Discovery of Nonsteroidal 17β-Hydroxysteroid Dehydrogenase 1 Inhibitors by Pharmacophore-Based Screening of Virtual Compound Libraries

Discovery of a Novel Potent and Selective HSD17B13 Inhibitor, BI-3231, a Well-Characterized Chemical Probe Available for Open Science

Genome-wide association studies in patients revealed HSD17B13 as a potential new target for the treatment of nonalcoholic steatohepatitis (NASH) and other liver diseases. However, the physiological function and the disease-relevant substrate of HSD17B13 remain unknown. In addition, no suitable chemical probe for HSD17B13 has been published yet. Herein, we report the identification of the novel potent and selective HSD17B13 inhibitor BI-3231. Through high-throughput screening (HTS), using estradiol as substrate, compound 1 was identified and selected for subsequent optimization resulting in compound 45 (BI-3231). In addition to the characterization of compound 45 for its functional, physicochemical, and drug metabolism and pharmacokinetic (DMPK) properties, NAD⁺ dependency was investigated. To support Open Science, the chemical HSD17B13 probe BI-3231 will be available to the scientific community for free via the opnMe platform, and thus can help to elucidate the pharmacology of HSD17B13.

In silico methods and tools for drug discovery

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.

Gut feelings about bacterial steroid-17,20-desmolase

Advances in technology are only beginning to reveal the complex interactions between hosts and their resident microbiota that have co-evolved over centuries. In this review, we present compelling evidence that implicates the host-associated microbiome in the generation of 11β-hydroxyandrostenedione, leading to the formation of potent 11-oxy-androgens. Microbial steroid-17,20-desmolase cleaves the side-chain of glucocorticoids (GC), including cortisol (and its derivatives of cortisone, 5α-dihydrocortisol, and also (allo)- 3α, 5α-tetrahydrocortisol, but not 3α-5β-tetrahydrocortisol) and drugs (prednisone and dexamethasone). In addition to side-chain cleavage, we discuss the gut microbiome's robust potential to transform a myriad of steroids, mirroring much of the host's metabolism. We also explore the overlooked role of intestinal steroidogenesis and efflux pumps as a potential route for GC transport into the gut. Lastly, we propose several health implications from microbial steroid-17,20-desmolase function, including aberrant mineralocorticoid, GC, and androgen receptor signaling in colonocytes, immune cells, and prostate cells, which may exacerbate disease states.

A Hybrid In Silico/In Vitro Target Fishing Study to Mine Novel Targets of Urolithin A and B: A Step Towards a Better Comprehension of Their Estrogenicity

SCOPE

Urolithin A and B are gut metabolites of ellagic acid and ellagitannins associated with many beneficial effects. Evidence in vitro pointed to their potential as estrogenic modulators. However, both molecular mechanisms and biological targets involved in such activity are still poorly characterized, preventing a comprehensive understanding of their bioactivity in living organisms. This study aimed at rationally identifying novel biological targets underlying the estrogenic-modulatory activity of urolithins.

METHODS AND RESULTS

The work relies on an in silico/in vitro target fishing study coupling molecular modeling with biochemical and cell-based assays. Estrogen sulfotransferase and 17β-hydroxysteroid dehydrogenase are identified as potentially subject to inhibition by the investigated urolithins. The inhibition of the latter undergoes experimental confirmation either in a cell-free or cell-based assay, validating computational outcomes.

CONCLUSIONS

The work describes target fishing as an effective tool to identify unexpected targets of food bioactives detailing the interaction at a molecular level. Specifically, it described, for the first time, 17β-hydroxysteroid dehydrogenase as a target of urolithins and highlighted the need of further investigations to widen the understanding of urolithins as estrogen modulators in living organisms.

Inhibitors of 17β-hydroxysteroid dehydrogenase type 1, 2 and 14: Structures, biological activities and future challenges

During the past 25 years, the modulation of estrogen action by inhibition of 17β-hydroxysteroid dehydrogenase types 1 and 2 (17β-HSD1 and 17β-HSD2), respectively, has been pursued intensively. In the search for novel treatment options for estrogen-dependent diseases (EDD) and in order to explore estrogenic signaling pathways, a large number of steroidal and nonsteroidal inhibitors of these enzymes has been described in the literature. The present review gives a survey on the development of inhibitor classes as well as the structural formulas and biological properties of their most interesting representatives. In addition, rationally designed dual inhibitors of both 17β-HSD1 and steroid sulfatase (STS) as well as the first inhibitors of 17β-HSD14 are covered.

Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures

Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: http://www.qsar4u.com/pages/pmapper.php and https://github.com/meddwl/psearch.

Hybrid in silico/in vitro target fishing to assign function to "orphan" compounds of food origin - The case of the fungal metabolite atromentin

Many small molecules of food origin may effect human health but lack an adequate description of their biological activity. To fill this knowledge gap, a first-line workflow is needed to assign putative functions, rank the endpoints for testing and guide wet-lab experiments. In this framework, the identification of potential biological targets can be used to probe the activity of orphan compounds using a so-called "target fishing" approach. Here, we present a proof of concept study using an in silico/in vitro target fishing approach on the fungal secondary metabolite atromentin. The procedure relies on a computational screening for activity identification coupled with experimental trials for dose-response characterization. Computational results identified estrogen receptors and 17-β-hydroxysteroid dehydrogenase as potential targets. Experiments confirmed a weak estrogenic activity, supporting the reliability of the procedure. Despite limited estrogenicity of atromentin, the proposed inhibition of 17-β-hydroxysteroid dehydrogenase should be considered as a source for endocrine disruptive effects.

Interference of Paraben Compounds with Estrogen Metabolism by Inhibition of 17β-Hydroxysteroid Dehydrogenases

Parabens are effective preservatives widely used in cosmetic products and processed food, with high human exposure. Recent evidence suggests that parabens exert estrogenic effects. This work investigated the potential interference of parabens with the estrogen-activating enzyme 17β-hydroxysteroid dehydrogenase (17β-HSD) 1 and the estrogen-inactivating 17β-HSD2. A ligand-based 17β-HSD2 pharmacophore model was applied to screen a cosmetic chemicals database, followed by in vitro testing of selected paraben compounds for inhibition of 17β-HSD1 and 17β-HSD2 activities. All tested parabens and paraben-like compounds, except their common metabolite p-hydroxybenzoic acid, inhibited 17β-HSD2. Ethylparaben and ethyl vanillate inhibited 17β-HSD2 with IC₅₀ values of 4.6 ± 0.8 and 1.3 ± 0.3 µM, respectively. Additionally, parabens size-dependently inhibited 17β-HSD1, whereby hexyl- and heptylparaben were most active with IC₅₀ values of 2.6 ± 0.6 and 1.8 ± 0.3 µM. Low micromolar concentrations of hexyl- and heptylparaben decreased 17β-HSD1 activity, and ethylparaben and ethyl vanillate decreased 17β-HSD2 activity. However, regarding the very rapid metabolism of these compounds to the inactive p-hydroxybenzoic acid by esterases, it needs to be determined under which conditions low micromolar concentrations of these parabens or their mixtures can occur in target cells to effectively disturb estrogen effects in vivo.

Structural basis for inhibition of 17β-hydroxysteroid dehydrogenases by phytoestrogens: The case of fungal 17β-HSDcl

Phytoestrogens are plant-derived compounds that functionally and structurally mimic mammalian estrogens. Phytoestrogens have broad inhibitory activities toward several steroidogenic enzymes, such as the 17β-hydroxysteroid dehydrogenases (17β-HSDs), which modulate the biological potency of androgens and estrogens in mammals. However, to date, no crystallographic data are available to explain phytoestrogens binding to mammalian 17β-HSDs. NADP(H)-dependent 17β-HSD from the filamentous fungus Cochliobolus lunatus (17β-HSDcl) has been the subject of extensive biochemical, kinetic and quantitative structure-activity relationship studies that have shown that the flavonols are the most potent inhibitors. In the present study, we investigated the structure-activity relationships of the ternary complexes between the holo form of 17β-HSDcl and the flavonols kaempferol and 3,7-dihydroxyflavone, in comparison with the isoflavones genistein and biochanin A. Crystallographic data are accompanied by kinetic analysis of the inhibition mechanisms for six flavonols (3-hydroxyflavone, 3,7-dihydroxyflavone, kaempferol, quercetin, fisetin, myricetin), one flavanone (naringenin), one flavone (luteolin), and two isoflavones (genistein, biochanin A). The kinetics analysis shows that the degree of hydroxylation of ring B significantly influences the overall inhibitory efficacy of the flavonols. A distinct binding mode defines the interactions between 17β-HSDcl and the flavones and isoflavones. Moreover, the complex with biochanin A reveals an unusual binding mode that appears to account for its greater inhibition of 17β-HSDcl with respect to genistein. Overall, these data provide a blueprint for identification of the distinct molecular determinants that underpin 17β-HSD inhibition by phytoestrogens.

Virtual screening applications in short-chain dehydrogenase/reductase research

Several members of the short-chain dehydrogenase/reductase (SDR) enzyme family play fundamental roles in adrenal and gonadal steroidogenesis as well as in the metabolism of steroids, oxysterols, bile acids, and retinoids in peripheral tissues, thereby controlling the local activation of their cognate receptors. Some of these SDRs are considered as promising therapeutic targets, for example to treat estrogen-/androgen-dependent and corticosteroid-related diseases, whereas others are considered as anti-targets as their inhibition may lead to disturbances of endocrine functions, thereby contributing to the development and progression of diseases. Nevertheless, the physiological functions of about half of all SDR members are still unknown. In this respect, in silico tools are highly valuable in drug discovery for lead molecule identification, in toxicology screenings to facilitate the identification of hazardous chemicals, and in fundamental research for substrate identification and enzyme characterization. Regarding SDRs, computational methods have been employed for a variety of applications including drug discovery, enzyme characterization and substrate identification, as well as identification of potential endocrine disrupting chemicals (EDC). This review provides an overview of the efforts undertaken in the field of virtual screening supported identification of bioactive molecules in SDR research. In addition, it presents an outlook and addresses the opportunities and limitations of computational modeling and in vitro validation methods.

Potential Antiosteoporotic Natural Product Lead Compounds That Inhibit 17β-Hydroxysteroid Dehydrogenase Type 2

17β-Hydroxysteroid dehydrogenase type 2 (17β-HSD2) converts the active steroid hormones estradiol, testosterone, and 5α-dihydrotestosterone into their weakly active forms estrone, Δ⁴-androstene-3,17-dione, and 5α-androstane-3,17-dione, respectively, thereby regulating cell- and tissue-specific steroid action. As reduced levels of active steroids are associated with compromised bone health and onset of osteoporosis, 17β-HSD2 is considered a target for antiosteoporotic treatment. In this study, a pharmacophore model based on 17β-HSD2 inhibitors was applied to a virtual screening of various databases containing natural products in order to discover new lead structures from nature. In total, 36 hit molecules were selected for biological evaluation. Of these compounds, 12 inhibited 17β-HSD2 with nanomolar to low micromolar IC₅₀ values. The most potent compounds, nordihydroguaiaretic acid (1), IC₅₀ 0.38 ± 0.04 μM, (-)-dihydroguaiaretic acid (4), IC₅₀ 0.94 ± 0.02 μM, isoliquiritigenin (6), IC₅₀ 0.36 ± 0.08 μM, and ethyl vanillate (12), IC₅₀ 1.28 ± 0.26 μM, showed 8-fold or higher selectivity over 17β-HSD1. As some of the identified compounds belong to the same structural class, structure-activity relationships were derived for these molecules. Thus, this study describes new 17β-HSD2 inhibitors from nature and provides insights into the binding pocket of 17β-HSD2, offering a promising starting point for further research in this area.

Hybrid Receptor-Bound/MM-GBSA-Per-residue Energy-Based Pharmacophore Modelling: Enhanced Approach for Identification of Selective LTA4H Inhibitors as Potential Anti-inflammatory Drugs

Leukotriene A₄ hydrolase has been identified as an enzyme with dual anti- and pro-inflammatory role, thus, the conversion of leukotriene to leukotriene B₄ in the initiation stage of inflammation and the removal of the chemotactic Pro-Gly-Pro tripeptide. These findings make leukotriene A₄ hydrolase an attractive drug target: suggesting an innovative approach towards the identification and design of novel class of compounds that can selectively inhibit leukotriene B₄ synthesis while sparing the aminopeptidase activity. Previous inhibitors block the dual activity of the enzyme. Recently, a small lead molecule inhibitor denoted as ARM1 has been identified to block the hydrolase activity of leukotriene A₄ hydrolase whilst sparing the aminopeptidase activity. In this study, a hybrid receptor-bound/MM-GBSA-per-residue energy based pharmacophore modeling approach was implemented to identify potential selective hydrolase inhibitors of leukotriene A₄ hydrolase. In this approach, active site residues that favorably contributed to the binding of the bound conformation of ARM1 were derived from MD ensembles and MM/GBSA thermodynamic calculations. These residues were then mapped to key pharmacophore features of ARM1. The generated pharmacophore model was used to search the ZINC database for 3D structures that match the pharmacophore. Five new compounds have been identified and proposed as potential epoxide hydrolase selective inhibitors of leukotriene A₄ hydrolase. Molecular docking and MM/GBSA analyses revealed that, these top five lead-like compounds ZINC00142747, ZINC94260794, ZINC01382396, ZINC02508448, and ZINC53994447 showed better binding affinities to the hydrolase active site pocket compared to ARM1. Per-residue energy decomposition analysis revealed that amino acid residues Phe314, Tyr378, Pro382, Trp311, Val367, and Ala377 are key residues critical in the selective inhibition of these hits. Information highlighted in this study may guide the the design the next generation of novel and potent epoxide hydrolase selective inhibitors of leukotriene A₄ hydrolase.

Identification of Novel Potential gp120 of HIV-1 Antagonist Using Per-Residue Energy Contribution-Based Pharmacophore modelling

Inhibition of HIV-1 target cell entry, by targeting gp120, has been identified as a promising approach for the identification and development of prophylactic and salvage HIV infection inhibitors. A small molecule compound 18A is an important chemotype in the development of novel and diverse viral cell entry inhibitors, as it inhibits a wide variety of HIV strains by disrupting allosteric structuring on gp120. This study combines residue energy contribution (REC) pharmacophore mapping of 18A and in silico molecular docking in a virtual screening campaign to identify novel and diverse antagonists of gp120. The binding free energy of a validated docked complex of gp120-18A and the quantitative contribution of interacting residues were obtained with a more accurate molecular mechanics/generalised born surface area (MM/GBSA) method followed by mapping the energetically favourable residue contributions onto atom centres in 18A to obtain a pharmacophore model. The generated pharmacophore hypothesis was used to search the ZINC database for 3D structures that match the pharmacophore. Further, molecular docking, molecular dynamics simulations and binding free energy analysis were performed on retrieved hits in order to rank hits based on their affinity and interactions in the CD4 binding cavity of a gp120. Interestingly, the top scoring compound designated with ZINC database ID as ZINC64700951 (docking score = -8.8 kcal/mol, ∆G = -43.77 kcal/mol) showed higher affinity compared to compound 18A docking score = -7.3 kcal/mol, ∆G = -31.97 kcal/mol) and interaction of ZN64700951 with validated allosteric hot spot residues, Asp368 and Met426, and binding hot spot residues, Asn425, Glu370, Gly473, Trp427 and Met475 in gp120, suggest that ZN64700951 is a promising antagonist of gp120. Thus, ZN64700951 could serve as an additional prototype for further optimisation as an HIV target cell viral entry inhibitor.

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies.

An insight into the potentially old-wonder molecule-quercetin: the perspectives in foresee

Use of phyto-medicine and digitalization of phyto-compounds has been fallen enthralling field of science in recent years. Quercetin, a flavonoid with brilliant citron yellow pigment, is typically found in fruits and leafy vegetables in reasonable amount. Quercetin's potentials as an antioxidant, immune-modulator, antiinflammatory, anti-cancer, and others have been the subject of interest in this review. Although, profiling the insights in to the molecular characterization of quercetin with various targets provided the loop-holes in understanding the knowledge for the aforementioned mechanisms, still necessitates research globally to unearth it completely. Thus, the available science on the synthesis and significant role played by the old molecule - quercetin which does wonders even now have been vividly explained in the present review to benefit the scientific community.

Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling

Biological effects of small molecules in an organism result from favorable interactions between the molecules and their target proteins. These interactions depend on chemical functionalities, bonds, and their 3D-orientations towards each other. These 3D-arrangements of chemical functionalities that make a small molecule active towards its target can be described by pharmacophore models. In these models, chemical functionalities are represented as so-called features. Commonly, they are obtained either from a set of active compounds or directly from the observed protein-ligand interactions as present in X-ray crystal structures, NMR structures, or docking poses. In this review, we explain the basics of pharmacophore modeling including dataset generation, 3D-representations and conformational analysis of small molecules, pharmacophore model construction, model validation, and its benefits to virtual screening and other applications.

Ligand-based pharmacophore modeling and virtual screening for the discovery of novel 17β-hydroxysteroid dehydrogenase 2 inhibitors

17β-Hydroxysteroid dehydrogenase 2 (17β-HSD2) catalyzes the inactivation of estradiol into estrone. This enzyme is expressed only in a few tissues, and therefore its inhibition is considered as a treatment option for osteoporosis to ameliorate estrogen deficiency. In this study, ligand-based pharmacophore models for 17β-HSD2 inhibitors were constructed and employed for virtual screening. From the virtual screening hits, 29 substances were evaluated in vitro for 17β-HSD2 inhibition. Seven compounds inhibited 17β-HSD2 with low micromolar IC50 values. To investigate structure-activity relationships (SAR), 30 more derivatives of the original hits were tested. The three most potent hits, 12, 22, and 15, had IC50 values of 240 nM, 1 μM, and 1.5 μM, respectively. All but 1 of the 13 identified inhibitors were selective over 17β-HSD1, the enzyme catalyzing conversion of estrone into estradiol. Three of the new, small, synthetic 17β-HSD2 inhibitors showed acceptable selectivity over other related HSDs, and six of them did not affect other HSDs.

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.

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.

Optimization of hydroxybenzothiazoles as novel potent and selective inhibitors of 17β-HSD1

17β-HSD1 is a novel target for the treatment of estrogen-dependent diseases, as it catalyzes intracellular estradiol formation. Starting from two recently described compounds, highly active and selective inhibitors were developed. Benzoyl 6 and benzamide 17 are the most selective compounds toward 17β-HSD2 described so far. They also showed a promising profile regarding activity in T47-D cells, selectivity toward ERα and ERβ, inhibition of hepatic CYP enzymes, metabolic stability, and inhibition of marmoset 17β-HSD1 and 17β-HSD2.

Hydroxybenzothiazoles as new nonsteroidal inhibitors of 17β-hydroxysteroid dehydrogenase type 1 (17β-HSD1)

17β-estradiol (E2), the most potent estrogen in humans, known to be involved in the development and progession of estrogen-dependent diseases (EDD) like breast cancer and endometriosis. 17β-HSD1, which catalyses the reduction of the weak estrogen estrone (E1) to E2, is often overexpressed in breast cancer and endometriotic tissues. An inhibition of 17β-HSD1 could selectively reduce the local E2-level thus allowing for a novel, targeted approach in the treatment of EDD. Continuing our search for new nonsteroidal 17β-HSD1 inhibitors, a novel pharmacophore model was derived from crystallographic data and used for the virtual screening of a small library of compounds. Subsequent experimental verification of the virtual hits led to the identification of the moderately active compound 5. Rigidification and further structure modifications resulted in the discovery of a novel class of 17β-HSD1 inhibitors bearing a benzothiazole-scaffold linked to a phenyl ring via keto- or amide-bridge. Their putative binding modes were investigated by correlating their biological data with features of the pharmacophore model. The most active keto-derivative 6 shows IC₅₀-values in the nanomolar range for the transformation of E1 to E2 by 17β-HSD1, reasonable selectivity against 17β-HSD2 but pronounced affinity to the estrogen receptors (ERs). On the other hand, the best amide-derivative 21 shows only medium 17β-HSD1 inhibitory activity at the target enzyme as well as fair selectivity against 17β-HSD2 and ERs. The compounds 6 and 21 can be regarded as first benzothiazole-type 17β-HSD1 inhibitors for the development of potential therapeutics.

Discovery of highly potent, nonsteroidal 17β-hydroxysteroid dehydrogenase type 1 inhibitors by virtual high-throughput screening

17β-Hydroxysteroid dehydrogenase type 1 (17β-HSD1) catalyzes the formation of the potent proliferation-stimulating hormone estradiol, and it is thus involved in the development of hormone-dependent breast cancer. Due to its high substrate specificity and the known relationships between its overexpression and disease incidence, 17β-HSD1 is considered an attractive target for drug development. Here, we have used structure-based virtual high-throughput screening to successfully identify potent nonsteroidal 17β-HSD1 inhibitors. Computational screening of a drug-like database containing 13 million compounds identified hits with a 2-benzylidenebenzofuran-3(2H)-one scaffold that we show to be highly potent 17β-HSD1 inhibitors. The most potent in the series, compound 1, showed an IC(50) of 45nM in our 17β-HSD1 inhibition assay, and also showed good selectivity for 17β-HSD1 over 17β-HSD2.

Human hydroxysteroid dehydrogenases and pre-receptor regulation: insights into inhibitor design and evaluation

Hydroxysteroid dehydrogenases (HSDs) represent a major class of NAD(P)(H) dependent steroid hormone oxidoreductases involved in the pre-receptor regulation of hormone action. This is achieved by HSDs working in pairs so that they can interconvert ketosteroids with hydroxysteroids resulting in a change in ligand potency for nuclear receptors. HSDs belong to two protein superfamilies the aldo-keto reductases and the short-chain dehydrogenase/reductases. In humans, many of the important enzymes have been thoroughly characterized including the elucidation of their three-dimensional structures. Because these enzymes play fundamental roles in steroid hormone action they can be considered to be drug targets for a variety of steroid driven diseases, e.g. metabolic syndrome and obesity, inflammation, and hormone dependent malignancies of the endometrium, prostate and breast. This article will review how fundamental knowledge of these enzymes can be exploited in the development of isoform specific HSD inhibitors from both protein superfamilies. Article from the Special issue on Targeted Inhibitors.

Identification of chemically diverse, novel inhibitors of 17β-hydroxysteroid dehydrogenase type 3 and 5 by pharmacophore-based virtual screening

17β-Hydroxysteroid dehydrogenase type 3 and 5 (17β-HSD3 and 17β-HSD5) catalyze testosterone biosynthesis and thereby constitute therapeutic targets for androgen-related diseases or endocrine-disrupting chemicals. As a fast and efficient tool to identify potential ligands for 17βHSD3/5, ligand- and structure-based pharmacophore models for both enzymes were developed. The models were evaluated first by in silico screening of commercial compound databases and further experimentally validated by enzymatic efficacy tests of selected virtual hits. Among the 35 tested compounds, 11 novel inhibitors with distinct chemical scaffolds, e.g. sulfonamides and triazoles, and with different selectivity properties were discovered. Thereby, we provide several potential starting points for further 17β-HSD3 and 17β-HSD5 inhibitor development. Article from the Special issue on Targeted Inhibitors.

Bicyclic substituted hydroxyphenylmethanones as novel inhibitors of 17β-hydroxysteroid dehydrogenase type 1 (17β-HSD1) for the treatment of estrogen-dependent diseases

Estradiol (E2), the most important estrogen in humans, is involved in the initiation and progression of estrogen-dependent diseases such as breast cancer and endometriosis. Its local production in the target cell is regulated by 17β-hydroxysteroid dehydrogenase type 1 (17β-HSD1), which catalyzes E2-formation by reduction of the weak estrogen estrone (E1). Because the enzyme is expressed in the diseased tissues, inhibition of 17β-HSD1 is considered as a promising therapy for the treatment of estrogen-dependent diseases. For the development of novel inhibitors, a structure- and ligand-based design strategy was applied, resulting in bicyclic substituted hydroxyphenylmethanones. In vitro testing revealed high inhibitory potencies toward human placental 17β-HSD1. Compounds were further evaluated with regard to selectivity (17β-HSD2, estrogen receptors ERα and ERβ), intracellular activity (T47D cells), and metabolic stability. The most promising compounds, 14 and 15, showed IC(50) values in the low nanomolar range in the cell-free and cellular assays (8-27 nM), more than 30-fold selectivity toward 17β-HSD2 and no affinity toward the ERs. The data obtained make these inhibitors interesting candidates for further preclinical evaluation.

Molecular therapy of breast cancer: progress and future directions

Breast cancer is a major cause of death in Western women, with a 10% lifetime risk of the disease. Most breast cancers are estrogen-dependent. Molecular therapies for breast cancer have developed rapidly in the past few decades and future treatment strategies are being investigated. The selective estrogen receptor (ER) modulator tamoxifen, which until now has served as a standard therapy, functions not only as an estrogen antagonist but also as an estrogen agonist in terms of bone maintenance. Aromatase inhibitors have performed well in international trials and have become a new standard therapy for estrogen-dependent breast cancer. The systematic study of estrogen activation pathways suggests that the enzymes steroid sulfatase and 17beta-hydroxysteroid dehydrogenase type 1, which both have pivotal roles in estrogen biosynthesis, are promising targets; the results of a phase I trial of steroid sulfatase inhibitors are encouraging. The activity of the human epidermal growth factor receptor (HER) pathway correlates negatively with that of the ER. HER2 is overexpressed in 22% of all breast cancers. In the decade since HER2 began being targeted, the monoclonal antibody trastuzumab has been used as well as pertuzumab and HER2 vaccines. Among the estrogen-independent breast cancers, the basal-like subtype has low survival, and therapeutic improvement is a priority. Crosstalk between ER and HER2 signaling pathways means that combinatory therapies may hold the key to enhancement of treatment responses. Other molecular therapies involving functional genomics and RNA interference studies also hold promise.

Novel method for generating structure-based pharmacophores using energetic analysis

We describe a novel method to develop energetically optimized, structure-based pharmacophores for use in rapid in silico screening. The method combines pharmacophore perception and database screening with protein-ligand energetic terms computed by the Glide XP scoring function to rank the importance of pharmacophore features. We derive energy-optimized pharmacophore hypotheses for 30 pharmaceutically relevant crystal structures and screen a database to assess the enrichment of active compounds. The method is compared to three other approaches: (1) pharmacophore hypotheses derived from a systematic assessment of receptor-ligand contacts, (2) Glide SP docking, and (3) 2D ligand fingerprint similarity. The method developed here shows better enrichments than the other three methods and yields a greater diversity of actives than the contact-based pharmacophores or the 2D ligand similarity. Docking produces the most cases (28/30) with enrichments greater than 10.0 in the top 1% of the database and on average produces the greatest diversity of active molecules. The combination of energy terms from a structure-based analysis with the speed of a ligand-based pharmacophore search results in a method that leverages the strengths of both approaches to produce high enrichments with a good diversity of active molecules.

Diazepane-acetamide derivatives as selective 11beta-hydroxysteroid dehydrogenase type 1 inhibitors

WO2008052638 describes the identification and synthesis of diazepane- acetamide derivatives as a novel class of selective small molecule inhibitors of 11beta-hydroxysteroid dehydrogenase type 1 (11beta-HSD1) for the treatment of metabolic syndrome. The generic structure of the disclosed diazepane-acetamide derivatives offers considerable possibilities for modifications that allow optimizing compound properties. Further studies to assess target selectivity, species-specificity, modulation of tissue-specific functions of 11beta-HSD1 as well as interference with alternative functions of this enzyme are needed to explore the therapeutic potential of these chemicals.

Hybrid structure-based virtual screening protocol for the identification of novel BACE1 inhibitors

BACE1, also called beta-secretase or memapsin 2, is an extensively studied aspartic protease, involved in etiopathogenesis and progression of Alzheimer's disease (AD). We report herein a modified structure-based virtual screening protocol that augments the lead identification process against BACE1 during virtual screening endeavors. A hybrid structure-based virtual screening protocol that incorporates elements from both ligand-based and structure-based techniques was used for the identification of prospective small molecule inhibitors. Virtual screening, using an active-site-derived pharmacophore, followed by ROCS (rapid overlay of chemical structures)-based GOLD (genetic optimization in ligand docking) docking was used to identify a library of focused candidates. The efficacy of the ROCS-based GOLD docking method together with our customized weighted consensus scoring function was evaluated against conventional docking methods for its ability to discern true positives from a screening library. An in-depth structural analysis of the binding mode of the top-ranking molecules reveals that emulation of the curial interaction patterns deemed necessary for BACE1 inhibition. The results obtained from our validation study ensure the superiority of our docking methodology over conventional docking methods in yielding higher enrichment rates.

Reductive 17beta-hydroxysteroid dehydrogenases in the sulfatase pathway: critical in the cell proliferation of breast cancer

Estradiol, the most potent estrogen, plays critical roles in tumor cell proliferation and breast cancer development. It can be synthesized via the aromatase pathway or the sulfatase pathway, and the later has been demonstrated to be more significant. Reductive 17beta-hydroxysteroid dehydrogenases (17beta-HSDs) catalyze the last step in estrogen activation and are thus critical in breast cancer development. 17beta-HSD Type 1 (17beta-HSD1) is of great importance since it efficiently synthesizes the most potent estrogen estradiol, as well as other estrogens as 5-androstene-3beta,17beta-diol and 5alpha-androstane-3beta,17beta-diol, and inactivates the most active androgen dihydrotestosterone (DHT), all contributing to the stimulation and development of breast cancers. Rational inhibitor design based on the new structure information has been developed, yielding interesting compounds and lead chemicals. This was demonstrated by a hybrid inhibitor that interacts with both the substrate and cofactor binding sites and a recently designed inhibitor 3-(3',17'beta-dihydroxyestra-1',3',5'(10')-trien-16'beta-methyl) benzamide which has been crystallized in complex with 17beta-HSD1. Both inhibitors demonstrate nM level K(i)in vitro. New non-steroidal inhibitors have been designed and reported very recently. The Type 7 17beta-HSD, expressed in several tissues including breast and ovary, can also contribute to estrogen synthesis and DHT inactivation in breast cancer cells. The enzyme role in steroid metabolism and cancer cell proliferation needs to be compared to that in cholesterogenesis. Breast cancer cell lines provide an excellent platform for such study. T47D, MCF-7 and MDA-MB-231-luc cells have been used to create xenografts in nude mice as animal models, now with the possibility of bioluminescent imaging to provide rapid, non-invasive, and quantitative analysis of tumor biomass and metastasis. Here we review the roles of the sulfatase and aromatase pathways and the contribution of the reductive 17beta-HSDs for hormone metabolism in breast cancer.

Structure-activity study in the class of 6-(3'-hydroxyphenyl)naphthalenes leading to an optimization of a pharmacophore model for 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) inhibitors

17beta-Hydroxysteroid dehydrogenase type 1 (17beta-HSD1) catalyzes the transformation of estrone (E1) into the most potent estrogen, estradiol (E2), which stimulates cell proliferation and decreases apoptosis. 17beta-HSD1 is often strongly overexpressed in estrogen-dependent diseases (like breast cancer and endometriosis). Thus, this over expressed enzyme is a promising novel target for the development of selective inhibitors, which could be used as drugs for the treatment of these diseases. Using a structure- and ligand-based approach, a pharmacophore model was proposed and a new class of non-steroidal inhibitors of 17beta-HSD1 was designed. Enzyme inhibition was evaluated in vitro using the human enzyme. After identification of the 6-(3'-hydroxyphenyl)-2-naphthol scaffold 1, the potency of this class of inhibitors was further improved by substitution of the 1-position of the naphthalene ring by a phenyl group (compound 18, IC(50)=20nM). Compound 18 also showed a good selectivity toward 17beta-HSD2 and the estrogen receptors alpha and beta.

Pharmacophore modelling of 17beta-HSD1 enzyme based on active inhibitors and enzyme structure

The 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) enzyme regulates the conversion of estrone (E1) to the biologically active estradiol (E2). Due to its role as a key enzyme in female hormone production, it has emerged as an attractive drug target for inhibitor development in relation to hormone-dependent breast cancer. Herein, we report four pharmacophore models of 17beta-HSD1 based on a crystal structure, a relaxed crystal structure, a library of 17beta-HSD1 inhibitors and on a docked complex of 17betaHSD1 enzyme and a potent inhibitor. The models were used in screening two databases, which produced novel compounds to be used as leads in our drug design project. The results were validated by docking the compounds to the active site of the 17beta-HSD1 enzyme. With the help of our 3D-QSAR model, these results will be used to develop new inhibitors of 17beta-HSD1 as drug candidates.