WorldWideScience

Sample records for structure-based drug discovery

  1. From crystal to compound: structure-based antimalarial drug discovery.

    Science.gov (United States)

    Drinkwater, Nyssa; McGowan, Sheena

    2014-08-01

    Despite a century of control and eradication campaigns, malaria remains one of the world's most devastating diseases. Our once-powerful therapeutic weapons are losing the war against the Plasmodium parasite, whose ability to rapidly develop and spread drug resistance hamper past and present malaria-control efforts. Finding new and effective treatments for malaria is now a top global health priority, fuelling an increase in funding and promoting open-source collaborations between researchers and pharmaceutical consortia around the world. The result of this is rapid advances in drug discovery approaches and technologies, with three major methods for antimalarial drug development emerging: (i) chemistry-based, (ii) target-based, and (iii) cell-based. Common to all three of these approaches is the unique ability of structural biology to inform and accelerate drug development. Where possible, SBDD (structure-based drug discovery) is a foundation for antimalarial drug development programmes, and has been invaluable to the development of a number of current pre-clinical and clinical candidates. However, as we expand our understanding of the malarial life cycle and mechanisms of resistance development, SBDD as a field must continue to evolve in order to develop compounds that adhere to the ideal characteristics for novel antimalarial therapeutics and to avoid high attrition rates pre- and post-clinic. In the present review, we aim to examine the contribution that SBDD has made to current antimalarial drug development efforts, covering hit discovery to lead optimization and prevention of parasite resistance. Finally, the potential for structural biology, particularly high-throughput structural genomics programmes, to identify future targets for drug discovery are discussed.

  2. Fragment approaches in structure-based drug discovery

    International Nuclear Information System (INIS)

    Hubbard, Roderick E.

    2008-01-01

    Fragment-based methods are successfully generating novel and selective drug-like inhibitors of protein targets, with a number of groups reporting compounds entering clinical trials. This paper summarizes the key features of the approach as one of the tools in structure-guided drug discovery. There has been considerable interest recently in what is known as 'fragment-based lead discovery'. The novel feature of the approach is to begin with small low-affinity compounds. The main advantage is that a larger potential chemical diversity can be sampled with fewer compounds, which is particularly important for new target classes. The approach relies on careful design of the fragment library, a method that can detect binding of the fragment to the protein target, determination of the structure of the fragment bound to the target, and the conventional use of structural information to guide compound optimization. In this article the methods are reviewed, and experiences in fragment-based discovery of lead series of compounds against kinases such as PDK1 and ATPases such as Hsp90 are discussed. The examples illustrate some of the key benefits and issues of the approach and also provide anecdotal examples of the patterns seen in selectivity and the binding mode of fragments across different protein targets

  3. Solution NMR Spectroscopy in Target-Based Drug Discovery.

    Science.gov (United States)

    Li, Yan; Kang, Congbao

    2017-08-23

    Solution NMR spectroscopy is a powerful tool to study protein structures and dynamics under physiological conditions. This technique is particularly useful in target-based drug discovery projects as it provides protein-ligand binding information in solution. Accumulated studies have shown that NMR will play more and more important roles in multiple steps of the drug discovery process. In a fragment-based drug discovery process, ligand-observed and protein-observed NMR spectroscopy can be applied to screen fragments with low binding affinities. The screened fragments can be further optimized into drug-like molecules. In combination with other biophysical techniques, NMR will guide structure-based drug discovery. In this review, we describe the possible roles of NMR spectroscopy in drug discovery. We also illustrate the challenges encountered in the drug discovery process. We include several examples demonstrating the roles of NMR in target-based drug discoveries such as hit identification, ranking ligand binding affinities, and mapping the ligand binding site. We also speculate the possible roles of NMR in target engagement based on recent processes in in-cell NMR spectroscopy.

  4. From Protein Structure to Small-Molecules: Recent Advances and Applications to Fragment-Based Drug Discovery.

    Science.gov (United States)

    Ferreira, Leonardo G; Andricopulo, Adriano D

    2017-01-01

    Fragment-based drug discovery (FBDD) is a broadly used strategy in structure-guided ligand design, whereby low-molecular weight hits move from lead-like to drug-like compounds. Over the past 15 years, an increasingly important role of the integration of these strategies into industrial and academic research platforms has been successfully established, allowing outstanding contributions to drug discovery. One important factor for the current prominence of FBDD is the better coverage of the chemical space provided by fragment-like libraries. The development of the field relies on two features: (i) the growing number of structurally characterized drug targets and (ii) the enormous chemical diversity available for experimental and virtual screenings. Indeed, fragment-based campaigns have contributed to address major challenges in lead optimization, such as the appropriate physicochemical profile of clinical candidates. This perspective paper outlines the usefulness and applications of FBDD approaches in medicinal chemistry and drug design. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  5. Computational methods in drug discovery

    Directory of Open Access Journals (Sweden)

    Sumudu P. Leelananda

    2016-12-01

    Full Text Available The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

  6. Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery.

    Science.gov (United States)

    Heifetz, Alexander; Southey, Michelle; Morao, Inaki; Townsend-Nicholson, Andrea; Bodkin, Mike J

    2018-01-01

    GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects.

  7. NMR in structure-based drug design.

    Science.gov (United States)

    Carneiro, Marta G; Ab, Eiso; Theisgen, Stephan; Siegal, Gregg

    2017-11-08

    NMR spectroscopy is a powerful technique that can provide valuable structural information for drug discovery endeavors. Here, we discuss the strengths (and limitations) of NMR applications to structure-based drug discovery, highlighting the different levels of resolution and throughput obtainable. Additionally, the emerging field of paramagnetic NMR in drug discovery and recent developments in approaches to speed up and automate protein-observed NMR data collection and analysis are discussed. © 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

  8. Discovery of potent, reversible MetAP2 inhibitors via fragment based drug discovery and structure based drug design-Part 2.

    Science.gov (United States)

    McBride, Christopher; Cheruvallath, Zacharia; Komandla, Mallareddy; Tang, Mingnam; Farrell, Pamela; Lawson, J David; Vanderpool, Darin; Wu, Yiqin; Dougan, Douglas R; Plonowski, Artur; Holub, Corine; Larson, Chris

    2016-06-15

    Methionine aminopeptidase-2 (MetAP2) is an enzyme that cleaves an N-terminal methionine residue from a number of newly synthesized proteins. This step is required before they will fold or function correctly. Pre-clinical and clinical studies with a MetAP2 inhibitor suggest that they could be used as a novel treatment for obesity. Herein we describe the discovery of a series of pyrazolo[4,3-b]indoles as reversible MetAP2 inhibitors. A fragment-based drug discovery (FBDD) approach was used, beginning with the screening of fragment libraries to generate hits with high ligand-efficiency (LE). An indazole core was selected for further elaboration, guided by structural information. SAR from the indazole series led to the design of a pyrazolo[4,3-b]indole core and accelerated knowledge-based fragment growth resulted in potent and efficient MetAP2 inhibitors, which have shown robust and sustainable body weight loss in DIO mice when dosed orally. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Introduction to fragment-based drug discovery.

    Science.gov (United States)

    Erlanson, Daniel A

    2012-01-01

    Fragment-based drug discovery (FBDD) has emerged in the past decade as a powerful tool for discovering drug leads. The approach first identifies starting points: very small molecules (fragments) that are about half the size of typical drugs. These fragments are then expanded or linked together to generate drug leads. Although the origins of the technique date back some 30 years, it was only in the mid-1990s that experimental techniques became sufficiently sensitive and rapid for the concept to be become practical. Since that time, the field has exploded: FBDD has played a role in discovery of at least 18 drugs that have entered the clinic, and practitioners of FBDD can be found throughout the world in both academia and industry. Literally dozens of reviews have been published on various aspects of FBDD or on the field as a whole, as have three books (Jahnke and Erlanson, Fragment-based approaches in drug discovery, 2006; Zartler and Shapiro, Fragment-based drug discovery: a practical approach, 2008; Kuo, Fragment based drug design: tools, practical approaches, and examples, 2011). However, this chapter will assume that the reader is approaching the field with little prior knowledge. It will introduce some of the key concepts, set the stage for the chapters to follow, and demonstrate how X-ray crystallography plays a central role in fragment identification and advancement.

  10. Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery.

    Science.gov (United States)

    Perualila-Tan, Nolen Joy; Shkedy, Ziv; Talloen, Willem; Göhlmann, Hinrich W H; Moerbeke, Marijke Van; Kasim, Adetayo

    2016-08-01

    The modern process of discovering candidate molecules in early drug discovery phase includes a wide range of approaches to extract vital information from the intersection of biology and chemistry. A typical strategy in compound selection involves compound clustering based on chemical similarity to obtain representative chemically diverse compounds (not incorporating potency information). In this paper, we propose an integrative clustering approach that makes use of both biological (compound efficacy) and chemical (structural features) data sources for the purpose of discovering a subset of compounds with aligned structural and biological properties. The datasets are integrated at the similarity level by assigning complementary weights to produce a weighted similarity matrix, serving as a generic input in any clustering algorithm. This new analysis work flow is semi-supervised method since, after the determination of clusters, a secondary analysis is performed wherein it finds differentially expressed genes associated to the derived integrated cluster(s) to further explain the compound-induced biological effects inside the cell. In this paper, datasets from two drug development oncology projects are used to illustrate the usefulness of the weighted similarity-based clustering approach to integrate multi-source high-dimensional information to aid drug discovery. Compounds that are structurally and biologically similar to the reference compounds are discovered using this proposed integrative approach.

  11. Fragment-based drug discovery and its application to challenging drug targets.

    Science.gov (United States)

    Price, Amanda J; Howard, Steven; Cons, Benjamin D

    2017-11-08

    Fragment-based drug discovery (FBDD) is a technique for identifying low molecular weight chemical starting points for drug discovery. Since its inception 20 years ago, FBDD has grown in popularity to the point where it is now an established technique in industry and academia. The approach involves the biophysical screening of proteins against collections of low molecular weight compounds (fragments). Although fragments bind to proteins with relatively low affinity, they form efficient, high quality binding interactions with the protein architecture as they have to overcome a significant entropy barrier to bind. Of the biophysical methods available for fragment screening, X-ray protein crystallography is one of the most sensitive and least prone to false positives. It also provides detailed structural information of the protein-fragment complex at the atomic level. Fragment-based screening using X-ray crystallography is therefore an efficient method for identifying binding hotspots on proteins, which can then be exploited by chemists and biologists for the discovery of new drugs. The use of FBDD is illustrated here with a recently published case study of a drug discovery programme targeting the challenging protein-protein interaction Kelch-like ECH-associated protein 1:nuclear factor erythroid 2-related factor 2. © 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

  12. Medicinal chemistry inspired fragment-based drug discovery.

    Science.gov (United States)

    Lanter, James; Zhang, Xuqing; Sui, Zhihua

    2011-01-01

    Lead generation can be a very challenging phase of the drug discovery process. The two principal methods for this stage of research are blind screening and rational design. Among the rational or semirational design approaches, fragment-based drug discovery (FBDD) has emerged as a useful tool for the generation of lead structures. It is particularly powerful as a complement to high-throughput screening approaches when the latter failed to yield viable hits for further development. Engagement of medicinal chemists early in the process can accelerate the progression of FBDD efforts by incorporating drug-friendly properties in the earliest stages of the design process. Medium-chain acyl-CoA synthetase 2b and ketohexokinase are chosen as examples to illustrate the importance of close collaboration of medicinal chemists, crystallography, and modeling. Copyright © 2011 Elsevier Inc. All rights reserved.

  13. Antibody informatics for drug discovery

    DEFF Research Database (Denmark)

    Shirai, Hiroki; Prades, Catherine; Vita, Randi

    2014-01-01

    to the antibody science in every project in antibody drug discovery. Recent experimental technologies allow for the rapid generation of large-scale data on antibody sequences, affinity, potency, structures, and biological functions; this should accelerate drug discovery research. Therefore, a robust bioinformatic...... infrastructure for these large data sets has become necessary. In this article, we first identify and discuss the typical obstacles faced during the antibody drug discovery process. We then summarize the current status of three sub-fields of antibody informatics as follows: (i) recent progress in technologies...... for antibody rational design using computational approaches to affinity and stability improvement, as well as ab-initio and homology-based antibody modeling; (ii) resources for antibody sequences, structures, and immune epitopes and open drug discovery resources for development of antibody drugs; and (iii...

  14. Bead-based screening in chemical biology and drug discovery

    DEFF Research Database (Denmark)

    Komnatnyy, Vitaly V.; Nielsen, Thomas Eiland; Qvortrup, Katrine

    2018-01-01

    libraries for early drug discovery. Among the various library forms, the one-bead-one-compound (OBOC) library, where each bead carries many copies of a single compound, holds the greatest potential for the rapid identification of novel hits against emerging drug targets. However, this potential has not yet...... been fully realized due to a number of technical obstacles. In this feature article, we review the progress that has been made towards bead-based library screening and applications to the discovery of bioactive compounds. We identify the key challenges of this approach and highlight key steps needed......High-throughput screening is an important component of the drug discovery process. The screening of libraries containing hundreds of thousands of compounds requires assays amanable to miniaturisation and automization. Combinatorial chemistry holds a unique promise to deliver structural diverse...

  15. Experiences in fragment-based drug discovery.

    Science.gov (United States)

    Murray, Christopher W; Verdonk, Marcel L; Rees, David C

    2012-05-01

    Fragment-based drug discovery (FBDD) has become established in both industry and academia as an alternative approach to high-throughput screening for the generation of chemical leads for drug targets. In FBDD, specialised detection methods are used to identify small chemical compounds (fragments) that bind to the drug target, and structural biology is usually employed to establish their binding mode and to facilitate their optimisation. In this article, we present three recent and successful case histories in FBDD. We then re-examine the key concepts and challenges of FBDD with particular emphasis on recent literature and our own experience from a substantial number of FBDD applications. Our opinion is that careful application of FBDD is living up to its promise of delivering high quality leads with good physical properties and that in future many drug molecules will be derived from fragment-based approaches. Copyright © 2012 Elsevier Ltd. All rights reserved.

  16. [Fragment-based drug discovery: concept and aim].

    Science.gov (United States)

    Tanaka, Daisuke

    2010-03-01

    Fragment-Based Drug Discovery (FBDD) has been recognized as a newly emerging lead discovery methodology that involves biophysical fragment screening and chemistry-driven fragment-to-lead stages. Although fragments, defined as structurally simple and small compounds (typically FBDD primarily turns our attention to weakly but specifically binding fragments (hit fragments) as the starting point of medicinal chemistry. Hit fragments are then promoted to more potent lead compounds through linking or merging with another hit fragment and/or attaching functional groups. Another positive aspect of FBDD is ligand efficiency. Ligand efficiency is a useful guide in screening hit selection and hit-to-lead phases to achieve lead-likeness. Owing to these features, a number of successful applications of FBDD to "undruggable targets" (where HTS and other lead identification methods failed to identify useful lead compounds) have been reported. As a result, FBDD is now expected to complement more conventional methodologies. This review, as an introduction of the following articles, will summarize the fundamental concepts of FBDD and will discuss its advantages over other conventional drug discovery approaches.

  17. Structural Genomics and Drug Discovery for Infectious Diseases

    International Nuclear Information System (INIS)

    Anderson, W.F.

    2009-01-01

    The application of structural genomics methods and approaches to proteins from organisms causing infectious diseases is making available the three dimensional structures of many proteins that are potential drug targets and laying the groundwork for structure aided drug discovery efforts. There are a number of structural genomics projects with a focus on pathogens that have been initiated worldwide. The Center for Structural Genomics of Infectious Diseases (CSGID) was recently established to apply state-of-the-art high throughput structural biology technologies to the characterization of proteins from the National Institute for Allergy and Infectious Diseases (NIAID) category A-C pathogens and organisms causing emerging, or re-emerging infectious diseases. The target selection process emphasizes potential biomedical benefits. Selected proteins include known drug targets and their homologs, essential enzymes, virulence factors and vaccine candidates. The Center also provides a structure determination service for the infectious disease scientific community. The ultimate goal is to generate a library of structures that are available to the scientific community and can serve as a starting point for further research and structure aided drug discovery for infectious diseases. To achieve this goal, the CSGID will determine protein crystal structures of 400 proteins and protein-ligand complexes using proven, rapid, highly integrated, and cost-effective methods for such determination, primarily by X-ray crystallography. High throughput crystallographic structure determination is greatly aided by frequent, convenient access to high-performance beamlines at third-generation synchrotron X-ray sources.

  18. Fragment-based approaches to anti-HIV drug discovery: state of the art and future opportunities.

    Science.gov (United States)

    Huang, Boshi; Kang, Dongwei; Zhan, Peng; Liu, Xinyong

    2015-12-01

    The search for additional drugs to treat HIV infection is a continuing effort due to the emergence and spread of HIV strains resistant to nearly all current drugs. The recent literature reveals that fragment-based drug design/discovery (FBDD) has become an effective alternative to conventional high-throughput screening strategies for drug discovery. In this critical review, the authors describe the state of the art in FBDD strategies for the discovery of anti-HIV drug-like compounds. The article focuses on fragment screening techniques, direct fragment-based design and early hit-to-lead progress. Rapid progress in biophysical detection and in silico techniques has greatly aided the application of FBDD to discover candidate agents directed at a variety of anti-HIV targets. Growing evidence suggests that structural insights on key proteins in the HIV life cycle can be applied in the early phase of drug discovery campaigns, providing valuable information on the binding modes and efficiently prompting fragment hit-to-lead progression. The combination of structural insights with improved methodologies for FBDD, including the privileged fragment-based reconstruction approach, fragment hybridization based on crystallographic overlays, fragment growth exploiting dynamic combinatorial chemistry, and high-speed fragment assembly via diversity-oriented synthesis followed by in situ screening, offers the possibility of more efficient and rapid discovery of novel drugs for HIV-1 prevention or treatment. Though the use of FBDD in anti-HIV drug discovery is still in its infancy, it is anticipated that anti-HIV agents developed via fragment-based strategies will be introduced into the clinic in the future.

  19. ACFIS: a web server for fragment-based drug discovery

    Science.gov (United States)

    Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu

    2016-01-01

    In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown ‘chemical space’ to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for ‘chemical space’, which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. PMID:27150808

  20. Fragment-based drug discovery using rational design.

    Science.gov (United States)

    Jhoti, H

    2007-01-01

    Fragment-based drug discovery (FBDD) is established as an alternative approach to high-throughput screening for generating novel small molecule drug candidates. In FBDD, relatively small libraries of low molecular weight compounds (or fragments) are screened using sensitive biophysical techniques to detect their binding to the target protein. A lower absolute affinity of binding is expected from fragments, compared to much higher molecular weight hits detected by high-throughput screening, due to their reduced size and complexity. Through the use of iterative cycles of medicinal chemistry, ideally guided by three-dimensional structural data, it is often then relatively straightforward to optimize these weak binding fragment hits into potent and selective lead compounds. As with most other lead discovery methods there are two key components of FBDD; the detection technology and the compound library. In this review I outline the two main approaches used for detecting the binding of low affinity fragments and also some of the key principles that are used to generate a fragment library. In addition, I describe an example of how FBDD has led to the generation of a drug candidate that is now being tested in clinical trials for the treatment of cancer.

  1. Structure and organization of drug-target networks: insights from genomic approaches for drug discovery.

    Science.gov (United States)

    Janga, Sarath Chandra; Tzakos, Andreas

    2009-12-01

    Recent years have seen an explosion in the amount of "omics" data and the integration of several disciplines, which has influenced all areas of life sciences including that of drug discovery. Several lines of evidence now suggest that the traditional notion of "one drug-one protein" for one disease does not hold any more and that treatment for most complex diseases can best be attempted using polypharmacological approaches. In this review, we formalize the definition of a drug-target network by decomposing it into drug, target and disease spaces and provide an overview of our understanding in recent years about its structure and organizational principles. We discuss advances made in developing promiscuous drugs following the paradigm of polypharmacology and reveal their advantages over traditional drugs for targeting diseases such as cancer. We suggest that drug-target networks can be decomposed to be studied at a variety of levels and argue that such network-based approaches have important implications in understanding disease phenotypes and in accelerating drug discovery. We also discuss the potential and scope network pharmacology promises in harnessing the vast amount of data from high-throughput approaches for therapeutic advantage.

  2. Structure-based drug discovery for combating influenza virus by targeting the PA-PB1 interaction.

    Science.gov (United States)

    Watanabe, Ken; Ishikawa, Takeshi; Otaki, Hiroki; Mizuta, Satoshi; Hamada, Tsuyoshi; Nakagaki, Takehiro; Ishibashi, Daisuke; Urata, Shuzo; Yasuda, Jiro; Tanaka, Yoshimasa; Nishida, Noriyuki

    2017-08-25

    Influenza virus infections are serious public health concerns throughout the world. The development of compounds with novel mechanisms of action is urgently required due to the emergence of viruses with resistance to the currently-approved anti-influenza viral drugs. We performed in silico screening using a structure-based drug discovery algorithm called Nagasaki University Docking Engine (NUDE), which is optimised for a GPU-based supercomputer (DEstination for Gpu Intensive MAchine; DEGIMA), by targeting influenza viral PA protein. The compounds selected by NUDE were tested for anti-influenza virus activity using a cell-based assay. The most potent compound, designated as PA-49, is a medium-sized quinolinone derivative bearing a tetrazole moiety, and it inhibited the replication of influenza virus A/WSN/33 at a half maximal inhibitory concentration of 0.47 μM. PA-49 has the ability to bind PA and its anti-influenza activity was promising against various influenza strains, including a clinical isolate of A(H1N1)pdm09 and type B viruses. The docking simulation suggested that PA-49 interrupts the PA-PB1 interface where important amino acids are mostly conserved in the virus strains tested, suggesting the strain independent utility. Because our NUDE/DEGIMA system is rapid and efficient, it may help effective drug discovery against the influenza virus and other emerging viruses.

  3. Fragment-based drug discovery and molecular docking in drug design.

    Science.gov (United States)

    Wang, Tao; Wu, Mian-Bin; Chen, Zheng-Jie; Chen, Hua; Lin, Jian-Ping; Yang, Li-Rong

    2015-01-01

    Fragment-based drug discovery (FBDD) has caused a revolution in the process of drug discovery and design, with many FBDD leads being developed into clinical trials or approved in the past few years. Compared with traditional high-throughput screening, it displays obvious advantages such as efficiently covering chemical space, achieving higher hit rates, and so forth. In this review, we focus on the most recent developments of FBDD for improving drug discovery, illustrating the process and the importance of FBDD. In particular, the computational strategies applied in the process of FBDD and molecular-docking programs are highlighted elaborately. In most cases, docking is used for predicting the ligand-receptor interaction modes and hit identification by structurebased virtual screening. The successful cases of typical significance and the hits identified most recently are discussed.

  4. ACFIS: a web server for fragment-based drug discovery.

    Science.gov (United States)

    Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu

    2016-07-08

    In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  5. Fragment-based drug discovery and protein–protein interactions

    Directory of Open Access Journals (Sweden)

    Turnbull AP

    2014-09-01

    Full Text Available Andrew P Turnbull,1 Susan M Boyd,2 Björn Walse31CRT Discovery Laboratories, Department of Biological Sciences, Birkbeck, University of London, London, UK; 2IOTA Pharmaceuticals Ltd, Cambridge, UK; 3SARomics Biostructures AB, Lund, SwedenAbstract: Protein–protein interactions (PPIs are involved in many biological processes, with an estimated 400,000 PPIs within the human proteome. There is significant interest in exploiting the relatively unexplored potential of these interactions in drug discovery, driven by the need to find new therapeutic targets. Compared with classical drug discovery against targets with well-defined binding sites, developing small-molecule inhibitors against PPIs where the contact surfaces are frequently more extensive and comparatively flat, with most of the binding energy localized in “hot spots”, has proven far more challenging. However, despite the difficulties associated with targeting PPIs, important progress has been made in recent years with fragment-based drug discovery playing a pivotal role in improving their tractability. Computational and empirical approaches can be used to identify hot-spot regions and assess the druggability and ligandability of new targets, whilst fragment screening campaigns can detect low-affinity fragments that either directly or indirectly perturb the PPI. Once fragment hits have been identified and confirmed using biochemical and biophysical approaches, three-dimensional structural data derived from nuclear magnetic resonance or X-ray crystallography can be used to drive medicinal chemistry efforts towards the development of more potent inhibitors. A small-scale comparison presented in this review of “standard” fragments with those targeting PPIs has revealed that the latter tend to be larger, be more lipophilic, and contain more polar (acid/base functionality, whereas three-dimensional descriptor data indicate that there is little difference in their three

  6. Lessons from Hot Spot Analysis for Fragment-Based Drug Discovery.

    Science.gov (United States)

    Hall, David R; Kozakov, Dima; Whitty, Adrian; Vajda, Sandor

    2015-11-01

    Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high-affinity, drug-like ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from a protein 3D structure, and how their strength, number, and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

    Directory of Open Access Journals (Sweden)

    Nicholas Ekow Thomford

    2018-05-01

    Full Text Available The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of “active compound” has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of ‘organ-on chip’ and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug

  8. Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery.

    Science.gov (United States)

    Thomford, Nicholas Ekow; Senthebane, Dimakatso Alice; Rowe, Arielle; Munro, Daniella; Seele, Palesa; Maroyi, Alfred; Dzobo, Kevin

    2018-05-25

    The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review

  9. Lessons from hot spot analysis for fragment-based drug discovery

    Science.gov (United States)

    Hall, David R.; Vajda, Sandor

    2015-01-01

    Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high affinity, druglike ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from protein three-dimensional structure, and how their strength, number and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery. PMID:26538314

  10. Hierarchical virtual screening approaches in small molecule drug discovery.

    Science.gov (United States)

    Kumar, Ashutosh; Zhang, Kam Y J

    2015-01-01

    Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. "Drug" Discovery with the Help of Organic Chemistry.

    Science.gov (United States)

    Itoh, Yukihiro; Suzuki, Takayoshi

    2017-01-01

    The first step in "drug" discovery is to find compounds binding to a potential drug target. In modern medicinal chemistry, the screening of a chemical library, structure-based drug design, and ligand-based drug design, or a combination of these methods, are generally used for identifying the desired compounds. However, they do not necessarily lead to success and there is no infallible method for drug discovery. Therefore, it is important to explore medicinal chemistry based on not only the conventional methods but also new ideas. So far, we have found various compounds as drug candidates. In these studies, some strategies based on organic chemistry have allowed us to find drug candidates, through 1) construction of a focused library using organic reactions and 2) rational design of enzyme inhibitors based on chemical reactions catalyzed by the target enzyme. Medicinal chemistry based on organic chemical reactions could be expected to supplement the conventional methods. In this review, we present drug discovery with the help of organic chemistry showing examples of our explorative studies on histone deacetylase inhibitors and lysine-specific demethylase 1 inhibitors.

  12. Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases.

    Science.gov (United States)

    Mello, Juliana da Fonseca Rezende E; Gomes, Renan Augusto; Vital-Fujii, Drielli Gomes; Ferreira, Glaucio Monteiro; Trossini, Gustavo Henrique Goulart

    2017-12-01

    Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs. © 2017 John Wiley & Sons A/S.

  13. Computer-Aided Drug Discovery in Plant Pathology.

    Science.gov (United States)

    Shanmugam, Gnanendra; Jeon, Junhyun

    2017-12-01

    Control of plant diseases is largely dependent on use of agrochemicals. However, there are widening gaps between our knowledge on plant diseases gained from genetic/mechanistic studies and rapid translation of the knowledge into target-oriented development of effective agrochemicals. Here we propose that the time is ripe for computer-aided drug discovery/design (CADD) in molecular plant pathology. CADD has played a pivotal role in development of medically important molecules over the last three decades. Now, explosive increase in information on genome sequences and three dimensional structures of biological molecules, in combination with advances in computational and informational technologies, opens up exciting possibilities for application of CADD in discovery and development of agrochemicals. In this review, we outline two categories of the drug discovery strategies: structure- and ligand-based CADD, and relevant computational approaches that are being employed in modern drug discovery. In order to help readers to dive into CADD, we explain concepts of homology modelling, molecular docking, virtual screening, and de novo ligand design in structure-based CADD, and pharmacophore modelling, ligand-based virtual screening, quantitative structure activity relationship modelling and de novo ligand design for ligand-based CADD. We also provide the important resources available to carry out CADD. Finally, we present a case study showing how CADD approach can be implemented in reality for identification of potent chemical compounds against the important plant pathogens, Pseudomonas syringae and Colletotrichum gloeosporioides .

  14. AutoDrug: fully automated macromolecular crystallography workflows for fragment-based drug discovery

    International Nuclear Information System (INIS)

    Tsai, Yingssu; McPhillips, Scott E.; González, Ana; McPhillips, Timothy M.; Zinn, Daniel; Cohen, Aina E.; Feese, Michael D.; Bushnell, David; Tiefenbrunn, Theresa; Stout, C. David; Ludaescher, Bertram; Hedman, Britt; Hodgson, Keith O.; Soltis, S. Michael

    2013-01-01

    New software has been developed for automating the experimental and data-processing stages of fragment-based drug discovery at a macromolecular crystallography beamline. A new workflow-automation framework orchestrates beamline-control and data-analysis software while organizing results from multiple samples. AutoDrug is software based upon the scientific workflow paradigm that integrates the Stanford Synchrotron Radiation Lightsource macromolecular crystallography beamlines and third-party processing software to automate the crystallography steps of the fragment-based drug-discovery process. AutoDrug screens a cassette of fragment-soaked crystals, selects crystals for data collection based on screening results and user-specified criteria and determines optimal data-collection strategies. It then collects and processes diffraction data, performs molecular replacement using provided models and detects electron density that is likely to arise from bound fragments. All processes are fully automated, i.e. are performed without user interaction or supervision. Samples can be screened in groups corresponding to particular proteins, crystal forms and/or soaking conditions. A single AutoDrug run is only limited by the capacity of the sample-storage dewar at the beamline: currently 288 samples. AutoDrug was developed in conjunction with RestFlow, a new scientific workflow-automation framework. RestFlow simplifies the design of AutoDrug by managing the flow of data and the organization of results and by orchestrating the execution of computational pipeline steps. It also simplifies the execution and interaction of third-party programs and the beamline-control system. Modeling AutoDrug as a scientific workflow enables multiple variants that meet the requirements of different user groups to be developed and supported. A workflow tailored to mimic the crystallography stages comprising the drug-discovery pipeline of CoCrystal Discovery Inc. has been deployed and successfully

  15. Advances in fragment-based drug discovery platforms.

    Science.gov (United States)

    Orita, Masaya; Warizaya, Masaichi; Amano, Yasushi; Ohno, Kazuki; Niimi, Tatsuya

    2009-11-01

    Fragment-based drug discovery (FBDD) has been established as a powerful alternative and complement to traditional high-throughput screening techniques for identifying drug leads. At present, this technique is widely used among academic groups as well as small biotech and large pharmaceutical companies. In recent years, > 10 new compounds developed with FBDD have entered clinical development, and more and more attention in the drug discovery field is being focused on this technique. Under the FBDD approach, a fragment library of relatively small compounds (molecular mass = 100 - 300 Da) is screened by various methods and the identified fragment hits which normally weakly bind to the target are used as starting points to generate more potent drug leads. Because FBDD is still a relatively new drug discovery technology, further developments and optimizations in screening platforms and fragment exploitation can be expected. This review summarizes recent advances in FBDD platforms and discusses the factors important for the successful application of this technique. Under the FBDD approach, both identifying the starting fragment hit to be developed and generating the drug lead from that starting fragment hit are important. Integration of various techniques, such as computational technology, X-ray crystallography, NMR, surface plasmon resonance, isothermal titration calorimetry, mass spectrometry and high-concentration screening, must be applied in a situation-appropriate manner.

  16. Traditional Chinese Medicine-Based Network Pharmacology Could Lead to New Multicompound Drug Discovery

    Directory of Open Access Journals (Sweden)

    Jian Li

    2012-01-01

    Full Text Available Current strategies for drug discovery have reached a bottleneck where the paradigm is generally “one gene, one drug, one disease.” However, using holistic and systemic views, network pharmacology may be the next paradigm in drug discovery. Based on network pharmacology, a combinational drug with two or more compounds could offer beneficial synergistic effects for complex diseases. Interestingly, traditional chinese medicine (TCM has been practicing holistic views for over 3,000 years, and its distinguished feature is using herbal formulas to treat diseases based on the unique pattern classification. Though TCM herbal formulas are acknowledged as a great source for drug discovery, no drug discovery strategies compatible with the multidimensional complexities of TCM herbal formulas have been developed. In this paper, we highlighted some novel paradigms in TCM-based network pharmacology and new drug discovery. A multiple compound drug can be discovered by merging herbal formula-based pharmacological networks with TCM pattern-based disease molecular networks. Herbal formulas would be a source for multiple compound drug candidates, and the TCM pattern in the disease would be an indication for a new drug.

  17. Post processing of protein-compound docking for fragment-based drug discovery (FBDD): in-silico structure-based drug screening and ligand-binding pose prediction.

    Science.gov (United States)

    Fukunishi, Yoshifumi

    2010-01-01

    For fragment-based drug development, both hit (active) compound prediction and docking-pose (protein-ligand complex structure) prediction of the hit compound are important, since chemical modification (fragment linking, fragment evolution) subsequent to the hit discovery must be performed based on the protein-ligand complex structure. However, the naïve protein-compound docking calculation shows poor accuracy in terms of docking-pose prediction. Thus, post-processing of the protein-compound docking is necessary. Recently, several methods for the post-processing of protein-compound docking have been proposed. In FBDD, the compounds are smaller than those for conventional drug screening. This makes it difficult to perform the protein-compound docking calculation. A method to avoid this problem has been reported. Protein-ligand binding free energy estimation is useful to reduce the procedures involved in the chemical modification of the hit fragment. Several prediction methods have been proposed for high-accuracy estimation of protein-ligand binding free energy. This paper summarizes the various computational methods proposed for docking-pose prediction and their usefulness in FBDD.

  18. SemaTyP: a knowledge graph based literature mining method for drug discovery.

    Science.gov (United States)

    Sang, Shengtian; Yang, Zhihao; Wang, Lei; Liu, Xiaoxia; Lin, Hongfei; Wang, Jian

    2018-05-30

    Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries. Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases. The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs. In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.

  19. Computational neuropharmacology: dynamical approaches in drug discovery.

    Science.gov (United States)

    Aradi, Ildiko; Erdi, Péter

    2006-05-01

    Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.

  20. e-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design.

    Science.gov (United States)

    Pihan, Emilie; Colliandre, Lionel; Guichou, Jean-François; Douguet, Dominique

    2012-06-01

    In the drug discovery field, new uses for old drugs, selective optimization of side activities and fragment-based drug design (FBDD) have proved to be successful alternatives to high-throughput screening. e-Drug3D is a database of 3D chemical structures of drugs that provides several collections of ready-to-screen SD files of drugs and commercial drug fragments. They are natural inputs in studies dedicated to drug repurposing and FBDD. e-Drug3D collections are freely available at http://chemoinfo.ipmc.cnrs.fr/e-drug3d.html either for download or for direct in silico web-based screenings.

  1. Toxins and drug discovery.

    Science.gov (United States)

    Harvey, Alan L

    2014-12-15

    Components from venoms have stimulated many drug discovery projects, with some notable successes. These are briefly reviewed, from captopril to ziconotide. However, there have been many more disappointments on the road from toxin discovery to approval of a new medicine. Drug discovery and development is an inherently risky business, and the main causes of failure during development programmes are outlined in order to highlight steps that might be taken to increase the chances of success with toxin-based drug discovery. These include having a clear focus on unmet therapeutic needs, concentrating on targets that are well-validated in terms of their relevance to the disease in question, making use of phenotypic screening rather than molecular-based assays, and working with development partners with the resources required for the long and expensive development process. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.

  2. Mass spectrometry-driven drug discovery for development of herbal medicine.

    Science.gov (United States)

    Zhang, Aihua; Sun, Hui; Wang, Xijun

    2018-05-01

    Herbal medicine (HM) has made a major contribution to the drug discovery process with regard to identifying products compounds. Currently, more attention has been focused on drug discovery from natural compounds of HM. Despite the rapid advancement of modern analytical techniques, drug discovery is still a difficult and lengthy process. Fortunately, mass spectrometry (MS) can provide us with useful structural information for drug discovery, has been recognized as a sensitive, rapid, and high-throughput technology for advancing drug discovery from HM in the post-genomic era. It is essential to develop an efficient, high-quality, high-throughput screening method integrated with an MS platform for early screening of candidate drug molecules from natural products. We have developed a new chinmedomics strategy reliant on MS that is capable of capturing the candidate molecules, facilitating their identification of novel chemical structures in the early phase; chinmedomics-guided natural product discovery based on MS may provide an effective tool that addresses challenges in early screening of effective constituents of herbs against disease. This critical review covers the use of MS with related techniques and methodologies for natural product discovery, biomarker identification, and determination of mechanisms of action. It also highlights high-throughput chinmedomics screening methods suitable for lead compound discovery illustrated by recent successes. © 2016 Wiley Periodicals, Inc.

  3. Combining NMR and X-ray crystallography in fragment-based drug discovery: discovery of highly potent and selective BACE-1 inhibitors.

    Science.gov (United States)

    Wyss, Daniel F; Wang, Yu-Sen; Eaton, Hugh L; Strickland, Corey; Voigt, Johannes H; Zhu, Zhaoning; Stamford, Andrew W

    2012-01-01

    Fragment-based drug discovery (FBDD) has become increasingly popular over the last decade. We review here how we have used highly structure-driven fragment-based approaches to complement more traditional lead discovery to tackle high priority targets and those struggling for leads. Combining biomolecular nuclear magnetic resonance (NMR), X-ray crystallography, and molecular modeling with structure-assisted chemistry and innovative biology as an integrated approach for FBDD can solve very difficult problems, as illustrated in this chapter. Here, a successful FBDD campaign is described that has allowed the development of a clinical candidate for BACE-1, a challenging CNS drug target. Crucial to this achievement were the initial identification of a ligand-efficient isothiourea fragment through target-based NMR screening and the determination of its X-ray crystal structure in complex with BACE-1, which revealed an extensive H-bond network with the two active site aspartate residues. This detailed 3D structural information then enabled the design and validation of novel, chemically stable and accessible heterocyclic acylguanidines as aspartic acid protease inhibitor cores. Structure-assisted fragment hit-to-lead optimization yielded iminoheterocyclic BACE-1 inhibitors that possess desirable molecular properties as potential therapeutic agents to test the amyloid hypothesis of Alzheimer's disease in a clinical setting.

  4. Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions.

    Science.gov (United States)

    Lipinski, Christopher A

    2016-06-01

    The rule of five (Ro5), based on physicochemical profiles of phase II drugs, is consistent with structural limitations in protein targets and the drug target ligands. Three of four parameters in Ro5 are fundamental to the structure of both target and drug binding sites. The chemical structure of the drug ligand depends on the ligand chemistry and design philosophy. Two extremes of chemical structure and design philosophy exist; ligands constructed in the medicinal chemistry synthesis laboratory without input from natural selection and natural product (NP) metabolites biosynthesized based on evolutionary selection. Exceptions to Ro5 are found mostly among NPs. Chemistry chameleon-like behavior of some NPs due to intra-molecular hydrogen bonding as exemplified by cyclosporine A is a strong contributor to NP Ro5 outliers. The fragment derived, drug Navitoclax is an example of the extensive expertise, resources, time and key decisions required for the rare discovery of a non-NP Ro5 outlier. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Binding thermodynamics discriminates fragments from druglike compounds: a thermodynamic description of fragment-based drug discovery.

    Science.gov (United States)

    Williams, Glyn; Ferenczy, György G; Ulander, Johan; Keserű, György M

    2017-04-01

    Small is beautiful - reducing the size and complexity of chemical starting points for drug design allows better sampling of chemical space, reveals the most energetically important interactions within protein-binding sites and can lead to improvements in the physicochemical properties of the final drug. The impact of fragment-based drug discovery (FBDD) on recent drug discovery projects and our improved knowledge of the structural and thermodynamic details of ligand binding has prompted us to explore the relationships between ligand-binding thermodynamics and FBDD. Information on binding thermodynamics can give insights into the contributions to protein-ligand interactions and could therefore be used to prioritise compounds with a high degree of specificity in forming key interactions. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. In silico pharmacology for a multidisciplinary drug discovery process.

    Science.gov (United States)

    Ortega, Santiago Schiaffino; Cara, Luisa Carlota López; Salvador, María Kimatrai

    2012-01-01

    The process of bringing new and innovative drugs, from conception and synthesis through to approval on the market can take the pharmaceutical industry 8-15 years and cost approximately $1.8 billion. Two key technologies are improving the hit-to-drug timeline: high-throughput screening (HTS) and rational drug design. In the latter case, starting from some known ligand-based or target-based information, a lead structure will be rationally designed to be tested in vitro or in vivo. Computational methods are part of many drug discovery programs, including the assessment of ADME (absorption-distribution-metabolism-excretion) and toxicity (ADMET) properties of compounds at the early stages of discovery/development with impressive results. The aim of this paper is to review, in a simple way, some of the most popular strategies used by modelers and some successful applications on computational chemistry to raise awareness of its importance and potential for an actual multidisciplinary drug discovery process.

  7. Conformational transition paths harbor structures useful for aiding drug discovery and understanding enzymatic mechanisms in protein kinases.

    Science.gov (United States)

    Wong, Chung F

    2016-01-01

    This short article examines the usefulness of fast simulations of conformational transition paths in elucidating enzymatic mechanisms and guiding drug discovery for protein kinases. It applies the transition path method in the MOIL software package to simulate the paths of conformational transitions between six pairs of structures from the Protein Data Bank. The structures along the transition paths were found to resemble experimental structures that mimic transient structures believed to form during enzymatic catalysis or conformational transitions, or structures that have drug candidates bound. These findings suggest that such simulations could provide quick initial insights into the enzymatic mechanisms or pathways of conformational transitions of proteins kinases, or could provide structures useful for aiding structure-based drug design. © 2015 The Protein Society.

  8. Use of machine learning approaches for novel drug discovery.

    Science.gov (United States)

    Lima, Angélica Nakagawa; Philot, Eric Allison; Trossini, Gustavo Henrique Goulart; Scott, Luis Paulo Barbour; Maltarollo, Vinícius Gonçalves; Honorio, Kathia Maria

    2016-01-01

    The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds. This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening. Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

  9. Virtual drug discovery: beyond computational chemistry?

    Science.gov (United States)

    Gilardoni, Francois; Arvanites, Anthony C

    2010-02-01

    This editorial looks at how a fully integrated structure that performs all aspects in the drug discovery process, under one company, is slowly disappearing. The steps in the drug discovery paradigm have been slowly increasing toward virtuality or outsourcing at various phases of product development in a company's candidate pipeline. Each step in the process, such as target identification and validation and medicinal chemistry, can be managed by scientific teams within a 'virtual' company. Pharmaceutical companies to biotechnology start-ups have been quick in adopting this new research and development business strategy in order to gain flexibility, access the best technologies and technical expertise, and decrease product developmental costs. In today's financial climate, the term virtual drug discovery has an organizational meaning. It represents the next evolutionary step in outsourcing drug development.

  10. Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery.

    Science.gov (United States)

    Gozalbes, Rafael; Carbajo, Rodrigo J; Pineda-Lucena, Antonio

    2010-01-01

    In the last decade, fragment-based drug discovery (FBDD) has evolved from a novel approach in the search of new hits to a valuable alternative to the high-throughput screening (HTS) campaigns of many pharmaceutical companies. The increasing relevance of FBDD in the drug discovery universe has been concomitant with an implementation of the biophysical techniques used for the detection of weak inhibitors, e.g. NMR, X-ray crystallography or surface plasmon resonance (SPR). At the same time, computational approaches have also been progressively incorporated into the FBDD process and nowadays several computational tools are available. These stretch from the filtering of huge chemical databases in order to build fragment-focused libraries comprising compounds with adequate physicochemical properties, to more evolved models based on different in silico methods such as docking, pharmacophore modelling, QSAR and virtual screening. In this paper we will review the parallel evolution and complementarities of biophysical techniques and computational methods, providing some representative examples of drug discovery success stories by using FBDD.

  11. Structural biology contributions to the discovery of drugs to treat chronic myelogenous leukaemia

    International Nuclear Information System (INIS)

    Cowan-Jacob, Sandra W.; Fendrich, Gabriele; Floersheimer, Andreas; Furet, Pascal; Liebetanz, Janis; Rummel, Gabriele; Rheinberger, Paul; Centeleghe, Mario; Fabbro, Doriano; Manley, Paul W.

    2006-01-01

    A case study showing how the determination of multiple cocrystal structures of the protein tyrosine kinase c-Abl was used to support drug discovery, resulting in a compound effective in the treatment of chronic myelogenous leukaemia. Chronic myelogenous leukaemia (CML) results from the Bcr-Abl oncoprotein, which has a constitutively activated Abl tyrosine kinase domain. Although most chronic phase CML patients treated with imatinib as first-line therapy maintain excellent durable responses, patients who have progressed to advanced-stage CML frequently fail to respond or lose their response to therapy owing to the emergence of drug-resistant mutants of the protein. More than 40 such point mutations have been observed in imatinib-resistant patients. The crystal structures of wild-type and mutant Abl kinase in complex with imatinib and other small-molecule Abl inhibitors were determined, with the aim of understanding the molecular basis of resistance and to aid in the design and optimization of inhibitors active against the resistance mutants. These results are presented in a way which illustrates the approaches used to generate multiple structures, the type of information that can be gained and the way that this information is used to support drug discovery

  12. Assessment of Dengue virus helicase and methyltransferase as targets for fragment-based drug discovery.

    Science.gov (United States)

    Coutard, Bruno; Decroly, Etienne; Li, Changqing; Sharff, Andrew; Lescar, Julien; Bricogne, Gérard; Barral, Karine

    2014-06-01

    Seasonal and pandemic flaviviruses continue to be leading global health concerns. With the view to help drug discovery against Dengue virus (DENV), a fragment-based experimental approach was applied to identify small molecule ligands targeting two main components of the flavivirus replication complex: the NS3 helicase (Hel) and the NS5 mRNA methyltransferase (MTase) domains. A library of 500 drug-like fragments was first screened by thermal-shift assay (TSA) leading to the identification of 36 and 32 fragment hits binding Hel and MTase from DENV, respectively. In a second stage, we set up a fragment-based X-ray crystallographic screening (FBS-X) in order to provide both validated fragment hits and structural binding information. No fragment hit was confirmed for DENV Hel. In contrast, a total of seven fragments were identified as DENV MTase binders and structures of MTase-fragment hit complexes were solved at resolution at least 2.0Å or better. All fragment hits identified contain either a five- or six-membered aromatic ring or both, and three novel binding sites were located on the MTase. To further characterize the fragment hits identified by TSA and FBS-X, we performed enzymatic assays to assess their inhibition effect on the N7- and 2'-O-MTase enzymatic activities: five of these fragment hits inhibit at least one of the two activities with IC50 ranging from 180μM to 9mM. This work validates the FBS-X strategy for identifying new anti-flaviviral hits targeting MTase, while Hel might not be an amenable target for fragment-based drug discovery (FBDD). This approach proved to be a fast and efficient screening method for FBDD target validation and discovery of starting hits for the development of higher affinity molecules that bind to novel allosteric sites. Copyright © 2014 Elsevier B.V. All rights reserved.

  13. The heat is on: thermodynamic analysis in fragment-based drug discovery

    NARCIS (Netherlands)

    Edink, E.S.; Jansen, C.J.W.; Leurs, R.; De Esch, I.J.

    2010-01-01

    Thermodynamic analysis provides access to the determinants of binding affinity, enthalpy and entropy. In fragment-based drug discovery (FBDD), thermodynamic analysis provides a powerful tool to discriminate fragments based on their potential for successful optimization. The thermodynamic data

  14. NMR approaches in structure-based lead discovery: recent developments and new frontiers for targeting multi-protein complexes.

    Science.gov (United States)

    Dias, David M; Ciulli, Alessio

    2014-01-01

    Nuclear magnetic resonance (NMR) spectroscopy is a pivotal method for structure-based and fragment-based lead discovery because it is one of the most robust techniques to provide information on protein structure, dynamics and interaction at an atomic level in solution. Nowadays, in most ligand screening cascades, NMR-based methods are applied to identify and structurally validate small molecule binding. These can be high-throughput and are often used synergistically with other biophysical assays. Here, we describe current state-of-the-art in the portfolio of available NMR-based experiments that are used to aid early-stage lead discovery. We then focus on multi-protein complexes as targets and how NMR spectroscopy allows studying of interactions within the high molecular weight assemblies that make up a vast fraction of the yet untargeted proteome. Finally, we give our perspective on how currently available methods could build an improved strategy for drug discovery against such challenging targets. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  15. MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development.

    Science.gov (United States)

    Korkmaz, Selcuk; Zararsiz, Gokmen; Goksuluk, Dincer

    2015-01-01

    Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/.

  16. The rise of fragment-based drug discovery.

    Science.gov (United States)

    Murray, Christopher W; Rees, David C

    2009-06-01

    The search for new drugs is plagued by high attrition rates at all stages in research and development. Chemists have an opportunity to tackle this problem because attrition can be traced back, in part, to the quality of the chemical leads. Fragment-based drug discovery (FBDD) is a new approach, increasingly used in the pharmaceutical industry, for reducing attrition and providing leads for previously intractable biological targets. FBDD identifies low-molecular-weight ligands (∼150 Da) that bind to biologically important macromolecules. The three-dimensional experimental binding mode of these fragments is determined using X-ray crystallography or NMR spectroscopy, and is used to facilitate their optimization into potent molecules with drug-like properties. Compared with high-throughput-screening, the fragment approach requires fewer compounds to be screened, and, despite the lower initial potency of the screening hits, offers more efficient and fruitful optimization campaigns. Here, we review the rise of FBDD, including its application to discovering clinical candidates against targets for which other chemistry approaches have struggled.

  17. Drugs from the Oceans: Marine Natural Products as Leads for Drug Discovery.

    Science.gov (United States)

    Altmann, Karl-Heinz

    2017-10-25

    The marine environment harbors a vast number of species that are the source of a wide array of structurally diverse bioactive secondary metabolites. At this point in time, roughly 27'000 marine natural products are known, of which eight are (were) at the origin of seven marketed drugs, mostly for the treatment of cancer. The majority of these drugs and also of drug candidates currently undergoing clinical evaluation (excluding antibody-drug conjugates) are unmodified natural products, but synthetic chemistry has played a central role in the discovery and/or development of all but one of the approved marine-derived drugs. More than 1000 new marine natural products have been isolated per year over the last decade, but the pool of new and unique structures is far from exhausted. To fully leverage the potential offered by the structural diversity of marine-produced secondary metabolites for drug discovery will require their broad assessment for different bioactivities and the productive interplay between new fermentation technologies, synthetic organic chemistry, and medicinal chemistry, in order to secure compound supply and enable lead optimization.

  18. In silico tools used for compound selection during target-based drug discovery and development.

    Science.gov (United States)

    Caldwell, Gary W

    2015-01-01

    The target-based drug discovery process, including target selection, screening, hit-to-lead (H2L) and lead optimization stage gates, is the most common approach used in drug development. The full integration of in vitro and/or in vivo data with in silico tools across the entire process would be beneficial to R&D productivity by developing effective selection criteria and drug-design optimization strategies. This review focuses on understanding the impact and extent in the past 5 years of in silico tools on the various stage gates of the target-based drug discovery approach. There are a large number of in silico tools available for establishing selection criteria and drug-design optimization strategies in the target-based approach. However, the inconsistent use of in vitro and/or in vivo data integrated with predictive in silico multiparameter models throughout the process is contributing to R&D productivity issues. In particular, the lack of reliable in silico tools at the H2L stage gate is contributing to the suboptimal selection of viable lead compounds. It is suggested that further development of in silico multiparameter models and organizing biologists, medicinal and computational chemists into one team with a single accountable objective to expand the utilization of in silico tools in all phases of drug discovery would improve R&D productivity.

  19. Science of the science, drug discovery and artificial neural networks.

    Science.gov (United States)

    Patel, Jigneshkumar

    2013-03-01

    Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

  20. West Nile Virus Drug Discovery

    Directory of Open Access Journals (Sweden)

    Siew Pheng Lim

    2013-12-01

    Full Text Available The outbreak of West Nile virus (WNV in 1999 in the USA, and its continued spread throughout the Americas, parts of Europe, the Middle East and Africa, underscored the need for WNV antiviral development. Here, we review the current status of WNV drug discovery. A number of approaches have been used to search for inhibitors of WNV, including viral infection-based screening, enzyme-based screening, structure-based virtual screening, structure-based rationale design, and antibody-based therapy. These efforts have yielded inhibitors of viral or cellular factors that are critical for viral replication. For small molecule inhibitors, no promising preclinical candidate has been developed; most of the inhibitors could not even be advanced to the stage of hit-to-lead optimization due to their poor drug-like properties. However, several inhibitors developed for related members of the family Flaviviridae, such as dengue virus and hepatitis C virus, exhibited cross-inhibition of WNV, suggesting the possibility to re-purpose these antivirals for WNV treatment. Most promisingly, therapeutic antibodies have shown excellent efficacy in mouse model; one of such antibodies has been advanced into clinical trial. The knowledge accumulated during the past fifteen years has provided better rationale for the ongoing WNV and other flavivirus antiviral development.

  1. Open source drug discovery--a new paradigm of collaborative research in tuberculosis drug development.

    Science.gov (United States)

    Bhardwaj, Anshu; Scaria, Vinod; Raghava, Gajendra Pal Singh; Lynn, Andrew Michael; Chandra, Nagasuma; Banerjee, Sulagna; Raghunandanan, Muthukurussi V; Pandey, Vikas; Taneja, Bhupesh; Yadav, Jyoti; Dash, Debasis; Bhattacharya, Jaijit; Misra, Amit; Kumar, Anil; Ramachandran, Srinivasan; Thomas, Zakir; Brahmachari, Samir K

    2011-09-01

    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. Copyright © 2011 Elsevier Ltd. All rights reserved.

  2. ASM Inaugural Lecture 2010: Single crystal X-ray structural determination: A powerful technique for natural products research and drug discovery

    International Nuclear Information System (INIS)

    Fun Hoong Kun; Chantrapromma, S.; Boonnak, N.; Lee, V.S.

    2010-01-01

    Drug discovery from natural products resources have been extensively studied worldwide because natural products with their great structural diversity have traditionally provided most of the drugs in use. They offer major opportunities for finding novel low molecular weight leading-structures that are active against a wide range of assay targets. The most important step in the discovery process is the identification of compounds with interesting biological activity. Single crystal X-ray structure determination is a powerful technique for natural products research and drug discovery. The detailed three-dimensional structures that emerge can be co-related to the activities to these structures. In this article the following is presented: (i) co-crystal and disorder structures; (ii) determination of absolute configuration and (iii) the ability to distinguish between whether a natural product compound is a natural product or a natural product artifact. All these three properties are unique to the technique of single crystal X-ray structure determination. Case (iii) was demonstrated with a compound containing a chromene ring, namely macluraxanthone (which was isolated from Cratoxylum formosum subsp. pruniflorum, a Thai medicinal plant). (author)

  3. Drug-Target Kinetics in Drug Discovery.

    Science.gov (United States)

    Tonge, Peter J

    2018-01-17

    The development of therapies for the treatment of neurological cancer faces a number of major challenges including the synthesis of small molecule agents that can penetrate the blood-brain barrier (BBB). Given the likelihood that in many cases drug exposure will be lower in the CNS than in systemic circulation, it follows that strategies should be employed that can sustain target engagement at low drug concentration. Time dependent target occupancy is a function of both the drug and target concentration as well as the thermodynamic and kinetic parameters that describe the binding reaction coordinate, and sustained target occupancy can be achieved through structural modifications that increase target (re)binding and/or that decrease the rate of drug dissociation. The discovery and deployment of compounds with optimized kinetic effects requires information on the structure-kinetic relationships that modulate the kinetics of binding, and the molecular factors that control the translation of drug-target kinetics to time-dependent drug activity in the disease state. This Review first introduces the potential benefits of drug-target kinetics, such as the ability to delineate both thermodynamic and kinetic selectivity, and then describes factors, such as target vulnerability, that impact the utility of kinetic selectivity. The Review concludes with a description of a mechanistic PK/PD model that integrates drug-target kinetics into predictions of drug activity.

  4. Cell and small animal models for phenotypic drug discovery

    Directory of Open Access Journals (Sweden)

    Szabo M

    2017-06-01

    Full Text Available Mihaly Szabo,1 Sara Svensson Akusjärvi,1 Ankur Saxena,1 Jianping Liu,2 Gayathri Chandrasekar,1 Satish S Kitambi1 1Department of Microbiology Tumor, and Cell Biology, 2Department of Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden Abstract: The phenotype-based drug discovery (PDD approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery. Keywords: phenotype, screening, PDD, discovery, zebrafish, drug

  5. Computational medicinal chemistry in fragment-based drug discovery: what, how and when.

    Science.gov (United States)

    Rabal, Obdulia; Urbano-Cuadrado, Manuel; Oyarzabal, Julen

    2011-01-01

    The use of fragment-based drug discovery (FBDD) has increased in the last decade due to the encouraging results obtained to date. In this scenario, computational approaches, together with experimental information, play an important role to guide and speed up the process. By default, FBDD is generally considered as a constructive approach. However, such additive behavior is not always present, therefore, simple fragment maturation will not always deliver the expected results. In this review, computational approaches utilized in FBDD are reported together with real case studies, where applicability domains are exemplified, in order to analyze them, and then, maximize their performance and reliability. Thus, a proper use of these computational tools can minimize misleading conclusions, keeping the credit on FBDD strategy, as well as achieve higher impact in the drug-discovery process. FBDD goes one step beyond a simple constructive approach. A broad set of computational tools: docking, R group quantitative structure-activity relationship, fragmentation tools, fragments management tools, patents analysis and fragment-hopping, for example, can be utilized in FBDD, providing a clear positive impact if they are utilized in the proper scenario - what, how and when. An initial assessment of additive/non-additive behavior is a critical point to define the most convenient approach for fragments elaboration.

  6. Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field.

    Science.gov (United States)

    Wójcikowski, Maciej; Zielenkiewicz, Piotr; Siedlecki, Pawel

    2015-01-01

    There has been huge progress in the open cheminformatics field in both methods and software development. Unfortunately, there has been little effort to unite those methods and software into one package. We here describe the Open Drug Discovery Toolkit (ODDT), which aims to fulfill the need for comprehensive and open source drug discovery software. The Open Drug Discovery Toolkit was developed as a free and open source tool for both computer aided drug discovery (CADD) developers and researchers. ODDT reimplements many state-of-the-art methods, such as machine learning scoring functions (RF-Score and NNScore) and wraps other external software to ease the process of developing CADD pipelines. ODDT is an out-of-the-box solution designed to be easily customizable and extensible. Therefore, users are strongly encouraged to extend it and develop new methods. We here present three use cases for ODDT in common tasks in computer-aided drug discovery. Open Drug Discovery Toolkit is released on a permissive 3-clause BSD license for both academic and industrial use. ODDT's source code, additional examples and documentation are available on GitHub (https://github.com/oddt/oddt).

  7. Academic Drug Discovery Centres

    DEFF Research Database (Denmark)

    Kirkegaard, Henriette Schultz; Valentin, Finn

    2014-01-01

    Academic drug discovery centres (ADDCs) are seen as one of the solutions to fill the innovation gap in early drug discovery, which has proven challenging for previous organisational models. Prior studies of ADDCs have identified the need to analyse them from the angle of their economic...

  8. [Artificial Intelligence in Drug Discovery].

    Science.gov (United States)

    Fujiwara, Takeshi; Kamada, Mayumi; Okuno, Yasushi

    2018-04-01

    According to the increase of data generated from analytical instruments, application of artificial intelligence(AI)technology in medical field is indispensable. In particular, practical application of AI technology is strongly required in "genomic medicine" and "genomic drug discovery" that conduct medical practice and novel drug development based on individual genomic information. In our laboratory, we have been developing a database to integrate genome data and clinical information obtained by clinical genome analysis and a computational support system for clinical interpretation of variants using AI. In addition, with the aim of creating new therapeutic targets in genomic drug discovery, we have been also working on the development of a binding affinity prediction system for mutated proteins and drugs by molecular dynamics simulation using supercomputer "Kei". We also have tackled for problems in a drug virtual screening. Our developed AI technology has successfully generated virtual compound library, and deep learning method has enabled us to predict interaction between compound and target protein.

  9. The current state of GPCR-based drug discovery to treat metabolic disease.

    Science.gov (United States)

    Sloop, Kyle W; Emmerson, Paul J; Statnick, Michael A; Willard, Francis S

    2018-02-02

    One approach of modern drug discovery is to identify agents that enhance or diminish signal transduction cascades in various cell types and tissues by modulating the activity of GPCRs. This strategy has resulted in the development of new medicines to treat many conditions, including cardiovascular disease, psychiatric disorders, HIV/AIDS, certain forms of cancer and Type 2 diabetes mellitus (T2DM). These successes justify further pursuit of GPCRs as disease targets and provide key learning that should help guide identifying future therapeutic agents. This report reviews the current landscape of GPCR drug discovery with emphasis on efforts aimed at developing new molecules for treating T2DM and obesity. We analyse historical efforts to generate GPCR-based drugs to treat metabolic disease in terms of causal factors leading to success and failure in this endeavour. © 2018 The British Pharmacological Society.

  10. Structural Biology Guides Antibiotic Discovery

    Science.gov (United States)

    Polyak, Steven

    2014-01-01

    Modern drug discovery programs require the contribution of researchers in a number of specialist areas. One of these areas is structural biology. Using X-ray crystallography, the molecular basis of how a drug binds to its biological target and exerts its mode of action can be defined. For example, a drug that binds into the active site of an…

  11. Applied metabolomics in drug discovery.

    Science.gov (United States)

    Cuperlovic-Culf, M; Culf, A S

    2016-08-01

    The metabolic profile is a direct signature of phenotype and biochemical activity following any perturbation. Metabolites are small molecules present in a biological system including natural products as well as drugs and their metabolism by-products depending on the biological system studied. Metabolomics can provide activity information about possible novel drugs and drug scaffolds, indicate interesting targets for drug development and suggest binding partners of compounds. Furthermore, metabolomics can be used for the discovery of novel natural products and in drug development. Metabolomics can enhance the discovery and testing of new drugs and provide insight into the on- and off-target effects of drugs. This review focuses primarily on the application of metabolomics in the discovery of active drugs from natural products and the analysis of chemical libraries and the computational analysis of metabolic networks. Metabolomics methodology, both experimental and analytical is fast developing. At the same time, databases of compounds are ever growing with the inclusion of more molecular and spectral information. An increasing number of systems are being represented by very detailed metabolic network models. Combining these experimental and computational tools with high throughput drug testing and drug discovery techniques can provide new promising compounds and leads.

  12. Computational methods in drug discovery

    OpenAIRE

    Sumudu P. Leelananda; Steffen Lindert

    2016-01-01

    The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery project...

  13. Assessing the structural conservation of protein pockets to study functional and allosteric sites: implications for drug discovery

    Directory of Open Access Journals (Sweden)

    Daura Xavier

    2010-03-01

    Full Text Available Abstract Background With the classical, active-site oriented drug-development approach reaching its limits, protein ligand-binding sites in general and allosteric sites in particular are increasingly attracting the interest of medicinal chemists in the search for new types of targets and strategies to drug development. Given that allostery represents one of the most common and powerful means to regulate protein function, the traditional drug discovery approach of targeting active sites can be extended by targeting allosteric or regulatory protein pockets that may allow the discovery of not only novel drug-like inhibitors, but activators as well. The wealth of available protein structural data can be exploited to further increase our understanding of allosterism, which in turn may have therapeutic applications. A first step in this direction is to identify and characterize putative effector sites that may be present in already available structural data. Results We performed a large-scale study of protein cavities as potential allosteric and functional sites, by integrating publicly available information on protein sequences, structures and active sites for more than a thousand protein families. By identifying common pockets across different structures of the same protein family we developed a method to measure the pocket's structural conservation. The method was first parameterized using known active sites. We characterized the predicted pockets in terms of sequence and structural conservation, backbone flexibility and electrostatic potential. Although these different measures do not tend to correlate, their combination is useful in selecting functional and regulatory sites, as a detailed analysis of a handful of protein families shows. We finally estimated the numbers of potential allosteric or regulatory pockets that may be present in the data set, finding that pockets with putative functional and effector characteristics are widespread across

  14. Medicinal Chemistry Projects Requiring Imaginative Structure-Based Drug Design Methods.

    Science.gov (United States)

    Moitessier, Nicolas; Pottel, Joshua; Therrien, Eric; Englebienne, Pablo; Liu, Zhaomin; Tomberg, Anna; Corbeil, Christopher R

    2016-09-20

    Computational methods for docking small molecules to proteins are prominent in drug discovery. There are hundreds, if not thousands, of documented examples-and several pertinent cases within our research program. Fifteen years ago, our first docking-guided drug design project yielded nanomolar metalloproteinase inhibitors and illustrated the potential of structure-based drug design. Subsequent applications of docking programs to the design of integrin antagonists, BACE-1 inhibitors, and aminoglycosides binding to bacterial RNA demonstrated that available docking programs needed significant improvement. At that time, docking programs primarily considered flexible ligands and rigid proteins. We demonstrated that accounting for protein flexibility, employing displaceable water molecules, and using ligand-based pharmacophores improved the docking accuracy of existing methods-enabling the design of bioactive molecules. The success prompted the development of our own program, Fitted, implementing all of these aspects. The primary motivation has always been to respond to the needs of drug design studies; the majority of the concepts behind the evolution of Fitted are rooted in medicinal chemistry projects and collaborations. Several examples follow: (1) Searching for HDAC inhibitors led us to develop methods considering drug-zinc coordination and its effect on the pKa of surrounding residues. (2) Targeting covalent prolyl oligopeptidase (POP) inhibitors prompted an update to Fitted to identify reactive groups and form bonds with a given residue (e.g., a catalytic residue) when the geometry allows it. Fitted-the first fully automated covalent docking program-was successfully applied to the discovery of four new classes of covalent POP inhibitors. As a result, efficient stereoselective syntheses of a few screening hits were prioritized rather than synthesizing large chemical libraries-yielding nanomolar inhibitors. (3) In order to study the metabolism of POP inhibitors by

  15. Bioinformatics in translational drug discovery.

    Science.gov (United States)

    Wooller, Sarah K; Benstead-Hume, Graeme; Chen, Xiangrong; Ali, Yusuf; Pearl, Frances M G

    2017-08-31

    Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications. © 2017 The Author(s).

  16. Protein crystallography and drug discovery: recollections of knowledge exchange between academia and industry

    Directory of Open Access Journals (Sweden)

    Tom L. Blundell

    2017-07-01

    Full Text Available The development of structure-guided drug discovery is a story of knowledge exchange where new ideas originate from all parts of the research ecosystem. Dorothy Crowfoot Hodgkin obtained insulin from Boots Pure Drug Company in the 1930s and insulin crystallization was optimized in the company Novo in the 1950s, allowing the structure to be determined at Oxford University. The structure of renin was developed in academia, on this occasion in London, in response to a need to develop antihypertensives in pharma. The idea of a dimeric aspartic protease came from an international academic team and was discovered in HIV; it eventually led to new HIV antivirals being developed in industry. Structure-guided fragment-based discovery was developed in large pharma and biotechs, but has been exploited in academia for the development of new inhibitors targeting protein–protein interactions and also antimicrobials to combat mycobacterial infections such as tuberculosis. These observations provide a strong argument against the so-called `linear model', where ideas flow only in one direction from academic institutions to industry. Structure-guided drug discovery is a story of applications of protein crystallography and knowledge exhange between academia and industry that has led to new drug approvals for cancer and other common medical conditions by the Food and Drug Administration in the USA, as well as hope for the treatment of rare genetic diseases and infectious diseases that are a particular challenge in the developing world.

  17. Protein crystallography and drug discovery: recollections of knowledge exchange between academia and industry.

    Science.gov (United States)

    Blundell, Tom L

    2017-07-01

    The development of structure-guided drug discovery is a story of knowledge exchange where new ideas originate from all parts of the research ecosystem. Dorothy Crowfoot Hodgkin obtained insulin from Boots Pure Drug Company in the 1930s and insulin crystallization was optimized in the company Novo in the 1950s, allowing the structure to be determined at Oxford University. The structure of renin was developed in academia, on this occasion in London, in response to a need to develop antihypertensives in pharma. The idea of a dimeric aspartic protease came from an international academic team and was discovered in HIV; it eventually led to new HIV antivirals being developed in industry. Structure-guided fragment-based discovery was developed in large pharma and biotechs, but has been exploited in academia for the development of new inhibitors targeting protein-protein interactions and also antimicrobials to combat mycobacterial infections such as tuberculosis. These observations provide a strong argument against the so-called 'linear model', where ideas flow only in one direction from academic institutions to industry. Structure-guided drug discovery is a story of applications of protein crystallography and knowledge exhange between academia and industry that has led to new drug approvals for cancer and other common medical conditions by the Food and Drug Administration in the USA, as well as hope for the treatment of rare genetic diseases and infectious diseases that are a particular challenge in the developing world.

  18. The SGC beyond structural genomics: redefining the role of 3D structures by coupling genomic stratification with fragment-based discovery.

    Science.gov (United States)

    Bradley, Anthony R; Echalier, Aude; Fairhead, Michael; Strain-Damerell, Claire; Brennan, Paul; Bullock, Alex N; Burgess-Brown, Nicola A; Carpenter, Elisabeth P; Gileadi, Opher; Marsden, Brian D; Lee, Wen Hwa; Yue, Wyatt; Bountra, Chas; von Delft, Frank

    2017-11-08

    The ongoing explosion in genomics data has long since outpaced the capacity of conventional biochemical methodology to verify the large number of hypotheses that emerge from the analysis of such data. In contrast, it is still a gold-standard for early phenotypic validation towards small-molecule drug discovery to use probe molecules (or tool compounds), notwithstanding the difficulty and cost of generating them. Rational structure-based approaches to ligand discovery have long promised the efficiencies needed to close this divergence; in practice, however, this promise remains largely unfulfilled, for a host of well-rehearsed reasons and despite the huge technical advances spearheaded by the structural genomics initiatives of the noughties. Therefore the current, fourth funding phase of the Structural Genomics Consortium (SGC), building on its extensive experience in structural biology of novel targets and design of protein inhibitors, seeks to redefine what it means to do structural biology for drug discovery. We developed the concept of a Target Enabling Package (TEP) that provides, through reagents, assays and data, the missing link between genetic disease linkage and the development of usefully potent compounds. There are multiple prongs to the ambition: rigorously assessing targets' genetic disease linkages through crowdsourcing to a network of collaborating experts; establishing a systematic approach to generate the protocols and data that comprise each target's TEP; developing new, X-ray-based fragment technologies for generating high quality chemical matter quickly and cheaply; and exploiting a stringently open access model to build multidisciplinary partnerships throughout academia and industry. By learning how to scale these approaches, the SGC aims to make structures finally serve genomics, as originally intended, and demonstrate how 3D structures systematically allow new modes of druggability to be discovered for whole classes of targets. © 2017 The

  19. Trypanosomatids topoisomerase re-visited. New structural findings and role in drug discovery

    Directory of Open Access Journals (Sweden)

    Rafael Balaña-Fouce

    2014-12-01

    Full Text Available The Trypanosomatidae family, composed of unicellular parasites, causes severe vector-borne diseases that afflict human populations worldwide. Chagas disease, sleeping sickness, as well as different sorts of leishmaniases are amongst the most important infectious diseases produced by Trypanosoma cruzi, Trypanosoma brucei and Leishmania spp., respectively. All these infections are closely related to weak health care services in low-income populations of less developed and least economically developed countries. Search for new therapeutic targets in order to hit these pathogens is of paramount priority, as no effective vaccine is currently in use against any of these parasites. Furthermore, present-day chemotherapy comprises old-fashioned drugs full of important side effects. Besides, they are prone to produce tolerance and resistance as a consequence of their continuous use for decades. DNA topoisomerases (Top are ubiquitous enzymes responsible for solving the torsional tensions caused during replication and transcription processes, as well as in maintaining genomic stability during DNA recombination. As the inhibition of these enzymes produces cell arrest and triggers cell death, Top inhibitors are among the most effective and most widely used drugs in both cancer and antibacterial therapies. Top relaxation and decatenation activities, which are based on a common nicking–closing cycle involving one or both DNA strands, have been pointed as a promising drug target. Specific inhibitors that bind to the interface of DNA-Top complexes can stabilize Top-mediated transient DNA breaks. In addition, important structural differences have been found between Tops from the Trypanosomatidae family members and Tops from the host. Such dissimilarities make these proteins very interesting for drug design and molecular intervention. The present review is a critical update of the last findings regarding trypanosomatid’s Tops, their new structural features

  20. Natural Products as Leads in Schistosome Drug Discovery

    Directory of Open Access Journals (Sweden)

    Bruno J. Neves

    2015-01-01

    Full Text Available Schistosomiasis is a neglected parasitic tropical disease that claims around 200,000 human lives every year. Praziquantel (PZQ, the only drug recommended by the World Health Organization for the treatment and control of human schistosomiasis, is now facing the threat of drug resistance, indicating the urgent need for new effective compounds to treat this disease. Therefore, globally, there is renewed interest in natural products (NPs as a starting point for drug discovery and development for schistosomiasis. Recent advances in genomics, proteomics, bioinformatics, and cheminformatics have brought about unprecedented opportunities for the rapid and more cost-effective discovery of new bioactive compounds against neglected tropical diseases. This review highlights the main contributions that NP drug discovery and development have made in the treatment of schistosomiasis and it discusses how integration with virtual screening (VS strategies may contribute to accelerating the development of new schistosomidal leads, especially through the identification of unexplored, biologically active chemical scaffolds and structural optimization of NPs with previously established activity.

  1. A Bioinorganic Approach to Fragment-Based Drug Discovery Targeting Metalloenzymes.

    Science.gov (United States)

    Cohen, Seth M

    2017-08-15

    Metal-dependent enzymes (i.e., metalloenzymes) make up a large fraction of all enzymes and are critically important in a wide range of biological processes, including DNA modification, protein homeostasis, antibiotic resistance, and many others. Consequently, metalloenzymes represent a vast and largely untapped space for drug development. The discovery of effective therapeutics that target metalloenzymes lies squarely at the interface of bioinorganic and medicinal chemistry and requires expertise, methods, and strategies from both fields to mount an effective campaign. In this Account, our research program that brings together the principles and methods of bioinorganic and medicinal chemistry are described, in an effort to bridge the gap between these fields and address an important class of medicinal targets. Fragment-based drug discovery (FBDD) is an important drug discovery approach that is particularly well suited for metalloenzyme inhibitor development. FBDD uses relatively small but diverse chemical structures that allow for the assembly of privileged molecular collections that focus on a specific feature of the target enzyme. For metalloenzyme inhibition, the specific feature is rather obvious, namely, a metal-dependent active site. Surprisingly, prior to our work, the exploration of diverse molecular fragments for binding the metal active sites of metalloenzymes was largely unexplored. By assembling a modest library of metal-binding pharmacophores (MBPs), we have been able to find lead hits for many metalloenzymes and, from these hits, develop inhibitors that act via novel mechanisms of action. A specific case study on the use of this strategy to identify a first-in-class inhibitor of zinc-dependent Rpn11 (a component of the proteasome) is highlighted. The application of FBDD for the development of metalloenzyme inhibitors has raised several other compelling questions, such as how the metalloenzyme active site influences the coordination chemistry of bound

  2. Fragment based drug discovery: practical implementation based on ¹⁹F NMR spectroscopy.

    Science.gov (United States)

    Jordan, John B; Poppe, Leszek; Xia, Xiaoyang; Cheng, Alan C; Sun, Yax; Michelsen, Klaus; Eastwood, Heather; Schnier, Paul D; Nixey, Thomas; Zhong, Wenge

    2012-01-26

    Fragment based drug discovery (FBDD) is a widely used tool for discovering novel therapeutics. NMR is a powerful means for implementing FBDD, and several approaches have been proposed utilizing (1)H-(15)N heteronuclear single quantum coherence (HSQC) as well as one-dimensional (1)H and (19)F NMR to screen compound mixtures against a target of interest. While proton-based NMR methods of fragment screening (FBS) have been well documented and are widely used, the use of (19)F detection in FBS has been only recently introduced (Vulpetti et al. J. Am. Chem. Soc.2009, 131 (36), 12949-12959) with the aim of targeting "fluorophilic" sites in proteins. Here, we demonstrate a more general use of (19)F NMR-based fragment screening in several areas: as a key tool for rapid and sensitive detection of fragment hits, as a method for the rapid development of structure-activity relationship (SAR) on the hit-to-lead path using in-house libraries and/or commercially available compounds, and as a quick and efficient means of assessing target druggability.

  3. Can biochemistry drive drug discovery beyond simple potency measurements?

    Science.gov (United States)

    Chène, Patrick

    2012-04-01

    Among the fields of expertise required to develop drugs successfully, biochemistry holds a key position in drug discovery at the interface between chemistry, structural biology and cell biology. However, taking the example of protein kinases, it appears that biochemical assays are mostly used in the pharmaceutical industry to measure compound potency and/or selectivity. This limited use of biochemistry is surprising, given that detailed biochemical analyses are commonly used in academia to unravel molecular recognition processes. In this article, I show that biochemistry can provide invaluable information on the dynamics and energetics of compound-target interactions that cannot be obtained on the basis of potency measurements and structural data. Therefore, an extensive use of biochemistry in drug discovery could facilitate the identification and/or development of new drugs. Copyright © 2012 Elsevier Ltd. All rights reserved.

  4. Native Mass Spectrometry in Fragment-Based Drug Discovery

    Directory of Open Access Journals (Sweden)

    Liliana Pedro

    2016-07-01

    Full Text Available The advent of native mass spectrometry (MS in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein–ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD. Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.

  5. Native Mass Spectrometry in Fragment-Based Drug Discovery.

    Science.gov (United States)

    Pedro, Liliana; Quinn, Ronald J

    2016-07-28

    The advent of native mass spectrometry (MS) in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein-ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD). Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.

  6. Maximum Entropy in Drug Discovery

    Directory of Open Access Journals (Sweden)

    Chih-Yuan Tseng

    2014-07-01

    Full Text Available Drug discovery applies multidisciplinary approaches either experimentally, computationally or both ways to identify lead compounds to treat various diseases. While conventional approaches have yielded many US Food and Drug Administration (FDA-approved drugs, researchers continue investigating and designing better approaches to increase the success rate in the discovery process. In this article, we provide an overview of the current strategies and point out where and how the method of maximum entropy has been introduced in this area. The maximum entropy principle has its root in thermodynamics, yet since Jaynes’ pioneering work in the 1950s, the maximum entropy principle has not only been used as a physics law, but also as a reasoning tool that allows us to process information in hand with the least bias. Its applicability in various disciplines has been abundantly demonstrated. We give several examples of applications of maximum entropy in different stages of drug discovery. Finally, we discuss a promising new direction in drug discovery that is likely to hinge on the ways of utilizing maximum entropy.

  7. Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review

    Science.gov (United States)

    Csermely, Peter; Korcsmáros, Tamás; Kiss, Huba J.M.; London, Gábor; Nussinov, Ruth

    2013-01-01

    Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach. PMID:23384594

  8. OSIRIS, an entirely in-house developed drug discovery informatics system.

    Science.gov (United States)

    Sander, Thomas; Freyss, Joel; von Korff, Modest; Reich, Jacqueline Renée; Rufener, Christian

    2009-02-01

    We present OSIRIS, an entirely in-house developed drug discovery informatics system. Its components cover all information handling aspects from compound synthesis via biological testing to preclinical development. Its design principles are platform and vendor independence, a consistent look and feel, and complete coverage of the drug discovery process by custom tailored applications. These include electronic laboratory notebook applications for biology and chemistry, tools for high-throughput and secondary screening evaluation, chemistry-aware data visualization, physicochemical property prediction, 3D-pharmacophore comparisons, interactive modeling, computing grid based ligand-protein docking, and more. Most applications are developed in Java and are built on top of a Java library layer that provides reusable cheminformatics functionality and GUI components such as chemical editors, structure canonicalization, substructure search, combinatorial enumeration, enhanced stereo perception, force field minimization, and conformation generation.

  9. Incorporation of rapid thermodynamic data in fragment-based drug discovery.

    Science.gov (United States)

    Kobe, Akihiro; Caaveiro, Jose M M; Tashiro, Shinya; Kajihara, Daisuke; Kikkawa, Masato; Mitani, Tomoya; Tsumoto, Kouhei

    2013-03-14

    Fragment-based drug discovery (FBDD) has enjoyed increasing popularity in recent years. We introduce SITE (single-injection thermal extinction), a novel thermodynamic methodology that selects high-quality hits early in FBDD. SITE is a fast calorimetric competitive assay suitable for automation that captures the essence of isothermal titration calorimetry but using significantly fewer resources. We describe the principles of SITE and identify a novel family of fragment inhibitors of the enzyme ketosteroid isomerase displaying high values of enthalpic efficiency.

  10. Targeting cysteine proteases in trypanosomatid disease drug discovery.

    Science.gov (United States)

    Ferreira, Leonardo G; Andricopulo, Adriano D

    2017-12-01

    Chagas disease and human African trypanosomiasis are endemic conditions in Latin America and Africa, respectively, for which no effective and safe therapy is available. Efforts in drug discovery have focused on several enzymes from these protozoans, among which cysteine proteases have been validated as molecular targets for pharmacological intervention. These enzymes are expressed during the entire life cycle of trypanosomatid parasites and are essential to many biological processes, including infectivity to the human host. As a result of advances in the knowledge of the structural aspects of cysteine proteases and their role in disease physiopathology, inhibition of these enzymes by small molecules has been demonstrated to be a worthwhile approach to trypanosomatid drug research. This review provides an update on drug discovery strategies targeting the cysteine peptidases cruzain from Trypanosoma cruzi and rhodesain and cathepsin B from Trypanosoma brucei. Given that current chemotherapy for Chagas disease and human African trypanosomiasis has several drawbacks, cysteine proteases will continue to be actively pursued as valuable molecular targets in trypanosomatid disease drug discovery efforts. Copyright © 2017. Published by Elsevier Inc.

  11. Revisiting lab-on-a-chip technology for drug discovery.

    Science.gov (United States)

    Neuži, Pavel; Giselbrecht, Stefan; Länge, Kerstin; Huang, Tony Jun; Manz, Andreas

    2012-08-01

    The field of microfluidics or lab-on-a-chip technology aims to improve and extend the possibilities of bioassays, cell biology and biomedical research based on the idea of miniaturization. Microfluidic systems allow more accurate modelling of physiological situations for both fundamental research and drug development, and enable systematic high-volume testing for various aspects of drug discovery. Microfluidic systems are in development that not only model biological environments but also physically mimic biological tissues and organs; such 'organs on a chip' could have an important role in expediting early stages of drug discovery and help reduce reliance on animal testing. This Review highlights the latest lab-on-a-chip technologies for drug discovery and discusses the potential for future developments in this field.

  12. Web-based services for drug design and discovery.

    Science.gov (United States)

    Frey, Jeremy G; Bird, Colin L

    2011-09-01

    Reviews of the development of drug discovery through the 20(th) century recognised the importance of chemistry and increasingly bioinformatics, but had relatively little to say about the importance of computing and networked computing in particular. However, the design and discovery of new drugs is arguably the most significant single application of bioinformatics and cheminformatics to have benefitted from the increases in the range and power of the computational techniques since the emergence of the World Wide Web, commonly now referred to as simply 'the Web'. Web services have enabled researchers to access shared resources and to deploy standardized calculations in their search for new drugs. This article first considers the fundamental principles of Web services and workflows, and then explores the facilities and resources that have evolved to meet the specific needs of chem- and bio-informatics. This strategy leads to a more detailed examination of the basic components that characterise molecules and the essential predictive techniques, followed by a discussion of the emerging networked services that transcend the basic provisions, and the growing trend towards embracing modern techniques, in particular the Semantic Web. In the opinion of the authors, the issues that require community action are: increasing the amount of chemical data available for open access; validating the data as provided; and developing more efficient links between the worlds of cheminformatics and bioinformatics. The goal is to create ever better drug design services.

  13. Deep Learning in Drug Discovery.

    Science.gov (United States)

    Gawehn, Erik; Hiss, Jan A; Schneider, Gisbert

    2016-01-01

    Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. The discovery of drug-induced illness.

    Science.gov (United States)

    Jick, H

    1977-03-03

    The increased use of drugs (and the concurrent increased risks of drug-induced illness) require definition of relevant research areas and strategy. For established marketed drugs, research needs depend on the magnitudes of risk of an illness from a drug and the base-line risk. With the drug risk high and the base-line risk low, the problem surfaces in premarketing studies or through the epidemic that develops after marketing. If the drug adds slightly to a high base-line risk, the effect is undetectable. When both risks are low, adverse effects can be discovered by chance, but systematic case-referent studies can speed discovery. If both risks are high, clinical trials and nonexperimental studies may be used. With both risks intermediate, systematic evaluations, especially case-referent studies are needed. Newly marketed drugs should be routinely evaluated through compulsory registration and follow-up study of the earliest users.

  15. Synthetic biology for pharmaceutical drug discovery

    Directory of Open Access Journals (Sweden)

    Trosset JY

    2015-12-01

    Full Text Available Jean-Yves Trosset,1 Pablo Carbonell2,3 1Bioinformation Research Laboratory, Sup’Biotech, Villejuif, France; 2Faculty of Life Sciences, SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK; 3Department of Experimental and Health Sciences (DCEXS, Research Programme on Biomedical Informatics (GRIB, Hospital del Mar Medical Research Institute (IMIM, Universitat Pompeu Fabra (UPF, Barcelona, Spain Abstract: Synthetic biology (SB is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell–cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity. Keywords: metabolic engineering, plant synthetic biology, natural products, synthetic quorum sensing, drug resistance

  16. Leveraging the contribution of thermodynamics in drug discovery with the help of fluorescence-based thermal shift assays.

    Science.gov (United States)

    Hau, Jean Christophe; Fontana, Patrizia; Zimmermann, Catherine; De Pover, Alain; Erdmann, Dirk; Chène, Patrick

    2011-06-01

    The development of new drugs with better pharmacological and safety properties mandates the optimization of several parameters. Today, potency is often used as the sole biochemical parameter to identify and select new molecules. Surprisingly, thermodynamics, which is at the core of any interaction, is rarely used in drug discovery, even though it has been suggested that the selection of scaffolds according to thermodynamic criteria may be a valuable strategy. This poor integration of thermodynamics in drug discovery might be due to difficulties in implementing calorimetry experiments despite recent technological progress in this area. In this report, the authors show that fluorescence-based thermal shift assays could be used as prescreening methods to identify compounds with different thermodynamic profiles. This approach allows a reduction in the number of compounds to be tested in calorimetry experiments, thus favoring greater integration of thermodynamics in drug discovery.

  17. Drug target ontology to classify and integrate drug discovery data.

    Science.gov (United States)

    Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande; Turner, John Paul; Vidovic, Dusica; Forlin, Michele; Koleti, Amar; Nguyen, Dac-Trung; Jensen, Lars Juhl; Guha, Rajarshi; Mathias, Stephen L; Ursu, Oleg; Stathias, Vasileios; Duan, Jianbin; Nabizadeh, Nooshin; Chung, Caty; Mader, Christopher; Visser, Ubbo; Yang, Jeremy J; Bologa, Cristian G; Oprea, Tudor I; Schürer, Stephan C

    2017-11-09

    One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. DTO was built based on the need for a formal semantic

  18. Arthritis Genetics Analysis Aids Drug Discovery

    Science.gov (United States)

    ... NIH Research Matters January 13, 2014 Arthritis Genetics Analysis Aids Drug Discovery An international research team identified 42 new ... Edition Distracted Driving Raises Crash Risk Arthritis Genetics Analysis Aids Drug Discovery Oxytocin Affects Facial Recognition Connect with Us ...

  19. A high content screening assay to predict human drug-induced liver injury during drug discovery.

    Science.gov (United States)

    Persson, Mikael; Løye, Anni F; Mow, Tomas; Hornberg, Jorrit J

    2013-01-01

    Adverse drug reactions are a major cause for failures of drug development programs, drug withdrawals and use restrictions. Early hazard identification and diligent risk avoidance strategies are therefore essential. For drug-induced liver injury (DILI), this is difficult using conventional safety testing. To reduce the risk for DILI, drug candidates with a high risk need to be identified and deselected. And, to produce drug candidates without that risk associated, risk factors need to be assessed early during drug discovery, such that lead series can be optimized on safety parameters. This requires methods that allow for medium-to-high throughput compound profiling and that generate quantitative results suitable to establish structure-activity-relationships during lead optimization programs. We present the validation of such a method, a novel high content screening assay based on six parameters (nuclei counts, nuclear area, plasma membrane integrity, lysosomal activity, mitochondrial membrane potential (MMP), and mitochondrial area) using ~100 drugs of which the clinical hepatotoxicity profile is known. We find that a 100-fold TI between the lowest toxic concentration and the therapeutic Cmax is optimal to classify compounds as hepatotoxic or non-hepatotoxic, based on the individual parameters. Most parameters have ~50% sensitivity and ~90% specificity. Drugs hitting ≥2 parameters at a concentration below 100-fold their Cmax are typically hepatotoxic, whereas non-hepatotoxic drugs typically hit based on nuclei count, MMP and human Cmax, we identified an area without a single false positive, while maintaining 45% sensitivity. Hierarchical clustering using the multi-parametric dataset roughly separates toxic from non-toxic compounds. We employ the assay in discovery projects to prioritize novel compound series during hit-to-lead, to steer away from a DILI risk during lead optimization, for risk assessment towards candidate selection and to provide guidance of safe

  20. Discovery of inhibitors of bacterial histidine kinases

    NARCIS (Netherlands)

    Velikova, N.R.

    2014-01-01

    Discovery of Inhibitors of Bacterial Histidine Kinases Summary

    The thesis is on novel antibacterial drug discovery (http://youtu.be/NRMWOGgeysM). Using structure-based and fragment-based drug discovery approach, we have identified small-molecule histidine-kinase

  1. Fragment-Based Drug Discovery in the Bromodomain and Extra-Terminal Domain Family.

    Science.gov (United States)

    Radwan, Mostafa; Serya, Rabah

    2017-08-01

    Bromodomain and extra-terminal domain (BET) inhibition has emerged recently as a potential therapeutic target for the treatment of many human disorders such as atherosclerosis, inflammatory disorders, chronic obstructive pulmonary disease (COPD), some viral infections, and cancer. Since the discovery of the two potent inhibitors, I-BET762 and JQ1, different research groups have used different techniques to develop novel potent and selective inhibitors. In this review, we will be concerned with the trials that used fragment-based drug discovery (FBDD) approaches to discover or optimize BET inhibitors, also showing fragments that can be further optimized in future projects to reach novel potent BET inhibitors. © 2017 Deutsche Pharmazeutische Gesellschaft.

  2. Freely Accessible Chemical Database Resources of Compounds for in Silico Drug Discovery.

    Science.gov (United States)

    Yang, JingFang; Wang, Di; Jia, Chenyang; Wang, Mengyao; Hao, GeFei; Yang, GuangFu

    2018-05-07

    In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to overcome these obstacles, freely accessible database resources of compounds have bloomed in recent years. Nevertheless, how to choose appropriate tools to treat these freely accessible databases are crucial. To the best of our knowledge, this is the first systematic review on this issue. The existed advantages and drawbacks of chemical databases were analyzed and summarized based on the collected six categories of freely accessible chemical databases from literature in this review. Suggestions on how and in which conditions the usage of these databases could be reasonable were provided. Tools and procedures for building 3D structure chemical libraries were also introduced. In this review, we described the freely accessible chemical database resources for in silico drug discovery. In particular, the chemical information for building chemical database appears as attractive resources for drug design to alleviate experimental pressure. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  3. Pathways to new drug discovery in neuropsychiatry

    Directory of Open Access Journals (Sweden)

    Berk Michael

    2012-11-01

    Full Text Available Abstract There is currently a crisis in drug discovery for neuropsychiatric disorders, with a profound, yet unexpected drought in new drug development across the spectrum. In this commentary, the sources of this dilemma and potential avenues to redress the issue are explored. These include a critical review of diagnostic issues and of selection of participants for clinical trials, and the mechanisms for identifying new drugs and new drug targets. Historically, the vast majority of agents have been discovered serendipitously or have been modifications of existing agents. Serendipitous discoveries, based on astute clinical observation or data mining, remain a valid option, as is illustrated by the suggestion in the paper by Wahlqvist and colleagues that treatment with sulfonylurea and metformin reduces the risk of affective disorder. However, the identification of agents targeting disorder-related biomarkers is currently proving particularly fruitful. There is considerable hope for genetics as a purist, pathophysiologically valid pathway to drug discovery; however, it is unclear whether the science is ready to meet this promise. Fruitful paradigms will require a break from the orthodoxy, and creativity and risk may well be the fingerprints of success. See related article http://www.biomedcentral.com/1741-7015/10/150

  4. Novel Technology for Protein-Protein Interaction-based Targeted Drug Discovery

    Directory of Open Access Journals (Sweden)

    Jung Me Hwang

    2011-12-01

    Full Text Available We have developed a simple but highly efficient in-cell protein-protein interaction (PPI discovery system based on the translocation properties of protein kinase C- and its C1a domain in live cells. This system allows the visual detection of trimeric and dimeric protein interactions including cytosolic, nuclear, and/or membrane proteins with their cognate ligands. In addition, this system can be used to identify pharmacological small compounds that inhibit specific PPIs. These properties make this PPI system an attractive tool for screening drug candidates and mapping the protein interactome.

  5. When fragments link : a bibliometric perspective on the development of fragment-based drug discovery

    NARCIS (Netherlands)

    Romasanta, A.K.S.; van der Sijde, P.C.; Hellsten, I.; Hubbard, Roderick E.; Keseru, Gyorgy M.; van Muijlwijk-Koezen, Jacqueline E.; de Esch, I.J.P.

    2018-01-01

    Fragment-based drug discovery (FBDD) is a highly interdisciplinary field, rich in ideas integrated from pharmaceutical sciences, chemistry, biology, and physics, among others. To enrich our understanding of the development of the field, we used bibliometric techniques to analyze 3642 publications in

  6. The in silico drug discovery toolbox: applications in lead discovery and optimization.

    Science.gov (United States)

    Bruno, Agostino; Costantino, Gabriele; Sartori, Luca; Radi, Marco

    2017-11-06

    Discovery and development of a new drug is a long lasting and expensive journey that takes around 15 years from starting idea to approval and marketing of new medication. Despite the R&D expenditures have been constantly increasing in the last few years, number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. From this point of view, it is clear that if we want to increase drug-discovery success rate and reduce costs associated with development of a new drug, a comprehensive evaluation/prediction of potential safety issues should be conducted as soon as possible during early drug discovery phase. In the present review, we will analyse the early steps of drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  7. Discovery of novel dengue virus NS5 methyltransferase non-nucleoside inhibitors by fragment-based drug design.

    Science.gov (United States)

    Benmansour, Fatiha; Trist, Iuni; Coutard, Bruno; Decroly, Etienne; Querat, Gilles; Brancale, Andrea; Barral, Karine

    2017-01-05

    With the aim to help drug discovery against dengue virus (DENV), a fragment-based drug design approach was applied to identify ligands targeting a main component of DENV replication complex: the NS5 AdoMet-dependent mRNA methyltransferase (MTase) domain, playing an essential role in the RNA capping process. Herein, we describe the identification of new inhibitors developed using fragment-based, structure-guided linking and optimization techniques. Thermal-shift assay followed by a fragment-based X-ray crystallographic screening lead to the identification of three fragment hits binding DENV MTase. We considered linking two of them, which bind to proximal sites of the AdoMet binding pocket, in order to improve their potency. X-ray crystallographic structures and computational docking were used to guide the fragment linking, ultimately leading to novel series of non-nucleoside inhibitors of flavivirus MTase, respectively N-phenyl-[(phenylcarbamoyl)amino]benzene-1-sulfonamide and phenyl [(phenylcarbamoyl)amino]benzene-1-sulfonate derivatives, that show a 10-100-fold stronger inhibition of 2'-O-MTase activity compared to the initial fragments. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  8. SWEETLEAD: an in silico database of approved drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery.

    Directory of Open Access Journals (Sweden)

    Paul A Novick

    Full Text Available In the face of drastically rising drug discovery costs, strategies promising to reduce development timelines and expenditures are being pursued. Computer-aided virtual screening and repurposing approved drugs are two such strategies that have shown recent success. Herein, we report the creation of a highly-curated in silico database of chemical structures representing approved drugs, chemical isolates from traditional medicinal herbs, and regulated chemicals, termed the SWEETLEAD database. The motivation for SWEETLEAD stems from the observance of conflicting information in publicly available chemical databases and the lack of a highly curated database of chemical structures for the globally approved drugs. A consensus building scheme surveying information from several publicly accessible databases was employed to identify the correct structure for each chemical. Resulting structures are filtered for the active pharmaceutical ingredient, standardized, and differing formulations of the same drug were combined in the final database. The publically available release of SWEETLEAD (https://simtk.org/home/sweetlead provides an important tool to enable the successful completion of computer-aided repurposing and drug discovery campaigns.

  9. Biomimicry as a basis for drug discovery.

    Science.gov (United States)

    Kolb, V M

    1998-01-01

    Selected works are discussed which clearly demonstrate that mimicking various aspects of the process by which natural products evolved is becoming a powerful tool in contemporary drug discovery. Natural products are an established and rich source of drugs. The term "natural product" is often used synonymously with "secondary metabolite." Knowledge of genetics and molecular evolution helps us understand how biosynthesis of many classes of secondary metabolites evolved. One proposed hypothesis is termed "inventive evolution." It invokes duplication of genes, and mutation of the gene copies, among other genetic events. The modified duplicate genes, per se or in conjunction with other genetic events, may give rise to new enzymes, which, in turn, may generate new products, some of which may be selected for. Steps of the inventive evolution can be mimicked in several ways for purpose of drug discovery. For example, libraries of chemical compounds of any imaginable structure may be produced by combinatorial synthesis. Out of these libraries new active compounds can be selected. In another example, genetic system can be manipulated to produce modified natural products ("unnatural natural products"), from which new drugs can be selected. In some instances, similar natural products turn up in species that are not direct descendants of each other. This is presumably due to a horizontal gene transfer. The mechanism of this inter-species gene transfer can be mimicked in therapeutic gene delivery. Mimicking specifics or principles of chemical evolution including experimental and test-tube evolution also provides leads for new drug discovery.

  10. Application of PBPK modelling in drug discovery and development at Pfizer.

    Science.gov (United States)

    Jones, Hannah M; Dickins, Maurice; Youdim, Kuresh; Gosset, James R; Attkins, Neil J; Hay, Tanya L; Gurrell, Ian K; Logan, Y Raj; Bungay, Peter J; Jones, Barry C; Gardner, Iain B

    2012-01-01

    Early prediction of human pharmacokinetics (PK) and drug-drug interactions (DDI) in drug discovery and development allows for more informed decision making. Physiologically based pharmacokinetic (PBPK) modelling can be used to answer a number of questions throughout the process of drug discovery and development and is thus becoming a very popular tool. PBPK models provide the opportunity to integrate key input parameters from different sources to not only estimate PK parameters and plasma concentration-time profiles, but also to gain mechanistic insight into compound properties. Using examples from the literature and our own company, we have shown how PBPK techniques can be utilized through the stages of drug discovery and development to increase efficiency, reduce the need for animal studies, replace clinical trials and to increase PK understanding. Given the mechanistic nature of these models, the future use of PBPK modelling in drug discovery and development is promising, however, some limitations need to be addressed to realize its application and utility more broadly.

  11. Application of Combination High-Throughput Phenotypic Screening and Target Identification Methods for the Discovery of Natural Product-Based Combination Drugs.

    Science.gov (United States)

    Isgut, Monica; Rao, Mukkavilli; Yang, Chunhua; Subrahmanyam, Vangala; Rida, Padmashree C G; Aneja, Ritu

    2018-03-01

    Modern drug discovery efforts have had mediocre success rates with increasing developmental costs, and this has encouraged pharmaceutical scientists to seek innovative approaches. Recently with the rise of the fields of systems biology and metabolomics, network pharmacology (NP) has begun to emerge as a new paradigm in drug discovery, with a focus on multiple targets and drug combinations for treating disease. Studies on the benefits of drug combinations lay the groundwork for a renewed focus on natural products in drug discovery. Natural products consist of a multitude of constituents that can act on a variety of targets in the body to induce pharmacodynamic responses that may together culminate in an additive or synergistic therapeutic effect. Although natural products cannot be patented, they can be used as starting points in the discovery of potent combination therapeutics. The optimal mix of bioactive ingredients in natural products can be determined via phenotypic screening. The targets and molecular mechanisms of action of these active ingredients can then be determined using chemical proteomics, and by implementing a reverse pharmacokinetics approach. This review article provides evidence supporting the potential benefits of natural product-based combination drugs, and summarizes drug discovery methods that can be applied to this class of drugs. © 2017 Wiley Periodicals, Inc.

  12. Use of allosteric targets in the discovery of safer drugs.

    Science.gov (United States)

    Grover, Ashok Kumar

    2013-01-01

    The need for drugs with fewer side effects cannot be overemphasized. Today, most drugs modify the actions of enzymes, receptors, transporters and other molecules by directly binding to their active (orthosteric) sites. However, orthosteric site configuration is similar in several proteins performing related functions and this leads to a lower specificity of a drug for the desired protein. Consequently, such drugs may have adverse side effects. A new basis of drug discovery is emerging based on the binding of the drug molecules to sites away (allosteric) from the orthosteric sites. It is possible to find allosteric sites which are unique and hence more specific as targets for drug discovery. Of many available examples, two are highlighted here. The first is caloxins - a new class of highly specific inhibitors of plasma membrane Ca²⁺ pumps. The second concerns the modulation of receptors for the neurotransmitter acetylcholine, which binds to 12 types of receptors. Exploitation of allosteric sites has led to the discovery of drugs which can selectively modulate the activation of only 1 (M1 muscarinic) out of the 12 different types of acetylcholine receptors. These drugs are being tested for schizophrenia treatment. It is anticipated that the drug discovery exploiting allosteric sites will lead to more effective therapeutic agents with fewer side effects. Copyright © 2013 S. Karger AG, Basel.

  13. Grid-based Continual Analysis of Molecular Interior for Drug Discovery, QSAR and QSPR.

    Science.gov (United States)

    Potemkin, Andrey V; Grishina, Maria A; Potemkin, Vladimir A

    2017-01-01

    In 1979, R.D.Cramer and M.Milne made a first realization of 3D comparison of molecules by aligning them in space and by mapping their molecular fields to a 3D grid. Further, this approach was developed as the DYLOMMS (Dynamic Lattice- Oriented Molecular Modelling System) approach. In 1984, H.Wold and S.Wold proposed the use of partial least squares (PLS) analysis, instead of principal component analysis, to correlate the field values with biological activities. Then, in 1988, the method which was called CoMFA (Comparative Molecular Field Analysis) was introduced and the appropriate software became commercially available. Since 1988, a lot of 3D QSAR methods, algorithms and their modifications are introduced for solving of virtual drug discovery problems (e.g., CoMSIA, CoMMA, HINT, HASL, GOLPE, GRID, PARM, Raptor, BiS, CiS, ConGO,). All the methods can be divided into two groups (classes):1. Methods studying the exterior of molecules; 2) Methods studying the interior of molecules. A series of grid-based computational technologies for Continual Molecular Interior analysis (CoMIn) are invented in the current paper. The grid-based analysis is fulfilled by means of a lattice construction analogously to many other grid-based methods. The further continual elucidation of molecular structure is performed in various ways. (i) In terms of intermolecular interactions potentials. This can be represented as a superposition of Coulomb, Van der Waals interactions and hydrogen bonds. All the potentials are well known continual functions and their values can be determined in all lattice points for a molecule. (ii) In the terms of quantum functions such as electron density distribution, Laplacian and Hamiltonian of electron density distribution, potential energy distribution, the highest occupied and the lowest unoccupied molecular orbitals distribution and their superposition. To reduce time of calculations using quantum methods based on the first principles, an original quantum

  14. Emerging Computational Methods for the Rational Discovery of Allosteric Drugs.

    Science.gov (United States)

    Wagner, Jeffrey R; Lee, Christopher T; Durrant, Jacob D; Malmstrom, Robert D; Feher, Victoria A; Amaro, Rommie E

    2016-06-08

    Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

  15. Scientific Prediction and Prophetic Patenting in Drug Discovery.

    Science.gov (United States)

    Curry, Stephen H; Schneiderman, Anne M

    2015-01-01

    Pharmaceutical patenting involves writing claims based on both discoveries already made, and on prophesy of future developments in an ongoing project. This is necessitated by the very different timelines involved in the drug discovery and product development process on the one hand, and successful patenting on the other. If patents are sought too early there is a risk that patent examiners will disallow claims because of lack of enablement. If patenting is delayed, claims are at risk of being denied on the basis of existence of prior art, because the body of relevant known science will have developed significantly while the project was being pursued. This review examines the role of prophetic patenting in relation to the essential predictability of many aspects of drug discovery science, promoting the concepts of discipline-related and project-related prediction. This is especially directed towards patenting activities supporting commercialization of academia-based discoveries, where long project timelines occur, and where experience, and resources to pay for patenting, are limited. The need for improved collaborative understanding among project scientists, technology transfer professionals in, for example, universities, patent attorneys, and patent examiners is emphasized.

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

    Science.gov (United States)

    Hellerstein, Marc K

    2008-01-01

    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 naïve 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.

  17. Applying genetics in inflammatory disease drug discovery

    DEFF Research Database (Denmark)

    Folkersen, Lasse; Biswas, Shameek; Frederiksen, Klaus Stensgaard

    2015-01-01

    , with several notable exceptions, the journey from a small-effect genetic variant to a functional drug has proven arduous, and few examples of actual contributions to drug discovery exist. Here, we discuss novel approaches of overcoming this hurdle by using instead public genetics resources as a pragmatic guide...... alongside existing drug discovery methods. Our aim is to evaluate human genetic confidence as a rationale for drug target selection....

  18. Structure-based discovery of clinically approved drugs as Zika virus NS2B-NS3 protease inhibitors that potently inhibit Zika virus infection in vitro and in vivo.

    Science.gov (United States)

    Yuan, Shuofeng; Chan, Jasper Fuk-Woo; den-Haan, Helena; Chik, Kenn Ka-Heng; Zhang, Anna Jinxia; Chan, Chris Chung-Sing; Poon, Vincent Kwok-Man; Yip, Cyril Chik-Yan; Mak, Winger Wing-Nga; Zhu, Zheng; Zou, Zijiao; Tee, Kah-Meng; Cai, Jian-Piao; Chan, Kwok-Hung; de la Peña, Jorge; Pérez-Sánchez, Horacio; Cerón-Carrasco, José Pedro; Yuen, Kwok-Yung

    2017-09-01

    Zika virus (ZIKV) infection may be associated with severe complications in fetuses and adults, but treatment options are limited. We performed an in silico structure-based screening of a large chemical library to identify potential ZIKV NS2B-NS3 protease inhibitors. Clinically approved drugs belonging to different drug classes were selected among the 100 primary hit compounds with the highest predicted binding affinities to ZIKV NS2B-NS3-protease for validation studies. ZIKV NS2B-NS3 protease inhibitory activity was validated in most of the selected drugs and in vitro anti-ZIKV activity was identified in two of them (novobiocin and lopinavir-ritonavir). Molecular docking and molecular dynamics simulations predicted that novobiocin bound to ZIKV NS2B-NS3-protease with high stability. Dexamethasone-immunosuppressed mice with disseminated ZIKV infection and novobiocin treatment had significantly (P < 0.05) higher survival rate (100% vs 0%), lower mean blood and tissue viral loads, and less severe histopathological changes than untreated controls. This structure-based drug discovery platform should facilitate the identification of additional enzyme inhibitors of ZIKV. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Comment on "drug discovery: turning the titanic".

    Science.gov (United States)

    Lesterhuis, W Joost; Bosco, Anthony; Lake, Richard A

    2014-03-26

    The pathobiology-based approach to research and development has been the dominant paradigm for successful drug discovery over the last decades. We propose that the molecular and cellular events that govern a resolving, rather than an evolving, disease may reveal new druggable pathways.

  20. [Application of Imaging Mass Spectrometry for Drug Discovery].

    Science.gov (United States)

    Hayasaka, Takahiro

    2016-01-01

    Imaging mass spectrometry (IMS) can reveal the distribution of biomolecules on tissue sections. In this process, the biomolecules are directly ionized within tissue sections using matrix-assisted laser desorption/ionization, and then their distribution is visualized by pseudo-color based on the relative signal intensity. The biomolecules, such as fatty acids, phospholipids, glycolipids, peptides, proteins, and neurotransmitters, have been analyzed at a spatial resolution of 5 μm. A special instrument for IMS analysis was developed by Shimadzu. The IMS analysis does not require the labeling of biomolecules and is capable of analyzing all the ionized biomolecules. Interest in this method has expanded to many research fields, including biology, agriculture, medicine, and pharmacology. The technique is especially relevant to the drug discovery process. As practiced currently, drug discovery is expensive and time consuming, requiring the preparation of probes for each drug and its metabolites, followed by systematic probe tracking in animal models. The IMS technique is expected to overcome these drawbacks by revealing the distribution of drugs and their metabolites using only a single analysis. In this symposium, I introduced the methodology and applications of IMS and discussed the feasibility of its application to drug discovery in the near future.

  1. Orphan diseases: state of the drug discovery art.

    Science.gov (United States)

    Volmar, Claude-Henry; Wahlestedt, Claes; Brothers, Shaun P

    2017-06-01

    Since 1983 more than 300 drugs have been developed and approved for orphan diseases. However, considering the development of novel diagnosis tools, the number of rare diseases vastly outpaces therapeutic discovery. Academic centers and nonprofit institutes are now at the forefront of rare disease R&D, partnering with pharmaceutical companies when academic researchers discover novel drugs or targets for specific diseases, thus reducing the failure risk and cost for pharmaceutical companies. Considerable progress has occurred in the art of orphan drug discovery, and a symbiotic relationship now exists between pharmaceutical industry, academia, and philanthropists that provides a useful framework for orphan disease therapeutic discovery. Here, the current state-of-the-art of drug discovery for orphan diseases is reviewed. Current technological approaches and challenges for drug discovery are considered, some of which can present somewhat unique challenges and opportunities in orphan diseases, including the potential for personalized medicine, gene therapy, and phenotypic screening.

  2. Prediction of potential drug targets based on simple sequence properties

    Directory of Open Access Journals (Sweden)

    Lai Luhua

    2007-09-01

    Full Text Available Abstract Background During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets. Results Based on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research. Conclusion We have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.

  3. A kernel for open source drug discovery in tropical diseases.

    Science.gov (United States)

    Ortí, Leticia; Carbajo, Rodrigo J; Pieper, Ursula; Eswar, Narayanan; Maurer, Stephen M; Rai, Arti K; Taylor, Ginger; Todd, Matthew H; Pineda-Lucena, Antonio; Sali, Andrej; Marti-Renom, Marc A

    2009-01-01

    Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such "kernels". HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i) comparative structure modeling of target proteins, (ii) predicting the localization of ligand binding sites on their surfaces, and (iii) assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases.

  4. A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing

    Science.gov (United States)

    Guerrero, Ginés D.; Imbernón, Baldomero; García, José M.

    2014-01-01

    Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC) platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs) has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO). This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor. PMID:25025055

  5. RAS - Screens & Assays - Drug Discovery

    Science.gov (United States)

    The RAS Drug Discovery group aims to develop assays that will reveal aspects of RAS biology upon which cancer cells depend. Successful assay formats are made available for high-throughput screening programs to yield potentially effective drug compounds.

  6. Molecular property diagnostic suite (MPDS): Development of disease-specific open source web portals for drug discovery.

    Science.gov (United States)

    Nagamani, S; Gaur, A S; Tanneeru, K; Muneeswaran, G; Madugula, S S; Consortium, Mpds; Druzhilovskiy, D; Poroikov, V V; Sastry, G N

    2017-11-01

    Molecular property diagnostic suite (MPDS) is a Galaxy-based open source drug discovery and development platform. MPDS web portals are designed for several diseases, such as tuberculosis, diabetes mellitus, and other metabolic disorders, specifically aimed to evaluate and estimate the drug-likeness of a given molecule. MPDS consists of three modules, namely data libraries, data processing, and data analysis tools which are configured and interconnected to assist drug discovery for specific diseases. The data library module encompasses vast information on chemical space, wherein the MPDS compound library comprises 110.31 million unique molecules generated from public domain databases. Every molecule is assigned with a unique ID and card, which provides complete information for the molecule. Some of the modules in the MPDS are specific to the diseases, while others are non-specific. Importantly, a suitably altered protocol can be effectively generated for another disease-specific MPDS web portal by modifying some of the modules. Thus, the MPDS suite of web portals shows great promise to emerge as disease-specific portals of great value, integrating chemoinformatics, bioinformatics, molecular modelling, and structure- and analogue-based drug discovery approaches.

  7. Fragment-based approaches to the discovery of kinase inhibitors.

    Science.gov (United States)

    Mortenson, Paul N; Berdini, Valerio; O'Reilly, Marc

    2014-01-01

    Protein kinases are one of the most important families of drug targets, and aberrant kinase activity has been linked to a large number of disease areas. Although eminently targetable using small molecules, kinases present a number of challenges as drug targets, not least obtaining selectivity across such a large and relatively closely related target family. Fragment-based drug discovery involves screening simple, low-molecular weight compounds to generate initial hits against a target. These hits are then optimized to more potent compounds via medicinal chemistry, usually facilitated by structural biology. Here, we will present a number of recent examples of fragment-based approaches to the discovery of kinase inhibitors, detailing the construction of fragment-screening libraries, the identification and validation of fragment hits, and their optimization into potent and selective lead compounds. The advantages of fragment-based methodologies will be discussed, along with some of the challenges associated with using this route. Finally, we will present a number of key lessons derived both from our own experience running fragment screens against kinases and from a large number of published studies.

  8. Accessing external innovation in drug discovery and development.

    Science.gov (United States)

    Tufféry, Pierre

    2015-06-01

    A decline in the productivity of the pharmaceutical industry research and development (R&D) pipeline has highlighted the need to reconsider the classical strategies of drug discovery and development, which are based on internal resources, and to identify new means to improve the drug discovery process. Accepting that the combination of internal and external ideas can improve innovation, ways to access external innovation, that is, opening projects to external contributions, have recently been sought. In this review, the authors look at a number of external innovation opportunities. These include increased interactions with academia via academic centers of excellence/innovation centers, better communication on projects using crowdsourcing or social media and new models centered on external providers such as built-to-buy startups or virtual pharmaceutical companies. The buzz for accessing external innovation relies on the pharmaceutical industry's major challenge to improve R&D productivity, a conjuncture favorable to increase interactions with academia and new business models supporting access to external innovation. So far, access to external innovation has mostly been considered during early stages of drug development, and there is room for enhancement. First outcomes suggest that external innovation should become part of drug development in the long term. However, the balance between internal and external developments in drug discovery can vary largely depending on the company strategies.

  9. From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

    Science.gov (United States)

    Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

    2017-11-01

    Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Network-based discovery through mechanistic systems biology. Implications for applications--SMEs and drug discovery: where the action is.

    Science.gov (United States)

    Benson, Neil

    2015-08-01

    Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. The mass-action law based algorithm for cost-effective approach for cancer drug discovery and development.

    Science.gov (United States)

    Chou, Ting-Chao

    2011-01-01

    The mass-action law based system analysis via mathematical induction and deduction lead to the generalized theory and algorithm that allows computerized simulation of dose-effect dynamics with small size experiments using a small number of data points in vitro, in animals, and in humans. The median-effect equation of the mass-action law deduced from over 300 mechanism specific-equations has been shown to be the unified theory that serves as the common-link for complicated biomedical systems. After using the median-effect principle as the common denominator, its applications are mechanism-independent, drug unit-independent, and dynamic order-independent; and can be used generally for single drug analysis or for multiple drug combinations in constant-ratio or non-constant ratios. Since the "median" is the common link and universal reference point in biological systems, these general enabling lead to computerized quantitative bio-informatics for econo-green bio-research in broad disciplines. Specific applications of the theory, especially relevant to drug discovery, drug combination, and clinical trials, have been cited or illustrated in terms of algorithms, experimental design and computerized simulation for data analysis. Lessons learned from cancer research during the past fifty years provide a valuable opportunity to reflect, and to improve the conventional divergent approach and to introduce a new convergent avenue, based on the mass-action law principle, for the efficient cancer drug discovery and the low-cost drug development.

  12. Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations.

    Science.gov (United States)

    Cournia, Zoe; Allen, Bryce; Sherman, Woody

    2017-12-26

    Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives

  13. Mathematical modeling for novel cancer drug discovery and development.

    Science.gov (United States)

    Zhang, Ping; Brusic, Vladimir

    2014-10-01

    Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

  14. A Performance/Cost Evaluation for a GPU-Based Drug Discovery Application on Volunteer Computing

    Directory of Open Access Journals (Sweden)

    Ginés D. Guerrero

    2014-01-01

    Full Text Available Bioinformatics is an interdisciplinary research field that develops tools for the analysis of large biological databases, and, thus, the use of high performance computing (HPC platforms is mandatory for the generation of useful biological knowledge. The latest generation of graphics processing units (GPUs has democratized the use of HPC as they push desktop computers to cluster-level performance. Many applications within this field have been developed to leverage these powerful and low-cost architectures. However, these applications still need to scale to larger GPU-based systems to enable remarkable advances in the fields of healthcare, drug discovery, genome research, etc. The inclusion of GPUs in HPC systems exacerbates power and temperature issues, increasing the total cost of ownership (TCO. This paper explores the benefits of volunteer computing to scale bioinformatics applications as an alternative to own large GPU-based local infrastructures. We use as a benchmark a GPU-based drug discovery application called BINDSURF that their computational requirements go beyond a single desktop machine. Volunteer computing is presented as a cheap and valid HPC system for those bioinformatics applications that need to process huge amounts of data and where the response time is not a critical factor.

  15. Drug Discovery Gets a Boost from Data Science.

    Science.gov (United States)

    Amaro, Rommie E

    2016-08-02

    In this issue of Structure, Schiebel et al. (2016) describe a workflow-driven approach to high-throughput X-ray crystallographic fragment screening and refinement. In doing so, they extend the applicability of X-ray crystallography as a primary fragment-screening tool and show how data science techniques can favorably impact drug discovery efforts. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Systems Pharmacology in Small Molecular Drug Discovery

    Directory of Open Access Journals (Sweden)

    Wei Zhou

    2016-02-01

    Full Text Available Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity, target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level.

  17. An Innovative Cell Microincubator for Drug Discovery Based on 3D Silicon Structures

    Directory of Open Access Journals (Sweden)

    Francesca Aredia

    2016-01-01

    Full Text Available We recently employed three-dimensional (3D silicon microstructures (SMSs consisting in arrays of 3 μm-thick silicon walls separated by 50 μm-deep, 5 μm-wide gaps, as microincubators for monitoring the biomechanical properties of tumor cells. They were here applied to investigate the in vitro behavior of HT1080 human fibrosarcoma cells driven to apoptosis by the chemotherapeutic drug Bleomycin. Our results, obtained by fluorescence microscopy, demonstrated that HT1080 cells exhibited a great ability to colonize the narrow gaps. Remarkably, HT1080 cells grown on 3D-SMS, when treated with the DNA damaging agent Bleomycin under conditions leading to apoptosis, tended to shrink, reducing their volume and mimicking the normal behavior of apoptotic cells, and were prone to leave the gaps. Finally, we performed label-free detection of cells adherent to the vertical silicon wall, inside the gap of 3D-SMS, by exploiting optical low coherence reflectometry using infrared, low power radiation. This kind of approach may become a new tool for increasing automation in the drug discovery area. Our results open new perspectives in view of future applications of the 3D-SMS as the core element of a lab-on-a-chip suitable for screening the effect of new molecules potentially able to kill tumor cells.

  18. Anticancer drug discovery and pharmaceutical chemistry: a history.

    Science.gov (United States)

    Braña, Miguel F; Sánchez-Migallón, Ana

    2006-10-01

    There are several procedures for the chemical discovery and design of new drugs from the point of view of the pharmaceutical or medicinal chemistry. They range from classical methods to the very new ones, such as molecular modeling or high throughput screening. In this review, we will consider some historical approaches based on the screening of natural products, the chances for luck, the systematic screening of new chemical entities and serendipity. Another group comprises rational design, as in the case of metabolic pathways, conformation versus configuration and, finally, a brief description on available new targets to be carried out. In each approach, the structure of some examples of clinical interest will be shown.

  19. DrugQuest - a text mining workflow for drug association discovery.

    Science.gov (United States)

    Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis

    2016-06-06

    Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

  20. 3D in vitro technology for drug discovery.

    Science.gov (United States)

    Hosseinkhani, Hossein

    2012-02-01

    Three-dimensional (3D) in vitro systems that can mimic organ and tissue structure and function in vivo, will be of great benefit for a variety of biological applications from basic biology to toxicity testing and drug discovery. There have been several attempts to generate 3D tissue models but most of these models require costly equipment, and the most serious disadvantage in them is that they are too far from the mature human organs in vivo. Because of these problems, research and development in drug discovery, toxicity testing and biotech industries are highly expensive, and involve sacrifice of countless animals and it takes several years to bring a single drug/product to the market or to find the toxicity or otherwise of chemical entities. Our group has been actively working on several alternative models by merging biomaterials science, nanotechnology and biological principles to generate 3D in vitro living organs, to be called "Human Organs-on-Chip", to mimic natural organ/tissues, in order to reduce animal testing and clinical trials. We have fabricated a novel type of mechanically and biologically bio-mimicking collagen-based hydrogel that would provide for interconnected mini-wells in which 3D cell/organ culture of human samples in a manner similar to human organs with extracellular matrix (ECM) molecules would be possible. These products mimic the physical, chemical, and biological properties of natural organs and tissues at different scales. This paper will review the outcome of our several experiments so far in this direction and the future perspectives.

  1. Role of Open Source Tools and Resources in Virtual Screening for Drug Discovery.

    Science.gov (United States)

    Karthikeyan, Muthukumarasamy; Vyas, Renu

    2015-01-01

    Advancement in chemoinformatics research in parallel with availability of high performance computing platform has made handling of large scale multi-dimensional scientific data for high throughput drug discovery easier. In this study we have explored publicly available molecular databases with the help of open-source based integrated in-house molecular informatics tools for virtual screening. The virtual screening literature for past decade has been extensively investigated and thoroughly analyzed to reveal interesting patterns with respect to the drug, target, scaffold and disease space. The review also focuses on the integrated chemoinformatics tools that are capable of harvesting chemical data from textual literature information and transform them into truly computable chemical structures, identification of unique fragments and scaffolds from a class of compounds, automatic generation of focused virtual libraries, computation of molecular descriptors for structure-activity relationship studies, application of conventional filters used in lead discovery along with in-house developed exhaustive PTC (Pharmacophore, Toxicophores and Chemophores) filters and machine learning tools for the design of potential disease specific inhibitors. A case study on kinase inhibitors is provided as an example.

  2. An overview of aldehyde oxidase: an enzyme of emerging importance in novel drug discovery.

    Science.gov (United States)

    Rashidi, Mohammad-Reza; Soltani, Somaieh

    2017-03-01

    Given the rising trend in medicinal chemistry strategy to reduce cytochrome P450-dependent metabolism, aldehyde oxidase (AOX) has recently gained increased attention in drug discovery programs and the number of drug candidates that are metabolized by AOX is steadily growing. Areas covered: Despite the emerging importance of AOX in drug discovery, there are certain major recognized problems associated with AOX-mediated metabolism of drugs. Intra- and inter-species variations in AOX activity, the lack of reliable and predictive animal models using the common experimental animals, and failure in the predictions of in vivo metabolic activity of AOX using traditional in vitro methods are among these issues that are covered in this article. A comprehensive review of computational human AOX (hAOX) related studies are also provided. Expert opinion: Following the recent progress in the stem cell field, the authors recommend the application of organoids technology as an effective tool to solve the fundamental problems associated with the evaluation of AOX in drug discovery. The recent success in resolving the hAOX crystal structure can too be another valuable data source for the study of AOX-catalyzed metabolism of new drug candidates, using computer-aided drug discovery methods.

  3. Molecular Networking As a Drug Discovery, Drug Metabolism, and Precision Medicine Strategy.

    Science.gov (United States)

    Quinn, Robert A; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C

    2017-02-01

    Molecular networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chemistry of molecules dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related molecules are likely to display similar fragment ion spectra. Molecular networking organizes the MS/MS data as a relational spectral network thereby mapping the chemistry that was detected in an MS/MS-based metabolomics experiment. Although the wider utility of molecular networking is just beginning to be recognized, in this review we highlight the principles behind molecular networking and its use for the discovery of therapeutic leads, monitoring drug metabolism, clinical diagnostics, and emerging applications in precision medicine. Copyright © 2016. Published by Elsevier Ltd.

  4. Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.

    Science.gov (United States)

    Simm, Jaak; Klambauer, Günter; Arany, Adam; Steijaert, Marvin; Wegner, Jörg Kurt; Gustin, Emmanuel; Chupakhin, Vladimir; Chong, Yolanda T; Vialard, Jorge; Buijnsters, Peter; Velter, Ingrid; Vapirev, Alexander; Singh, Shantanu; Carpenter, Anne E; Wuyts, Roel; Hochreiter, Sepp; Moreau, Yves; Ceulemans, Hugo

    2018-05-17

    In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Insights into Integrated Lead Generation and Target Identification in Malaria and Tuberculosis Drug Discovery.

    Science.gov (United States)

    Okombo, John; Chibale, Kelly

    2017-07-18

    protease-mediated hydrolysis of hemoglobin. We further expound on the recombinant enzyme assays, heme fractionation experiments, and genomic and chemoproteomic methods that we employed to identify Plasmodium falciparum falcipain 2 (PfFP2), hemozoin formation, phosphatidylinositol 4-kinase (PfPI4K) and Mycobacterium tuberculosis cytochrome bc1 complex as the targets of the antimalarial chalcones, pyrido[1,2-a]benzimidazoles, aminopyridines, and antimycobacterial pyrrolo[3,4-c]pyridine-1,3(2H)-diones, respectively. In conclusion, we argue for the expansion of chemical space through exploitation of privileged natural product scaffolds and diversity-oriented synthesis, as well as the broadening of druggable spaces by exploiting available protein crystal structures, -omics data, and bioinformatics infrastructure to explore hitherto untargeted spaces like lipid metabolism and protein kinases in P. falciparum. Finally, we audit the merits of both target-based and whole-cell phenotypic screening in steering antimalarial and antituberculosis chemical matter toward populating drug discovery pipelines with new lead molecules.

  6. An integrated structure- and system-based framework to identify new targets of metabolites and known drugs

    KAUST Repository

    Naveed, Hammad; Hameed, Umar Farook Shahul; Harrus, Deborah; Bourguet, William; Arold, Stefan T.; Gao, Xin

    2015-01-01

    Results: Here, we present a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called ‘drugs’). For a given drug, our method uses sequence order–independent structure alignment, hierarchical clustering, and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug’s PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of eleven drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the nuclear receptor PPARγ and the oncogene Bcl-2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development.

  7. International Drug Discovery Science and Technology--BIT's Seventh Annual Congress.

    Science.gov (United States)

    Bodovitz, Steven

    2010-01-01

    BIT's Seventh Annual International Drug Discovery Science and Technology Congress, held in Shanghai, included topics covering new therapeutic and technological developments in the field of drug discovery. This conference report highlights selected presentations on open-access approaches to R&D, novel and multifactorial targets, and technologies that assist drug discovery. Investigational drugs discussed include the anticancer agents astuprotimut-r (GlaxoSmithKline plc) and AS-1411 (Antisoma plc).

  8. Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

    Science.gov (United States)

    Dobchev, Dimitar; Karelson, Mati

    2016-07-01

    Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

  9. Four disruptive strategies for removing drug discovery bottlenecks.

    Science.gov (United States)

    Ekins, Sean; Waller, Chris L; Bradley, Mary P; Clark, Alex M; Williams, Antony J

    2013-03-01

    Drug discovery is shifting focus from industry to outside partners and, in the process, creating new bottlenecks. Technologies like high throughput screening (HTS) have moved to a larger number of academic and institutional laboratories in the USA, with little coordination or consideration of the outputs and creating a translational gap. Although there have been collaborative public-private partnerships in Europe to share pharmaceutical data, the USA has seemingly lagged behind and this may hold it back. Sharing precompetitive data and models may accelerate discovery across the board, while finding the best collaborators, mining social media and mobile approaches to open drug discovery should be evaluated in our efforts to remove drug discovery bottlenecks. We describe four strategies to rectify the current unsustainable situation. Copyright © 2012 Elsevier Ltd. All rights reserved.

  10. Hot-spot analysis for drug discovery targeting protein-protein interactions.

    Science.gov (United States)

    Rosell, Mireia; Fernández-Recio, Juan

    2018-04-01

    Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

  11. Systems pharmacology-based drug discovery for marine resources: an example using sea cucumber (Holothurians).

    Science.gov (United States)

    Guo, Yingying; Ding, Yan; Xu, Feifei; Liu, Baoyue; Kou, Zinong; Xiao, Wei; Zhu, Jingbo

    2015-05-13

    Sea cucumber, a kind of marine animal, have long been utilized as tonic and traditional remedies in the Middle East and Asia because of its effectiveness against hypertension, asthma, rheumatism, cuts and burns, impotence, and constipation. In this study, an overall study performed on sea cucumber was used as an example to show drug discovery from marine resource by using systems pharmacology model. The value of marine natural resources has been extensively considered because these resources can be potentially used to treat and prevent human diseases. However, the discovery of drugs from oceans is difficult, because of complex environments in terms of composition and active mechanisms. Thus, a comprehensive systems approach which could discover active constituents and their targets from marine resource, understand the biological basis for their pharmacological properties is necessary. In this study, a feasible pharmacological model based on systems pharmacology was established to investigate marine medicine by incorporating active compound screening, target identification, and network and pathway analysis. As a result, 106 candidate components of sea cucumber and 26 potential targets were identified. Furthermore, the functions of sea cucumber in health improvement and disease treatment were elucidated in a holistic way based on the established compound-target and target-disease networks, and incorporated pathways. This study established a novel strategy that could be used to explore specific active mechanisms and discover new drugs from marine sources. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  12. Financing drug discovery via dynamic leverage.

    Science.gov (United States)

    Montazerhodjat, Vahid; Frishkopf, John J; Lo, Andrew W

    2016-03-01

    We extend the megafund concept for funding drug discovery to enable dynamic leverage in which the portfolio of candidate therapeutic assets is predominantly financed initially by equity, and debt is introduced gradually as assets mature and begin generating cash flows. Leverage is adjusted so as to maintain an approximately constant level of default risk throughout the life of the fund. Numerical simulations show that applying dynamic leverage to a small portfolio of orphan drug candidates can boost the return on equity almost twofold compared with securitization with a static capital structure. Dynamic leverage can also add significant value to comparable all-equity-financed portfolios, enhancing the return on equity without jeopardizing debt performance or increasing risk to equity investors. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  13. The AEROPATH project targeting Pseudomonas aeruginosa: crystallographic studies for assessment of potential targets in early-stage drug discovery

    International Nuclear Information System (INIS)

    Moynie, Lucille; Schnell, Robert; McMahon, Stephen A.; Sandalova, Tatyana; Boulkerou, Wassila Abdelli; Schmidberger, Jason W.; Alphey, Magnus; Cukier, Cyprian; Duthie, Fraser; Kopec, Jolanta; Liu, Huanting; Jacewicz, Agata; Hunter, William N.; Naismith, James H.; Schneider, Gunter

    2012-01-01

    A focused strategy has been directed towards the structural characterization of selected proteins from the bacterial pathogen P. aeruginosa. The objective is to exploit the resulting structural data, in combination with ligand-binding studies, and to assess the potential of these proteins for early-stage antimicrobial drug discovery. Bacterial infections are increasingly difficult to treat owing to the spread of antibiotic resistance. A major concern is Gram-negative bacteria, for which the discovery of new antimicrobial drugs has been particularly scarce. In an effort to accelerate early steps in drug discovery, the EU-funded AEROPATH project aims to identify novel targets in the opportunistic pathogen Pseudomonas aeruginosa by applying a multidisciplinary approach encompassing target validation, structural characterization, assay development and hit identification from small-molecule libraries. Here, the strategies used for target selection are described and progress in protein production and structure analysis is reported. Of the 102 selected targets, 84 could be produced in soluble form and the de novo structures of 39 proteins have been determined. The crystal structures of eight of these targets, ranging from hypothetical unknown proteins to metabolic enzymes from different functional classes (PA1645, PA1648, PA2169, PA3770, PA4098, PA4485, PA4992 and PA5259), are reported here. The structural information is expected to provide a firm basis for the improvement of hit compounds identified from fragment-based and high-throughput screening campaigns

  14. Three-dimensional compound comparison methods and their application in drug discovery.

    Science.gov (United States)

    Shin, Woong-Hee; Zhu, Xiaolei; Bures, Mark Gregory; Kihara, Daisuke

    2015-07-16

    Virtual screening has been widely used in the drug discovery process. Ligand-based virtual screening (LBVS) methods compare a library of compounds with a known active ligand. Two notable advantages of LBVS methods are that they do not require structural information of a target receptor and that they are faster than structure-based methods. LBVS methods can be classified based on the complexity of ligand structure information utilized: one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D). Unlike 1D and 2D methods, 3D methods can have enhanced performance since they treat the conformational flexibility of compounds. In this paper, a number of 3D methods will be reviewed. In addition, four representative 3D methods were benchmarked to understand their performance in virtual screening. Specifically, we tested overall performance in key aspects including the ability to find dissimilar active compounds, and computational speed.

  15. Cancer in silico drug discovery: a systems biology tool for identifying candidate drugs to target specific molecular tumor subtypes.

    Science.gov (United States)

    San Lucas, F Anthony; Fowler, Jerry; Chang, Kyle; Kopetz, Scott; Vilar, Eduardo; Scheet, Paul

    2014-12-01

    Large-scale cancer datasets such as The Cancer Genome Atlas (TCGA) allow researchers to profile tumors based on a wide range of clinical and molecular characteristics. Subsequently, TCGA-derived gene expression profiles can be analyzed with the Connectivity Map (CMap) to find candidate drugs to target tumors with specific clinical phenotypes or molecular characteristics. This represents a powerful computational approach for candidate drug identification, but due to the complexity of TCGA and technology differences between CMap and TCGA experiments, such analyses are challenging to conduct and reproduce. We present Cancer in silico Drug Discovery (CiDD; scheet.org/software), a computational drug discovery platform that addresses these challenges. CiDD integrates data from TCGA, CMap, and Cancer Cell Line Encyclopedia (CCLE) to perform computational drug discovery experiments, generating hypotheses for the following three general problems: (i) determining whether specific clinical phenotypes or molecular characteristics are associated with unique gene expression signatures; (ii) finding candidate drugs to repress these expression signatures; and (iii) identifying cell lines that resemble the tumors being studied for subsequent in vitro experiments. The primary input to CiDD is a clinical or molecular characteristic. The output is a biologically annotated list of candidate drugs and a list of cell lines for in vitro experimentation. We applied CiDD to identify candidate drugs to treat colorectal cancers harboring mutations in BRAF. CiDD identified EGFR and proteasome inhibitors, while proposing five cell lines for in vitro testing. CiDD facilitates phenotype-driven, systematic drug discovery based on clinical and molecular data from TCGA. ©2014 American Association for Cancer Research.

  16. Toward Omics-Based, Systems Biomedicine, and Path and Drug Discovery Methodologies for Depression-Inflammation Research.

    Science.gov (United States)

    Maes, Michael; Nowak, Gabriel; Caso, Javier R; Leza, Juan Carlos; Song, Cai; Kubera, Marta; Klein, Hans; Galecki, Piotr; Noto, Cristiano; Glaab, Enrico; Balling, Rudi; Berk, Michael

    2016-07-01

    Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular

  17. Structure-based drug design of a highly potent CDK1,2,4,6 inhibitor with novel macrocyclic quinoxalin-2-one structure.

    Science.gov (United States)

    Kawanishi, Nobuhiko; Sugimoto, Tetsuya; Shibata, Jun; Nakamura, Kaori; Masutani, Kouta; Ikuta, Mari; Hirai, Hiroshi

    2006-10-01

    The design of a novel series of cyclin-dependent kinase (CDK) inhibitors containing a macrocyclic quinoxaline-2-one is reported. Structure-based drug design and optimization from the starting point of diarylurea 2, which we previously reported as a moderate CDK1,2,4,6 inhibitor [J. Biol.Chem.2001, 276, 27548], led to the discovery of potent CDK1,2,4,6 inhibitor that were suitable for iv administration for in vivo study.

  18. The impact of genetics on future drug discovery in schizophrenia.

    Science.gov (United States)

    Matsumoto, Mitsuyuki; Walton, Noah M; Yamada, Hiroshi; Kondo, Yuji; Marek, Gerard J; Tajinda, Katsunori

    2017-07-01

    Failures of investigational new drugs (INDs) for schizophrenia have left huge unmet medical needs for patients. Given the recent lackluster results, it is imperative that new drug discovery approaches (and resultant drug candidates) target pathophysiological alterations that are shared in specific, stratified patient populations that are selected based on pre-identified biological signatures. One path to implementing this paradigm is achievable by leveraging recent advances in genetic information and technologies. Genome-wide exome sequencing and meta-analysis of single nucleotide polymorphism (SNP)-based association studies have already revealed rare deleterious variants and SNPs in patient populations. Areas covered: Herein, the authors review the impact that genetics have on the future of schizophrenia drug discovery. The high polygenicity of schizophrenia strongly indicates that this disease is biologically heterogeneous so the identification of unique subgroups (by patient stratification) is becoming increasingly necessary for future investigational new drugs. Expert opinion: The authors propose a pathophysiology-based stratification of genetically-defined subgroups that share deficits in particular biological pathways. Existing tools, including lower-cost genomic sequencing and advanced gene-editing technology render this strategy ever more feasible. Genetically complex psychiatric disorders such as schizophrenia may also benefit from synergistic research with simpler monogenic disorders that share perturbations in similar biological pathways.

  19. The future of drug discovery: enabling technologies for enhancing lead characterization and profiling therapeutic potential.

    Science.gov (United States)

    Janero, David R

    2014-08-01

    Technology often serves as a handmaiden and catalyst of invention. The discovery of safe, effective medications depends critically upon experimental approaches capable of providing high-impact information on the biological effects of drug candidates early in the discovery pipeline. This information can enable reliable lead identification, pharmacological compound differentiation and successful translation of research output into clinically useful therapeutics. The shallow preclinical profiling of candidate compounds promulgates a minimalistic understanding of their biological effects and undermines the level of value creation necessary for finding quality leads worth moving forward within the development pipeline with efficiency and prognostic reliability sufficient to help remediate the current pharma-industry productivity drought. Three specific technologies discussed herein, in addition to experimental areas intimately associated with contemporary drug discovery, appear to hold particular promise for strengthening the preclinical valuation of drug candidates by deepening lead characterization. These are: i) hydrogen-deuterium exchange mass spectrometry for characterizing structural and ligand-interaction dynamics of disease-relevant proteins; ii) activity-based chemoproteomics for profiling the functional diversity of mammalian proteomes; and iii) nuclease-mediated precision gene editing for developing more translatable cellular and in vivo models of human diseases. When applied in an informed manner congruent with the clinical understanding of disease processes, technologies such as these that span levels of biological organization can serve as valuable enablers of drug discovery and potentially contribute to reducing the current, unacceptably high rates of compound clinical failure.

  20. In silico fragment-based drug design.

    Science.gov (United States)

    Konteatis, Zenon D

    2010-11-01

    In silico fragment-based drug design (FBDD) is a relatively new approach inspired by the success of the biophysical fragment-based drug discovery field. Here, we review the progress made by this approach in the last decade and showcase how it complements and expands the capabilities of biophysical FBDD and structure-based drug design to generate diverse, efficient drug candidates. Advancements in several areas of research that have enabled the development of in silico FBDD and some applications in drug discovery projects are reviewed. The reader is introduced to various computational methods that are used for in silico FBDD, the fragment library composition for this technique, special applications used to identify binding sites on the surface of proteins and how to assess the druggability of these sites. In addition, the reader will gain insight into the proper application of this approach from examples of successful programs. In silico FBDD captures a much larger chemical space than high-throughput screening and biophysical FBDD increasing the probability of developing more diverse, patentable and efficient molecules that can become oral drugs. The application of in silico FBDD holds great promise for historically challenging targets such as protein-protein interactions. Future advances in force fields, scoring functions and automated methods for determining synthetic accessibility will all aid in delivering more successes with in silico FBDD.

  1. Drug design: structure- and ligand-based approaches

    National Research Council Canada - National Science Library

    Merz, Kenneth M; Ringe, Dagmar; Reynolds, Charles H

    2010-01-01

    ..., computational ADME-Tox, and drug discovery case studies. A variety of authors from academic and commercial institutions all over the world have contributed to this book, which is illustrated with more than 200 images...

  2. Three-Dimensional Compound Comparison Methods and Their Application in Drug Discovery

    Directory of Open Access Journals (Sweden)

    Woong-Hee Shin

    2015-07-01

    Full Text Available Virtual screening has been widely used in the drug discovery process. Ligand-based virtual screening (LBVS methods compare a library of compounds with a known active ligand. Two notable advantages of LBVS methods are that they do not require structural information of a target receptor and that they are faster than structure-based methods. LBVS methods can be classified based on the complexity of ligand structure information utilized: one-dimensional (1D, two-dimensional (2D, and three-dimensional (3D. Unlike 1D and 2D methods, 3D methods can have enhanced performance since they treat the conformational flexibility of compounds. In this paper, a number of 3D methods will be reviewed. In addition, four representative 3D methods were benchmarked to understand their performance in virtual screening. Specifically, we tested overall performance in key aspects including the ability to find dissimilar active compounds, and computational speed.

  3. Current status and future prospects for enabling chemistry technology in the drug discovery process.

    Science.gov (United States)

    Djuric, Stevan W; Hutchins, Charles W; Talaty, Nari N

    2016-01-01

    This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of "dangerous" reagents. Also featured are advances in the "computer-assisted drug design" area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities.

  4. Advancing Drug Discovery through Enhanced Free Energy Calculations.

    Science.gov (United States)

    Abel, Robert; Wang, Lingle; Harder, Edward D; Berne, B J; Friesner, Richard A

    2017-07-18

    A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates

  5. Protein Complex Production from the Drug Discovery Standpoint.

    Science.gov (United States)

    Moarefi, Ismail

    2016-01-01

    Small molecule drug discovery critically depends on the availability of meaningful in vitro assays to guide medicinal chemistry programs that are aimed at optimizing drug potency and selectivity. As it becomes increasingly evident, most disease relevant drug targets do not act as a single protein. In the body, they are instead generally found in complex with protein cofactors that are highly relevant for their correct function and regulation. This review highlights selected examples of the increasing trend to use biologically relevant protein complexes for rational drug discovery to reduce costly late phase attritions due to lack of efficacy or toxicity.

  6. Recent advances in inkjet dispensing technologies: applications in drug discovery.

    Science.gov (United States)

    Zhu, Xiangcheng; Zheng, Qiang; Yang, Hu; Cai, Jin; Huang, Lei; Duan, Yanwen; Xu, Zhinan; Cen, Peilin

    2012-09-01

    Inkjet dispensing technology is a promising fabrication methodology widely applied in drug discovery. The automated programmable characteristics and high-throughput efficiency makes this approach potentially very useful in miniaturizing the design patterns for assays and drug screening. Various custom-made inkjet dispensing systems as well as specialized bio-ink and substrates have been developed and applied to fulfill the increasing demands of basic drug discovery studies. The incorporation of other modern technologies has further exploited the potential of inkjet dispensing technology in drug discovery and development. This paper reviews and discusses the recent developments and practical applications of inkjet dispensing technology in several areas of drug discovery and development including fundamental assays of cells and proteins, microarrays, biosensors, tissue engineering, basic biological and pharmaceutical studies. Progression in a number of areas of research including biomaterials, inkjet mechanical systems and modern analytical techniques as well as the exploration and accumulation of profound biological knowledge has enabled different inkjet dispensing technologies to be developed and adapted for high-throughput pattern fabrication and miniaturization. This in turn presents a great opportunity to propel inkjet dispensing technology into drug discovery.

  7. Current status and future prospects for enabling chemistry technology in the drug discovery process

    Science.gov (United States)

    Djuric, Stevan W.; Hutchins, Charles W.; Talaty, Nari N.

    2016-01-01

    This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of “dangerous” reagents. Also featured are advances in the “computer-assisted drug design” area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities. PMID:27781094

  8. Integration of distributed computing into the drug discovery process.

    Science.gov (United States)

    von Korff, Modest; Rufener, Christian; Stritt, Manuel; Freyss, Joel; Bär, Roman; Sander, Thomas

    2011-02-01

    Grid computing offers an opportunity to gain massive computing power at low costs. We give a short introduction into the drug discovery process and exemplify the use of grid computing for image processing, docking and 3D pharmacophore descriptor calculations. The principle of a grid and its architecture are briefly explained. More emphasis is laid on the issues related to a company-wide grid installation and embedding the grid into the research process. The future of grid computing in drug discovery is discussed in the expert opinion section. Most needed, besides reliable algorithms to predict compound properties, is embedding the grid seamlessly into the discovery process. User friendly access to powerful algorithms without any restrictions, that is, by a limited number of licenses, has to be the goal of grid computing in drug discovery.

  9. Use of combinatorial chemistry to speed drug discovery.

    Science.gov (United States)

    Rádl, S

    1998-10-01

    IBC's International Conference on Integrating Combinatorial Chemistry into the Discovery Pipeline was held September 14-15, 1998. The program started with a pre-conference workshop on High-Throughput Compound Characterization and Purification. The agenda of the main conference was divided into sessions of Synthesis, Automation and Unique Chemistries; Integrating Combinatorial Chemistry, Medicinal Chemistry and Screening; Combinatorial Chemistry Applications for Drug Discovery; and Information and Data Management. This meeting was an excellent opportunity to see how big pharma, biotech and service companies are addressing the current bottlenecks in combinatorial chemistry to speed drug discovery. (c) 1998 Prous Science. All rights reserved.

  10. Target based drug design - a reality in virtual sphere.

    Science.gov (United States)

    Verma, Saroj; Prabhakar, Yenamandra S

    2015-01-01

    The target based drug design approaches are a series of computational procedures, including visualization tools, to support the decision systems of drug design/discovery process. In the essence of biological targets shaping the potential lead/drug molecules, this review presents a comprehensive position of different components of target based drug design which include target identification, protein modeling, molecular dynamics simulations, binding/catalytic sites identification, docking, virtual screening, fragment based strategies, substructure treatment of targets in tackling drug resistance, in silico ADMET, structural vaccinology, etc along with the key issues involved therein and some well investigated case studies. The concepts and working of these procedures are critically discussed to arouse interest and to advance the drug research.

  11. Recent advances in combinatorial biosynthesis for drug discovery

    Directory of Open Access Journals (Sweden)

    Sun H

    2015-02-01

    Full Text Available Huihua Sun,1,* Zihe Liu,1,* Huimin Zhao,1,2 Ee Lui Ang1 1Metabolic Engineering Research Laboratory, Institute of Chemical and Engineering Sciences, Agency for Science, Technology and Research, Singapore; 2Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA *These authors contributed equally to this work Abstract: Because of extraordinary structural diversity and broad biological activities, natural products have played a significant role in drug discovery. These therapeutically important secondary metabolites are assembled and modified by dedicated biosynthetic pathways in their host living organisms. Traditionally, chemists have attempted to synthesize natural product analogs that are important sources of new drugs. However, the extraordinary structural complexity of natural products sometimes makes it challenging for traditional chemical synthesis, which usually involves multiple steps, harsh conditions, toxic organic solvents, and byproduct wastes. In contrast, combinatorial biosynthesis exploits substrate promiscuity and employs engineered enzymes and pathways to produce novel “unnatural” natural products, substantially expanding the structural diversity of natural products with potential pharmaceutical value. Thus, combinatorial biosynthesis provides an environmentally friendly way to produce natural product analogs. Efficient expression of the combinatorial biosynthetic pathway in genetically tractable heterologous hosts can increase the titer of the compound, eventually resulting in less expensive drugs. In this review, we will discuss three major strategies for combinatorial biosynthesis: 1 precursor-directed biosynthesis; 2 enzyme-level modification, which includes swapping of the entire domains, modules and subunits, site-specific mutagenesis, and directed evolution; 3 pathway-level recombination. Recent examples of combinatorial biosynthesis employing these

  12. Increasing the Structural Coverage of Tuberculosis Drug Targets

    OpenAIRE

    Baugh, Loren; Phan, Isabelle; Begley, Darren W.; Clifton, Matthew C.; Armour, Brianna; Dranow, David M.; Taylor, Brandy M.; Muruthi, Marvin M.; Abendroth, Jan; Fairman, James W.; Fox, David; Dieterich, Shellie H.; Staker, Bart L.; Gardberg, Anna S.; Choi, Ryan

    2014-01-01

    High-resolution three-dimensional structures of essential Mycobacterium tuberculosis (Mtb) proteins provide templates for TB drug design, but are available for only a small fraction of the Mtb proteome. Here we evaluate an intra-genus “homolog-rescue” strategy to increase the structural information available for TB drug discovery by using mycobacterial homologs with conserved active sites. Of 179 potential TB drug targets selected for x-ray structure determination, only 16 yielded a crystal s...

  13. Drug design and discovery: translational biomedical science varies among countries.

    Science.gov (United States)

    Weaver, Ian N; Weaver, Donald F

    2013-10-01

    Drug design and discovery is an innovation process that translates the outcomes of fundamental biomedical research into therapeutics that are ultimately made available to people with medical disorders in many countries throughout the world. To identify which nations succeed, exceed, or fail at the drug design/discovery endeavor--more specifically, which countries, within the context of their national size and wealth, are "pulling their weight" when it comes to developing medications targeting the myriad of diseases that afflict humankind--we compiled and analyzed a comprehensive survey of all new drugs (small molecular entities and biologics) approved annually throughout the world over the 20-year period from 1991 to 2010. Based upon this analysis, we have devised prediction algorithms to ascertain which countries are successful (or not) in contributing to the worldwide need for effective new therapeutics. © 2013 Wiley Periodicals, Inc.

  14. Perspectives of biomolecular NMR in drug discovery: the blessing and curse of versatility

    International Nuclear Information System (INIS)

    Jahnke, Wolfgang

    2007-01-01

    The versatility of NMR and its broad applicability to several stages in the drug discovery process is well known and generally considered one of the major strengths of NMR (Pellecchia et al., Nature Rev Drug Discov 1:211-219, 2002; Stockman and Dalvit, Prog Nucl Magn Reson Spectrosc 41:187-231, 2002; Lepre et al., Comb Chem High throughput screen 5:583-590, 2002; Wyss et al., Curr Opin Drug Discov Devel 5:630-647, 2002; Jahnke and Widmer, Cell Mol Life Sci 61:580-599, 2004; Huth et al., Methods Enzymol 394:549-571, 2005b; Klages et al., Mol Biosyst 2:318-332, 2006; Takeuchi and Wagner, Curr Opin Struct Biol 16:109-117, 2006; Zartler and Shapiro, Curr Pharm Des 12:3963-3972, 2006). Indeed, NMR is the only biophysical technique which can detect and quantify molecular interactions, and at the same time provide detailed structural information with atomic level resolution. NMR should therefore be ideally suited and widely requested as a tool for drug discovery research, and numerous examples of drug discovery projects which have substantially benefited from NMR contributions or were even driven by NMR have been described in the literature. However, not all pharmaceutical companies have rigorously implemented NMR as integral tool of their research processes. Some companies invest with limited resources, and others do not use biomolecular NMR at all. This discrepancy in assessing the value of a technology is striking, and calls for clarification-under which circumstances can NMR provide added value to the drug discovery process? What kind of contributions can NMR make, and how is it implemented and integrated for maximum impact? This perspectives article suggests key areas of impact for NMR, and a model of integrating NMR with other technologies to realize synergies and maximize their value for drug discovery

  15. Stem cells: a model for screening, discovery and development of drugs.

    Science.gov (United States)

    Kitambi, Satish Srinivas; Chandrasekar, Gayathri

    2011-01-01

    The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficacy studies using stem cells offers a reliable platform for the drug discovery process. Advances made in the generation of induced pluripotent stem cells from normal or diseased tissue serves as a platform to perform drug screens aimed at developing cell-based therapies against conditions like Parkinson's disease and diabetes. This review discusses the application of stem cells and cancer stem cells in drug screening and their role in complementing, reducing, and replacing animal testing. In addition to this, target identification and major advances in the field of personalized medicine using induced pluripotent cells are also discussed.

  16. A role for physicians in ethnopharmacology and drug discovery.

    Science.gov (United States)

    Raza, Mohsin

    2006-04-06

    Ethnopharmacology investigations classically involved traditional healers, botanists, anthropologists, chemists and pharmacologists. The role of some groups of researchers but not of physician has been highlighted and well defined in ethnopharmacological investigations. Historical data shows that discovery of several important modern drugs of herbal origin owe to the medical knowledge and clinical expertise of physicians. Current trends indicate negligible role of physicians in ethnopharmacological studies. Rising cost of modern drug development is attributed to the lack of classical ethnopharmacological approach. Physicians can play multiple roles in the ethnopharmacological studies to facilitate drug discovery as well as to rescue authentic traditional knowledge of use of medicinal plants. These include: (1) Ethnopharmacological field work which involves interviewing healers, interpreting traditional terminologies into their modern counterparts, examining patients consuming herbal remedies and identifying the disease for which an herbal remedy is used. (2) Interpretation of signs and symptoms mentioned in ancient texts and suggesting proper use of old traditional remedies in the light of modern medicine. (3) Clinical studies on herbs and their interaction with modern medicines. (4) Advising pharmacologists to carryout laboratory studies on herbs observed during field studies. (5) Work in collaboration with local healers to strengthen traditional system of medicine in a community. In conclusion, physician's involvement in ethnopharmacological studies will lead to more reliable information on traditional use of medicinal plants both from field and ancient texts, more focused and cheaper natural product based drug discovery, as well as bridge the gap between traditional and modern medicine.

  17. Novel opportunities for computational biology and sociology in drug discovery

    Science.gov (United States)

    Yao, Lixia

    2009-01-01

    Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies. PMID:19674801

  18. Financing drug discovery for orphan diseases

    OpenAIRE

    Fagnan, David Erik; Gromatzky, Austin A.; Stein, Roger Mark; Fernandez, Jose-Maria; Lo, Andrew W.

    2014-01-01

    Recently proposed ‘megafund’ financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures...

  19. Providing data science support for systems pharmacology and its implications to drug discovery.

    Science.gov (United States)

    Hart, Thomas; Xie, Lei

    2016-01-01

    The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology. This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction. Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.

  20. Zebrafish models in neuropsychopharmacology and CNS drug discovery.

    Science.gov (United States)

    Khan, Kanza M; Collier, Adam D; Meshalkina, Darya A; Kysil, Elana V; Khatsko, Sergey L; Kolesnikova, Tatyana; Morzherin, Yury Yu; Warnick, Jason E; Kalueff, Allan V; Echevarria, David J

    2017-07-01

    Despite the high prevalence of neuropsychiatric disorders, their aetiology and molecular mechanisms remain poorly understood. The zebrafish (Danio rerio) is increasingly utilized as a powerful animal model in neuropharmacology research and in vivo drug screening. Collectively, this makes zebrafish a useful tool for drug discovery and the identification of disordered molecular pathways. Here, we discuss zebrafish models of selected human neuropsychiatric disorders and drug-induced phenotypes. As well as covering a broad range of brain disorders (from anxiety and psychoses to neurodegeneration), we also summarize recent developments in zebrafish genetics and small molecule screening, which markedly enhance the disease modelling and the discovery of novel drug targets. © 2017 The British Pharmacological Society.

  1. Integration of Antibody Array Technology into Drug Discovery and Development.

    Science.gov (United States)

    Huang, Wei; Whittaker, Kelly; Zhang, Huihua; Wu, Jian; Zhu, Si-Wei; Huang, Ruo-Pan

    Antibody arrays represent a high-throughput technique that enables the parallel detection of multiple proteins with minimal sample volume requirements. In recent years, antibody arrays have been widely used to identify new biomarkers for disease diagnosis or prognosis. Moreover, many academic research laboratories and commercial biotechnology companies are starting to apply antibody arrays in the field of drug discovery. In this review, some technical aspects of antibody array development and the various platforms currently available will be addressed; however, the main focus will be on the discussion of antibody array technologies and their applications in drug discovery. Aspects of the drug discovery process, including target identification, mechanisms of drug resistance, molecular mechanisms of drug action, drug side effects, and the application in clinical trials and in managing patient care, which have been investigated using antibody arrays in recent literature will be examined and the relevance of this technology in progressing this process will be discussed. Protein profiling with antibody array technology, in addition to other applications, has emerged as a successful, novel approach for drug discovery because of the well-known importance of proteins in cell events and disease development.

  2. Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery.

    Science.gov (United States)

    Pérot, Stéphanie; Sperandio, Olivier; Miteva, Maria A; Camproux, Anne-Claude; Villoutreix, Bruno O

    2010-08-01

    Detection, comparison and analyses of binding pockets are pivotal to structure-based drug design endeavors, from hit identification, screening of exosites and de-orphanization of protein functions to the anticipation of specific and non-specific binding to off- and anti-targets. Here, we analyze protein-ligand complexes and discuss methods that assist binding site identification, prediction of druggability and binding site comparison. The full potential of pockets is yet to be harnessed, and we envision that better understanding of the pocket space will have far-reaching implications in the field of drug discovery, such as the design of pocket-specific compound libraries and scoring functions.

  3. DenguePredict: An Integrated Drug Repositioning Approach towards Drug Discovery for Dengue.

    Science.gov (United States)

    Wang, QuanQiu; Xu, Rong

    2015-01-01

    Dengue is a viral disease of expanding global incidence without cures. Here we present a drug repositioning system (DenguePredict) leveraging upon a unique drug treatment database and vast amounts of disease- and drug-related data. We first constructed a large-scale genetic disease network with enriched dengue genetics data curated from biomedical literature. We applied a network-based ranking algorithm to find dengue-related diseases from the disease network. We then developed a novel algorithm to prioritize FDA-approved drugs from dengue-related diseases to treat dengue. When tested in a de-novo validation setting, DenguePredict found the only two drugs tested in clinical trials for treating dengue and ranked them highly: chloroquine ranked at top 0.96% and ivermectin at top 22.75%. We showed that drugs targeting immune systems and arachidonic acid metabolism-related apoptotic pathways might represent innovative drugs to treat dengue. In summary, DenguePredict, by combining comprehensive disease- and drug-related data and novel algorithms, may greatly facilitate drug discovery for dengue.

  4. Preclinical experimental models of drug metabolism and disposition in drug discovery and development

    Directory of Open Access Journals (Sweden)

    Donglu Zhang

    2012-12-01

    Full Text Available Drug discovery and development involve the utilization of in vitro and in vivo experimental models. Different models, ranging from test tube experiments to cell cultures, animals, healthy human subjects, and even small numbers of patients that are involved in clinical trials, are used at different stages of drug discovery and development for determination of efficacy and safety. The proper selection and applications of correct models, as well as appropriate data interpretation, are critically important in decision making and successful advancement of drug candidates. In this review, we discuss strategies in the applications of both in vitro and in vivo experimental models of drug metabolism and disposition.

  5. Twenty years on: the impact of fragments on drug discovery.

    Science.gov (United States)

    Erlanson, Daniel A; Fesik, Stephen W; Hubbard, Roderick E; Jahnke, Wolfgang; Jhoti, Harren

    2016-09-01

    After 20 years of sometimes quiet growth, fragment-based drug discovery (FBDD) has become mainstream. More than 30 drug candidates derived from fragments have entered the clinic, with two approved and several more in advanced trials. FBDD has been widely applied in both academia and industry, as evidenced by the large number of papers from universities, non-profit research institutions, biotechnology companies and pharmaceutical companies. Moreover, FBDD draws on a diverse range of disciplines, from biochemistry and biophysics to computational and medicinal chemistry. As the promise of FBDD strategies becomes increasingly realized, now is an opportune time to draw lessons and point the way to the future. This Review briefly discusses how to design fragment libraries, how to select screening techniques and how to make the most of information gleaned from them. It also shows how concepts from FBDD have permeated and enhanced drug discovery efforts.

  6. Applications of fiber-optics-based nanosensors to drug discovery.

    Science.gov (United States)

    Vo-Dinh, Tuan; Scaffidi, Jonathan; Gregas, Molly; Zhang, Yan; Seewaldt, Victoria

    2009-08-01

    Fiber-optic nanosensors are fabricated by heating and pulling optical fibers to yield sub-micron diameter tips and have been used for in vitro analysis of individual living mammalian cells. Immobilization of bioreceptors (e.g., antibodies, peptides, DNA) selective to targeting analyte molecules of interest provides molecular specificity. Excitation light can be launched into the fiber, and the resulting evanescent field at the tip of the nanofiber can be used to excite target molecules bound to the bioreceptor molecules. The fluorescence or surface-enhanced Raman scattering produced by the analyte molecules is detected using an ultra-sensitive photodetector. This article provides an overview of the development and application of fiber-optic nanosensors for drug discovery. The nanosensors provide minimally invasive tools to probe subcellular compartments inside single living cells for health effect studies (e.g., detection of benzopyrene adducts) and medical applications (e.g., monitoring of apoptosis in cells treated with anticancer drugs).

  7. Fragment-Based Discovery of Pyrimido[1,2-b]indazole PDE10A Inhibitors.

    Science.gov (United States)

    Chino, Ayaka; Seo, Ryushi; Amano, Yasushi; Namatame, Ichiji; Hamaguchi, Wataru; Honbou, Kazuya; Mihara, Takuma; Yamazaki, Mayako; Tomishima, Masaki; Masuda, Naoyuki

    2018-01-01

    In this study, we report the identification of potent pyrimidoindazoles as phosphodiesterase10A (PDE10A) inhibitors by using the method of fragment-based drug discovery (FBDD). The pyrazolopyridine derivative 2 was found to be a fragment hit compound which could occupy a part of the binding site of PDE10A enzyme by using the method of the X-ray co-crystal structure analysis. On the basis of the crystal structure of compound 2 and PDE10A protein, a number of compounds were synthesized and evaluated, by means of structure-activity relationship (SAR) studies, which culminated in the discovery of a novel pyrimidoindazole derivative 13 having good physicochemical properties.

  8. A Multimodal Data Analysis Approach for Targeted Drug Discovery Involving Topological Data Analysis (TDA).

    Science.gov (United States)

    Alagappan, Muthuraman; Jiang, Dadi; Denko, Nicholas; Koong, Albert C

    In silico drug discovery refers to a combination of computational techniques that augment our ability to discover drug compounds from compound libraries. Many such techniques exist, including virtual high-throughput screening (vHTS), high-throughput screening (HTS), and mechanisms for data storage and querying. However, presently these tools are often used independent of one another. In this chapter, we describe a new multimodal in silico technique for the hit identification and lead generation phases of traditional drug discovery. Our technique leverages the benefits of three independent methods-virtual high-throughput screening, high-throughput screening, and structural fingerprint analysis-by using a fourth technique called topological data analysis (TDA). We describe how a compound library can be independently tested with vHTS, HTS, and fingerprint analysis, and how the results can be transformed into a topological data analysis network to identify compounds from a diverse group of structural families. This process of using TDA or similar clustering methods to identify drug leads is advantageous because it provides a mechanism for choosing structurally diverse compounds while maintaining the unique advantages of already established techniques such as vHTS and HTS.

  9. Novel Small Molecule Inhibitors of Choline Kinase Identified by Fragment-Based Drug Discovery.

    Science.gov (United States)

    Zech, Stephan G; Kohlmann, Anna; Zhou, Tianjun; Li, Feng; Squillace, Rachel M; Parillon, Lois E; Greenfield, Matthew T; Miller, David P; Qi, Jiwei; Thomas, R Mathew; Wang, Yihan; Xu, Yongjin; Miret, Juan J; Shakespeare, William C; Zhu, Xiaotian; Dalgarno, David C

    2016-01-28

    Choline kinase α (ChoKα) is an enzyme involved in the synthesis of phospholipids and thereby plays key roles in regulation of cell proliferation, oncogenic transformation, and human carcinogenesis. Since several inhibitors of ChoKα display antiproliferative activity in both cellular and animal models, this novel oncogene has recently gained interest as a promising small molecule target for cancer therapy. Here we summarize our efforts to further validate ChoKα as an oncogenic target and explore the activity of novel small molecule inhibitors of ChoKα. Starting from weakly binding fragments, we describe a structure based lead discovery approach, which resulted in novel highly potent inhibitors of ChoKα. In cancer cell lines, our lead compounds exhibit a dose-dependent decrease of phosphocholine, inhibition of cell growth, and induction of apoptosis at low micromolar concentrations. The druglike lead series presented here is optimizable for improvements in cellular potency, drug target residence time, and pharmacokinetic parameters. These inhibitors may be utilized not only to further validate ChoKα as antioncogenic target but also as novel chemical matter that may lead to antitumor agents that specifically interfere with cancer cell metabolism.

  10. An integrated structure- and system-based framework to identify new targets of metabolites and known drugs

    KAUST Repository

    Naveed, Hammad

    2015-08-18

    Motivation: The inherent promiscuity of small molecules towards protein targets impedes our understanding of healthy versus diseased metabolism. This promiscuity also poses a challenge for the pharmaceutical industry as identifying all protein targets is important to assess (side) effects and repositioning opportunities for a drug. Results: Here, we present a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called ‘drugs’). For a given drug, our method uses sequence order–independent structure alignment, hierarchical clustering, and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug’s PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of eleven drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the nuclear receptor PPARγ and the oncogene Bcl-2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development.

  11. Translational medicine and drug discovery

    National Research Council Canada - National Science Library

    Littman, Bruce H; Krishna, Rajesh

    2011-01-01

    ..., and examples of their application to real-life drug discovery and development. The latest thinking is presented by researchers from many of the world's leading pharmaceutical companies, including Pfizer, Merck, Eli Lilly, Abbott, and Novartis, as well as from academic institutions and public- private partnerships that support translational research...

  12. Generation of Polar Semi-Saturated Bicyclic Pyrazoles for Fragment-Based Drug Discovery Campaigns.

    Science.gov (United States)

    Luise, Nicola; Wyatt, Paul

    2018-05-07

    Synthesising polar semi-saturated bicyclic heterocycles can lead to better starting points for fragment-based drug discovery (FBDD) programs. This communication highlights the application of diverse chemistry to construct bicyclic systems from a common intermediate, where pyrazole, a privileged heteroaromatic able to bind effectively to biological targets, is fused to diverse saturated counterparts. The generated fragments can be further developed either after confirmation of their binding pose or early in the process, as their synthetic intermediates. Essential quality control (QC) for selection of small molecules to add to a fragment library is discussed. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. Drug discovery for Chagas disease should consider Trypanosoma cruzi strain diversity

    Directory of Open Access Journals (Sweden)

    Bianca Zingales

    2014-09-01

    Full Text Available This opinion piece presents an approach to standardisation of an important aspect of Chagas disease drug discovery and development: selecting Trypanosoma cruzi strains for in vitro screening. We discuss the rationale for strain selection representing T. cruzi diversity and provide recommendations on the preferred parasite stage for drug discovery, T. cruzi discrete typing units to include in the panel of strains and the number of strains/clones for primary screens and lead compounds. We also consider experimental approaches for in vitro drug assays. The Figure illustrates the current Chagas disease drug-discovery and development landscape.

  14. Net present value approaches for drug discovery.

    Science.gov (United States)

    Svennebring, Andreas M; Wikberg, Jarl Es

    2013-12-01

    Three dedicated approaches to the calculation of the risk-adjusted net present value (rNPV) in drug discovery projects under different assumptions are suggested. The probability of finding a candidate drug suitable for clinical development and the time to the initiation of the clinical development is assumed to be flexible in contrast to the previously used models. The rNPV of the post-discovery cash flows is calculated as the probability weighted average of the rNPV at each potential time of initiation of clinical development. Practical considerations how to set probability rates, in particular during the initiation and termination of a project is discussed.

  15. Structure-Based Drug Design of Small Molecule Peptide Deformylase Inhibitors to Treat Cancer

    Directory of Open Access Journals (Sweden)

    Jian Gao

    2016-03-01

    Full Text Available Human peptide deformylase (HsPDF is an important target for anticancer drug discovery. In view of the limited HsPDF, inhibitors were reported, and high-throughput virtual screening (HTVS studies based on HsPDF for developing new PDF inhibitors remain to be reported. We reported here on diverse small molecule inhibitors with excellent anticancer activities designed based on HTVS and molecular docking studies using the crystal structure of HsPDF. The compound M7594_0037 exhibited potent anticancer activities against HeLa, A549 and MCF-7 cell lines with IC50s of 35.26, 29.63 and 24.63 μM, respectively. Molecular docking studies suggested that M7594_0037 and its three derivatives could interact with HsPDF by several conserved hydrogen bonds. Moreover, the pharmacokinetic and toxicity properties of M7594_0037 and its derivatives were predicted using the OSIRIS property explorer. Thus, M7594_0037 and its derivatives might represent a promising scaffold for the further development of novel anticancer drugs.

  16. Stem cells: a model for screening, discovery and development of drugs

    Directory of Open Access Journals (Sweden)

    Kitambi SS

    2011-09-01

    Full Text Available Satish Srinivas Kitambi1, Gayathri Chandrasekar21Department of Medical Biochemistry and Biophysics; 2Department of Biosciences, Karolinska Institutet, Stockholm, SwedenAbstract: The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficacy studies using stem cells offers a reliable platform for the drug discovery process. Advances made in the generation of induced pluripotent stem cells from normal or diseased tissue serves as a platform to perform drug screens aimed at developing cell-based therapies against conditions like Parkinson's disease and diabetes. This review discusses the application of stem cells and cancer stem cells in drug screening and their role in complementing, reducing, and replacing animal testing. In addition to this, target identification and major advances in the field of personalized medicine using induced pluripotent cells are also discussed.Keywords: therapeutics, stem cells, cancer stem cells, screening models, drug development, high throughput screening

  17. Renaissance in Antibiotic Discovery: Some Novel Approaches for Finding Drugs to Treat Bad Bugs.

    Science.gov (United States)

    Gadakh, Bharat; Van Aerschot, Arthur

    2015-01-01

    With the alarming resistance to currently used antibiotics, there is a serious worldwide threat to public health. Therefore, there is an urgent need to search for new antibiotics or new cellular targets which are essential for survival of the pathogens. However, during the past 50 years, only two new classes of antibiotics (oxazolidinone and lipopeptides) have reached the clinic. This suggests that the success rate in discovering new/novel antibiotics using conventional approaches is limited and that we must reconsider our antibiotic discovery approaches. While many new strategies are being pursued lately, this review primarily focuses only on a few of these novel/new approaches for antibiotic discovery. These include structure-based drug design (SBDD), the genomic approach, anti-virulence strategy, targeting nonmultiplying bacteria and the use of bacteriophages. In general, recent advancements in nuclear magnetic resonance, Xcrystallography, and genomic evolution have significant impact on antibacterial drug research. This review therefore aims to discuss recent strategies in searching new antibacterial agents making use of these technical novelties, their advantages, disadvantages and limitations.

  18. Structured evaluation of rodent behavioral tests used in drug discovery research

    Directory of Open Access Journals (Sweden)

    Anders eHånell

    2014-07-01

    Full Text Available A large variety of rodent behavioral tests are currently being used to evaluate traits such as sensory-motor function, social interactions, anxiety-like and depressive-like behavior, substance dependence and various forms of cognitive function. Most behavioral tests have an inherent complexity, and their use requires consideration of several aspects such as the source of motivation in the test, the interaction between experimenter and animal, sources of variability, the sensory modality required by the animal to solve the task as well as costs and required work effort. Of particular importance is a test’s validity because of its influence on the chance of successful translation of preclinical results to clinical settings. High validity may, however, have to be balanced against practical constraints and there are no behavioral tests with optimal characteristics. The design and development of new behavioral tests is therefore an ongoing effort and there are now well over one hundred tests described in the contemporary literature. Some of them are well established following extensive use, while others are novel and still unproven. The task of choosing a behavioral test for a particular project may therefore be daunting and the aim of the present review is to provide a structured way to evaluate rodent behavioral tests aimed at drug discovery research.

  19. Oncology drug discovery: planning a turnaround.

    Science.gov (United States)

    Toniatti, Carlo; Jones, Philip; Graham, Hilary; Pagliara, Bruno; Draetta, Giulio

    2014-04-01

    We have made remarkable progress in our understanding of the pathophysiology of cancer. This improved understanding has resulted in increasingly effective targeted therapies that are better tolerated than conventional cytotoxic agents and even curative in some patients. Unfortunately, the success rate of drug approval has been limited, and therapeutic improvements have been marginal, with too few exceptions. In this article, we review the current approach to oncology drug discovery and development, identify areas in need of improvement, and propose strategies to improve patient outcomes. We also suggest future directions that may improve the quality of preclinical and early clinical drug evaluation, which could lead to higher approval rates of anticancer drugs.

  20. Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.

    Science.gov (United States)

    Carpenter, Kristy A; Huang, Xudong

    2018-06-07

    Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity. The study aims to review ML-based methods used for VS and applications to Alzheimer's disease (AD) drug discovery. To update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). All techniques have found success in VS, but the future of VS is likely to lean more heavily toward the use of neural networks - and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics of for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation. Different ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  1. Dynamic Docking: A Paradigm Shift in Computational Drug Discovery

    Directory of Open Access Journals (Sweden)

    Dario Gioia

    2017-11-01

    Full Text Available Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking. Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.

  2. Organic synthesis provides opportunities to transform drug discovery

    Science.gov (United States)

    Blakemore, David C.; Castro, Luis; Churcher, Ian; Rees, David C.; Thomas, Andrew W.; Wilson, David M.; Wood, Anthony

    2018-03-01

    Despite decades of ground-breaking research in academia, organic synthesis is still a rate-limiting factor in drug-discovery projects. Here we present some current challenges in synthetic organic chemistry from the perspective of the pharmaceutical industry and highlight problematic steps that, if overcome, would find extensive application in the discovery of transformational medicines. Significant synthesis challenges arise from the fact that drug molecules typically contain amines and N-heterocycles, as well as unprotected polar groups. There is also a need for new reactions that enable non-traditional disconnections, more C-H bond activation and late-stage functionalization, as well as stereoselectively substituted aliphatic heterocyclic ring synthesis, C-X or C-C bond formation. We also emphasize that syntheses compatible with biomacromolecules will find increasing use, while new technologies such as machine-assisted approaches and artificial intelligence for synthesis planning have the potential to dramatically accelerate the drug-discovery process. We believe that increasing collaboration between academic and industrial chemists is crucial to address the challenges outlined here.

  3. Pharmaceutical structure montages as catalysts for design and discovery.

    Science.gov (United States)

    Njarðarson, Jon T

    2012-05-01

    Majority of pharmaceuticals are small molecule organic compounds. Their structures are most effectively described and communicated using the graphical language of organic chemistry. A few years ago we decided to harness this powerful language to create new educational tools that could serve well for data mining and as catalysts for discovery. The results were the Top 200 drug posters, which we have posted online for everyone to enjoy and update yearly. This article details the origin and motivation for our design and highlights the value of this graphical format by presenting and analyzing a new pharmaceutical structure montage (poster) focused on US FDA approved drugs in 2011.

  4. Application of lean manufacturing concepts to drug discovery: rapid analogue library synthesis.

    Science.gov (United States)

    Weller, Harold N; Nirschl, David S; Petrillo, Edward W; Poss, Michael A; Andres, Charles J; Cavallaro, Cullen L; Echols, Martin M; Grant-Young, Katherine A; Houston, John G; Miller, Arthur V; Swann, R Thomas

    2006-01-01

    The application of parallel synthesis to lead optimization programs in drug discovery has been an ongoing challenge since the first reports of library synthesis. A number of approaches to the application of parallel array synthesis to lead optimization have been attempted over the years, ranging from widespread deployment by (and support of) individual medicinal chemists to centralization as a service by an expert core team. This manuscript describes our experience with the latter approach, which was undertaken as part of a larger initiative to optimize drug discovery. In particular, we highlight how concepts taken from the manufacturing sector can be applied to drug discovery and parallel synthesis to improve the timeliness and thus the impact of arrays on drug discovery.

  5. The Critical Role of Organic Chemistry in Drug Discovery.

    Science.gov (United States)

    Rotella, David P

    2016-10-19

    Small molecules remain the backbone for modern drug discovery. They are conceived and synthesized by medicinal chemists, many of whom were originally trained as organic chemists. Support from government and industry to provide training and personnel for continued development of this critical skill set has been declining for many years. This Viewpoint highlights the value of organic chemistry and organic medicinal chemists in the complex journey of drug discovery as a reminder that basic science support must be restored.

  6. The Proteomics Big Challenge for Biomarkers and New Drug-Targets Discovery

    Science.gov (United States)

    Savino, Rocco; Paduano, Sergio; Preianò, Mariaimmacolata; Terracciano, Rosa

    2012-01-01

    In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets. PMID:23203042

  7. Scientific workflows as productivity tools for drug discovery.

    Science.gov (United States)

    Shon, John; Ohkawa, Hitomi; Hammer, Juergen

    2008-05-01

    Large pharmaceutical companies annually invest tens to hundreds of millions of US dollars in research informatics to support their early drug discovery processes. Traditionally, most of these investments are designed to increase the efficiency of drug discovery. The introduction of do-it-yourself scientific workflow platforms has enabled research informatics organizations to shift their efforts toward scientific innovation, ultimately resulting in a possible increase in return on their investments. Unlike the handling of most scientific data and application integration approaches, researchers apply scientific workflows to in silico experimentation and exploration, leading to scientific discoveries that lie beyond automation and integration. This review highlights some key requirements for scientific workflow environments in the pharmaceutical industry that are necessary for increasing research productivity. Examples of the application of scientific workflows in research and a summary of recent platform advances are also provided.

  8. Drug discovery for alopecia: gone today, hair tomorrow.

    Science.gov (United States)

    Santos, Zenildo; Avci, Pinar; Hamblin, Michael R

    2015-03-01

    Hair loss or alopecia affects the majority of the population at some time in their life, and increasingly, sufferers are demanding treatment. Three main types of alopecia (androgenic [AGA], areata [AA] and chemotherapy-induced [CIA]) are very different, and have their own laboratory models and separate drug-discovery efforts. In this article, the authors review the biology of hair, hair follicle (HF) cycling, stem cells and signaling pathways. AGA, due to dihydrotesterone, is treated by 5-α reductase inhibitors, androgen receptor blockers and ATP-sensitive potassium channel-openers. AA, which involves attack by CD8(+)NK group 2D-positive (NKG2D(+)) T cells, is treated with immunosuppressives, biologics and JAK inhibitors. Meanwhile, CIA is treated by apoptosis inhibitors, cytokines and topical immunotherapy. The desire to treat alopecia with an easy topical preparation is expected to grow with time, particularly with an increasing aging population. The discovery of epidermal stem cells in the HF has given new life to the search for a cure for baldness. Drug discovery efforts are being increasingly centered on these stem cells, boosting the hair cycle and reversing miniaturization of HF. Better understanding of the molecular mechanisms underlying the immune attack in AA will yield new drugs. New discoveries in HF neogenesis and low-level light therapy will undoubtedly have a role to play.

  9. Skin too thin? The developing utility of zebrafish skin (neuro)pharmacology for CNS drug discovery research.

    Science.gov (United States)

    Nguyen, Michael; Poudel, Manoj K; Stewart, Adam Michael; Kalueff, Allan V

    2013-09-01

    Skin coloration can be affected by many genetic, environmental and pharmacological factors. Zebrafish (Danio rerio) are a useful and versatile model organism in biomedical research due to their genetic tractability, physiological homology to mammals, low cost, reproducibility and high throughput. Zebrafish coloration is mediated by chromatophores - the skin color pigment cells largely controlled by endocrine and neural mechanisms. The characteristic darkening of zebrafish skin is caused by the dispersion (and paling - by aggregation) of melanosomes (pigment-containing organelles), which show high homology to mammalian structures. Various pharmacological agents potently affect zebrafish coloration - the phenotype that often accompanies behavioral effects of the drugs, and may be used for drug discovery. Although zebrafish behavior and skin responses are usually not directly related, they share common regulatory (neural, endocrine) mechanisms, and therefore may be assessed in parallel during psychotropic drug screening. For example, some psychoactive drugs can potently affect zebrafish skin coloration. Can we use this knowledge to refine phenotype-driven psychotropic drug discovery? Here, we present current models using zebrafish skin coloration assays, and discuss how these models may be applied to enhance in vivo CNS drug discovery. Copyright © 2013 Elsevier Inc. All rights reserved.

  10. Advances in immobilized artificial membrane (IAM) chromatography for novel drug discovery.

    Science.gov (United States)

    Tsopelas, Fotios; Vallianatou, Theodosia; Tsantili-Kakoulidou, Anna

    2016-01-01

    The development of immobilized artificial membrane (IAM) chromatography has unfolded new perspectives for the use of chromatographic techniques in drug discovery, combining simulation of the environment of cell membranes with rapid measurements. The present review describes the characteristics of phosphatidylcholine-based stationary phases and analyses the molecular factors governing IAM retention in comparison to n-octanol-water and liposomes partitioning systems as well as to reversed phase chromatography. Other biomimetic stationary phases are also briefly discussed. The potential of IAM chromatography to model permeability through the main physiological barriers and drug membrane interactions is outlined. Further applications to calculate complex pharmacokinetic properties, related to tissue binding, and to screen drug candidates for phospholipidosis, as well as to estimate cell accumulation/retention are surveyed. The ambivalent nature of IAM chromatography, as a border case between passive diffusion and binding, defines its multiple potential applications. However, despite its successful performance in many permeability and drug-membrane interactions studies, IAM chromatography is still used as a supportive and not a stand-alone technique. Further studies looking at IAM chromatography in different biological processes are still required if this technique is to have a more focused and consistent application in drug discovery.

  11. [Chapter 2. Transitions in drug-discovery technology and drug-development in Japan (1980-2010)].

    Science.gov (United States)

    Sakakibara, Noriko; Yoshioka, Ryuzo; Matsumoto, Kazuo

    2014-01-01

    In 1970s, the material patent system was introduced in Japan. Since then, many Japanese pharmaceutical companies have endeavored to create original in-house products. From 1980s, many of the innovative products were small molecular drugs and were developed using powerful medicinal-chemical technologies. Among them were antibiotics and effective remedies for the digestive organs and circulatory organs. During this period, Japanese companies were able to launch some blockbuster drugs. At the same time, the pharmaceutical market, which had grown rapidly for two decades, was beginning to level off. From the late 1990s, drug development was slowing down due to the lack of expertise in biotechnology such as genetic engineering. In response to the circumstances, the research and development on biotechnology-based drugs such as antibody drugs have become more dynamic and popular at companies than small molecule drugs. In this paper, the writers reviewed in detail the transitions in drug discovery and development between 1980 and 2010.

  12. An invertebrate model for CNS drug discovery

    DEFF Research Database (Denmark)

    Al-Qadi, Sonia; Schiøtt, Morten; Hansen, Steen Honoré

    2015-01-01

    BACKGROUND: ABC efflux transporters at the blood brain barrier (BBB), namely the P-glycoprotein (P-gp), restrain the development of central nervous system (CNS) drugs. Consequently, early screening of CNS drug candidates is pivotal to identify those affected by efflux activity. Therefore, simple,...... barriers. CONCLUSION: Findings suggest a conserved mechanism of brain efflux activity between insects and vertebrates, confirming that this model holds promise for inexpensive and high-throughput screening relative to in vivo models, for CNS drug discovery....

  13. Perspectives on bioanalytical mass spectrometry and automation in drug discovery.

    Science.gov (United States)

    Janiszewski, John S; Liston, Theodore E; Cole, Mark J

    2008-11-01

    The use of high speed synthesis technologies has resulted in a steady increase in the number of new chemical entities active in the drug discovery research stream. Large organizations can have thousands of chemical entities in various stages of testing and evaluation across numerous projects on a weekly basis. Qualitative and quantitative measurements made using LC/MS are integrated throughout this process from early stage lead generation through candidate nomination. Nearly all analytical processes and procedures in modern research organizations are automated to some degree. This includes both hardware and software automation. In this review we discuss bioanalytical mass spectrometry and automation as components of the analytical chemistry infrastructure in pharma. Analytical chemists are presented as members of distinct groups with similar skillsets that build automated systems, manage test compounds, assays and reagents, and deliver data to project teams. The ADME-screening process in drug discovery is used as a model to highlight the relationships between analytical tasks in drug discovery. Emerging software and process automation tools are described that can potentially address gaps and link analytical chemistry related tasks. The role of analytical chemists and groups in modern 'industrialized' drug discovery is also discussed.

  14. Integrated Teaching of Structure-Based Drug Design and Biopharmaceutics: A Computer-Based Approach

    Science.gov (United States)

    Sutch, Brian T.; Romero, Rebecca M.; Neamati, Nouri; Haworth, Ian S.

    2012-01-01

    Rational drug design requires expertise in structural biology, medicinal chemistry, physiology, and related fields. In teaching structure-based drug design, it is important to develop an understanding of the need for early recognition of molecules with "drug-like" properties as a key component. That is, it is not merely sufficient to teach…

  15. Increasing the Structural Coverage of Tuberculosis Drug Targets

    Science.gov (United States)

    Baugh, Loren; Phan, Isabelle; Begley, Darren W.; Clifton, Matthew C.; Armour, Brianna; Dranow, David M.; Taylor, Brandy M.; Muruthi, Marvin M.; Abendroth, Jan; Fairman, James W.; Fox, David; Dieterich, Shellie H.; Staker, Bart L.; Gardberg, Anna S.; Choi, Ryan; Hewitt, Stephen N.; Napuli, Alberto J.; Myers, Janette; Barrett, Lynn K.; Zhang, Yang; Ferrell, Micah; Mundt, Elizabeth; Thompkins, Katie; Tran, Ngoc; Lyons-Abbott, Sally; Abramov, Ariel; Sekar, Aarthi; Serbzhinskiy, Dmitri; Lorimer, Don; Buchko, Garry W.; Stacy, Robin; Stewart, Lance J.; Edwards, Thomas E.; Van Voorhis, Wesley C.; Myler, Peter J.

    2015-01-01

    High-resolution three-dimensional structures of essential Mycobacterium tuberculosis (Mtb) proteins provide templates for TB drug design, but are available for only a small fraction of the Mtb proteome. Here we evaluate an intra-genus “homolog-rescue” strategy to increase the structural information available for TB drug discovery by using mycobacterial homologs with conserved active sites. Of 179 potential TB drug targets selected for x-ray structure determination, only 16 yielded a crystal structure. By adding 1675 homologs from nine other mycobacterial species to the pipeline, structures representing an additional 52 otherwise intractable targets were solved. To determine whether these homolog structures would be useful surrogates in TB drug design, we compared the active sites of 106 pairs of Mtb and non-TB mycobacterial (NTM) enzyme homologs with experimentally determined structures, using three metrics of active site similarity, including superposition of continuous pharmacophoric property distributions. Pair-wise structural comparisons revealed that 19/22 pairs with >55% overall sequence identity had active site Cα RMSD 85% side chain identity, and ≥80% PSAPF (similarity based on pharmacophoric properties) indicating highly conserved active site shape and chemistry. Applying these results to the 52 NTM structures described above, 41 shared >55% sequence identity with the Mtb target, thus increasing the effective structural coverage of the 179 Mtb targets over three-fold (from 9% to 32%). The utility of these structures in TB drug design can be tested by designing inhibitors using the homolog structure and assaying the cognate Mtb enzyme; a promising test case, Mtb cytidylate kinase, is described. The homolog-rescue strategy evaluated here for TB is also generalizable to drug targets for other diseases. PMID:25613812

  16. High throughput electrophysiology: new perspectives for ion channel drug discovery

    DEFF Research Database (Denmark)

    Willumsen, Niels J; Bech, Morten; Olesen, Søren-Peter

    2003-01-01

    . A cornerstone in current drug discovery is high throughput screening assays which allow examination of the activity of specific ion channels though only to a limited extent. Conventional patch clamp remains the sole technique with sufficiently high time resolution and sensitivity required for precise and direct....... The introduction of new powerful HTS electrophysiological techniques is predicted to cause a revolution in ion channel drug discovery....

  17. Structure-Guided Discovery of Novel, Potent, and Orally Bioavailable Inhibitors of Lipoprotein-Associated Phospholipase A2.

    Science.gov (United States)

    Liu, Qiufeng; Huang, Fubao; Yuan, Xiaojing; Wang, Kai; Zou, Yi; Shen, Jianhua; Xu, Yechun

    2017-12-28

    Lipoprotein-associated phospholipase A2 (Lp-PLA2) is a promising therapeutic target for atherosclerosis, Alzheimer's disease, and diabetic macular edema. Here we report the identification of novel sulfonamide scaffold Lp-PLA2 inhibitors derived from a relatively weak fragment. Similarity searching on this fragment followed by molecular docking leads to the discovery of a micromolar inhibitor with a 300-fold potency improvement. Subsequently, by the application of a structure-guided design strategy, a successful hit-to-lead optimization was achieved and a number of Lp-PLA2 inhibitors with single-digit nanomolar potency were obtained. After preliminary evaluation of the properties of drug-likeness in vitro and in vivo, compound 37 stands out from this congeneric series of inhibitors for good inhibitory activity and favorable oral bioavailability in male Sprague-Dawley rats, providing a quality candidate for further development. The present study thus clearly demonstrates the power and advantage of integrally employing fragment screening, crystal structures determination, virtual screening, and medicinal chemistry in an efficient lead discovery project, providing a good example for structure-based drug design.

  18. Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data

    Science.gov (United States)

    Wang, Yongcui; Chen, Shilong; Deng, Naiyang; Wang, Yong

    2013-01-01

    Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems. PMID:24244318

  19. Weak affinity chromatography for evaluation of stereoisomers in early drug discovery.

    Science.gov (United States)

    Duong-Thi, Minh-Dao; Bergström, Maria; Fex, Tomas; Svensson, Susanne; Ohlson, Sten; Isaksson, Roland

    2013-07-01

    In early drug discovery (e.g., in fragment screening), recognition of stereoisomeric structures is valuable and guides medicinal chemists to focus only on useful configurations. In this work, we concurrently screened mixtures of stereoisomers and estimated their affinities to a protein target (thrombin) using weak affinity chromatography-mass spectrometry (WAC-MS). Affinity determinations by WAC showed that minor changes in stereoisomeric configuration could have a major impact on affinity. The ability of WAC-MS to provide instant information about stereoselectivity and binding affinities directly from analyte mixtures is a great advantage in fragment library screening and drug lead development.

  20. Plant natural products research in tuberculosis drug discovery and ...

    African Journals Online (AJOL)

    Plant natural products research in tuberculosis drug discovery and development: A situation report ... African Journal of Biotechnology ... tuberculosis (XDR-TB), call for the development of new anti-tuberculosis drugs to combat this disease.

  1. Structural systems pharmacology: a new frontier in discovering novel drug targets.

    Science.gov (United States)

    Tan, Hepan; Ge, Xiaoxia; Xie, Lei

    2013-08-01

    The modern target-based drug discovery process, characterized by the one-drug-one-gene paradigm, has been of limited success. In contrast, phenotype-based screening produces thousands of active compounds but gives no hint as to what their molecular targets are or which ones merit further research. This presents a question: What is a suitable target for an efficient and safe drug? In this paper, we argue that target selection should take into account the proteome-wide energetic and kinetic landscape of drug-target interactions, as well as their cellular and organismal consequences. We propose a new paradigm of structural systems pharmacology to deconvolute the molecular targets of successful drugs as well as to identify druggable targets and their drug-like binders. Here we face two major challenges in structural systems pharmacology: How do we characterize and analyze the structural and energetic origins of drug-target interactions on a proteome scale? How do we correlate the dynamic molecular interactions to their in vivo activity? We will review recent advances in developing new computational tools for biophysics, bioinformatics, chemoinformatics, and systems biology related to the identification of genome-wide target profiles. We believe that the integration of these tools will realize structural systems pharmacology, enabling us to both efficiently develop effective therapeutics for complex diseases and combat drug resistance.

  2. Structures of Pseudomonas aeruginosa β-ketoacyl-(acyl-carrier-protein) synthase II (FabF) and a C164Q mutant provide templates for antibacterial drug discovery and identify a buried potassium ion and a ligand-binding site that is an artefact of the crystal form

    International Nuclear Information System (INIS)

    Baum, Bernhard; Lecker, Laura S. M.; Zoltner, Martin; Jaenicke, Elmar; Schnell, Robert; Hunter, William N.; Brenk, Ruth

    2015-01-01

    Three crystal structures of recombinant P. aeruginosa FabF are reported: the apoenzyme, an active-site mutant and a complex with a fragment of a natural product inhibitor. The characterization provides reagents and new information to support antibacterial drug discovery. Bacterial infections remain a serious health concern, in particular causing life-threatening infections of hospitalized and immunocompromised patients. The situation is exacerbated by the rise in antibacterial drug resistance, and new treatments are urgently sought. In this endeavour, accurate structures of molecular targets can support early-stage drug discovery. Here, crystal structures, in three distinct forms, of recombinant Pseudomonas aeruginosa β-ketoacyl-(acyl-carrier-protein) synthase II (FabF) are presented. This enzyme, which is involved in fatty-acid biosynthesis, has been validated by genetic and chemical means as an antibiotic target in Gram-positive bacteria and represents a potential target in Gram-negative bacteria. The structures of apo FabF, of a C164Q mutant in which the binding site is altered to resemble the substrate-bound state and of a complex with 3-(benzoylamino)-2-hydroxybenzoic acid are reported. This compound mimics aspects of a known natural product inhibitor, platensimycin, and surprisingly was observed binding outside the active site, interacting with a symmetry-related molecule. An unusual feature is a completely buried potassium-binding site that was identified in all three structures. Comparisons suggest that this may represent a conserved structural feature of FabF relevant to fold stability. The new structures provide templates for structure-based ligand design and, together with the protocols and reagents, may underpin a target-based drug-discovery project for urgently needed antibacterials

  3. Structures of Pseudomonas aeruginosa β-ketoacyl-(acyl-carrier-protein) synthase II (FabF) and a C164Q mutant provide templates for antibacterial drug discovery and identify a buried potassium ion and a ligand-binding site that is an artefact of the crystal form

    Energy Technology Data Exchange (ETDEWEB)

    Baum, Bernhard [Johannes Gutenberg-Universität, Staudinger Weg 5, 55128 Mainz (Germany); Lecker, Laura S. M.; Zoltner, Martin [University of Dundee, Dundee DD1 4EH, Scotland (United Kingdom); Jaenicke, Elmar [Johannes Gutenberg-Universität, Jakob Welder Weg 26, 55128 Mainz (Germany); Schnell, Robert [Karolinska Institutet, 17 177 Stockholm (Sweden); Hunter, William N., E-mail: w.n.hunter@dundee.ac.uk [University of Dundee, Dundee DD1 4EH, Scotland (United Kingdom); Brenk, Ruth, E-mail: w.n.hunter@dundee.ac.uk [Johannes Gutenberg-Universität, Staudinger Weg 5, 55128 Mainz (Germany)

    2015-07-28

    Three crystal structures of recombinant P. aeruginosa FabF are reported: the apoenzyme, an active-site mutant and a complex with a fragment of a natural product inhibitor. The characterization provides reagents and new information to support antibacterial drug discovery. Bacterial infections remain a serious health concern, in particular causing life-threatening infections of hospitalized and immunocompromised patients. The situation is exacerbated by the rise in antibacterial drug resistance, and new treatments are urgently sought. In this endeavour, accurate structures of molecular targets can support early-stage drug discovery. Here, crystal structures, in three distinct forms, of recombinant Pseudomonas aeruginosa β-ketoacyl-(acyl-carrier-protein) synthase II (FabF) are presented. This enzyme, which is involved in fatty-acid biosynthesis, has been validated by genetic and chemical means as an antibiotic target in Gram-positive bacteria and represents a potential target in Gram-negative bacteria. The structures of apo FabF, of a C164Q mutant in which the binding site is altered to resemble the substrate-bound state and of a complex with 3-(benzoylamino)-2-hydroxybenzoic acid are reported. This compound mimics aspects of a known natural product inhibitor, platensimycin, and surprisingly was observed binding outside the active site, interacting with a symmetry-related molecule. An unusual feature is a completely buried potassium-binding site that was identified in all three structures. Comparisons suggest that this may represent a conserved structural feature of FabF relevant to fold stability. The new structures provide templates for structure-based ligand design and, together with the protocols and reagents, may underpin a target-based drug-discovery project for urgently needed antibacterials.

  4. Systems biology-embedded target validation: improving efficacy in drug discovery.

    Science.gov (United States)

    Vandamme, Drieke; Minke, Benedikt A; Fitzmaurice, William; Kholodenko, Boris N; Kolch, Walter

    2014-01-01

    The pharmaceutical industry is faced with a range of challenges with the ever-escalating costs of drug development and a drying out of drug pipelines. By harnessing advances in -omics technologies and moving away from the standard, reductionist model of drug discovery, there is significant potential to reduce costs and improve efficacy. Embedding systems biology approaches in drug discovery, which seek to investigate underlying molecular mechanisms of potential drug targets in a network context, will reduce attrition rates by earlier target validation and the introduction of novel targets into the currently stagnant market. Systems biology approaches also have the potential to assist in the design of multidrug treatments and repositioning of existing drugs, while stratifying patients to give a greater personalization of medical treatment. © 2013 Wiley Periodicals, Inc.

  5. Drug Discovery in an Academic Setting: Playing to the Strengths

    OpenAIRE

    Huryn, Donna M.

    2013-01-01

    Drug discovery and medicinal chemistry initiatives in academia provide an opportunity to create a unique environment that is distinct from the traditional industrial model. Two characteristics of a university setting that are not usually associated with pharma are the ability to pursue high-risk projects and a depth of expertise, infrastructure, and capabilities in focused areas. Encouraging, supporting, and fostering drug discovery efforts that take advantage of these an...

  6. Drug discovery and developments in developing countries ...

    African Journals Online (AJOL)

    the major burden being in developing countries. Many of ... The driving force for drug discovery and development by pharmaceutical firms ... world and particularly in the third world countries ..... GFHR (2000) Global Forum for Health Research:.

  7. Optogenetic Approaches to Drug Discovery in Neuroscience and Beyond.

    Science.gov (United States)

    Zhang, Hongkang; Cohen, Adam E

    2017-07-01

    Recent advances in optogenetics have opened new routes to drug discovery, particularly in neuroscience. Physiological cellular assays probe functional phenotypes that connect genomic data to patient health. Optogenetic tools, in particular tools for all-optical electrophysiology, now provide a means to probe cellular disease models with unprecedented throughput and information content. These techniques promise to identify functional phenotypes associated with disease states and to identify compounds that improve cellular function regardless of whether the compound acts directly on a target or through a bypass mechanism. This review discusses opportunities and unresolved challenges in applying optogenetic techniques throughout the discovery pipeline - from target identification and validation, to target-based and phenotypic screens, to clinical trials. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Influence networks based on coexpression improve drug target discovery for the development of novel cancer therapeutics

    Science.gov (United States)

    2014-01-01

    Background The demand for novel molecularly targeted drugs will continue to rise as we move forward toward the goal of personalizing cancer treatment to the molecular signature of individual tumors. However, the identification of targets and combinations of targets that can be safely and effectively modulated is one of the greatest challenges facing the drug discovery process. A promising approach is to use biological networks to prioritize targets based on their relative positions to one another, a property that affects their ability to maintain network integrity and propagate information-flow. Here, we introduce influence networks and demonstrate how they can be used to generate influence scores as a network-based metric to rank genes as potential drug targets. Results We use this approach to prioritize genes as drug target candidates in a set of ER + breast tumor samples collected during the course of neoadjuvant treatment with the aromatase inhibitor letrozole. We show that influential genes, those with high influence scores, tend to be essential and include a higher proportion of essential genes than those prioritized based on their position (i.e. hubs or bottlenecks) within the same network. Additionally, we show that influential genes represent novel biologically relevant drug targets for the treatment of ER + breast cancers. Moreover, we demonstrate that gene influence differs between untreated tumors and residual tumors that have adapted to drug treatment. In this way, influence scores capture the context-dependent functions of genes and present the opportunity to design combination treatment strategies that take advantage of the tumor adaptation process. Conclusions Influence networks efficiently find essential genes as promising drug targets and combinations of targets to inform the development of molecularly targeted drugs and their use. PMID:24495353

  9. Organ/body-on-a-chip based on microfluidic technology for drug discovery.

    Science.gov (United States)

    Kimura, Hiroshi; Sakai, Yasuyuki; Fujii, Teruo

    2018-02-01

    Although animal experiments are indispensable for preclinical screening in the drug discovery process, various issues such as ethical considerations and species differences remain. To solve these issues, cell-based assays using human-derived cells have been actively pursued. However, it remains difficult to accurately predict drug efficacy, toxicity, and organs interactions, because cultivated cells often do not retain their original organ functions and morphologies in conventional in vitro cell culture systems. In the μTAS research field, which is a part of biochemical engineering, the technologies of organ-on-a-chip, based on microfluidic devices built using microfabrication, have been widely studied recently as a novel in vitro organ model. Since it is possible to physically and chemically mimic the in vitro environment by using microfluidic device technology, maintenance of cellular function and morphology, and replication of organ interactions can be realized using organ-on-a-chip devices. So far, functions of various organs and tissues, such as the lung, liver, kidney, and gut have been reproduced as in vitro models. Furthermore, a body-on-a-chip, integrating multi organ functions on a microfluidic device, has also been proposed for prediction of organ interactions. We herein provide a background of microfluidic systems, organ-on-a-chip, Body-on-a-chip technologies, and their challenges in the future. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

  10. Increasing the structural coverage of tuberculosis drug targets.

    Science.gov (United States)

    Baugh, Loren; Phan, Isabelle; Begley, Darren W; Clifton, Matthew C; Armour, Brianna; Dranow, David M; Taylor, Brandy M; Muruthi, Marvin M; Abendroth, Jan; Fairman, James W; Fox, David; Dieterich, Shellie H; Staker, Bart L; Gardberg, Anna S; Choi, Ryan; Hewitt, Stephen N; Napuli, Alberto J; Myers, Janette; Barrett, Lynn K; Zhang, Yang; Ferrell, Micah; Mundt, Elizabeth; Thompkins, Katie; Tran, Ngoc; Lyons-Abbott, Sally; Abramov, Ariel; Sekar, Aarthi; Serbzhinskiy, Dmitri; Lorimer, Don; Buchko, Garry W; Stacy, Robin; Stewart, Lance J; Edwards, Thomas E; Van Voorhis, Wesley C; Myler, Peter J

    2015-03-01

    High-resolution three-dimensional structures of essential Mycobacterium tuberculosis (Mtb) proteins provide templates for TB drug design, but are available for only a small fraction of the Mtb proteome. Here we evaluate an intra-genus "homolog-rescue" strategy to increase the structural information available for TB drug discovery by using mycobacterial homologs with conserved active sites. Of 179 potential TB drug targets selected for x-ray structure determination, only 16 yielded a crystal structure. By adding 1675 homologs from nine other mycobacterial species to the pipeline, structures representing an additional 52 otherwise intractable targets were solved. To determine whether these homolog structures would be useful surrogates in TB drug design, we compared the active sites of 106 pairs of Mtb and non-TB mycobacterial (NTM) enzyme homologs with experimentally determined structures, using three metrics of active site similarity, including superposition of continuous pharmacophoric property distributions. Pair-wise structural comparisons revealed that 19/22 pairs with >55% overall sequence identity had active site Cα RMSD 85% side chain identity, and ≥80% PSAPF (similarity based on pharmacophoric properties) indicating highly conserved active site shape and chemistry. Applying these results to the 52 NTM structures described above, 41 shared >55% sequence identity with the Mtb target, thus increasing the effective structural coverage of the 179 Mtb targets over three-fold (from 9% to 32%). The utility of these structures in TB drug design can be tested by designing inhibitors using the homolog structure and assaying the cognate Mtb enzyme; a promising test case, Mtb cytidylate kinase, is described. The homolog-rescue strategy evaluated here for TB is also generalizable to drug targets for other diseases. Copyright © 2014 Elsevier Ltd. All rights reserved.

  11. Discovery of Boolean metabolic networks: integer linear programming based approach.

    Science.gov (United States)

    Qiu, Yushan; Jiang, Hao; Ching, Wai-Ki; Cheng, Xiaoqing

    2018-04-11

    Traditional drug discovery methods focused on the efficacy of drugs rather than their toxicity. However, toxicity and/or lack of efficacy are produced when unintended targets are affected in metabolic networks. Thus, identification of biological targets which can be manipulated to produce the desired effect with minimum side-effects has become an important and challenging topic. Efficient computational methods are required to identify the drug targets while incurring minimal side-effects. In this paper, we propose a graph-based computational damage model that summarizes the impact of enzymes on compounds in metabolic networks. An efficient method based on Integer Linear Programming formalism is then developed to identify the optimal enzyme-combination so as to minimize the side-effects. The identified target enzymes for known successful drugs are then verified by comparing the results with those in the existing literature. Side-effects reduction plays a crucial role in the study of drug development. A graph-based computational damage model is proposed and the theoretical analysis states the captured problem is NP-completeness. The proposed approaches can therefore contribute to the discovery of drug targets. Our developed software is available at " http://hkumath.hku.hk/~wkc/APBC2018-metabolic-network.zip ".

  12. An Ontology for Description of Drug Discovery Investigations

    Directory of Open Access Journals (Sweden)

    Qi Da

    2010-12-01

    Full Text Available The paper presents an ontology for the description of Drug Discovery Investigation (DDI. This has been developed through the use of a Robot Scientist “Eve”, and in consultation with industry. DDI aims to define the principle entities and the relations in the research and development phase of the drug discovery pipeline. DDI is highly transferable and extendable due to its adherence to accepted standards, and compliance with existing ontology resources. This enables DDI to be integrated with such related ontologies as the Vaccine Ontology, the Advancing Clinico-Genomic Trials on Cancer Master Ontology, etc. DDI is available at http://purl.org/ddi/wikipedia or http://purl.org/ddi/home

  13. Simulation with quantum mechanics/molecular mechanics for drug discovery.

    Science.gov (United States)

    Barbault, Florent; Maurel, François

    2015-10-01

    Biological macromolecules, such as proteins or nucleic acids, are (still) molecules and thus they follow the same chemical rules that any simple molecule follows, even if their size generally renders accurate studies unhelpful. However, in the context of drug discovery, a detailed analysis of ligand association is required for understanding or predicting their interactions and hybrid quantum mechanics/molecular mechanics (QM/MM) computations are relevant tools to help elucidate this process. In this review, the authors explore the use of QM/MM for drug discovery. After a brief description of the molecular mechanics (MM) technique, the authors describe the subtractive and additive techniques for QM/MM computations. The authors then present several application cases in topics involved in drug discovery. QM/MM have been widely employed during the last decades to study chemical processes such as enzyme-inhibitor interactions. However, despite the enthusiasm around this area, plain MM simulations may be more meaningful than QM/MM. To obtain reliable results, the authors suggest fixing several keystone parameters according to the underlying chemistry of each studied system.

  14. Discovery and development of new antibacterial drugs: learning from experience?

    Science.gov (United States)

    Jackson, Nicole; Czaplewski, Lloyd; Piddock, Laura J V

    2018-06-01

    Antibiotic (antibacterial) resistance is a serious global problem and the need for new treatments is urgent. The current antibiotic discovery model is not delivering new agents at a rate that is sufficient to combat present levels of antibiotic resistance. This has led to fears of the arrival of a 'post-antibiotic era'. Scientific difficulties, an unfavourable regulatory climate, multiple company mergers and the low financial returns associated with antibiotic drug development have led to the withdrawal of many pharmaceutical companies from the field. The regulatory climate has now begun to improve, but major scientific hurdles still impede the discovery and development of novel antibacterial agents. To facilitate discovery activities there must be increased understanding of the scientific problems experienced by pharmaceutical companies. This must be coupled with addressing the current antibiotic resistance crisis so that compounds and ultimately drugs are delivered to treat the most urgent clinical challenges. By understanding the causes of the failures and successes of the pharmaceutical industry's research history, duplication of discovery programmes will be reduced, increasing the productivity of the antibiotic drug discovery pipeline by academia and small companies. The most important scientific issues to address are getting molecules into the Gram-negative bacterial cell and avoiding their efflux. Hence screening programmes should focus their efforts on whole bacterial cells rather than cell-free systems. Despite falling out of favour with pharmaceutical companies, natural product research still holds promise for providing new molecules as a basis for discovery.

  15. Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics.

    Science.gov (United States)

    de Lange, Elizabeth C M; van den Brink, Willem; Yamamoto, Yumi; de Witte, Wilhelmus E A; Wong, Yin Cheong

    2017-12-01

    CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

  16. Cloud computing approaches to accelerate drug discovery value chain.

    Science.gov (United States)

    Garg, Vibhav; Arora, Suchir; Gupta, Chitra

    2011-12-01

    Continued advancements in the area of technology have helped high throughput screening (HTS) evolve from a linear to parallel approach by performing system level screening. Advanced experimental methods used for HTS at various steps of drug discovery (i.e. target identification, target validation, lead identification and lead validation) can generate data of the order of terabytes. As a consequence, there is pressing need to store, manage, mine and analyze this data to identify informational tags. This need is again posing challenges to computer scientists to offer the matching hardware and software infrastructure, while managing the varying degree of desired computational power. Therefore, the potential of "On-Demand Hardware" and "Software as a Service (SAAS)" delivery mechanisms cannot be denied. This on-demand computing, largely referred to as Cloud Computing, is now transforming the drug discovery research. Also, integration of Cloud computing with parallel computing is certainly expanding its footprint in the life sciences community. The speed, efficiency and cost effectiveness have made cloud computing a 'good to have tool' for researchers, providing them significant flexibility, allowing them to focus on the 'what' of science and not the 'how'. Once reached to its maturity, Discovery-Cloud would fit best to manage drug discovery and clinical development data, generated using advanced HTS techniques, hence supporting the vision of personalized medicine.

  17. Inositol Polyphosphate Kinases, Fungal Virulence and Drug Discovery

    Directory of Open Access Journals (Sweden)

    Cecilia Li

    2016-09-01

    Full Text Available Opportunistic fungi are a major cause of morbidity and mortality world-wide, particularly in immunocompromised individuals. Developing new treatments to combat invasive fungal disease is challenging given that fungal and mammalian host cells are eukaryotic, with similar organization and physiology. Even therapies targeting unique fungal cell features have limitations and drug resistance is emerging. New approaches to the development of antifungal drugs are therefore needed urgently. Cryptococcus neoformans, the commonest cause of fungal meningitis worldwide, is an accepted model for studying fungal pathogenicity and driving drug discovery. We recently characterized a phospholipase C (Plc1-dependent pathway in C. neoformans comprising of sequentially-acting inositol polyphosphate kinases (IPK, which are involved in synthesizing inositol polyphosphates (IP. We also showed that the pathway is essential for fungal cellular function and pathogenicity. The IP products of the pathway are structurally diverse, each consisting of an inositol ring, with phosphate (P and pyrophosphate (PP groups covalently attached at different positions. This review focuses on (1 the characterization of the Plc1/IPK pathway in C. neoformans; (2 the identification of PP-IP5 (IP7 as the most crucial IP species for fungal fitness and virulence in a mouse model of fungal infection; and (3 why IPK enzymes represent suitable candidates for drug development.

  18. Residual Complexity Does Impact Organic Chemistry and Drug Discovery: The Case of Rufomyazine and Rufomycin.

    Science.gov (United States)

    Choules, Mary P; Klein, Larry L; Lankin, David C; McAlpine, James B; Cho, Sang-Hyun; Cheng, Jinhua; Lee, Hanki; Suh, Joo-Won; Jaki, Birgit U; Franzblau, Scott G; Pauli, Guido F

    2018-05-24

    Residual complexity (RC) involves the impact of subtle but critical structural and biological features on drug lead validation, including unexplained effects related to unidentified impurities. RC commonly plagues drug discovery efforts due to the inherent imperfections of chromatographic separation methods. The new diketopiperazine, rufomyazine (6), and the previously known antibiotic, rufomycin (7), represent a prototypical case of RC that (almost) resulted in the misassignment of biological activity. The case exemplifies that impurities well below the natural abundance of 13 C (1.1%) can be highly relevant and calls for advanced analytical characterization of drug leads with extended molar dynamic ranges of >1:1,000 using qNMR and LC-MS. Isolated from an actinomycete strain, 6 was originally found to be active against Mycobacterium tuberculosis with a minimum inhibitory concentration (MIC) of 2 μg/mL and high selectivity. As a part of lead validation, the dipeptide was synthesized and surprisingly found to be inactive. The initially observed activity was eventually attributed to a very minor contamination (0.24% [m/m]) with a highly active cyclic peptide (MIC ∼ 0.02 μM), subsequently identified as an analogue of 7. This study illustrates the serious implications RC can exert on organic chemistry and drug discovery, and what efforts are vital to improve lead validation and efficiency, especially in NP-related drug discovery programs.

  19. Open Innovation Drug Discovery (OIDD): a potential path to novel therapeutic chemical space.

    Science.gov (United States)

    Alvim-Gaston, Maria; Grese, Timothy; Mahoui, Abdelaziz; Palkowitz, Alan D; Pineiro-Nunez, Marta; Watson, Ian

    2014-01-01

    The continued development of computational and synthetic methods has enabled the enumeration or preparation of a nearly endless universe of chemical structures. Nevertheless, the ability of this chemical universe to deliver small molecules that can both modulate biological targets and have drug-like physicochemical properties continues to be a topic of interest to the pharmaceutical industry and academic researchers alike. The chemical space described by public, commercial, in-house and virtual compound collections has been interrogated by multiple approaches including biochemical, cellular and virtual screening, diversity analysis, and in-silico profiling. However, current drugs and known chemical probes derived from these efforts are contained within a remarkably small volume of the predicted chemical space. Access to more diverse classes of chemical scaffolds that maintain the properties relevant for drug discovery is certainly needed to meet the increasing demands for pharmaceutical innovation. The Lilly Open Innovation Drug Discovery platform (OIDD) was designed to tackle barriers to innovation through the identification of novel molecules active in relevant disease biology models. In this article we will discuss several computational approaches towards describing novel, biologically active, drug-like chemical space and illustrate how the OIDD program may facilitate access to previously untapped molecules that may aid in the search for innovative pharmaceuticals.

  20. Resource Discovery in Activity-Based Sensor Networks

    DEFF Research Database (Denmark)

    Bucur, Doina; Bardram, Jakob

    This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networ....... ABSN enhances the generic Extended Zone Routing Protocol with logical sensor grouping and greatly lowers network overhead during the process of discovery, while keeping discovery latency close to optimal.......This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

  1. Switch in Site of Inhibition: A Strategy for Structure-Based Discovery of Human Topoisomerase IIα Catalytic Inhibitors

    Science.gov (United States)

    2015-01-01

    A study of structure-based modulation of known ligands of hTopoIIα, an important enzyme involved in DNA processes, coupled with synthesis and in vitro assays led to the establishment of a strategy of rational switch in mode of inhibition of the enzyme’s catalytic cycle. 6-Arylated derivatives of known imidazopyridine ligands were found to be selective inhibitors of hTopoIIα, while not showing TopoI inhibition and DNA binding. Interestingly, while the parent imidazopyridines acted as ATP-competitive inhibitors, arylated derivatives inhibited DNA cleavage similar to merbarone, indicating a switch in mode of inhibition from ATP-hydrolysis to the DNA-cleavage stage of catalytic cycle of the enzyme. The 6-aryl-imidazopyridines were relatively more cytotoxic than etoposide in cancer cells and less toxic to normal cells. Such unprecedented strategy will encourage research on “choice-based change” in target-specific mode of action for rapid drug discovery. PMID:25941559

  2. Fragment Based Strategies for Discovery of Novel HIV-1 Reverse Transcriptase and Integrase Inhibitors.

    Science.gov (United States)

    Latham, Catherine F; La, Jennifer; Tinetti, Ricky N; Chalmers, David K; Tachedjian, Gilda

    2016-01-01

    Human immunodeficiency virus (HIV) remains a global health problem. While combined antiretroviral therapy has been successful in controlling the virus in patients, HIV can develop resistance to drugs used for treatment, rendering available drugs less effective and limiting treatment options. Initiatives to find novel drugs for HIV treatment are ongoing, although traditional drug design approaches often focus on known binding sites for inhibition of established drug targets like reverse transcriptase and integrase. These approaches tend towards generating more inhibitors in the same drug classes already used in the clinic. Lack of diversity in antiretroviral drug classes can result in limited treatment options, as cross-resistance can emerge to a whole drug class in patients treated with only one drug from that class. A fresh approach in the search for new HIV-1 drugs is fragment-based drug discovery (FBDD), a validated strategy for drug discovery based on using smaller libraries of low molecular weight molecules (FBDD is aimed at not only finding novel drug scaffolds, but also probing the target protein to find new, often allosteric, inhibitory binding sites. Several fragment-based strategies have been successful in identifying novel inhibitory sites or scaffolds for two proven drug targets for HIV-1, reverse transcriptase and integrase. While any FBDD-generated HIV-1 drugs have yet to enter the clinic, recent FBDD initiatives against these two well-characterised HIV-1 targets have reinvigorated antiretroviral drug discovery and the search for novel classes of HIV-1 drugs.

  3. The development of high-content screening (HCS) technology and its importance to drug discovery.

    Science.gov (United States)

    Fraietta, Ivan; Gasparri, Fabio

    2016-01-01

    High-content screening (HCS) was introduced about twenty years ago as a promising analytical approach to facilitate some critical aspects of drug discovery. Its application has spread progressively within the pharmaceutical industry and academia to the point that it today represents a fundamental tool in supporting drug discovery and development. Here, the authors review some of significant progress in the HCS field in terms of biological models and assay readouts. They highlight the importance of high-content screening in drug discovery, as testified by its numerous applications in a variety of therapeutic areas: oncology, infective diseases, cardiovascular and neurodegenerative diseases. They also dissect the role of HCS technology in different phases of the drug discovery pipeline: target identification, primary compound screening, secondary assays, mechanism of action studies and in vitro toxicology. Recent advances in cellular assay technologies, such as the introduction of three-dimensional (3D) cultures, induced pluripotent stem cells (iPSCs) and genome editing technologies (e.g., CRISPR/Cas9), have tremendously expanded the potential of high-content assays to contribute to the drug discovery process. Increasingly predictive cellular models and readouts, together with the development of more sophisticated and affordable HCS readers, will further consolidate the role of HCS technology in drug discovery.

  4. Resource Discovery in Activity-Based Sensor Networks

    DEFF Research Database (Denmark)

    Bucur, Doina; Bardram, Jakob

    This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

  5. Current approaches for the discovery of drugs that deter substance and drug abuse.

    Science.gov (United States)

    Yasgar, Adam; Simeonov, Anton

    2014-11-01

    Much has been presented and debated on the topic of drug abuse and its multidimensional nature, including the role of society and its customs and laws, economical factors, and the magnitude and nature of the burden. Given the complex nature of the receptors and pathways implicated in regulation of the cognitive and behavioral processes associated with addiction, a large number of molecular targets have been interrogated during recent years to discover starting points for development of small-molecule interventions. This review describes recent developments in the field of early drug discovery for drug abuse interventions with an emphasis on the advances published during the 2012 - 2014 period. Technologically, the processes/platforms utilized in drug abuse drug discovery are nearly identical to those used in the other disease areas. A key complicating factor in drug abuse research is the enormous biological complexity surrounding the brain processes involved and the associated difficulty in finding 'good' targets and achieving exquisite selectivity of treatment agents. While tremendous progress has been made during recent years to use the power of high-throughput technologies to discover proof-of-principle molecules for many new targets, next-generation models will be especially important in this field. Examples include: seeking advantageous drug-drug combinations, the use of automated whole-animal behavioral screening systems, advancing our understanding of the role of epigenetics in drug addiction and the employment of organoid-level 3D test platforms (also referred to as tissue-chip or organs-on-chip).

  6. The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery.

    Science.gov (United States)

    Spyrakis, Francesca; Ahmed, Mostafa H; Bayden, Alexander S; Cozzini, Pietro; Mozzarelli, Andrea; Kellogg, Glen E

    2017-08-24

    The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.

  7. Current status and future prospects for enabling chemistry technology in the drug discovery process [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Stevan W. Djuric

    2016-09-01

    Full Text Available This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of “dangerous” reagents. Also featured are advances in the “computer-assisted drug design” area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities.

  8. Conference Abstracts: Translational Science and Drug Discovery ...

    African Journals Online (AJOL)

    ... and Drug Discovery: Impact on Health, Wellness, Environment and Economics" conference, July 27-29th, 2015, at the Hennessy Park Hotel, Ebène Cybercity, Mauritius. The conference was hosted by the Society for Free radical Research Africa and the International Association of Medical and Biomedical Researchers.

  9. Establishing MALDI-TOF as Versatile Drug Discovery Readout to Dissect the PTP1B Enzymatic Reaction.

    Science.gov (United States)

    Winter, Martin; Bretschneider, Tom; Kleiner, Carola; Ries, Robert; Hehn, Jörg P; Redemann, Norbert; Luippold, Andreas H; Bischoff, Daniel; Büttner, Frank H

    2018-07-01

    Label-free, mass spectrometric (MS) detection is an emerging technology in the field of drug discovery. Unbiased deciphering of enzymatic reactions is a proficient advantage over conventional label-based readouts suffering from compound interference and intricate generation of tailored signal mediators. Significant evolvements of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS, as well as associated liquid handling instrumentation, triggered extensive efforts in the drug discovery community to integrate the comprehensive MS readout into the high-throughput screening (HTS) portfolio. Providing speed, sensitivity, and accuracy comparable to those of conventional, label-based readouts, combined with merits of MS-based technologies, such as label-free parallelized measurement of multiple physiological components, emphasizes the advantages of MALDI-TOF for HTS approaches. Here we describe the assay development for the identification of protein tyrosine phosphatase 1B (PTP1B) inhibitors. In the context of this precious drug target, MALDI-TOF was integrated into the HTS environment and cross-compared with the well-established AlphaScreen technology. We demonstrate robust and accurate IC 50 determination with high accordance to data generated by AlphaScreen. Additionally, a tailored MALDI-TOF assay was developed to monitor compound-dependent, irreversible modification of the active cysteine of PTP1B. Overall, the presented data proves the promising perspective for the integration of MALDI-TOF into drug discovery campaigns.

  10. New perspectives on innovative drug discovery: an overview.

    Science.gov (United States)

    Pan, Si Yuan; Pan, Shan; Yu, Zhi-Ling; Ma, Dik-Lung; Chen, Si-Bao; Fong, Wang-Fun; Han, Yi-Fan; Ko, Kam-Ming

    2010-01-01

    Despite advances in technology, drug discovery is still a lengthy, expensive, difficult, and inefficient process, with a low rate of success. Today, advances in biomedical science have brought about great strides in therapeutic interventions for a wide spectrum of diseases. The advent of biochemical techniques and cutting-edge bio/chemical technologies has made available a plethora of practical approaches to drug screening and design. In 2010, the total sales of the global pharmaceutical market will reach 600 billion US dollars and expand to over 975 billion dollars by 2013. The aim of this review is to summarize available information on contemporary approaches and strategies in the discovery of novel therapeutic agents, especially from the complementary and alternative medicines, including natural products and traditional remedies such as Chinese herbal medicine.

  11. Stem cell technology for drug discovery and development.

    Science.gov (United States)

    Hook, Lilian A

    2012-04-01

    Stem cells have enormous potential to revolutionise the drug discovery process at all stages, from target identification through to toxicology studies. Their ability to generate physiologically relevant cells in limitless supply makes them an attractive alternative to currently used recombinant cell lines or primary cells. However, realisation of the full potential of stem cells is currently hampered by the difficulty in routinely directing stem cell differentiation to reproducibly and cost effectively generate pure populations of specific cell types. In this article we discuss how stem cells have already been used in the drug discovery process and how novel technologies, particularly in relation to stem cell differentiation, can be applied to attain widespread adoption of stem cell technology by the pharmaceutical industry. Copyright © 2011 Elsevier Ltd. All rights reserved.

  12. Drug discovery in an academic setting: playing to the strengths.

    Science.gov (United States)

    Huryn, Donna M

    2013-03-14

    Drug discovery and medicinal chemistry initiatives in academia provide an opportunity to create a unique environment that is distinct from the traditional industrial model. Two characteristics of a university setting that are not usually associated with pharma are the ability to pursue high-risk projects and a depth of expertise, infrastructure, and capabilities in focused areas. Encouraging, supporting, and fostering drug discovery efforts that take advantage of these and other distinguishing characteristics of an academic setting can lead to novel and innovative therapies that might not be discovered otherwise.

  13. Software Infrastructure for Computer-aided Drug Discovery and Development, a Practical Example with Guidelines.

    Science.gov (United States)

    Moretti, Loris; Sartori, Luca

    2016-09-01

    In the field of Computer-Aided Drug Discovery and Development (CADDD) the proper software infrastructure is essential for everyday investigations. The creation of such an environment should be carefully planned and implemented with certain features in order to be productive and efficient. Here we describe a solution to integrate standard computational services into a functional unit that empowers modelling applications for drug discovery. This system allows users with various level of expertise to run in silico experiments automatically and without the burden of file formatting for different software, managing the actual computation, keeping track of the activities and graphical rendering of the structural outcomes. To showcase the potential of this approach, performances of five different docking programs on an Hiv-1 protease test set are presented. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Financing drug discovery for orphan diseases.

    Science.gov (United States)

    Fagnan, David E; Gromatzky, Austin A; Stein, Roger M; Fernandez, Jose-Maria; Lo, Andrew W

    2014-05-01

    Recently proposed 'megafund' financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures among disease targets) the amount of capital needed to de-risk such portfolios is much lower in this field. Numerical simulations suggest that an orphan disease megafund of only US$575 million can yield double-digit expected rates of return with only 10-20 projects in the portfolio. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

  15. Polyphony: superposition independent methods for ensemble-based drug discovery.

    Science.gov (United States)

    Pitt, William R; Montalvão, Rinaldo W; Blundell, Tom L

    2014-09-30

    Structure-based drug design is an iterative process, following cycles of structural biology, computer-aided design, synthetic chemistry and bioassay. In favorable circumstances, this process can lead to the structures of hundreds of protein-ligand crystal structures. In addition, molecular dynamics simulations are increasingly being used to further explore the conformational landscape of these complexes. Currently, methods capable of the analysis of ensembles of crystal structures and MD trajectories are limited and usually rely upon least squares superposition of coordinates. Novel methodologies are described for the analysis of multiple structures of a protein. Statistical approaches that rely upon residue equivalence, but not superposition, are developed. Tasks that can be performed include the identification of hinge regions, allosteric conformational changes and transient binding sites. The approaches are tested on crystal structures of CDK2 and other CMGC protein kinases and a simulation of p38α. Known interaction - conformational change relationships are highlighted but also new ones are revealed. A transient but druggable allosteric pocket in CDK2 is predicted to occur under the CMGC insert. Furthermore, an evolutionarily-conserved conformational link from the location of this pocket, via the αEF-αF loop, to phosphorylation sites on the activation loop is discovered. New methodologies are described and validated for the superimposition independent conformational analysis of large collections of structures or simulation snapshots of the same protein. The methodologies are encoded in a Python package called Polyphony, which is released as open source to accompany this paper [http://wrpitt.bitbucket.org/polyphony/].

  16. Chimeric mice with humanized liver: Application in drug metabolism and pharmacokinetics studies for drug discovery.

    Science.gov (United States)

    Naritomi, Yoichi; Sanoh, Seigo; Ohta, Shigeru

    2018-02-01

    Predicting human drug metabolism and pharmacokinetics (PK) is key to drug discovery. In particular, it is important to predict human PK, metabolite profiles and drug-drug interactions (DDIs). Various methods have been used for such predictions, including in vitro metabolic studies using human biological samples, such as hepatic microsomes and hepatocytes, and in vivo studies using experimental animals. However, prediction studies using these methods are often inconclusive due to discrepancies between in vitro and in vivo results, and interspecies differences in drug metabolism. Further, the prediction methods have changed from qualitative to quantitative to solve these issues. Chimeric mice with humanized liver have been developed, in which mouse liver cells are mostly replaced with human hepatocytes. Since human drug metabolizing enzymes are expressed in the liver of these mice, they are regarded as suitable models for mimicking the drug metabolism and PK observed in humans; therefore, these mice are useful for predicting human drug metabolism and PK. In this review, we discuss the current state, issues, and future directions of predicting human drug metabolism and PK using chimeric mice with humanized liver in drug discovery. Copyright © 2017 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

  17. Open Access Could Transform Drug Discovery: A Case Study of JQ1.

    Science.gov (United States)

    Arshad, Zeeshaan; Smith, James; Roberts, Mackenna; Lee, Wen Hwa; Davies, Ben; Bure, Kim; Hollander, Georg A; Dopson, Sue; Bountra, Chas; Brindley, David

    2016-01-01

    The cost to develop a new drug from target discovery to market is a staggering $1.8 billion, largely due to the very high attrition rate of drug candidates and the lengthy transition times during development. Open access is an emerging model of open innovation that places no restriction on the use of information and has the potential to accelerate the development of new drugs. To date, no quantitative assessment has yet taken place to determine the effects and viability of open access on the process of drug translation. This need is addressed within this study. The literature and intellectual property landscapes of the drug candidate JQ1, which was made available on an open access basis when discovered, and conventionally developed equivalents that were not are compared using the Web of Science and Thomson Innovation software, respectively. Results demonstrate that openly sharing the JQ1 molecule led to a greater uptake by a wider and more multi-disciplinary research community. A comparative analysis of the patent landscapes for each candidate also found that the broader scientific diaspora of the publically released JQ1 data enhanced innovation, evidenced by a greater number of downstream patents filed in relation to JQ1. The authors' findings counter the notion that open access drug discovery would leak commercial intellectual property. On the contrary, JQ1 serves as a test case to evidence that open access drug discovery can be an economic model that potentially improves efficiency and cost of drug discovery and its subsequent commercialization.

  18. Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy.

    Science.gov (United States)

    Kirk, Jon; Shah, Nirav; Noll, Braxton; Stevens, Craig B; Lawler, Marshall; Mougeot, Farah B; Mougeot, Jean-Luc C

    2018-08-01

    Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy. Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways. OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM. Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success. Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

  19. Discovery of novel inhibitors for Leishmania nucleoside diphosphatase kinase (NDK) based on its structural and functional characterization

    Science.gov (United States)

    Mishra, Arjun K.; Singh, Nidhi; Agnihotri, Pragati; Mishra, Shikha; Singh, Saurabh P.; Kolli, Bala K.; Chang, Kwang Poo; Sahasrabuddhe, Amogh A.; Siddiqi, M. I.; Pratap, J. Venkatesh

    2017-06-01

    Nucleoside diphosphate kinases (NDKs) are ubiquitous enzymes that catalyze the transfer of the γ-phosphate moiety from an NTP donor to an NDP acceptor, crucial for maintaining the cellular level of nucleoside triphosphates (NTPs). The inability of trypanosomatids to synthesize purines de novo and their dependence on the salvage pathway makes NDK an attractive target to develop drugs for the diseases they cause. Here we report the discovery of novel inhibitors for Leishmania NDK based on the structural and functional characterization of purified recombinant NDK from Leishmania amazonensis. Recombinant LaNDK possesses auto-phosphorylation, phosphotransferase and kinase activities with Histidine 117 playing an essential role. LaNDK crystals were grown by hanging drop vapour diffusion method in a solution containing 18% PEG-MME 500, 100 mM Bis-Tris propane pH 6.0 and 50 mM MgCl2. It belongs to the hexagonal space group P6322 with unit cell parameters a = b = 115.18, c = 62.18 Å and α = β = 90°, γ = 120°. The structure solved by molecular replacement methods was refined to crystallographic R-factor and Rfree values of 22.54 and 26.52%, respectively. Molecular docking and dynamics simulation -based virtual screening identified putative binding compounds. Protein inhibition studies of selected hits identified five inhibitors effective at micromolar concentrations. One of the compounds showed 45% inhibition of Leishmania promastigotes proliferation. Analysis of inhibitor-NDK complexes reveals the mode of their binding, facilitating design of new compounds for optimization of activities as drugs against leishmaniasis.

  20. Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia.

    Science.gov (United States)

    Keserű, György M; Erlanson, Daniel A; Ferenczy, György G; Hann, Michael M; Murray, Christopher W; Pickett, Stephen D

    2016-09-22

    Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function; it can cover broad swaths of chemical space and allows the use of creative chemistry. FBDD is widely implemented for lead discovery in industry but is sometimes used less systematically in academia. Design principles and implementation approaches for fragment libraries are continually evolving, and the lack of up-to-date guidance may prevent more effective application of FBDD in academia. This Perspective explores many of the theoretical, practical, and strategic considerations that occur within FBDD programs, including the optimal size, complexity, physicochemical profile, and shape profile of fragments in FBDD libraries, as well as compound storage, evaluation, and screening technologies. This compilation of industry experience in FBDD will hopefully be useful for those pursuing FBDD in academia.

  1. Preparative Scale Resolution of Enantiomers Enables Accelerated Drug Discovery and Development

    Directory of Open Access Journals (Sweden)

    Hanna Leek

    2017-01-01

    Full Text Available The provision of pure enantiomers is of increasing importance not only for the pharmaceutical industry but also for agro-chemistry and biotechnology. In drug discovery and development, the enantiomers of a chiral drug depict unique chemical and pharmacological behaviors in a chiral environment, such as the human body, in which the stereochemistry of the chiral drugs determines their pharmacokinetic, pharmacodynamic and toxicological properties. We present a number of challenging case studies of up-to-kilogram separations of racemic or enriched isomer mixtures using preparative liquid chromatography and super critical fluid chromatography to generate individual enantiomers that have enabled the development of new candidate drugs within AstraZeneca. The combination of chromatography and racemization as well as strategies on when to apply preparative chiral chromatography of enantiomers in a multi-step synthesis of a drug compound can further facilitate accelerated drug discovery and the early clinical evaluation of the drug candidates.

  2. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines.

    Science.gov (United States)

    Ru, Jinlong; Li, Peng; Wang, Jinan; Zhou, Wei; Li, Bohui; Huang, Chao; Li, Pidong; Guo, Zihu; Tao, Weiyang; Yang, Yinfeng; Xu, Xue; Li, Yan; Wang, Yonghua; Yang, Ling

    2014-01-01

    Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed. The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 Chinese herbs registered in the Chinese pharmacopoeia with 29,384 ingredients, 3,311 targets and 837 associated diseases. Twelve important ADME-related properties like human oral bioavailability, half-life, drug-likeness, Caco-2 permeability, blood-brain barrier and Lipinski's rule of five are provided for drug screening and evaluation. TCMSP also provides drug targets and diseases of each active compound, which can automatically establish the compound-target and target-disease networks that let users view and analyze the drug action mechanisms. It is designed to fuel the development of herbal medicines and to promote integration of modern medicine and traditional medicine for drug discovery and development. The particular strengths of TCMSP are the composition of the large number of herbal entries, and the ability to identify drug-target networks and drug-disease networks, which will help revealing the mechanisms of action of Chinese herbs, uncovering the nature of TCM theory and developing new herb-oriented drugs. TCMSP is freely available at http://sm.nwsuaf.edu.cn/lsp/tcmsp.php.

  3. Computer-Aided Drug Design Applied to Marine Drug Discovery: Meridianins as Alzheimer's Disease Therapeutic Agents.

    Science.gov (United States)

    Llorach-Pares, Laura; Nonell-Canals, Alfons; Sanchez-Martinez, Melchor; Avila, Conxita

    2017-11-27

    Computer-aided drug discovery/design (CADD) techniques allow the identification of natural products that are capable of modulating protein functions in pathogenesis-related pathways, constituting one of the most promising lines followed in drug discovery. In this paper, we computationally evaluated and reported the inhibitory activity found in meridianins A-G, a group of marine indole alkaloids isolated from the marine tunicate Aplidium , against various protein kinases involved in Alzheimer's disease (AD), a neurodegenerative pathology characterized by the presence of neurofibrillary tangles (NFT). Balance splitting between tau kinase and phosphate activities caused tau hyperphosphorylation and, thereby, its aggregation and NTF formation. Inhibition of specific kinases involved in its phosphorylation pathway could be one of the key strategies to reverse tau hyperphosphorylation and would represent an approach to develop drugs to palliate AD symptoms. Meridianins bind to the adenosine triphosphate (ATP) binding site of certain protein kinases, acting as ATP competitive inhibitors. These compounds show very promising scaffolds to design new drugs against AD, which could act over tau protein kinases Glycogen synthetase kinase-3 Beta (GSK3β) and Casein kinase 1 delta (CK1δ, CK1D or KC1D), and dual specificity kinases as dual specificity tyrosine phosphorylation regulated kinase 1 (DYRK1A) and cdc2-like kinases (CLK1). This work is aimed to highlight the role of CADD techniques in marine drug discovery and to provide precise information regarding the binding mode and strength of meridianins against several protein kinases that could help in the future development of anti-AD drugs.

  4. Utility of Glioblastoma Patient-Derived Orthotopic Xenografts in Drug Discovery and Personalized Therapy

    Directory of Open Access Journals (Sweden)

    Michele Patrizii

    2018-02-01

    Full Text Available Despite substantial effort and resources dedicated to drug discovery and development, new anticancer agents often fail in clinical trials. Among many reasons, the lack of reliable predictive preclinical cancer models is a fundamental one. For decades, immortalized cancer cell cultures have been used to lay the groundwork for cancer biology and the quest for therapeutic responses. However, cell lines do not usually recapitulate cancer heterogeneity or reveal therapeutic resistance cues. With the rapidly evolving exploration of cancer “omics,” the scientific community is increasingly investigating whether the employment of short-term patient-derived tumor cell cultures (two- and three-dimensional and/or patient-derived xenograft models might provide a more representative delineation of the cancer core and its therapeutic response. Patient-derived cancer models allow the integration of genomic with drug sensitivity data on a personalized basis and currently represent the ultimate approach for preclinical drug development and biomarker discovery. The proper use of these patient-derived cancer models might soon influence clinical outcomes and allow the implementation of tailored personalized therapy. When assessing drug efficacy for the treatment of glioblastoma multiforme (GBM, currently, the most reliable models are generated through direct injection of patient-derived cells or more frequently the isolation of glioblastoma cells endowed with stem-like features and orthotopically injecting these cells into the cerebrum of immunodeficient mice. Herein, we present the key strengths, weaknesses, and potential applications of cell- and animal-based models of GBM, highlighting our experience with the glioblastoma stem-like patient cell-derived xenograft model and its utility in drug discovery.

  5. Structure-based synthesis from natural products to drug prototypes

    International Nuclear Information System (INIS)

    Hanessian, S.

    2009-01-01

    X-Ray crystallographic data available from complexes of natural and synthetic molecules with the enzyme thrombin has aided to the design and synthesis of truncated and hybrid molecules exhibiting excellent inhibition in vitro. The vital importance of natural products for the well-being of man has been known lor millennia. Their therapeutic benefits to alleviate pain or cure diseases continue to rank natural products among the primary sources of potential drugs. Great advances have been made in the methods of isolation, identification, and structure elucidation of some of the most complex natural products in recent years. The advent of molecular biology and genetic mapping has also aided in our understanding of the intriguing biosynthetic pathways leading to various classes of therapeutically relevant antibiotic, anticancer, and related natural products. Elegant and practical methodology has been developed leading to the total synthesis of virtually every class of medicinally important natural product. In some cases, natural products or their chemically modified congeners have been manufactured by total synthesis on an industrial level which is a testament to the ingenuity of process chemists. In spite of their potent activities HI enzymatic ox receptor-mediated assays, not all natural products are amenable to being developed as marketable drags. In many instances unfavorable pharmacological effects cannot be overcome without drastic structural and functional modifications, which may also result in altered efficacy. Structure modification through truncation, functional group variations, isosteric replacements, and skeletal rigidifications aided by molecular modeling, X ray crystallography of protein targets, or NMR data are valid objectives in the context of small molecule drug discovery starting with bioactive natural products. A large proportion of these pertain to chemotherapeutic agents against cancer

  6. Fungal Anticancer Metabolites: Synthesis Towards Drug Discovery.

    Science.gov (United States)

    Barbero, Margherita; Artuso, Emma; Prandi, Cristina

    2018-01-01

    Fungi are a well-known and valuable source of compounds of therapeutic relevance, in particular of novel anticancer compounds. Although seldom obtainable through isolation from the natural source, the total organic synthesis still remains one of the most efficient alternatives to resupply them. Furthermore, natural product total synthesis is a valuable tool not only for discovery of new complex biologically active compounds but also for the development of innovative methodologies in enantioselective organic synthesis. We undertook an in-depth literature searching by using chemical bibliographic databases (SciFinder, Reaxys) in order to have a comprehensive insight into the wide research field. The literature has been then screened, refining the obtained results by subject terms focused on both biological activity and innovative synthetic procedures. The literature on fungal metabolites has been recently reviewed and these publications have been used as a base from which we consider the synthetic feasibility of the most promising compounds, in terms of anticancer properties and drug development. In this paper, compounds are classified according to their chemical structure. This review summarizes the anticancer potential of fungal metabolites, highlighting the role of total synthesis outlining the feasibility of innovative synthetic procedures that facilitate the development of fungal metabolites into drugs that may become a real future perspective. To our knowledge, this review is the first effort to deal with the total synthesis of these active fungi metabolites and demonstrates that total chemical synthesis is a fruitful means of yielding fungal derivatives as aided by recent technological and innovative advancements. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  7. Outsourcing drug discovery to India and China: from surviving to thriving.

    Science.gov (United States)

    Subramaniam, Swaminathan; Dugar, Sundeep

    2012-10-01

    Global pharmaceutical companies face an increasingly harsh environment for their primary business of selling medicines. They have to contend with a spiraling decline in the productivity of their R&D programs that is guaranteed to severely diminish their growth prospects. Outsourcing of drug discovery activities to low-cost locations is a growing response to this crisis. However, the upsides to outsourcing are capped by the failure of global pharmaceutical companies to take advantage of the full range of possibilities that this model provides. Companies that radically rethink and transform the way they conduct R&D, such as seeking the benefits of low-cost locations in India and China will be the ones that thrive in this environment. In this article we present our views on how the outsourcing model in drug discovery should go beyond increasing the efficiency of existing drug discovery processes to a fundamental rethink and re-engineering of these processes. Copyright © 2012. Published by Elsevier Ltd.

  8. Development of novel, 384-well high-throughput assay panels for human drug transporters: drug interaction and safety assessment in support of discovery research.

    Science.gov (United States)

    Tang, Huaping; Shen, Ding Ren; Han, Yong-Hae; Kong, Yan; Balimane, Praveen; Marino, Anthony; Gao, Mian; Wu, Sophie; Xie, Dianlin; Soars, Matthew G; O'Connell, Jonathan C; Rodrigues, A David; Zhang, Litao; Cvijic, Mary Ellen

    2013-10-01

    Transporter proteins are known to play a critical role in affecting the overall absorption, distribution, metabolism, and excretion characteristics of drug candidates. In addition to efflux transporters (P-gp, BCRP, MRP2, etc.) that limit absorption, there has been a renewed interest in influx transporters at the renal (OATs, OCTs) and hepatic (OATPs, BSEP, NTCP, etc.) organ level that can cause significant clinical drug-drug interactions (DDIs). Several of these transporters are also critical for hepatobiliary disposition of bilirubin and bile acid/salts, and their inhibition is directly implicated in hepatic toxicities. Regulatory agencies took action to address transporter-mediated DDI with the goal of ensuring drug safety in the clinic and on the market. To meet regulatory requirements, advanced bioassay technology and automation solutions were implemented for high-throughput transporter screening to provide structure-activity relationship within lead optimization. To enhance capacity, several functional assay formats were miniaturized to 384-well throughput including novel fluorescence-based uptake and efflux inhibition assays using high-content image analysis as well as cell-based radioactive uptake and vesicle-based efflux inhibition assays. This high-throughput capability enabled a paradigm shift from studying transporter-related issues in the development space to identifying and dialing out these concerns early on in discovery for enhanced mechanism-based efficacy while circumventing DDIs and transporter toxicities.

  9. The multiple roles of computational chemistry in fragment-based drug design

    Science.gov (United States)

    Law, Richard; Barker, Oliver; Barker, John J.; Hesterkamp, Thomas; Godemann, Robert; Andersen, Ole; Fryatt, Tara; Courtney, Steve; Hallett, Dave; Whittaker, Mark

    2009-08-01

    Fragment-based drug discovery (FBDD) represents a change in strategy from the screening of molecules with higher molecular weights and physical properties more akin to fully drug-like compounds, to the screening of smaller, less complex molecules. This is because it has been recognised that fragment hit molecules can be efficiently grown and optimised into leads, particularly after the binding mode to the target protein has been first determined by 3D structural elucidation, e.g. by NMR or X-ray crystallography. Several studies have shown that medicinal chemistry optimisation of an already drug-like hit or lead compound can result in a final compound with too high molecular weight and lipophilicity. The evolution of a lower molecular weight fragment hit therefore represents an attractive alternative approach to optimisation as it allows better control of compound properties. Computational chemistry can play an important role both prior to a fragment screen, in producing a target focussed fragment library, and post-screening in the evolution of a drug-like molecule from a fragment hit, both with and without the available fragment-target co-complex structure. We will review many of the current developments in the area and illustrate with some recent examples from successful FBDD discovery projects that we have conducted.

  10. G-protein-coupled receptors: new approaches to maximise the impact of GPCRS in drug discovery.

    Science.gov (United States)

    Davey, John

    2004-04-01

    IBC's Drug Discovery Technology Series is a group of conferences highlighting technological advances and applications in niche areas of the drug discovery pipeline. This 2-day meeting focused on G-protein-coupled receptors (GPCRs), probably the most important and certainly the most valuable class of targets for drug discovery. The meeting was chaired by J Beesley (Vice President, European Business Development for LifeSpan Biosciences, Seattle, USA) and included 17 presentations on various aspects of GPCR activity, drug screens and therapeutic analyses. Keynote Addresses covered two of the emerging areas in GPCR regulation; receptor dimerisation (G Milligan, Professor of Molecular Pharmacology and Biochemistry, University of Glasgow, UK) and proteins that interact with GPCRs (J Bockaert, Laboratory of Functional Genomics, CNRS Montpellier, France). A third Keynote Address from W Thomsen (Director of GPCR Drug Screening, Arena Pharmaceuticals, USA) discussed Arena's general approach to drug discovery and illustrated this with reference to the development of an agonist with potential efficacy in Type II diabetes.

  11. Advantages of crystallographic fragment screening: functional and mechanistic insights from a powerful platform for efficient drug discovery.

    Science.gov (United States)

    Patel, Disha; Bauman, Joseph D; Arnold, Eddy

    2014-01-01

    X-ray crystallography has been an under-appreciated screening tool for fragment-based drug discovery due to the perception of low throughput and technical difficulty. Investigators in industry and academia have overcome these challenges by taking advantage of key factors that contribute to a successful crystallographic screening campaign. Efficient cocktail design and soaking methodologies have evolved to maximize throughput while minimizing false positives/negatives. In addition, technical improvements at synchrotron beamlines have dramatically increased data collection rates thus enabling screening on a timescale comparable to other techniques. The combination of available resources and efficient experimental design has resulted in many successful crystallographic screening campaigns. The three-dimensional crystal structure of the bound fragment complexed to its target, a direct result of the screening effort, enables structure-based drug design while revealing insights regarding protein dynamics and function not readily obtained through other experimental approaches. Furthermore, this "chemical interrogation" of the target protein crystals can lead to the identification of useful reagents for improving diffraction resolution or compound solubility. Copyright © 2014. Published by Elsevier Ltd.

  12. Advantages of Crystallographic Fragment Screening: Functional and Mechanistic Insights from a Powerful Platform for Efficient Drug Discovery

    Science.gov (United States)

    Patel, Disha; Bauman, Joseph D.; Arnold, Eddy

    2015-01-01

    X-ray crystallography has been an under-appreciated screening tool for fragment-based drug discovery due to the perception of low throughput and technical difficulty. Investigators in industry and academia have overcome these challenges by taking advantage of key factors that contribute to a successful crystallographic screening campaign. Efficient cocktail design and soaking methodologies have evolved to maximize throughput while minimizing false positives/negatives. In addition, technical improvements at synchrotron beamlines have dramatically increased data collection rates thus enabling screening on a timescale comparable to other techniques. The combination of available resources and efficient experimental design has resulted in many successful crystallographic screening campaigns. The three-dimensional crystal structure of the bound fragment complexed to its target, a direct result of the screening effort, enables structure-based drug design while revealing insights regarding protein dynamics and function not readily obtained through other experimental approaches. Furthermore, this “chemical interrogation” of the target protein crystals can lead to the identification of useful reagents for improving diffraction resolution or compound solubility. PMID:25117499

  13. The use of web ontology languages and other semantic web tools in drug discovery.

    Science.gov (United States)

    Chen, Huajun; Xie, Guotong

    2010-05-01

    To optimize drug development processes, pharmaceutical companies require principled approaches to integrate disparate data on a unified infrastructure, such as the web. The semantic web, developed on the web technology, provides a common, open framework capable of harmonizing diversified resources to enable networked and collaborative drug discovery. We survey the state of art of utilizing web ontologies and other semantic web technologies to interlink both data and people to support integrated drug discovery across domains and multiple disciplines. Particularly, the survey covers three major application categories including: i) semantic integration and open data linking; ii) semantic web service and scientific collaboration and iii) semantic data mining and integrative network analysis. The reader will gain: i) basic knowledge of the semantic web technologies; ii) an overview of the web ontology landscape for drug discovery and iii) a basic understanding of the values and benefits of utilizing the web ontologies in drug discovery. i) The semantic web enables a network effect for linking open data for integrated drug discovery; ii) The semantic web service technology can support instant ad hoc collaboration to improve pipeline productivity and iii) The semantic web encourages publishing data in a semantic way such as resource description framework attributes and thus helps move away from a reliance on pure textual content analysis toward more efficient semantic data mining.

  14. When fragments link: a bibliometric perspective on the development of fragment-based drug discovery.

    Science.gov (United States)

    Romasanta, Angelo K S; van der Sijde, Peter; Hellsten, Iina; Hubbard, Roderick E; Keseru, Gyorgy M; van Muijlwijk-Koezen, Jacqueline; de Esch, Iwan J P

    2018-05-05

    Fragment-based drug discovery (FBDD) is a highly interdisciplinary field, rich in ideas integrated from pharmaceutical sciences, chemistry, biology, and physics, among others. To enrich our understanding of the development of the field, we used bibliometric techniques to analyze 3642 publications in FBDD, complementing accounts by key practitioners. Mapping its core papers, we found the transfer of knowledge from academia to industry. Co-authorship analysis showed that university-industry collaboration has grown over time. Moreover, we show how ideas from other scientific disciplines have been integrated into the FBDD paradigm. Keyword analysis showed that the field is organized into four interconnected practices: library design, fragment screening, computational methods, and optimization. This study highlights the importance of interactions among various individuals and institutions from diverse disciplines in newly emerging scientific fields. Copyright © 2018. Published by Elsevier Ltd.

  15. Common characteristics of open source software development and applicability for drug discovery: a systematic review.

    Science.gov (United States)

    Ardal, Christine; Alstadsæter, Annette; Røttingen, John-Arne

    2011-09-28

    Innovation through an open source model has proven to be successful for software development. This success has led many to speculate if open source can be applied to other industries with similar success. We attempt to provide an understanding of open source software development characteristics for researchers, business leaders and government officials who may be interested in utilizing open source innovation in other contexts and with an emphasis on drug discovery. A systematic review was performed by searching relevant, multidisciplinary databases to extract empirical research regarding the common characteristics and barriers of initiating and maintaining an open source software development project. Common characteristics to open source software development pertinent to open source drug discovery were extracted. The characteristics were then grouped into the areas of participant attraction, management of volunteers, control mechanisms, legal framework and physical constraints. Lastly, their applicability to drug discovery was examined. We believe that the open source model is viable for drug discovery, although it is unlikely that it will exactly follow the form used in software development. Hybrids will likely develop that suit the unique characteristics of drug discovery. We suggest potential motivations for organizations to join an open source drug discovery project. We also examine specific differences between software and medicines, specifically how the need for laboratories and physical goods will impact the model as well as the effect of patents.

  16. An integrated dataset for in silico drug discovery

    Directory of Open Access Journals (Sweden)

    Cockell Simon J

    2010-12-01

    Full Text Available Drug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.

  17. Semiconductor technology in protein kinase research and drug discovery: sensing a revolution.

    Science.gov (United States)

    Bhalla, Nikhil; Di Lorenzo, Mirella; Estrela, Pedro; Pula, Giordano

    2017-02-01

    Since the discovery of protein kinase activity in 1954, close to 600 kinases have been discovered that have crucial roles in cell physiology. In several pathological conditions, aberrant protein kinase activity leads to abnormal cell and tissue physiology. Therefore, protein kinase inhibitors are investigated as potential treatments for several diseases, including dementia, diabetes, cancer and autoimmune and cardiovascular disease. Modern semiconductor technology has recently been applied to accelerate the discovery of novel protein kinase inhibitors that could become the standard-of-care drugs of tomorrow. Here, we describe current techniques and novel applications of semiconductor technologies in protein kinase inhibitor drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Computer-aided drug discovery [v1; ref status: indexed, http://f1000r.es/5ij

    Directory of Open Access Journals (Sweden)

    Jürgen Bajorath

    2015-08-01

    Full Text Available Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.

  19. Small Molecule Drug Discovery at the Glucagon-Like Peptide-1 Receptor

    Directory of Open Access Journals (Sweden)

    Francis S. Willard

    2012-01-01

    Full Text Available The therapeutic success of peptide glucagon-like peptide-1 (GLP-1 receptor agonists for the treatment of type 2 diabetes mellitus has inspired discovery efforts aimed at developing orally available small molecule GLP-1 receptor agonists. Although the GLP-1 receptor is a member of the structurally complex class B1 family of GPCRs, in recent years, a diverse array of orthosteric and allosteric nonpeptide ligands has been reported. These compounds include antagonists, agonists, and positive allosteric modulators with intrinsic efficacy. In this paper, a comprehensive review of currently disclosed small molecule GLP-1 receptor ligands is presented. In addition, examples of “ligand bias” and “probe dependency” for the GLP-1 receptor are discussed; these emerging concepts may influence further optimization of known molecules or persuade designs of expanded screening strategies to identify novel chemical starting points for GLP-1 receptor drug discovery.

  20. The evolution of the matrix metalloproteinase inhibitor drug discovery program at abbott laboratories.

    Science.gov (United States)

    Wada, Carol K

    2004-01-01

    Matrix metalloproteinases (MMPs) have been implicated in several pathologies. At Abbott Laboratories, the matrix metalloproteinases inhibitor drug discovery program has focused on the discovery of a potent, selective, orally bioavailable MMP inhibitor for the treatment of cancer. The program evolved from early succinate-based inhibitors to utilizing in-house technology such as SAR by NMR to develop a novel class of biaryl hydroxamate MMP inhibitors. The metabolic instability of the biaryl hydroxamates led to the discovery of a new class of N-formylhydroxylamine (retrohydroxamate) biaryl ethers, exemplified by ABT-770 (16). Toxicity issues with this pre-clinical candidate led to the discovery of another novel class of retrohydroxamate MMP inhibitors, the phenoxyphenyl sulfones such as ABT-518 (19j). ABT-518 is a potent, orally bioavailable, selective inhibitor of MMP-2 and 9 over MMP-1 that has been evaluated in Phase I clinical trials in cancer patients.

  1. LEGO-inspired drug design: Discovery of novel fungal Plasma membrane H+-ATPase (Pma1) inhibitors from small molecule libraries: An introduction of HFSA-SBS_DOS-RD strategy in drug discovery

    DEFF Research Database (Denmark)

    Tung, Truong Thanh; Dao, Trong Tuan; Palmgren, Michael B.

    to extracellular, this enzyme generates a transmembrane electrochem. gradient, as a consequence, fungi can uptake nutrients by secondary transport systems. Until now, only low resoln. of protein structure has been reported, and notably there a no report of co-crystal structure of Pma1 with inhibitors. Therefore......-oriented synthesis (SBS_DOS) and rational design (RD), so called HFSA-SBS_DOS-RD strategy in drug discovery and development process. Using HFSA-SBS_DOS-RD, our group successfully designed, synthesized, and performed SAR studies of novel compds. potent Pma1 inhibitors. An expeditious, high yield and scalable...... microwave-assisted synthesis was developed and applied for synthesis of library compds. To our delight, ours compd. libraries were able to inhibit Pma1 activity and growth inhibitory activity of C. albican and S. cerevisiae revealed the most promising example for future development of antifungal drugs...

  2. From Medicinal Chemistry to Human Health: Current Approaches to Drug Discovery for Cancer and Neglected Tropical Diseases

    Directory of Open Access Journals (Sweden)

    LEONARDO G. FERREIRA

    2018-02-01

    Full Text Available ABSTRACT Scientific and technological breakthroughs have compelled the current players in drug discovery to increasingly incorporate knowledge-based approaches. This evolving paradigm, which has its roots attached to the recent advances in medicinal chemistry, molecular and structural biology, has unprecedentedly demanded the development of up-to-date computational approaches, such as bio- and chemo-informatics. These tools have been pivotal to catalyzing the ever-increasing amount of data generated by the molecular sciences, and to converting the data into insightful guidelines for use in the research pipeline. As a result, ligand- and structure-based drug design have emerged as key pathways to address the pharmaceutical industry’s striking demands for innovation. These approaches depend on a keen integration of experimental and molecular modeling methods to surmount the main challenges faced by drug candidates - in vivo efficacy, pharmacodynamics, metabolism, pharmacokinetics and safety. To that end, the Laboratório de Química Medicinal e Computacional (LQMC of the Universidade de São Paulo has developed forefront research on highly prevalent and life-threatening neglected tropical diseases and cancer. By taking part in global initiatives for pharmaceutical innovation, the laboratory has contributed to the advance of these critical therapeutic areas through the use of cutting-edge strategies in medicinal chemistry.

  3. BPS Pharmacology 2014 - Drug Discovery Pathways symposium Report

    OpenAIRE

    Marsh, Andrew

    2015-01-01

    Report on BPS Pharmacology 2014, BPS Industry Committe and Learned Societies Drug Discovery Pathways Group symposium: "Realizing the potential of new approaches to target identification and validation" by Dr Andrew Marsh Associate Professor Department of Chemistry University of Warwick go.warwick.ac.uk/marshgroup Twitter @marshgroup

  4. Alkaloids as Cyclooxygenase Inhibitors in Anticancer Drug Discovery.

    Science.gov (United States)

    Hashmi, Muhammad Ali; Khan, Afsar; Farooq, Umar; Khan, Sehroon

    2018-01-01

    Cancer is the leading cause of death worldwide and anticancer drug discovery is a very hot area of research at present. There are various factors which control and affect cancer, out of which enzymes like cyclooxygenase-2 (COX-2) play a vital role in the growth of tumor cells. Inhibition of this enzyme is a very useful target for the prevention of various types of cancers. Alkaloids are a diverse group of naturally occurring compounds which have shown great COX-2 inhibitory activity both in vitro and in vivo. In this mini-review, we have discussed different alkaloids with COX-2 inhibitory activities and anticancer potential which may act as leads in modern anticancer drug discovery. Different classes of alkaloids including isoquinoline alkaloids, indole alkaloids, piperidine alkaloids, quinazoline alkaloids, and various miscellaneous alkaloids obtained from natural sources have been discussed in detail in this review. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  5. Polymorphism in phenobarbital: discovery of a new polymorph and crystal structure of elusive form V.

    Science.gov (United States)

    Roy, Saikat; Goud, N Rajesh; Matzger, Adam J

    2016-03-21

    This report highlights the discovery of a new polymorph of the anticonvulsant drug phenobarbital (PB) using polymer-induced heteronucleation (PIHn) and unravelling the crystal structure of the elusive form V. Both forms are characterized by structural, thermal and VT-Raman spectroscopy methods to elucidate phase transformation behavior and shed light on stability relationships.

  6. The Use of Physiology-Based Pharmacokinetic and Pharmacodynamic Modeling in the Discovery of the Dual Orexin Receptor Antagonist ACT-541468.

    Science.gov (United States)

    Treiber, Alexander; de Kanter, Ruben; Roch, Catherine; Gatfield, John; Boss, Christoph; von Raumer, Markus; Schindelholz, Benno; Muehlan, Clemens; van Gerven, Joop; Jenck, Francois

    2017-09-01

    The identification of new sleep drugs poses particular challenges in drug discovery owing to disease-specific requirements such as rapid onset of action, sleep maintenance throughout major parts of the night, and absence of residual next-day effects. Robust tools to estimate drug levels in human brain are therefore key for a successful discovery program. Animal models constitute an appropriate choice for drugs without species differences in receptor pharmacology or pharmacokinetics. Translation to man becomes more challenging when interspecies differences are prominent. This report describes the discovery of the dual orexin receptor 1 and 2 (OX 1 and OX 2 ) antagonist ACT-541468 out of a class of structurally related compounds, by use of physiology-based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling applied early in drug discovery. Although all drug candidates exhibited similar target receptor potencies and efficacy in a rat sleep model, they exhibited large interspecies differences in key factors determining their pharmacokinetic profile. Human PK models were built on the basis of in vitro metabolism and physicochemical data and were then used to predict the time course of OX 2 receptor occupancy in brain. An active ACT-541468 dose of 25 mg was estimated on the basis of OX 2 receptor occupancy thresholds of about 65% derived from clinical data for two other orexin antagonists, almorexant and suvorexant. Modeling predictions for ACT-541468 in man were largely confirmed in a single-ascending dose trial in healthy subjects. PBPK-PD modeling applied early in drug discovery, therefore, has great potential to assist in the identification of drug molecules when specific pharmacokinetic and pharmacodynamic requirements need to be met. Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.

  7. The principle of safety evaluation in medicinal drug - how can toxicology contribute to drug discovery and development as a multidisciplinary science?

    Science.gov (United States)

    Horii, Ikuo

    2016-01-01

    Pharmaceutical (drug) safety assessment covers a diverse science-field in the drug discovery and development including the post-approval and post-marketing phases in order to evaluate safety and risk management. The principle in toxicological science is to be placed on both of pure and applied sciences that are derived from past/present scientific knowledge and coming new science and technology. In general, adverse drug reactions are presented as "biological responses to foreign substances." This is the basic concept of thinking about the manifestation of adverse drug reactions. Whether or not toxic expressions are extensions of the pharmacological effect, adverse drug reactions as seen from molecular targets are captured in the category of "on-target" or "off-target", and are normally expressed as a biological defense reaction. Accordingly, reactions induced by pharmaceuticals can be broadly said to be defensive reactions. Recent molecular biological conception is in line with the new, remarkable scientific and technological developments in the medical and pharmaceutical areas, and the viewpoints in the field of toxicology have shown that they are approaching toward the same direction as well. This paper refers to the basic concept of pharmaceutical toxicology, the differences for safety assessment in each stage of drug discovery and development, regulatory submission, and the concept of scientific considerations for risk assessment and management from the viewpoint of "how can multidisciplinary toxicology contribute to innovative drug discovery and development?" And also realistic translational research from preclinical to clinical application is required to have a significant risk management in post market by utilizing whole scientific data derived from basic and applied scientific research works. In addition, the significance for employing the systems toxicology based on AOP (Adverse Outcome Pathway) analysis is introduced, and coming challenges on precision

  8. Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design.

    Science.gov (United States)

    Du, Qi-Shi; Huang, Ri-Bo; Wei, Yu-Tuo; Pang, Zong-Wen; Du, Li-Qin; Chou, Kuo-Chen

    2009-01-30

    In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus. (c) 2008 Wiley Periodicals, Inc.

  9. An In Vivo Platform for Rapid High-Throughput Antitubercular Drug Discovery

    Directory of Open Access Journals (Sweden)

    Kevin Takaki

    2012-07-01

    Full Text Available Treatment of tuberculosis, like other infectious diseases, is increasingly hindered by the emergence of drug resistance. Drug discovery efforts would be facilitated by facile screening tools that incorporate the complexities of human disease. Mycobacterium marinum-infected zebrafish larvae recapitulate key aspects of tuberculosis pathogenesis and drug treatment. Here, we develop a model for rapid in vivo drug screening using fluorescence-based methods for serial quantitative assessment of drug efficacy and toxicity. We provide proof-of-concept that both traditional bacterial-targeting antitubercular drugs and newly identified host-targeting drugs would be discovered through the use of this model. We demonstrate the model’s utility for the identification of synergistic combinations of antibacterial drugs and demonstrate synergy between bacterial- and host-targeting compounds. Thus, the platform can be used to identify new antibacterial agents and entirely new classes of drugs that thwart infection by targeting host pathways. The methods developed here should be widely applicable to small-molecule screens for other infectious and noninfectious diseases.

  10. A magnetic bead-based ligand binding assay to facilitate human kynurenine 3-monooxygenase drug discovery.

    Science.gov (United States)

    Wilson, Kris; Mole, Damian J; Homer, Natalie Z M; Iredale, John P; Auer, Manfred; Webster, Scott P

    2015-02-01

    Human kynurenine 3-monooxygenase (KMO) is emerging as an important drug target enzyme in a number of inflammatory and neurodegenerative disease states. Recombinant protein production of KMO, and therefore discovery of KMO ligands, is challenging due to a large membrane targeting domain at the C-terminus of the enzyme that causes stability, solubility, and purification difficulties. The purpose of our investigation was to develop a suitable screening method for targeting human KMO and other similarly challenging drug targets. Here, we report the development of a magnetic bead-based binding assay using mass spectrometry detection for human KMO protein. The assay incorporates isolation of FLAG-tagged KMO enzyme on protein A magnetic beads. The protein-bound beads are incubated with potential binding compounds before specific cleavage of the protein-compound complexes from the beads. Mass spectrometry analysis is used to identify the compounds that demonstrate specific binding affinity for the target protein. The technique was validated using known inhibitors of KMO. This assay is a robust alternative to traditional ligand-binding assays for challenging protein targets, and it overcomes specific difficulties associated with isolating human KMO. © 2014 Society for Laboratory Automation and Screening.

  11. Structure-based drug design for G protein-coupled receptors.

    Science.gov (United States)

    Congreve, Miles; Dias, João M; Marshall, Fiona H

    2014-01-01

    Our understanding of the structural biology of G protein-coupled receptors has undergone a transformation over the past 5 years. New protein-ligand complexes are described almost monthly in high profile journals. Appreciation of how small molecules and natural ligands bind to their receptors has the potential to impact enormously how medicinal chemists approach this major class of receptor targets. An outline of the key topics in this field and some recent examples of structure- and fragment-based drug design are described. A table is presented with example views of each G protein-coupled receptor for which there is a published X-ray structure, including interactions with small molecule antagonists, partial and full agonists. The possible implications of these new data for drug design are discussed. © 2014 Elsevier B.V. All rights reserved.

  12. Phenotypic and genomic comparison of Mycobacterium aurum and surrogate model species to Mycobacterium tuberculosis: implications for drug discovery.

    Science.gov (United States)

    Namouchi, Amine; Cimino, Mena; Favre-Rochex, Sandrine; Charles, Patricia; Gicquel, Brigitte

    2017-07-13

    Tuberculosis (TB) is caused by Mycobacterium tuberculosis and represents one of the major challenges facing drug discovery initiatives worldwide. The considerable rise in bacterial drug resistance in recent years has led to the need of new drugs and drug regimens. Model systems are regularly used to speed-up the drug discovery process and circumvent biosafety issues associated with manipulating M. tuberculosis. These include the use of strains such as Mycobacterium smegmatis and Mycobacterium marinum that can be handled in biosafety level 2 facilities, making high-throughput screening feasible. However, each of these model species have their own limitations. We report and describe the first complete genome sequence of Mycobacterium aurum ATCC23366, an environmental mycobacterium that can also grow in the gut of humans and animals as part of the microbiota. This species shows a comparable resistance profile to that of M. tuberculosis for several anti-TB drugs. The aims of this study were to (i) determine the drug resistance profile of a recently proposed model species, Mycobacterium aurum, strain ATCC23366, for anti-TB drug discovery as well as Mycobacterium smegmatis and Mycobacterium marinum (ii) sequence and annotate the complete genome sequence of this species obtained using Pacific Bioscience technology (iii) perform comparative genomics analyses of the various surrogate strains with M. tuberculosis (iv) discuss how the choice of the surrogate model used for drug screening can affect the drug discovery process. We describe the complete genome sequence of M. aurum, a surrogate model for anti-tuberculosis drug discovery. Most of the genes already reported to be associated with drug resistance are shared between all the surrogate strains and M. tuberculosis. We consider that M. aurum might be used in high-throughput screening for tuberculosis drug discovery. We also highly recommend the use of different model species during the drug discovery screening process.

  13. Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active site water molecules description.

    Science.gov (United States)

    Spyrakis, Francesca; Cavasotto, Claudio N

    2015-10-01

    Structure-based virtual screening is currently an established tool in drug lead discovery projects. Although in the last years the field saw an impressive progress in terms of algorithm development, computational performance, and retrospective and prospective applications in ligand identification, there are still long-standing challenges where further improvement is needed. In this review, we consider the conceptual frame, state-of-the-art and recent developments of three critical "structural" issues in structure-based drug lead discovery: the use of homology modeling to accurately model the binding site when no experimental structures are available, the necessity of accounting for the dynamics of intrinsically flexible systems as proteins, and the importance of considering active site water molecules in lead identification and optimization campaigns. Copyright © 2015 Elsevier Inc. All rights reserved.

  14. Patient-derived stem cells: pathways to drug discovery for brain diseases

    Directory of Open Access Journals (Sweden)

    Alan eMackay-Sim

    2013-03-01

    Full Text Available The concept of drug discovery through stem cell biology is based on technological developments whose genesis is now coincident. The first is automated cell microscopy with concurrent advances in image acquisition and analysis, known as high content screening (HCS. The second is patient-derived stem cells for modelling the cell biology of brain diseases. HCS has developed from the requirements of the pharmaceutical industry for high throughput assays to screen thousands of chemical compounds in the search for new drugs. HCS combines new fluorescent probes with automated microscopy and computational power to quantify the effects of compounds on cell functions. Stem cell biology has advanced greatly since the discovery of genetic reprogramming of somatic cells into induced pluripotent stem cells (iPSCs. There is now a rush of papers describing their generation from patients with various diseases of the nervous system. Although the majority of these have been genetic diseases, iPSCs have been generated from patients with complex diseases (schizophrenia and sporadic Parkinson’s disease. Some genetic diseases are also modelled in embryonic stem cells generated from blastocysts rejected during in vitro fertilisation. Neural stem cells have been isolated from post-mortem brain of Alzheimer’s patients and neural stem cells generated from biopsies of the olfactory organ of patients is another approach. These olfactory neurosphere-derived cells demonstrate robust disease-specific phenotypes in patients with schizophrenia and Parkinson’s disease. High content screening is already in use to find small molecules for the generation and differentiation of embryonic stem cells and induced pluripotent stem cells. The challenges for using stem cells for drug discovery are to develop robust stem cell culture methods that meet the rigorous requirements for repeatable, consistent quantities of defined cell types at the industrial scale necessary for high

  15. Current perspectives in fragment-based lead discovery (FBLD)

    Science.gov (United States)

    Lamoree, Bas; Hubbard, Roderick E.

    2017-01-01

    It is over 20 years since the first fragment-based discovery projects were disclosed. The methods are now mature for most ‘conventional’ targets in drug discovery such as enzymes (kinases and proteases) but there has also been growing success on more challenging targets, such as disruption of protein–protein interactions. The main application is to identify tractable chemical startpoints that non-covalently modulate the activity of a biological molecule. In this essay, we overview current practice in the methods and discuss how they have had an impact in lead discovery – generating a large number of fragment-derived compounds that are in clinical trials and two medicines treating patients. In addition, we discuss some of the more recent applications of the methods in chemical biology – providing chemical tools to investigate biological molecules, mechanisms and systems. PMID:29118093

  16. Traditional Medicine Collection Tracking System (TM-CTS): a database for ethnobotanically driven drug-discovery programs.

    Science.gov (United States)

    Harris, Eric S J; Erickson, Sean D; Tolopko, Andrew N; Cao, Shugeng; Craycroft, Jane A; Scholten, Robert; Fu, Yanling; Wang, Wenquan; Liu, Yong; Zhao, Zhongzhen; Clardy, Jon; Shamu, Caroline E; Eisenberg, David M

    2011-05-17

    Ethnobotanically driven drug-discovery programs include data related to many aspects of the preparation of botanical medicines, from initial plant collection to chemical extraction and fractionation. The Traditional Medicine Collection Tracking System (TM-CTS) was created to organize and store data of this type for an international collaborative project involving the systematic evaluation of commonly used Traditional Chinese Medicinal plants. The system was developed using domain-driven design techniques, and is implemented using Java, Hibernate, PostgreSQL, Business Intelligence and Reporting Tools (BIRT), and Apache Tomcat. The TM-CTS relational database schema contains over 70 data types, comprising over 500 data fields. The system incorporates a number of unique features that are useful in the context of ethnobotanical projects such as support for information about botanical collection, method of processing, quality tests for plants with existing pharmacopoeia standards, chemical extraction and fractionation, and historical uses of the plants. The database also accommodates data provided in multiple languages and integration with a database system built to support high throughput screening based drug discovery efforts. It is accessed via a web-based application that provides extensive, multi-format reporting capabilities. This new database system was designed to support a project evaluating the bioactivity of Chinese medicinal plants. The software used to create the database is open source, freely available, and could potentially be applied to other ethnobotanically driven natural product collection and drug-discovery programs. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  17. Traditional Medicine Collection Tracking System (TM-CTS): A Database for Ethnobotanically-Driven Drug-Discovery Programs

    Science.gov (United States)

    Harris, Eric S. J.; Erickson, Sean D.; Tolopko, Andrew N.; Cao, Shugeng; Craycroft, Jane A.; Scholten, Robert; Fu, Yanling; Wang, Wenquan; Liu, Yong; Zhao, Zhongzhen; Clardy, Jon; Shamu, Caroline E.; Eisenberg, David M.

    2011-01-01

    Aim of the study. Ethnobotanically-driven drug-discovery programs include data related to many aspects of the preparation of botanical medicines, from initial plant collection to chemical extraction and fractionation. The Traditional Medicine-Collection Tracking System (TM-CTS) was created to organize and store data of this type for an international collaborative project involving the systematic evaluation of commonly used Traditional Chinese Medicinal plants. Materials and Methods. The system was developed using domain-driven design techniques, and is implemented using Java, Hibernate, PostgreSQL, Business Intelligence and Reporting Tools (BIRT), and Apache Tomcat. Results. The TM-CTS relational database schema contains over 70 data types, comprising over 500 data fields. The system incorporates a number of unique features that are useful in the context of ethnobotanical projects such as support for information about botanical collection, method of processing, quality tests for plants with existing pharmacopoeia standards, chemical extraction and fractionation, and historical uses of the plants. The database also accommodates data provided in multiple languages and integration with a database system built to support high throughput screening based drug discovery efforts. It is accessed via a web-based application that provides extensive, multi-format reporting capabilities. Conclusions. This new database system was designed to support a project evaluating the bioactivity of Chinese medicinal plants. The software used to create the database is open source, freely available, and could potentially be applied to other ethnobotanically-driven natural product collection and drug-discovery programs. PMID:21420479

  18. Towards novel therapeutics for HIV through fragment-based screening and drug design.

    Science.gov (United States)

    Tiefendbrunn, Theresa; Stout, C David

    2014-01-01

    Fragment-based drug discovery has been applied with varying levels of success to a number of proteins involved in the HIV (Human Immunodeficiency Virus) life cycle. Fragment-based approaches have led to the discovery of novel binding sites within protease, reverse transcriptase, integrase, and gp41. Novel compounds that bind to known pockets within CCR5 have also been identified via fragment screening, and a fragment-based approach to target the TAR-Tat interaction was explored. In the context of HIV-1 reverse transcriptase (RT), fragment-based approaches have yielded fragment hits with mid-μM activity in an in vitro activity assay, as well as fragment hits that are active against drug-resistant variants of RT. Fragment-based drug discovery is a powerful method to elucidate novel binding sites within proteins, and the method has had significant success in the context of HIV proteins.

  19. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

    Science.gov (United States)

    Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola

    2016-01-01

    Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.

  20. Cunninghamella Biotransformation--Similarities to Human Drug Metabolism and Its Relevance for the Drug Discovery Process.

    Science.gov (United States)

    Piska, Kamil; Żelaszczyk, Dorota; Jamrozik, Marek; Kubowicz-Kwaśny, Paulina; Pękala, Elżbieta

    2016-01-01

    Studies of drug metabolism are one of the most significant issues in the process of drug development, its introduction to the market and also in treatment. Even the most promising molecule may show undesirable metabolic properties that would disqualify it as a potential drug. Therefore, such studies are conducted in the early phases of drug discovery and development process. Cunninghamella is a filamentous fungus known for its catalytic properties, which mimics mammalian drug metabolism. It has been proven that C. elegans carries at least one gene coding for a CYP enzyme closely related to the CYP51 family. The transformation profile of xenobiotics in Cunninghamella spp. spans a number of reactions catalyzed by different mammalian CYP isoforms. This paper presents detailed data on similar biotransformation drug products in humans and Cunninghamella spp. and covers the most important aspects of preparative biosynthesis of metabolites, since this model allows to obtain metabolites in sufficient quantities to conduct the further detailed investigations, as quantification, structure analysis and pharmacological activity and toxicity testing. The metabolic activity of three mostly used Cunninghamella species in obtaining hydroxylated, dealkylated and oxidated metabolites of different drugs confirmed its convergence with human biotransformation. Though it cannot replace the standard methods, it can provide support in the field of biotransformation and identifying metabolic soft spots of new chemicals and in predicting possible metabolic pathways. Another aspect is the biosynthesis of metabolites. In this respect, techniques using Cunninghamella spp. seem to be competitive to the chemical methods currently used.

  1. Drug discovery and development tomorrow -- changing the mindset.

    Science.gov (United States)

    Coleman, Robert A

    2009-09-01

    Today's drug discovery and development paradigm is not working, and something needs to be done about it. There is good reason to believe that a move away from reliance on animal surrogates for human subjects in the Pharma Industry's R&D programmes could provide an important step forward. However, no serious move will be made in that direction until there is some hard evidence that it will be rewarded with improved productivity outcomes. The Safer Medicines Trust are proposing that a study be undertaken, involving a range of drugs that have been approved for human use, but have subsequently proved to have limitations in terms of safety and/or efficacy. The aim is to determine the efficiency of a battery of human-based test methods to identify a compound's safety and efficacy profiles, and to compare this with that of the more traditional, largely animal-based methods that were employed in their original development. Should such an approach prove more reliable, the authorities will be faced with important decisions relating to the role of human biological test data in regulatory submissions, while the Pharma Industry will be faced with the key logistical issue of how to acquire the human biomaterials necessary to make possible the routine application of such test methods. 2009 FRAME.

  2. Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery.

    Directory of Open Access Journals (Sweden)

    Albert H Gough

    Full Text Available One of the greatest challenges in biomedical research, drug discovery and diagnostics is understanding how seemingly identical cells can respond differently to perturbagens including drugs for disease treatment. Although heterogeneity has become an accepted characteristic of a population of cells, in drug discovery it is not routinely evaluated or reported. The standard practice for cell-based, high content assays has been to assume a normal distribution and to report a well-to-well average value with a standard deviation. To address this important issue we sought to define a method that could be readily implemented to identify, quantify and characterize heterogeneity in cellular and small organism assays to guide decisions during drug discovery and experimental cell/tissue profiling. Our study revealed that heterogeneity can be effectively identified and quantified with three indices that indicate diversity, non-normality and percent outliers. The indices were evaluated using the induction and inhibition of STAT3 activation in five cell lines where the systems response including sample preparation and instrument performance were well characterized and controlled. These heterogeneity indices provide a standardized method that can easily be integrated into small and large scale screening or profiling projects to guide interpretation of the biology, as well as the development of therapeutics and diagnostics. Understanding the heterogeneity in the response to perturbagens will become a critical factor in designing strategies for the development of therapeutics including targeted polypharmacology.

  3. Medicinal chemistry in drug discovery in big pharma: past, present and future.

    Science.gov (United States)

    Campbell, Ian B; Macdonald, Simon J F; Procopiou, Panayiotis A

    2018-02-01

    The changes in synthetic and medicinal chemistry and related drug discovery science as practiced in big pharma over the past few decades are described. These have been predominantly driven by wider changes in society namely the computer, internet and globalisation. Thoughts about the future of medicinal chemistry are also discussed including sharing the risks and costs of drug discovery and the future of outsourcing. The continuing impact of access to substantial computing power and big data, the use of algorithms in data analysis and drug design are also presented. The next generation of medicinal chemists will communicate in ways that reflect social media and the results of constantly being connected to each other and data. Copyright © 2017. Published by Elsevier Ltd.

  4. FLIPR assays of intracellular calcium in GPCR drug discovery

    DEFF Research Database (Denmark)

    Hansen, Kasper Bø; Bräuner-Osborne, Hans

    2009-01-01

    Fluorescent dyes sensitive to changes in intracellular calcium have become increasingly popular in G protein-coupled receptor (GPCR) drug discovery for several reasons. First of all, the assays using the dyes are easy to perform and are of low cost compared to other assays. Second, most non...

  5. Microfluidic-Based Multi-Organ Platforms for Drug Discovery

    Directory of Open Access Journals (Sweden)

    Ahmad Rezaei Kolahchi

    2016-09-01

    Full Text Available Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.

  6. Small Molecules from Nature Targeting G-Protein Coupled Cannabinoid Receptors: Potential Leads for Drug Discovery and Development

    Directory of Open Access Journals (Sweden)

    Charu Sharma

    2015-01-01

    Full Text Available The cannabinoid molecules are derived from Cannabis sativa plant which acts on the cannabinoid receptors types 1 and 2 (CB1 and CB2 which have been explored as potential therapeutic targets for drug discovery and development. Currently, there are numerous cannabinoid based synthetic drugs used in clinical practice like the popular ones such as nabilone, dronabinol, and Δ9-tetrahydrocannabinol mediates its action through CB1/CB2 receptors. However, these synthetic based Cannabis derived compounds are known to exert adverse psychiatric effect and have also been exploited for drug abuse. This encourages us to find out an alternative and safe drug with the least psychiatric adverse effects. In recent years, many phytocannabinoids have been isolated from plants other than Cannabis. Several studies have shown that these phytocannabinoids show affinity, potency, selectivity, and efficacy towards cannabinoid receptors and inhibit endocannabinoid metabolizing enzymes, thus reducing hyperactivity of endocannabinoid systems. Also, these naturally derived molecules possess the least adverse effects opposed to the synthetically derived cannabinoids. Therefore, the plant based cannabinoid molecules proved to be promising and emerging therapeutic alternative. The present review provides an overview of therapeutic potential of ligands and plants modulating cannabinoid receptors that may be of interest to pharmaceutical industry in search of new and safer drug discovery and development for future therapeutics.

  7. Bioactive secondary metabolites from marine microbes for drug discovery.

    Science.gov (United States)

    Nikapitiya, Chamilani

    2012-01-01

    The isolation and extraction of novel bioactive secondary metabolites from marine microorganisms have a biomedical potential for future drug discovery as the oceans cover 70% of the planet's surface and life on earth originates from sea. Wide range of novel bioactive secondary metabolites exhibiting pharmacodynamic properties has been isolated from marine microorganisms and many to be discovered. The compounds isolated from marine organisms (macro and micro) are important in their natural form and also as templates for synthetic modifications for the treatments for variety of deadly to minor diseases. Many technical issues are yet to overcome before wide-scale bioprospecting of marine microorganisms becomes a reality. This chapter focuses on some novel secondary metabolites having antitumor, antivirus, enzyme inhibitor, and other bioactive properties identified and isolated from marine microorganisms including bacteria, actinomycetes, fungi, and cyanobacteria, which could serve as potentials for drug discovery after their clinical trials. Copyright © 2012 Elsevier Inc. All rights reserved.

  8. Melodic pattern discovery by structural analysis via wavelets and clustering techniques

    DEFF Research Database (Denmark)

    Velarde, Gissel; Meredith, David

    We present an automatic method to support melodic pattern discovery by structural analysis of symbolic representations by means of wavelet analysis and clustering techniques. In previous work, we used the method to recognize the parent works of melodic segments, or to classify tunes into tune......-means to cluster melodic segments into groups of measured similarity and obtain a raking of the most prototypical melodic segments or patterns and their occurrences. We test the method on the JKU Patterns Development Database and evaluate it based on the ground truth defined by the MIREX 2013 Discovery of Repeated...... Themes & Sections task. We compare the results of our method to the output of geometric approaches. Finally, we discuss about the relevance of our wavelet-based analysis in relation to structure, pattern discovery, similarity and variation, and comment about the considerations of the method when used...

  9. The application of molecular topology for ulcerative colitis drug discovery.

    Science.gov (United States)

    Bellera, Carolina L; Di Ianni, Mauricio E; Talevi, Alan

    2018-01-01

    Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.

  10. Competitive intelligence and patent analysis in drug discovery.

    Science.gov (United States)

    Grandjean, Nicolas; Charpiot, Brigitte; Pena, Carlos Andres; Peitsch, Manuel C

    2005-01-01

    Patents are a major source of information in drug discovery and, when properly processed and analyzed, can yield a wealth of information on competitors activities, R&D trends, emerging fields, collaborations, among others. This review discusses the current state-of-the-art in textual data analysis and exploration methods as applied to patent analysis.: © 2005 Elsevier Ltd . All rights reserved.

  11. Large-scale prediction of drug-target interactions using protein sequences and drug topological structures

    Energy Technology Data Exchange (ETDEWEB)

    Cao Dongsheng [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Liu Shao [Xiangya Hospital, Central South University, Changsha 410008 (China); Xu Qingsong [School of Mathematical Sciences and Computing Technology, Central South University, Changsha 410083 (China); Lu Hongmei; Huang Jianhua [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Hu Qiannan [Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071 (China); Liang Yizeng, E-mail: yizeng_liang@263.net [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China)

    2012-11-08

    Highlights: Black-Right-Pointing-Pointer Drug-target interactions are predicted using an extended SAR methodology. Black-Right-Pointing-Pointer A drug-target interaction is regarded as an event triggered by many factors. Black-Right-Pointing-Pointer Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. Black-Right-Pointing-Pointer Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug-target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug-target interactions in a timely manner. In this article, we aim at extending current structure-activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug-target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug

  12. The pivotal role of multimodality reporter sensors in drug discovery: from cell based assays to real time molecular imaging.

    Science.gov (United States)

    Ray, Pritha

    2011-04-01

    Development and marketing of new drugs require stringent validation that are expensive and time consuming. Non-invasive multimodality molecular imaging using reporter genes holds great potential to expedite these processes at reduced cost. New generations of smarter molecular imaging strategies such as Split reporter, Bioluminescence resonance energy transfer, Multimodality fusion reporter technologies will further assist to streamline and shorten the drug discovery and developmental process. This review illustrates the importance and potential of molecular imaging using multimodality reporter genes in drug development at preclinical phases.

  13. Exploiting pluripotent stem cell technology for drug discovery, screening, safety, and toxicology assessments.

    Science.gov (United States)

    McGivern, Jered V; Ebert, Allison D

    2014-04-01

    In order for the pharmaceutical industry to maintain a constant flow of novel drugs and therapeutics into the clinic, compounds must be thoroughly validated for safety and efficacy in multiple biological and biochemical systems. Pluripotent stem cells, because of their ability to develop into any cell type in the body and recapitulate human disease, may be an important cellular system to add to the drug development repertoire. This review will discuss some of the benefits of using pluripotent stem cells for drug discovery and safety studies as well as some of the recent applications of stem cells in drug screening studies. We will also address some of the hurdles that need to be overcome in order to make stem cell-based approaches an efficient and effective tool in the quest to produce clinically successful drug compounds. Copyright © 2013 Elsevier B.V. All rights reserved.

  14. Pharmacokinetic properties and in silico ADME modeling in drug discovery.

    Science.gov (United States)

    Honório, Kathia M; Moda, Tiago L; Andricopulo, Adriano D

    2013-03-01

    The discovery and development of a new drug are time-consuming, difficult and expensive. This complex process has evolved from classical methods into an integration of modern technologies and innovative strategies addressed to the design of new chemical entities to treat a variety of diseases. The development of new drug candidates is often limited by initial compounds lacking reasonable chemical and biological properties for further lead optimization. Huge libraries of compounds are frequently selected for biological screening using a variety of techniques and standard models to assess potency, affinity and selectivity. In this context, it is very important to study the pharmacokinetic profile of the compounds under investigation. Recent advances have been made in the collection of data and the development of models to assess and predict pharmacokinetic properties (ADME--absorption, distribution, metabolism and excretion) of bioactive compounds in the early stages of drug discovery projects. This paper provides a brief perspective on the evolution of in silico ADME tools, addressing challenges, limitations, and opportunities in medicinal chemistry.

  15. Pharmacologically directed strategies in academic anticancer drug discovery based on the European NCI compounds initiative.

    Science.gov (United States)

    Hendriks, Hans R; Govaerts, Anne-Sophie; Fichtner, Iduna; Burtles, Sally; Westwell, Andrew D; Peters, Godefridus J

    2017-07-11

    The European NCI compounds programme, a joint initiative of the EORTC Research Branch, Cancer Research Campaign and the US National Cancer Institute, was initiated in 1993. The objective was to help the NCI in reducing the backlog of in vivo testing of potential anticancer compounds, synthesised in Europe that emerged from the NCI in vitro 60-cell screen. Over a period of more than twenty years the EORTC-Cancer Research Campaign panel reviewed ∼2000 compounds of which 95 were selected for further evaluation. Selected compounds were stepwise developed with clear go/no go decision points using a pharmacologically directed programme. This approach eliminated quickly compounds with unsuitable pharmacological properties. A few compounds went into Phase I clinical evaluation. The lessons learned and many of the principles outlined in the paper can easily be applied to current and future drug discovery and development programmes. Changes in the review panel, restrictions regarding numbers and types of compounds tested in the NCI in vitro screen and the appearance of targeted agents led to the discontinuation of the European NCI programme in 2017 and its transformation into an academic platform of excellence for anticancer drug discovery and development within the EORTC-PAMM group. This group remains open for advice and collaboration with interested parties in the field of cancer pharmacology.

  16. Drug repurposing based on drug-drug interaction.

    Science.gov (United States)

    Zhou, Bin; Wang, Rong; Wu, Ping; Kong, De-Xin

    2015-02-01

    Given the high risk and lengthy procedure of traditional drug development, drug repurposing is gaining more and more attention. Although many types of drug information have been used to repurpose drugs, drug-drug interaction data, which imply possible physiological effects or targets of drugs, remain unexploited. In this work, similarity of drug interaction was employed to infer similarity of the physiological effects or targets for the drugs. We collected 10,835 drug-drug interactions concerning 1074 drugs, and for 700 of them, drug similarity scores based on drug interaction profiles were computed and rendered using a drug association network with 589 nodes (drugs) and 2375 edges (drug similarity scores). The 589 drugs were clustered into 98 groups with Markov Clustering Algorithm, most of which were significantly correlated with certain drug functions. This indicates that the network can be used to infer the physiological effects of drugs. Furthermore, we evaluated the ability of this drug association network to predict drug targets. The results show that the method is effective for 317 of 561 drugs that have known targets. Comparison of this method with the structure-based approach shows that they are complementary. In summary, this study demonstrates the feasibility of drug repurposing based on drug-drug interaction data. © 2014 John Wiley & Sons A/S.

  17. Simulated drug discovery process to conduct a synoptic assessment of pharmacy students.

    Science.gov (United States)

    Richardson, Alan; Curtis, Anthony D M; Moss, Gary P; Pearson, Russell J; White, Simon; Rutten, Frank J M; Perumal, Dhaya; Maddock, Katie

    2014-03-12

    OBJECTIVE. To implement and assess a task-based learning exercise that prompts pharmacy students to integrate their understanding of different disciplines. DESIGN. Master of pharmacy (MPharm degree) students were provided with simulated information from several preclinical science and from clinical trials and asked to synthesize this into a marketing authorization application for a new drug. Students made a link to pharmacy practice by creating an advice leaflet for pharmacists. ASSESSMENT. Students' ability to integrate information from different disciplines was evaluated by oral examination. In 2 successive academic years, 96% and 82% of students demonstrated an integrated understanding of their proposed new drug. Students indicated in a survey that their understanding of the links between different subjects improved. CONCLUSION. Simulated drug discovery provides a learning environment that emphasizes the connectivity of the preclinical sciences with each other and the practice of pharmacy.

  18. A Fully Automated High-Throughput Flow Cytometry Screening System Enabling Phenotypic Drug Discovery.

    Science.gov (United States)

    Joslin, John; Gilligan, James; Anderson, Paul; Garcia, Catherine; Sharif, Orzala; Hampton, Janice; Cohen, Steven; King, Miranda; Zhou, Bin; Jiang, Shumei; Trussell, Christopher; Dunn, Robert; Fathman, John W; Snead, Jennifer L; Boitano, Anthony E; Nguyen, Tommy; Conner, Michael; Cooke, Mike; Harris, Jennifer; Ainscow, Ed; Zhou, Yingyao; Shaw, Chris; Sipes, Dan; Mainquist, James; Lesley, Scott

    2018-05-01

    The goal of high-throughput screening is to enable screening of compound libraries in an automated manner to identify quality starting points for optimization. This often involves screening a large diversity of compounds in an assay that preserves a connection to the disease pathology. Phenotypic screening is a powerful tool for drug identification, in that assays can be run without prior understanding of the target and with primary cells that closely mimic the therapeutic setting. Advanced automation and high-content imaging have enabled many complex assays, but these are still relatively slow and low throughput. To address this limitation, we have developed an automated workflow that is dedicated to processing complex phenotypic assays for flow cytometry. The system can achieve a throughput of 50,000 wells per day, resulting in a fully automated platform that enables robust phenotypic drug discovery. Over the past 5 years, this screening system has been used for a variety of drug discovery programs, across many disease areas, with many molecules advancing quickly into preclinical development and into the clinic. This report will highlight a diversity of approaches that automated flow cytometry has enabled for phenotypic drug discovery.

  19. Open Access Target Validation Is a More Efficient Way to Accelerate Drug Discovery

    Science.gov (United States)

    Lee, Wen Hwa

    2015-01-01

    There is a scarcity of novel treatments to address many unmet medical needs. Industry and academia are finally coming to terms with the fact that the prevalent models and incentives for innovation in early stage drug discovery are failing to promote progress quickly enough. Here we will examine how an open model of precompetitive public–private research partnership is enabling efficient derisking and acceleration in the early stages of drug discovery, whilst also widening the range of communities participating in the process, such as patient and disease foundations. PMID:26042736

  20. Neurotransmitter transporters in schistosomes: structure, function and prospects for drug discovery.

    Science.gov (United States)

    Ribeiro, Paula; Patocka, Nicholas

    2013-12-01

    Neurotransmitter transporters (NTTs) play a fundamental role in the control of neurotransmitter signaling and homeostasis. Sodium symporters of the plasma membrane mediate the cellular uptake of neurotransmitter from the synaptic cleft, whereas proton-driven vesicular transporters sequester the neurotransmitter into synaptic vesicles for subsequent release. Together these transporters control how much transmitter is released and how long it remains in the synaptic cleft, thereby regulating the intensity and duration of signaling. NTTs have been the subject of much research in mammals and there is growing interest in their activities among invertebrates as well. In this review we will focus our attention on NTTs of the parasitic flatworm Schistosoma mansoni. Bloodflukes of the genus Schistosoma are the causative agents of human schistosomiasis, a devastating disease that afflicts over 200 million people worldwide. Schistosomes have a well-developed nervous system and a rich diversity of neurotransmitters, including many of the small-molecule ("classical") neurotransmitters that normally employ NTTs in their mechanism of signaling. Recent advances in schistosome genomics have unveiled numerous NTTs in this parasite, some of which have now been cloned and characterized in vitro. Moreover new genetic and pharmacological evidence suggests that NTTs are required for proper control of neuromuscular signaling and movement of the worm. Among these carriers are proteins that have been successfully targeted for drug discovery in other organisms, in particular sodium symporters for biogenic amine neurotransmitters such as serotonin and dopamine. Our goal in this chapter is to review the current status of research on schistosome NTTs, with emphasis on biogenic amine sodium symporters, and to evaluate their potential for anti-schistosomal drug targeting. Through this discussion we hope to draw attention to this important superfamily of parasite proteins and to identify new

  1. Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond

    Science.gov (United States)

    Van Voorhis, Wesley C.; Adams, John H.; Adelfio, Roberto; Ahyong, Vida; Akabas, Myles H.; Alano, Pietro; Alday, Aintzane; Alemán Resto, Yesmalie; Alsibaee, Aishah; Alzualde, Ainhoa; Andrews, Katherine T.; Avery, Simon V.; Avery, Vicky M.; Ayong, Lawrence; Baker, Mark; Baker, Stephen; Ben Mamoun, Choukri; Bhatia, Sangeeta; Bickle, Quentin; Bounaadja, Lotfi; Bowling, Tana; Bosch, Jürgen; Boucher, Lauren E.; Boyom, Fabrice F.; Brea, Jose; Brennan, Marian; Burton, Audrey; Caffrey, Conor R.; Camarda, Grazia; Carrasquilla, Manuela; Carter, Dee; Belen Cassera, Maria; Chih-Chien Cheng, Ken; Chindaudomsate, Worathad; Chubb, Anthony; Colon, Beatrice L.; Colón-López, Daisy D.; Corbett, Yolanda; Crowther, Gregory J.; Cowan, Noemi; D’Alessandro, Sarah; Le Dang, Na; Delves, Michael; Du, Alan Y.; Duffy, Sandra; Abd El-Salam El-Sayed, Shimaa; Ferdig, Michael T.; Fernández Robledo, José A.; Fidock, David A.; Florent, Isabelle; Fokou, Patrick V. T.; Galstian, Ani; Gamo, Francisco Javier; Gold, Ben; Golub, Todd; Goldgof, Gregory M.; Guha, Rajarshi; Guiguemde, W. Armand; Gural, Nil; Guy, R. Kiplin; Hansen, Michael A. E.; Hanson, Kirsten K.; Hemphill, Andrew; Hooft van Huijsduijnen, Rob; Horii, Takaaki; Horrocks, Paul; Hughes, Tyler B.; Huston, Christopher; Igarashi, Ikuo; Ingram-Sieber, Katrin; Itoe, Maurice A.; Jadhav, Ajit; Naranuntarat Jensen, Amornrat; Jensen, Laran T.; Jiang, Rays H. Y.; Kaiser, Annette; Keiser, Jennifer; Ketas, Thomas; Kicka, Sebastien; Kim, Sunyoung; Kirk, Kiaran; Kumar, Vidya P.; Kyle, Dennis E.; Lafuente, Maria Jose; Landfear, Scott; Lee, Nathan; Lee, Sukjun; Lehane, Adele M.; Li, Fengwu; Little, David; Liu, Liqiong; Llinás, Manuel; Loza, Maria I.; Lubar, Aristea; Lucantoni, Leonardo; Lucet, Isabelle; Maes, Louis; Mancama, Dalu; Mansour, Nuha R.; March, Sandra; McGowan, Sheena; Medina Vera, Iset; Meister, Stephan; Mercer, Luke; Mestres, Jordi; Mfopa, Alvine N.; Misra, Raj N.; Moon, Seunghyun; Moore, John P.; Morais Rodrigues da Costa, Francielly; Müller, Joachim; Muriana, Arantza; Nakazawa Hewitt, Stephen; Nare, Bakela; Nathan, Carl; Narraidoo, Nathalie; Nawaratna, Sujeevi; Ojo, Kayode K.; Ortiz, Diana; Panic, Gordana; Papadatos, George; Parapini, Silvia; Patra, Kailash; Pham, Ngoc; Prats, Sarah; Plouffe, David M.; Poulsen, Sally-Ann; Pradhan, Anupam; Quevedo, Celia; Quinn, Ronald J.; Rice, Christopher A.; Abdo Rizk, Mohamed; Ruecker, Andrea; St. Onge, Robert; Salgado Ferreira, Rafaela; Samra, Jasmeet; Robinett, Natalie G.; Schlecht, Ulrich; Schmitt, Marjorie; Silva Villela, Filipe; Silvestrini, Francesco; Sinden, Robert; Smith, Dennis A.; Soldati, Thierry; Spitzmüller, Andreas; Stamm, Serge Maximilian; Sullivan, David J.; Sullivan, William; Suresh, Sundari; Suzuki, Brian M.; Suzuki, Yo; Swamidass, S. Joshua; Taramelli, Donatella; Tchokouaha, Lauve R. Y.; Theron, Anjo; Thomas, David; Tonissen, Kathryn F.; Townson, Simon; Tripathi, Abhai K.; Trofimov, Valentin; Udenze, Kenneth O.; Ullah, Imran; Vallieres, Cindy; Vigil, Edgar; Vinetz, Joseph M.; Voong Vinh, Phat; Vu, Hoan; Watanabe, Nao-aki; Weatherby, Kate; White, Pamela M.; Wilks, Andrew F.; Winzeler, Elizabeth A.; Wojcik, Edward; Wree, Melanie; Wu, Wesley; Yokoyama, Naoaki; Zollo, Paul H. A.; Abla, Nada; Blasco, Benjamin; Burrows, Jeremy; Laleu, Benoît; Leroy, Didier; Spangenberg, Thomas; Wells, Timothy; Willis, Paul A.

    2016-01-01

    A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal

  2. Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond.

    Directory of Open Access Journals (Sweden)

    Wesley C Van Voorhis

    2016-07-01

    Full Text Available A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34% of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments

  3. Protein-Templated Fragment Ligations-From Molecular Recognition to Drug Discovery.

    Science.gov (United States)

    Jaegle, Mike; Wong, Ee Lin; Tauber, Carolin; Nawrotzky, Eric; Arkona, Christoph; Rademann, Jörg

    2017-06-19

    Protein-templated fragment ligation is a novel concept to support drug discovery and can help to improve the efficacy of protein ligands. Protein-templated fragment ligations are chemical reactions between small molecules ("fragments") utilizing a protein's surface as a reaction vessel to catalyze the formation of a protein ligand with increased binding affinity. The approach exploits the molecular recognition of reactive small-molecule fragments by proteins both for ligand assembly and for the identification of bioactive fragment combinations. In this way, chemical synthesis and bioassay are integrated in one single step. This Review discusses the biophysical basis of reversible and irreversible fragment ligations and gives an overview of the available methods to detect protein-templated ligation products. The chemical scope and recent applications as well as future potential of the concept in drug discovery are reviewed. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. Designing multi-targeted agents: An emerging anticancer drug discovery paradigm.

    Science.gov (United States)

    Fu, Rong-Geng; Sun, Yuan; Sheng, Wen-Bing; Liao, Duan-Fang

    2017-08-18

    The dominant paradigm in drug discovery is to design ligands with maximum selectivity to act on individual drug targets. With the target-based approach, many new chemical entities have been discovered, developed, and further approved as drugs. However, there are a large number of complex diseases such as cancer that cannot be effectively treated or cured only with one medicine to modulate the biological function of a single target. As simultaneous intervention of two (or multiple) cancer progression relevant targets has shown improved therapeutic efficacy, the innovation of multi-targeted drugs has become a promising and prevailing research topic and numerous multi-targeted anticancer agents are currently at various developmental stages. However, most multi-pharmacophore scaffolds are usually discovered by serendipity or screening, while rational design by combining existing pharmacophore scaffolds remains an enormous challenge. In this review, four types of multi-pharmacophore modes are discussed, and the examples from literature will be used to introduce attractive lead compounds with the capability of simultaneously interfering with different enzyme or signaling pathway of cancer progression, which will reveal the trends and insights to help the design of the next generation multi-targeted anticancer agents. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  5. Membrane Transporters: Structure, Function and Targets for Drug Design

    Science.gov (United States)

    Ravna, Aina W.; Sager, Georg; Dahl, Svein G.; Sylte, Ingebrigt

    Current therapeutic drugs act on four main types of molecular targets: enzymes, receptors, ion channels and transporters, among which a major part (60-70%) are membrane proteins. This review discusses the molecular structures and potential impact of membrane transporter proteins on new drug discovery. The three-dimensional (3D) molecular structure of a protein contains information about the active site and possible ligand binding, and about evolutionary relationships within the protein family. Transporters have a recognition site for a particular substrate, which may be used as a target for drugs inhibiting the transporter or acting as a false substrate. Three groups of transporters have particular interest as drug targets: the major facilitator superfamily, which includes almost 4000 different proteins transporting sugars, polyols, drugs, neurotransmitters, metabolites, amino acids, peptides, organic and inorganic anions and many other substrates; the ATP-binding cassette superfamily, which plays an important role in multidrug resistance in cancer chemotherapy; and the neurotransmitter:sodium symporter family, which includes the molecular targets for some of the most widely used psychotropic drugs. Recent technical advances have increased the number of known 3D structures of membrane transporters, and demonstrated that they form a divergent group of proteins with large conformational flexibility which facilitates transport of the substrate.

  6. Harnessing the potential of natural products in drug discovery from a cheminformatics vantage point.

    Science.gov (United States)

    Rodrigues, Tiago

    2017-11-15

    Natural products (NPs) present a privileged source of inspiration for chemical probe and drug design. Despite the biological pre-validation of the underlying molecular architectures and their relevance in drug discovery, the poor accessibility to NPs, complexity of the synthetic routes and scarce knowledge of their macromolecular counterparts in phenotypic screens still hinder their broader exploration. Cheminformatics algorithms now provide a powerful means of circumventing the abovementioned challenges and unlocking the full potential of NPs in a drug discovery context. Herein, I discuss recent advances in the computer-assisted design of NP mimics and how artificial intelligence may accelerate future NP-inspired molecular medicine.

  7. Discovery and Development of Therapeutic Drugs against Lethal Human RNA Viruses: a Multidisciplinary Assault.

    Science.gov (United States)

    1991-07-16

    AD-A239 742 AD GRANT NO: DAMD17-89-Z-9021 TITLE: DISCOVERY AND DEVELOPMENT OF THERAPEUTIC DRUGS AGAINST LETHAL HUMAN RNA VIRUSES: A MULTIDISCIPLINARY...62787A871 AB WrJDA317987 11. TITLE (Include Securty Classification) DISCOVERY AND DEVELOPMENT OF THERAPEUTIC DRUGS AGAINST LETHAL HUMAN RNA VIRUSES: A...G. R. Pettit, III, D.-S. Huang, and G. R. Pettit, 23rd Int’l. Horticulture Congress, Italy, 8/27 - 9/1/90. "Bryostatins Define the Role of Protein

  8. RNA Editing and Drug Discovery for Cancer Therapy

    Directory of Open Access Journals (Sweden)

    Wei-Hsuan Huang

    2013-01-01

    Full Text Available RNA editing is vital to provide the RNA and protein complexity to regulate the gene expression. Correct RNA editing maintains the cell function and organism development. Imbalance of the RNA editing machinery may lead to diseases and cancers. Recently, RNA editing has been recognized as a target for drug discovery although few studies targeting RNA editing for disease and cancer therapy were reported in the field of natural products. Therefore, RNA editing may be a potential target for therapeutic natural products. In this review, we provide a literature overview of the biological functions of RNA editing on gene expression, diseases, cancers, and drugs. The bioinformatics resources of RNA editing were also summarized.

  9. Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

    Science.gov (United States)

    Basith, Shaherin; Cui, Minghua; Macalino, Stephani J. Y.; Park, Jongmi; Clavio, Nina A. B.; Kang, Soosung; Choi, Sun

    2018-01-01

    The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the “golden age for GPCR structural biology.” Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand– and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed. PMID:29593527

  10. Genetics of rheumatoid arthritis conributes to biology and drug discovery

    NARCIS (Netherlands)

    Okada, Yukinori; Wu, Di; Trynka, Gosia; Raj, Towfique; Terao, Chikashi; Ikari, Katsunori; Kochi, Yuta; Ohmura, Koichiro; Suzuki, A.; Yoshida, S.; Graham, R.R.; Manoharan, A.; Ortmann, W.; Bhangale, T.; Denny, J.C.; Carroll, R.J.; Eyler, A.E.; Greenberg, J.D.; Kremer, J.M.; Pappas, D.A.; Jiang, L.; Yin, L.; Ye, L.; Su, D.F.; Yang, J.; Xie, G.; Keystone, E.; Westra, H.J.; Esko, T.; Metspalu, A.; Zhou, X.; Gupta, N.; Mirel, D.; Stahl, Eli A.; Diogo, D.; Cui, J.; Liao, K.; Guo, M.H.; Myouzen, K.; Kawaguchi, T.; Coenen, M.J.; van Riel, P.L.; van de Laar, Mart A.F.J.; Guchelaar, H.J.; Huizinga, T.W.; Dieudé, P.; Mariette, X.; Louis Bridges Jr, S.; Zhernakova, A.; Toes, R.E.; Tak, P.P.; Miceli-Richard, C.; Bang, S.Y.; Lee, H.S.; Martin, J.; Gonzales-Gay, M.A.; Rodriguez-Rodriguez, L.; Rantapää-Dhlqvist, S.; Arlestig, L.; Choi, H.K.; Kamatani, Y.; Galan, P.; Lathrop, M.; Eyre, S.; Bowes, J.; Barton, A.; de Vries, N.; Moreland, L.W.; Criswell, L.A.; Karlson, E.W.; Taniguchi, A.; Yamada, R; Kubo, M.; Bae, S.C.; Worthington, J.; Padyukov, L.; Klareskog, L.; Gregersen, Peter K.; Raychaudhuri, S.; Stranger, B.E.; de Jager, P.L.; Franke, L.; Visscher, P.M.; Brown, M.A.; Yamanaka, H.; Mimori, T.; Takahashi, A.; Xu, H.; Behrens, T.W.; Siminovitch, K.A.; Momohara, S.; Matsuda, F.; Yamamoto, K.; Plenge, Robert M.

    2013-01-01

    A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here we performed

  11. Fluorescence lifetime assays: current advances and applications in drug discovery.

    Science.gov (United States)

    Pritz, Stephan; Doering, Klaus; Woelcke, Julian; Hassiepen, Ulrich

    2011-06-01

    Fluorescence lifetime assays complement the portfolio of established assay formats available in drug discovery, particularly with the recent advances in microplate readers and the commercial availability of novel fluorescent labels. Fluorescence lifetime assists in lowering complexity of compound screening assays, affording a modular, toolbox-like approach to assay development and yielding robust homogeneous assays. To date, materials and procedures have been reported for biochemical assays on proteases, as well as on protein kinases and phosphatases. This article gives an overview of two assay families, distinguished by the origin of the fluorescence signal modulation. The pharmaceutical industry demands techniques with a robust, integrated compound profiling process and short turnaround times. Fluorescence lifetime assays have already helped the drug discovery field, in this sense, by enhancing productivity during the hit-to-lead and lead optimization phases. Future work will focus on covering other biochemical molecular modifications by investigating the detailed photo-physical mechanisms underlying the fluorescence signal.

  12. Drug repositioning for enzyme modulator based on human metabolite-likeness.

    Science.gov (United States)

    Lee, Yoon Hyeok; Choi, Hojae; Park, Seongyong; Lee, Boah; Yi, Gwan-Su

    2017-05-31

    Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme's metabolites and drugs. We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden's index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness. In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for

  13. Fighting obesity with a sugar-based library: discovery of novel MCH-1R antagonists by a new computational-VAST approach for exploration of GPCR binding sites.

    Science.gov (United States)

    Heifetz, Alexander; Barker, Oliver; Verquin, Geraldine; Wimmer, Norbert; Meutermans, Wim; Pal, Sandeep; Law, Richard J; Whittaker, Mark

    2013-05-24

    Obesity is an increasingly common disease. While antagonism of the melanin-concentrating hormone-1 receptor (MCH-1R) has been widely reported as a promising therapeutic avenue for obesity treatment, no MCH-1R antagonists have reached the market. Discovery and optimization of new chemical matter targeting MCH-1R is hindered by reduced HTS success rates and a lack of structural information about the MCH-1R binding site. X-ray crystallography and NMR, the major experimental sources of structural information, are very slow processes for membrane proteins and are not currently feasible for every GPCR or GPCR-ligand complex. This situation significantly limits the ability of these methods to impact the drug discovery process for GPCR targets in "real-time", and hence, there is an urgent need for other practical and cost-efficient alternatives. We present here a conceptually pioneering approach that integrates GPCR modeling with design, synthesis, and screening of a diverse library of sugar-based compounds from the VAST technology (versatile assembly on stable templates) to provide structural insights on the MCH-1R binding site. This approach creates a cost-efficient new avenue for structure-based drug discovery (SBDD) against GPCR targets. In our work, a primary VAST hit was used to construct a high-quality MCH-1R model. Following model validation, a structure-based virtual screen yielded a 14% hit rate and 10 novel chemotypes of potent MCH-1R antagonists, including EOAI3367472 (IC50 = 131 nM) and EOAI3367474 (IC50 = 213 nM).

  14. Epothilones as lead structures for the synthesis-based discovery of new chemotypes for microtubule stabilization.

    Science.gov (United States)

    Feyen, Fabian; Cachoux, Frédéric; Gertsch, Jürg; Wartmann, Markus; Altmann, Karl-Heinz

    2008-01-01

    Epothilones are macrocyclic bacterial natural products with potent microtubule-stabilizing and antiproliferative activity. They have served as successful lead structures for the development of several clinical candidates for anticancer therapy. However, the structural diversity of this group of clinical compounds is rather limited, as their structures show little divergence from the original natural product leads. Our own research has explored the question of whether epothilones can serve as a basis for the development of new structural scaffolds, or chemotypes, for microtubule stabilization that might serve as a basis for the discovery of new generations of anticancer drugs. We have elaborated a series of epothilone-derived macrolactones whose overall structural features significantly deviate from those of the natural epothilone scaffold and thus define new structural families of microtubule-stabilizing agents. Key elements of our hypermodification strategy are the change of the natural epoxide geometry from cis to trans, the incorporation of a conformationally constrained side chain, the removal of the C3-hydroxyl group, and the replacement of C12 with nitrogen. So far, this approach has yielded analogs 30 and 40 that are the most advanced, the most rigorously modified, structures, both of which are potent antiproliferative agents with low nanomolar activity against several human cancer cell lines in vitro. The synthesis was achieved through a macrolactone-based strategy or a high-yielding RCM reaction. The 12-aza-epothilone ("azathilone" 40) may be considered a "non-natural" natural product that still retains most of the overall structural characteristics of a true natural product but is structurally unique, because it lies outside of the general scope of Nature's biosynthetic machinery for polyketide synthesis. Like natural epothilones, both 30 and 40 promote tubulin polymerization in vitro and at the cellular level induce cell cycle arrest in mitosis. These

  15. Stem cells in drug discovery, tissue engineering, and regenerative medicine: emerging opportunities and challenges.

    Science.gov (United States)

    Nirmalanandhan, Victor Sanjit; Sittampalam, G Sitta

    2009-08-01

    Stem cells, irrespective of their origin, have emerged as valuable reagents or tools in human health in the past 2 decades. Initially, a research tool to study fundamental aspects of developmental biology is now the central focus of generating transgenic animals, drug discovery, and regenerative medicine to address degenerative diseases of multiple organ systems. This is because stem cells are pluripotent or multipotent cells that can recapitulate developmental paths to repair damaged tissues. However, it is becoming clear that stem cell therapy alone may not be adequate to reverse tissue and organ damage in degenerative diseases. Existing small-molecule drugs and biologicals may be needed as "molecular adjuvants" or enhancers of stem cells administered in therapy or adult stem cells in the diseased tissues. Hence, a combination of stem cell-based, high-throughput screening and 3D tissue engineering approaches is necessary to advance the next wave of tools in preclinical drug discovery. In this review, the authors have attempted to provide a basic account of various stem cells types, as well as their biology and signaling, in the context of research in regenerative medicine. An attempt is made to link stem cells as reagents, pharmacology, and tissue engineering as converging fields of research for the next decade.

  16. Phenotypic Screening Approaches to Develop Aurora Kinase Inhibitors: Drug Discovery Perspectives.

    Science.gov (United States)

    Marugán, Carlos; Torres, Raquel; Lallena, María José

    2015-01-01

    Targeting mitotic regulators as a strategy to fight cancer implies the development of drugs against key proteins, such as Aurora-A and -B. Current drugs, which target mitosis through a general mechanism of action (stabilization/destabilization of microtubules), have several side effects (neutropenia, alopecia, and emesis). Pharmaceutical companies aim at avoiding these unwanted effects by generating improved and selective drugs that increase the quality of life of the patients. However, the development of these drugs is an ambitious task that involves testing thousands of compounds through biochemical and cell-based assays. In addition, molecules usually target complex biological processes, involving several proteins and different molecular pathways, further emphasizing the need for high-throughput screening techniques and multiplexing technologies in order to identify drugs with the desired phenotype. We will briefly describe two multiplexing technologies [high-content imaging (HCI) and flow cytometry] and two key processes for drug discovery research (assay development and validation) following our own published industry quality standards. We will further focus on HCI as a useful tool for phenotypic screening and will provide a concrete example of HCI assay to detect Aurora-A or -B selective inhibitors discriminating the off-target effects related to the inhibition of other cell cycle or non-cell cycle key regulators. Finally, we will describe other assays that can help to characterize the in vitro pharmacology of the inhibitors.

  17. Molecular docking as a popular tool in drug design, an in silico travel

    Directory of Open Access Journals (Sweden)

    de Ruyck J

    2016-06-01

    Full Text Available Jerome de Ruyck, Guillaume Brysbaert, Ralf Blossey, Marc F Lensink University Lille, CNRS UMR8576 UGSF, Lille, FranceAbstract: New molecular modeling approaches, driven by rapidly improving computational platforms, have allowed many success stories for the use of computer-assisted drug design in the discovery of new mechanism- or structure-based drugs. In this overview, we highlight three aspects of the use of molecular docking. First, we discuss the combination of molecular and quantum mechanics to investigate an unusual enzymatic mechanism of a flavoprotein. Second, we present recent advances in anti-infectious agents' synthesis driven by structural insights. At the end, we focus on larger biological complexes made by protein–protein interactions and discuss their relevance in drug design. This review provides information on how these large systems, even in the presence of the solvent, can be investigated with the outlook of drug discovery.Keywords: structure-based drug design, protein–protein docking, quaternary structure prediction, residue interaction networks, RINs, water position

  18. How Phenotypic Screening Influenced Drug Discovery: Lessons from Five Years of Practice.

    Science.gov (United States)

    Haasen, Dorothea; Schopfer, Ulrich; Antczak, Christophe; Guy, Chantale; Fuchs, Florian; Selzer, Paul

    Since 2011, phenotypic screening has been a trend in the pharmaceutical industry as well as in academia. This renaissance was triggered by analyses that suggested that phenotypic screening is a superior strategy to discover first-in-class drugs. Despite these promises and considerable investments, pharmaceutical research organizations have encountered considerable challenges with the approach. Few success stories have emerged in the past 5 years and companies are questioning their investment in this area. In this contribution, we outline what we have learned about success factors and challenges of phenotypic screening. We then describe how our efforts in phenotypic screening have influenced our approach to drug discovery in general. We predict that concepts from phenotypic screening will be incorporated into target-based approaches and will thus remain influential beyond the current trend.

  19. Structural genomics of infectious disease drug targets: the SSGCID

    International Nuclear Information System (INIS)

    Stacy, Robin; Begley, Darren W.; Phan, Isabelle; Staker, Bart L.; Van Voorhis, Wesley C.; Varani, Gabriele; Buchko, Garry W.; Stewart, Lance J.; Myler, Peter J.

    2011-01-01

    An introduction and overview of the focus, goals and overall mission of the Seattle Structural Genomics Center for Infectious Disease (SSGCID) is given. The Seattle Structural Genomics Center for Infectious Disease (SSGCID) is a consortium of researchers at Seattle BioMed, Emerald BioStructures, the University of Washington and Pacific Northwest National Laboratory that was established to apply structural genomics approaches to drug targets from infectious disease organisms. The SSGCID is currently funded over a five-year period by the National Institute of Allergy and Infectious Diseases (NIAID) to determine the three-dimensional structures of 400 proteins from a variety of Category A, B and C pathogens. Target selection engages the infectious disease research and drug-therapy communities to identify drug targets, essential enzymes, virulence factors and vaccine candidates of biomedical relevance to combat infectious diseases. The protein-expression systems, purified proteins, ligand screens and three-dimensional structures produced by SSGCID constitute a valuable resource for drug-discovery research, all of which is made freely available to the greater scientific community. This issue of Acta Crystallographica Section F, entirely devoted to the work of the SSGCID, covers the details of the high-throughput pipeline and presents a series of structures from a broad array of pathogenic organisms. Here, a background is provided on the structural genomics of infectious disease, the essential components of the SSGCID pipeline are discussed and a survey of progress to date is presented

  20. Trends in GPCR drug discovery: new agents, targets and indications.

    Science.gov (United States)

    Hauser, Alexander S; Attwood, Misty M; Rask-Andersen, Mathias; Schiöth, Helgi B; Gloriam, David E

    2017-12-01

    G protein-coupled receptors (GPCRs) are the most intensively studied drug targets, mostly due to their substantial involvement in human pathophysiology and their pharmacological tractability. Here, we report an up-to-date analysis of all GPCR drugs and agents in clinical trials, which reveals current trends across molecule types, drug targets and therapeutic indications, including showing that 475 drugs (~34% of all drugs approved by the US Food and Drug Administration (FDA)) act at 108 unique GPCRs. Approximately 321 agents are currently in clinical trials, of which ~20% target 66 potentially novel GPCR targets without an approved drug, and the number of biological drugs, allosteric modulators and biased agonists has increased. The major disease indications for GPCR modulators show a shift towards diabetes, obesity and Alzheimer disease, although several central nervous system disorders are also highly represented. The 224 (56%) non-olfactory GPCRs that have not yet been explored in clinical trials have broad untapped therapeutic potential, particularly in genetic and immune system disorders. Finally, we provide an interactive online resource to analyse and infer trends in GPCR drug discovery.

  1. ChEMBL web services: streamlining access to drug discovery data and utilities.

    Science.gov (United States)

    Davies, Mark; Nowotka, Michał; Papadatos, George; Dedman, Nathan; Gaulton, Anna; Atkinson, Francis; Bellis, Louisa; Overington, John P

    2015-07-01

    ChEMBL is now a well-established resource in the fields of drug discovery and medicinal chemistry research. The ChEMBL database curates and stores standardized bioactivity, molecule, target and drug data extracted from multiple sources, including the primary medicinal chemistry literature. Programmatic access to ChEMBL data has been improved by a recent update to the ChEMBL web services (version 2.0.x, https://www.ebi.ac.uk/chembl/api/data/docs), which exposes significantly more data from the underlying database and introduces new functionality. To complement the data-focused services, a utility service (version 1.0.x, https://www.ebi.ac.uk/chembl/api/utils/docs), which provides RESTful access to commonly used cheminformatics methods, has also been concurrently developed. The ChEMBL web services can be used together or independently to build applications and data processing workflows relevant to drug discovery and chemical biology. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  2. The Drug Discovery and Development Industry in India-Two Decades of Proprietary Small-Molecule R&D.

    Science.gov (United States)

    Differding, Edmond

    2017-06-07

    This review provides a comprehensive survey of proprietary drug discovery and development efforts performed by Indian companies between 1994 and mid-2016. It is based on the identification and detailed analysis of pharmaceutical, biotechnology, and contract research companies active in proprietary new chemical entity (NCE) research and development (R&D) in India. Information on preclinical and clinical development compounds was collected by company, therapeutic indication, mode of action, target class, and development status. The analysis focuses on the overall pipeline and its evolution over two decades, contributions by type of company, therapeutic focus, attrition rates, and contribution to Western pharmaceutical pipelines through licensing agreements. This comprehensive analysis is the first of its kind, and, in our view, represents a significant contribution to the understanding of the current state of the drug discovery and development industry in India. © 2017 The Author. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

  3. Discovery of potent, reversible MetAP2 inhibitors via fragment based drug discovery and structure based drug design-Part 1.

    Science.gov (United States)

    Cheruvallath, Zacharia; Tang, Mingnam; McBride, Christopher; Komandla, Mallareddy; Miura, Joanne; Ton-Nu, Thu; Erikson, Phil; Feng, Jun; Farrell, Pamela; Lawson, J David; Vanderpool, Darin; Wu, Yiqin; Dougan, Douglas R; Plonowski, Artur; Holub, Corine; Larson, Chris

    2016-06-15

    Methionine aminopeptidase 2 (MetAP2) is an enzyme that cleaves an N-terminal methionine residue from a number of newly synthesized proteins. Pre-clinical and clinical studies suggest that MetAP2 inhibitors could be used as a novel treatment for obesity. Herein we describe our use of fragment screening methods and structural biology to quickly identify and elaborate an indazole fragment into a series of reversible MetAP2 inhibitors with <10nM potency, excellent selectivity, and favorable in vitro safety profiles. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects.

    Science.gov (United States)

    Lagorce, David; Sperandio, Olivier; Galons, Hervé; Miteva, Maria A; Villoutreix, Bruno O

    2008-09-24

    Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making. This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python) based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries. We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after) experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules) that can be easily tuned.

  5. Lost in translation? Role of metabolomics in solving translational problems in drug discovery and development

    NARCIS (Netherlands)

    Greef, J. van der; Adourian, A.; Muntendam, P.; McBurney, R.N.

    2006-01-01

    Too few drug discovery projects generate a marketed drug product, often because preclinical studies fail to predict the clinical experience with a drug candidate. Improving the success of preclinical-to-clinical translation is of paramount importance in optimizing the pharmaceutical value chain.

  6. In vivo brain microdialysis: advances in neuropsychopharmacology and drug discovery.

    Science.gov (United States)

    Darvesh, Altaf S; Carroll, Richard T; Geldenhuys, Werner J; Gudelsky, Gary A; Klein, Jochen; Meshul, Charles K; Van der Schyf, Cornelis J

    2011-02-01

    INTRODUCTION: Microdialysis is an important in vivo sampling technique, useful in the assay of extracellular tissue fluid. The technique has both pre-clinical and clinical applications but is most widely used in neuroscience. The in vivo microdialysis technique allows measurement of neurotransmitters such as acetycholine (ACh), the biogenic amines including dopamine (DA), norepinephrine (NE) and serotonin (5-HT), amino acids such as glutamate (Glu) and gamma aminobutyric acid (GABA), as well as the metabolites of the aforementioned neurotransmitters, and neuropeptides in neuronal extracellular fluid in discrete brain regions of laboratory animals such as rodents and non-human primates. AREAS COVERED: In this review we present a brief overview of the principles and procedures related to in vivo microdialysis and detail the use of this technique in the pre-clinical measurement of drugs designed to be used in the treatment of chemical addiction, neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD) and as well as psychiatric disorders such as attention-deficit/hyperactivity disorder (ADHD) and schizophrenia. This review offers insight into the tremendous utility and versatility of this technique in pursuing neuropharmacological investigations as well its significant potential in rational drug discovery. EXPERT OPINION: In vivo microdialysis is an extremely versatile technique, routinely used in the neuropharmacological investigation of drugs used for the treatment of neurological disorders. This technique has been a boon in the elucidation of the neurochemical profile and mechanism of action of several classes of drugs especially their effects on neurotransmitter systems. The exploitation and development of this technique for drug discovery in the near future will enable investigational new drug candidates to be rapidly moved into the clinical trial stages and to market thus providing new successful therapies for neurological diseases

  7. In silico discovery of novel Retinoic Acid Receptor agonist structures

    Directory of Open Access Journals (Sweden)

    Samuels Herbert H

    2001-06-01

    Full Text Available Abstract Background Several Retinoic Acid Receptors (RAR agonists have therapeutic activity against a variety of cancer types; however, unacceptable toxicity profiles have hindered the development of drugs. RAR agonists presenting novel structural and chemical features could therefore open new avenues for the discovery of leads against breast, lung and prostate cancer or leukemia. Results We have analysed the induced fit of the active site residues upon binding of a known ligand. The derived binding site models were used to dock over 150,000 molecules in silico (or virtually to the structure of the receptor with the Internal Coordinates Mechanics (ICM program. Thirty ligand candidates were tested in vitro. Conclusions Two novel agonists resulting from the predicted receptor model were active at 50 nM. One of them displays novel structural features which may translate into the development of new ligands for cancer therapy.

  8. Screening the Medicines for Malaria Venture Pathogen Box across Multiple Pathogens Reclassifies Starting Points for Open-Source Drug Discovery.

    Science.gov (United States)

    Duffy, Sandra; Sykes, Melissa L; Jones, Amy J; Shelper, Todd B; Simpson, Moana; Lang, Rebecca; Poulsen, Sally-Ann; Sleebs, Brad E; Avery, Vicky M

    2017-09-01

    Open-access drug discovery provides a substantial resource for diseases primarily affecting the poor and disadvantaged. The open-access Pathogen Box collection is comprised of compounds with demonstrated biological activity against specific pathogenic organisms. The supply of this resource by the Medicines for Malaria Venture has the potential to provide new chemical starting points for a number of tropical and neglected diseases, through repurposing of these compounds for use in drug discovery campaigns for these additional pathogens. We tested the Pathogen Box against kinetoplastid parasites and malaria life cycle stages in vitro Consequently, chemical starting points for malaria, human African trypanosomiasis, Chagas disease, and leishmaniasis drug discovery efforts have been identified. Inclusive of this in vitro biological evaluation, outcomes from extensive literature reviews and database searches are provided. This information encompasses commercial availability, literature reference citations, other aliases and ChEMBL number with associated biological activity, where available. The release of this new data for the Pathogen Box collection into the public domain will aid the open-source model of drug discovery. Importantly, this will provide novel chemical starting points for drug discovery and target identification in tropical disease research. Copyright © 2017 Duffy et al.

  9. [Frontiers in Live Bone Imaging Researches. Novel drug discovery by means of intravital bone imaging technology].

    Science.gov (United States)

    Ishii, Masaru

    2015-06-01

    Recent advances in intravital bone imaging technology has enabled us to grasp the real cellular behaviors and functions in vivo , revolutionizing the field of drug discovery for novel therapeutics against intractable bone diseases. In this chapter, I introduce various updated information on pharmacological actions of several antibone resorptive agents, which could only be derived from advanced imaging techniques, and also discuss the future perspectives of this new trend in drug discovery.

  10. Ebola virus: A gap in drug design and discovery - experimental and computational perspective.

    Science.gov (United States)

    Balmith, Marissa; Faya, Mbuso; Soliman, Mahmoud E S

    2017-03-01

    The Ebola virus, formally known as the Ebola hemorrhagic fever, is an acute viral syndrome causing sporadic outbreaks that have ravaged West Africa. Due to its extreme virulence and highly transmissible nature, Ebola has been classified as a category A bioweapon organism. Only recently have vaccine or drug regimens for the Ebola virus been developed, including Zmapp and peptides. In addition, existing drugs which have been repurposed toward anti-Ebola virus activity have been re-examined and are seen to be promising candidates toward combating Ebola. Drug development involving computational tools has been widely employed toward target-based drug design. Screening large libraries have greatly stimulated research toward effective anti-Ebola virus drug regimens. Current emphasis has been placed on the investigation of host proteins and druggable viral targets. There is a huge gap in the literature regarding guidelines in the discovery of Ebola virus inhibitors, which may be due to the lack of information on the Ebola drug targets, binding sites, and mechanism of action of the virus. This review focuses on Ebola virus inhibitors, drugs which could be repurposed to combat the Ebola virus, computational methods which study drug-target interactions as well as providing further insight into the mode of action of the Ebola virus. © 2016 John Wiley & Sons A/S.

  11. An evolutionary perspective on drug discovery in the plant genus Euphorbia L. (Euphorbiaceae)

    DEFF Research Database (Denmark)

    Ernst, Madeleine

    herbivory and physical stresses or to attract pollinators. Consequently, specializedmetabolites, as well as plants used in traditional medicine, are not randomly distributed across phylogenetictrees. Evolutionary approaches to plant-based drug discovery suggest that this informationcan be used to guide...... healthcarethreats, urge for systematic and time-efficient approaches in finding new drug candidates. Manydrugs are derived from plant specialized metabolites, chemical compounds, which are synthesizedby the plants in response to evolutionary adaptation to environmental and ecological factors, for example,to combat...... evolution and diversification. Also, Euphorbia species producean often chemically highly diverse latex exhibiting an exceptional number of biological activities withpharmaceutical interest. In this PhD project, the genus Euphorbia was chosen as a model group forstudying evolutionary approaches to plant...

  12. Japan-China Joint Medical Workshop on Drug Discoveries and Therapeutics 2008: The need of Asian pharmaceutical researchers' cooperation.

    Science.gov (United States)

    Nakata, M; Tang, W

    2008-10-01

    research in Asian countries. (reported on October 1st, with grateful thanks to all participants) Main program Session I. Research Advances in Drug Discoveries and Therapeutics ● Design, synthesis and preliminary activity assay of influenza virus neuraminidase inhibitors by Wenfang Xu (Shandong University, China) ● Infection disease models with silkworms to evaluate the therapeutic effects of drug candidates by Kazuhisa Sekimizu (The University of Tokyo, Japan) ● Japan's governmental approaches to facilitate drug development process by Makoto Shimoaraiso (Ministry of Foreign Affairs of Japan, Japan) ● Effective detection of the epidermal growth factor receptor mutation by the peptide nucleic acid-locked nucleic acid PCR Clamp by Sakuo Hoshi (The University of Tokyo Hospital, Japan) ● Design and synthesis of p53-MDM2 binding inhibitors by Yongzhou Hu (Zhejiang University, China) Session II. Drug Synthesis/Clinical Therapeutics ● Pharmacogenomics-based clinical studies using a novel fully-automated genotyping system by Setsuo Hasegawa (Sekino Clinical Pharmacology Clinic, Japan) ● Synthesis and biological evaluation of pentacyclic triterpenes as anti-tumor agents by Hongbin Sun (China Pharmaceutical University, China) ● Drug discovery and therapeutics using silkworm as experimental animal by Yasuyuki Ogata (The University of Tokyo, Japan) ● Novel selective estrogen recetpor modulators (SERMs) with unusual structure and biological activities by Haibing Zhou (Wuhan University, China) Session III. Medicinal Chemistry/Natural Products ● Synthesis and properties of isonucleosides incorporated oligonucleotides by Zhenjun Yang (Peking University, China) ● Isolation of antiviral compounds from plant resources using silkworm bioassay by Yutaka Orihara (The University of Tokyo, Japan) ● Synthesis and structural modifcation of tasiamide and the effect of these modifications on in vitro anticancer activity by Yingxia Li (Ocean University of China, China)

  13. Novel, customizable scoring functions, parameterized using N-PLS, for structure-based drug discovery.

    Science.gov (United States)

    Catana, Cornel; Stouten, Pieter F W

    2007-01-01

    The ability to accurately predict biological affinity on the basis of in silico docking to a protein target remains a challenging goal in the CADD arena. Typically, "standard" scoring functions have been employed that use the calculated docking result and a set of empirical parameters to calculate a predicted binding affinity. To improve on this, we are exploring novel strategies for rapidly developing and tuning "customized" scoring functions tailored to a specific need. In the present work, three such customized scoring functions were developed using a set of 129 high-resolution protein-ligand crystal structures with measured Ki values. The functions were parametrized using N-PLS (N-way partial least squares), a multivariate technique well-known in the 3D quantitative structure-activity relationship field. A modest correlation between observed and calculated pKi values using a standard scoring function (r2 = 0.5) could be improved to 0.8 when a customized scoring function was applied. To mimic a more realistic scenario, a second scoring function was developed, not based on crystal structures but exclusively on several binding poses generated with the Flo+ docking program. Finally, a validation study was conducted by generating a third scoring function with 99 randomly selected complexes from the 129 as a training set and predicting pKi values for a test set that comprised the remaining 30 complexes. Training and test set r2 values were 0.77 and 0.78, respectively. These results indicate that, even without direct structural information, predictive customized scoring functions can be developed using N-PLS, and this approach holds significant potential as a general procedure for predicting binding affinity on the basis of in silico docking.

  14. Discovery and Development of ATP-Competitive mTOR Inhibitors Using Computational Approaches.

    Science.gov (United States)

    Luo, Yao; Wang, Ling

    2017-11-16

    The mammalian target of rapamycin (mTOR) is a central controller of cell growth, proliferation, metabolism, and angiogenesis. This protein is an attractive target for new anticancer drug development. Significant progress has been made in hit discovery, lead optimization, drug candidate development and determination of the three-dimensional (3D) structure of mTOR. Computational methods have been applied to accelerate the discovery and development of mTOR inhibitors helping to model the structure of mTOR, screen compound databases, uncover structure-activity relationship (SAR) and optimize the hits, mine the privileged fragments and design focused libraries. Besides, computational approaches were also applied to study protein-ligand interactions mechanisms and in natural product-driven drug discovery. Herein, we survey the most recent progress on the application of computational approaches to advance the discovery and development of compounds targeting mTOR. Future directions in the discovery of new mTOR inhibitors using computational methods are also discussed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  15. An update on the use of C. elegans for preclinical drug discovery: screening and identifying anti-infective drugs.

    Science.gov (United States)

    Kim, Wooseong; Hendricks, Gabriel Lambert; Lee, Kiho; Mylonakis, Eleftherios

    2017-06-01

    The emergence of antibiotic-resistant and -tolerant bacteria is a major threat to human health. Although efforts for drug discovery are ongoing, conventional bacteria-centered screening strategies have thus far failed to yield new classes of effective antibiotics. Therefore, new paradigms for discovering novel antibiotics are of critical importance. Caenorhabditis elegans, a model organism used for in vivo, offers a promising solution for identification of anti-infective compounds. Areas covered: This review examines the advantages of C. elegans-based high-throughput screening over conventional, bacteria-centered in vitro screens. It discusses major anti-infective compounds identified from large-scale C. elegans-based screens and presents the first clinically-approved drugs, then known bioactive compounds, and finally novel small molecules. Expert opinion: There are clear advantages of using a C. elegans-infection based screening method. A C. elegans-based screen produces an enriched pool of non-toxic, efficacious, potential anti-infectives, covering: conventional antimicrobial agents, immunomodulators, and anti-virulence agents. Although C. elegans-based screens do not denote the mode of action of hit compounds, this can be elucidated in secondary studies by comparing the results to target-based screens, or conducting subsequent target-based screens, including the genetic knock-down of host or bacterial genes.

  16. Phenotypic screening approaches to develop Aurora kinase inhibitors: Drug Discovery perspectives

    Directory of Open Access Journals (Sweden)

    Carlos eMarugán

    2016-01-01

    Full Text Available Targeting mitotic regulators as a strategy to fight cancer implies the development of drugs against key proteins such as Aurora A and B. Current drugs which target mitosis through a general mechanism of action (stabilization/destabilization of microtubules, have several side effects (neutropenia, alopecia, emesis. Pharmaceutical companies aim at avoiding these unwanted effects by generating improved and selective drugs that increase the quality of life of the patients. However, the development of these drugs is an ambitious task that involves testing thousands of compounds through biochemical and cell-based assays. In addition, molecules usually target complex biological processes, involving several proteins and different molecular pathways, further emphasizing the need for high-throughput screening techniques and multiplexing technologies in order to identify drugs with the desired phenotype.We will briefly describe two multiplexing technologies (high-content imaging, microarrays and flow cytometry and two key processes for drug discovery research (assay development and validation following our own published industry quality standards. We will further focus on high-content imaging as a useful tool for phenotypic screening and will provide a concrete example of high-content imaging assay to detect Aurora A or B selective inhibitors discriminating the off-target effects related to inhibition of other cell cycle or non-cell cycle key regulators. Finally, we will describe other assays that can help to characterize the in vitro pharmacology of the inhibitors.

  17. Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

    Science.gov (United States)

    Ekins, Sean; Madrid, Peter B; Sarker, Malabika; Li, Shao-Gang; Mittal, Nisha; Kumar, Pradeep; Wang, Xin; Stratton, Thomas P; Zimmerman, Matthew; Talcott, Carolyn; Bourbon, Pauline; Travers, Mike; Yadav, Maneesh; Freundlich, Joel S

    2015-01-01

    Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC50 in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10-6 cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.

  18. Drug and bioactive molecule screening based on a bioelectrical impedance cell culture platform

    Directory of Open Access Journals (Sweden)

    Ramasamy S

    2014-12-01

    Full Text Available Sakthivel Ramasamy,1 Devasier Bennet,1 Sanghyo Kim1,2 1Department of Bionanotechnology, Gachon University, Gyeonggi-Do, Republic of Korea; 2Graduate Gachon Medical Research Institute, Gil Medical Center, Incheon, Republic of Korea Abstract: This review will present a brief discussion on the recent advancements of bioelectrical impedance cell-based biosensors, especially the electric cell-substrate impedance sensing (ECIS system for screening of various bioactive molecules. The different technical integrations of various chip types, working principles, measurement systems, and applications for drug targeting of molecules in cells are highlighted in this paper. Screening of bioactive molecules based on electric cell-substrate impedance sensing is a trial-and-error process toward the development of therapeutically active agents for drug discovery and therapeutics. In general, bioactive molecule screening can be used to identify active molecular targets for various diseases and toxicity at the cellular level with nanoscale resolution. In the innovation and screening of new drugs or bioactive molecules, the activeness, the efficacy of the compound, and safety in biological systems are the main concerns on which determination of drug candidates is based. Further, drug discovery and screening of compounds are often performed in cell-based test systems in order to reduce costs and save time. Moreover, this system can provide more relevant results in in vivo studies, as well as high-throughput drug screening for various diseases during the early stages of drug discovery. Recently, MEMS technologies and integration with image detection techniques have been employed successfully. These new technologies and their possible ongoing transformations are addressed. Select reports are outlined, and not all the work that has been performed in the field of drug screening and development is covered. Keywords: screening of bioactive agents, impedance-based cell

  19. Computer aided drug design

    Science.gov (United States)

    Jain, A.

    2017-08-01

    Computer based method can help in discovery of leads and can potentially eliminate chemical synthesis and screening of many irrelevant compounds, and in this way, it save time as well as cost. Molecular modeling systems are powerful tools for building, visualizing, analyzing and storing models of complex molecular structure that can help to interpretate structure activity relationship. The use of various techniques of molecular mechanics and dynamics and software in Computer aided drug design along with statistics analysis is powerful tool for the medicinal chemistry to synthesis therapeutic and effective drugs with minimum side effect.

  20. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim; Fokoue, Achille; Hassanzadeh, Oktie; Zhang, Ping; Sadoghi, Mohammad

    2017-01-01

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  1. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug-drug interactions

    KAUST Repository

    Abdelaziz, Ibrahim

    2017-06-12

    Drug-Drug Interactions (DDIs) are a major cause of preventable Adverse Drug Reactions (ADRs), causing a significant burden on the patients’ health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. In this paper, we present Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction. Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. In particular, Tiresias utilizes two classes of features in a knowledge graph: local and global features. Local features are derived from the information directly associated to each drug (i.e., one hop away) while global features are learnt by minimizing a global loss function that considers the complete structure of the knowledge graph. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed Tiresias and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs as well as newly developed drugs.

  2. Drug-domain interaction networks in myocardial infarction.

    Science.gov (United States)

    Wang, Haiying; Zheng, Huiru; Azuaje, Francisco; Zhao, Xing-Ming

    2013-09-01

    It has been well recognized that the pace of the development of new drugs and therapeutic interventions lags far behind biological knowledge discovery. Network-based approaches have emerged as a promising alternative to accelerate the discovery of new safe and effective drugs. Based on the integration of several biological resources including two recently published datasets i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was firstly constructed, followed by topological analysis and functional characterization of the network. The results show that My-DDome has a very clear modular structure, where drugs interacting with the same domain(s) within each module tend to have similar therapeutic effects. Moreover it has been found that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome (p drugs, their known targets, and seemingly unrelated proteins can be revealed.

  3. FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects

    Directory of Open Access Journals (Sweden)

    Miteva Maria A

    2008-09-01

    Full Text Available Abstract Background Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses and as such, compound collections' property profiling can be performed in silico. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making. Results This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries. Conclusion We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules that can be easily tuned.

  4. Price Discovery from Cross-Currency and FX Swaps: A Structural Analysis

    OpenAIRE

    Yasuaki Amatatsu; Naohiko Baba

    2007-01-01

    This paper investigates the relative role of price discovery between two long-term swap contracts that exchange between the U.S. dollar and the Japanese yen: cross-currency basis swap and FX (foreign exchange) swap. First, we show that these two swaps should be in a no-arbitrage relationship by allowing for differential risk premiums. Second, we empirically investigate the relative role of price discovery using the structural-form approach based on the state space models. Main finding are as ...

  5. Production of heparanase constructs suitable for nuclear magnetic resonance and drug discovery studies.

    Science.gov (United States)

    Mosulén, Silvia; Ortí, Leticia; Bas, Esperanza; Carbajo, Rodrigo J; Pineda-Lucena, Antonio

    2011-02-01

    Heparanase is an endo-β-D-glucosidase capable of specifically degrading heparan sulphate, one of the main components of the extracellular matrix. This 65 kDa polypeptide is implicated in cancer processes such as tumour formation, angiogenesis and metastasis, making it a very attractive target in antitumour treatments. Structure-based approaches to find inhibitors of heparanase have been historically hampered by the lack of success in crystallizing the protein. With the aim to undertake the NMR structural characterisation of heparanase, we have designed and produced, using recombinant methods, smaller constructs of heparanase containing the catalytically active glutamic acids and the two binding sites for heparan sulphate. An extensive range of expression and purification conditions were evaluated to alleviate the intrinsic low solubility and aggregation propensity of heparanase, allowing the obtention of the enzyme in milligram quantities, both unlabelled and ¹⁵N-labelled for NMR studies. Using the smallest of the designed constructs and applying NMR and SPR methodologies, we have demonstrated that known inhibitors of heparanase bind to this construct specifically and selectively with K(D) values in the range of those reported for human heparanase, validating it for future drug discovery projects focused on the identification of novel inhibitors of this enzyme. © 2010 Wiley Periodicals, Inc.

  6. Synthesis and structural characterization of some trisulfide analoges of thiouracil-based antithyroid drugs

    Science.gov (United States)

    Bhabak, Krishna P.; Bhowmick, Debasish

    2012-08-01

    Thiourea-based antithyroid drugs are effectively used for the treatment of hyperthyroidism. In this paper, we describe the synthesis of new trisulfides (11-12) from the commonly used thiourea-based antithyroid drugs such as 6-n-propyl-2-thiouracil (PTU) and 6-methyl-2-thiouracil (MTU) in the reaction with I2/KI system. Structural analysis by single crystal X-ray diffraction studies revealed the stabilization of trisulfides by a lactam-lactim tautomerism facilitating effective intramolecular as well as intermolecular non-covalent interactions. Although the structures of both trisulfides were found to be quite similar, a notable difference in the intermolecular interactions was observed between compounds 11 and 12 leading to different structural patterns. Structural stabilization of these trisulfides by tautomerism followed by intramolecular as well as intermolecular H-bonds makes these molecules as perfect examples in molecular recognition with self-complementary donor and acceptor units within a single molecule.

  7. Laboratory informatics tools integration strategies for drug discovery: integration of LIMS, ELN, CDS, and SDMS.

    Science.gov (United States)

    Machina, Hari K; Wild, David J

    2013-04-01

    There are technologies on the horizon that could dramatically change how informatics organizations design, develop, deliver, and support applications and data infrastructures to deliver maximum value to drug discovery organizations. Effective integration of data and laboratory informatics tools promises the ability of organizations to make better informed decisions about resource allocation during the drug discovery and development process and for more informed decisions to be made with respect to the market opportunity for compounds. We propose in this article a new integration model called ELN-centric laboratory informatics tools integration.

  8. Key drivers of biomedical innovation in cancer drug discovery

    OpenAIRE

    Huber, Margit A; Kraut, Norbert

    2014-01-01

    Discovery and translational research has led to the identification of a series of ?cancer drivers??genes that, when mutated or otherwise misregulated, can drive malignancy. An increasing number of drugs that directly target such drivers have demonstrated activity in clinical trials and are shaping a new landscape for molecularly targeted cancer therapies. Such therapies rely on molecular and genetic diagnostic tests to detect the presence of a biomarker that predicts response. Here, we highli...

  9. Big Data in Drug Discovery.

    Science.gov (United States)

    Brown, Nathan; Cambruzzi, Jean; Cox, Peter J; Davies, Mark; Dunbar, James; Plumbley, Dean; Sellwood, Matthew A; Sim, Aaron; Williams-Jones, Bryn I; Zwierzyna, Magdalena; Sheppard, David W

    2018-01-01

    Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation. © 2018 Elsevier B.V. All rights reserved.

  10. Partial filling affinity capillary electrophoresis as a useful tool for fragment-based drug discovery: A proof of concept on thrombin.

    Science.gov (United States)

    Farcaş, E; Bouckaert, C; Servais, A-C; Hanson, J; Pochet, L; Fillet, M

    2017-09-01

    With the emergence of more challenging targets, a relatively new approach, fragment-based drug discovery (FBDD), proved its efficacy and gained increasing importance in the pharmaceutical industry. FBDD identifies low molecular-weight (MW) ligands (fragments) that bind to biologically important macromolecules, then a structure-guided fragment growing or merging approach is performed, contributing to the quality of the lead. However, to select the appropriate fragment to be evolved, sensitive analytical screening methods must be used to measure the affinity in the μM or even mM range. In this particular context, we developed a robust and selective partial filling affinity CE (ACE) method for the direct binding screening of a small fragment library in order to identify new thrombin inhibitors. To demonstrate the accuracy of our assay, the complex dissociation constants of three known thrombin inhibitors, namely benzamidine, p-aminobenzamidine and nafamostat were determined and found to be in good concordance with the previously reported values. Finally, the screening of a small library was performed and demonstrated the high discriminatory power of our method towards weak binders compared to classical spectrophotometric activity assay, proving the interest of our method in the context of FBDD. Copyright © 2017 Elsevier B.V. All rights reserved.

  11. Ultrafast protein structure-based virtual screening with Panther

    Science.gov (United States)

    Niinivehmas, Sanna P.; Salokas, Kari; Lätti, Sakari; Raunio, Hannu; Pentikäinen, Olli T.

    2015-10-01

    Molecular docking is by far the most common method used in protein structure-based virtual screening. This paper presents Panther, a novel ultrafast multipurpose docking tool. In Panther, a simple shape-electrostatic model of the ligand-binding area of the protein is created by utilizing the protein crystal structure. The features of the possible ligands are then compared to the model by using a similarity search algorithm. On average, one ligand can be processed in a few minutes by using classical docking methods, whereas using Panther processing takes Panther protocol can be used in several applications, such as speeding up the early phases of drug discovery projects, reducing the number of failures in the clinical phase of the drug development process, and estimating the environmental toxicity of chemicals. Panther-code is available in our web pages (http://www.jyu.fi/panther) free of charge after registration.

  12. G-LoSA: An efficient computational tool for local structure-centric biological studies and drug design.

    Science.gov (United States)

    Lee, Hui Sun; Im, Wonpil

    2016-04-01

    Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G-LoSA. G-LoSA aligns protein local structures in a sequence order independent way and provides a GA-score, a chemical feature-based and size-independent structure similarity score. Our benchmark validation shows the robust performance of G-LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure-centric comparative biology studies. In particular, G-LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G-LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer-aided drug design. We hope that G-LoSA can be a useful computational method for exploring interesting biological problems through large-scale comparison of protein local structures and facilitating drug discovery research and development. G-LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/. © 2016 The Protein Society.

  13. Computational discovery of picomolar Q(o) site inhibitors of cytochrome bc1 complex.

    Science.gov (United States)

    Hao, Ge-Fei; Wang, Fu; Li, Hui; Zhu, Xiao-Lei; Yang, Wen-Chao; Huang, Li-Shar; Wu, Jia-Wei; Berry, Edward A; Yang, Guang-Fu

    2012-07-11

    A critical challenge to the fragment-based drug discovery (FBDD) is its low-throughput nature due to the necessity of biophysical method-based fragment screening. Herein, a method of pharmacophore-linked fragment virtual screening (PFVS) was successfully developed. Its application yielded the first picomolar-range Q(o) site inhibitors of the cytochrome bc(1) complex, an important membrane protein for drug and fungicide discovery. Compared with the original hit compound 4 (K(i) = 881.80 nM, porcine bc(1)), the most potent compound 4f displayed 20 507-fold improved binding affinity (K(i) = 43.00 pM). Compound 4f was proved to be a noncompetitive inhibitor with respect to the substrate cytochrome c, but a competitive inhibitor with respect to the substrate ubiquinol. Additionally, we determined the crystal structure of compound 4e (K(i) = 83.00 pM) bound to the chicken bc(1) at 2.70 Å resolution, providing a molecular basis for understanding its ultrapotency. To our knowledge, this study is the first application of the FBDD method in the discovery of picomolar inhibitors of a membrane protein. This work demonstrates that the novel PFVS approach is a high-throughput drug discovery method, independent of biophysical screening techniques.

  14. Exploring Natural Products from the Biodiversity of Pakistan for Computational Drug Discovery Studies: Collection, Optimization, Design and Development of A Chemical Database (ChemDP).

    Science.gov (United States)

    Mirza, Shaher Bano; Bokhari, Habib; Fatmi, Muhammad Qaiser

    2015-01-01

    Pakistan possesses a rich and vast source of natural products (NPs). Some of these secondary metabolites have been identified as potent therapeutic agents. However, the medicinal usage of most of these compounds has not yet been fully explored. The discoveries for new scaffolds of NPs as inhibitors of certain enzymes or receptors using advanced computational drug discovery approaches are also limited due to the unavailability of accurate 3D structures of NPs. An organized database incorporating all relevant information, therefore, can facilitate to explore the medicinal importance of the metabolites from Pakistani Biodiversity. The Chemical Database of Pakistan (ChemDP; release 01) is a fully-referenced, evolving, web-based, virtual database which has been designed and developed to introduce natural products (NPs) and their derivatives from the biodiversity of Pakistan to Global scientific communities. The prime aim is to provide quality structures of compounds with relevant information for computer-aided drug discovery studies. For this purpose, over 1000 NPs have been identified from more than 400 published articles, for which 2D and 3D molecular structures have been generated with a special focus on their stereochemistry, where applicable. The PM7 semiempirical quantum chemistry method has been used to energy optimize the 3D structure of NPs. The 2D and 3D structures can be downloaded as .sdf, .mol, .sybyl, .mol2, and .pdb files - readable formats by many chemoinformatics/bioinformatics software packages. Each entry in ChemDP contains over 100 data fields representing various molecular, biological, physico-chemical and pharmacological properties, which have been properly documented in the database for end users. These pieces of information have been either manually extracted from the literatures or computationally calculated using various computational tools. Cross referencing to a major data repository i.e. ChemSpider has been made available for overlapping

  15. Dual-acting of Hybrid Compounds - A New Dawn in the Discovery of Multi-target Drugs: Lead Generation Approaches.

    Science.gov (United States)

    Abdolmaleki, Azizeh; Ghasemi, Jahan B

    2017-01-01

    Finding high quality beginning compounds is a critical job at the start of the lead generation stage for multi-target drug discovery (MTDD). Designing hybrid compounds as selective multitarget chemical entity is a challenge, opportunity, and new idea to better act against specific multiple targets. One hybrid molecule is formed by two (or more) pharmacophore group's participation. So, these new compounds often exhibit two or more activities going about as multi-target drugs (mtdrugs) and may have superior safety or efficacy. Application of integrating a range of information and sophisticated new in silico, bioinformatics, structural biology, pharmacogenomics methods may be useful to discover/design, and synthesis of the new hybrid molecules. In this regard, many rational and screening approaches have followed by medicinal chemists for the lead generation in MTDD. Here, we review some popular lead generation approaches that have been used for designing multiple ligands (DMLs). This paper focuses on dual- acting chemical entities that incorporate a part of two drugs or bioactive compounds to compose hybrid molecules. Also, it presents some of key concepts and limitations/strengths of lead generation methods by comparing combination framework method with screening approaches. Besides, a number of examples to represent applications of hybrid molecules in the drug discovery are included. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  16. Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.

    Science.gov (United States)

    Verbist, Bie; Klambauer, Günter; Vervoort, Liesbet; Talloen, Willem; Shkedy, Ziv; Thas, Olivier; Bender, Andreas; Göhlmann, Hinrich W H; Hochreiter, Sepp

    2015-05-01

    The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  17. Public and private sector contributions to the discovery and development of "impact" drugs.

    Science.gov (United States)

    Reichert, Janice M; Milne, Christopher-Paul

    2002-01-01

    Recently, well-publicized reports by Public Citizen and the Joint Economic Committee (JEC) of the US Congress questioned the role of the drug industry in the discovery and development of therapeutically important drugs. To gain a better understanding of the relative roles of the public and private sectors in pharmaceutic innovation, the Tufts Center for the Study of Drug Development evaluated the underlying National Institutes of Health (NIH) and academic research cited in the Public Citizen and JEC reports and performed its own assessment of the relationship between the private and public sectors in drug discovery and development of 21 "impact" drugs. We found that, ultimately, any attempt to measure the relative contribution of the public and private sectors to the research and development (R&D) of therapeutically important drugs by output alone, such as counting publications or even product approvals, is flawed. Several key factors (eg, degree of uncertainty, expected market value, potential social benefit) affect investment decisions and determine whether public or private sector funds, or both, are most appropriate. Because of the competitiveness and complexity of today's R&D environment, both sectors are increasingly challenged to show returns on their investment and the traditional boundaries separating the roles of the private and public research spheres have become increasingly blurred. What remains clear, however, is that the process still starts with good science and ends with good medicine.

  18. Cardiac Arrhythmia: In vivo screening in the zebrafish to overcome complexity in drug discovery.

    Science.gov (United States)

    Macrae, Calum A

    2010-07-01

    IMPORTANCE OF THE FIELD: Cardiac arrhythmias remain a major challenge for modern drug discovery. Clinical events are paroxysmal, often rare and may be asymptomatic until a highly morbid complication. Target selection is often based on limited information and though highly specific agents are identified in screening, the final efficacy is often compromised by unanticipated systemic responses, a narrow therapeutic index and substantial toxicities. AREAS COVERED IN THIS REVIEW: Our understanding of complexity of arrhythmogenesis has grown dramatically over the last two decades, and the range of potential disease mechanisms now includes pathways previously thought only tangentially involved in arrhythmia. This review surveys the literature on arrhythmia mechanisms from 1965 to the present day, outlines the complex biology underlying potentially each and every rhythm disturbance, and highlights the problems for rational target identification. The rationale for in vivo screening is described and the utility of the zebrafish for this approach and for complementary work in functional genomics is discussed. Current limitations of the model in this setting and the need for careful validation in new disease areas are also described. WHAT THE READER WILL GAIN: An overview of the complex mechanisms underlying most clinical arrhythmias, and insight into the limits of ion channel conductances as drug targets. An introduction to the zebrafish as a model organism, in particular for cardiovascular biology. Potential approaches to overcoming the hurdles to drug discovery in the face of complex biology including in vivo screening of zebrafish genetic disease models. TAKE HOME MESSAGE: In vivo screening in faithful disease models allows the effects of drugs on integrative physiology and disease biology to be captured during the screening process, in a manner agnostic to potential drug target or targets. This systematic strategy bypasses current gaps in our understanding of disease

  19. Applications of Dynamic Clamp to Cardiac Arrhythmia Research: Role in Drug Target Discovery and Safety Pharmacology Testing

    Directory of Open Access Journals (Sweden)

    Francis A. Ortega

    2018-01-01

    Full Text Available Dynamic clamp, a hybrid-computational-experimental technique that has been used to elucidate ionic mechanisms underlying cardiac electrophysiology, is emerging as a promising tool in the discovery of potential anti-arrhythmic targets and in pharmacological safety testing. Through the injection of computationally simulated conductances into isolated cardiomyocytes in a real-time continuous loop, dynamic clamp has greatly expanded the capabilities of patch clamp outside traditional static voltage and current protocols. Recent applications include fine manipulation of injected artificial conductances to identify promising drug targets in the prevention of arrhythmia and the direct testing of model-based hypotheses. Furthermore, dynamic clamp has been used to enhance existing experimental models by addressing their intrinsic limitations, which increased predictive power in identifying pro-arrhythmic pharmacological compounds. Here, we review the recent advances of the dynamic clamp technique in cardiac electrophysiology with a focus on its future role in the development of safety testing and discovery of anti-arrhythmic drugs.

  20. Recent Advances in Drug Discovery from South African Marine Invertebrates

    Directory of Open Access Journals (Sweden)

    Michael T. Davies-Coleman

    2015-10-01

    Full Text Available Recent developments in marine drug discovery from three South African marine invertebrates, the tube worm Cephalodiscus gilchristi, the ascidian Lissoclinum sp. and the sponge Topsentia pachastrelloides, are presented. Recent reports of the bioactivity and synthesis of the anti-cancer secondary metabolites cephalostatin and mandelalides (from C. gilchristi and Lissoclinum sp., respectively and various analogues are presented. The threat of drug-resistant pathogens, e.g., methicillin-resistant Staphylococcus aureus (MRSA, is assuming greater global significance, and medicinal chemistry strategies to exploit the potent MRSA PK inhibition, first revealed by two marine secondary metabolites, cis-3,4-dihydrohamacanthin B and bromodeoxytopsentin from T. pachastrelloides, are compared.

  1. Machine-learning techniques applied to antibacterial drug discovery.

    Science.gov (United States)

    Durrant, Jacob D; Amaro, Rommie E

    2015-01-01

    The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field. © 2015 John Wiley & Sons A/S.

  2. NCI Program for Natural Product Discovery: A Publicly-Accessible Library of Natural Product Fractions for High-Throughput Screening.

    Science.gov (United States)

    Thornburg, Christopher C; Britt, John R; Evans, Jason R; Akee, Rhone K; Whitt, James A; Trinh, Spencer K; Harris, Matthew J; Thompson, Jerell R; Ewing, Teresa L; Shipley, Suzanne M; Grothaus, Paul G; Newman, David J; Schneider, Joel P; Grkovic, Tanja; O'Keefe, Barry R

    2018-06-13

    The US National Cancer Institute's (NCI) Natural Product Repository is one of the world's largest, most diverse collections of natural products containing over 230,000 unique extracts derived from plant, marine, and microbial organisms that have been collected from biodiverse regions throughout the world. Importantly, this national resource is available to the research community for the screening of extracts and the isolation of bioactive natural products. However, despite the success of natural products in drug discovery, compatibility issues that make extracts challenging for liquid handling systems, extended timelines that complicate natural product-based drug discovery efforts and the presence of pan-assay interfering compounds have reduced enthusiasm for the high-throughput screening (HTS) of crude natural product extract libraries in targeted assay systems. To address these limitations, the NCI Program for Natural Product Discovery (NPNPD), a newly launched, national program to advance natural product discovery technologies and facilitate the discovery of structurally defined, validated lead molecules ready for translation will create a prefractionated library from over 125,000 natural product extracts with the aim of producing a publicly-accessible, HTS-amenable library of >1,000,000 fractions. This library, representing perhaps the largest accumulation of natural-product based fractions in the world, will be made available free of charge in 384-well plates for screening against all disease states in an effort to reinvigorate natural product-based drug discovery.

  3. Rediscovering Don Swanson: The Past, Present and Future of Literature-based Discovery

    Directory of Open Access Journals (Sweden)

    Neil R. Smalheiser

    2017-12-01

    Full Text Available Purpose: The late Don R. Swanson was well appreciated during his lifetime as Dean of the Graduate Library School at University of Chicago, as winner of the American Society for Information Science Award of Merit for 2000, and as author of many seminal articles. In this informal essay, I will give my personal perspective on Don’s contributions to science, and outline some current and future directions in literature-based discovery that are rooted in concepts that he developed. Design/methodology/approach: Personal recollections and literature review. Findings: The Swanson A-B-C model of literature-based discovery has been successfully used by laboratory investigators analyzing their findings and hypotheses. It continues to be a fertile area of research in a wide range of application areas including text mining, drug repurposing, studies of scientific innovation, knowledge discovery in databases, and bioinformatics. Recently, additional modes of discovery that do not follow the A-B-C model have also been proposed and explored (e.g. so-called storytelling, gaps, analogies, link prediction, negative consensus, outliers, and revival of neglected or discarded research questions. Research limitations: This paper reflects the opinions of the author and is not a comprehensive nor technically based review of literature-based discovery. Practical implications: The general scientific public is still not aware of the availability of tools for literature-based discovery. Our Arrowsmith project site maintains a suite of discovery tools that are free and open to the public (http://arrowsmith.psych.uic.edu, as does BITOLA which is maintained by Dmitar Hristovski (http:// http://ibmi.mf.uni-lj.si/bitola, and Epiphanet which is maintained by Trevor Cohen (http://epiphanet.uth.tmc.edu/. Bringing user-friendly tools to the public should be a high priority, since even more than advancing basic research in informatics, it is vital that we ensure that scientists

  4. Ligand and Structure-Based Approaches for the Identification of Peptide Deformylase Inhibitors as Antibacterial Drugs.

    Science.gov (United States)

    Gao, Jian; Liang, Li; Zhu, Yasheng; Qiu, Shengzhi; Wang, Tao; Zhang, Ling

    2016-07-15

    Peptide deformylase (PDF) is a metalloprotease catalyzing the removal of a formyl group from newly synthesized proteins, which makes it an important antibacterial drug target. Given the importance of PDF inhibitors like actinonin in antibacterial drug discovery, several reported potent PDF inhibitors were used to develop pharmacophore models using the Galahad module of Sybyl 7.1 software. Generated pharmacophore models were composed of two donor atom centers, four acceptor atom centers and two hydrophobic groups. Model-1 was screened against the Zinc database and several compounds were retrieved as hits. Compounds with Qfit values of more than 60 were employed to perform a molecular docking study with the receptor Escherichia coli PDF, then compounds with docking score values of more than 6 were used to predict the in silico pharmacokinetic and toxicity risk via OSIRIS property explorer. Two known PDF inhibitors were also used to perform a molecular docking study with E. coli PDF as reference molecules. The results of the molecular docking study were validated by reproducing the crystal structure of actinonin. Molecular docking and in silico pharmacokinetic and toxicity prediction studies suggested that ZINC08740166 has a relatively high docking score of 7.44 and a drug score of 0.78.

  5. De Novo Discovery of Structured ncRNA Motifs in Genomic Sequences

    DEFF Research Database (Denmark)

    Ruzzo, Walter L; Gorodkin, Jan

    2014-01-01

    De novo discovery of "motifs" capturing the commonalities among related noncoding ncRNA structured RNAs is among the most difficult problems in computational biology. This chapter outlines the challenges presented by this problem, together with some approaches towards solving them, with an emphas...... on an approach based on the CMfinder CMfinder program as a case study. Applications to genomic screens for novel de novo structured ncRNA ncRNA s, including structured RNA elements in untranslated portions of protein-coding genes, are presented.......De novo discovery of "motifs" capturing the commonalities among related noncoding ncRNA structured RNAs is among the most difficult problems in computational biology. This chapter outlines the challenges presented by this problem, together with some approaches towards solving them, with an emphasis...

  6. Microemulsions based transdermal drug delivery systems.

    Science.gov (United States)

    Vadlamudi, Harini C; Narendran, Hyndavi; Nagaswaram, Tejeswari; Yaga, Gowri; Thanniru, Jyotsna; Yalavarthi, Prasanna R

    2014-01-01

    Since the discovery of microemulsions by Jack H Schulman, there has been huge progress made in applying microemulsion systems in plethora of research and industrial process. Microemulsions are optically isotropic systems consisting of water, oil and amphiphile. These systems are beneficial due to their thermodynamic stability, optical clarity, ease of preparation, higher diffusion and absorption rates. Moreover, it has been reported that the ingredients of microemulsion can effectively overcome the diffusion barrier and penetrate through the stratum corneum of the skin. Hence it becomes promising for both transdermal and dermal drug delivery. However, low viscosity of microemulsion restrains its applicability in pharmaceutical industry. To overcome the above drawback, the low viscous microemulsions were added to viscous gel bases to potentiate its applications as topical drug delivery systems so that various drug related toxic effects and erratic drug absorption can be avoided. The present review deals with the microemulsions, various techniques involved in the development of organic nanoparticles. The review emphasized on microemulsion based systems such as hydrogels and organogels. The physicochemical characteristics, mechanical properties, rheological and stability principles involved in microemulsion based viscous gels were also explored.

  7. The case for open-source software in drug discovery.

    Science.gov (United States)

    DeLano, Warren L

    2005-02-01

    Widespread adoption of open-source software for network infrastructure, web servers, code development, and operating systems leads one to ask how far it can go. Will "open source" spread broadly, or will it be restricted to niches frequented by hopeful hobbyists and midnight hackers? Here we identify reasons for the success of open-source software and predict how consumers in drug discovery will benefit from new open-source products that address their needs with increased flexibility and in ways complementary to proprietary options.

  8. Brazil: An emerging partner in drug R&D.

    Science.gov (United States)

    Rodrigues, Debora G

    2009-08-01

    With the need for innovation in drug discovery and development and changes to patent laws that are enabling greater IP protection, many pharmaceutical companies are pursuing international cooperation agreements with foreign companies as part of a global development strategy to enhance product pipelines. Brazil, the largest pharmaceutical market in Latin America, has improved its infrastructure, scientific and technological capabilities and has created a sustainable strategy to promote drug discovery research activities. Positive economic growth, a stable political structure, expanding patient populations an increasing governmental, private and foreign investments are creating a new landscape for drug R&D in the country. As Brazilian-based pharmaceutical companies become further established, new opportunities for partnerships and collaborative alliances are becoming available for the drug discovery process, as well as for co-manufacturing and co-marketing efforts. This feature review provides an overview of the Brazilian pharmaceutical market and discusses current opportunities, emerging trends and challenges for this expanding market.

  9. Cardiovascular drug discovery in the academic setting: building infrastructure, harnessing strengths, and seeking synergies.

    Science.gov (United States)

    Gardell, Stephen J; Roth, Gregory P; Kelly, Daniel P

    2010-10-01

    The flow of innovative, effective, and safe new drugs from pharmaceutical laboratories for the treatment and prevention of cardiovascular disease has slowed to a trickle. While the need for breakthrough cardiovascular disease drugs is still paramount, the incentive to develop these agents has been blunted by burgeoning clinical development costs coupled with a heightened risk of failure due to the unprecedented nature of the emerging drug targets and increasingly challenging regulatory environment. A fuller understanding of the drug targets and employing novel biomarker strategies in clinical trials should serve to mitigate the risk. In any event, these current challenges have evoked changing trends in the pharmaceutical industry, which have created an opportunity for non-profit biomedical research institutions to play a pivotal partnering role in early stage drug discovery. The obvious strengths of academic research institutions is the breadth of their scientific programs and the ability and motivation to "go deep" to identify and characterize new target pathways that unlock the specific mysteries of cardiovascular diseases--leading to a bounty of novel therapeutic targets and prescient biomarkers. However, success in the drug discovery arena within the academic environment is contingent upon assembling the requisite infrastructure, annexing the talent to interrogate and validate the drug targets, and building translational bridges with pharmaceutical organizations and patient-oriented researchers.

  10. Silk Fibroin-Based Nanoparticles for Drug Delivery

    Science.gov (United States)

    Zhao, Zheng; Li, Yi; Xie, Mao-Bin

    2015-01-01

    Silk fibroin (SF) is a protein-based biomacromolecule with excellent biocompatibility, biodegradability and low immunogenicity. The development of SF-based nanoparticles for drug delivery have received considerable attention due to high binding capacity for various drugs, controlled drug release properties and mild preparation conditions. By adjusting the particle size, the chemical structure and properties, the modified or recombinant SF-based nanoparticles can be designed to improve the therapeutic efficiency of drugs encapsulated into these nanoparticles. Therefore, they can be used to deliver small molecule drugs (e.g., anti-cancer drugs), protein and growth factor drugs, gene drugs, etc. This paper reviews recent progress on SF-based nanoparticles, including chemical structure, properties, and preparation methods. In addition, the applications of SF-based nanoparticles as carriers for therapeutic drugs are also reviewed. PMID:25749470

  11. [From the discovery of antibiotics to emerging highly drug-resistant bacteria].

    Science.gov (United States)

    Meunier, Olivier

    2015-01-01

    The discovery of antibiotics has enabled serious infections to be treated. However, bacteria resistant to several families of antibiotics and the emergence of new highly drug-resistant bacteria constitute a public health issue in France and across the world. Actions to prevent their transmission are being put in place. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  12. Lab-on-a-chip platform for high throughput drug discovery with DNA-encoded chemical libraries

    Science.gov (United States)

    Grünzner, S.; Reddavide, F. V.; Steinfelder, C.; Cui, M.; Busek, M.; Klotzbach, U.; Zhang, Y.; Sonntag, F.

    2017-02-01

    The fast development of DNA-encoded chemical libraries (DECL) in the past 10 years has received great attention from pharmaceutical industries. It applies the selection approach for small molecular drug discovery. Because of the limited choices of DNA-compatible chemical reactions, most DNA-encoded chemical libraries have a narrow structural diversity and low synthetic yield. There is also a poor correlation between the ranking of compounds resulted from analyzing the sequencing data and the affinity measured through biochemical assays. By combining DECL with dynamical chemical library, the resulting DNA-encoded dynamic library (EDCCL) explores the thermodynamic equilibrium of reversible reactions as well as the advantages of DNA encoded compounds for manipulation/detection, thus leads to enhanced signal-to-noise ratio of the selection process and higher library quality. However, the library dynamics are caused by the weak interactions between the DNA strands, which also result in relatively low affinity of the bidentate interaction, as compared to a stable DNA duplex. To take advantage of both stably assembled dual-pharmacophore libraries and EDCCLs, we extended the concept of EDCCLs to heat-induced EDCCLs (hi-EDCCLs), in which the heat-induced recombination process of stable DNA duplexes and affinity capture are carried out separately. To replace the extremely laborious and repetitive manual process, a fully automated device will facilitate the use of DECL in drug discovery. Herein we describe a novel lab-on-a-chip platform for high throughput drug discovery with hi-EDCCL. A microfluidic system with integrated actuation was designed which is able to provide a continuous sample circulation by reducing the volume to a minimum. It consists of a cooled and a heated chamber for constant circulation. The system is capable to generate stable temperatures above 75 °C in the heated chamber to melt the double strands of the DNA and less than 15 °C in the cooled chamber

  13. Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations.

    Science.gov (United States)

    Singh, Pankaj Kumar; Negi, Arvind; Gupta, Pawan Kumar; Chauhan, Monika; Kumar, Raj

    2016-08-01

    Toxicity is a common drawback of newly designed chemotherapeutic agents. With the exception of pharmacophore-induced toxicity (lack of selectivity at higher concentrations of a drug), the toxicity due to chemotherapeutic agents is based on the toxicophore moiety present in the drug. To date, methodologies implemented to determine toxicophores may be broadly classified into biological, bioanalytical and computational approaches. The biological approach involves analysis of bioactivated metabolites, whereas the computational approach involves a QSAR-based method, mapping techniques, an inverse docking technique and a few toxicophore identification/estimation tools. Being one of the major steps in drug discovery process, toxicophore identification has proven to be an essential screening step in drug design and development. The paper is first of its kind, attempting to cover and compare different methodologies employed in predicting and determining toxicophores with an emphasis on their scope and limitations. Such information may prove vital in the appropriate selection of methodology and can be used as screening technology by researchers to discover the toxicophoric potentials of their designed and synthesized moieties. Additionally, it can be utilized in the manipulation of molecules containing toxicophores in such a manner that their toxicities might be eliminated or removed.

  14. ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research.

    Science.gov (United States)

    Wang, Nanyi; Wang, Lirong; Xie, Xiang-Qun

    2017-11-27

    Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.

  15. Open source drug discovery in practice: a case study.

    Science.gov (United States)

    Årdal, Christine; Røttingen, John-Arne

    2012-01-01

    Open source drug discovery offers potential for developing new and inexpensive drugs to combat diseases that disproportionally affect the poor. The concept borrows two principle aspects from open source computing (i.e., collaboration and open access) and applies them to pharmaceutical innovation. By opening a project to external contributors, its research capacity may increase significantly. To date there are only a handful of open source R&D projects focusing on neglected diseases. We wanted to learn from these first movers, their successes and failures, in order to generate a better understanding of how a much-discussed theoretical concept works in practice and may be implemented. A descriptive case study was performed, evaluating two specific R&D projects focused on neglected diseases. CSIR Team India Consortium's Open Source Drug Discovery project (CSIR OSDD) and The Synaptic Leap's Schistosomiasis project (TSLS). Data were gathered from four sources: interviews of participating members (n = 14), a survey of potential members (n = 61), an analysis of the websites and a literature review. Both cases have made significant achievements; however, they have done so in very different ways. CSIR OSDD encourages international collaboration, but its process facilitates contributions from mostly Indian researchers and students. Its processes are formal with each task being reviewed by a mentor (almost always offline) before a result is made public. TSLS, on the other hand, has attracted contributors internationally, albeit significantly fewer than CSIR OSDD. Both have obtained funding used to pay for access to facilities, physical resources and, at times, labor costs. TSLS releases its results into the public domain, whereas CSIR OSDD asserts ownership over its results. Technically TSLS is an open source project, whereas CSIR OSDD is a crowdsourced project. However, both have enabled high quality research at low cost. The critical success factors appear to be clearly

  16. Nanodiamond-Based Composite Structures for Biomedical Imaging and Drug Delivery.

    Science.gov (United States)

    Rosenholm, Jessica M; Vlasov, Igor I; Burikov, Sergey A; Dolenko, Tatiana A; Shenderova, Olga A

    2015-02-01

    Nanodiamond particles are widely recognized candidates for biomedical applications due to their excellent biocompatibility, bright photoluminescence based on color centers and outstanding photostability. Recently, more complex architectures with a nanodiamond core and an external shell or nanostructure which provides synergistic benefits have been developed, and their feasibility for biomedical applications has been demonstrated. This review is aimed at summarizing recent achievements in the fabrication and functional demonstrations of nanodiamond-based composite structures, along with critical considerations that should be taken into account in the design of such structures from a biomedical point of view. A particular focus of the review is core/shell structures of nanodiamond surrounded by porous silica shells, which demonstrate a remarkable increase in drug loading efficiency; as well as nanodiamonds decorated with carbon dots, which have excellent potential as bioimaging probes. Other combinations are also considered, relying on the discussed inherent properties of the inorganic materials being integrated in a way to advance inorganic nanomedicine in the quest for better health-related nanotechnology.

  17. Therapeutics discovery: From bench to first in-human trials.

    Science.gov (United States)

    Al-Hujaily, Ensaf M; Khatlani, Tanvir; Alehaideb, Zeyad; Ali, Rizwan; Almuzaini, Bader; Alrfaei, Bahauddeen M; Iqbal, Jahangir; Islam, Imadul; Malik, Shuja; Marwani, Bader A; Massadeh, Salam; Nehdi, Atef; Alsomaie, Barrak; Debasi, Bader; Bushnak, Ibraheem; Noibi, Saeed; Hussain, Syed; Wajid, Wahid Abdul; Armand, Jean-Pierre; Gul, Sheraz; Oyarzabal, Julen; Rais, Rana; Bountra, Chas; Alaskar, Ahmed; Knawy, Bander Al; Boudjelal, Mohamed

    2018-03-01

    The 'Therapeutics discovery: From bench to first in-human trials' conference, held at the King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard Health Affairs (MNGHA), Kingdom of Saudi Arabia (KSA) from October 10-12, 2017, provided a unique opportunity for experts worldwide to discuss advances in drug discovery and development, focusing on phase I clinical trials. It was the first event of its kind to be hosted at the new research center, which was constructed to boost drug discovery and development in the KSA in collaboration with institutions, such as the Academic Drug Discovery Consortium in the United States of America (USA), Structural Genomics Consortium of the University of Oxford in the United Kingdom (UK), and Institute of Materia Medica of the Chinese Academy of Medical Sciences in China. The program was divided into two parts. A pre-symposium day took place on October 10, during which courses were conducted on clinical trials, preclinical drug discovery, molecular biology and nanofiber research. The attendees had the opportunity for one-to-one meetings with international experts to exchange information and foster collaborations. In the second part of the conference, which took place on October 11 and 12, the clinical trials pipeline, design and recruitment of volunteers, and economic impact of clinical trials were discussed. The Saudi Food and Drug Administration presented the regulations governing clinical trials in the KSA. The process of preclinical drug discovery from small molecules, cellular and immunologic therapies, and approaches to identifying new targets were also presented. The recommendation of the conference was that researchers in the KSA must invest more fund, talents and infrastructure to lead the region in phase I clinical trials and preclinical drug discovery. Diseases affecting the local population, such as Middle East Respiratory Syndrome and resistant bacterial infections, represent the optimal

  18. The Open Form Inducer Approach for Structure-Based Drug Design.

    Directory of Open Access Journals (Sweden)

    Daniel Ken Inaoka

    Full Text Available Many open form (OF structures of drug targets were obtained a posteriori by analysis of co-crystals with inhibitors. Therefore, obtaining the OF structure of a drug target a priori will accelerate development of potent inhibitors. In addition to its small active site, Trypanosoma cruzi dihydroorotate dehydrogenase (TcDHODH is fully functional in its monomeric form, making drug design approaches targeting the active site and protein-protein interactions unrealistic. Therefore, a novel a priori approach was developed to determination the TcDHODH active site in OF. This approach consists of generating an "OF inducer" (predicted in silico to bind the target and cause steric repulsion with flexible regions proximal to the active site that force it open. We provide the first proof-of-concept of this approach by predicting and crystallizing TcDHODH in complex with an OF inducer, thereby obtaining the OF a priori with its subsequent use in designing potent and selective inhibitors. Fourteen co-crystal structures of TcDHODH with the designed inhibitors are presented herein. This approach has potential to encourage drug design against diseases where the molecular targets are such difficult proteins possessing small AS volume. This approach can be extended to study open/close conformation of proteins in general, the identification of allosteric pockets and inhibitors for other drug targets where conventional drug design approaches are not applicable, as well as the effective exploitation of the increasing number of protein structures deposited in Protein Data Bank.

  19. Open Science Meets Stem Cells: A New Drug Discovery Approach for Neurodegenerative Disorders.

    Science.gov (United States)

    Han, Chanshuai; Chaineau, Mathilde; Chen, Carol X-Q; Beitel, Lenore K; Durcan, Thomas M

    2018-01-01

    Neurodegenerative diseases are a challenge for drug discovery, as the biological mechanisms are complex and poorly understood, with a paucity of models that faithfully recapitulate these disorders. Recent advances in stem cell technology have provided a paradigm shift, providing researchers with tools to generate human induced pluripotent stem cells (iPSCs) from patient cells. With the potential to generate any human cell type, we can now generate human neurons and develop "first-of-their-kind" disease-relevant assays for small molecule screening. Now that the tools are in place, it is imperative that we accelerate discoveries from the bench to the clinic. Using traditional closed-door research systems raises barriers to discovery, by restricting access to cells, data and other research findings. Thus, a new strategy is required, and the Montreal Neurological Institute (MNI) and its partners are piloting an "Open Science" model. One signature initiative will be that the MNI biorepository will curate and disseminate patient samples in a more accessible manner through open transfer agreements. This feeds into the MNI open drug discovery platform, focused on developing industry-standard assays with iPSC-derived neurons. All cell lines, reagents and assay findings developed in this open fashion will be made available to academia and industry. By removing the obstacles many universities and companies face in distributing patient samples and assay results, our goal is to accelerate translational medical research and the development of new therapies for devastating neurodegenerative disorders.

  20. Open Science Meets Stem Cells: A New Drug Discovery Approach for Neurodegenerative Disorders

    Directory of Open Access Journals (Sweden)

    Chanshuai Han

    2018-02-01

    Full Text Available Neurodegenerative diseases are a challenge for drug discovery, as the biological mechanisms are complex and poorly understood, with a paucity of models that faithfully recapitulate these disorders. Recent advances in stem cell technology have provided a paradigm shift, providing researchers with tools to generate human induced pluripotent stem cells (iPSCs from patient cells. With the potential to generate any human cell type, we can now generate human neurons and develop “first-of-their-kind” disease-relevant assays for small molecule screening. Now that the tools are in place, it is imperative that we accelerate discoveries from the bench to the clinic. Using traditional closed-door research systems raises barriers to discovery, by restricting access to cells, data and other research findings. Thus, a new strategy is required, and the Montreal Neurological Institute (MNI and its partners are piloting an “Open Science” model. One signature initiative will be that the MNI biorepository will curate and disseminate patient samples in a more accessible manner through open transfer agreements. This feeds into the MNI open drug discovery platform, focused on developing industry-standard assays with iPSC-derived neurons. All cell lines, reagents and assay findings developed in this open fashion will be made available to academia and industry. By removing the obstacles many universities and companies face in distributing patient samples and assay results, our goal is to accelerate translational medical research and the development of new therapies for devastating neurodegenerative disorders.

  1. The discovery of the structure of DNA

    Science.gov (United States)

    Squires, G. L.

    2003-04-01

    On 25 April 1953, Nature published a letter by Francis Crick and James Watson, at the Cavendish Laboratory, Cambridge, proposing a structure for DNA. This letter marked the beginning of a revolution in biology. Besides Crick and Watson, two other scientists, Rosalind Franklin and Maurice Wilkins, played key roles in the discovery. After sketching the early careers of the four scientists, the present article gives an account of the physics and chemistry involved in the discovery, and the events leading up to it.

  2. Pharmacological screening technologies for venom peptide discovery.

    Science.gov (United States)

    Prashanth, Jutty Rajan; Hasaballah, Nojod; Vetter, Irina

    2017-12-01

    Venomous animals occupy one of the most successful evolutionary niches and occur on nearly every continent. They deliver venoms via biting and stinging apparatuses with the aim to rapidly incapacitate prey and deter predators. This has led to the evolution of venom components that act at a number of biological targets - including ion channels, G-protein coupled receptors, transporters and enzymes - with exquisite selectivity and potency, making venom-derived components attractive pharmacological tool compounds and drug leads. In recent years, plate-based pharmacological screening approaches have been introduced to accelerate venom-derived drug discovery. A range of assays are amenable to this purpose, including high-throughput electrophysiology, fluorescence-based functional and binding assays. However, despite these technological advances, the traditional activity-guided fractionation approach is time-consuming and resource-intensive. The combination of screening techniques suitable for miniaturization with sequence-based discovery approaches - supported by advanced proteomics, mass spectrometry, chromatography as well as synthesis and expression techniques - promises to further improve venom peptide discovery. Here, we discuss practical aspects of establishing a pipeline for venom peptide drug discovery with a particular emphasis on pharmacology and pharmacological screening approaches. This article is part of the Special Issue entitled 'Venom-derived Peptides as Pharmacological Tools.' Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Structure-based assessment of disease-related mutations in human voltage-gated sodium channels

    Directory of Open Access Journals (Sweden)

    Weiyun Huang

    2017-02-01

    Full Text Available ABSTRACT Voltage-gated sodium (Nav channels are essential for the rapid upstroke of action potentials and the propagation of electrical signals in nerves and muscles. Defects of Nav channels are associated with a variety of channelopathies. More than 1000 disease-related mutations have been identified in Nav channels, with Nav1.1 and Nav1.5 each harboring more than 400 mutations. Nav channels represent major targets for a wide array of neurotoxins and drugs. Atomic structures of Nav channels are required to understand their function and disease mechanisms. The recently determined atomic structure of the rabbit voltage-gated calcium (Cav channel Cav1.1 provides a template for homology-based structural modeling of the evolutionarily related Nav channels. In this Resource article, we summarized all the reported disease-related mutations in human Nav channels, generated a homologous model of human Nav1.7, and structurally mapped disease-associated mutations. Before the determination of structures of human Nav channels, the analysis presented here serves as the base framework for mechanistic investigation of Nav channelopathies and for potential structure-based drug discovery.

  4. Structure-based assessment of disease-related mutations in human voltage-gated sodium channels.

    Science.gov (United States)

    Huang, Weiyun; Liu, Minhao; Yan, S Frank; Yan, Nieng

    2017-06-01

    Voltage-gated sodium (Na v ) channels are essential for the rapid upstroke of action potentials and the propagation of electrical signals in nerves and muscles. Defects of Na v channels are associated with a variety of channelopathies. More than 1000 disease-related mutations have been identified in Na v channels, with Na v 1.1 and Na v 1.5 each harboring more than 400 mutations. Na v channels represent major targets for a wide array of neurotoxins and drugs. Atomic structures of Na v channels are required to understand their function and disease mechanisms. The recently determined atomic structure of the rabbit voltage-gated calcium (Ca v ) channel Ca v 1.1 provides a template for homology-based structural modeling of the evolutionarily related Na v channels. In this Resource article, we summarized all the reported disease-related mutations in human Na v channels, generated a homologous model of human Na v 1.7, and structurally mapped disease-associated mutations. Before the determination of structures of human Na v channels, the analysis presented here serves as the base framework for mechanistic investigation of Na v channelopathies and for potential structure-based drug discovery.

  5. Bigger Data, Collaborative Tools and the Future of Predictive Drug Discovery

    Science.gov (United States)

    Clark, Alex M.; Swamidass, S. Joshua; Litterman, Nadia; Williams, Antony J.

    2014-01-01

    Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas. PMID:24943138

  6. Feature Issue Introduction: Bio-Optics in Clinical Applications, Nanotechnology, and Drug Discovery

    OpenAIRE

    Nordstrom, Robert J.; Almutairi, Adah; Hillman, Elizabeth M.C.

    2010-01-01

    The editors introduce the Biomedical Optics Express feature issue, “Bio-Optics in Clinical Applications, Nanotechnology, and Drug Discovery,” which combines three technical areas from the 2010 Optical Society of America (OSA), Biomedical Optics (BIOMED) Topical Meeting held on 11–14 April in Miami, FL and includes contributions from conference attendees.

  7. Functional principles of registry-based service discovery

    NARCIS (Netherlands)

    Sundramoorthy, V.; Tan, C.; Hartel, P.H.; Hartog, den J.I.; Scholten, J.

    2005-01-01

    As Service Discovery Protocols (SDP) are becoming increasingly important for ubiquitous computing, they must behave according to predefined principles. We present the functional Principles of Service Discovery for robust, registry-based service discovery. A methodology to guarantee adherence to

  8. Software-aided approach to investigate peptide structure and metabolic susceptibility of amide bonds in peptide drugs based on high resolution mass spectrometry.

    Directory of Open Access Journals (Sweden)

    Tatiana Radchenko

    Full Text Available Interest in using peptide molecules as therapeutic agents due to high selectivity and efficacy is increasing within the pharmaceutical industry. However, most peptide-derived drugs cannot be administered orally because of low bioavailability and instability in the gastrointestinal tract due to protease activity. Therefore, structural modifications peptides are required to improve their stability. For this purpose, several in-silico software tools have been developed such as PeptideCutter or PoPS, which aim to predict peptide cleavage sites for different proteases. Moreover, several databases exist where this information is collected and stored from public sources such as MEROPS and ExPASy ENZYME databases. These tools can help design a peptide drug with increased stability against proteolysis, though they are limited to natural amino acids or cannot process cyclic peptides, for example. We worked to develop a new methodology to analyze peptide structure and amide bond metabolic stability based on the peptide structure (linear/cyclic, natural/unnatural amino acids. This approach used liquid chromatography / high resolution, mass spectrometry to obtain the analytical data from in vitro incubations. We collected experimental data for a set (linear/cyclic, natural/unnatural amino acids of fourteen peptide drugs and four substrate peptides incubated with different proteolytic media: trypsin, chymotrypsin, pepsin, pancreatic elastase, dipeptidyl peptidase-4 and neprilysin. Mass spectrometry data was analyzed to find metabolites and determine their structures, then all the results were stored in a chemically aware manner, which allows us to compute the peptide bond susceptibility by using a frequency analysis of the metabolic-liable bonds. In total 132 metabolites were found from the various in vitro conditions tested resulting in 77 distinct cleavage sites. The most frequent observed cleavage sites agreed with those reported in the literature. The

  9. Microbial P450 Enzymes in Bioremediation and Drug Discovery: Emerging Potentials and Challenges.

    Science.gov (United States)

    Bhattacharya, Sukanta S; Yadav, Jagjit S

    2018-01-01

    Cytochrome P450 enzymes are a structurally conserved but functionally diverse group of heme-containing mixed function oxidases found across both prokaryotic and eukaryotic forms of the microbial world. Microbial P450s are known to perform diverse functions ranging from the synthesis of cell wall components to xenobiotic/drug metabolism to biodegradation of environmental chemicals. Conventionally, many microbial systems have been reported to mimic mammalian P450-like activation of drugs and were proposed as the in-vitro models of mammalian drug metabolism. Recent reports suggest that native or engineered forms of specific microbial P450s from these and other microbial systems could be employed for desired specific biotransformation reactions toward natural and synthetic (drug) compounds underscoring their emerging potential in drug improvement and discovery. On the other hand, microorganisms particularly fungi and actinomycetes have been shown to possess catabolic P450s with unusual potential to degrade toxic environmental chemicals including persistent organic pollutants (POPs). Wood-rotting basidiomycete fungi in particular have revealed the presence of exceptionally large P450 repertoire (P450ome) in their genomes, majority of which are however orphan (with no known function). Our pre- and post-genomic studies have led to functional characterization of several fungal P450s inducible in response to exposure to several environmental toxicants and demonstration of their potential in bioremediation of these chemicals. This review is an attempt to summarize the postgenomic unveiling of this versatile enzyme superfamily in microbial systems and investigation of their potential to synthesize new drugs and degrade persistent pollutants, among other biotechnological applications. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  10. Drug discovery for male subfertility using high-throughput screening: a new approach to an unsolved problem.

    Science.gov (United States)

    Martins da Silva, Sarah J; Brown, Sean G; Sutton, Keith; King, Louise V; Ruso, Halil; Gray, David W; Wyatt, Paul G; Kelly, Mark C; Barratt, Christopher L R; Hope, Anthony G

    2017-05-01

    Can pharma drug discovery approaches be utilized to transform investigation into novel therapeutics for male infertility? High-throughput screening (HTS) is a viable approach to much-needed drug discovery for male factor infertility. There is both huge demand and a genuine clinical need for new treatment options for infertile men. However, the time, effort and resources required for drug discovery are currently exorbitant, due to the unique challenges of the cellular, physical and functional properties of human spermatozoa and a lack of appropriate assay platform. Spermatozoa were obtained from healthy volunteer research donors and subfertile patients undergoing IVF/ICSI at a hospital-assisted reproductive techniques clinic between January 2012 and November 2016. A HTS assay was developed and validated using intracellular calcium ([Ca2+]i) as a surrogate for motility in human spermatozoa. Calcium fluorescence was detected using a Flexstation microplate reader (384-well platform) and compared with responses evoked by progesterone, a compound known to modify a number of biologically relevant behaviours in human spermatozoa. Hit compounds identified following single point drug screen (10 μM) of an ion channel-focussed library assembled by the University of Dundee Drug Discovery Unit were rescreened to ensure potency using standard 10 point half-logarithm concentration curves, and tested for purity and integrity using liquid chromatography and mass spectrometry. Hit compounds were grouped by structure activity relationships and five representative compounds then further investigated for direct effects on spermatozoa, using computer-assisted sperm assessment, sperm penetration assay and whole-cell patch clamping. Of the 3242 ion channel library ligands screened, 384 compounds (11.8%) elicited a statistically significant increase in calcium fluorescence, with greater than 3× median absolute deviation above the baseline. Seventy-four compounds eliciting ≥50% increase

  11. Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database

    Science.gov (United States)

    2012-01-01

    Herein we review our recent efforts in searching for bioactive ligands by enumeration and virtual screening of the unknown chemical space of small molecules. Enumeration from first principles shows that almost all small molecules (>99.9%) have never been synthesized and are still available to be prepared and tested. We discuss open access sources of molecules, the classification and representation of chemical space using molecular quantum numbers (MQN), its exhaustive enumeration in form of the chemical universe generated databases (GDB), and examples of using these databases for prospective drug discovery. MQN-searchable GDB, PubChem, and DrugBank are freely accessible at www.gdb.unibe.ch. PMID:23019491

  12. Alignment-independent comparison of binding sites based on DrugScore potential fields encoded by 3D Zernike descriptors.

    Science.gov (United States)

    Nisius, Britta; Gohlke, Holger

    2012-09-24

    Analyzing protein binding sites provides detailed insights into the biological processes proteins are involved in, e.g., into drug-target interactions, and so is of crucial importance in drug discovery. Herein, we present novel alignment-independent binding site descriptors based on DrugScore potential fields. The potential fields are transformed to a set of information-rich descriptors using a series expansion in 3D Zernike polynomials. The resulting Zernike descriptors show a promising performance in detecting similarities among proteins with low pairwise sequence identities that bind identical ligands, as well as within subfamilies of one target class. Furthermore, the Zernike descriptors are robust against structural variations among protein binding sites. Finally, the Zernike descriptors show a high data compression power, and computing similarities between binding sites based on these descriptors is highly efficient. Consequently, the Zernike descriptors are a useful tool for computational binding site analysis, e.g., to predict the function of novel proteins, off-targets for drug candidates, or novel targets for known drugs.

  13. Discovery of potent inhibitors of soluble epoxide hydrolase by combinatorial library design and structure-based virtual screening.

    Science.gov (United States)

    Xing, Li; McDonald, Joseph J; Kolodziej, Steve A; Kurumbail, Ravi G; Williams, Jennifer M; Warren, Chad J; O'Neal, Janet M; Skepner, Jill E; Roberds, Steven L

    2011-03-10

    Structure-based virtual screening was applied to design combinatorial libraries to discover novel and potent soluble epoxide hydrolase (sEH) inhibitors. X-ray crystal structures revealed unique interactions for a benzoxazole template in addition to the conserved hydrogen bonds with the catalytic machinery of sEH. By exploitation of the favorable binding elements, two iterations of library design based on amide coupling were employed, guided principally by the docking results of the enumerated virtual products. Biological screening of the libraries demonstrated as high as 90% hit rate, of which over two dozen compounds were single digit nanomolar sEH inhibitors by IC(50) determination. In total the library design and synthesis produced more than 300 submicromolar sEH inhibitors. In cellular systems consistent activities were demonstrated with biochemical measurements. The SAR understanding of the benzoxazole template provides valuable insights into discovery of novel sEH inhibitors as therapeutic agents.

  14. High-throughput screening platform for natural product-based drug discovery against 3 neglected tropical diseases: human African trypanosomiasis, leishmaniasis, and Chagas disease.

    Science.gov (United States)

    Annang, F; Pérez-Moreno, G; García-Hernández, R; Cordon-Obras, C; Martín, J; Tormo, J R; Rodríguez, L; de Pedro, N; Gómez-Pérez, V; Valente, M; Reyes, F; Genilloud, O; Vicente, F; Castanys, S; Ruiz-Pérez, L M; Navarro, M; Gamarro, F; González-Pacanowska, D

    2015-01-01

    African trypanosomiasis, leishmaniasis, and Chagas disease are 3 neglected tropical diseases for which current therapeutic interventions are inadequate or toxic. There is an urgent need to find new lead compounds against these diseases. Most drug discovery strategies rely on high-throughput screening (HTS) of synthetic chemical libraries using phenotypic and target-based approaches. Combinatorial chemistry libraries contain hundreds of thousands of compounds; however, they lack the structural diversity required to find entirely novel chemotypes. Natural products, in contrast, are a highly underexplored pool of unique chemical diversity that can serve as excellent templates for the synthesis of novel, biologically active molecules. We report here a validated HTS platform for the screening of microbial extracts against the 3 diseases. We have used this platform in a pilot project to screen a subset (5976) of microbial extracts from the MEDINA Natural Products library. Tandem liquid chromatography-mass spectrometry showed that 48 extracts contain potentially new compounds that are currently undergoing de-replication for future isolation and characterization. Known active components included actinomycin D, bafilomycin B1, chromomycin A3, echinomycin, hygrolidin, and nonactins, among others. The report here is, to our knowledge, the first HTS of microbial natural product extracts against the above-mentioned kinetoplastid parasites. © 2014 Society for Laboratory Automation and Screening.

  15. Computational prediction of drug-drug interactions based on drugs functional similarities.

    Science.gov (United States)

    Ferdousi, Reza; Safdari, Reza; Omidi, Yadollah

    2017-06-01

    Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs. Copyright © 2017. Published by Elsevier Inc.

  16. A collaborative filtering-based approach to biomedical knowledge discovery.

    Science.gov (United States)

    Lever, Jake; Gakkhar, Sitanshu; Gottlieb, Michael; Rashnavadi, Tahereh; Lin, Santina; Siu, Celia; Smith, Maia; Jones, Martin R; Krzywinski, Martin; Jones, Steven J M; Wren, Jonathan

    2018-02-15

    The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers. We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations. All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery. sjones@bcgsc.ca. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  17. Computer-Aided Drug Design in Epigenetics

    Science.gov (United States)

    Lu, Wenchao; Zhang, Rukang; Jiang, Hao; Zhang, Huimin; Luo, Cheng

    2018-03-01

    Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.

  18. Computer-Aided Drug Design in Epigenetics

    Science.gov (United States)

    Lu, Wenchao; Zhang, Rukang; Jiang, Hao; Zhang, Huimin; Luo, Cheng

    2018-01-01

    Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field. PMID:29594101

  19. Overcoming the challenges of drug discovery for neglected tropical diseases: the A·WOL experience.

    Science.gov (United States)

    Johnston, Kelly L; Ford, Louise; Taylor, Mark J

    2014-03-01

    Neglected tropical diseases (NTDs) are a group of 17 diseases that typically affect poor people in tropical countries. Each has been neglected for decades in terms of funding, research, and policy, but the recent grouping of them into one unit, which can be targeted using integrated control measures, together with increased advocacy has helped to place them on the global health agenda. The World Health Organization has set ambitious goals to control or eliminate 10 NTDs by 2020 and launched a roadmap in January 2012 to guide this global plan. The result of the launch meeting, which brought together representatives from the pharmaceutical industry, donors, and politicians, was the London Declaration: a series of commitments to provide more drugs, research, and funds to achieve the 2020 goals. Drug discovery and development for these diseases are extremely challenging, and this article highlights these challenges in the context of the London Declaration, before focusing on an example of a drug discovery and development program for the NTDs onchocerciasis and lymphatic filariasis (the anti-Wolbachia consortium, A·WOL).

  20. Predicting drug?drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge

    OpenAIRE

    Takeda, Takako; Hao, Ming; Cheng, Tiejun; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    Drug?drug interactions (DDIs) may lead to adverse effects and potentially result in drug withdrawal from the market. Predicting DDIs during drug development would help reduce development costs and time by rigorous evaluation of drug candidates. The primary mechanisms of DDIs are based on pharmacokinetics (PK) and pharmacodynamics (PD). This study examines the effects of 2D structural similarities of drugs on DDI prediction through interaction networks including both PD and PK knowledge. Our a...

  1. Drugability of extracellular targets: discovery of small molecule drugs targeting allosteric, functional, and subunit-selective sites on GPCRs and ion channels.

    Science.gov (United States)

    Grigoriadis, Dimitri E; Hoare, Samuel R J; Lechner, Sandra M; Slee, Deborah H; Williams, John A

    2009-01-01

    Beginning with the discovery of the structure of deoxyribose nucleic acid in 1953, by James Watson and Francis Crick, the sequencing of the entire human genome some 50 years later, has begun to quantify the classes and types of proteins that may have relevance to human disease with the promise of rapidly identifying compounds that can modulate these proteins so as to have a beneficial and therapeutic outcome. This so called 'drugable space' involves a variety of membrane-bound proteins including the superfamily of G-protein-coupled receptors (GPCRs), ion channels, and transporters among others. The recent number of novel therapeutics targeting membrane-bound extracellular proteins that have reached the market in the past 20 years however pales in magnitude when compared, during the same timeframe, to the advancements made in the technologies available to aid in the discovery of these novel therapeutics. This review will consider select examples of extracellular drugable targets and focus on the GPCRs and ion channels highlighting the corticotropin releasing factor (CRF) type 1 and gamma-aminobutyric acid receptors, and the Ca(V)2.2 voltage-gated ion channel. These examples will elaborate current technological advancements in drug discovery and provide a prospective framework for future drug development.

  2. Pharmacogenetics in diverse ethnic populations--implications for drug discovery and development.

    Science.gov (United States)

    McCarthy, Linda C; Davies, Kirstie J; Campbell, David A

    2002-07-01

    It is widely acknowledged that the vast quantities of data now publicly available as a result of the human genome initiative have the potential to revolutionize the pharmaceutical industry. More tangibly to the drug development business, the dawn of the pharmacogenetics era has the potential to impact not only the discovery of new medicines but also the safety and efficacy of pharmaceutical agents. Coincident with these scientific advances is the emergence of new markets for pharmaceutical agents. Japan, which represents the world's second biggest market, is a good example. With the ICH E5 agreement in 1998 and a rapid change in the drug registration process in Japan, there are increasing opportunities to improve access to more medicines in all parts of the world. However, it is increasingly clear that significant genetic variation still exists between populations, with a host of data on interethnic variation in drug metabolizing enzyme and drug transporter activity. Evidence suggesting that this genetic variation may play an important role in defining some of the interethnic variation in drug response to currently marketed compounds is reviewed here, and future possibilities of using such information to better streamline the drug development process are discussed.

  3. Discovery and structure-activity relationships study of thieno[2,3-b]pyridine analogues as hepatic gluconeogenesis inhibitors.

    Science.gov (United States)

    Ma, Fei; Liu, Jian; Zhou, Tingting; Lei, Min; Chen, Jing; Wang, Xiachang; Zhang, Yinan; Shen, Xu; Hu, Lihong

    2018-05-25

    Type 2 diabetes mellitus (T2DM) is a chronic, complex and multifactorial metabolic disorder, and targeting gluconeogenesis inhibition is a promising strategy for anti-diabetic drug discovery. This study discovered a new class of thieno[2,3-b]pyridine derivatives as hepatic gluconeogenesis inhibitors. First, a hit compound (DMT: IC 50  = 33.8 μM) characterized by a thienopyridine core was identified in a cell-based screening of our privileged small molecule library. Structure activity relationships (SARs) study showed that replaced the CF 3 in the thienopyridine core could improve the potency and led to the discovery of 8e (IC 50  = 16.8 μM) and 9d (IC 50  = 12.3 μM) with potent inhibition of hepatic glucose production and good drug-like properties. Furthermore, the mechanism of 8e for the inhibition of hepatic glucose production was also identified, which could be effective through the reductive expression of the mRNA transcription level of gluconeogenic genes, including glucose-6-phosphatase (G6Pase) and hepatic phosphoenolpyruvate carboxykinase (PEPCK). Additionally, 8e could also reduce the fasting blood glucose and improve the oral glucose tolerance and pyruvate tolerance in db/db mice. The optimization of this class of derivatives had provided us a start point to develop new anti-hepatic gluconeogenesis agents. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

  4. Stem cells: a model for screening, discovery and development of drugs

    OpenAIRE

    Kitambi, Satish Srinivas; Chandrasekar, Gayathri

    2011-01-01

    Satish Srinivas Kitambi1, Gayathri Chandrasekar21Department of Medical Biochemistry and Biophysics; 2Department of Biosciences, Karolinska Institutet, Stockholm, SwedenAbstract: The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficac...

  5. Designing an intuitive web application for drug discovery scientists.

    Science.gov (United States)

    Karamanis, Nikiforos; Pignatelli, Miguel; Carvalho-Silva, Denise; Rowland, Francis; Cham, Jennifer A; Dunham, Ian

    2018-01-11

    We discuss how we designed the Open Targets Platform (www.targetvalidation.org), an intuitive application for bench scientists working in early drug discovery. To meet the needs of our users, we applied lean user experience (UX) design methods: we started engaging with users very early and carried out research, design and evaluation activities within an iterative development process. We also emphasize the collaborative nature of applying lean UX design, which we believe is a foundation for success in this and many other scientific projects. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  6. Drug Discovery of Host CLK1 Inhibitors for Influenza Treatment

    Directory of Open Access Journals (Sweden)

    Mian Zu

    2015-11-01

    Full Text Available The rapid evolution of influenza virus makes antiviral drugs less effective, which is considered to be a major bottleneck in antiviral therapy. The key proteins in the host cells, which are related with the replication cycle of influenza virus, are regarded as potential drug targets due to their distinct advantage of lack of evolution and drug resistance. Cdc2-like kinase 1 (CLK1 in the host cells is responsible for alternative splicing of the M2 gene of influenza virus during influenza infection and replication. In this study, we carried out baculovirus-mediated expression and purification of CLK1 and established a reliable screening assay for CLK1 inhibitors. After a virtual screening of CLK1 inhibitors was performed, the activities of the selected compounds were evaluated. Finally, several compounds with strong inhibitory activity against CLK1 were discovered and their in vitro anti-influenza virus activities were validated using a cytopathic effect (CPE reduction assay. The assay results showed that clypearin, corilagin, and pinosylvine were the most potential anti-influenza virus compounds as CLK1 inhibitors among the compounds tested. These findings will provide important information for new drug design and development in influenza treatment, and CLK1 may be a potent drug target for anti-influenza drug screening and discovery.

  7. Web-based drug repurposing tools: a survey.

    Science.gov (United States)

    Sam, Elizabeth; Athri, Prashanth

    2017-10-06

    Drug repurposing (a.k.a. drug repositioning) is the search for new indications or molecular targets distinct from a drug's putative activity, pharmacological effect or binding specificities. With the ever-increasing rates of termination of drugs in clinical trials, drug repositioning has risen as one of the effective solutions against the risk of drug failures. Repositioning finds a way to reverse the grim but real trend that Eroom's law portends for the pharmaceutical and biotech industry, and drug discovery in general. Further, the advent of high-throughput technologies to explore biological systems has enabled the generation of zeta bytes of data and a massive collection of databases that store them. Computational analytics and mining are frequently used as effective tools to explore this byzantine series of biological and biomedical data. However, advanced computational tools are often difficult to understand or use, thereby limiting their accessibility to scientists without a strong computational background. Hence it is of great importance to build user-friendly interfaces to extend the user-base beyond computational scientists, to include life scientists who may have deeper chemical and biological insights. This survey is focused on systematically presenting the available Web-based tools that aid in repositioning drugs. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  8. Therapeutic approaches to genetic ion channelopathies and perspectives in drug discovery

    Directory of Open Access Journals (Sweden)

    Paola eImbrici

    2016-05-01

    Full Text Available In the human genome more than 400 genes encode ion channels, which are transmembrane proteins mediating ion fluxes across membranes. Being expressed in all cell types, they are involved in almost all physiological processes, including sense perception, neurotransmission, muscle contraction, secretion, immune response, cell proliferation and differentiation. Due to the widespread tissue distribution of ion channels and their physiological functions, mutations in genes encoding ion channel subunits, or their interacting proteins, are responsible for inherited ion channelopathies. These diseases can range from common to very rare disorders and their severity can be mild, disabling, or life-threatening. In spite of this, ion channels are the primary target of only about 5% of the marketed drugs suggesting their potential in drug discovery. The current review summarizes the therapeutic management of the principal ion channelopathies of central and peripheral nervous system, heart, kidney, bone, skeletal muscle and pancreas, resulting from mutations in calcium, sodium, potassium and chloride ion channels. For most channelopathies the therapy is mainly empirical and symptomatic, often limited by lack of efficacy and tolerability for a significant number of patients. Other channelopathies can exploit ion channel targeted drugs, such as marketed sodium channel blockers. Developing new and more specific therapeutic approaches is therefore required. To this aim, a major advancement in the pharmacotherapy of channelopathies has been the discovery that ion channel mutations lead to change in biophysics that can in turn specifically modify the sensitivity to drugs: this opens the way to a pharmacogenetics strategy, allowing the development of a personalized therapy with increased efficacy and reduced side effects. In addition, the identification of disease modifiers in ion channelopathies appears an alternative strategy to discover novel druggable targets.

  9. Open Source Drug Discovery in Practice: A Case Study

    Science.gov (United States)

    Årdal, Christine; Røttingen, John-Arne

    2012-01-01

    Background Open source drug discovery offers potential for developing new and inexpensive drugs to combat diseases that disproportionally affect the poor. The concept borrows two principle aspects from open source computing (i.e., collaboration and open access) and applies them to pharmaceutical innovation. By opening a project to external contributors, its research capacity may increase significantly. To date there are only a handful of open source R&D projects focusing on neglected diseases. We wanted to learn from these first movers, their successes and failures, in order to generate a better understanding of how a much-discussed theoretical concept works in practice and may be implemented. Methodology/Principal Findings A descriptive case study was performed, evaluating two specific R&D projects focused on neglected diseases. CSIR Team India Consortium's Open Source Drug Discovery project (CSIR OSDD) and The Synaptic Leap's Schistosomiasis project (TSLS). Data were gathered from four sources: interviews of participating members (n = 14), a survey of potential members (n = 61), an analysis of the websites and a literature review. Both cases have made significant achievements; however, they have done so in very different ways. CSIR OSDD encourages international collaboration, but its process facilitates contributions from mostly Indian researchers and students. Its processes are formal with each task being reviewed by a mentor (almost always offline) before a result is made public. TSLS, on the other hand, has attracted contributors internationally, albeit significantly fewer than CSIR OSDD. Both have obtained funding used to pay for access to facilities, physical resources and, at times, labor costs. TSLS releases its results into the public domain, whereas CSIR OSDD asserts ownership over its results. Conclusions/Significance Technically TSLS is an open source project, whereas CSIR OSDD is a crowdsourced project. However, both have enabled high quality

  10. Optimizing Oral Bioavailability in Drug Discovery: An Overview of Design and Testing Strategies and Formulation Options.

    Science.gov (United States)

    Aungst, Bruce J

    2017-04-01

    For discovery teams working toward new, orally administered therapeutic agents, one requirement is to attain adequate systemic exposure after oral dosing, which is best accomplished when oral bioavailability is optimized. This report summarizes the bioavailability challenges currently faced in drug discovery, and the design and testing methods and strategies currently utilized to address the challenges. Profiling of discovery compounds usually includes separate assessments of solubility, permeability, and susceptibility to first-pass metabolism, which are the 3 most likely contributors to incomplete oral bioavailability. An initial assessment of absorption potential may be made computationally, and high throughput in vitro assays are typically performed to prioritize compounds for in vivo studies. The initial pharmacokinetic study is a critical decision point in compound evaluation, and the importance of the effect the dosing vehicle or formulation can have on oral bioavailability, especially for poorly water soluble compounds, is emphasized. Dosing vehicles and bioavailability-enabling formulations that can be used for discovery and preclinical studies are described. Optimizing oral bioavailability within a chemical series or for a lead compound requires identification of the barrier limiting bioavailability, and methods used for this purpose are outlined. Finally, a few key guidelines are offered for consideration when facing the challenges of optimizing oral bioavailability in drug discovery. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  11. Theory and Applications of Covalent Docking in Drug Discovery: Merits and Pitfalls

    Directory of Open Access Journals (Sweden)

    Hezekiel Mathambo Kumalo

    2015-01-01

    Full Text Available he present art of drug discovery and design of new drugs is based on suicidal irreversible inhibitors. Covalent inhibition is the strategy that is used to achieve irreversible inhibition. Irreversible inhibitors interact with their targets in a time-dependent fashion, and the reaction proceeds to completion rather than to equilibrium. Covalent inhibitors possessed some significant advantages over non-covalent inhibitors such as covalent warheads can target rare, non-conserved residue of a particular target protein and thus led to development of highly selective inhibitors, covalent inhibitors can be effective in targeting proteins with shallow binding cleavage which will led to development of novel inhibitors with increased potency than non-covalent inhibitors. Several computational approaches have been developed to simulate covalent interactions; however, this is still a challenging area to explore. Covalent molecular docking has been recently implemented in the computer-aided drug design workflows to describe covalent interactions between inhibitors and biological targets. In this review we highlight: (i covalent interactions in biomolecular systems; (ii the mathematical framework of covalent molecular docking; (iii implementation of covalent docking protocol in drug design workflows; (iv applications covalent docking: case studies and (v shortcomings and future perspectives of covalent docking. To the best of our knowledge; this review is the first account that highlights different aspects of covalent docking with its merits and pitfalls. We believe that the method and applications highlighted in this study will help future efforts towards the design of irreversible inhibitors.

  12. Open-access public-private partnerships to enable drug discovery--new approaches.

    Science.gov (United States)

    Müller, Susanne; Weigelt, Johan

    2010-03-01

    The productivity of the pharmaceutical industry, as assessed by the number of NMEs produced per US dollar spent in R&D, has been in steady decline during the past 40 years. This decline in productivity not only poses a significant challenge to the pharmaceutical industry, but also to society because of the importance of developing drugs for the treatment of unmet medical needs. The major challenge in progressing a new drug to the market is the successful completion of clinical trials. However, the failure rate of drugs entering trials has not decreased, despite various technological and scientific breakthroughs in recent decades, and despite intense target validation efforts. This lack of success suggests limitations in the fundamental understanding of target biology and human pharmacology. One contributing factor may be the traditional secrecy of the pharmaceutical sector, a characteristic that does not promote scientific discovery in an optimal manner. Access to broader knowledge relating to target biology and human pharmacology is difficult to obtain because interactions between researchers in industry and academia are typically restricted to closed collaborations in which the knowledge gained is confidential.However, open-access collaborative partnerships are gaining momentum in industry, and are also favored by funding agencies. Such open-access collaborations may be a powerful alternative to closed collaborations; the sharing of early-stage research data is expected to enable scientific discovery by engaging a broader section of the scientific community in the exploration of new findings. Potentially, the sharing of data could contribute to an increased understanding of biological processes and a decrease in the attrition of clinical programs.

  13. Computer-Aided Drug Design in Epigenetics

    Directory of Open Access Journals (Sweden)

    Wenchao Lu

    2018-03-01

    Full Text Available Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.

  14. Blood-brain barrier in vitro models as tools in drug discovery: assessment of the transport ranking of antihistaminic drugs.

    Science.gov (United States)

    Neuhaus, W; Mandikova, J; Pawlowitsch, R; Linz, B; Bennani-Baiti, B; Lauer, R; Lachmann, B; Noe, C R

    2012-05-01

    In the course of our validation program testing blood-brain barrier (BBB) in vitro models for their usability as tools in drug discovery it was evaluated whether an established Transwell model based on porcine cell line PBMEC/C1-2 was able to differentiate between the transport properties of first and second generation antihistaminic drugs. First generation antihistamines can permeate the BBB and act in the central nervous system (CNS), whereas entry to the CNS of second generation antihistamines is restricted by efflux pumps such as P-glycoprotein (P-gP) located in brain endothelial cells. P-gP functionality of PBMEC/C1-2 cells grown on Transwell filter inserts was proven by transport studies with P-gP substrate rhodamine 123 and P-gP blocker verapamil. Subsequent drug transport studies with the first generation antihistamines promethazine, diphenhydramine and pheniramine and the second generation antihistamines astemizole, ceterizine, fexofenadine and loratadine were accomplished in single substance as well as in group studies. Results were normalised to diazepam, an internal standard for the transcellular transport route. Moreover, effects after addition of P-gP inhibitor verapamil were investigated. First generation antihistamine pheniramine permeated as fastest followed by diphenhydramine, diazepam, promethazine and second generation antihistaminic drugs ceterizine, fexofenadine, astemizole and loratadine reflecting the BBB in vivo permeability ranking well. Verapamil increased the transport rates of all second generation antihistamines, which suggested involvement of P-gP during their permeation across the BBB model. The ranking after addition of verapamil was significantly changed, only fexofenadine and ceterizine penetrated slower than internal standard diazepam in the presence of verapamil. In summary, permeability data showed that the BBB model based on porcine cell line PBMEC/C1-2 was able to reflect the BBB in vivo situation for the transport of

  15. The impact of assay technology as applied to safety assessment in reducing compound attrition in drug discovery.

    Science.gov (United States)

    Thomas, Craig E; Will, Yvonne

    2012-02-01

    Attrition in the drug industry due to safety findings remains high and requires a shift in the current safety testing paradigm. Many companies are now positioning safety assessment at each stage of the drug development process, including discovery, where an early perspective on potential safety issues is sought, often at chemical scaffold level, using a variety of emerging technologies. Given the lengthy development time frames of drugs in the pharmaceutical industry, the authors believe that the impact of new technologies on attrition is best measured as a function of the quality and timeliness of candidate compounds entering development. The authors provide an overview of in silico and in vitro models, as well as more complex approaches such as 'omics,' and where they are best positioned within the drug discovery process. It is important to take away that not all technologies should be applied to all projects. Technologies vary widely in their validation state, throughput and cost. A thoughtful combination of validated and emerging technologies is crucial in identifying the most promising candidates to move to proof-of-concept testing in humans. In spite of the challenges inherent in applying new technologies to drug discovery, the successes and recognition that we cannot continue to rely on safety assessment practices used for decades have led to rather dramatic strategy shifts and fostered partnerships across government agencies and industry. We are optimistic that these efforts will ultimately benefit patients by delivering effective and safe medications in a timely fashion.

  16. CRITICAL ASSESSMENT OF CONTRIBUTION FROM INDIAN PUBLICATIONS: THE ROLE OF IN SILICO DESIGNING METHODS LEADING TO DRUGS OR DRUG-LIKE COMPOUNDS USING TEXT BASED MINING AND ASSOCIATION

    Directory of Open Access Journals (Sweden)

    Pawan Kumar

    2017-09-01

    Full Text Available Over the several decades, India is constantly challenged by communicable and non-communicable diseases which are originated either by poor lifestyle or by environmental factors. The pools of diseases are constantly posing serious threats to mankind especially among the poverty-stricken families. Scientific communities across the globe are working continuously to design drug molecules to overcome the burden of these life threaten diseases. In last three decades, many computational algorithms and tools have been developed to identify potential drug targets and their inhibitors. It is believed that computational techniques have reduced the time and money required to develop an inhibitor into drug. However, applicability and deliverability of these in silico techniques in rational drug designing are not fully evaluated. In the present study, PubMed/Medline extracted data driven analysis has been performed to highlight the influence and progress of the theoretical methods in the field of drug discovery across India and compared with the world. Drug discovery related keyword dictionary has been built and utilized to select only drug discovery related PubMed abstract. A second keyword set (related to bioinformatics tools is used for normalized pointwise mutual information (PMI based association analysis. Observations show that drug discovery has been an interdisciplinary research and used many tools starting with QSAR, docking, pharmacophore, Molecular Simulations etc. The publications contributed from India (2% are similar as compared to the contribution in total world publications, suggesting large scope in future. Data coverage as represented since 1990-2015 in PubMed as indicated by number of publications associated with drug discovery is almost same in world and India (~75%. Emerging institutes/Universities are contributing since last 10 years as observed from Indian publication list. However, this method has many limitations as discussed.

  17. Therapeutic Potential of Foldamers: From Chemical Biology Tools To Drug Candidates?

    Science.gov (United States)

    Gopalakrishnan, Ranganath; Frolov, Andrey I; Knerr, Laurent; Drury, William J; Valeur, Eric

    2016-11-10

    Over the past decade, foldamers have progressively emerged as useful architectures to mimic secondary structures of proteins. Peptidic foldamers, consisting of various amino acid based backbones, have been the most studied from a therapeutic perspective, while polyaromatic foldamers have barely evolved from their nascency and remain perplexing for medicinal chemists due to their poor drug-like nature. Despite these limitations, this compound class may still offer opportunities to study challenging targets or provide chemical biology tools. The potential of foldamer drug candidates reaching the clinic is still a stretch. Nevertheless, advances in the field have demonstrated their potential for the discovery of next generation therapeutics. In this perspective, the current knowledge of foldamers is reviewed in a drug discovery context. Recent advances in the early phases of drug discovery including hit finding, target validation, and optimization and molecular modeling are discussed. In addition, challenges and focus areas are debated and gaps highlighted.

  18. Zebrafish-Based Discovery of Antiseizure Compounds from the Red Sea: Pseurotin A2 and Azaspirofuran A.

    Science.gov (United States)

    Copmans, Daniëlle; Rateb, Mostafa; Tabudravu, Jioji N; Pérez-Bonilla, Mercedes; Dirkx, Nina; Vallorani, Riccardo; Diaz, Caridad; Pérez Del Palacio, José; Smith, Alan J; Ebel, Rainer; Reyes, Fernando; Jaspars, Marcel; de Witte, Peter A M

    2018-04-19

    In search for novel antiseizure drugs (ASDs), the European FP7-funded PharmaSea project used zebrafish embryos and larvae as a drug discovery platform to screen marine natural products to identify promising antiseizure hits in vivo for further development. Within the framework of this project, seven known heterospirocyclic γ-lactams, namely, pseurotin A, pseurotin A 2 , pseurotin F1, 11- O-methylpseurotin A, pseurotin D, azaspirofuran A, and azaspirofuran B, were isolated from the bioactive marine fungus Aspergillus fumigatus, and their antiseizure activity was evaluated in the larval zebrafish pentylenetetrazole (PTZ) seizure model. Pseurotin A 2 and azaspirofuran A were identified as antiseizure hits, while their close chemical analogues were inactive. Besides, electrophysiological analysis from the zebrafish midbrain demonstrated that pseurotin A 2 and azaspirofuran A also ameliorate PTZ-induced epileptiform discharges. Next, to determine whether these findings translate to mammalians, both compounds were analyzed in the mouse 6 Hz (44 mA) psychomotor seizure model. They lowered the seizure duration dose-dependently, thereby confirming their antiseizure properties and suggesting activity against drug-resistant seizures. Finally, in a thorough ADMET assessment, pseurotin A 2 and azaspirofuran A were found to be drug-like. Based on the prominent antiseizure activity in both species and the drug-likeness, we propose pseurotin A 2 and azaspirofuran A as lead compounds that are worth further investigation for the treatment of epileptic seizures. This study not only provides the first evidence of antiseizure activity of pseurotins and azaspirofurans, but also demonstrates the value of the zebrafish model in (marine) natural product drug discovery in general, and for ASD discovery in particular.

  19. Test systems in drug discovery for hazard identification and risk assessment of human drug-induced liver injury.

    Science.gov (United States)

    Weaver, Richard J; Betts, Catherine; Blomme, Eric A G; Gerets, Helga H J; Gjervig Jensen, Klaus; Hewitt, Philip G; Juhila, Satu; Labbe, Gilles; Liguori, Michael J; Mesens, Natalie; Ogese, Monday O; Persson, Mikael; Snoeys, Jan; Stevens, James L; Walker, Tracy; Park, B Kevin

    2017-07-01

    The liver is an important target for drug-induced toxicities. Early detection of hepatotoxic drugs requires use of well-characterized test systems, yet current knowledge, gaps and limitations of tests employed remains an important issue for drug development. Areas Covered: The current state of the science, understanding and application of test systems in use for the detection of drug-induced cytotoxicity, mitochondrial toxicity, cholestasis and inflammation is summarized. The test systems highlighted herein cover mostly in vitro and some in vivo models and endpoint measurements used in the assessment of small molecule toxic liabilities. Opportunities for research efforts in areas necessitating the development of specific tests and improved mechanistic understanding are highlighted. Expert Opinion: Use of in vitro test systems for safety optimization will remain a core activity in drug discovery. Substantial inroads have been made with a number of assays established for human Drug-induced Liver Injury. There nevertheless remain significant gaps with a need for improved in vitro tools and novel tests to address specific mechanisms of human Drug-Induced Liver Injury. Progress in these areas will necessitate not only models fit for application, but also mechanistic understanding of how chemical insult on the liver occurs in order to identify translational and quantifiable readouts for decision-making.

  20. Marine Microorganism-Invertebrate Assemblages: Perspectives to Solve the “Supply Problem” in the Initial Steps of Drug Discovery

    Science.gov (United States)

    Leal, Miguel Costa; Sheridan, Christopher; Osinga, Ronald; Dionísio, Gisela; Rocha, Rui Jorge Miranda; Silva, Bruna; Rosa, Rui; Calado, Ricardo

    2014-01-01

    The chemical diversity associated with marine natural products (MNP) is unanimously acknowledged as the “blue gold” in the urgent quest for new drugs. Consequently, a significant increase in the discovery of MNP published in the literature has been observed in the past decades, particularly from marine invertebrates. However, it remains unclear whether target metabolites originate from the marine invertebrates themselves or from their microbial symbionts. This issue underlines critical challenges associated with the lack of biomass required to supply the early stages of the drug discovery pipeline. The present review discusses potential solutions for such challenges, with particular emphasis on innovative approaches to culture invertebrate holobionts (microorganism-invertebrate assemblages) through in toto aquaculture, together with methods for the discovery and initial production of bioactive compounds from these microbial symbionts. PMID:24983638

  1. Marine Microorganism-Invertebrate Assemblages: Perspectives to Solve the “Supply Problem” in the Initial Steps of Drug Discovery

    Directory of Open Access Journals (Sweden)

    Miguel Costa Leal

    2014-06-01

    Full Text Available The chemical diversity associated with marine natural products (MNP is unanimously acknowledged as the “blue gold” in the urgent quest for new drugs. Consequently, a significant increase in the discovery of MNP published in the literature has been observed in the past decades, particularly from marine invertebrates. However, it remains unclear whether target metabolites originate from the marine invertebrates themselves or from their microbial symbionts. This issue underlines critical challenges associated with the lack of biomass required to supply the early stages of the drug discovery pipeline. The present review discusses potential solutions for such challenges, with particular emphasis on innovative approaches to culture invertebrate holobionts (microorganism-invertebrate assemblages through in toto aquaculture, together with methods for the discovery and initial production of bioactive compounds from these microbial symbionts.

  2. 2013 Philip S. Portoghese Medicinal Chemistry Lectureship: Drug Discovery Targeting Allosteric Sites†

    Science.gov (United States)

    2015-01-01

    The identification of sites on receptors topographically distinct from the orthosteric sites, so-called allosteric sites, has heralded novel approaches and modes of pharmacology for target modulation. Over the past 20 years, our understanding of allosteric modulation has grown significantly, and numerous advantages, as well as caveats (e.g., flat structure–activity relationships, species differences, “molecular switches”), have been identified. For multiple receptors and proteins, numerous examples have been described where unprecedented levels of selectivity are achieved along with improved physiochemical properties. While not a panacea, these novel approaches represent exciting opportunities for tool compound development to probe the pharmacology and therapeutic potential of discrete molecular targets, as well as new medicines. In this Perspective, in commemoration of the 2013 Philip S. Portoghese Medicinal Chemistry Lectureship (LindsleyC. W.Adventures in allosteric drug discovery. Presented at the 246th National Meeting of the American Chemical Society, Indianapolis, IN, September 10, 2013; The 2013 Portoghese Lectureship), several vignettes of drug discovery campaigns targeting novel allosteric mechanisms will be recounted, along with lessons learned and guidelines that have emerged for successful lead optimization. PMID:25180768

  3. High throughput electrophysiology: new perspectives for ion channel drug discovery

    DEFF Research Database (Denmark)

    Willumsen, Niels J; Bech, Morten; Olesen, Søren-Peter

    2003-01-01

    Proper function of ion channels is crucial for all living cells. Ion channel dysfunction may lead to a number of diseases, so-called channelopathies, and a number of common diseases, including epilepsy, arrhythmia, and type II diabetes, are primarily treated by drugs that modulate ion channels....... A cornerstone in current drug discovery is high throughput screening assays which allow examination of the activity of specific ion channels though only to a limited extent. Conventional patch clamp remains the sole technique with sufficiently high time resolution and sensitivity required for precise and direct...... characterization of ion channel properties. However, patch clamp is a slow, labor-intensive, and thus expensive, technique. New techniques combining the reliability and high information content of patch clamping with the virtues of high throughput philosophy are emerging and predicted to make a number of ion...

  4. 21 CFR 17.23 - Discovery.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 1 2010-04-01 2010-04-01 false Discovery. 17.23 Section 17.23 Food and Drugs FOOD... HEARINGS § 17.23 Discovery. (a) No later than 60 days prior to the hearing, unless otherwise ordered by the..., depositions, and any forms of discovery, other than those permitted under paragraphs (a) and (e) of this...

  5. Calculating Water Thermodynamics in the Binding Site of Proteins - Applications of WaterMap to Drug Discovery.

    Science.gov (United States)

    Cappel, Daniel; Sherman, Woody; Beuming, Thijs

    2017-01-01

    The ability to accurately characterize the solvation properties (water locations and thermodynamics) of biomolecules is of great importance to drug discovery. While crystallography, NMR, and other experimental techniques can assist in determining the structure of water networks in proteins and protein-ligand complexes, most water molecules are not fully resolved and accurately placed. Furthermore, understanding the energetic effects of solvation and desolvation on binding requires an analysis of the thermodynamic properties of solvent involved in the interaction between ligands and proteins. WaterMap is a molecular dynamics-based computational method that uses statistical mechanics to describe the thermodynamic properties (entropy, enthalpy, and free energy) of water molecules at the surface of proteins. This method can be used to assess the solvent contributions to ligand binding affinity and to guide lead optimization. In this review, we provide a comprehensive summary of published uses of WaterMap, including applications to lead optimization, virtual screening, selectivity analysis, ligand pose prediction, and druggability assessment. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  6. Design of diversity and focused combinatorial libraries in drug discovery.

    Science.gov (United States)

    Young, S Stanley; Ge, Nanxiang

    2004-05-01

    Using well-characterized chemical reactions and readily available monomers, chemists are able to create sets of compounds, termed libraries, which are useful in drug discovery processes. The design of combinatorial chemical libraries can be complex and there has been much information recently published offering suggestions on how the design process can be carried out. This review focuses on literature with the goal of organizing current thinking. At this point in time, it is clear that benchmarking of current suggested methods is required as opposed to further new methods.

  7. Advances in phage display technology for drug discovery.

    Science.gov (United States)

    Omidfar, Kobra; Daneshpour, Maryam

    2015-06-01

    Over the past decade, several library-based methods have been developed to discover ligands with strong binding affinities for their targets. These methods mimic the natural evolution for screening and identifying ligand-target interactions with specific functional properties. Phage display technology is a well-established method that has been applied to many technological challenges including novel drug discovery. This review describes the recent advances in the use of phage display technology for discovering novel bioactive compounds. Furthermore, it discusses the application of this technology to produce proteins and peptides as well as minimize the use of antibodies, such as antigen-binding fragment, single-chain fragment variable or single-domain antibody fragments like VHHs. Advances in screening, manufacturing and humanization technologies demonstrate that phage display derived products can play a significant role in the diagnosis and treatment of disease. The effects of this technology are inevitable in the development pipeline for bringing therapeutics into the market, and this number is expected to rise significantly in the future as new advances continue to take place in display methods. Furthermore, a widespread application of this methodology is predicted in different medical technological areas, including biosensing, monitoring, molecular imaging, gene therapy, vaccine development and nanotechnology.

  8. Comparative psychology and the grand challenge of drug discovery in psychiatry and neurodegeneration.

    Science.gov (United States)

    Brunner, Dani; Balcı, Fuat; Ludvig, Elliot A

    2012-02-01

    Drug discovery for brain disorders is undergoing a period of upheaval. Faced with an empty drug pipeline and numerous failures of potential new drugs in clinical trials, many large pharmaceutical companies have been shrinking or even closing down their research divisions that focus on central nervous system (CNS) disorders. In this paper, we argue that many of the difficulties facing CNS drug discovery stem from a lack of robustness in pre-clinical (i.e., non-human animal) testing. There are two main sources for this lack of robustness. First, there is the lack of replicability of many results from the pre-clinical stage, which we argue is driven by a combination of publication bias and inappropriate selection of statistical and experimental designs. Second, there is the frequent failure to translate results in non-human animals to parallel results in humans in the clinic. This limitation can only be overcome by developing new behavioral tests for non-human animals that have predictive, construct, and etiological validity. Here, we present these translational difficulties as a "grand challenge" to researchers from comparative cognition, who are well positioned to provide new methods for testing behavior and cognition in non-human animals. These new experimental protocols will need to be both statistically robust and target behavioral and cognitive processes that allow for better connection with human CNS disorders. Our hope is that this downturn in industrial research may represent an opportunity to develop new protocols that will re-kindle the search for more effective and safer drugs for CNS disorders. Copyright © 2011 Elsevier B.V. All rights reserved.

  9. Membrane Interactions of Phytochemicals as Their Molecular Mechanism Applicable to the Discovery of Drug Leads from Plants

    Directory of Open Access Journals (Sweden)

    Hironori Tsuchiya

    2015-10-01

    Full Text Available In addition to interacting with functional proteins such as receptors, ion channels, and enzymes, a variety of drugs mechanistically act on membrane lipids to change the physicochemical properties of biomembranes as reported for anesthetic, adrenergic, cholinergic, non-steroidal anti-inflammatory, analgesic, antitumor, antiplatelet, antimicrobial, and antioxidant drugs. As well as these membrane-acting drugs, bioactive plant components, phytochemicals, with amphiphilic or hydrophobic structures, are presumed to interact with biological membranes and biomimetic membranes prepared with phospholipids and cholesterol, resulting in the modification of membrane fluidity, microviscosity, order, elasticity, and permeability with the potencies being consistent with their pharmacological effects. A novel mechanistic point of view of phytochemicals would lead to a better understanding of their bioactivities, an insight into their medicinal benefits, and a strategic implication for discovering drug leads from plants. This article reviews the membrane interactions of different classes of phytochemicals by highlighting their induced changes in membrane property. The phytochemicals to be reviewed include membrane-interactive flavonoids, terpenoids, stilbenoids, capsaicinoids, phloroglucinols, naphthodianthrones, organosulfur compounds, alkaloids, anthraquinonoids, ginsenosides, pentacyclic triterpene acids, and curcuminoids. The membrane interaction’s applicability to the discovery of phytochemical drug leads is also discussed while referring to previous screening and isolating studies.

  10. Recreational drug discovery: natural products as lead structures for the synthesis of smart drugs.

    Science.gov (United States)

    Appendino, Giovanni; Minassi, Alberto; Taglialatela-Scafati, Orazio

    2014-07-01

    Covering: up to December 2013. Over the past decade, there has been a growing transition in recreational drugs from natural materials (marijuana, hashish, opium), natural products (morphine, cocaine), or their simple derivatives (heroin), to synthetic agents more potent than their natural prototypes, which are sometimes less harmful in the short term, or that combine properties from different classes of recreational prototypes. These agents have been named smart drugs, and have become popular both for personal consumption and for collective intoxication at rave parties. The reasons for this transition are varied, but are mainly regulatory and commercial. New analogues of known illegal intoxicants are invisible to most forensic detection techniques, while the alleged natural status and the lack of avert acute toxicity make them appealing to a wide range of users. On the other hand, the advent of the internet has made possible the quick dispersal of information among users and the on-line purchase of these agents and/or the precursors for their synthesis. Unlike their natural products chemotypes (ephedrine, mescaline, cathinone, psilocybin, THC), most new drugs of abuse are largely unfamiliar to the organic chemistry community as well as to health care providers. To raise awareness of the growing plague of smart drugs we have surveyed, in a medicinal chemistry fashion, their development from natural products leads, their current methods of production, and the role that clandestine home laboratories and underground chemists have played in the surge of popularity of these drugs.

  11. Recent development in software and automation tools for high-throughput discovery bioanalysis.

    Science.gov (United States)

    Shou, Wilson Z; Zhang, Jun

    2012-05-01

    Bioanalysis with LC-MS/MS has been established as the method of choice for quantitative determination of drug candidates in biological matrices in drug discovery and development. The LC-MS/MS bioanalytical support for drug discovery, especially for early discovery, often requires high-throughput (HT) analysis of large numbers of samples (hundreds to thousands per day) generated from many structurally diverse compounds (tens to hundreds per day) with a very quick turnaround time, in order to provide important activity and liability data to move discovery projects forward. Another important consideration for discovery bioanalysis is its fit-for-purpose quality requirement depending on the particular experiments being conducted at this stage, and it is usually not as stringent as those required in bioanalysis supporting drug development. These aforementioned attributes of HT discovery bioanalysis made it an ideal candidate for using software and automation tools to eliminate manual steps, remove bottlenecks, improve efficiency and reduce turnaround time while maintaining adequate quality. In this article we will review various recent developments that facilitate automation of individual bioanalytical procedures, such as sample preparation, MS/MS method development, sample analysis and data review, as well as fully integrated software tools that manage the entire bioanalytical workflow in HT discovery bioanalysis. In addition, software tools supporting the emerging high-resolution accurate MS bioanalytical approach are also discussed.

  12. A High Throughput, 384-Well, Semi-Automated, Hepatocyte Intrinsic Clearance Assay for Screening New Molecular Entities in Drug Discovery.

    Science.gov (United States)

    Heinle, Lance; Peterkin, Vincent; de Morais, Sonia M; Jenkins, Gary J; Badagnani, Ilaria

    2015-01-01

    A high throughput, semi-automated clearance screening assay in hepatocytes was developed allowing a scientist to generate data for 96 compounds in one week. The 384-well format assay utilizes a Thermo Multidrop Combi and an optimized LC-MS/MS method. The previously reported LCMS/ MS method reduced the analytical run time by 3-fold, down to 1.2 min injection-to-injection. The Multidrop was able to deliver hepatocytes to 384-well plates with minimal viability loss. Comparison of results from the new 384-well and historical 24-well assays yielded a correlation of 0.95. In addition, results obtained for 25 marketed drugs with various metabolism pathways had a correlation of 0.75 when compared with literature values. Precision was maintained in the new format as 8 compounds tested in ≥39 independent experiments had coefficients of variation ≤21%. The ability to predict in vivo clearances using the new stability assay format was also investigated using 22 marketed drugs and 26 AbbVie compounds. Correction of intrinsic clearance values with binding to hepatocytes (in vitro data) and plasma (in vivo data) resulted in a higher in vitro to in vivo correlation when comparing 22 marketed compounds in human (0.80 vs 0.35) and 26 AbbVie Discovery compounds in rat (0.56 vs 0.17), demonstrating the importance of correcting for binding in clearance studies. This newly developed high throughput, semi-automated clearance assay allows for rapid screening of Discovery compounds to enable Structure Activity Relationship (SAR) analysis based on high quality hepatocyte stability data in sufficient quantity and quality to drive the next round of compound synthesis.

  13. Human embryonic stem cell technologies and drug discovery.

    Science.gov (United States)

    Jensen, Janne; Hyllner, Johan; Björquist, Petter

    2009-06-01

    Development of new drugs is costly and takes huge resources into consideration. The big pharmaceutical companies are currently facing increasing developmental costs and a lower success-rate of bringing new compounds to the market. Therefore, it is now of outmost importance that the drug-hunting companies minimize late attritions due to sub-optimal pharmacokinetic properties or unexpected toxicity when entering the clinical programs. To achieve this, a strong need to test new candidate drugs in assays of high human relevance in vitro as early as possible has been identified. The traditionally used cell systems are however remarkably limited in this sense, and new improved technologies are of greatest importance. The human embryonic stem cells (hESC) is one of the most powerful cell types known. They have not only the possibility to divide indefinitely; these cells can also differentiate into all mature cell types of the human body. This makes them potentially very valuable for pharmaceutical development, spanning from use as tools in early target studies, DMPK or safety assessment, as screening models to find new chemical entities modulating adult stem cell fate, or as the direct use in cell therapies. This review illustrates the use of hESC in the drug discovery process, today, as well as in a future perspective. This will specifically be exemplified with the most important cell type for pharmaceutical development-the hepatocyte. We discuss how hESC-derived hepatocyte-like cells could improve this process, and how these cells should be cultured if optimized functionality and usefulness should be achieved. J. Cell. Physiol. 219: 513-519, 2009. (c) 2009 Wiley-Liss, Inc.

  14. Report on the 10th anniversary of international drug discovery science and technology conference, 8 - 10 november 2012, nanjing, china.

    Science.gov (United States)

    Everett, Jeremy R

    2013-03-01

    The 10th Anniversary of International Drug Discovery Science and Technology (IDDST) Conference was held in Nanjing, China from 8 to 10 November 2012. The conference ran in parallel with the 2nd Annual Symposium of Drug Delivery Systems. Over 400 delegates from both conferences came together for the Opening Ceremony and Keynote Addresses but otherwise pursued separate paths in the huge facilities of the Nanjing International Expo Centre. The IDDST was arranged into 19 separate Chapters covering drug discovery biology, target validation, chemistry, rational drug design, pharmacology and toxicology, drug screening technology, 'omics' technologies, analytical, automation and enabling technologies, informatics, stem cells and regenerative medicine, bioprocessing, generics, biosimilars and biologicals and seven disease areas: cancer, CNS, respiratory and inflammation, autoimmune, emerging infectious, bone and orphan diseases. There were also two sessions of a 'Bench to Bedside to Business' Program and a Chinese Scientist programme. In each period of the IDDST conference, up to seven sessions were running in parallel. This Meeting Highlight samples just a fraction of the content of this large meeting. The talks included have as a link, the use of new approaches to drug discovery. Many other excellent talks could have been highlighted and the author has necessarily had to be selective.

  15. A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

    Science.gov (United States)

    Pizzo, Fabiola; Lombardo, Anna; Manganaro, Alberto; Benfenati, Emilio

    2016-01-01

    The prompt identification of chemical molecules with potential effects on liver may help in drug discovery and in raising the levels of protection for human health. Besides in vitro approaches, computational methods in toxicology are drawing attention. We built a structure-activity relationship (SAR) model for evaluating hepatotoxicity. After compiling a data set of 950 compounds using data from the literature, we randomly split it into training (80%) and test sets (20%). We also compiled an external validation set (101 compounds) for evaluating the performance of the model. To extract structural alerts (SAs) related to hepatotoxicity and non-hepatotoxicity we used SARpy, a statistical application that automatically identifies and extracts chemical fragments related to a specific activity. We also applied the chemical grouping approach for manually identifying other SAs. We calculated accuracy, specificity, sensitivity and Matthews correlation coefficient (MCC) on the training, test and external validation sets. Considering the complexity of the endpoint, the model performed well. In the training, test and external validation sets the accuracy was respectively 81, 63, and 68%, specificity 89, 33, and 33%, sensitivity 93, 88, and 80% and MCC 0.63, 0.27, and 0.13. Since it is preferable to overestimate hepatotoxicity rather than not to recognize unsafe compounds, the model's architecture followed a conservative approach. As it was built using human data, it might be applied without any need for extrapolation from other species. This model will be freely available in the VEGA platform. PMID:27920722

  16. In vitro membrane binding and protein binding (IAM MB/PB technology to estimate in vivo distribution: applications in early drug discovery

    Directory of Open Access Journals (Sweden)

    Klara Livia Valko

    2017-03-01

    Full Text Available The drug discovery process can be accelerated by chromatographic profiling of the analogs to model in vivo distribution and the major non-specific binding. A balanced potency and chromatographically determined membrane and protein binding (IAM MB/PB data enable selecting drug discovery compounds for further analysis that have the highest probability to show the desired in vivo distribution behavior for efficacy and reduced chance for toxicity. Although the basic principles of the technology have already appeared in numerous publications, the lack of standardized procedures limited its widespread applications especially in academia and small drug discovery biotech companies. In this paper, the standardized procedures are described that has been trademarked as Regis IAM MB/PB Technology®. Comparison between the Drug Efficiency Index (DEI=pIC50-logVdu+2 and generally used Ligand Lipophilicity Efficiency (LLE has been made, demonstrating the advantage of measured IAM and HSA binding over calculated log P. The power of the proposed chromatographic technology is demonstrated using the data of marketed drugs.

  17. Weighted voting-based consensus clustering for chemical structure databases

    Science.gov (United States)

    Saeed, Faisal; Ahmed, Ali; Shamsir, Mohd Shahir; Salim, Naomie

    2014-06-01

    The cluster-based compound selection is used in the lead identification process of drug discovery and design. Many clustering methods have been used for chemical databases, but there is no clustering method that can obtain the best results under all circumstances. However, little attention has been focused on the use of combination methods for chemical structure clustering, which is known as consensus clustering. Recently, consensus clustering has been used in many areas including bioinformatics, machine learning and information theory. This process can improve the robustness, stability, consistency and novelty of clustering. For chemical databases, different consensus clustering methods have been used including the co-association matrix-based, graph-based, hypergraph-based and voting-based methods. In this paper, a weighted cumulative voting-based aggregation algorithm (W-CVAA) was developed. The MDL Drug Data Report (MDDR) benchmark chemical dataset was used in the experiments and represented by the AlogP and ECPF_4 descriptors. The results from the clustering methods were evaluated by the ability of the clustering to separate biologically active molecules in each cluster from inactive ones using different criteria, and the effectiveness of the consensus clustering was compared to that of Ward's method, which is the current standard clustering method in chemoinformatics. This study indicated that weighted voting-based consensus clustering can overcome the limitations of the existing voting-based methods and improve the effectiveness of combining multiple clusterings of chemical structures.

  18. The application of mass-spectrometry-based protein biomarker discovery to theragnostics

    OpenAIRE

    Street, Jonathan M; Dear, James W

    2010-01-01

    Over the last decade rapid developments in mass spectrometry have allowed the identification of multiple proteins in complex biological samples. This proteomic approach has been applied to biomarker discovery in the context of clinical pharmacology (the combination of biomarker and drug now being termed ‘theragnostics’). In this review we provide a roadmap for early protein biomarker discovery studies, focusing on some key questions that regularly confront researchers.

  19. Integrating medicinal chemistry, organic/combinatorial chemistry, and computational chemistry for the discovery of selective estrogen receptor modulators with Forecaster, a novel platform for drug discovery.

    Science.gov (United States)

    Therrien, Eric; Englebienne, Pablo; Arrowsmith, Andrew G; Mendoza-Sanchez, Rodrigo; Corbeil, Christopher R; Weill, Nathanael; Campagna-Slater, Valérie; Moitessier, Nicolas

    2012-01-23

    As part of a large medicinal chemistry program, we wish to develop novel selective estrogen receptor modulators (SERMs) as potential breast cancer treatments using a combination of experimental and computational approaches. However, one of the remaining difficulties nowadays is to fully integrate computational (i.e., virtual, theoretical) and medicinal (i.e., experimental, intuitive) chemistry to take advantage of the full potential of both. For this purpose, we have developed a Web-based platform, Forecaster, and a number of programs (e.g., Prepare, React, Select) with the aim of combining computational chemistry and medicinal chemistry expertise to facilitate drug discovery and development and more specifically to integrate synthesis into computer-aided drug design. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries, filter and extract a highly diverse library from the NCI database, and dock them to the estrogen receptor (ER), with all of these steps being fully automated by computational chemists for use by medicinal chemists. As a result, virtual screening of a diverse library seeded with active compounds followed by a search for analogs yielded an enrichment factor of 129, with 98% of the seeded active compounds recovered, while the screening of a designed virtual combinatorial library including known actives yielded an area under the receiver operating characteristic (AU-ROC) of 0.78. The lead optimization proved less successful, further demonstrating the challenge to simulate structure activity relationship studies.

  20. Current Landscape of Antiviral Drug Discovery [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Wade Blair

    2016-02-01

    Full Text Available Continued discovery and development of new antiviral medications are paramount for global human health, particularly as new pathogens emerge and old ones evolve to evade current therapeutic agents. Great success has been achieved in developing effective therapies to suppress human immunodeficiency virus (HIV and hepatitis B virus (HBV; however, the therapies are not curative and therefore current efforts in HIV and HBV drug discovery are directed toward longer-acting therapies and/or developing new mechanisms of action that could potentially lead to cure, or eradication, of the virus. Recently, exciting early clinical data have been reported for novel antivirals targeting respiratory syncytial virus (RSV and influenza (flu. Preclinical data suggest that these new approaches may be effective in treating high-risk patients afflicted with serious RSV or flu infections. In this review, we highlight new directions in antiviral approaches for HIV, HBV, and acute respiratory virus infections.

  1. Open innovation for phenotypic drug discovery: The PD2 assay panel.

    Science.gov (United States)

    Lee, Jonathan A; Chu, Shaoyou; Willard, Francis S; Cox, Karen L; Sells Galvin, Rachelle J; Peery, Robert B; Oliver, Sarah E; Oler, Jennifer; Meredith, Tamika D; Heidler, Steven A; Gough, Wendy H; Husain, Saba; Palkowitz, Alan D; Moxham, Christopher M

    2011-07-01

    Phenotypic lead generation strategies seek to identify compounds that modulate complex, physiologically relevant systems, an approach that is complementary to traditional, target-directed strategies. Unlike gene-specific assays, phenotypic assays interrogate multiple molecular targets and signaling pathways in a target "agnostic" fashion, which may reveal novel functions for well-studied proteins and discover new pathways of therapeutic value. Significantly, existing compound libraries may not have sufficient chemical diversity to fully leverage a phenotypic strategy. To address this issue, Eli Lilly and Company launched the Phenotypic Drug Discovery Initiative (PD(2)), a model of open innovation whereby external research groups can submit compounds for testing in a panel of Lilly phenotypic assays. This communication describes the statistical validation, operations, and initial screening results from the first PD(2) assay panel. Analysis of PD(2) submissions indicates that chemical diversity from open source collaborations complements internal sources. Screening results for the first 4691 compounds submitted to PD(2) have confirmed hit rates from 1.6% to 10%, with the majority of active compounds exhibiting acceptable potency and selectivity. Phenotypic lead generation strategies, in conjunction with novel chemical diversity obtained via open-source initiatives such as PD(2), may provide a means to identify compounds that modulate biology by novel mechanisms and expand the innovation potential of drug discovery.

  2. Predicting Drug-Target Interactions Based on Small Positive Samples.

    Science.gov (United States)

    Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

    2018-01-01

    A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance

  3. Using concepts in literature-based discovery : Simulating Swanson's Raynaud-fish oil and migraine-magnesium discoveries

    NARCIS (Netherlands)

    Weeber, M; Klein, Henny; de Jong-van den Berg, LTW; Vos, R

    Literature-based discovery has resulted in new knowledge. In the biomedical context, Don R. Swanson has generated several literature-based hypotheses that have been corroborated experimentally and clinically. In this paper, we propose a two-step model of the discovery process in which hypotheses are

  4. Supramolecular Nanostructures Based on Cyclodextrin and Poly(ethylene oxide: Syntheses, Structural Characterizations and Applications for Drug Delivery

    Directory of Open Access Journals (Sweden)

    Yue Zheng

    2016-05-01

    Full Text Available Cyclodextrins (CDs have been extensively studied as drug delivery carriers through host–guest interactions. CD-based poly(pseudorotaxanes, which are composed of one or more CD rings threading on the polymer chain with or without bulky groups (or stoppers, have attracted great interest in the development of supramolecular biomaterials. Poly(ethylene oxide (PEO is a water-soluble, biocompatible polymer. Depending on the molecular weight, PEO can be used as a plasticizer or as a toughening agent. Moreover, the hydrogels of PEO are also extensively studied because of their outstanding characteristics in biological drug delivery systems. These biomater