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

Protective effects and mechanisms of the Erzhi formula on glucocorticoid induced primary cortical neuron injury

High concentrations of glucocorticoids (GC) can cross the blood-brain barrier into the brain parenchyma, triggering a stress state that can lead to a range of physiological changes. This study investigated whether Erzhi formula has neuroprotective effects against glucocorticoid damage by establishing a dexamethasone-induced primary cortical neuron injury model in vitro. The results showed that Erzhi formula could reduce dexamethasone-induced apoptosis in primary cultured cortical neurons and improve synaptic damage. Further, network pharmacological analysis revealed that Erzhi formula may exert antidepressant effects by multi-component, multi-target, and multi-pathway characteristics, in which Salidroside, Biochanin-A and other ingredients are key components, HSD11B1, NR3C1, and other proteins are key targets, and steroid metabolism may be a key process in its action. Moreover, our study found that the neuroprotective effect of Erzhi formula might be related to the 11β-HSD1-GC/glucocorticoid receptor (GR) signaling pathway. The Erzhi formula could significantly inhibit the activity of 11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) in vitro using homogeneous time-resolved fluorescence. In addition to providing evidence for the pharmacological effects of the Erzhi formula, the present study lays down the foundation for subsequent experiments.

An Overview of Computer-aided Drug Design Tools and Recent Applications in Designing of Anti-diabetic Agents

BACKGROUND

In this fast-growing era, high throughput data is now being easily accessed by getting transformed into datasets which store the information. Such information is valuable to optimize the hypothesis and drug design via computer-aided drug design (CADD). Nowadays, we can explore the role of CADD in various disciplines like Nanotechnology, Biochemistry, Medical Sciences, Molecular Biology, etc. Methods: We searched the valuable literature using a pertinent database with given keywords like computer-aided drug design, anti-diabetic, drug design, etc. We retrieved all valuable articles which are recent and discussing the role of computation in the designing of anti-diabetic agents.

RESULTS

To facilitate the drug discovery process, the computational approach has set landmarks in the whole pipeline for drug discovery from target identification and mechanism of action to the identification of leads and drug candidates. Along with this, there is a determined endeavor to describe the significance of in-silico studies in predicting the absorption, distribution, metabolism, excretion, and toxicity profile. Thus, globally, CADD is accepted with a variety of tools for studying QSAR, virtual screening, protein structure prediction, quantum chemistry, material design, physical and biological property prediction.

CONCLUSION

Computer-assisted tools are used as the drug discovery tool in the area of different diseases, and here we reviewed the collaborative aspects of information technologies and chemoinformatic tools in the discovery of anti-diabetic agents, keeping in view the growing importance for treating diabetes.

Combined strategies in structure-based virtual screening

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

Design, synthesis, molecular docking and in vitro evaluation of benzothiazole derivatives as 11β-hydroxysteroid dehydrogenase type 1 inhibitors

11-Beta hydroxysteroid dehydrogenase type 1 (11β-HSD1) regulates cortisol levels mainly in adipose, hepatic and brain tissues. There is a relationship between the high activity of this enzyme and the development of obesity and metabolic disorders. The inhibition of 11β-HSD1 has been shown to attenuate the development of type 2 diabetes mellitus, insulin resistance, metabolic syndrome and other diseases mediated by excessive cortisol production. In this work, fifteen benzothiazole derivatives substituted with electron-withdrawing and electron-donating groups were designed to explore their affinity for 11β-HSD1 using in silico methods. The results show that (E)-5-((benzo[d]thiazol-2-ylimino)(methylthio)methylamino)-2-hydroxybenzoic acid (C1) has good physicochemical properties and favorable interactions with 11β-HSD1 through hydrogen bonding and hydrophobic interactions in the catalytic site formed by Y183, S170 and Y177. Furthermore, C1 was synthesized and evaluated in vitro and ex vivo using clobenzorex (CLX) as a reference drug in obese Zucker rats. The in vitro results showed that C1 was a better inhibitor of human 11β-HSD1 than CLX. The ex vivo assay results demonstrated that C1 was capable of reducing 11β-HSD1 overexpression in mesenteric adipose tissue. Therefore, C1 was able to decrease the activity and expression of 11β-HSD1 better than CLX.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

Pantoprazole, an FDA-approved proton-pump inhibitor, suppresses colorectal cancer growth by targeting T-cell-originated protein kinase

T-cell-originated protein kinase (TOPK) is highly expressed in several cancer cells and promotes tumorigenesis and progression, and therefore, it is an important target for drug treatment of tumor. Pantoprazole (PPZ) was identified to be a TOPK inhibitor from FDA-approved drug database by structure based virtual ligand screening. Herein, the data indicated that pantoprazole inhibited TOPK activities by directly binding with TOPK in vitro and in vivo. Ex vivo studies showed that pantoprazole inhibited TOPK activities in JB6 Cl41 cells and HCT 116 colorectal cancer cells. Moreover, knockdown of TOPK in HCT 116 cells decreased their sensitivities to pantoprazole. Results of an in vivo study demonstrated that i.p. injection of pantoprazole in HCT 116 colon tumor-bearing mice effectively suppressed cancer growth. The TOPK downstream signaling molecule phospho-histone H3 in tumor tissues was also decreased after pantoprazole treatment. In short, pantoprazole can suppress growth of colorectal cancer cells as a TOPK inhibitor both in vitro and in vivo.

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.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.

Cortisone and hydrocortisone inhibit human Kv1.3 activity in a non-genomic manner

Glucocorticoids are hormones released in response to stress that are involved in various physiological processes including immune functions. One immune-modulating mechanism is achieved by the Kv1.3 voltage-dependent potassium channel, which is expressed highly in lymphocytes including effector memory T lymphocytes (TEM). Although glucocorticoids are known to inhibit Kv1.3 function, the detailed inhibitory mechanism is not yet fully understood. Here we studied the rapid non-genomic effects of cortisone and hydrocortisone on the human Kv1.3 channel expressed in Xenopus oocytes. Both cortisone and hydrocortisone reduced the amplitude of the Kv1.3 channel current in a concentration-dependent manner. Both cortisone and hydrocortisone rapidly and irreversibly inhibited Kv1.3 currents, eliminating the possibility of genomic regulation. Inhibition rate was stable relative to the degree of depolarization. Kinetically, cortisone altered the activating gate of Kv1.3 and hydrocortisone interacted with this channel in an open state. These results suggest that cortisone and hydrocortisone inhibit Kv1.3 currents via a non-genomic mechanism, providing a mechanism for the immunosuppressive effects of glucocorticoids.