Drug screening for influenza neuraminidase inhibitors

Neuraminidase (NA) is one of the most important targets to screen the drugs of anti-influenza virus A and B. After virtual screening approaches were applied to a compound database which possesses more than 10000 compound structures, 160 compounds were selected for bioactivity assay, then a High Throughput Screening (HTS) model established for influenza virus NA inhibitors was applied to detect these compounds. Finally, three compounds among them displayed higher inhibitory activities, the range of their IC50 was from 0.1 μmol/L to 3μmol/L. Their structural scaffolds are novel and different from those of NA inhibitors approved for influenza treatment, and will be useful for the design and research of new NA inhibitors. The resuit indicated that the combination of virtual screening with HTS was very significant to drug screening and drug discovery.     

Using <Emphasis Type="Italic">C. elegans</Emphasis> to screen for targets of ethanol and behavior-altering drugs

Caenorhabditis elegans is an attractive model system for determining the targets of neuroactive compounds. Genetic screens in C. elegans provide a relatively unbiased approach to the identification of genes that are essential for behavioral effects of drugs and neuroactive compounds such as alcohol. Much work in vertebrate systems has identified multiple potential targets of ethanol but which, if any, of those candidates are responsible for the behavioral effects of alcohol is uncertain. Here we provide detailed methodology for a genetic screen for mutants of C. elegans that are resistant to the depressive effects of ethanol on locomotion and for the subsequent behavioral analysis of those mutants. The methods we describe should also be applicable for use in screening for mutants that are resistant or hypersensitive to many neuroactive compounds and for identifying the molecular targets or biochemical pathways mediating drug responses. Published: June 8, 2004.     

Joint effects of acetochlor and urea on germinating characteristics of crop seeds

Neuraminidase (NA) is one of the most important targets to screen the drugs of anti-influenza virus A and B. After virtual screening approaches were applied to a compound database which possesses more than 10000 compound structures, 160 compounds were selected for bioactivity assay, then a High Throughput Screening (HTS) model established for influenza virus NA inhibitors was applied to detect these compounds. Finally, three compounds among them displayed higher inhibitory activities, the range of their IC5o was from 0.1 μmol/L to 3 μmol/L. Their structural scaffolds are novel and different from those of NA inhibitors approved for influenza treatment, and will be useful for the design and research of new NA inhibitors. The result indicated that the combination of virtual screening with HTS was very significant to drug screening and drug discovery.     

Prediction of off-target effects on angiotensin-converting enzyme 2

The authors describe a structure-based strategy to identify therapeutically beneficial off-target effects by screening a chemical library of Food and Drug Administration (FDA)-approved small-molecule drugs matching pharmacophores defined for specific target proteins. They applied this strategy to angiotensin-converting enzyme 2 (ACE2), an enzyme that generates vasodilatory peptides and promotes protection from hypertension-associated cardiovascular disease. The conformation-based structural selection method by molecular docking using DOCK allowed them to identify a series of FDA-approved drugs that enhance catalytic efficiency of ACE2 in vitro. These data demonstrate that libraries of approved drugs can be rapidly screened to identify potential side effects due to interactions with specific proteins other than the intended targets.     

New molecular scaffolds for the design of Mycobacterium tuberculosis type II dehydroquinase inhibitors identified using ligand and receptor based virtual screening

Using ligand and receptor based virtual screening approaches we have identified potential virtual screening hits targeting type II dehydroquinase from Mycobacterium tuberculosis, an effective and validated anti-mycobacterial target. Initially, we applied a virtual screening workflow based on a combination of 2D structural fingerprints, 3D pharmacophore and molecular docking to identify compounds that rigidly match specific aspects of ligand bioactive conformation. Subsequently, the resulting compounds were ranked and prioritized using receptor interaction fingerprint based scoring and quantitative structure activity relationship model developed using already known actives. The virtual screening hits prioritized belong to several classes of molecular scaffolds with several available substitution positions that could allow chemical modification to enhance binding affinity. Finally, identified hits may be useful to a medicinal chemist or combinatorial chemist to pick up the new molecular starting points for medicinal chemistry optimization for the design of novel type II dehydroquinase inhibitors.     

The quest for surrogate markers of angiogenesis: a paradigm for translational research in tumor angiogenesis and anti-angiogenesis trials

Inhibition of tumor angiogenesis suppresses tumor growth and metastatic spreading in many experimental models, suggesting that anti-angiogenic drugs may be used to treat human cancer. During the past decade more than eighty molecules that showed anti-angiogenic activity in preclinical studies were tested in clinical cancer trials, but most of them failed to demonstrate any measurable anti-tumor activity and none have been approved for clinical use. Recent results stemming from trials with anti-VEGF antibodies, used alone or in combination with chemotherapy, suggest that systemic anti-angiogenic therapy may indeed have a measurable impact on cancer progression and patient survival. From the clinical studies it became nevertheless clear that the classical endpoints used in anti-cancer trials do not bring sufficient discriminative power to monitor the effects of anti-angiogenic drugs. It is therefore necessary to identify and validate molecular, cellular and functional surrogate markers of angiogenesis to monitor activity and efficacy of anti-angiogenic drugs in patients. Availability of such markers will be instrumental to re-evaluate the role of tumor angiogenesis in human cancer, to identify new molecular targets and drugs, and to improve planning, monitoring and interpretation of future studies. Future anti-angiogenesis trials integrating biological endpoints and surrogate markers or angiogenesis will require close collaboration between clinical investigators and laboratory-based researchers.     

A chemoinformatics analysis of hit lists obtained from high-throughput affinity-selection screening

The high-throughput affinity-selection screening platform SpeedScreen was recently reported by the Novartis Institutes for BioMedical Research as a homogeneous, label-free screening technology with mass-spectrometry readout. SpeedScreen relies on the screening of compound mixtures with various target proteins and uses fast size-exclusion chromatography to separate target-bound from unbound substances. After disintegration of the target-binder complex, the binder molecules are identified by their molecular masses using liquid chromatography/mass spectrometry. The authors report an analysis of the molecular properties of hits obtained with SpeedScreen on 26 targets screened within the past few years at Novartis using this technology. Affinity-based SpeedScreen is a robust high-throughput screening technology that does not accumulate frequent hitters or potential covalent binders. The hits are representative of the most commonly identified scaffold classes observed for known drugs. Validated SpeedScreen hits tend to be enriched on more lipophilic and larger-molecular-weight compounds compared to the whole library. The potential for a reduced SpeedScreen screening set to be used in case only limited protein quantities are available is evaluated. Such a reduced compound set should also maximize the coverage of the high-performing regions of the chemical property and class spaces; chemoinformatics methods including genetic algorithms and divisive K-means clustering are used for this aim.     

Potential Compounds for Oral Cancer Treatment: Resveratrol,Nimbolide, Lovastatin,Bortezomib, Vorinostat,Berberine, Pterostilbene,Deguelin, Andrographolide,and Colchicine

Oral cancer is one of the main causes of cancer-related deaths in South-Asian countries. There are very limited treatment options available for oral cancer. Research endeavors focused on discovery and development of novel therapies for oral cancer, is necessary to control the ever rising oral cancer related mortalities. We mined the large pool of compounds from the publicly available compound databases, to identify potential therapeutic compounds for oral cancer. Over 84 million compounds were screened for the possible anti-cancer activity by custom build SVM classifier. The molecular targets of the predicted anti-cancer compounds were mined from reliable sources like experimental bioassays studies associated with the compound, and from protein-compound interaction databases. Therapeutic compounds from DrugBank, and a list of natural anti-cancer compounds derived from literature mining of published studies, were used for building partial least squares regression model. The regression model thus built, was used for the estimation of oral cancer specific weights based on the molecular targets. These weights were used to compute scores for screening the predicted anti-cancer compounds for their potential to treat oral cancer. The list of potential compounds was annotated with corresponding physicochemical properties, cancer specific bioactivity evidences, and literature evidences. In all, 288 compounds with the potential to treat oral cancer were identified in the current study. The majority of the compounds in this list are natural products, which are well-tolerated and have minimal side-effects compared to the synthetic counterparts. Some of the potential therapeutic compounds identified in the current study are resveratrol, nimbolide, lovastatin, bortezomib, vorinostat, berberine, pterostilbene, deguelin, andrographolide, and colchicine.     

Playing only one instrument may be not enough: limitations and future of the antiangiogenic treatment of cancer

Angiogenesis plays an essential role in tumor growth, invasion and metastasis. After initial pessimism about the usefulness of the antiangiogenic therapeutic approach for cancer, interest has increased in the development of antiangiogenic compounds after the first clinical approval of an antiangiogenic therapy. The anti-vascular endothelial growth factor (VEGF) antibody bevacizumab has recently been approved for use in combination with chemotherapy for the treatment of metastatic colorectal and non-small cell lung cancer patients. However, no survival benefit has been demonstrated in anti-VEGF monotherapy trials, probably due to the high complexity of tumor angiogenesis regulation. Experimental and clinical data, including the approval of the multitargeted drugs sunitinib and sorafenib, indicate that exciting results, including tumor regression, can be expected from the combined targeting of different pathways in the tumor angiogenesis scenario. Several obstacles, including the high cost of new molecular targeted drugs make this therapeutic approach difficult.     

Quantitative analysis on the characteristics of targets with FDA approved drugs

Accumulated knowledge of genomic information, systems biology, and disease mechanisms provide an unprecedented opportunity to elucidate the genetic basis of diseases, and to discover new and novel therapeutic targets from the wealth of genomic data. With hundreds to a few thousand potential targets available in the human genome alone, target selection and validation has become a critical component of drug discovery process. The explorations on quantitative characteristics of the currently explored targets (those without any marketed drug) and successful targets (targeted by at least one marketed drug) could help discern simple rules for selecting a putative successful target. Here we use integrative in silico (computational) approaches to quantitatively analyze the characteristics of 133 targets with FDA approved drugs and 3120 human disease genes (therapeutic targets) not targeted by FDA approved drugs. This is the first attempt to comparatively analyze targets with FDA approved drugs and targets with no FDA approved drug or no drugs available for them. Our results show that proteins with 5 or fewer number of homologs outside their own family, proteins with single-exon gene architecture and proteins interacting with more than 3 partners are more likely to be targetable. These quantitative characteristics could serve as criteria to search for promising targetable disease genes.     

Virtual screening to identify novel potential inhibitors for Glutamine synthetase of Mycobacterium tuberculosis

Abstract

Glutamine synthetase (GS) of Mycobacterium tuberculosis (Mtb) is an essential enzyme which is involved in nitrogen metabolism and cell wall synthesis. It is involved in the inhibition of phagosome-lysosome fusion by preventing acidification. Targeting GS can be helpful to control the infection of Mtb. In order to identify potential inhibitors, we screened chemical libraries (56,400 compounds of ChEMBL anti-mycobacterial, 1596 FDA approved drugs, 419 Natural product and 916 phytochemical) against this target using the virtual screening approach. Screening by molecular docking identified ten top-ranked compounds as GSMtb inhibitors and they were compared with known inhibitors (as control). Since GS enzyme (GSHs) is also present in human. We have compared the protein sequence of GS from Mtb and human using the P-BLAST in NCBI. We found ～27% identity in between these two sequences, so we also compared the binding affinity of inhibitor between Mtb and human. Finally, we identified top two compounds namely CHEMBL387509, CHEMBL226198 from ChEMBL anti-mycobacterial dataset, and Eriocitrin and Malvidin from phytochemical dataset which showed lees binding affinity towards GSHs whereas Pamidronate, and Phentermine from FDA approved drugs and (-)-Quinic Acid, Hexopyranuronic acid, Quebrachit, and Castanospermine from natural product showed protein-ligand interaction with Mtb protein while no interaction with GSHs. The top two docked complexes were subjected to molecular dynamic simulation to understand the stability of the molecule. Further, we calculated the binding free energy of the docked complex and analyzed hydrogen bond, salt bridge, pie stacking, and hydrophobic interaction in the docking region. These ligands exhibited very good binding affinity GSMtb enzymes. Therefore, these ligands are novel and drug-likeness compounds, and they may be potential inhibitors of M tuberculosis.

Communicated by Ramaswamy H. Sarma     

Identification of inhibitors for MDM2 ubiquitin ligase activity from natural product extracts by a novel high-throughput electrochemiluminescent screen

High-throughput screening technologies have revolutionized the manner in which potential therapeutics are identified. Although they are the source of lead compounds for ~65% of anticancer and antimicrobial drugs approved by the Food and Drug Administration between 1981 and 2002, natural products have largely been excluded from modern screening programs. This is due, at least in part, to the inherent difficulties in testing complex extract mixtures, which often contain nuisance compounds, in modern bioassay systems. In this article, the authors present a novel electrochemiluminescent assay system for inhibition of MDM2 activity that is suitable for testing natural product extracts in high-throughput screening systems. The assay was used to screen more than 144,000 natural product extracts. The authors identified 1 natural product, sempervirine, that inhibited MDM2 auto-ubiquitination, MDM2-mediated p53 degradation, and led to accumulation of p53 in cells. Sempervirine preferentially induced apoptosis in transformed cells expressing wild-type p53, suggesting that it could be a potential lead for anticancer therapeutics.     

Clinical-translational strategies for the elevation of Nm23-H1 metastasis suppressor gene expression

Interruption of the tumor metastatic process is a new, thought provoking molecular target for the treatment of cancer. The Nm23-H1 metastasis suppressor gene stands as a validated molecular target owing to its reduced expression in many aggressive human tumors, and the reduction in metastatic potential in vivo upon re-expression in multiple cell lines. Several compounds have been identified which elevate Nm23-H1 expression in vitro including indomethacin, γ Linolenic Acid, trichostatin A, 5-aza-deoxycytidine, and high dose medroxyprogesterone acetate. Using a model of lung metastatic colonization by MDA-MB-231 human breast carcinoma cells, we demonstrated that high dose MPA reduced the formation of overt lung metastases by 37–46% and those metastases that formed were statistically smaller. A Phase II clinical trial of high dose MPA, alone or in combination with metronomic chemotherapy has recently opened.     

Identification of Potential Inhibitors of H5N1 Influenza A Virus Neuraminidase by Ligand-Based Virtual Screening Approach

The neuraminidase (NA) of the influenza virus is the target of antiviral drug, oseltamivir. Recently, cases were reported that influenza virus becoming resistant to oseltamivir, necessitating the development of new long-acting antiviral compounds. In this report, a novel class of lead molecule with potential NA inhibitory activity was identified using a combination of virtual screening (VS), molecular docking, and molecular dynamic approach. The PubChem database was used to perform the VS analysis by employing oseltamivir as query. Subsequently, the data reduction was carried out by employing molecular docking study. Furthermore, the screened lead molecules were analyzed with respect to the Lipinski rule of five, drug-likeness, toxicity profiles, and other physico-chemical properties of drugs by suitable software program. Final screening was carried out by normal mode analysis and molecular dynamic simulation approach. The result indicates that CID 25145634, deuterium-enriched oseltamivir, become a promising lead compound and be effective in treating oseltamivir sensitive as well as resistant influenza virus strains.     

The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective

The human kinome is one of the most productive classes of drug target, and there is emerging necessity for treating complex diseases by means of polypharmacology (multi-target drugs and combination products). However, the advantages of the multi-target drugs and the combination products are still under debate. A comparative analysis between FDA approved multi-target drugs and combination products, targeting the human kinome, was conducted by mapping targets onto the phylogenetic tree of the human kinome. The approach of network medicine illustrating the drug-target interactions was applied to identify popular targets of multi-target drugs and combination products. As identified, the multi-target drugs tended to inhibit target pairs in the human kinome, especially the receptor tyrosine kinase family, while the combination products were able to against targets of distant homology relationship. This finding asked for choosing the combination products as a better solution for designing drugs aiming at targets of distant homology relationship. Moreover, sub-networks of drug-target interactions in specific disease were generated, and mechanisms shared by multi-target drugs and combination products were identified. In conclusion, this study performed an analysis between approved multi-target drugs and combination products against the human kinome, which could assist the discovery of next generation polypharmacology.     

Structural characterization of fungus-specific histone deacetylase Hos3 provides insights into developing selective inhibitors with antifungal activity

Fungal infection has long been a chronic and even life-threatening problem for humans. The demand for new antifungal drugs has increased dramatically as fungal infections have continued to increase, yet no new classes of drugs have been approved for nearly 15 years due to either high toxicity or development of drug resistance. Thus, validating new drug targets, especially fungus-specific targets, may facilitate future drug design. Here, we report the crystal structure of yeast Hos3 (ScHos3), a fungus-specific histone deacetylase (HDAC) that plays an important role in the life span of fungi. As acetylation modifications are important to many aspects of fungal infection, the species specificity of Hos3 makes it an ideal target for the development of new antifungal drugs. In this study, we show that ScHos3 forms a functional homodimer in solution, and key residues for dimerization crucial for its deacetylation activity were identified. We used molecular dynamics simulation and structural comparison with mammalian hHDAC6 to determine unique features of the ScHos3 catalytic core. In addition, a small-molecule inhibitor with a preference for ScHos3 was identified through structure-based virtual screening and in vitro enzymatic assays. The structural information and regulatory interferences of ScHos3 reported here provide new insights for the design of selective inhibitors that target fungal HDAC with high efficiency and low toxicity or that have the potential to overcome the prevailing problem of drug resistance in combination therapy with other drugs.     

A critique of the molecular target-based drug discovery paradigm based on principles of metabolic control: advantages of pathway-based discovery

Contemporary drug discovery and development (DDD) is dominated by a molecular target-based paradigm. Molecular targets that are potentially important in disease are physically characterized; chemical entities that interact with these targets are identified by ex vivo high-throughput screening assays, and optimized lead compounds enter testing as drugs. Contrary to highly publicized claims, the ascendance of this approach has in fact resulted in the lowest rate of new drug approvals in a generation. The primary explanation for low rates of new drugs is attrition, or the failure of candidates identified by molecular target-based methods to advance successfully through the DDD process. In this essay, I advance the thesis that this failure was predictable, based on modern principles of metabolic control that have emerged and been applied most forcefully in the field of metabolic engineering. These principles, such as the robustness of flux distributions, address connectivity relationships in complex metabolic networks and make it unlikely a priori that modulating most molecular targets will have predictable, beneficial functional outcomes. These same principles also suggest, however, that unexpected therapeutic actions will be common for agents that have any effect (i.e., that complexity can be exploited therapeutically). A potential operational solution (pathway-based DDD), based on observability rather than predictability, is described, focusing on emergent properties of key metabolic pathways in vivo. Recent examples of pathway-based DDD are described. In summary, the molecular target-based DDD paradigm is built on a na?ve and misleading model of biologic control and is not heuristically adequate for advancing the mission of modern therapeutics. New approaches that take account of and are built on principles described by metabolic engineers are needed for the next generation of DDD.     

Discovering modes of action for therapeutic compounds using a genome-wide screen of yeast heterozygotes

Modern medicine faces the challenge of developing safer and more effective therapies to treat human diseases. Many drugs currently in use were discovered without knowledge of their underlying molecular mechanisms. Understanding their biological targets and modes of action will be essential to design improved second-generation compounds. Here, we describe the use of a genome-wide pool of tagged heterozygotes to assess the cellular effects of 78 compounds in Saccharomyces cerevisiae. Specifically, lanosterol synthase in the sterol biosynthetic pathway was identified as a target of the antianginal drug molsidomine, which may explain its cholesterol-lowering effects. Further, the rRNA processing exosome was identified as a potential target of the cell growth inhibitor 5-fluorouracil. This genome-wide screen validated previously characterized targets or helped identify potentially new modes of action for over half of the compounds tested, providing proof of this principle for analyzing the modes of action of clinically relevant compounds.     

High throughput magnetic resonance imaging for evaluating targeted nanoparticle probes.

The ability to image specific molecular targets in vivo would have significant impact in allowing earlier disease detection and in tailoring molecular therapies. One of the rate-limiting steps in the development of novel compounds as reporter probes has been the lack of cell-based, biologically relevant, high throughput screening methods. Here we describe the development and validation of magnetic resonance imaging (MRI) as a technique to rapidly screen compounds that are potential MR reporter agents for their interaction with specific cellular targets. We show that MR imaging can (1) evaluate thousands of samples simultaneously and rapidly, (2) provide exceedingly accurate measurements, and (3) provide receptor binding/internalization data as validated by radioactive assays. The technique allows the screening of libraries of peptide-nanoparticle conjugates against target cells and the identification of conjugates that may be subsequently used as reporter agents in vivo. The technology should greatly accelerate the development of target-specific or cell-specific MR contrast agents.     

Structure based virtual screening, 3D-QSAR,molecular dynamics and ADMET studies for selection of natural inhibitors against structural and non-structural targets of Chikungunya

The transmission of mosquito-borne Chikungunya virus (CHIKV) has large epidemics worldwide. Till date, there are neither anti-viral drugs nor vaccines available for the treatment of Chikungunya. Accumulated evidences suggest that some natural compounds i.e., Epigallocatechin gallate, Harringtonine, Apigenin, Chrysin, Silybin, etc. have the capability to inhibit CHIKV replication in vitro. Natural compounds are known to possess less or no side effects. Therefore, natural compound in its purified or crude extracts form could be the preeminent and safe mode of therapies for Chikungunya. Wet lab screening and identification of natural compounds against Chikungunya targets is a time consuming and expensive exercise. In the present study, we used in silico techniques like receptor-ligand docking, Molecular dynamic (MD), Three Dimensional Quantitative Structure Activity Relation (3D-QSAR) and ADME properties to screen out potential compounds. Aim of the study is to identify potential lead/s from natural sources using in silico techniques that can be developed as a drug like molecule against Chikungunya infection and replication. Three softwares were used for molecular docking studies. Potential ligands selected by docking studies were subsequently subjected 3D-QSAR studies to predict biological activity. Based on docking scores and pIC₅₀ value, potential anti-Chikungunya compounds were identified. Best docked receptor-ligands were also subjected to MD for more accurate estimation. Lipinski’s rule and ADME studies of the identified compounds were also studied to assess their drug likeness properties. Results of in silico findings, led to identification of few best fit compounds of natural origin against targets of Chikungunya virus which may lead to discovery of new drugs for Chikungunya.

Communicated by Ramaswamy H. Sarma