Structure-Based Design of Potent Aromatase Inhibitors by High-Throughput Docking

Novel N-allyl/propargyl tetrahydroquinolines: Synthesis via Three-component Cationic Imino Diels-Alder Reaction, Binding Prediction, and Evaluation as Cholinesterase Inhibitors

New N‐allyl/propargyl 4‐substituted 1,2,3,4‐tetrahydroquinolines derivatives were efficiently synthesized using acid‐catalyzed three components cationic imino Diels–Alder reaction (70–95%). All compounds were tested in vitro as dual acetylcholinesterase and butyryl‐cholinesterase inhibitors and their potential binding modes, and affinity, were predicted by molecular docking and binding free energy calculations (∆G) respectively. The compound 4af (IC₅₀ = 72 μm) presented the most effective inhibition against acetylcholinesterase despite its poor selectivity (SI = 2), while the best inhibitory activity on butyryl‐cholinesterase was exhibited by compound 4ae (IC₅₀ = 25.58 μm) with considerable selectivity (SI = 0.15). Molecular docking studies indicated that the most active compounds fit in the reported acetylcholinesterase and butyryl‐cholinesterase active sites. Moreover, our computational data indicated a high correlation between the calculated ∆G and the experimental activity values in both targets.

Is It Reliable to Use Common Molecular Docking Methods for Comparing the Binding Affinities of Enantiomer Pairs for Their Protein Target?

Molecular docking is a computational chemistry method which has become essential for the rational drug design process. In this context, it has had great impact as a successful tool for the study of ligand–receptor interaction modes, and for the exploration of large chemical datasets through virtual screening experiments. Despite their unquestionable merits, docking methods are not reliable for predicting binding energies due to the simple scoring functions they use. However, comparisons between two or three complexes using the predicted binding energies as a criterion are commonly found in the literature. In the present work we tested how wise is it to trust the docking energies when two complexes between a target protein and enantiomer pairs are compared. For this purpose, a ligand library composed by 141 enantiomeric pairs was used, including compounds with biological activities reported against seven protein targets. Docking results using the software Glide (considering extra precision (XP), standard precision (SP), and high-throughput virtual screening (HTVS) modes) and AutoDock Vina were compared with the reported biological activities using a classification scheme. Our test failed for all modes and targets, demonstrating that an accurate prediction when binding energies of enantiomers are compared using docking may be due to chance. We also compared pairs of compounds with different molecular weights and found the same results.

Theoretical Study of the Mechanism of Exemestane Hydroxylation Catalyzed by Human Aromatase Enzyme

Human aromatase (CYP19A1) aromatizes the androgens to form estrogens via a three-step oxidative process. The estrogens are necessary in humans, mainly in women, because of the role they play in sexual and reproductive development. However, these also are involved in the development and growth of hormone-dependent breast cancer. Therefore, inhibition of the enzyme aromatase, by means of drugs known as aromatase inhibitors, is the frontline therapy for these types of cancers. Exemestane is a suicidal third-generation inhibitor of aromatase, currently used in breast cancer treatment. In this study, the hydroxylation of exemestane catalyzed by aromatase has been studied by means of hybrid QM/MM methods. The Free Energy Perturbation calculations provided a free energy of activation for the hydrogen abstraction step (rate-limiting step) of 17 kcal/mol. The results reveal that the hydroxylation of exemestane is not the inhibition stage, suggesting a possible competitive mechanism between the inhibitor and the natural substrate androstenedione in the first catalytic subcycle of the enzyme. Furthermore, the analysis of the interaction energy for the substrate and the cofactor in the active site shows that the role of the enzymatic environment during this reaction consists of a transition state stabilization by means of electrostatic effects.

Recent Progress in the Discovery of Next Generation Inhibitors of Aromatase from the Structure-Function Perspective

Human aromatase catalyzes the synthesis of estrogen from androgen with high substrate specificity. For the past 40 years, aromatase has been a target of intense inhibitor discovery research for the prevention and treatment of estrogen-dependent breast cancer. The so-called third generation aromatase inhibitors (AIs) letrozole, anastrozole, and the steroidal exemestane were approved in the U.S. in the late 1990s for estrogen-dependent postmenopausal breast cancer. Efforts to develop better AIs with higher selectivity and lower side effects were handicapped by the lack of an experimental structure of this unique P450. The year 2009 marked the publication of the crystal structure of aromatase purified from human placenta, revealing an androgen-specific active site. The structure has reinvigorated research activities on this fascinating enzyme and served as the catalyst for next generation AI discovery research. Here, we present an account of recent developments in the AI field from the perspective of the enzyme's structure-function relationships.

High-throughput virtual screening with e-pharmacophore and molecular simulations study in the designing of pancreatic lipase inhibitors

BACKGROUND

Obesity is a progressive metabolic disorder in the current world population, and is characterized by the excess deposition of fat in the adipose tissue. Pancreatic lipase is one of the key enzymes in the hydrolysis of triglycerides into monoglycerides and free fatty acids, and is thus considered a promising target for the treatment of obesity. The present drugs used for treating obesity do not give satisfactory results, and on prolonged usage result in severe side effects. In view of the drastic increase in the obese population day-to-day, there is a greater need to discover new drugs with lesser side effects.

MATERIALS AND METHODS

High-throughput virtual screening combined with e-pharmacophore screening and ADME (absorption, distribution, metabolism, and excretion) and PAINS (pan-assay interference compounds) filters were applied to screen out the ligand molecules from the ZINC natural molecule database. The screened molecules were subjected to Glide XP docking to study the molecular interactions broadly. Further, molecular dynamic simulations were used to validate the stability of the enzyme-ligand complexes. Finally, the molecules with better results were optimized for in vitro testing.

RESULTS

The screening protocols identified eight hits from the natural molecule database, which were further filtered through pharmacological filters. The final four hits were subjected to extra precision docking, and the complexes were finally studied with molecular dynamic simulations. The results pointed to the zinc 85893731 molecule as the most stable in the binding pocket, producing consistent H-bond interaction with Ser152 (G=-7.18). The optimized lead molecule exhibited good docking score, better fit, and improved ADME profile.

CONCLUSION

The present study specifies zinc 85893731 as a lead molecule with higher binding score and energetically stable complex with pancreatic lipase. This lead molecule, along with its various analogs, can be further tested as a novel inhibitor against pancreatic lipase using in vitro protocols.

Coumarins as anticancer agents: a review on synthetic strategies, mechanism of action and SAR studies

Coumarins are fused benzene and pyrone ring systems which prompt biological investigation to assess their potential therapeutic significance. It possesses immeasurable anticancer potential with minimum side effects depending on the substitutions on the basic nucleus. Coumarins have a tremendous ability to regulate diverse range of cellular pathways that can be explored for selective anticancer activity. This is the first standalone review that emphasis on the assorted retrosynthetic approaches, important targets for molecularly targeted cancer therapy and structure activity relationship studies that highlight the chemical groups responsible for evoking the anticancer potential of coumarin derivatives reported from 2011 to 2014.

Computational insights into inhibitory mechanism of azole compounds against human aromatase

We investigated the inhibitory mechanism of azole aromatase inhibitors. The results showed that letrozole and imazalil prefer different unbinding pathways.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

Design, synthesis, docking study and cytotoxic activity evaluation of some novel letrozole analogs

Background

Breast cancer is the most common type of female cancer. One class of hormonal therapy for breast cancer drugs -non steroidal aromatase inhibitors- are triazole analogues. In this work, some derivatives of these drugs was designed and synthesized. All synthesized compounds were evaluated for their cytotoxic activities on breast cancer cell lines (MDA-MB-231, T47D and MCF-7).

Methods

Our synthetic route for designed compounds started from 4-bromotolunitrile which was reacted with 1H-1,2,4-triazole to afford 4-(4-cyanobenzyl)-1,2,4-triazole. The reaction of later compound with aromatic aldehydes led to formation of the designed compounds. Eleven novel derivatives 1a-k were tested for their cytotoxic activities on three human breast cancer cell lines.

Results

Among the synthesized compound, 4-[2-(3-chlorophenyl)-1-(1H-1,2,4-triazol-1-yl)ethenyl]benzonitrile (1c) showed the highest activity against MCF-7 and MDA-MB-231 cell lines and 4-[2-(4-methoxyphenyl)-1-(1H-1,2,4-triazol-1-yl)ethenyl]benzonitrile (1 h) exhibited highest activity against T47D cell line. According to cytotoxic activities results, compound 4-[2-(4-dimethylamino)-1-(1H-1,2,4-triazol-1-yl)ethenyl]benzonitrile (1 k) showed comparative activity against T47D and MDA-MB-231 cell lines with compound (1 h) and our reference drug Etoposide.

Conclusion

In the process of anti-cancer drug discovery, to find new potential anti-breast cancer agents, we designed and synthesized a novel series of letrozole analogs. Cytotoxicity evaluation revealed that compounds (1c) and (1 k) were the most potent compounds with comparative activity with Etoposide. The results revealed that π-π interactions are responsible for the enzyme inhibitions of compounds (1 c) and (1 k).

Metformin, cancer and glucose metabolism

Metformin is the first-line treatment for type 2 diabetes. Results from several clinical studies have indicated that type 2 diabetic patients treated with metformin might have a lower cancer risk. One of the primary metabolic changes observed in malignant cell transformation is an increased catabolic glucose metabolism. In this context, once it has entered the cell through organic cation transporters, metformin decreases mitochondrial respiration chain activity and ATP production that, in turn, activates AMP-activated protein kinase, which regulates energy homeostasis. In addition, metformin reduces cellular energy availability and glucose entrapment by inhibiting hexokinase-II, which catalyses the glucose phosphorylation reaction. In this review, we discuss recent findings on molecular mechanisms that sustain the anticancer effect of metformin through regulation of glucose metabolism. In particular, we have focused on the emerging action of metformin on glycolysis in normal and cancer cells, with a drug discovery perspective.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Design of the influenza virus inhibitors targeting the PA endonuclease using 3D-QSAR modeling, side-chain hopping, and docking

With the emergence of drug resistance and the structural determination of the PA N-terminal domain (PAN), influenza endonucleases have become an attractive target for antiviral therapies for influenza infection. Here, we combined 3D-QSAR with side-chain hopping and molecular docking to produce novel structures as endonuclease inhibitors. First, a new molecular library was generated with side-chain hopping on an existing template molecule, L-742001, using an in-house fragment library that targets bivalent-cation-binding proteins. Then, the best 3D-QSAR model (AAAHR.500), with q(2)=0.76 and r(2)=0.97 from phase modeling, was constructed from 23 endonuclease inhibitors and validated with 17 test compounds. The AAAHR.500 model was then used to select effective candidates from the new molecular library. Combining 3D-QSAR with docking using Glide and Autodock, 13 compounds were considered the most likely candidate inhibitors. Docking studies showed that the binding modes of these compounds were consistent with the crystal structures of known inhibitors. These compounds could serve as potential endonuclease inhibitors for further biological activity tests.

Identification of PDZ ligands by docking-based virtual screening for the development of novel analgesic agents

Disrupting the interaction between the PDZ protein, PSD-95, and its target ligands (such as the glutamate NMDA receptor or the serotonin 5-HT2A receptor) was found to reduce hyperalgesia in various models of neuropathic pain. Here, we set out to identify lead molecules which would interact with PSD-95, and hence, would potentially display analgesic activity. We describe the virtual screening of the Asinex and Cambridge databases which together contain almost one million molecules. Using three successive docking filters and visual inspection, we identified three structural classes of molecules and synthesized a potential lead compound from each class. The binding of the molecules with the PDZ domains of PSD-95 was assessed by (1)H-(15)N HSQC NMR experiments. The analgesic activity of the best ligand, quinoline 2, was evaluated in vivo in a model of neuropathic pain and showed promising results.

Chemoinformatic analysis of GRAS (Generally Recognized as Safe) flavor chemicals and natural products

Food materials designated as "Generally Recognized as Safe" (GRAS) are attracting the attention of researchers in their attempts to systematically identify compounds with putative health-related benefits. In particular, there is currently a great deal of interest in exploring possible secondary benefits of flavor ingredients, such as those relating to health and wellness. One step in this direction is the comprehensive characterization of the chemical structures contained in databases of flavoring substances. Herein, we report a comprehensive analysis of the recently updated FEMA GRAS list of flavoring substances (discrete chemical entities only). Databases of natural products, approved drugs and a large set of commercial molecules were used as references. Remarkably, natural products continue to be an important source of bioactive compounds for drug discovery and nutraceutical purposes. The comparison of five collections of compounds of interest was performed using molecular properties, rings, atom counts and structural fingerprints. It was found that the molecular size of the GRAS flavoring substances is, in general, smaller cf. members of the other databases analyzed. The lipophilicity profile of the GRAS database, a key property to predict human bioavailability, is similar to approved drugs. Several GRAS chemicals overlap to a broad region of the property space occupied by drugs. The GRAS list analyzed in this work has high structural diversity, comparable to approved drugs, natural products and libraries of screening compounds. This study represents one step towards the use of the distinctive features of the flavoring chemicals contained in the GRAS list and natural products to systematically search for compounds with potential health-related benefits.

Novel aromatase inhibitors by structure-guided design

Human cytochrome P450 aromatase catalyzes with high specificity the synthesis of estrogens from androgens. Aromatase inhibitors (AIs) such as exemestane, 6-methylideneandrosta-1,4-diene-3,17-dione, are preeminent drugs for the treatment of estrogen-dependent breast cancer. The crystal structure of human placental aromatase has shown an androgen-specific active site. By utilization of the structural data, novel C6-substituted androsta-1,4-diene-3,17-dione inhibitors have been designed. Several of the C6-substituted 2-alkynyloxy compounds inhibit purified placental aromatase with IC(50) values in the nanomolar range. Antiproliferation studies in a MCF-7 breast cancer cell line demonstrate that some of these compounds have EC(50) values better than 1 nM, exceeding that for exemestane. X-ray structures of aromatase complexes of two potent compounds reveal that, per their design, the novel side groups protrude into the opening to the access channel unoccupied in the enzyme-substrate/exemestane complexes. The observed structure-activity relationship is borne out by the X-ray data. Structure-guided design permits utilization of the aromatase-specific interactions for the development of next generation AIs.

Analysis of structure-based virtual screening studies and characterization of identified active compounds

Structure-based virtual screening makes explicit or implicit use of 3D target structure information to detect novel active compounds. Results of nearly 300 currently available original applications have been analyzed to characterize the state-of-the-art in this field. Compound selection from docking calculations is much influenced by subjective criteria. Although submicromolar compounds are identified, the majority of docking hits are only weakly potent. However, only a small percentage of docking hits can be reproduced by ligand-based methods. When docking calculations identify potent hits, they often originate from specialized compound sources (e.g., pharmaceutical compound decks or target-focused libraries) and also display a notable bias towards kinase targets. Structure-based virtual screening is the dominant approach to computational hit identification. Docking calculations frequently identify active compounds. Limited accuracy of compound scoring and ranking currently presents a major caveat of the approach that is often compensated for by chemical intuition and knowledge.

Structure-based virtual screening for drug discovery: a problem-centric review

Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers' practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques.

Development of a new class of aromatase inhibitors: design, synthesis and inhibitory activity of 3-phenylchroman-4-one (isoflavanone) derivatives

Aromatase (CYP19) catalyzes the aromatization reaction of androgen substrates to estrogens, the last and rate-limiting step in estrogen biosynthesis. Inhibition of aromatase is a new and promising approach to treat hormone-dependent breast cancer. We present here the design and development of isoflavanone derivatives as potential aromatase inhibitors. Structural modifications were performed on the A and B rings of isoflavanones via microwave-assisted, gold-catalyzed annulation reactions of hydroxyaldehydes and alkynes. The in vitro aromatase inhibition of these compounds was determined by fluorescence-based assays utilizing recombinant human aromatase (baculovirus/insect cell-expressed). The compounds 3-(4-phenoxyphenyl)chroman-4-one (1h), 6-methoxy-3-phenylchroman-4-one (2a) and 3-(pyridin-3-yl)chroman-4-one (3b) exhibited potent inhibitory effects against aromatase with IC(50) values of 2.4 μM, 0.26 μM and 5.8 μM, respectively. Docking simulations were employed to investigate crucial enzyme/inhibitor interactions such as hydrophobic interactions, hydrogen bonding and heme iron coordination. This report provides useful information on aromatase inhibition and serves as a starting point for the development of new flavonoid aromatase inhibitors.