Competitive molecular docking approach for predicting estrogen receptor subtype α agonists and antagonists

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.

A preliminary understanding of the relationship between synthetic phenolic antioxidants and early pregnancy loss: Uncovering the potential molecular mechanisms

Mounting evidence suggests that environmental pollutants may affect reproductive health, potentially leading to adverse outcomes like pregnancy loss. However, it remains unclear whether exposure to synthetic phenolic antioxidants (SPAs) correlates with early pregnancy loss (EPL). This study explores SPA exposure's link to EPL and its potential molecular mechanisms. From 2021 to 2022, 265 early pregnant women (136 serum and 129 villus samples) with and without EPL were enrolled. We quantified 17 SPAs in serum and chorionic villus, with AO1010, AO3114, BHT, AO2246, and BHT-Q frequently being detected, suggesting their ability to cross the placental barrier. AO1135 showed a positive relationship with EPL in sera, indicating a significant monotonic dose-response relationship (p-trend <0.001). BHT-Q exhibited a similar relationship with EPL in villi. Inhibitory effects of BHT-Q on estradiol (E2) were observed. Molecular docking revealed SPA-protein interactions involved in E2 synthesis. SPA-induced EPL might occur with specific serum levels of AO1135 and certain villus levels of AO1010, BHT-Q, and AO2246. BHT-Q emerges as a potential biomarker for assessing EPL risk. This study provides insights into understanding of the exposure to SPAs and potential adverse outcomes in pregnant women.

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.

Aspirin as a potential drug repurposing candidate targeting estrogen receptor alpha in breast cancer: a molecular dynamics and in-vitro study

Estrogen receptor alpha (ERα) is expressed by 70% of breast cancers (BCs). Any deregulation in ERα signaling is crucial for the initiation and progression of BC. Because of development of resistance to anti-estrogenic compounds, repurposing existing drugs is an apt strategy to avoid a long drug-discovery process. Substantial epidemiologic evidence suggests that Aspirin use reduces the risk of different cancers including BC, while its role as an adjuvant or a possible antineoplastic agent in cancer treatment is being investigated. In this study, we attempted to explore possibilities of ERα inhibition by Aspirin which may act through competitive binding to the ligand binding domain (LBD) of ERα. A list of 48 ERα-LBD crystal structures bound with agonists, antagonists, and selective ER modulators (SERMs) was thoroughly analysed to determine interaction patterns specific to each ligand category. Exhaustive docking and 500 ns molecular dynamics (MD) studies were performed on three ERα - Aspirin complexes generated using agonist, antagonist, and SERM-bound crystal structures. Besides, three ERα crystal structures bound to agonist, antagonist, and SERM respectively were also subjected to MD simulations. Aspirin showed good affinity to LBD of ERα. Comparative analyses of binding patterns, conformational changes and molecular interaction profiles from the docking results and MD trajectories suggests that Aspirin was most stable in complex generated using SERM bound crystal structure of ERα and showed interactions with Gly-521, Ala-350, Leu-525 and Thr-347 like SERMs. In addition, in-vitro assays, qPCR, and immunofluorescent assay demonstrated the decline in the expression of ERα in MCF-7 upon treatment with Aspirin. These preliminary bioinformatical and in-vitro findings may form the basis to consider Aspirin as a potential candidate for targeting ERα, especially in tamoxifen-resistant cancers.Communicated by Ramaswamy H. Sarma.

Computational Screening of Some Phytochemicals to Identify Best Modulators for Ligand Binding Domain of Estrogen Receptor Alpha

OBJECTIVE

The peculiar aim of this study is to discover and identify the most effective and potential inhibitors against the most influential target ERα receptor by in silico studies of 45 phytochemicals from six diverse ayurvedic medicinal plants.

METHODS

The molecular docking investigation was carried out by the genetic algorithm program of AutoDock Vina. The molecular dynamic (MD) simulation investigations were conducted using the Desmond tool of Schrödinger molecular modelling. This study identified the top ten highest binding energy phytochemicals that were taken for drug-likeness test and ADMET profile prediction with the help of the web-based server QikpropADME.

RESULTS

Molecular docking study revealed that ellagic acid (-9.3 kcal/mol), emodin (-9.1 kcal/mol), rhein (-9.1 kcal/mol), andquercetin (-9.0 kcal/mol) phytochemicals showed similar binding affinity as standard tamoxifen towards the target protein ERα. MD studies showed that all four compounds possess comparatively stable ligand-protein complexes with ERα target compared to the tamoxifen-ERα complex. Among the four compounds, phytochemical rhein formed a more stable complex than standard tamoxifen. ADMET studies for the top ten highest binding energy phytochemicals showed a better safety profile.

CONCLUSION

Additionally, these compounds are being reported for the first time in this study as possible inhibitors of ERα for treating breast cancer, according to the notion of drug repurposing. Hence, these phytochemicals can be further studied and used as a parent core molecule to develop innovative lead molecules for breast cancer therapy.

In silico evaluation of binding interaction and ADME study of new 1,3-diazetidin-2-one derivatives with high antiproliferative activity

A series of eight novels' 1,3-diazetidin-2-ones have been proposed to assess their potential activities. They are intended to examine antiproliferative effects through inhibition of epidermal growth factor receptor (EGFR) expression. These eight compounds strongly interact with the EGFR protein, responsible for the activity. As part of a present study, these compounds were docked to the crystal structure of the EGFR (Protein Data Bank code: 1 M17) to determine their binding affinity at the active site. Based on computer predictions, two compounds were demonstrated high scores of 80.80 and 85.89. After analyzing ADME properties, these compounds were found to have significant potential for binding. Consequently, the abilities of gefitinib, erlotinib, imatinib, and sorafenib were selected for comparison as controls. Computational methods were performed to predict the critical disposition of eight novels' 1,3-diazetidin-2-one derivatives to the EGFR. Moreover, a docking technique employing the Genetic Optimization for Ligand Docking program was conducted. Compounds 2 and 7 demonstrate a high docking peace-wise scoring function (PLP) fitness of 85.89 and 80.80, respectively. They fulfilled the Lipinski's rule, topological descriptors, and fingerprints of drug-like molecular structure keys. These compounds can be used as lead compounds to develop novel antiproliferative agents. The outcome of applying this study is novel series of 1,3-diazetidin-2-one compounds as new analogs were designed and evaluated for their antiproliferative activity with a higher potency profile and binding affinity within the active sites of EGFR.

Development of a Novel 18F-Labeled Probe for PET Imaging of Estrogen Receptor β

Estrogen receptor beta (ERβ) is an important ER subtype that plays crucial roles in many physiological and pathological disorders. Herein, we developed the probe [¹⁸F]PVBO for in vivo ERβ targeted PET imaging and obtained promising results. The nonradioactive PVBO showed a 12.5-fold stronger binding affinity to ERβ than to ERα in vitro. In vitro assays revealed the specific uptake of [¹⁸F]PVBO by DU145 cells. The uptake of [¹⁸F]PVBO by DU145 xenografts increased during the 120 min dynamic scanning, with a maximum uptake of 2.80 ± 0.30% ID/g. Based on time activity curves (TACs), the injection of [¹⁸F]PVBO with unlabeled PVBO or ERB-041 resulted in a significant signal reduction with the tumor/muscle (T/M) ratio <1 at 30, 60, 75, and 120 min post-injection (p < 0.05). [¹⁸F]PVBO demonstrates the feasibility of noninvasively imaging ERβ-positive tumors by small-animal PET and provides a new strategy for visualizing ERβ in vivo.

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.

Boesenbergia Pandurata as an Anti-Breast Cancer Agent: Molecular Docking and ADMET Study

Background:

Boesenbergia pandurata or fingerroot is known to have various pharmacological activities, including anticancer properties. Extracts from these plants are known to inhibit the growth of cancer cells, including breast cancer. Anti-breast cancer activity is significantly influenced by the inhibition of two receptors: ER-α and HER2. However, it is unknown which metabolites of B. pandurata play the most crucial role in exerting anticancer activity.

Objective:

This study aimed to determine the metabolites of B. pandurata with the best potential as ER-α and HER2 inhibitors.

Method:

The method used was molecular docking of several B. pandurata metabolites to ER-α and HER2 receptors, followed by an ADMET study of several metabolites with the best docking results.

Results:

The docking results showed eight metabolites with the best docking results for the two receptors based on the docking score and ligand-receptor interactions. Of these eight compounds, compounds 11 ((2S)-7,8-dihydro-5-hydroxy-2-methyl-2-(4''-methyl-3''-pentenyl)-8-phenyl-2H,6H-benzo(1,2-b-5,4- b')dipyran-6-one) and 34 (geranyl-2,4-dihydroxy-6-phenethylbenzoate) showed the potential to inhibit both receptors. Both ADMET profiles also showed mixed results; however, there is a possibility of further development.

Conclusion:

In conclusion, the metabolites of B. pandurata, especially compounds 11 and 34, can be developed as anti-breast cancer agents by inhibiting ER-α and HER2.

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.

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

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

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.

Identification of steroidal saponins from Tribulus terrestris and their in silico docking studies

Tribulus terrestris is known to possess many pharmacological properties, most notably its anticancer activities, owing to its rich steroidal saponin contents. Even though many reports are available elucidating the anticancer potential of the herb, we, for the very first time have attempted to isolate and identified the active compound present in seed crude saponin extract and confers its in silico docking ability with various cellular targets proteins. High performance thin layer chromatography confirms the presence of active saponins in leaf and seed saponin extracts which were further fractionated by silica gel column chromatography. Fractions collected were assessed for cytotoxicity on human breast cancer cells. High resolution liquid chromatography and mass spectroscopy was employ to identify the active components present in fraction with highest cytotoxicity. Intriguingly, Nautigenin type of steriodal saponin was identified to present in the active fraction of seed extract (SF11) and the identified compound was further analyzed for its in silico docking interaction using PyRx AutodockVina. Docking studies revealed the high binding affinity of Nuatigenin at significant sites with apoptotic proteins Bcl-2, Bax, caspase-3, caspase-8, p53 and apoptosis inducing factor along with cell surface receptors estrogen receptor, projesterone receptor, epidermal growth factor receptor, and human epidermal growth factor receptor-2. Thus, the conclusions were drawn that saponin fraction of Tribulus terrestis possesses active compounds having anticancer property and specifically, Nuatigenin saponin can be considered as an important therapeutic drug for the breast cancer treatment.

Dual targeting of cytokine storm and viral replication in COVID-19 by plant-derived steroidal pregnanes: An in silico perspective

The high morbidity and mortality rate of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection arises majorly from the Acute Respiratory Distress Syndrome and "cytokine storm" syndrome, which is sustained by an aberrant systemic inflammatory response and elevated pro-inflammatory cytokines. Thus, phytocompounds with broad-spectrum anti-inflammatory activity that target multiple SARS-CoV-2 proteins will enhance the development of effective drugs against the disease. In this study, an in-house library of 117 steroidal plant-derived pregnanes (PDPs) was docked in the active regions of human glucocorticoid receptors (hGRs) in a comparative molecular docking analysis. Based on the minimal binding energy and a comparative dexamethasone binding mode analysis, a list of top twenty ranked PDPs docked in the agonist conformation of hGR, with binding energies ranging between -9.8 and -11.2 kcal/mol, was obtained and analyzed for possible interactions with the human Janus kinases 1 and Interleukins-6 and SARS-CoV-2 3-chymotrypsin-like protease, Papain-like protease and RNA-dependent RNA polymerase. For each target protein, the top three ranked PDPs were selected. Eight PDPs (bregenin, hirundigenin, anhydroholantogenin, atratogenin A, atratogenin B, glaucogenin A, glaucogenin C and glaucogenin D) with high binding tendencies to the catalytic residues of multiple targets were identified. A high degree of structural stability was observed from the 100 ns molecular dynamics simulation analyses of glaucogenin C and hirundigenin complexes of hGR. The selected top-eight ranked PDPs demonstrated high druggable potentials and favourable in silico ADMET properties. Thus, the therapeutic potentials of glaucogenin C and hirundigenin can be explored for further in vitro and in vivo studies.

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.

Machine Learning Methods in Drug Discovery

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

2-D chemical structure image-based in silico model to predict agonist activity for androgen receptor

BACKGROUND

Abnormal activation of human nuclear hormone receptors disrupts endocrine systems and thereby affects human health. There have been machine learning-based models to predict androgen receptor agonist activity. However, the models were constructed based on limited numerical features such as molecular descriptors and fingerprints.

RESULT

In this study, instead of the numerical features, 2-D chemical structure images of compounds were used to build an androgen receptor toxicity prediction model. The images may provide unknown features that were not represented by conventional numerical features. As a result, the new strategy resulted in a construction of highly accurate prediction model: Mathews correlation coefficient (MCC) of 0.688, positive predictive value (PPV) of 0.933, sensitivity of 0.519, specificity of 0.998, and overall accuracy of 0.981 in 10-fold cross-validation. Validation on a test dataset showed MCC of 0.370, sensitivity of 0.211, specificity of 0.991, PPV of 0.882, and overall accuracy of 0.801. Our chemical image-based prediction model outperforms conventional models based on numerical features.

CONCLUSION

Our constructed prediction model successfully classified molecular images into androgen receptor agonists or inactive compounds. The result indicates that 2-D molecular mimetic diagram would be used as another feature to construct molecular activity prediction models.

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

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

Computer-Aided Ligand Discovery for Estrogen Receptor Alpha

Breast cancer (BCa) is one of the most predominantly diagnosed cancers in women. Notably, 70% of BCa diagnoses are Estrogen Receptor α positive (ERα+) making it a critical therapeutic target. With that, the two subtypes of ER, ERα and ERβ, have contrasting effects on BCa cells. While ERα promotes cancerous activities, ERβ isoform exhibits inhibitory effects on the same. ER-directed small molecule drug discovery for BCa has provided the FDA approved drugs tamoxifen, toremifene, raloxifene and fulvestrant that all bind to the estrogen binding site of the receptor. These ER-directed inhibitors are non-selective in nature and may eventually induce resistance in BCa cells as well as increase the risk of endometrial cancer development. Thus, there is an urgent need to develop novel drugs with alternative ERα targeting mechanisms that can overcome the limitations of conventional anti-ERα therapies. Several functional sites on ERα, such as Activation Function-2 (AF2), DNA binding domain (DBD), and F-domain, have been recently considered as potential targets in the context of drug research and discovery. In this review, we summarize methods of computer-aided drug design (CADD) that have been employed to analyze and explore potential targetable sites on ERα, discuss recent advancement of ERα inhibitor development, and highlight the potential opportunities and challenges of future ERα-directed drug discovery.

Development of a Nicotinic Acetylcholine Receptor nAChR α7 Binding Activity Prediction Model

Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR-α7 subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR-α7 is an important component for assessing addictive potential of tobacco constituents. We developed an α7 binding activity prediction model based on a large training data set of 843 chemicals with human α7 binding activity data extracted from PubChem and ChEMBL. The model was tested using 1215 chemicals with rat α7 binding activity data from the same databases. Based on the competitive docking results, the docking scores were partitioned to the key residues that play important roles in the receptor-ligand binding. A decision forest was used to train the human α7 binding activity prediction model based on the partition of docking scores. Five-fold cross validations were conducted to estimate the performance of the decision forest models. The developed model was used to predict the potential human α7 binding activity for 5275 tobacco constituents. The human α7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm and empirically validate the prediction results. The prediction accuracy, sensitivity, and specificity were 64.3, 40.0, and 81.6%, respectively. The developed prediction model of human α7 may be a useful tool for high-throughput screening of potential addictive tobacco constituents.

Alkaloids and flavonoids from African phytochemicals as potential inhibitors of SARS-Cov-2 RNA-dependent RNA polymerase: an in silico perspective

Corona Virus Disease 2019 (COVID-19) is a pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Exploiting the potentials of phytocompounds is an integral component of the international response to this pandemic. In this study, a virtual screening through molecular docking analysis was used to screen a total of 226 bioactive compounds from African herbs and medicinal plants for direct interactions with SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). From these, 36 phytocompounds with binding affinities higher than the approved reference drugs (remdesivir and sobosivir), were further docked targeting the active sites of SARS-CoV-2, as well as SARS-CoV and HCV RdRp. A hit list of 7 compounds alongside two positive controls (remdesivir and sofosbuvir) and two negative controls (cinnamaldehyde and Thymoquinone) were further docked into the active site of 8 different conformations of SARS-CoV-2 RdRp gotten from molecular dynamics simulation (MDS) system equilibration. The top docked compounds were further subjected to predictive druglikeness and ADME/tox filtering analyses. Drugable alkaloids (10'-hydroxyusambarensine, cryptospirolepine, strychnopentamine) and flavonoids (usararotenoid A, and 12α-epi-millettosin), were reported to exhibit strong affinity binding and interactions with key amino acid residues in the catalytic site, the divalent-cation-binding site, and the NTP entry channel in the active region of the RdRp enzyme as the positive controls. These phytochemicals, in addition to other promising antivirals such as remdesivir and sofosbuvir, may be exploited towards the development of a cocktail of anti-coronavirus treatments in COVID-19. Experimental studies are recommended to validate these study.

In silico study of molecular mechanisms of action: Estrogenic disruptors among phthalate esters

Phthalate esters (PAEs), as widely used plasticizers, have been concerned for their possible disruption of estrogen functions via binding to and activating the transcription of estrogen receptors (ERs). Nevertheless, the computational interpretation of the mechanism of ERs activities modulated by PAEs at the molecular level is still insufficient, which hinders the reliable screening of the ERs-active PAEs with high speed and high throughput. To bridge the gap, the in silico simulations considering the effects of coactivators were accomplished to explore the molecular mechanism of action for the purpose of predicting the estrogenic potencies of PAEs. The transcriptional activation functions of human ERα (hERα) modulated by PAEs is predicted via the simulations including binding interaction of PAEs and hERα, conformational changes of PAEs-hERα complexes and recruitment of coactivators. Molecular insight into the diverse estrogen mechanism of action among PAEs with regard to hERα agonists and selective estrogen receptor modulators (SERMs) is provided. Agonist-modulated conformational change of hERα leads to the optimal exposure of its Activation Function 2 (AF-2) surface which, in turn, facilitates the recruitment of coactivators, therefore promoting the transcriptional activation functions of hERα. Conversely, binding interaction of hERα with SERMs among PAEs leads to the conformational change with blocked AF-2 surface, thus preventing the recruitment of coactivators and consequently inhibiting the AF-2 activity. The two-hybrid recombinant yeast is experimentally used for verification. The established in silico evaluation methodology exhibits great promise to speed up the prediction of chemicals which work as hERα agonist or SERMs.

Determination and analysis of agonist and antagonist potential of naturally occurring flavonoids for estrogen receptor (ERα) by various parameters and molecular modelling approach

Most estrogen receptor α (ERα) ligands target the ligand binding domain (LBD). Agonist 17β-estradiol (E₂) and tamoxifen (TM, known SERM), bind to the same site within the LBD. However, structures of ligand-bound complexes show that E₂ and TM induce different conformations of helix 12 (H12). During the molecular modelling studies of some naturally occurring flavonoids such as quercetin, luteolin, myricetin, kaempferol, naringin, hesperidin, galangin, baicalein and epicatechin with human ERα (3ERT and 1GWR), we observed that most of the ligands bound to the active site pocket of both 3ERT and 1GWR. The docking scores, interaction analyses, and conformation of H12 provided the data to support for the estrogenic or antiestrogenic potential of these flavonoids to a limited degree. Explicit molecular dynamics for 50 ns was performed to identify the stability and compatibility pattern of protein-ligand complex and RMSD were obtained. Baicalein, epicatechin, and kaempferol with 1GWR complex showed similar RMSD trend with minor deviations in the protein backbone RMSD against 1GWR-E₂ complex that provided clear indications that ligands were stable throughout the explicit molecular simulations in the protein and outcome of naringin-3ERT complex had an upward trend but stable throughout the simulations and all molecular dynamics showed stability with less than overall 1 Å deviation throughout the simulations. To examine their estrogenic or antiestrogenic potential, we studied the effect of the flavonoids on viability, progesterone receptor expression and 3xERE/3XERRE-driven reporter gene expression in ERα positive and estrogen responsive MCF-7 breast cancer cells. Epicatechin, myricetin, and kaempferol showed estrogenic potential at 5 µM concentration.

Virtual screening of novel compounds as potential ER-alpha inhibitors

Majority of breast cancers diagnosed today are estrogen receptor (ER)-positive, however, progesterone receptor-positive (PR-positive) is also responsible for breast cancer. Tumors that are ER/PR-positive are much more likely to respond to hormone therapy than tumors that are ER/PR-negative. Nearly 105 ERa inhibitors from literature when docked resulted in 31 compounds (pyrazolo[1,5-a]pyrimidine analogs and chromen-2-one derivatives) with better binding affinities. The maximum score obtained was -175.282 kcal/mol for compound, [2-(4- Fluoro-phenylamino)-pyridin-3-yl]-{4-[2-phenyl-7- (3, 4, 5-trimethoxy-phenyl)-pyrazolo[1,5-a]pyrimidine-5-carbonyl]-piperazin-1-yl}-methanone. The major H-bond interactions are observed with Thr347. In pursuit to identify novel ERa inhibitory ligands, virtual screening was carried out by docking pyrazole, bipyrazole, thiazole, thiadiazole etc scaffold analogs from literature.34 bipyrazoles from literature revealed Compound 2, ethyl 5-amino-1-(5-amino-3-anilino-4-ethoxycarbonyl-pyrazol-1-yl)-3-anilino-pyrazole-4-carboxylate, with -175.9 kcal/mol binding affinity with the receptor, where a favourable H-bond was formed with Thr347.On the other hand, screening 2035 FDA approved drugs from Drug Bank database resulted in 11 drugs which showed better binding affinities than ERa bound tamoxifen. Consensus scoring using 5 scoring schemes such as Mol Dock score, mcule, SwissDock, Pose&Rank and DSX respectively resulted in better rank-sumsfor Lomitapide, Itraconazole, Cobicistat, Azilsartanmedoxomil, and Zafirlukast.

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.

Development of leads targeting ER-α in breast cancer: An in silico exploration from natural domain

The steroid, estrogen has been recognized as being important for stimulating the growth of breast cancers primarily mediated via the steroidal estrogen receptor-α (ER-α). Inhibition of estrogen activity by small molecules with increased target specificity has proven to be an effective treatment for breast cancer. After the success stories of SERMs and fulvestrant, there is a need for the development of new small molecule modulating ER-α is due to developing resistance and side effects to current breast cancer therapy. In this pursuit, we virtually screened 227 chemically diverse bioactive natural products to get the best hits having an ER-α binding affinity. The docking scores and protein-ligand interactions of the obtained hits were emulated with the clinically used selective estrogen modulators and ER-antagonists. The results revealed 18 potential hits, which were putatively classified as hits belonging to ER agonists, modulators, and antagonists. Furthermore, as most of the hits were found to comprise the chromene nucleus, the 2D and 3D QSAR studies were performed using a set of natural products and synthesized compounds containing this scaffold, to understand the structural requirements for improving activity against breast cancer. Additionally, a pharmacophore model was generated to investigate the pharmacophoric features of the explored scaffolds for an optimal anticancer activity. The results signify that these compounds with structural modification could serve as potential leads in the drug discovery process for the treatment of breast cancer.

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 Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs

Drug-induced liver injury (DILI) presents a significant challenge to drug development and regulatory science. The FDA’s Liver Toxicity Knowledge Base (LTKB) evaluated >1000 drugs for their likelihood of causing DILI in humans, of which >700 drugs were classified into three categories (most-DILI, less-DILI, and no-DILI). Based on this dataset, we developed and compared 2-class and 3-class DILI prediction models using the machine learning algorithm of Decision Forest (DF) with Mold2 structural descriptors. The models were evaluated through 1000 iterations of 5-fold cross-validations, 1000 bootstrapping validations and 1000 permutation tests (that assessed the chance correlation). Furthermore, prediction confidence analysis was conducted, which provides an additional parameter for proper interpretation of prediction results. We revealed that the 3-class model not only had a higher resolution to estimate DILI risk but also showed an improved capability to differentiate most-DILI drugs from no-DILI drugs in comparison with the 2-class DILI model. We demonstrated the utility of the models for drug ingredients with warnings very recently issued by the FDA. Moreover, we identified informative molecular features important for assessing DILI risk. Our results suggested that the 3-class model presents a better option than the binary model (which most publications are focused on) for drug safety evaluation.

Virtual screening and biological evaluation of biofilm inhibitors on dual targets in quorum sensing system

AIM

Resistance to conventional antibiotics has spurred interest in exploring new antimicrobial strategies. Suppressing quorum sensing within biofilm is a promising antimicrobial strategy. LasR in quorum sensing system of the Gram-negative bacteria, Pseudomonas aeruginosa, directly enhances virulence and antibiotic resistance, with QscR as its indirect suppressor, so targeting both of them can synergistically take the effect.

METHODOLOGY/RESULTS

An in silico protocol combining pharmacophores with molecular docking was applied. Pharmacophores of QscR agonists and LasR antagonists were prepared for preliminary screening, followed by counter-screen using a pharmacophore model of LasR agonists and molecular docking of LasR. Four compounds with novel scaffolds were confirmed as potential biofilm inhibitors with preliminary experimental data.

CONCLUSION

Novel biofilm inhibitors can be found with the method.

Discriminating Agonist from Antagonist Ligands of the Nuclear Receptors Using Different Chemoinformatics Approaches

Nuclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4 years, we tackled the pharmacological profile assessment of NR ligands for which we constructed the NRLiSt BDB. We evaluated and compared the performance of different virtual screening approaches: mean of molecular descriptor distribution values, molecular docking and 3D pharmacophore models. The simple comparison of the distribution profiles of 4885 molecular descriptors between the agonist and antagonist datasets didn't provide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonist ligands. But the availability of PDB structures in the "pharmacological-profile-to-predict-bound-state" (agonist-bound or antagonist-bound) and the availability of enough ligands of both pharmacological profiles constituted limits to generalize this protocol for all NRs. Finally, the 3D pharmacophore modeling approach, allowed us to generate selective agonist pharmacophores and selective antagonist pharmacophores that covered more than 99 % of the whole NRLiSt BDB. This study allowed a better understanding of the pharmacological modulation of NRs with small molecules and could be extended to other therapeutic classes.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

In silico prediction and in vivo analysis of antiestrogenic potential of 2-isopropylthioxanthone (2-ITX) in juvenile goldfish (Carassius auratus)

Previous studies have shown both anti-estrogenic and anti-androgenic activities of 2-isopropylthioxanthone (2-ITX), a well known food contaminant, in in vitro assays. However, no data are available on the anti-estrogenic potentials and risks of 2-ITX in aquatic organisms. This work evaluated the potential endocrine disrupting effects of 2-ITX at the level of estrogen receptor (ER) signaling cascade using juvenile goldfish (Carassius auratus) as model. Firstly, we investigated the ligand binding efficiency of 2-ITX to the ligand binding domains (LBD) of goldfish ER subtypes using a molecular docking approach. Secondly, we assessed the effects of 2-ITX on E2-induced hepatic expression of ERα1, ERβ1, ERβ2, and vitellogenin (VTG) in vivo. Crosstalk between ER-VTG and aryl hydrocarbon receptor 2 (AhR2)-cytochrome P4501A (CYP1A) was also investigated. Fish were injected with increasing doses of 2-ITX ranging from 2 to 10µg/g BW, and results were compared to the effect of tamoxifen, a well-known ER modulator. We observed that compared to ERβ, the interaction potentials of 2-ITX to goldfish ERα1 LBD was more stable in the inactive receptor conformation. The in silico docking simulation analysis also revealed that 2-ITX acted as agonist for the goldfish AhR2 LBDs suggesting the ability of this compound to activate the cross-talk between the ERα- and AhR-signaling pathways. In vivo experiments confirm in silico simulation predictions demonstrating that 2-ITX reduced the estrogenicity of E2 at both transcriptional and post-transcriptional levels, indicating a clear anti-estrogenic effect. Co-exposure of E2 and 2-ITX also resulted in a significant decrease of CYP1A gene expression with respect to 2-ITX alone. Results from these studies collectively revealed that the antiestrogenic property of 2-ITX can be ascribed to a combination of effects on multiple signaling pathways suggesting the potential for this environmental contaminant to affect the hormonal control of reproductive processes in fish.

Discriminating agonist and antagonist ligands of the nuclear receptors using 3D-pharmacophores

Nuclear receptors (NRs) constitute an important class of therapeutic targets. We evaluated the performance of 3D structure-based and ligand-based pharmacophore models in predicting the pharmacological profile of NRs ligands using the NRLiSt BDB database. We could generate selective pharmacophores for agonist and antagonist ligands and we found that the best performances were obtained by combining the structure-based and the ligand-based approaches. The combination of pharmacophores that were generated allowed to cover most of the chemical space of the NRLiSt BDB datasets. By screening the whole NRLiSt BDB on our 3D pharmacophores, we demonstrated their selectivity towards their dedicated NRs ligands. The 3D pharmacophores herein presented can thus be used as a predictor of the pharmacological activity of NRs ligands.Graphical AbstractUsing a combination of structure-based and ligand-based pharmacophores, agonist and antagonist ligands of the Nuclear Receptors included in the NRLiSt BDB database could be separated.

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.

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.

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.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

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.

Biomarker-based drug safety assessment in the age of systems pharmacology: from foundational to regulatory science

Improved biomarker-based assessment of drug safety is needed in drug discovery and development as well as regulatory evaluation. However, identifying drug safety-related biomarkers such as genes, proteins, miRNA and single-nucleotide polymorphisms remains a big challenge. The advances of 'omics' and computational technologies such as genomics, transcriptomics, metabolomics, proteomics, systems biology, network biology and systems pharmacology enable us to explore drug actions at the organ and organismal levels. Computational and experimental systems pharmacology approaches could be utilized to facilitate biomarker-based drug safety assessment for drug discovery and development and to inform better regulatory decisions. In this article, we review the current status and advances of systems pharmacology approaches for the development of predictive models to identify biomarkers for drug safety assessment.

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.