Discovery of a novel IKK-β inhibitor by ligand-based virtual screening techniques

Salicylanilides and Their Anticancer Properties

Salicylanilides are pharmacologically active compounds with a wide spectrum of biological effects. Halogenated salicylanilides, which have been used for decades in human and veterinary medicine as anthelmintics, have recently emerged as candidates for drug repurposing in oncology. The most prominent example of salicylanilide anthelmintic, that is intensively studied for its potential anticancer properties, is niclosamide. Nevertheless, recent studies have discovered extensive anticancer potential in a number of other salicylanilides. This potential of their anticancer action is mediated most likely by diverse mechanisms of action such as uncoupling of oxidative phosphorylation, inhibition of protein tyrosine kinase epidermal growth factor receptor, modulation of different signaling pathways as Wnt/β-catenin, mTORC1, STAT3, NF-κB and Notch signaling pathways or induction of B-Raf V600E inhibition. Here we provide a comprehensive overview of the current knowledge about the proposed mechanisms of action of anticancer activity of salicylanilides based on preclinical in vitro and in vivo studies, or structural requirements for such an activity.

Nanoinformatics and Personalized Medicine: An Advanced Cumulative Approach for Cancer Management

BACKGROUND

Even though the battle against one of the deadliest diseases, cancer, has advanced remarkably in the last few decades and the survival rate has improved significantly; the search for an ultimate cure remains a utopia. Nanoinformatics, which is bioinformatics coupled with nanotechnology, endows many novel research opportunities in the preclinical and clinical development of personalized nanosized drug carriers in cancer therapy. Personalized nanomedicines serve as a promising treatment option for cancer owing to their noninvasiveness and their novel approach. Explicitly, the field of personalized medicine is expected to have an enormous impact soon because of its many advantages, namely its versatility to adapt a drug to a cohort of patients.

OBJECTIVE

The current review explains the application of this newly emerging field called nanoinformatics to the field of precision medicine. This review also recapitulates how nanoinformatics could hasten the development of personalized nanomedicine for cancer, which is undoubtedly the need of the hour.

CONCLUSION

This approach has been facilitated by a humongous impending field named Nanoinformatics. These breakthroughs and advances have provided insight into the future of personalized medicine. Imperatively, they have been enabling landmark research to merge all advances, creating nanosized particles that contain drugs targeting cell surface receptors and other potent molecules designed to kill cancerous cells. Nanoparticle- based medicine has been developing and has become a center of attention in recent years, focusing primely on proficient delivery systems for various chemotherapy drugs.

Functional network analysis reveals potential repurposing of β-blocker atenolol for pancreatic cancer therapy

BACKGROUND

The survival rate of patients with pancreatic cancer is low; therefore, continuous discovery and development of novel pancreatic cancer drugs are required. Functional network analysis is an integrated bioinformatics approach based on gene, target, and disease networks interaction, and it is extensively used in drug discovery and development.

OBJECTIVE

This study aimed to identify if atenolol, a selective adrenergic inhibitor, can be repurposed for the treatment of pancreatic cancer using functional network analysis.

METHODS

Direct target proteins (DTPs) and indirect target proteins (ITPs) were obtained from STITCH and STRING databases, respectively. Atenolol-mediated proteins (AMPs) were collected from DTPs and ITPs and further analyzed for gene ontology, KEGG pathway enrichment, genetic alterations, overall survival, and molecular docking.

RESULTS

We obtained 176 AMPs that consisted of 10 DTPs and 166 ITPs. Among the AMPs involved in the pancreatic cancer pathways, several AMPs such as MAPK1, RELA, MAPK8, STAT1, and STAT3 were identified. Genetic alterations in seven AMPs were identified in 0.9%-16% of patients. Patients with high mRNA levels of MAPK1, RELA, STAT3, GNB1, and MMP9 had significantly worse overall survival rates compared with patients with low expression. Molecular docking studies showed that RELA and MMP9 are potential target candidates of atenolol in the treatment of patients with pancreatic cancer.

CONCLUSION

In conclusion, atenolol can potentially be repurposed to target pancreatic cancer cells by modulating MMP9 and NF-κB signaling. The results of this study need to be further validated in vitro and in vivo.

Computational Studies in Drug Design Against Cancer

Background:

The application of in silico tools in the development of anti cancer drugs.

Objective:

The summing of different computer aided drug design approaches that have been applied in the development of anti cancer drugs.

Methods:

Structure based, ligand based, hybrid protein-ligand pharmacophore methods, Homology modeling, molecular docking aids in different steps of drug discovery pipeline with considerable saving in time and expenditure. In silico tools also find applications in the domain of cancer drug development.

Results:

Structure-based pharmacophore modeling aided in the identification of PUMA inhibitors, structure based approach with high throughput screening for the development of Bcl-2 inhibitors, to derive the most relevant protein-protein interactions, anti mitotic agents; I-Kappa-B Kinase β (IKK- β) inhibitor, screening of new class of aromatase inhibitors that can be important targets in cancer therapy.

Conclusion:

Application of computational methods in the design of anti cancer drugs was found to be effective.

A Strength-Weaknesses-Opportunities-Threats (SWOT) Analysis of Cheminformatics in Natural Product Research

Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.

Discovery of novel potent nuclear factor kappa-B inhibitors (IKK-β) via extensive ligand-based modeling and virtual screening

Inhibitor kappa-B kinase-beta (IKK-β) controls the activation of nuclear transcription factor kappa-B and has been linked to inflammation and cancer. Therefore, inhibitors of this kinase should have potent anti-inflammatory and anticancer properties. Accordingly, we explored the pharmacophoric space of 218 IKK-β inhibitors to identify high-quality binding models. Subsequently, genetic algorithm-based quantitative structure activity relationship (QSAR) analysis was employed to select the best possible combination of pharmacophoric models and physicochemical descriptors that explain bioactivity variation among training compounds. Three successful pharmacophores emerged in 2 optimal QSAR equations (r₁²₁₇₅  = 0.733, r₁²_LOO  = 0.52, F1 = 65.62, r₁²_PRESS against 43 test inhibitors = 0.63 and r₂²₁₇₅  = 0.683, r₂²_LOO  = 0.52, F2 = 72.66, r₂²_PRESS against 43 test inhibitors = 0.65). Two pharmacophores were merged in a single binding model. Receiver operating characteristic curve validation proved the excellent qualities of this model. The merged pharmacophore and the associated QSAR equations were applied to screen the National Cancer Institute list of compounds. Ten hits were found to exhibit potent anti-IKK-β bioactivity, out of which, one illustrates IC50 of 11.0nM.

Application of computational methods for anticancer drug discovery, design, and optimization

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

Application of computational methods for anticancer drug discovery, design, and optimization

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

Synthesis and Biological Screening of Silicon-Containing Ibuprofen Derivatives: A Study of Their NF-κβ Inhibitory Activity, Cytotoxicity, and Their Ability to Bind IKKβ

The synthesis and characterisation of new silicon-containing amides and esters derived from ibuprofen is reported. These compounds were tested against nuclear transcription factor κβ (NF-κβ). Higher inhibition values than those of ibuprofen were achieved by the new amides 10a–10d; ester derivatives did not show inhibitory activity. The cytotoxicity of these new derivatives was screened; none of them displayed significant toxicity at the screened doses. A molecular docking calculation on IKKβ (an enzyme related to NF-κβ activation) was carried out and the results showed that the amides interact better than ibuprofen with key residues, which are important to the inhibition of IKKβ.

Leveraging Data Fusion Strategies in Multireceptor Lead Optimization MM/GBSA End-Point Methods

Accurate and efficient affinity calculations are critical to enhancing the contribution of in silico modeling during the lead optimization phase of a drug discovery campaign. Here, we present a large-scale study of the efficacy of data fusion strategies to leverage results from end-point MM/GBSA calculations in multiple receptors to identify potent inhibitors among an ensemble of congeneric ligands. The retrospective analysis of 13 congeneric ligand series curated from publicly available data across seven biological targets demonstrates that in 90% of the individual receptor structures MM/GBSA scores successfully identify subsets of inhibitors that are more potent than a random selection, and data fusion strategies that combine MM/GBSA scores from each of the receptors significantly increase the robustness of the predictions. Among nine different data fusion metrics based on consensus scores or receptor rankings, the SumZScore (i.e., converting MM/GBSA scores into standardized Z-Scores within a receptor and computing the sum of the Z-Scores for a given ligand across the ensemble of receptors) is found to be a robust and physically meaningful metric for combining results across multiple receptors. Perhaps most surprisingly, even with relatively low to modest overall correlations between SumZScore and experimental binding affinities, SumZScore tends to reliably prioritize subsets of inhibitors that are at least as potent as those that are prioritized from a "best" single receptor identified from known compounds within the congeneric series.

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.

The role of HTS in drug discovery at the University of Michigan

High throughput screening (HTS) is an integral part of a highly collaborative approach to drug discovery at the University of Michigan. The HTS lab is one of four core centers that provide services to identify, produce, screen and follow-up on biomedical targets for faculty. Key features of this system are: protein cloning and purification, protein crystallography, small molecule and siRNA HTS, medicinal chemistry and pharmacokinetics. Therapeutic areas that have been targeted include anti-bacterial, metabolic, neurodegenerative, cardiovascular, anti-cancer and anti-viral. The centers work in a coordinated, interactive environment to affordably provide academic investigators with the technology, informatics and expertise necessary for successful drug discovery. This review provides an overview of these centers at the University of Michigan, along with case examples of successful collaborations with faculty.

Binding mode analysis of zerumbone to key signal proteins in the tumor necrosis factor pathway

Breast cancer is the second most common cancer among women worldwide. Several signaling pathways have been implicated as causative and progression agents. The tumor necrosis factor (TNF) α protein plays a dual role in promoting and inhibiting cancer depending largely on the pathway initiated by the binding of the protein to its receptor. Zerumbone, an active constituent of Zingiber zerumbet, Smith, is known to act on the tumor necrosis factor pathway upregulating tumour necrosis factor related apoptosis inducing ligand (TRAIL) death receptors and inducing apoptosis in cancer cells. Zerumbone is a sesquiterpene that is able to penetrate into the hydrophobic pockets of proteins to exert its inhibiting activity with several proteins. We found a good binding with the tumor necrosis factor, kinase κB (IKKβ) and the Nuclear factor κB (NF-κB) component proteins along the TNF pathway. Our results suggest that zerumbone can exert its apoptotic activities by inhibiting the cytoplasmic proteins. It inhibits the IKKβ kinase that activates the NF-κB and also binds to the NF-κB complex in the TNF pathway. Blocking both proteins can lead to inhibition of cell proliferating proteins to be downregulated and possibly ultimate induction of apoptosis.

Identification of plumericin as a potent new inhibitor of the NF-κB pathway with anti-inflammatory activity in vitro and in vivo

BACKGROUND AND PURPOSE

The transcription factor NF-κB orchestrates many pro-inflammatory signals and its inhibition is considered a promising strategy to combat inflammation. Here we report the characterization of the natural product plumericin as a highly potent inhibitor of the NF-κB pathway with a novel chemical scaffold, which was isolated via a bioactivity-guided approach, from extracts of Himatanthus sucuuba, an Amazonian plant traditionally used to treat inflammation-related disorders.

EXPERIMENTAL APPROACH

A NF-κB luciferase reporter gene assay was used to identify NF-κB pathway inhibitors from H. sucuuba extracts. Monitoring of TNF-α-induced expression of the adhesion molecules VCAM-1, ICAM-1 and E-selectin by flow cytometry was used to confirm NF-κB inhibition in endothelial cells, and thioglycollate-induced peritonitis in mice to confirm effects in vivo. Western blotting and transfection experiments were used to investigate the mechanism of action of plumericin.

KEY RESULTS

Plumericin inhibited NF-κB-mediated transactivation of a luciferase reporter gene (IC₅₀ 1 μM), abolished TNF-α-induced expression of the adhesion molecules VCAM-1, ICAM-1 and E-selectin in endothelial cells and suppressed thioglycollate-induced peritonitis in mice. Plumericin exerted its NF-κB pathway inhibitory effect by blocking IκB phosphorylation and degradation. Plumericin also inhibited NF-κB activation induced by transfection with the constitutively active catalytic subunit of the IκB kinase (IKK-β), suggesting IKK involvement in the inhibitory action of this natural product.

CONCLUSION AND IMPLICATIONS

Plumericin is a potent inhibitor of NF-κB pathways with a new chemical scaffold. It could be further explored as a novel anti-inflammatory lead compound.

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.

Imbricaric acid and perlatolic acid: multi-targeting anti-inflammatory depsides from Cetrelia monachorum

In vitro screening of 17 Alpine lichen species for their inhibitory activity against 5-lipoxygenase, microsomal prostaglandin E₂ synthase-1 and nuclear factor kappa B revealed Cetrelia monachorum (Zahlbr.) W.L. Culb. & C.F. Culb. As conceivable source for novel anti-inflammatory compounds. Phytochemical investigation of the ethanolic crude extract resulted in the isolation and identification of 11 constituents, belonging to depsides and derivatives of orsellinic acid, olivetolic acid and olivetol. The two depsides imbricaric acid (4) and perlatolic acid (5) approved dual inhibitory activities on microsomal prostaglandin E₂ synthase-1 (IC₅₀ = 1.9 and 0.4 µM, resp.) and on 5-lipoxygenase tested in a cell-based assay (IC₅₀ = 5.3 and 1.8 µM, resp.) and on purified enzyme (IC₅₀ = 3.5 and 0.4 µM, resp.). Additionally, these two main constituents quantified in the extract with 15.22% (4) and 9.10% (5) showed significant inhibition of tumor necrosis factor alpha-induced nuclear factor kappa B activation in luciferase reporter cells with IC₅₀ values of 2.0 and 7.0 µM, respectively. In a murine in vivo model of inflammation, 5 impaired the inflammatory, thioglycollate-induced recruitment of leukocytes to the peritoneum.

The potent inhibitory effects on the three identified targets attest 4 and 5 a pronounced multi-target anti-inflammatory profile which warrants further investigation on their pharmacokinetics and in vivo efficacy.

Small-molecule inhibitors of IκB kinase (IKK) and IKK-related kinases

The transcription factors NF-κB and IFN control important signaling cascades and mediate the expression of a number of important pro-inflammatory cytokines, adhesion molecules, growth factors and anti-apoptotic survival proteins. IκB kinase (IKK) and IKK-related kinases (IKKε and TBK1) are key regulators of these biological pathways and, as such, modulators of these enzymes may be useful in the treatment of inflammatory diseases and cancer. We have reviewed the most recent IKK patent literature (2008–2012), added publications of interest overlooked in previous patent reviews and identified all the players involved in small-molecule inhibitors of the IKKs. This will provide the reader with a decisive summary of the IKK arena, a field that has reached maturity over a decade of research.

Shape and pharmacophore-based virtual screening to identify potential cytochrome P450 sterol 14α-demethylase inhibitors

Sterol 14α-demethylase (CYP51) is a cytochrome P450 heme thiolate containing enzyme involved in biosynthesis of membrane sterols, including sterol in animals, ergosterol in fungi, and a variety of C24-modified sterols in plants and protozoa. Several clinical drugs have been developed to reduce the impact of fungal diseases, but their clinical uses have been limited by the emergence of drug resistance and insufficiencies in their antifungal activity. Therefore, in order to identify potential CYP51 inhibitors, we have implemented a virtual screening (VS) protocol by using both phase shape and pharmacophore model (AHHRR) against Asinex, ChemBridge and Maybridge databases. A filtering protocol, including Lipinski filter, number of rotatable bonds and different precisions of molecular docking was applied in hits selection. The results indicated that both shape-based and pharmacophore-based screening yielded the best result with potential inhibitors. The searched compounds were also evaluated with ADME properties, which show excellent pharmacokinetic properties under the acceptable range. We identified potential CYP51 inhibitors for further investigation, they could also be employed to design ligands with enhanced inhibitory potencies and to predict the potencies of analogs to guide synthesis/or prepare synthetic antifungal analogs against CYP51.

Induction in gastric mucosal prostaglandin and nitric oxide by Helicobacter pylori is dependent on MAPK/ERK-mediated activation of IKK-β and cPLA2: modulatory effect of ghrelin

Among the key factors defining the extent of gastric mucosal inflammatory involvement in response to Helicobacter pylori is the excessive generation of prostaglandin (PGE2) and nitric oxide (NO), caused by the overexpression of cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS), and triggered by the activation of mitogen-activated protein kinase (MAPK)/c-Jun N-terminal kinase, p38 and ERK, and nuclear translocation of the cognate transcription factors. In this study, we report on the role of MAPK/ERK in the regulation of H. pylori LPS-induced gastric mucosal expression of COX-2 and iNOS. We show that ERK activation by the LPS leads to phosphorylation of the inhibitory κB kinase-β (IKK-β) and cytosolic phospholipase A2 (cPLA2), and is reflected in the upsurge in NF-κB nuclear translocation, induction in COX-2 and iNOS expression, and up-regulation in cPLA2 activity. The modulatory effect of peptide hormone, ghrelin, on the LPS-induced changes, although associated with further enhancement in ERK, IKK-β, and cPLA2 phosphorylation, was reflected in the suppression of IKK-β and cPLA2 activity through S-nitrosylation. While the effect of ghrelin on S-nitrosylation was susceptible to suppression by the inhibitors of Src/Akt pathway, the inhibition of ERK activation caused the blockage in IKK-β and cPLA2 phosphorylation as well as S-nitrosylation. Taken together, our data show that H. pylori-induced ERK activation plays a critical role in up-regulation of gastric mucosal PGE2 and NO generation at the level of IKK-β and cPLA2 activation, and that ghrelin counters these proinflammatory consequences of the LPS through Src/Akt-dependent S-nitrosylation.

Novel IKKβ inhibitors discovery based on the co-crystal structure by using binding-conformation-based and ligand-based method

IκB kinase β (IKKβ), an attractive anti-inflammation and anti-cancer target, plays a crucial role in the activation of NF-κB signalling pathway. To identify novel IKKβ inhibitors, we combined structure-based and ligand-based methods based on the co-crystal structure of IKKβ. According to the chemical similarity, 162 reported IKKβ inhibitors were divided into five classes. For each class, a 3D pharmacophore model was established based on the binding conformations of the compounds. The validated models were further used in virtual screening. Twelve drugable compounds were retained for biological test, resulting in two novel inhibitors with IC50 values lower than 10 μM. Compared to other models, our method considers the crystal structure of IKKβ for the first time.

Involvement of p38 MAPK-dependent activator protein (AP-1) activation in modulation of gastric mucosal inflammatory responses to Helicobacter pylori by ghrelin

A peptide hormone, ghrelin, plays an important role in modulation of gastric mucosal inflammatory responses to Helicobacter pylori infection by controlling the cross-talk between nitric oxide synthase (NOS) and cyclooxygenase (COX) enzyme systems. In this study, we report that H. pylori LPS-elicited induction in gastric mucosal COX-2 and inducible (i) iNOS protein expression, and the impairment in constitutive (c) cNOS phosphorylation, was associated with mitogen-activated protein kinase, c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase and p38 activation, and occurred with the involvement of transcription factors, CCATT/enhancer-binding protein (C/EBP) δ, cAMP response element-binding protein, activator protein-1 (AP-1), and NF-κB. The modulatory effect of ghrelin on the LPS-induced changes was manifested in the inhibition of nuclear translocation of p65 NF-κB and C/EBPδ, and suppression in AP-1 activation, and the inhibition in phosphorylation of JNK and p38, as well as their respective downstream targets, c-Jun and ATF-2. However, only the inhibition of p38-mediated ATF-2 phosphorylation was reflected in the reduced expression of COX-2 protein. Further, the effect of ghrelin of the LPS-induced changes was reflected in the increase in Src/Akt-dependent cNOS activation through phosphorylation and the inhibition of cNOS-mediated IKK-β S-nitrosylation. Our findings indicate ghrelin counters the proinflammatory consequences of H. pylori by interfering with the p38/ATF-2-induced AP-1 activation in association with concurrent up-regulation in Src/Akt-dependent cNOS phosphorylation.

Identification of novel liver X receptor activators by structure-based modeling

Liver X receptors (LXRs) are members of the nuclear receptor family. Activators of LXRs are of high pharmacological interest as LXRs regulate cholesterol, fatty acid, and carbohydrate metabolism as well as inflammatory processes. On the basis of different X-ray crystal structures, we established a virtual screening workflow for the identification of novel LXR modulators. A two-step screening concept to identify active compounds included 3D-pharmacophore filters and rescoring by shape alignment. Eighteen virtual hits were tested in vitro applying a reporter gene assay, where concentration-dependent activity was proven for four novel lead structures. The most active compound 10, a 1,4-naphthochinone, has an estimated EC₅₀ of around 5 μM.

Pharmacophore-based discovery of a novel cytosolic phospholipase A(2)α inhibitor

The release of arachidonic acid, a precursor in the production of prostaglandins and leukotrienes, is achieved by activity of the cytosolic phospholipase A(2)α (cPLA(2)α). Signaling mediated by this class of bioactive lipids, which are collectively referred to as eicosanoids, has numerous effects in physiological and pathological processes. Herein, we report the development of a ligand-based pharmacophore model and pharmacophore-based virtual screening of the National Cancer Institute (NCI) database, leading to the identification of 4-(hexadecyloxy)-3-(2-(hydroxyimino)-3-oxobutanamido)benzoic acid (NSC 119957) as cPLA(2)α inhibitor in cell-free and cell-based in vitro assays.

In silico virtual screening approaches for anti-viral drug discovery

Despite the considerable advances in medical and pharmaceutical research during the past years, diseases caused by viruses have remained a major burden to public health. Virtual in silico screening has repeatedly proven to be useful to meet the special challenges of antiviral drug discovery. Large virtual compound libraries are filtered by different computational screening methods such as docking, ligand-based similarity searches or pharmacophore-based screening, reducing the number of candidate molecules to a smaller set of promising candidates that are then tested biologically. This rational approach makes the drug discovery process more goal-oriented and saves resources in terms of time and money. In this review we discuss how different virtual screening techniques can be applied to antiviral drug discovery, present recent success stories in this field and finally address the main differences between the methods.:

Lignan derivatives from Krameria lappacea roots inhibit acute inflammation in vivo and pro-inflammatory mediators in vitro

The roots of Krameria lappacea are used traditionally against oropharyngeal inflammation. So far, the astringent and antimicrobial properties of its proanthocyanidin constituents are considered to account for the anti-inflammatory effect. The aim of the present study was to characterize pharmacologically a lipophilic extract of K. lappacea roots and several isolated lignan derivatives (1-11) in terms of their putative anti-inflammatory activity. The dichloromethane extract (ID₅₀ 77 μg/cm²) as well compounds 1-11 (ID₅₀ 0.31-0.60 μmol/cm²) exhibited topical antiedematous properties comparable to those of indomethacin (ID₅₀ 0.29 μmol/cm²) in a mouse ear in vivo model. Two of the most potent compounds, 2-(2-hydroxy-4-methoxyphenyl)-5-(3-hydroxypropyl)benzofuran (5) and (+)-conocarpan (7), were studied regarding their time-dependent edema development and leukocyte infiltration up to 48 h after croton oil-induced dermatitis induction, and they showed activity profiles similar to that of hydrocortisone. In vitro studies of the isolated lignan derivatives demonstrated the inhibition of NF-κB, cyclooxygenase-1 and -2, 5-lipoxygenase, and microsomal prostaglandin E₂ synthase-1 as well as antioxidant properties, as mechanisms possibly contributing to the observed in vivo effects. The present findings not only support the ethnopharmacological use of K. lappacea roots but also reveal that the isolated lignan derivatives contribute strongly to the anti-inflammatory activity of this herbal drug.

The role of computational methods in the identification of bioactive compounds

Computational methods play an ever increasing role in lead finding. A vast repertoire of molecular design and virtual screening methods emerged in the past two decades and are today routinely used. There is increasing awareness that there is no single best computational protocol and correspondingly there is a shift recommending the combination of complementary methods. A promising trend for the application of computational methods in lead finding is to take advantage of the vast amounts of HTS (High Throughput Screening) data to allow lead assessment by detailed systems-based data analysis, especially for phenotypic screens where the identification of compound-target pairs is the primary goal. Herein, we review trends and provide examples of successful applications of computational methods in lead finding.

Identification of human IKK-2 inhibitors of natural origin (part I): modeling of the IKK-2 kinase domain, virtual screening and activity assays

BACKGROUND

Their large scaffold diversity and properties, such as structural complexity and drug similarity, form the basis of claims that natural products are ideal starting points for drug design and development. Consequently, there has been great interest in determining whether such molecules show biological activity toward protein targets of pharmacological relevance. One target of particular interest is hIKK-2, a serine-threonine protein kinase belonging to the IKK complex that is the primary component responsible for activating NF-κB in response to various inflammatory stimuli. Indeed, this has led to the development of synthetic ATP-competitive inhibitors for hIKK-2. Therefore, the main goals of this study were (a) to use virtual screening to identify potential hIKK-2 inhibitors of natural origin that compete with ATP and (b) to evaluate the reliability of our virtual-screening protocol by experimentally testing the in vitro activity of selected natural-product hits.

METHODOLOGY/PRINCIPAL FINDINGS

We thus predicted that 1,061 out of the 89,425 natural products present in the studied database would inhibit hIKK-2 with good ADMET properties. Notably, when these 1,061 molecules were merged with the 98 synthetic hIKK-2 inhibitors used in this study and the resulting set was classified into ten clusters according to chemical similarity, there were three clusters that contained only natural products. Five molecules from these three clusters (for which no anti-inflammatory activity has been previously described) were then selected for in vitro activity testing, in which three out of the five molecules were shown to inhibit hIKK-2.

CONCLUSIONS/SIGNIFICANCE

We demonstrated that our virtual-screening protocol was successful in identifying lead compounds for developing new inhibitors for hIKK-2, a target of great interest in medicinal chemistry. Additionally, all the tools developed during the current study (i.e., the homology model for the hIKK-2 kinase domain and the pharmacophore) will be made available to interested readers upon request.