The discovery of new 11beta-hydroxysteroid dehydrogenase type 1 inhibitors by common feature pharmacophore modeling and virtual screening

Drug Design by Pharmacophore and Virtual Screening Approach

Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

The Pharmacophore Concept and Its Applications in Computer-Aided Drug Design

Pharmacophore-based techniques currently are an integral part of many computer-aided drug design workflows and have been successfully and extensively applied for tasks such as virtual screening, de novo design, and lead optimization. Pharmacophore models can be derived both in a receptor-based and in a ligand-based manner, and provide an abstract description of essential non-bonded interactions that typically occur between small-molecule ligands and macromolecular targets. Due to their simplistic and abstract nature, pharmacophores are both perfectly suited for efficient computer processing and easy to comprehend by life and physical scientists. As a consequence, they have also proven to be a valuable tool for communicating between computational and medicinal chemists.This chapter aims to provide a short overview of the pharmacophore concept and its applications in modern computer-aided drug design. The chapter is divided into three distinct parts. The first section contains a brief introduction to the pharmacophore concept. The second section provides a description of the most common nonbonded interaction types and their representation as pharmacophoric features. Furthermore, it gives an overview of the various methods for pharmacophore generation and important pharmacophore-based techniques in drug design. This part concludes with examples for recent pharmacophore concept-related research and development. The last section is dedicated to a review of research in the field of natural product chemistry as carried out by employing pharmacophore-based drug design methods.

Absence of hexose-6-phosphate dehydrogenase results in reduced overall glucose consumption but does not prevent 11β-hydroxysteroid dehydrogenase-1-dependent glucocorticoid activation

Hexose-6-phosphate dehydrogenase (H6PD) is thought to be the major source of NADPH within the endoplasmic reticulum (ER), determining 11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) reaction direction to convert inert 11-oxo- to potent 11β-hydroxyglucocorticoids. Here, we tested the hypothesis whether H6pd knock-out (KO) in primary murine bone marrow-derived macrophages results in a switch from 11β-HSD1 oxoreduction to dehydrogenation, thereby inactivating glucocorticoids (GC) and affecting macrophage phenotypic activation as well as causing a more aggressive M1 macrophage phenotype. H6pdKO did not lead to major disturbances of macrophage activation state, although a slightly more pronounced M1 phenotype was observed with enhanced proinflammatory cytokine release, an effect explained by the decreased 11β-HSD1-dependent GC activation. Unexpectedly, ablation of H6pd did not switch 11β-HSD1 reaction direction. A moderately decreased 11β-HSD1 oxoreduction activity by 40-50% was observed in H6pdKO M1 macrophages but dehydrogenation activity was undetectable, providing strong evidence for the existence of an alternative source of NADPH in the ER. H6pdKO M1 activated macrophages showed decreased phagocytic activity, most likely a result of the reduced 11β-HSD1-dependent GC activation. Other general macrophage functions reported to be influenced by GC, such as nitrite production and cholesterol efflux, were altered negligibly or not at all. Importantly, assessment of energy metabolism using an extracellular flux analyzer and lactate measurements revealed reduced overall glucose consumption in H6pdKO M1 activated macrophages, an effect that was GC independent. The GC-independent influence of H6PD on energy metabolism and the characterization of the alternative source of NADPH in the ER warrant further investigations. ENZYMES: 11β-HSD1, EC 1.1.1.146; H6PD, EC 1.1.1.47.

A Proposal for a Structural Model of the Feline Calicivirus Protease Bound to the Substrate Peptide under Physiological Conditions

Feline calicivirus (FCV) protease functions to cleave viral precursor proteins during productive infection. Previous studies have mapped a protease-coding region and six cleavage sites in viral precursor proteins. However, how the FCV protease interacts with its substrates remains unknown. To gain insights into the interactions, we constructed a molecular model of the FCV protease bound with the octapeptide containing a cleavage site of the capsid precursor protein by homology modeling and docking simulation. The complex model was used to screen for the substrate mimic from a chemical library by pharmacophore-based in silico screening. With this structure-based approach, we identified a compound that has physicochemical features and arrangement of the P3 and P4 sites of the substrate in the protease, is predicted to bind to FCV proteases in a mode similar to that of the authentic substrate, and has the ability to inhibit viral protease activity in vitro and in the cells, and to suppress viral replication in FCV-infected cells. The complex model was further subjected to molecular dynamics simulation to refine the enzyme-substrate interactions in solution. The simulation along with a variation study predicted that the authentic substrate and anti-FCV compound share a highly conserved binding site. These results suggest the validity of our in silico model for elucidating protease-substrate interactions during FCV replication and for developing antivirals.

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.

DHRS7 (SDR34C1) - A new player in the regulation of androgen receptor function by inactivation of 5α-dihydrotestosterone?

DHRS7 (SDR34C1) has been associated with potential tumor suppressor effects in prostate cancer; however, its function remains largely unknown. Recent experiments using purified recombinant human DHRS7 suggested several potential substrates, including the steroids cortisone and Δ4-androstene-3,17-dione (androstenedione). However, the substrate and cofactor concentrations used in these experiments were very high and the physiological relevance of these observations needed to be further investigated. In the present study, recombinant human DHRS7 was expressed in intact HEK-293 cells in order to investigate whether glucocorticoids and androgens serve as substrates at sub-micromolar concentrations and at physiological cofactor concentrations. Furthermore, the membrane topology of DHRS7 was revisited using redox-sensitive green-fluorescent protein fusions in living cells. The results revealed that (1) cortisone is a substrate of DHRS7; however, it is not reduced to cortisol but to 20β-dihydrocortisone, (2) androstenedione is not a relevant substrate of DHRS7, (3) DHRS7 catalyzes the oxoreduction of 5α-dihydrotestosterone (5αDHT) to 3α-androstanediol (3αAdiol), with a suppressive effect on androgen receptor (AR) transcriptional activity, and (4) DHRS7 is anchored in the endoplasmic reticulum membrane with a cytoplasmic orientation. Together, the results show that DHRS7 is a cytoplasmic oriented enzyme exhibiting 3α/20β-hydroxysteroid dehydrogenase activity, with a possible role in the modulation of AR function. Further research needs to address the physiological relevance of DHRS7 in the inactivation of 5αDHT and AR regulation.

Inhibition of 11β-hydroxysteroid dehydrogenase 2 by the fungicides itraconazole and posaconazole

Impaired 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2)-dependent cortisol inactivation can lead to electrolyte dysbalance, hypertension and cardiometabolic disease. Furthermore, placental 11β-HSD2 essentially protects the fetus from high maternal glucocorticoid levels, and its impaired function has been associated with altered fetal growth and a higher risk for cardio-metabolic diseases in later life. Despite its important role, 11β-HSD2 is not included in current off-target screening approaches. To identify potential 11β-HSD inhibitors among approved drugs, a pharmacophore model was used for virtual screening, followed by biological assessment of selected hits. This led to the identification of several azole fungicides as 11β-HSD inhibitors, showing a significant structure-activity relationship between azole scaffold size, 11β-HSD enzyme selectivity and inhibitory potency. A hydrophobic linker connecting the azole ring to the other, more polar end of the molecule was observed to be favorable for 11β-HSD2 inhibition and selectivity over 11β-HSD1. The most potent 11β-HSD2 inhibition, using cell lysates expressing recombinant human 11β-HSD2, was obtained for itraconazole (IC₅₀ 139±14nM), its active metabolite hydroxyitraconazole (IC₅₀ 223±31nM) and posaconazole (IC₅₀ 460±98nM). Interestingly, experiments with mouse and rat kidney homogenates showed considerably lower inhibitory activity of these compounds towards 11β-HSD2, indicating important species-specific differences. Thus, 11β-HSD2 inhibition by these compounds is likely to be overlooked in preclinical rodent studies. Inhibition of placental 11β-HSD2 by these compounds, in addition to the known inhibition of cytochrome P450 enzymes and P-glycoprotein efflux transport, might contribute to elevated local cortisol levels, thereby affecting fetal programming.

Molecular Modeling Studies of 11β-Hydroxysteroid Dehydrogenase Type 1 Inhibitors through Receptor-Based 3D-QSAR and Molecular Dynamics Simulations

11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) is a potential target for the treatment of numerous human disorders, such as diabetes, obesity, and metabolic syndrome. In this work, molecular modeling studies combining molecular docking, 3D-QSAR, MESP, MD simulations and free energy calculations were performed on pyridine amides and 1,2,4-triazolopyridines as 11β-HSD1 inhibitors to explore structure-activity relationships and structural requirement for the inhibitory activity. 3D-QSAR models, including CoMFA and CoMSIA, were developed from the conformations obtained by docking strategy. The derived pharmacophoric features were further supported by MESP and Mulliken charge analyses using density functional theory. In addition, MD simulations and free energy calculations were employed to determine the detailed binding process and to compare the binding modes of inhibitors with different bioactivities. The binding free energies calculated by MM/PBSA showed a good correlation with the experimental biological activities. Free energy analyses and per-residue energy decomposition indicated the van der Waals interaction would be the major driving force for the interactions between an inhibitor and 11β-HSD1. These unified results may provide that hydrogen bond interactions with Ser170 and Tyr183 are favorable for enhancing activity. Thr124, Ser170, Tyr177, Tyr183, Val227, and Val231 are the key amino acid residues in the binding pocket. The obtained results are expected to be valuable for the rational design of novel potent 11β-HSD1 inhibitors.

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.

Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target

A search of broader range of chemical space is important for drug discovery. Different methods of computer-aided drug discovery (CADD) are known to propose compounds in different chemical spaces as hit molecules for the same target protein. This study aimed at using multiple CADD methods through open innovation to achieve a level of hit molecule diversity that is not achievable with any particular single method. We held a compound proposal contest, in which multiple research groups participated and predicted inhibitors of tyrosine-protein kinase Yes. This showed whether collective knowledge based on individual approaches helped to obtain hit compounds from a broad range of chemical space and whether the contest-based approach was effective.

Pharmacophore-Map-Pick: A Method to Generate Pharmacophore Models for All Human GPCRs

GPCR-based drug discovery is hindered by a lack of effective screening methods for most GPCRs that have neither ligands nor high-quality structures. With the aim to identify lead molecules for these GPCRs, we developed a new method called Pharmacophore-Map-Pick to generate pharmacophore models for all human GPCRs. The model of ADRB2 generated using this method not only predicts the binding mode of ADRB2-ligands correctly but also performs well in virtual screening. Findings also demonstrate that this method is powerful for generating high-quality pharmacophore models. The average enrichment for the pharmacophore models of the 15 targets in different GPCR families reached 15-fold at 0.5 % false-positive rate. Therefore, the pharmacophore models can be applied in virtual screening directly with no requirement for any ligand information or shape constraints. A total of 2386 pharmacophore models for 819 different GPCRs (99 % coverage (819/825)) were generated and are available at http://bsb.kiz.ac.cn/GPCRPMD.

Reprint of "In silico methods in the discovery of endocrine disrupting chemicals"

The prevalence of sex hormone-dependent cancers, reproductive problems, obesity, and cardiovascular complications has risen especially in the Western world. It has been suggested, that the exposure to various endocrine disrupting chemicals (EDCs) contributes to the development and progression of these diseases. EDCs can interfere with various proteins: nuclear steroid hormone receptors, such as estrogen-, androgen-, glucocorticoid- and mineralocorticoid receptors (ER, AR, GR, MR), and enzymes that are involved in steroid hormone synthesis and metabolism, for example hydroxysteroid dehydrogenases (HSDs). Numerous chemicals are known as endocrine disruptors. However, the mechanism of action for most of these EDCs is still unknown. It is exhaustive and time consuming to test in vitro all chemicals - potential EDCs - used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in drug development. The same methods could also aid the research on EDCs. So far, the computational methods in the search of EDCs have been retrospective. There are, however, some prospective studies reporting the use of in silico methods: five studies reporting the identification of previously unknown 17β-HSD3 inhibitors, MR agonists, and ER antagonists/agonists. This review provides an overview of case studies and in silico methods that are used in the search of EDCs. This article is part of a Special Issue entitled 'CSR 2013'.

Accessing biological actions of Ganoderma secondary metabolites by in silico profiling

The species complex around the medicinal fungus Ganoderma lucidum Karst. (Ganodermataceae) is widely known in traditional medicines, as well as in modern applications such as functional food or nutraceuticals. A considerable number of publications reflects its abundance and variety in biological actions either provoked by primary metabolites, such as polysaccharides, or secondary metabolites, such as lanostane-type triterpenes. However, due to this remarkable amount of information, a rationalization of the individual Ganoderma constituents to biological actions on a molecular level is quite challenging. To overcome this issue, a database was generated containing meta-information, i.e., chemical structures and biological actions of hitherto identified Ganoderma constituents (279). This was followed by a computational approach subjecting this 3D multi-conformational molecular dataset to in silico parallel screening against an in-house collection of validated structure- and ligand-based 3D pharmacophore models. The predictive power of the evaluated in silico tools and hints from traditional application fields served as criteria for the model selection. Thus, the focus was laid on representative druggable targets in the field of viral infections (5) and diseases related to the metabolic syndrome (22). The results obtained from this in silico approach were compared to bioactivity data available from the literature. 89 and 197 Ganoderma compounds were predicted as ligands of at least one of the selected pharmacological targets in the antiviral and the metabolic syndrome screening, respectively. Among them only a minority of individual compounds (around 10%) has ever been investigated on these targets or for the associated biological activity. Accordingly, this study discloses putative ligand target interactions for a plethora of Ganoderma constituents in the empirically manifested field of viral diseases and metabolic syndrome which serve as a basis for future applications to access yet undiscovered biological actions of Ganoderma secondary metabolites on a molecular level.

Structure- and ligand-based virtual screening identifies new scaffolds for inhibitors of the oncoprotein MDM2

A major challenge in the field of ligand discovery is to identify chemically useful fragments that can be developed into inhibitors of specific protein-protein interactions. Low molecular weight fragments (with molecular weight less than 250 Da) are likely to bind weakly to a protein’s surface. Here we use a new virtual screening procedure which uses a combination of similarity searching and docking to identify chemically tractable scaffolds that bind to the p53-interaction site of MDM2. The binding has been verified using capillary electrophoresis which has proven to be an excellent screening method for such small, weakly binding ligands.

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.

Identification of novel 11β-HSD1 inhibitors by combined ligand- and structure-based virtual screening

11 beta-hydroxysteroid dehydrogenase type 1 (11β-HSD1) converts cortisone to cortisol in a NADPH dependent manner. Overexpression of 11β-HSD1 in key metabolic tissues is related to the development of type 2 diabetes, obesity, hypertension and metabolic syndrome. Using crystal structures of human 11β-HSD1 in complex with inhibitors as source of structural information, a combined ligand and structure-based virtual screening approach was implemented to identify novel 11β-HSD1 inhibitors. A selected group of compounds was identified in silico and further evaluated in cell-based assays for cytotoxicity and 11β-HSD1 mediated cortisol production inhibitory capacity. The expression of 11β-HSD1 and 11β-HSD2 in human LS14 adipocytes was assessed during differentiation. Biological evaluation of 39 compounds in adipocytes and steroids quantification by HPLC-MS/MS identify 4 compounds that exhibit 11β-HSD1 mediated cortisol production inhibitory activity with potencies in the micromolar range. Two compounds showed to be selective for the 11β-HSD1 reductase activity and over 11β-HSD2 isoform, and thus represent novel leads for the development of more active derivatives with higher efficacies targeting intracellular cortisol levels in type 2 diabetes and metabolic syndrome.

New strategy for receptor-based pharmacophore query construction: a case study for 5-HT₇ receptor ligands

In this paper, a new approach for generating receptor-based 3D pharmacophore models for rapid in silico virtual screening is presented. The method combines information from docking poses of known ligands of different structures and further ligand-receptor complexes analyses using structural interaction fingerprints (SIFts). Next, the best linear combination of three-, four-, and five-feature pharmacophores in terms of selected performance parameter (i.e., recall, F-score, and MCC) is constructed. The resultant queries showed significantly better VS performance and new scaffold recognition when compared with the known ligand- and receptor-based pharmacophore models. The approach was developed and validated on 5-HT₇ receptor homology models created on available crystal structure templates. The efficiency of the obtained linear combinations exhibited only a minor dependence on the template selection.

Pharmacophore Model Refinement for 11β-Hydroxysteroid Dehydrogenase Inhibitors: Search for Modulators of Intracellular Glucocorticoid Concentrations

11β-Hydroxysteroid dehydrogenases (11β-HSD) control the intracellular concentrations of glucocorticoids: 11β-HSD1 converts the inactive cortisone to the active cortisol, and 11β-HSD2 is responsible for the opposite reaction. Inhibition of 11β-HSD1 is beneficial in the treatment of metabolic syndrome, whereas 11β-HSD2 inhibition leads to hypertension. Therefore, 11β-HSD1 inhibitors should be selective over 11β-HSD2. To support drug discovery and toxicological studies, we have previously reported pharmacophore models for 11β-HSD1 and 2 inhibition. These models represent the common chemical features of 11β-HSD inhibitors, which were used as virtual screening filter. Since new inhibitors are constantly discovered, the quality of the pharmacophore models has to be evaluated in order to maintain a good predictive power. In this study, we report a systematic evaluation and refinement of our pharmacophore model collection. We employed our models for virtual screening, especially focusing on the 11β-HSD2 inhibition. In total, 42 compounds were biologically evaluated and among these we discovered 17 11β-HSD inhibitors that decreased the residual enzyme activity to 50% or less at the concentration of 20 µM. The experimental 11β-HSD1 and 2 readouts from these compounds were used for further model refinement. Evaluation metrics were applied for a quantitative comparison of the old and newly generated models which resulted in a set of improved pharmacophore models offering reliable in silico tools for the identification of novel and selective 11β-HSD inhibitors.

In silico methods in the discovery of endocrine disrupting chemicals

The prevalence of sex hormone-dependent cancers, reproductive problems, obesity, and cardiovascular complications has risen especially in the Western world. It has been suggested, that the exposure to various endocrine disrupting chemicals (EDCs) contributes to the development and progression of these diseases. EDCs can interfere with various proteins: nuclear steroid hormone receptors, such as estrogen-, androgen-, glucocorticoid- and mineralocorticoid receptors (ER, AR, GR, MR), and enzymes that are involved in steroid hormone synthesis and metabolism, for example hydroxysteroid dehydrogenases (HSDs). Numerous chemicals are known as endocrine disruptors. However, the mechanism of action for most of these EDCs is still unknown. It is exhaustive and time consuming to test in vitro all chemicals - potential EDCs - used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in drug development. The same methods could also aid the research on EDCs. So far, the computational methods in the search of EDCs have been retrospective. There are, however, some prospective studies reporting the use of in silico methods: five studies reporting the identification of previously unknown 17β-HSD3 inhibitors, MR agonists, and ER antagonists/agonists. This review provides an overview of case studies and in silico methods that are used in the search of EDCs. This article is part of a Special Issue entitled 'CSR 2013'.

Synthesis and biological analysis of benzazol-2-yl piperazine sulfonamides as 11β-hydroxysteroid dehydrogenase 1 inhibitors

In the last decade the inhibition of the enzyme 11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) emerged as a promising new strategy to treat diabetes and several metabolic syndrome phenotypes. Using a molecular modeling approach and classical bioisosteric studies, we discovered a new class of 11β-HSD1 inhibitors bearing an arylsulfonylpiperazine scaffold. Optimization of the initial lead resulted in compound 11 that selectively inhibits 11β-HSD1 (IC50=0.7 μM).

Diabetes Mellitus: New Challenges and Innovative Therapies

Diabetes is a common chronic disease affecting an estimated 285 million adults worldwide. The rising incidence of diabetes, metabolic syndrome, and subsequent vascular diseases is a major public health problem in industrialized countries. This chapter summarizes current pharmacological approaches to treat diabetes mellitus and focuses on novel therapies for diabetes mellitus that are under development.

There is great potential for developing a new generation of therapeutics that offer better control of diabetes, its co-morbidities and its complications. Preclinical results are discussed for new approaches including AMPK activation, the FGF21 target, cell therapy approaches, adiponectin mimetics and novel insulin formulations. Gene-based therapies are among the most promising emerging alternatives to conventional treatments. Therapies based on gene silencing using vector systems to deliver interference RNA to cells (i.e. against VEGF in diabetic retinopathy) are also a promising therapeutic option for the treatment of several diabetic complications.

In conclusion, treatment of diabetes faces now a new era that is characterized by a variety of innovative therapeutic approaches that will improve quality of life in the near future.

Mineralocorticoid and glucocorticoid receptors differentially regulate NF-kappaB activity and pro-inflammatory cytokine production in murine BV-2 microglial cells

Background

Microglia, the resident macrophage-like cells in the brain, regulate innate immune responses in the CNS to protect neurons. However, excessive activation of microglia contributes to neurodegenerative diseases. Corticosteroids are potent modulators of inflammation and mediate their effects by binding to mineralocorticoid receptors (MR) and glucocorticoid receptors (GR). Here, the coordinated activities of GR and MR on the modulation of the nuclear factor-κB (NF-κB) pathway in murine BV-2 microglial cells were studied.

Methods

BV-2 cells were treated with different corticosteroids in the presence or absence of MR and GR antagonists. The impact of the glucocorticoid-activating enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) was determined by incubating cells with 11-dehydrocorticosterone, with or without selective inhibitors. Expression of interleukin-6 (IL-6), tumor necrosis factor receptor 2 (TNFR2), and 11β-HSD1 mRNA was analyzed by RT-PCR and IL-6 protein expression by ELISA. NF-κB activation and translocation upon treatment with various corticosteroids were visualized by western blotting, immunofluorescence microscopy, and translocation assays.

Results

GR and MR differentially regulate NF-κB activation and neuroinflammatory parameters in BV-2 cells. By converting inactive 11-dehydrocorticosterone to active corticosterone, 11β-HSD1 essentially modulates the coordinated action of GR and MR. Biphasic effects were observed for 11-dehydrocorticosterone and corticosterone, with an MR-dependent potentiation of IL-6 and tumor necrosis factor-α (TNF-α) expression and NF-κB activation at low/moderate concentrations and a GR-dependent suppression at high concentrations. The respective effects were confirmed using the MR ligand aldosterone and the antagonist spironolactone as well as the GR ligand dexamethasone and the antagonist RU-486. NF-κB activation could be blocked by spironolactone and the inhibitor of NF-κB translocation Cay-10512. Moreover, an increased expression of TNFR2 was observed upon treatment with 11-dehydrocorticosterone and aldosterone, which was reversed by 11β-HSD1 inhibitors and/or spironolactone and Cay-10512.

Conclusions

A tightly coordinated GR and MR activity regulates the NF-κB pathway and the control of inflammatory mediators in microglia cells. The balance of GR and MR activity is locally modulated by the action of 11β-HSD1, which is upregulated by pro-inflammatory mediators and may represent an important feedback mechanism involved in resolution of inflammation.

Virtual screening as a strategy for the identification of xenobiotics disrupting corticosteroid action

BACKGROUND

Impaired corticosteroid action caused by genetic and environmental influence, including exposure to hazardous xenobiotics, contributes to the development and progression of metabolic diseases, cardiovascular complications and immune disorders. Novel strategies are thus needed for identifying xenobiotics that interfere with corticosteroid homeostasis. 11β-hydroxysteroid dehydrogenase 2 (11β-HSD2) and mineralocorticoid receptors (MR) are major regulators of corticosteroid action. 11β-HSD2 converts the active glucocorticoid cortisol to the inactive cortisone and protects MR from activation by glucocorticoids. 11β-HSD2 has also an essential role in the placenta to protect the fetus from high maternal cortisol concentrations.

METHODS AND PRINCIPAL FINDINGS

We employed a previously constructed 3D-structural library of chemicals with proven and suspected endocrine disrupting effects for virtual screening using a chemical feature-based 11β-HSD pharmacophore. We tested several in silico predicted chemicals in a 11β-HSD2 bioassay. The identified antibiotic lasalocid and the silane-coupling agent AB110873 were found to concentration-dependently inhibit 11β-HSD2. Moreover, the silane AB110873 was shown to activate MR and stimulate mitochondrial ROS generation and the production of the proinflammatory cytokine interleukin-6 (IL-6). Finally, we constructed a MR pharmacophore, which successfully identified the silane AB110873.

CONCLUSIONS

Screening of virtual chemical structure libraries can facilitate the identification of xenobiotics inhibiting 11β-HSD2 and/or activating MR. Lasalocid and AB110873 belong to new classes of 11β-HSD2 inhibitors. The silane AB110873 represents to the best of our knowledge the first industrial chemical shown to activate MR. Furthermore, the MR pharmacophore can now be used for future screening purposes.

Structural basis for specific recognition of substrates by sapovirus protease

Sapovirus (SaV) protease catalyzes cleavage of the peptide bonds at six sites of a viral polyprotein for the viral replication and maturation. However, the mechanisms by which the protease recognizes the distinct sequences of the six cleavage sites remain poorly understood. Here we examined this issue by computational and experimental approaches. A structural modeling and docking study disclosed two small clefts on the SaV protease cavity that allow the stable and functional binding of substrates to the catalytic cavity via aromatic stacking and electrostatic interactions. An information entropy study and a site-directed mutagenesis study consistently suggested variability of the two clefts under functional constraints. Using this information, we identified three chemical compounds that had structural and spatial features resembling those of the substrate amino acid residues bound to the two clefts and that exhibited an inhibitory effect on SaV protease in vitro. These results suggest that the two clefts provide structural base points to realize the functional binding of various substrates.

Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ∼75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.

Hepatic reduction of the secondary bile acid 7-oxolithocholic acid is mediated by 11β-hydroxysteroid dehydrogenase 1

The oxidized bile acid 7-oxoLCA (7-oxolithocholic acid), formed primarily by gut micro-organisms, is reduced in human liver to CDCA (chenodeoxycholic acid) and, to a lesser extent, UDCA (ursodeoxycholic acid). The enzyme(s) responsible remained unknown. Using human liver microsomes, we observed enhanced 7-oxoLCA reduction in the presence of detergent. The reaction was dependent on NADPH and stimulated by glucose 6-phosphate, suggesting localization of the enzyme in the ER (endoplasmic reticulum) and dependence on NADPH-generating H6PDH (hexose-6-phosphate dehydrogenase). Using recombinant human 11β-HSD1 (11β-hydroxysteroid dehydrogenase 1), we demonstrate efficient conversion of 7-oxoLCA into CDCA and, to a lesser extent, UDCA. Unlike the reversible metabolism of glucocorticoids, 11β-HSD1 mediated solely 7-oxo reduction of 7-oxoLCA and its taurine and glycine conjugates. Furthermore, we investigated the interference of bile acids with 11β-HSD1-dependent interconversion of glucocorticoids. 7-OxoLCA and its conjugates preferentially inhibited cortisone reduction, and CDCA and its conjugates inhibited cortisol oxidation. Three-dimensional modelling provided an explanation for the binding mode and selectivity of the bile acids studied. The results reveal that 11β-HSD1 is responsible for 7-oxoLCA reduction in humans, providing a further link between hepatic glucocorticoid activation and bile acid metabolism. These findings also suggest the need for animal and clinical studies to explore whether inhibition of 11β-HSD1 to reduce cortisol levels would also lead to an accumulation of 7-oxoLCA, thereby potentially affecting bile acid-mediated functions.

Advances in the treatment of type 2 diabetes mellitus

There is a rising worldwide prevalence of diabetes, especially type 2 diabetes mellitus (T2DM), which is one of the most challenging health problems in the 21st century. The associated complications of diabetes, such as cardiovascular disease, peripheral vascular disease, stroke, diabetic neuropathy, amputations, renal failure, and blindness result in increasing disability, reduced life expectancy, and enormous health costs. T2DM is a polygenic disease characterized by multiple defects in insulin action in tissues and defects in pancreatic insulin secretion, which eventually leads to loss of pancreatic insulin-secreting cells. The treatment goals for T2DM patients are effective control of blood glucose, blood pressure, and lipids (if elevated) and, ultimately, to avert the serious complications associated with sustained tissue exposure to excessively high glucose concentrations. Prevention and control of diabetes with diet, weight control, and physical activity has been difficult. Treatment of T2DM has centered on increasing insulin levels, either by direct insulin administration or oral agents that promote insulin secretion, improving sensitivity to insulin in tissues, or reducing the rate of carbohydrate absorption from the gastrointestinal tract. This review presents comprehensive and up-to-date information on the mechanism(s) of action, efficacy, pharmacokinetics, pleiotropic effects, drug interactions, and adverse effects of the newer antidiabetic drugs, including (1) peroxisome proliferator-activated-receptor-γ agonists (thiazolidinediones, pioglitazone, and rosiglitazone); (2) the incretin, glucagon-like peptide-) receptor agonists (incretin-mimetics, exenatide. and liraglutide), (3) inhibitors of dipeptidyl-peptidase-4 (incretin enhancers, sitagliptin, and vildagliptin), (4) short-acting, nonsulfonylurea secretagogue, meglitinides (repaglinide and nateglinide), (5) amylin anlog-pramlintide, (6) α-glucosidase inhibitors (miglitol and voglibose), and (7) colesevelam (a bile acid sequestrant). In addition, information is presented on drug candidates in clinical trials, experimental compounds, and some plants used in the traditional treatment of diabetes based on experimental evidence. In the opinion of this reviewer, therapy based on orally active incretins and incretin mimetics with long duration of action that will be efficacious, preserve the β-cell number/function, and block the progression of diabetes will be highly desirable. However, major changes in lifestyle factors such as diet and, especially, exercise will also be needed if the growing burden of diabetes is to be contained.

Molecular docking and structural analysis of cofactor-protein interaction between NAD⁺ and 11β-hydroxysteroid dehydrogenase type 2

Molecular docking and structural analysis of the cofactor-protein interaction between NAD(+) and human (h) or mouse (m) 11β-hydroxysteroid dehydrogenase type 2 (11βHSD2) were performed with the molecular operating environment (MOE). 11βHSD1 (PDB code: 3HFG) was selected as a template for the 3D structure modeling of 11βHSD2. The MOE docking (MOE-dock) and the alpha sphere and excluded volume-based ligand-protein docking (ASE-dock) showed that both NAD(+)-h11βHSD2 and NAD(+)-m11βHSD2 models have a similar binding orientation to the template cofactor-protein model. Our present study also revealed that Asp91, Phe94, Tyr232 and Thr267 could be of importance in the interaction between NAD(+) and 11βHSD2. NADP(+) was incapable of entering into the cofactor-binding site of the 11βHSD2 models. The present study proposes the latest models for 11βHSD2 and its cofactor NAD(+), and to the best of our knowledge, this is the first report of a m11βHSD2 model with NAD(+).

17β-Hydroxysteroid dehydrogenases (17β-HSDs) as therapeutic targets: protein structures, functions, and recent progress in inhibitor development

17β-Hydroxysteroid dehydrogenases (17β-HSDs) are oxidoreductases, which play a key role in estrogen and androgen steroid metabolism by catalyzing final steps of the steroid biosynthesis. Up to now, 14 different subtypes have been identified in mammals, which catalyze NAD(P)H or NAD(P)(+) dependent reductions/oxidations at the 17-position of the steroid. Depending on their reductive or oxidative activities, they modulate the intracellular concentration of inactive and active steroids. As the genomic mechanism of steroid action involves binding to a steroid nuclear receptor, 17β-HSDs act like pre-receptor molecular switches. 17β-HSDs are thus key enzymes implicated in the different functions of the reproductive tissues in both males and females. The crucial role of estrogens and androgens in the genesis and development of hormone dependent diseases is well recognized. Considering the pivotal role of 17β-HSDs in steroid hormone modulation and their substrate specificity, these proteins are promising therapeutic targets for diseases like breast cancer, endometriosis, osteoporosis, and prostate cancer. The selective inhibition of the concerned enzymes might provide an effective treatment and a good alternative to the existing endocrine therapies. Herein, we give an overview of functional and structural aspects for the different 17β-HSDs. We focus on steroidal and non-steroidal inhibitors recently published for each subtype and report on existing animal models for the different 17β-HSDs and the respective diseases. 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.

Characterization of activity and binding mode of glycyrrhetinic acid derivatives inhibiting 11β-hydroxysteroid dehydrogenase type 2

Modulation of intracellular glucocorticoid availability is considered as a promising strategy to treat glucocorticoid-dependent diseases. 18β-Glycyrrhetinic acid (GA), the biologically active triterpenoid metabolite of glycyrrhizin, which is contained in the roots and rhizomes of licorice (Glycyrrhiza spp.), represents a well-known but non-selective inhibitor of 11β-hydroxysteroid dehydrogenases (11β-HSDs). However, to assess the physiological functions of the respective enzymes and for potential therapeutic applications selective inhibitors are needed. In the present study, we applied bioassays and 3D-structure modeling to characterize nine 11β-HSD1 and fifteen 11β-HSD2 inhibiting GA derivatives. Comparison of the GA derivatives in assays using cell lysates revealed that modifications at the 3-hydroxyl and/or the carboxyl led to highly selective and potent 11β-HSD2 inhibitors. The data generated significantly extends our knowledge on structure-activity relationship of GA derivatives as 11β-HSD inhibitors. Using recombinant enzymes we found also potent inhibition of mouse 11β-HSD2, despite significant species-specific differences. The selected GA derivatives potently inhibited 11β-HSD2 in intact SW-620 colon cancer cells, although the rank order of inhibitory potential differed from that obtained in cell lysates. The biological activity of compounds was further demonstrated in glucocorticoid receptor (GR) transactivation assays in cells coexpressing GR and 11β-HSD1 or 11β-HSD2. 3D-structure modeling provides an explanation for the differences in the selectivity and activity of the GA derivatives investigated. The most potent and selective 11β-HSD2 inhibitors should prove useful as mechanistic tools for further anti-inflammatory and anti-cancer in vitro and in vivo studies. Article from the Special issue on Targeted Inhibitors.

Design and synthesis of anti-MRSA benzimidazolylbenzene-sulfonamides. QSAR studies for prediction of antibacterial activity

A series of benzimidazolylbenzenesulfonamide compounds containing electron-releasing and electron-withdrawing substituents were synthesized and tested for their in vitro antibacterial activity. Two BZS compounds showed strong antibacterial activity against methicillin-resistant Staphylococcus aureus and Bacillus subtilis. Quantitative studies of their structure-activity relationship using a simple linear regression analysis were applied to explore the correlation between the biological activity and the charges on acidic hydrogen atoms in the synthesized compounds.

Morphinans and isoquinolines: acetylcholinesterase inhibition, pharmacophore modeling, and interaction with opioid receptors

Following indications from pharmacophore-based virtual screening of natural product databases, morphinan and isoquinoline compounds were tested in vitro for acetylcholinesterase (AChE) inhibition. After the first screen, active and inactive compounds were used to build a ligand-based pharmacophore model in order to prioritize compounds for biological testing. Among the virtual hits tested, the enrichment of actives was significantly higher than in a random selection of test compounds. The most active compounds were biochemically tested for their activity on mu, delta, and kappa opioid receptors.