Selective ligands of estrogen receptor β discovered using pharmacophore mapping and structure-based virtual screening

The identification of cianidanol as a selective estrogen receptor beta agonist and evaluation of its neuroprotective effects on Parkinson's disease models

AIM

The present study aims to identify selective estrogen receptor beta (ERβ) agonists and to evaluate the neuroprotective mechanism in Parkinson's disease (PD) models.

MAIN METHODS

In-silico studies were carried out using Maestro and GROMACS. Neuroprotective activity and apoptosis were evaluated using cytotoxicity assay and flow cytometry respectively. Gene expression studies were carried out by reverse transcription polymerase chain reaction. Motor and cognitive functions were assessed by actophotometer, rotarod, catalepsy, and elevated plus maze. The neuronal population in the substantia nigra and striatum of rats was assessed by hematoxylin and eosin staining.

KEY FINDINGS

Cianidanol was identified as a selective ERβ agonist through virtual screening. The cianidanol-ERβ complex is stable during the 200 ns simulation and was able to retain the interactions with key amino acid residues. Cianidanol (25 μM) prevents neuronal toxicity and apoptosis induced by rotenone in differentiated SH-SY5Y cells. Additionally, cianidanol (25 μM) increases the expression of ERβ, cathepsin D, and Nrf2 transcripts. The neuroprotective effects of cianidanol (25 μM) were reversed in the presence of a selective ERβ antagonist. In this study, we found that selective activation of ERβ could decrease the transcription of α-synuclein gene. Additionally, cianidanol (10, 20, 30 mg/kg, oral) improves the motor and cognitive deficit in rats induced by rotenone.

SIGNIFICANCE

Cianidanol shows neuroprotective action in PD models and has the potential to serve as a novel therapeutic agent for the treatment of PD.

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.

Silencing of cystatin SN abrogates cancer progression and stem cell properties in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) accounts for approximately 80% of total thyroid cancers worldwide. Although the prognosis for early‐stage PTC is favorable, the 5‐year survival rate of patients with late‐stage PTC is still very poor. Cystatin SN (cystatin 1, CST1) facilitates the progression of multiple cancers, but its role in regulating PTC pathogenesis is still largely unknown. In this study, we measured the expression levels of CST1 in PTC clinical tissues and cell lines by real‐time quantitative PCR and western blot analysis, and we performed gain‐ and loss‐of‐function experiments to examine the effects of CST1 on PTC cell growth, invasion, migration, epithelial–mesenchymal transition and stemness. Tumorigenicity was assessed using in vivo tumor‐bearing nude mouse models. As expected, upregulated CST1 was observed in PTC tissues (P < 0.05) and cells, compared with their normal counterparts (P < 0.05); furthermore, patients with PTC with higher levels of CST1 exhibited unfavorable prognosis (P < 0.05). In addition, CST1 ablation inhibited PTC cell growth (P < 0.05) in vivo and in vitro. Silencing of CST1 also inhibited cell motility and epithelial–mesenchymal transition in PTC cells (P < 0.05), whereas CST1 overexpression had the opposite effects on the earlier cellular functions. Notably, up‐regulation of CST1 promoted cell spheroid formation (P < 0.05) and increased the expression levels of stemness signatures (P < 0.05) in PTC cells. Collectively, these findings suggest that CST1 functions as an oncogene to facilitate cancer development and promote cancer stem cell properties in PTC cells, increasing our understanding of PTC pathogenesis mechanisms and possibly aiding in the development of potential therapeutic strategies.

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.

Estrogen receptor-positive (ER+) breast cancer treatment: Are multi-target compounds the next promising approach?

Endocrine therapy is currently the main therapeutic approach for estrogen receptor-positive (ER⁺) breast cancer, the most frequent subtype of breast cancer in women worldwide. For this subtype of tumors, the current clinical treatment includes aromatase inhibitors (AIs) and anti-estrogenic compounds, such as Tamoxifen and Fulvestrant, being AIs the first-line treatment option for post-menopausal women. Moreover, the recent guidelines also suggest the use of these compounds by pre-menopausal women after suppressing ovaries function. However, besides its therapeutic efficacy, the prolonged use of this type of therapies may lead to the development of several adverse effects, as well as, endocrine resistance, limiting the effectiveness of such treatments. In order to surpass this issues and clinical concerns, during the last years, several studies have been suggesting alternative therapeutic approaches, considering the function of aromatase, ERα and ERβ. Here, we review the structural and functional features of these three targets and their importance in ER⁺ breast cancer treatment, as well as, the current treatment strategies used in clinic, emphasizing the importance of the development of multi-target compounds able to simultaneously modulate these key targets, as a novel and promising therapeutic strategy for this type of cancer.

Elucidating the aryl hydrocarbon receptor antagonism from a chemical-structural perspective

The aryl hydrocarbon receptor (AhR) plays an important role in several biological processes such as reproduction, immunity and homoeostasis. However, little is known on the chemical-structural and physicochemical features that influence the activity of AhR antagonistic modulators. In the present report, in vitro AhR antagonistic activity evaluations, based on a chemical-activated luciferase gene expression (AhR-CALUX) bioassay, and an extensive literature review were performed with the aim of constructing a structurally diverse database of contaminants and potentially toxic chemicals. Subsequently, QSAR models based on Linear Discriminant Analysis and Logistic Regression, as well as two toxicophoric hypotheses were proposed to model the AhR antagonistic activity of the built dataset. The QSAR models were rigorously validated yielding satisfactory performance for all classification parameters. Likewise, the toxicophoric hypotheses were validated using a diverse set of 350 decoys, demonstrating adequate robustness and predictive power. Chemical interpretations of both the QSAR and toxicophoric models suggested that hydrophobic constraints, the presence of aromatic rings and electron-acceptor moieties are critical for the AhR antagonism. Therefore, it is hoped that the deductions obtained in the present study will contribute to elucidate further on the structural and physicochemical factors influencing the AhR antagonistic activity of chemical compounds.

A synthesis strategy for tetracyclic terpenoids leads to agonists of ERβ

Natural product and natural product-like molecules continue to be important for the development of pharmaceutical agents, as molecules in this class play a vital role in the pipeline for new therapeutics. Among these, tetracyclic terpenoids are privileged, with >100 being FDA-approved drugs. Despite this significant pharmaceutical success, there remain considerable limitations to broad medicinal exploitation of the class due to lingering scientific challenges associated with compound availability. Here, we report a concise asymmetric route to forging natural and unnatural (enantiomeric) C19 and C20 tetracyclic terpenoid skeletons suitable to drive medicinal exploration. While efforts have been focused on establishing the chemical science, early investigations reveal that the emerging chemical technology can deliver compositions of matter that are potent and selective agonists of the estrogen receptor beta, and that are selectively cytotoxic in two different glioblastoma cell lines (U251 and U87).

Many natural-product like drugs have a tetracyclic terpenoid core. Here, the authors developed a synthesis of triterpene-like tetracyclic systems, and apply this method to the preparation of a number of enantiomeric compounds, two of which are very selective ligands for estrogen receptor beta

Network-Based Methods for Prediction of Drug-Target Interactions

Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.

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.

In silico prediction of chemical mechanism of action via an improved network-based inference method

BACKGROUND AND PURPOSE

Deciphering chemical mechanism of action (MoA) enables the development of novel therapeutics (e.g. drug repositioning) and evaluation of drug side effects. Development of novel computational methods for chemical MoA assessment under a systems pharmacology framework would accelerate drug discovery and development with greater efficiency and low cost.

EXPERIMENTAL APPROACH

In this study, we proposed an improved network-based inference method, balanced substructure-drug-target network-based inference (bSDTNBI), to predict MoA for old drugs, clinically failed drugs and new chemical entities. Specifically, three parameters were introduced into network-based resource diffusion processes to adjust the initial resource allocation of different node types, the weighted values of different edge types and the influence of hub nodes. The performance of the method was systematically validated by benchmark datasets and bioassays.

KEY RESULTS

High performance was yielded for bSDTNBI in both 10-fold and leave-one-out cross validations. A global drug-target network was built to explore MoA of anticancer drugs and repurpose old drugs for 15 cancer types/subtypes. In a case study, 27 predicted candidates among 56 commercially available compounds were experimentally validated to have binding affinities on oestrogen receptor α with IC₅₀ or EC₅₀ values ≤10 μM. Furthermore, two dual ligands with both agonistic and antagonistic activities ≤1 μM would provide potential lead compounds for the development of novel targeted therapy in breast cancer or osteoporosis.

CONCLUSION AND IMPLICATIONS

In summary, bSDTNBI would provide a powerful tool for the MoA assessment on both old drugs and novel compounds in drug discovery and development.

Prediction of selective estrogen receptor beta agonist using open data and machine learning approach

Background

Estrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis, and cardiovascular disease. ERs have two subtypes, ER-α and ER-β. Emerging data suggest that the development of subtype-selective ligands that specifically target ER-β could be a more optimal approach to elicit beneficial estrogen-like activities and reduce side effects.

Methods

Herein, we focused on ER-β and developed its in silico quantitative structure-activity relationship models using machine learning (ML) methods.

Results

The chemical structures and ER-β bioactivity data were extracted from public chemogenomics databases. Four types of popular fingerprint generation methods including MACCS fingerprint, PubChem fingerprint, 2D atom pairs, and Chemistry Development Kit extended fingerprint were used as descriptors. Four ML methods including Naïve Bayesian classifier, k-nearest neighbor, random forest, and support vector machine were used to train the models. The range of classification accuracies was 77.10% to 88.34%, and the range of area under the ROC (receiver operating characteristic) curve values was 0.8151 to 0.9475, evaluated by the 5-fold cross-validation. Comparison analysis suggests that both the random forest and the support vector machine are superior for the classification of selective ER-β agonists. Chemistry Development Kit extended fingerprints and MACCS fingerprint performed better in structural representation between active and inactive agonists.

Conclusion

These results demonstrate that combining the fingerprint and ML approaches leads to robust ER-β agonist prediction models, which are potentially applicable to the identification of selective ER-β agonists.