Open source drug discovery- A new paradigm of collaborative research in tuberculosis drug development

It is being realized that the traditional closed-door and market driven approaches for drug discovery may not be the best suited model for the diseases of the developing world such as tuberculosis and malaria, because most patients suffering from these diseases have poor paying capacity. To ensure that new drugs are created for patients suffering from these diseases, it is necessary to formulate an alternate paradigm of drug discovery process. The current model constrained by limitations for collaboration and for sharing of resources with confidentiality hampers the opportunities for bringing expertise from diverse fields. These limitations hinder the possibilities of lowering the cost of drug discovery. The Open Source Drug Discovery project initiated by Council of Scientific and Industrial Research, India has adopted an open source model to power wide participation across geographical borders. Open Source Drug Discovery emphasizes integrative science through collaboration, open-sharing, taking up multi-faceted approaches and accruing benefits from advances on different fronts of new drug discovery. Because the open source model is based on community participation, it has the potential to self-sustain continuous development by generating a storehouse of alternatives towards continued pursuit for new drug discovery. Since the inventions are community generated, the new chemical entities developed by Open Source Drug Discovery will be taken up for clinical trial in a non-exclusive manner by participation of multiple companies with majority funding from Open Source Drug Discovery. This will ensure availability of drugs through a lower cost community driven drug discovery process for diseases afflicting people with poor paying capacity. Hopefully what LINUX the World Wide Web have done for the information technology, Open Source Drug Discovery will do for drug discovery. (C) 2011 Elsevier Ltd. All rights reserved.

Deorphanization of Malonyl CoA:ACP Transacylase Drug Target in Plasmodium falciparum (PfFabD) Using Bacterial Antagonists: A Piggyback' Approach for Antimalarial Drug Discovery

Quest for new drug targets in Plasmodium sp. has underscored malonyl CoA:ACP transacylase (PfFabD) of fatty acid biosynthetic pathway in apicoplast. In this study, a piggyback approach was employed for the receptor deorphanization using inhibitors of bacterial FabD enzymes. Due to the lack of crystal structure, theoretical model was constructed using the structural details of homologous enzymes. Sequence and structure analysis has localized the presence of two conserved pentapeptide motifs: GQGXG and GXSXG and five key invariant residues viz., Gln109, Ser193, Arg218, His305 and Gln354 characteristic of FabD enzyme. Active site mapping of PfFabD using substrate molecules has disclosed the spatial arrangement of key residues in the cavity. As structurally similar molecules exhibit similar biological activities, signature pharmacophore fingerprints of FabD antagonists were generated using 0D-3D descriptors for molecular similarity-based cluster analysis and to correlate with their binding profiles. It was observed that antagonists showing good geometrical fitness score were grouped in cluster-1, whereas those exhibiting high binding affinities in cluster-2. This study proves important to shed light on the active site environment to reveal the hotspot for binding with higher affinity and to narrow down the virtual screening process by searching for close neighbors of the active compounds.

Network approaches to drug discovery

Introduction: Advances in genomics technologies are providing a very large amount of data on genome-wide gene expression profiles, protein molecules and their interactions with other macromolecules and metabolites. Molecular interaction networks provide a useful way to capture this complex data and comprehend it. Networks are beginning to be used in drug discovery, in many steps of the modern discovery pipeline, with large-scale molecular networks being particularly useful for the understanding of the molecular basis of the disease. Areas covered: The authors discuss network approaches used for drug target discovery and lead identification in the drug discovery pipeline. By reconstructing networks of targets, drugs and drug candidates as well as gene expression profiles under normal and disease conditions, the paper illustrates how it is possible to find relationships between different diseases, find biomarkers, explore drug repurposing and study emergence of drug resistance. Furthermore, the authors also look at networks which address particular important aspects such as off-target effects, combination-targets, mechanism of drug action and drug safety. Expert opinion: The network approach represents another paradigm shift in drug discovery science. A network approach provides a fresh perspective of understanding important proteins in the context of their cellular environments, providing a rational basis for deriving useful strategies in drug design. Besides drug target identification and inferring mechanism of action, networks enable us to address new ideas that could prove to be extremely useful for new drug discovery, such as drug repositioning, drug synergy, polypharmacology and personalized medicine.

Suramin is a potent and selective inhibitor of Mycobacterium tuberculosis RecA protein and the SOS response: RecA as a potential target for antibacterial drug discovery

In eubacteria, RecA is essential for recombinational DNA repair and for stalled replication forks to resume DNA synthesis. Recent work has implicated a role for RecA in the development of antibiotic resistance in pathogenic bacteria. Consequently, our goal is to identify and characterize small-molecule inhibitors that target RecA both in vitro and in vivo. We employed ATPase, DNA strand exchange and LexA cleavage assays to elucidate the inhibitory effects of suramin on Mycobacterium tuberculosis RecA. To gain insights into the mechanism of suramin action, we directly visualized the structure of RecA nucleoprotein filaments by atomic force microscopy. To determine the specificity of suramin action in vivo, we investigated its effect on the SOS response by pull-down and western blot assays as well as for its antibacterial activity. We show that suramin is a potent inhibitor of DNA strand exchange and ATPase activities of bacterial RecA proteins with IC50 values in the low micromolar range. Additional evidence shows that suramin inhibits RecA-catalysed proteolytic cleavage of the LexA repressor. The mechanism underlying such inhibitory actions of suramin involves its ability to disassemble RecA-single-stranded DNA filaments. Notably, suramin abolished ciprofloxacin-induced recA gene expression and the SOS response and augmented the bactericidal action of ciprofloxacin. Our findings suggest a strategy to chemically disrupt the vital processes controlled by RecA and hence the promise of small molecules for use against drug-susceptible as well as drug-resistant strains of M. tuberculosis for better infection control and the development of new therapies.

Drug Discovery Research in India: Current State and Future Prospects

Indian civilization developed a strong system of traditional medicine and was one of the first nations to develop a synthetic drug. In the postindependence era, Indian pharmaceutical industry developed a strong base for production of generic drugs. Challenges for the future are to give its traditional medicine a strong scientific base and develop research and clinical capability to consistently produce new drugs based on advances in modem biological sciences.

CANDO and the infinite drug discovery frontier

The Computational Analysis of Novel Drug Opportunities (CANDO) platform (http://protinfo.org/cando) uses similarity of compound-proteome interaction signatures to infer homology of compound/drug behavior. We constructed interaction signatures for 3733 human ingestible compounds covering 48,278 protein structures mapping to 2030 indications based on basic science methodologies to predict and analyze protein structure, function, and interactions developed by us and others. Our signature comparison and ranking approach yielded benchmarking accuracies of 12-25% for 1439 indications with at least two approved compounds. We prospectively validated 49/82 `high value' predictions from nine studies covering seven indications, with comparable or better activity to existing drugs, which serve as novel repurposed therapeutics. Our approach may be generalized to compounds beyond those approved by the FDA, and can also consider mutations in protein structures to enable personalization. Our platform provides a holistic multiscale modeling framework of complex atomic, molecular, and physiological systems with broader applications in medicine and engineering.

Computational systems approach for drug target discovery

Importance of the field: The shift in focus from ligand based design approaches to target based discovery over the last two to three decades has been a major milestone in drug discovery research. Currently, it is witnessing another major paradigm shift by leaning towards the holistic systems based approaches rather the reductionist single molecule based methods. The effect of this new trend is likely to be felt strongly in terms of new strategies for therapeutic intervention, new targets individually and in combinations, and design of specific and safer drugs. Computational modeling and simulation form important constituents of new-age biology because they are essential to comprehend the large-scale data generated by high-throughput experiments and to generate hypotheses, which are typically iterated with experimental validation. Areas covered in this review: This review focuses on the repertoire of systems-level computational approaches currently available for target identification. The review starts with a discussion on levels of abstraction of biological systems and describes different modeling methodologies that are available for this purpose. The review then focuses on how such modeling and simulations can be applied for drug target discovery. Finally, it discusses methods for studying other important issues such as understanding targetability, identifying target combinations and predicting drug resistance, and considering them during the target identification stage itself. What the reader will gain: The reader will get an account of the various approaches for target discovery and the need for systems approaches, followed by an overview of the different modeling and simulation approaches that have been developed. An idea of the promise and limitations of the various approaches and perspectives for future development will also be obtained. Take home message: Systems thinking has now come of age enabling a `bird's eye view' of the biological systems under study, at the same time allowing us to `zoom in', where necessary, for a detailed description of individual components. A number of different methods available for computational modeling and simulation of biological systems can be used effectively for drug target discovery.

Recognizing drug targets using evolutionary information: implications for repurposing FDA-approved drugs against Mycobacterium tuberculosis H37Rv

Drug repurposing to explore target space has been gaining pace over the past decade with the upsurge in the use of systematic approaches for computational drug discovery. Such a cost and time-saving approach gains immense importance for pathogens of special interest, such as Mycobacterium tuberculosis H37Rv. We report a comprehensive approach to repurpose drugs, based on the exploration of evolutionary relationships inferred from the comparative sequence and structural analyses between targets of FDA-approved drugs and the proteins of M. tuberculosis. This approach has facilitated the identification of several polypharmacological drugs that could potentially target unexploited M. tuberculosis proteins. A total of 130 FDA-approved drugs, originally intended against other diseases, could be repurposed against 78 potential targets in M. tuberculosis. Additionally, we have also made an attempt to augment the chemical space by recognizing compounds structurally similar to FDA-approved drugs. For three of the attractive cases we have investigated the probable binding modes of the drugs in their corresponding M. tuberculosis targets by means of structural modelling. Such prospective targets and small molecules could be prioritized for experimental endeavours, and could significantly influence drug-discovery and drug-development programmes for tuberculosis.

targetTB: A target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

Background: Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation. Results: We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed. Conclusion: The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

Novel peptides of therapeutic promise from Indian Conidae

Highly structured small peptides are the major toxic constituents of the venom of cone snails, a family of widely distributed predatory marine molluscs. These animals use the venom for rapid prey immobilization. The peptide components in the venom target a wide variety of membrane-bound ion channels and receptors. Many have been found to be highly selective for a diverse range of mammalian ion channels and receptors associated with pain-signaling pathways. Their small size, structural stability, and target specificity make them attractive pharmacologic agents. A select number of laboratories mainly from the United States, Europe, Australia, Israel, and China have been engaged in intense drug discovery programs based on peptides from a few snail species. Coastal India has an estimated 20-30% of the known cone species; however, few serious studies have been reported so far. We have begun a comprehensive program for the identification and characterization of peptides from cone snails found in Indian Coastal waters. This presentation reviews our progress over the last 2 years. As expected from the evolutionary history of these venom components, our search has yielded novel peptides of therapeutic promise from the new species that we have studied.

Anti-amyloidogenic property of leaf aqueous extract of Caesalpinia crista

Amyloid beta (A beta) is the major etiological factor implicated in Alzheimer's disease (AD). A beta(42) self-assembles to form oligomers and fibrils via multiple aggregation process. The recent studies aimed to decrease A beta levels or prevention of A beta aggregation which are the major targets for therapeutic intervention. Natural products as alternatives for AD drug discovery are a current trend. We evidenced that Caesalpinia crista leaf aqueous extract has anti-amyloidogenic potential. The studies on pharmacological properties of C. crista are very limited. Our study focused on ability of C. crista leaf aqueous extract on the prevention of (i) the formation of oligomers and aggregates from monomers (Phase I: A beta(42) + extract co-incubation); (ii) the formation of fibrils from oligomers (Phase II: extract added after oligomers formation); and (iii) dis-aggregation of pre-formedfibrils (Phase III: aqueous extract added to matured fibrils and incubated for 9 days). The aggregation kinetics was monitored using thioflavin-T assay and transmission electron microscopy (TEM). The results showed that C. crista aqueous extract could able to inhibit the A beta(42) aggregation from monomers and oligomers and also able todis-aggregate the pre-formed fibrils. The study provides an insight on finding new natural products for AD therapeutics. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Antibacterial & antiplasmid activities of Helicteres isora L.

Background & objectives: The multiple drug resistance (MDR) is a serious health problem and major challenge to the global drug discovery programmes. Most of the genetic determinants that confer resistance to antibiotics are located on R-plasmids in bacteria. The present investigation was undertaken to investigate the ability of organic extract of the fruits of Helicteres isora to cure R-plasmids from certain clinical isolates. mMethods: Active fractions demonstrating antibacterial and antiplasmid activities were isolated from the acetone extracts of shade dried fruits of H. isora by bioassay guided fractionation. Minimal inhibitory concentration (MIC) of antibiotics and organic extracts was determined by agar dilution method. Plasmid curing activity of organic fractions was determined by evaluating the ability of bacterial colonies (pre treated with organic fraction for 18 h) to grow in the presence of antibiotics. The physical loss of plasmid DNA in the cured derivatives was further confirmed by agarose gel electrophoresis. Results: The active fraction did not inhibit the growth of either the clinical isolates or the strains harbouring reference plasmids even at a concentration of 400 mu g/ml. However, the same fraction could cure plasmids from Enterococcus faecalis, Escherichia coli, Bacillus cereus and E. coli (RP4) at curing efficiencies of 14, 26, 22 and 2 per cent respectively. The active fraction mediated plasmid curing resulted in the subsequent loss of antibiotic resistance encoded in the plasmids as revealed by antibiotic resistance profile of cured strains. The physical loss of plasmid was also confirmed by agarose gel electrophoresis. Interpretation & conclusions: The active fraction of acetone extract of H. isora fruits cured R-plasmids from Gram-positive and Gram-negative clinical isolates as well as reference strains. Such plasmid loss reversed the multiple antibiotic resistance in cured derivatives making them sensitive to low concentrations of antibiotics. Acetone fractions of H. isora may be a source to develop antiplasmid agents of natural origin to contain the development and spread of plasmid borne multiple antibiotic resistance.

DEPTH: a web server to compute depth and predict small-molecule binding cavities in proteins

Depth measures the extent of atom/residue burial within a protein. It correlates with properties such as protein stability, hydrogen exchange rate, protein-protein interaction hot spots, post-translational modification sites and sequence variability. Our server, DEPTH, accurately computes depth and solvent-accessible surface area (SASA) values. We show that depth can be used to predict small molecule ligand binding cavities in proteins. Often, some of the residues lining a ligand binding cavity are both deep and solvent exposed. Using the depth-SASA pair values for a residue, its likelihood to form part of a small molecule binding cavity is estimated. The parameters of the method were calibrated over a training set of 900 high-resolution X-ray crystal structures of single-domain proteins bound to small molecules (molecular weight < 1.5 KDa). The prediction accuracy of DEPTH is comparable to that of other geometry-based prediction methods including LIGSITE, SURFNET and Pocket-Finder (all with Matthew's correlation coefficient of similar to 0.4) over a testing set of 225 single and multi-chain protein structures. Users have the option of tuning several parameters to detect cavities of different sizes, for example, geometrically flat binding sites. The input to the server is a protein 3D structure in PDB format. The users have the option of tuning the values of four parameters associated with the computation of residue depth and the prediction of binding cavities. The computed depths, SASA and binding cavity predictions are displayed in 2D plots and mapped onto 3D representations of the protein structure using Jmol. Links are provided to download the outputs. Our server is useful for all structural analysis based on residue depth and SASA, such as guiding site-directed mutagenesis experiments and small molecule docking exercises, in the context of protein functional annotation and drug discovery.

Efficient syntheses of benzothiazepines as antagonists for the mitochondrial sodium-calcium exchanger: Potential therapeutics for type II diabetes

Type II diabetes mellitus is a chronic metabolic disorder that can lead to serious cardiovascular, renal, neurologic, and retinal complications. While several drugs are currently prescribed to treat type II diabetes, their efficacy is limited by mechanism-related side effects (weight gain, hypoglycemia, gastrointestinal distress), inadequate efficacy for use as monotherapy, and the development of tolerance to the agents. Consequently, combination therapies are frequently employed to effectively regulate blood glucose levels. We have focused on the mitochondrial sodium-calcium exchanger (mNCE) as a novel target for diabetes drug discovery. We have proposed that inhibition of the mNCE can be used to regulate calcium flux across the mitochondrial membrane, thereby enhancing mitochondrial oxidative metabolism, which in turn enhances glucose-stimulated insulin secretion (GSIS) in the pancreatic beta-cell. In this paper, we report the facile synthesis of benzothiazepines and derivatives by S-alkylation using 2-aminobenzhydrols. The syntheses of other bicyclic analogues based on benzothiazepine, benzothiazecine, benzodiazecine, and benzodiazepine templates are also described. These compounds have been evaluated for their inhibition of mNCE activity, and the results from the structure-activity relationship (SAR) studies are discussed.