Molecular docking and 3D-QSAR-based virtual screening of flavonoids as potential aromatase inhibitors against estrogen-dependent breast cancer

Evaluating flavonoids as potential aromatase inhibitors for breast cancer treatment: In vitro studies and in silico predictions

Aromatase inhibitors are commonly employed in the treatment of hormone-dependent breast cancers, and flavonoids have emerged as a promising alternative to existing drug classes with unfavorable side effects. In this study, we conducted in vitro investigations into CYP19A1 (aromatase) inhibitory potential of 14 flavonoids, including pinocembrin, sakuranetin, eriodictyol, liquiritigenin, naringenin, hesperetin, flavanone, baicalein, chrysin, nobiletin, luteolin, sinensetin, tricin, and primuletin. Flavonoids displaying inhibitory activity were further assessed using in silico tools, such as molecular docking to predict binding affinities, as well as SwissADME, admetSAR, and QED (Quantitative Estimate of Drug-likeness) for drug-likeness prediction. Flavonoids with IC50 values less than 10 μM, pinocembrin, eriodictyol, naringenin, liquirtigenin, sakuranetin, and chrysin, exhibited favorable physicochemical properties and ADME profiles, suggesting their potential for development as novel flavonoid-based aromatase inhibitors. This study would provide valuable insights for the development of flavonoid-based aromatase inhibitors for the treatment of breast cancer.

Design and Pharmacophore Study of Triazole Analogues as Aromatase Inhibitors

BACKGROUND

In current scenario breast cancer measured as one of the dangerous health issues. An effective therapeutic class of drug known as aromatase inhibitors (AIs) is dominant against estrogen receptorpositive breast cancer. However, there is an urgent need to create target-specific AIs with better anti-breast cancer profiles due to the increased toxicity and adverse effects related to currently existing anti-breast cancer drugs.

OBJECTIVES

In the present study, we have designed of 100 novel tiazole analogues as aromatase inhibitors their pharmacophoric features were explored.

METHOD

Molecular docking was applied to a series of 4-substituted-1, 2, 3-triazoles containing letrozole for their aromatase inhibitory effects. The aromatase inhibitory activity of the compound in a series varies in the range of (IC50 = 0.008-31.26 μM). A hydrogen atom positioned at R1 of the triazole ring in compound (01) was responsible for the most potent compound (IC50 = 0.008 μM) in the series of 28 compounds as compared to letrozole. The self-organizing molecular field study was used to assess the molecular characteristics and biological activities of the compounds. The four models were developed using PLS and MLR methods. The PLS method was good for statistical analysis. The letrozole scaffold-based 100 compounds were designed by selecting an effective pharmacophore responsible for aromatase inhibitory activity. The designed compound was placed on the previous model as a test set, and its IC50 values were calculated.

RESULT

Hydrogen bonds were established between the potent molecule (01) and the essential residues Met 374 and Arg 115, which were responsible for the aromatase-inhibiting action. Cross-validated q2 (0.6349) & noncross- validated r2 (0.7163) were discovered in the statistical findings as having reliable predictive power. Among 100 designed compounds, seven compounds showed good aromatase inhibitory activities.

CONCLUSION

The additional final SOMFA model created for the interactions between the aromatase and the triazole inhibitors may be helpful for future modification and enhancement of the inhibitors of this crucial enzyme.

Doxorubicin-Induced Cardiomyopathy: A Preliminary Study on the Cardioprotective Benefits of 7-Hydroxyflavanone

The therapeutic properties of flavonoids are reported to offer cardioprotective benefits against doxorubicin (Dox)-induced cardiotoxicity (DIC). In the current study, we aimed to investigate the prophylactic properties of 7-hydroxyflavanone (7H), a flavonoid with antioxidative properties, against DIC. An in vitro model of DIC was established by exposing H9c2 cardiomyoblasts to Dox for 6 days. Similarly, cells were also co-treated with 7H to assess its ability to mitigate DIC. The data obtained indicate that 7H, as a co-treatment, alleviates Dox-induced oxidative stress by enhancing total glutathione content (p ≤ 0.001) and superoxide dismutase activity (p ≤ 0.001) whilst decreasing ROS (p ≤ 0.001), malondialdehyde production (p ≤ 0.001) and the secretion of interleukin-6 (p ≤ 0.001). The data also showed an improvement in mitochondrial function as shown via enhanced bioenergetics, mitochondrial membrane potential, and PGC1-alpha (p ≤ 0.05) and pAMPK (p ≤ 0.001) expression. The cardioprotective potential of 7H was further highlighted by its ability attenuate Dox-induced caspase 3/7 activity (p ≤ 0.001), apoptosis (p ≤ 0.001) and necrosis (p ≤ 0.05). In conclusion, our findings demonstrated the cardioprotective benefits of 7H and thus suggests that it could be a suitable candidate cardioprotective agent against DIC.

Assessment of ACC and P450 Genes Expression in Wild Oat (Avena ludoviciana) in Different Tissues Under Herbicide Application

Target-site resistance (TSR) and non-target-site resistance (NTSR) to herbicides in arable weeds are increasing rapidly all over the world and threatening universal food safety. Resistance to herbicides that inhibit ACCase activity has been identified in wild oat. In this study, expression of ACC1, ACC2, CYP71R4 and CYP81B1 genes under herbicide stress conditions were studied in two TSR (resistant in the residue Ile1781-Leu and Ile2041-Asn of ACCase) biotypes, two NTSR biotypes and one susceptible biotype of A. ludoviciana for the first time. Treated and untreated biotypes with ACCase-inhibitor clodinafop propargyl herbicide were sampled from the stem and leaf tissues at 24 h after treatment. Our results showed an increase in gene expression levels in different tissues of both types of resistance biotypes that occurred under herbicide treatment compared with non-herbicide treatment. In all samples, the expression levels of leaf tissue in all studied genes were higher than in stem tissue. The results of ACC gene expression showed that the expression level of ACC1 was significantly higher than that of ACC2. Also, expression levels of TSR biotypes were higher than NTSR biotypes for the ACC1 gene. For both CYP71R4 and CYP81B1 genes, the expression ratio increased significantly in TSR and NTSR biotypes in different tissues after herbicide treatment. In contrast, the expression levels of CYP genes in NTSR biotypes were higher than in TSR biotypes. Our results support the hypothesis that the reaction of plants to herbicide is carried out through a different regulation of genes, which can be the result of the interaction of resistance type in the target or non-target-site.

LC-MS metabolomic evidence metabolites from Oenothera rosea L´ Hér. ex Ait with antiproliferative properties on DU145 human prostate cancer cell line

Prostate cancer remains one of the leading health issues without a fully effective treatment. Medicinal plants are one of the primary sources of compounds for treating numerous ailments. In this sense, the Oenothera genus contains metabolites with antiproliferative activity on cancer cells. For this, the study aimed to explore the antiproliferative activity of its extracts against prostate cancer and identify its metabolites (under metabolomics analyses) associated with anticancer and/or antiproliferative properties. For this reason, a LC-MS/MS-based metabolomic analysis was performed to demonstrate the possible metabolites present in O. rosea. In addition, the antiproliferative activity of different extracts in the human prostate cancer cell line DU145 was evaluated. All extracts have antiproliferative effects on DU145 cells at 72 h, with moderate activity being the best ethanolic either 48 or 72 h. Finally, by LC-MS/MS-based metabolomics, 307 compounds from aqueous, methanolic, ethanolic, and ethyl acetate extracts from which 40 putative metabolites identified were organized as anti-inflammatory, anticancer, and/or antiproliferative activities according to previously reported. These results provide evidence that O. rosea could be used as an antiproliferative agent due to its chemical contents used as polypharmacy with low concentration levels.

The inhibitory effect and mechanism of Resina Draconis on the proliferation of MCF-7 breast cancer cells: a network pharmacology-based analysis

Resina Draconis (RD) is known as the "holy medicine for promoting blood circulation" and possesses antitumor properties against various types of cancer, including breast cancer (BC); however, the underlying mechanism is not well understood. To explore the potential mechanism of RD against BC using network pharmacology and experimental validation, data on bioactive compounds, potential targets of RD, and related genes of BC were obtained from multiple public databases. Gene Ontology (GO) and KEGG pathway analyses were performed via the DAVID database. Protein interactions were downloaded from the STRING database. The mRNA and protein expression levels and survival analysis of the hub targets were analyzed using the UALCAN, HPA, Kaplan‒Meier mapper, and cBioPortal databases. Subsequently, molecular docking was used to verify the selected key ingredients and hub targets. Finally, the predicted results of network pharmacology methods were verified by cell experiments. In total, 160 active ingredients were obtained, and 148 RD target genes for the treatment of BC were identified. KEGG pathway analysis indicated that RD exerted its therapeutic effects on BC by regulating multiple pathways. Of these, the PI3K-AKT pathway was indicated to play an important role. In addition, RD treatment of BC seemed to involve the regulation of hub targets that were identified based on PPI interaction network analysis. Validation in different databases showed that AKT1, ESR1, HSP90AA1, CASP3, SRC and MDM2 may be involved in the carcinogenesis and progression of BC and that ESR1, IGF1 and HSP90AA1 were correlated with worse overall survival (OS) in BC patients. Molecular docking results showed that 103 active compounds have good binding activity with the hub targets, among which flavonoid compounds were the most important active components. Therefore, the sanguis draconis flavones (SDF) were selected for subsequent cell experiments. The experimental results showed that SDF significantly inhibited the cell cycle and cell proliferation of MCF-7 cells through the PI3K/AKT pathway and induced MCF-7 cell apoptosis. This study has preliminarily reported on the active ingredients, potential targets, and molecular mechanism of RD against BC, and RD was shown to exert its therapeutic effects on BC by regulating the PI3K/AKT pathway and related gene targets. Importantly, our work could provide a theoretical basis for further study of the complex anti-BC mechanism of RD.

In silico docking and ADME study of deketene curcumin derivatives (DKC) as an aromatase inhibitor or antagonist to the estrogen-alpha positive receptor (Erα+): potent application of breast cancer

Regardless of many extensive studies, hormonal-based breast cancer is the most common cause of cancer-related mortality of females worldwide. Indeed, estrogen receptor-positive (ER +) is the communal subtype in breast cancer. To treat this, three types of medications are typically used: selective estrogen receptor modulators (SERMs), selective estrogen receptor down modulators (SERDMs), and aromatase inhibitors (AIs), all of which directly interact with the activation of the estrogen signaling pathway and its formation. Despite their effectiveness, the development of new treatments is required since clinical efficacy is restricted owing to resistance. As a result, in silico studies for drug discovery are booming over the decades because of their affordability and less time-consuming features. Here, 25 deketene curcumin derivatives have been selected for docking studies through MVD software over the positive type of breast cancer through both the treatment hosts Erα + receptor and aromatase. DKC compounds are used because they have several pharmacological uses, including anti-cancer, anti-diabetic, anti-viral, anti-fungal, and anti-bacterial properties. Moreover, an ADME study was carried out for DKC derivatives that reveal the optimum drug-likeness profile. From 25 derivatives, the results showed a better MolDock score, hydrogen bonding, and steric interaction between compounds DKC-10, DKC-20, and DKC-21 with Erα + and aromatase. Although the study was done on both the treatable path hosts, better results were obtained with Erα + as an antagonist. Therefore, it is proposed that three selected DKC derivatives would be better therapeutic agents against breast cancer.

A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening

Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set (e.g.,QCV2) or the test set (e.g., specificity, selectivity or QF1/F2/F32). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.

A comprehensive review: Biological activity, modification and synthetic methodologies of prenylated flavonoids

Prenylated flavonoids, a unique class of flavonoids which combine a flavonoid skeleton and a lipophilic prenyl side-chain, possess great potential biological activities including cytotoxicity, anti-inflammation, anti-Alzheimer, anti-microbial, anti-oxidant, anti-diabetes, estrogenic, vasorelaxant and enzyme inhibition. Recently, prenylated flavonoids have become an indispensable anchor for the development of new therapeutic agents, and have received increasing from medicinal chemists. The prenylated flavonoids have been outstanding developed through isolation, semi or fully synthesis in a very short period of time, which proves the great value in medicinal chemistry researches. In this review, research progress of prenylated flavonoids including natural prenylated flavonoids, structural modification, synthetic methodologies and pharmacological activities was summarized comprehensively. Furthermore, the structure-activity relationships (SARs) of prenylated flavonoids were summarized which provided a basis for the selective design and optimization of multifunctional prenylated flavonoid derivatives for the treatment of multi-factorial diseases in clinic.

Therapeutic Effects of Modified Gengnianchun Formula on Stress-Induced Diminished Ovarian Reserve Based on Experimental Approaches and Network Pharmacology

Aim

To verify the effects of modified Gengnianchun formula (MGNC), a traditional Chinese medicine, on a stressed diminished ovarian reserve (DOR) animal model and predict the underlying mechanisms through network pharmacology strategies.

Methods

Sexually mature female C57BL/6 mice were allocated to five groups, abbreviated as the control (C) group, stress manipulated model (M) group, stress with normal saline gavage (N) group, stress with low-dose MGNC gavage (L) group, and stress with high-dose MGNC gavage (H) group. Body weight and the estrous cycle were monitored during the stress and gavage process. Serum stress hormones and reproductive hormones were evaluated by ELISA. Ovarian follicle counts were calculated, and ovarian follicle-stimulating hormone receptor (FSHR) and anti-Müllerian hormone (AMH) expression were assessed by Western blotting and immunohistochemistry. Network pharmacology strategies included active compound screening, drug and disease target analysis, gene ontology analysis, pathway analysis, and visualization of results.

Results

MGNC treatment significantly decreased serum corticosterone (CORT) and follicle-stimulating hormone (FSH) levels and increased testosterone (T) levels in the H group compared with the M and N groups. Primordial and preantral follicle counts and ovarian AMH and FSHR expression were significantly increased in the H group compared to those in the M and N groups. Through pharmacokinetic screening, we found 244 active compounds in MGNC. A total of 186 candidate intersection target genes of disease and MGNC were further screened to construct the interaction network. Gene ontology and KEGG pathway enrichment analysis ultimately unveiled a series of key targets that mainly mediated the effects of MGNC on DOR induced by chronic stress. The PI3K-Akt pathway may serve as the critical pathway underlying this therapeutic mechanism.

Conclusion

MGNC is a promising formula to treat DOR induced by chronic stress, and the PI3K-Akt pathway may play an essential role in this effect.

Targeting Peptidyl-prolyl Cis-trans Isomerase NIMA-interacting 1: A Structure-based Virtual Screening Approach to Find Novel Inhibitors

BACKGROUND

Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (Pin1) is an enzyme that isomerizes phosphorylated serine or threonine motifs adjacent to proline residues. Pin1 has important roles in several cellular signaling pathways, consequently impacting the development of multiple types of cancers.

METHODS

Based on the previously reported inhibitory activity of pentacyclic triterpenoids isolated from the gum resin of Boswellia genus against Pin1, we designed a computational experiment using molecular docking, pharmacophore filtering, and structural clustering allied to molecular dynamics (MD) simulations and binding free energy calculations to explore the inhibitory activity of new triterpenoids against Pin1 structure.

RESULTS

Here, we report different computational evidence that triterpenoids from neem (Azadirachta indica A. Juss), such as 6-deacetylnimbinene, 6-Oacetylnimbandiol, and nimbolide, replicate the binding mode of the Pin1 substrate peptide, interacting with high affinity with the binding site and thus destabilizing the Pin1 structure.

CONCLUSIONS

Our results are supported by experimental data, and provide interesting structural insights into their molecular mechanism of action, indicating that their structural scaffolds could be used as a start point to develop new inhibitors against Pin1.

Computer-Aided Drug Design Applied to Secondary Metabolites as Anticancer Agents

Computer-Aided Drug Design (CADD) techniques have garnered a great deal of attention in academia and industry because of their great versatility, low costs, possibilities of cost reduction in in vitro screening and in the development of synthetic steps; these techniques are compared with highthroughput screening, in particular for candidate drugs. The secondary metabolism of plants and other organisms provide substantial amounts of new chemical structures, many of which have numerous biological and pharmacological properties for virtually every existing disease, including cancer. In oncology, compounds such as vimblastine, vincristine, taxol, podophyllotoxin, captothecin and cytarabine are examples of how important natural products enhance the cancer-fighting therapeutic arsenal. In this context, this review presents an update of Ligand-Based Drug Design and Structure-Based Drug Design techniques applied to flavonoids, alkaloids and coumarins in the search of new compounds or fragments that can be used in oncology. A systematical search using various databases was performed. The search was limited to articles published in the last 10 years. The great diversity of chemical structures (coumarin, flavonoids and alkaloids) with cancer properties, associated with infinite synthetic possibilities for obtaining analogous compounds, creates a huge chemical environment with potential to be explored, and creates a major difficulty, for screening studies to select compounds with more promising activity for a selected target. CADD techniques appear to be the least expensive and most efficient alternatives to perform virtual screening studies, aiming to selected compounds with better activity profiles and better "drugability".

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.

6,N 2-Diaryl-1,3,5-triazine-2,4-diamines: synthesis, antiproliferative activity and 3D-QSAR modeling

A library of 126 compounds with a 6,N²-diaryl-1,3,5-triazine-2,4-diamine scaffold was prepared using a one-pot, microwave-assisted method from readily available cyanoguanidine, aromatic aldehydes and arylamines. The three-component condensation of these reagents in the presence of hydrochloric acid was followed by the treatment with a base, which promoted a rearrangement of the dihydrotriazine ring and its dehydrogenative aromatization. The antiproliferative properties of the prepared compounds were evaluated using three breast cancer cell lines. The most promising results were obtained in the growth inhibition of the triple negative MDA-MB231 breast cancer cells. The active compounds were also selective against cancer cells and did not affect growth of the non-cancerous MCF-10A breast cell line. Analyzing the structure–activity relationship within the series, we built a 3D-QSAR model for the further design of more potent anticancer compounds.

New compounds selectively targeting the triple negative MDA-MB231 breast cancer cells were used to build a 3D-QSAR model.

Structure-based design of human pancreatic amylase inhibitors from the natural anthocyanin database for type 2 diabetes

Malvidin 3-O-arabinoside is identified as a novel human pancreatic amylase inhibitor from the natural anthocyanin database with a structure-based design approach.

In silico identification of natural products with anticancer activity using a chemo-structural database of Brazilian biodiversity

Cancer is one of the leading causes of death worldwide, and the number of patients has only increased each year, despite the considerable efforts and investments in scientific research. Since natural products (NPs) may serve as suitable sources for drug development, the cytotoxicity against cancer cells of 2221 compounds from the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE_DB) was predicted using CDRUG algorithm. Molecular modeling, chemoinformatics, and chemometric tools were then used to analyze the structural and physicochemical properties of these compounds. We compared the positive NPs with FDA-approved anticancer drugs and predicted the molecular targets involved in the anticancer activity. In the present study, 46 families comprising potential anticancer compounds and at least 19 molecular targets involved in oncogenesis. To the best of our knowledge, this is the first large-scale study conducted to evaluate the potentiality of NPs sourced from Brazilian biodiversity as anticancer agents, using in silico approaches. Our results provided interesting insights about the mechanism of action of these compounds, and also suggested that their structural diversity may aid structure-based optimization strategies for developing novel drugs for cancer therapy.

Natural Compound Modulates the Cervical Cancer Microenvironment-A Pharmacophore Guided Molecular Modelling Approaches

Cervical cancer is regarded as one of the major burdens noticed in women next to breast cancer. Although, human papilloma viruses (HPVs) are regarded as the principal causative agents, they require certain other factors such as oestrogen hormone to induce cervical cancer. Aromatase is an enzyme that converts androgens into oestrogens and hindering this enzyme could subsequently hamper the formation of oestrogen thereby alleviating the disease. Accordingly, in the current investigation, a structure based pharmacophore was generated considering two proteins bearing the Protein Data Bank (PDB) codes 3EQM (pharm 1) and 3S7S (pharm 2), respectively. The two models were employed as the 3D query to screen the in-house built natural compounds database. The obtained 51 compounds were escalated to molecular docking studies to decipher on the binding affinities and to predict the quintessential binding modes which were affirmed by molecular dynamics (MD) simulations. The compound has induced dose-dependent down regulation of PP2B, Nitric oxide synthase-2 (NOS2), and Interleukin 6 (IL-6) genes in the HeLa cells and has modulated the expression of apoptotic genes such as Bax, Bcl2, and caspases-3 at different concentrations. These results guide us to comprehend that the identified aromatase inhibitor was effective against the cervical cancer cells and additionally could server as scaffolds in designing new drugs.

Integrated in silico-in vitro strategy for screening of some traditional Egyptian plants for human aromatase inhibitors

ETHNOPHARMACOLOGICAL RELEVANCE

Aromatase enzyme (CYP19) is widely known as a critical target protein for treating hormone-dependent breast cancer. Natural products from traditional medicinal plants continue to be an active source of aromatase inhibitors. Meanwhile, high cost of experimental work and low hit rate associated with HTS have stimulated the implementation of in-silico virtual screening to resolve these pitfalls, where coupling of both classical wet lab procedure and VS may offer a more deepened access to bioactive compounds with less work and time waste.

AIM OF THE STUDY

In this work, a sequential structure-based and ligand-based virtual screening strategy was utilized for investigating an in-house database of 1720 phytochemical constituents of 29 medicinal plants and natural products used in traditional Egyptian medicine to search for compounds with the potential to be used as inhibitors of the human aromatase enzyme.

MATERIALS AND METHODS

The suggested strategy included using Glide docking with its feature 'extra precision (XP)' for carrying out structure-based virtual screening (SBVS) where the resulting hits were further promoted to ligand-based virtual screening (LBVS) through the development of two pharmacophore and QSAR models; one for steroidal and the other for non-steroidal aromatase inhibitors.

RESULTS

The combined results revealed that Artemisia annua, Zingiber officinale, Cicer arietinum, Annona muricata and Vitex agnus castus were the top scoring plants in terms of in-silico activity scores, respectively. The hydro-alcoholic extracts and different solvent fractions of the top scoring plants were subsequently tested experimentally for their aromatase inhibitory activity, by the aid of in-vitro fluorometric assay. The rank ordering of the activities for the plants agreed with the ordering predicted on the basis of SBVS and LBVS workflow implemented.

CONCLUSION

The suggested strategy provides a reliable means of prospecting in-silico screening of natural products databases in the search for new dug leads as aromatase inhibitors. The hits so obtained can then be subjected to further phytochemical studies, to isolate and identify suitable compounds for further in-vitro testing.

Towards understanding aromatase inhibitory activity via QSAR modeling

Aromatase is a rate-limiting enzyme for estrogen biosynthesis that is overproduced in breast cancer tissue. To block the growth of breast tumors, aromatase inhibitors (AIs) are employed to bind and inhibit aromatase in order to lower the amount of estrogen produced in the body. Although a number of synthetic aromatase inhibitors have been released for clinical use in the treatment of hormone-receptor positive breast cancer, these inhibitors may lead to undesirable side effects (e.g. increased rash, diarrhea and vomiting; effects on the bone, brain and heart) and therefore, the search for novel AIs continues. Over the past decades, there has been an intense effort in employing medicinal chemistry and quantitative structure-activity relationship (QSAR) to shed light on the mechanistic basis of aromatase inhibition. To the best of our knowledge, this article constitutes the first comprehensive review of all QSAR studies of both steroidal and non-steroidal AIs that have been published in the field. Herein, we summarize the experimental setup of these studies as well as summarizing the key features that are pertinent for robust aromatase inhibition.

Structural insight into the antagonistic action of diarylheptanoid on human estrogen receptor alpha

Estrogen receptor α (ER α) is an important therapeutic target in the regulation of ligand dependent signaling in breast cancer. The current study investigates the anti-estrogenic potential of the Diarylheptanoid, 5-hydroxy-7-(4-hydroxy-3 methoxyphenyl)-1-phenyl-3-heptanone (DAH) in silico. Rigid Docking analysis of DAH at the ligand binding domain (LBD) of ER α showed hydrogen bond interactions with Arg394 and Glu353 at the active site, similar to the positive controls 4-Hydroxy Tamoxifen (4-OHT) and Fulvestrant (FUL). The protein and the protein-DAH complexes were further analyzed using molecular dynamics simulations for a time scale of 50 ns using GROMACS. Root mean square fluctuation (RMSF) analysis showed large fluctuations at the N-terminal region of Helices (H) 3, 9 and at the C-terminal region of H11, which could be involved in the antagonistic conformational change. Interestingly, H12 appeared to move away from the ligand binding pocket and occupy the co-activator binding groove at the LBD of ER α. Secondary structure analysis of the protein upon binding of DAH and CUR showed structural change from α-helix to Turn conformation at H4. We hypothesize that this structural change at H4, similar to the positive control, could hinder the activity of AF-2 by blocking the binding of co-activator. These conformational changes in ER α indicate an anti-estrogenic and therapeutic potential of the DAH.

Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein-ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol-1), with good cross-validation statistics.

Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in Breast Cancer

Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and p-p interactions are other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR, CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.

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.

Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in Breast Cancer

Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and p-p interactions are other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR, CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.