Estrogen Receptor α Antagonists. Part 1: 3-D QSAR-Driven Rational Design of Innovative Coumarin-Related Antiestrogens as Breast Cancer Suppressants through Structure-Based and Ligand-Based Studies

The estrogen receptor α (ERα) represents a 17β-estradiol-inducible transcriptional regulator that initiates the RNA polymerase II-dependent transcriptional machinery, pointed for breast cancer (BC) development via either genomic direct or genomic indirect (i.e., tethered) pathway. To develop innovative ligands, structure-based (SB) three-dimensional (3-D) quantitative structure-activity relationship (QSAR) studies have been undertaken from structural data taken from partial agonists, mixed agonists/antagonists (selective estrogen receptor modulators (SERMs)), and full antagonists (selective ERα downregulators (SERDs)) correlated with either wild-type or mutated ERα receptors. SB and ligand-based (LB) alignments allow us to rule out guidelines for the SB/LB alignment of untested compounds. 3-D QSAR models for ERα ligands, coupled with SB/LB alignment, were revealed to be useful tools to dissect the chemical determinants for ERα-based anticancer activity as well as to predict their potency. The herein developed protocol procedure was verified through the design and potency prediction of 12 new coumarin-based SERMs, namely, 3DQ-1a to 3DQ-1e, that upon synthesis turned to be potent ERα antagonists by means of either in vitro or in vivo assays (described in the second part of this study).

MINDY1 promotes breast cancer cell proliferation by stabilizing estrogen receptor α

Breast cancer is the most commonly diagnosed malignant tumor among females. Estrogen receptor α (ERα) is initially expressed in 70% of breast cancers and is a well-known target of endocrine therapy for ERα-positive breast cancer. In the present study, we identified MINDY1, a member belongs to the motif interacting with Ubcontaining novel DUB family (MINDY), as a potential deubiquitylase of ERα in breast cancer. There was a positive correlation between ERα and MINDY1 protein levels in human breast cancer tissues. We found that high expression of MINDY1 was associated with poor prognosis. MINDY1 interacted with ERα, thereby mediating the deubiquitination of ERα and increased its stability in a deubiquitylation activity-dependent manner. MINDY1 depletion significantly decreased the ERα protein level and ERα signaling activity in breast cancer cells. Specifically, MINDY1 associated with the N-terminal of ERα via its catalytic domain, thus inhibiting K48-specific poly-ubiquitination process on ERα protein. In addition, MINDY1 depletion led to growth inhibition and cell cycle arrest of ERα-positive breast cancer cells. Finally, overexpression of ERα could rescue the MINDY1 depletion-induced growth inhibition both in vitro and in vivo, suggesting that MINDY1 promotes breast carcinogenesis through increasing ERα stability. Overall, our study proposed a novel post-translational mechanism of ERα in supporting breast cancer progression. Targeting the MINDY1 may prove to be a promising strategy for patients with ERα-positive breast cancer.

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.

OTUD7B stabilizes estrogen receptor α and promotes breast cancer cell proliferation

Breast cancer is the most common malignancy in women worldwide. Estrogen receptor α (ERα) is expressed in ∼70% of breast cancer cases and promotes estrogen-dependent cancer progression. In the present study, we identified OTU domain-containing 7B (OTUD7B), a deubiquitylase belonging to A20 subgroup of ovarian tumor protein superfamily, as a bona fide deubiquitylase of ERα in breast cancer. OTUD7B expression was found to be positively correlated with ERα in breast cancer and associated with poor prognosis. OTUD7B could interact with, deubiquitylate, and stabilize ERα in a deubiquitylation activity-dependent manner. Depletion of OTUD7B decreased ERα protein level, the expression of ERα target genes, and the activity of estrogen response element in breast cancer cells. In addition, OTUD7B depletion significantly decreased ERα-positive breast cancer cell proliferation and migration. Finally, overexpression of ERα could rescue the suppressive effect induced by OTUD7B depletion, suggesting that the ERα status was essential to the function of OTUD7B in breast carcinogenesis. In conclusion, our study revealed an interesting post-translational mechanism between ERα and OTUD7B in ERα-positive breast cancer. Targeting the OTUD7B–ERα complex may prove to be a potential approach to treat patients with ERα-positive breast cancer.

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.

In-silico identification of potential inhibitors targeting the DNA binding domain of estrogen receptor α for the treatment of hormone therapy-resistant breast cancer

Estrogen receptor α (ERα) plays a critical role in breast cancer (BC) development. The standard therapeutic strategies for ERα- positive (ERα+) BC consist of impairing ERα signalling pathway by either estrogen competitors blocking its interaction with the ligand binding domain (LBD) or agents inhibiting the production of estrogen. These strategies are limited by many factors that lead to constitutive activation of ERα and consequently, resistance to treatment. Targeting the DNA binding domain (DBD) of ERα instead of its LBD with small-molecule inhibitors could be an alternative to impair ERα's signalling pathway. For this purpose, we conducted a structure based virtual screening of DrugBank against the crystal structure of ERα-DBD (PDB ID: 1HCQ) using the Glide module in standard precision (SP) and extra precision (XP) mode of docking. Molecules with XP Gscore less than -8 kcal/mol were selected and visually inspected to keep only the reasonable docking poses. Subsequently, these molecules were clustered using structural interaction fingerprints analysis and the complexes of the top ranked molecules of each cluster based on XP Gscore were subjected to 200 ns molecular dynamics simulations followed by MM-GBSA binding free energy calculation for the last 100 ns of each complex. In this study, we identified three molecules from DrugBank namely DB03450, DB02593 and DB08001 showing significant stability and strong interaction with the key amino acids during MD simulation suggesting a potential inhibition of the target. These molecules could be used as promising lead compounds to impair the ERα signalisation in hormone therapy-resistant breast cancer.Communicated by Ramaswamy H. Sarma.

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

Endocrine-disrupting chemicals (EDCs) can interact with nuclear receptors, including estrogen receptor α (ERα) and androgen receptor (AR), to affect the normal endocrine system function, causing severe symptoms. Limited studies queried the EDC mechanisms, focusing on limited chemicals or a set of structurally similar compounds. It remained uncertain how hundreds of diverse EDCs could bind to ERα and AR and cause distinct functional consequences. Here, we employed a series of computational methodologies to investigate the structural features of EDCs that bind to and activate ERα and AR based on more than 4000 compounds. We used molecular docking and molecular dynamics simulations to elucidate the functional consequences and validated structure-function correlations experimentally using a time-resolved fluorescence resonance energy-transfer assay. We found that EDCs share three levels of key fragments. Primary (20 for ERα and 18 for AR) and secondary fragments (38 for ERα and 29 for AR) are responsible for the binding to receptors, and tertiary fragments determine the activity type (agonist, antagonist, or mixed). In summary, our study provides a general mechanism for the EDC function. Discovering the three levels of key fragments may drive fast screening and evaluation of potential EDCs from large sets of commercially used synthetic compounds.

Species differences in ligand interaction and activation of estrogen receptors in fish and human

Estrogen receptor (ER) sequences vary between species and this suggests that there are differences in the ligand-specificity, leading to species-specific effects. This would indicate that it is not possible to generalize effects across species. In this study, we investigated the differences in activation potencies and binding affinities of ER´s alpha (α) and beta (β) in human, zebrafish and sea bream to elucidate species differences in response to estradiol, estrone, estriol and methyltestosterone. In vitro analysis showed that estradiol had the highest activity for all the ER´s except for human ERβ and seabream ERβ2. Alignment of the ligand binding domain and ligand binding pocket (LBP) residues of the three species showed that different residues were involved in the LBPs which led to differences in pocket volume, affected binding affinity and orientation of the ligands. By combining in silico and in vitro results, it was possible to identify the ligand specificities of ER´s. The results demonstrated that the human ER´s show lower resolution in ligand-dependent activation, suggesting higher promiscuity, than the zebrafish and seabream ER´s. These results show species-specificity of ER´s and suggest that species-specific differences must be taken into consideration when studying different exposure scenarios.

Estrogen Receptor α (ERα)-targeting Compounds and Derivatives: Recent Advances in Structural Modification and Bioactivity

Breast cancer is the most common cancer suffered by female, and the second highest cause of cancer-related death among women worldwide. At present, hormone therapy is still the main treatment route and can be divided into three main categories: selective estrogen receptor modulators (SERMs), selective estrogen receptor downregulators (SERDs), and aromatase inhibitors (AIs). However, breast cancer is difficult to cure even after several rounds of anti-estrogen therapy and most drugs have serious side-effects. Here, we review the literature published over the past five years regarding the isolation and synthesis of analogs and their derivatives.

Non-genomic effects of nuclear receptors: insights from the anucleate platelet

Nuclear receptors (NRs) have the ability to elicit two different kinds of responses, genomic and non-genomic. Although genomic responses control gene expression by influencing the rate of transcription, non-genomic effects occur rapidly and independently of transcriptional regulation. Due to their anucleate nature and mechanistically well-characterized and rapid responses, platelets provide a model system for the study of any non-genomic effects of the NRs. Several NRs have been found to be present in human platelets, and multiple NR agonists have been shown to elicit anti-platelet effects by a variety of mechanisms. The non-genomic functions of NRs vary, including the regulation of kinase and phosphatase activity, ion channel function, intracellular calcium levels, and production of second messengers. Recently, the characterization of mechanisms and identification of novel binding partners of NRs have further strengthened the prospects of developing their ligands into potential therapeutics that offer cardio-protective properties in addition to their other defined genomic effects.

Disinfection Byproducts Bind Human Estrogen Receptor-α

Disinfection byproducts are formed during most drinking water treatment and presently number >800, some of which are implicated in human health outcomes including bladder cancer and infertility, with unknown mechanisms of action. In particular, it is not yet understood whether these compounds can disrupt the estrogen-signaling pathway through binding to the human estrogen receptor (ER). In the present study, 21 disinfection byproducts, selected for their predicted involvement in endocrine-related diseases and their structural diversity, were individually evaluated for their binding affinity to the human ER and in silico, and then a subset of these chemicals was studied in binary mixtures with the known weak estrogen, 4-n-nonylphenol. Individually, 9 of the 21 disinfection byproducts were able to weakly bind to the ER, with affinities ranging from log median inhibitory concentration values of -3.83 to -2.19 M. In binary mixtures, the chemicals followed concentration addition, with their weak binding affinities having little contribution to the overall mixture affinity. These results demonstrate the variety of small-molecule disinfection byproduct structures that are capable of binding to the ER, and that their weak binding can still be of importance when overall human exposure to mixtures of disinfection byproducts in disinfected drinking water is considered. Environ Toxicol Chem 2019;9999:1-9. © 2019 SETAC.

Immunotoxicity of estrogen and nonylphenol on apoptosis and expression of ERs in goldfish macrophage: Opening new avenue for discovering the role of experimental model systems and sexes

The expression of estrogen receptors (ERs) and their roles in important cell processes such as apoptosis in the macrophages exposed to estrogen/xenoestrogen have remained a complex secret. This study focused on the expression of estrogen receptors (ERs) and the stimulation of apoptosis in the macrophages from the two sexes of goldfish (Carassius auratus) exposed to 17-βestradiol (E2) and nonylphenol (NP) under in vivo and in vitro conditions. For the in vivo experiment, fish were exposed to NP (10^-6 M and 10^-7 M) and E2 (10^-6 M) for 24 days. Then, the head kidney macrophages from the male and the female goldfish were isolated and assayed. For the in vitro experiments, the macrophages derived from the two sexes were cultured in L-15 medium and exposed to E2 (150 nM) and NP (10 nM and 150 nM) for 3 days. The results showed that the three isoforms of ERs (ERα, ERβ₁, ERβ₂) were expressed in the goldfish macrophages. After the exposure of macrophages to NP and E2, sex-specific increase of ERs expression and apoptosis were observed (P < 0.05). The expression of ERα after NP treatment showed the highest alteration, with the response being concentration-dependent. The most alteration of ERs expression were observed in the in vivo experiment. This study provides insight to understand how exposure of the goldfish macrophages to E2 and NP can up-regulate the transcript levels of estrogen receptor subtypes and stimulate apoptosis.

Estrogenic activity of multicyclic aromatic hydrocarbons in rainbow trout (Oncorhynchus mykiss) in vitro assays

A representative group of multicyclic aromatic hydrocarbons (MAHC) which can be further classified as bridged-ring (bridged-MAHC) or fused-ring (fused-MAHC) were examined for their ability to interact with the estrogen receptor of rainbow trout (rtER) in a hepatic cytosolic estrogen receptor competitive binding assay (cyto rtERαβ) and the vitellogenin (Vtg) mRNA gene activation liver slice assay. All five fused-MAHCs; naphthalene (NAFT), fluorene (FE), Fluoranthene (FAT), pyrene (PY), and 9,10-dihydroanthracene (DAC) had no estrogenic activity in the in vitro assays used. Five of the eight bridged-MAHCs; triphenylethylene (3PE), o-terphenyl (OTP), triphenylmethane (TPM), 1,1-diphenylethylene (DPE), and cis-stilbene (CSB) were positive in the rtER-binding assay. The additional three bridged-MAHC's; trans-stilbene (TSB), tetraphenylethylene (4PE), and 4,4-di-tertbutylphenyl (DtBB) were determined to be non-binders due to isomeric configuration, solubility limitation, and possible steric hinderance. It is possible that the bridged-MAHCs bind to the rtER through a proposed aromatic-aromatic stacking (π-π interaction) facilitated by perpendicular ring orientation achieved through free rotation of the bridged rings. The fused-ring structures are locked in a planar configuration which doesn't allow for rotation of rings perpendicular to one another. This first report of the rtER-binding of bridged-MAHCs in fish demonstrates binding for a class of chemicals normally not thought of as having an affinity for the estrogen receptor and further supports the versatility or promiscuity of ER ligand selectivity.

Cancer in wildlife: patterns of emergence

Cancer is ubiquitous in wildlife, affecting animals from bivalves to pachyderms and cetaceans. Reports of increasing frequency demonstrate that neoplasia is associated with substantial mortality in wildlife species. Anthropogenic activities and global weather changes are shaping new geographical limitations for many species, and alterations in living niches are associated with visible examples of genetic bottlenecks, toxin exposures, oncogenic pathogens, stress and immunosuppression, which can all contribute to cancers in wild species. Nations that devote resources to monitoring the health of wildlife often do so for human-centric reasons, including for the prediction of the potential for zoonotic disease, shared contaminants, chemicals and medications, and for observing the effect of exposure from crowding and loss of habitat. Given the increasing human footprint on land and in the sea, wildlife conservation should also become a more important motivating factor. Greater attention to the patterns of the emergence of wildlife cancer is imperative because growing numbers of species are existing at the interface between humans and the environment, making wildlife sentinels for both animal and human health. Therefore, monitoring wildlife cancers could offer interesting and novel insights into potentially unique non-age-related mechanisms of carcinogenesis across species.

Polyubiquitination inhibition of estrogen receptor alpha and its implications in breast cancer

Estrogen receptor alpha (ERα) is detected in more than 70% of the cases of breast cancer. Nuclear activity of ERα, a transcriptional regulator, is linked to the development of mammary tumors, whereas the extranuclear activity of ERα is related to endocrine therapy resistance. ERα polyubiquitination is induced by the estradiol hormone, and also by selective estrogen receptor degraders, resulting in ERα degradation via the ubiquitin proteasome system. Moreover, polyubiquitination is related to the ERα transcription cycle, and some E3-ubiquitin ligases also function as coactivators for ERα. Several studies have demonstrated that ERα polyubiquitination is inhibited by multiple mechanisms that include posttranslational modifications, interactions with coregulators, and formation of specific protein complexes with ERα. These events are responsible for an increase in ERα protein levels and deregulation of its signaling in breast cancers. Thus, ERα polyubiquitination inhibition may be a key factor in the progression of breast cancer and resistance to endocrine therapy.

In silico identification of protein targets for chemical neurotoxins using ToxCast in vitro data and read-across within the QSAR toolbox

This study evaluates the application of QSAR Toolbox and ToxCast screening data to identify neurological targets for pyrethroids.

There are many mechanisms of neurotoxicity that are initiated by the interaction of chemicals with different neurological targets. Under the U.S. Environmental Protection Agency's ToxCast program, the biological activity of thousands of chemicals was screened in biochemical and cell-based assays in a high-throughput manner. Two hundred sixteen assays in the ToxCast screening database were identified as targeting a total of 123 proteins having neurological functions according to the Gene Ontology database. Data from these assays were imported into the Organization for Economic Co-operation and Development QSAR Toolbox and used to predict neurological targets for chemical neurotoxins. Two sets of data were generated: one set was used to classify compounds as active or inactive and another set, composed of AC₅₀s for only active compounds, was used to predict AC₅₀ values for unknown chemicals. Chemical grouping and read-across within the QSAR Toolbox were used to identify neurologic targets and predict interactions for pyrethroids, a class of compounds known to elicit neurotoxic effects in humans. The classification prediction results showed 79% accuracy while AC₅₀ predictions demonstrated mixed accuracy compared with the ToxCast screening data.

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.

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.

Dynamic changes in binding interaction networks of sex steroids establish their non-classical effects

Non-classical signaling in the intracellular second messenger system plays a pivotal role in the cytoprotective effect of estradiol. Estrogen receptor is a common target of sex steroids and important in mediating estradiol-induced neuroprotection. Whereas the mechanism of genomic effects of sex steroids is fairly understood, their non-classical effects have not been elucidated completely. We use real time molecular dynamics calculations to uncover the interaction network of estradiol and activator estren. Besides steroid interactions, we also investigate the co-activation of the receptor. We show how steroid binding to the alternative binding site of the non-classical action is facilitated by the presence of a steroid in the classical binding site and the absence of the co-activator peptide. Uncovering such dynamic mechanisms behind steroid action will help the structure-based design of new drugs with non-classical responses and cytoprotective potential.

Nonsteroidal estrogen receptor isoform-selective biphenyls

Estrogen receptor (ER) has been a therapeutic target to treat ER-positive breast cancer, most notably by agents known as selective estrogen receptor modulators (SERMs). However, resistance and severe adverse effects of known drugs gave impetus to the search for newer agents with better therapeutic profile. ERα and ERβ are two isoforms sharing 56% identity and having different physiological functions and expressions in various tissues. Only two residues differ in the active sites of the two isoforms motivating us to design isoform-selective ligands. Guided by computational docking and molecular dynamics simulations, we have designed, synthesized, and tested, substituted biphenyl-2,6-diethanones and their derivatives as potential agents targeting ERα. Four of the molecules synthesized exhibited preferential cytotoxicity in ERα+ cell line (MCF-7) compared to ERβ+ cell line (MDA-MB-231). Molecular dynamics (MD) in combination with molecular mechanics-generalized Born surface area (MM-GBSA) methods could account for binding selectivity. Further cotreatment and E-screen studies with known ER ligands-estradiol (E₂ ) and tamoxifen (Tam)-indicated isoform-selective anti-estrogenicity in ERα+ cell line which might be ER-mediated. ERα siRNA silencing experiments further confirmed the ER selective nature of ligands.

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.

Nucleo-cytoplasmic transport of estrogen receptor alpha in breast cancer cells

Approximately 70% cases of breast cancers exhibit high expression and activity levels of estrogen receptor alpha (ERα), a transcription regulator that induces the expression of genes associated with cellular proliferation and survival. These nuclear functions of the receptor are associated with the development of breast cancer. However, ERα localization is not static, but rather, dynamic with continuous shuttling between the nucleus and the cytoplasm. Interestingly, both the nuclear import and export of ERα are modulated by several stimuli that include estradiol, antiestrogens, and growth factors. As ERα nuclear accumulation is critical to the regulation of gene expression, nuclear export of this receptor modulates the intensity and duration of its transcriptional activity. Thus, the subcellular spatial distribution of ERα ensures tight modulation of its concentration in cellular compartments, as well as of its nuclear and extranuclear functions. In this review, we will discuss current findings regarding the biological importance of molecular mechanisms of, and proteins responsible for, the nuclear import and export of ERα in breast cancer cells.

Phthalate exposure, flavonoid consumption and breast cancer risk among Mexican women

OBJECTIVE

To evaluate if selected phthalate exposure and flavonoid intake interact on breast cancer (BC) risk.

MATERIAL AND METHODS

Interviews and urine samples were obtained from 233 women with histologically confirmed BC and 221 healthy controls matched by age and place of residence, from various states of northern Mexico. Urinary metabolites concentrations of diethyl phthalate (DEP), butyl benzyl phthalate (BBzP) and dioctyl phthalate (DOP) were determined by solid-phase extraction coupled with high-performance liquid chromatography/isotope dilution/tandem mass spectrometry. Using a semiquantitative food frequency questionnaire, consumption of five types of flavonoids (anthocyanidins, flavan-3-ols, flavanones, flavones and flavonols) was estimated according to three food groups: vegetables, fruits and legumes-oil seeds.

RESULTS

A higher intake of anthocyanidins and flavan-3-ols (from vegetables), synergistically increased the negative association between BBzP and BC. No other significant flavonoid-phthalate multiplicative interactions on the risk for BC were found.

CONCLUSION

The consumption of some flavonoids may interact with exposure to phthalates on the risk of BC. Epidemiological and underlying mechanisms information is still insufficient and requires further investigations.

Phytochemical study and biological evaluation of chemical constituents of Platanus orientalis and Platanus × acerifolia buds

One flavonol glycoside, two O-isoprenylated flavonols, one α,α-dimethylallyl flavonol, one dihydrochalcone, two furanocoumarins and one terpenoid previously undescribed, along with 42 known compounds were isolated from the buds of two European Platanaceae, Platanus orientalis and Platanus × acerifolia. Their chemical structures were elucidated on the basis of spectroscopic analysis, including homonuclear and heteronuclear correlation NMR (COSY, NOESY, HSQC, and HMBC) experiments, as well as HRMS data. The estrogen-like and antiestrogen-like activity of dichloromethane and methanol extracts of P. orientalis and P. × acerifolia buds and isolated compounds was evaluated using estrogen-responsive cell lines. The potency of selected estrogen agonists to regulate gene expression through ERα and/or ERβ was compared with their in vitro osteoblastogenic activity. Kaempferol and 8-C-(1,1-dimethyl-2-propen-1-yl)-5,7-dihydroxyflavonol displayed osteoblastogenic as well as ERα-mediated estrogenic activity similar to estradiol.

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.

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.

Tamoxifen Resistance: Emerging Molecular Targets

17β-Estradiol (E2) plays a pivotal role in the development and progression of breast cancer. As a result, blockade of the E2 signal through either tamoxifen (TAM) or aromatase inhibitors is an important therapeutic strategy to treat or prevent estrogen receptor (ER) positive breast cancer. However, resistance to TAM is the major obstacle in endocrine therapy. This resistance occurs either de novo or is acquired after an initial beneficial response. The underlying mechanisms for TAM resistance are probably multifactorial and remain largely unknown. Considering that breast cancer is a very heterogeneous disease and patients respond differently to treatment, the molecular analysis of TAM’s biological activity could provide the necessary framework to understand the complex effects of this drug in target cells. Moreover, this could explain, at least in part, the development of resistance and indicate an optimal therapeutic option. This review highlights the implications of TAM in breast cancer as well as the role of receptors/signal pathways recently suggested to be involved in the development of TAM resistance. G protein—coupled estrogen receptor, Androgen Receptor and Hedgehog signaling pathways are emerging as novel therapeutic targets and prognostic indicators for breast cancer, based on their ability to mediate estrogenic signaling in ERα-positive or -negative breast cancer.

Charge Density and Electrostatic Potential Study of 16α,17β-Estriol and the Binding of Estrogen Molecules to the Estrogen Receptors ERα and ERβ

An accurate X-ray diffraction study at 20 K combined with DFT theoretical calculations has been performed for the estriol crystal with two conformationally different molecules in the asymmetric unit. The electron density has been modeled via a multipole expansion, using both experimental and theoretical structure factors, and a topological analysis has been performed. The experimental molecular geometry, hydrogen bonding, atomic charges, dipole moments, and other topological characteristics are compared with those calculated theoretically. In particular, the molecular electrostatic potential has been extracted and compared with those reported for other estrogen molecules exhibiting different binding affinities to the estrogen receptors (ERα and ERβ).

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.

Mechanisms that Increase Stability of Estrogen Receptor Alpha in Breast Cancer

Estrogen receptor alpha (ER) is a transcriptional regulator that controls the expression of genes related to cellular proliferation and differentiation in normal mammary tissue. However, the expression, abundance, and activity of this receptor are increased in 70% of breast cancers. The ER upregulation is facilitated by several molecular mechanisms, including protein stability, which represents an important strategy to maintain an active and functional repertoire of ER. Several proteins interact and protect ER from degradation by the ubiquitin-proteasome system. Through diverse mechanisms, these proteins prevent polyubiquitination and degradation of ER, leading to an increase in ER protein levels; consequently, estrogen signaling and its physiologic effects are enhanced in breast cancer cells. Thus, increased protein stability seems to be one of the main reasons that ER is upregulated in breast cancer. Here, we highlight findings on the proteins and mechanisms that participate directly or indirectly in ER stability and their relevance to breast cancer.

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.

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.

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.

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.

Analysis of the ToxCast Chemical-Assay Space Using the Comparative Toxicogenomics Database

Many studies have attempted to predict in vivo hazards based on the ToxCast in vitro assay results with the goal of using these predictions to prioritize compounds for conventional toxicity testing. Most of these conventional studies rely on in vivo end points observed using preclinical species (e.g., mice and rats). Although the preclinical animal studies provide valuable insights, there can often be significant disconnects between these studies and safety concerns in humans. One way to address these concerns, for an admittedly more limited set of compounds, is to explore relationships between the in vitro data from human cell lines and observations from human related studies. The Comparative Toxicogenomics Database (CTD; http://ctdbase.org ) is a rich source of data linking chemicals to human diseases/adverse events and pathways. In this study we explored the relationships between ToxCast chemicals, their ToxCast in vitro test results, and their annotations of human disease/adverse event end points as captured in the CTD database. We mined these associations to identify potentially interesting, statistically significant in vitro assay and in vivo toxicity correlations. To the best of our knowledge, this is one of the first studies analyzing the relationships between the ToxCast in vitro assays results and the CTD disease/adverse event end point annotations. The in vitro profiles identified in this analysis may prove useful for prioritizing compounds for toxicity testing, suggesting mechanisms of toxicity, and forecasting potential in vivo human drug induced injury.

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.