WorldWideScience

Sample records for system drug discovery

  1. Systems Pharmacology in Small Molecular Drug Discovery.

    Science.gov (United States)

    Zhou, Wei; Wang, Yonghua; Lu, Aiping; Zhang, Ge

    2016-02-18

    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.

  2. 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.

  3. Using quantitative systems pharmacology for novel drug discovery.

    Science.gov (United States)

    Pérez-Nueno, Violeta I

    2015-12-01

    Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology. This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects. The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.

  4. Rescuing drug discovery: In vivo systems pathology and systems pharmacology

    NARCIS (Netherlands)

    Greef, J. van der; McBurney, R.N.

    2005-01-01

    The pharmaceutical industry is currently beleaguered by close scrutiny from the financial community, regulators and the general public. Productivity, in terms of new drug approvals, has generally been falling for almost a decade and the safety of a number of highly successful drugs has recently been

  5. 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 and organi......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...... their performance....

  6. 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.

  7. 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.

  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. Cannabinoid CB1 receptor-interacting proteins: novel targets for central nervous system drug discovery?

    OpenAIRE

    Smith, Tricia H; Sim-Selley, Laura J; Selley, Dana E

    2010-01-01

    The main pharmacological effects of marijuana, as well as synthetic and endogenous cannabinoids, are mediated through G-protein-coupled receptors (GPCRs), including CB1 and CB2 receptors. The CB1 receptor is the major cannabinoid receptor in the central nervous system and has gained increasing interest as a target for drug discovery for treatment of nausea, cachexia, obesity, pain, spasticity, neurodegenerative diseases and mood and substance abuse disorders. Evidence has accumulated to sugge...

  10. 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.

  11. Systems pharmacology in drug discovery and therapeutic insight for herbal medicines.

    Science.gov (United States)

    Huang, Chao; Zheng, Chunli; Li, Yan; Wang, Yonghua; Lu, Aiping; Yang, Ling

    2014-09-01

    Systems pharmacology is an emerging field that integrates systems biology and pharmacology to advance the process of drug discovery, development and the understanding of therapeutic mechanisms. The aim of the present work is to highlight the role that the systems pharmacology plays across the traditional herbal medicines discipline, which is exemplified by a case study of botanical drugs applied in the treatment of depression. First, based on critically examined pharmacology and clinical knowledge, we propose a large-scale statistical analysis to evaluate the efficiency of herbs used in traditional medicines. Second, we focus on the exploration of the active ingredients and targets by carrying out complex structure-, omics- and network-based systematic investigations. Third, specific informatics methods are developed to infer drug-disease connections, with purpose to understand how drugs work on the specific targets and pathways. Finally, we propose a new systems pharmacology method, which is further applied to an integrated platform (Herbal medicine Systems Pharmacology) of blended herbal medicine and omics data sets, allowing for the systematization of current and traditional knowledge of herbal medicines and, importantly, for the application of this emerging body of knowledge to the development of new drugs for complex human diseases. © The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  12. 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.

  13. Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System.

    Science.gov (United States)

    El-Kattan, Ayman F; Varma, Manthena V; Steyn, Stefan J; Scott, Dennis O; Maurer, Tristan S; Bergman, Arthur

    2016-12-01

    To assess the utility of Extended Clearance Classification System (ECCS) in understanding absorption, distribution, metabolism, and elimination (ADME) attributes and enabling victim drug-drug interaction (DDI) predictions. A database of 368 drugs with relevant ADME parameters, main metabolizing enzymes, uptake transporters, efflux transporters, and highest change in exposure (%AUC) in presence of inhibitors was developed using published literature. Drugs were characterized according to ECCS using ionization, molecular weight and estimated permeability. Analyses suggested that ECCS class 1A drugs are well absorbed and systemic clearance is determined by metabolism mediated by CYP2C, esterases, and UGTs. For class 1B drugs, oral absorption is high and the predominant clearance mechanism is hepatic uptake mediated by OATP transporters. High permeability neutral/basic drugs (class 2) showed high oral absorption, with metabolism mediated generally by CYP3A, CYP2D6 and UGTs as the predominant clearance mechanism. Class 3A/4 drugs showed moderate absorption with dominant renal clearance involving OAT/OCT2 transporters. Class 3B drugs showed low to moderate absorption with hepatic uptake (OATPs) and/or renal clearance as primary clearance mechanisms. The highest DDI risk is typically seen with class 2/1B/3B compounds manifested by inhibition of either CYP metabolism or active hepatic uptake. Class 2 showed a wider range in AUC change likely due to a variety of enzymes involved. DDI risk for class 3A/4 is small and associated with inhibition of renal transporters. ECCS provides a framework to project ADME profiles and further enables prediction of victim DDI liabilities in drug discovery and development.

  14. Optogenetics enlightens neuroscience drug discovery.

    Science.gov (United States)

    Song, Chenchen; Knöpfel, Thomas

    2016-02-01

    Optogenetics - the use of light and genetics to manipulate and monitor the activities of defined cell populations - has already had a transformative impact on basic neuroscience research. Now, the conceptual and methodological advances associated with optogenetic approaches are providing fresh momentum to neuroscience drug discovery, particularly in areas that are stalled on the concept of 'fixing the brain chemistry'. Optogenetics is beginning to translate and transit into drug discovery in several key domains, including target discovery, high-throughput screening and novel therapeutic approaches to disease states. Here, we discuss the exciting potential of optogenetic technologies to transform neuroscience drug discovery.

  15. 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.

  16. 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...

  17. Label-free drug discovery

    Directory of Open Access Journals (Sweden)

    Ye eFang

    2014-03-01

    Full Text Available Current drug discovery is dominated by label-dependent molecular approaches, which screen drugs in the context of a predefined and target-based hypothesis in vitro. Given that target-based discovery has not transformed the industry, phenotypic screen that identifies drugs based on a specific phenotype of cells, tissues, or animals has gained renewed interest. However, owing to the intrinsic complexity in drug-target interactions, there is often a significant gap between the phenotype screened and the ultimate molecular mechanism of action sought. This paper presents a label-free strategy for early drug discovery. This strategy combines label-free cell phenotypic profiling with computational approaches, and holds promise to bridge the gap by offering a kinetic and holistic representation of the functional consequences of drugs in disease relevant cells that is amenable to mechanistic deconvolution.

  18. 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.

  19. 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).

  20. 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.

  1. Lysophospholipid receptors in drug discovery.

    Science.gov (United States)

    Kihara, Yasuyuki; Mizuno, Hirotaka; Chun, Jerold

    2015-05-01

    Lysophospholipids (LPs), including lysophosphatidic acid (LPA), sphingosine 1-phospate (S1P), lysophosphatidylinositol (LPI), and lysophosphatidylserine (LysoPS), are bioactive lipids that transduce signals through their specific cell-surface G protein-coupled receptors, LPA1-6, S1P1-5, LPI1, and LysoPS1-3, respectively. These LPs and their receptors have been implicated in both physiological and pathophysiological processes such as autoimmune diseases, neurodegenerative diseases, fibrosis, pain, cancer, inflammation, metabolic syndrome, bone formation, fertility, organismal development, and other effects on most organ systems. Advances in the LP receptor field have enabled the development of novel small molecules targeting LP receptors for several diseases. Most notably, fingolimod (FTY720, Gilenya, Novartis), an S1P receptor modulator, became the first FDA-approved medicine as an orally bioavailable drug for treating relapsing forms of multiple sclerosis. This success is currently being followed by multiple, mechanistically related compounds targeting S1P receptor subtypes, which are in various stages of clinical development. In addition, an LPA1 antagonist, BMS-986020 (Bristol-Myers Squibb), is in Phase 2 clinical development for treating idiopathic pulmonary fibrosis, as a distinct compound, SAR100842 (Sanofi) for the treatment of systemic sclerosis and related fibrotic diseases. This review summarizes the current state of drug discovery in the LP receptor field. Copyright © 2014 Elsevier Inc. All rights reserved.

  2. 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...

  3. 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

  4. 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.

  5. Transgenic parasites accelerate drug discovery

    Science.gov (United States)

    Rodriguez, Ana; Tarleton, Rick L.

    2013-01-01

    Parasitic neglected diseases are in dire need of new drugs either to replace old drugs rendered ineffective because of resistance development, to cover clinical needs that had never been addressed or to tackle other associated problems of existing drugs such as high cost, difficult administration, restricted coverage or toxicity. The availability of transgenic parasites expressing reporter genes facilitates the discovery of new drugs through high throughput screenings, but also by allowing rapid screening in animal models of disease. Taking advantage of these, we propose an alternative pathway of drug development for neglected diseases, going from high throughput screening directly into in vivo testing of the top ranked compounds selected by medicinal chemistry. Rapid assessment animal models allow for identification of compounds with bona fide activity in vivo early in the development chain, constituting a solid basis for further development and saving valuable time and resources. PMID:22277131

  6. 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 ...

  7. Biomaterials and biotechnology: from the discovery of the first angiogenesis inhibitors to the development of controlled drug delivery systems and the foundation of tissue engineering.

    Science.gov (United States)

    Langer, Robert

    2013-09-01

    This paper describes the discovery of the first inhibitors of angiogenesis; the discoveries that led to the development of the first biocompatible controlled release systems for macromolecules, and findings that helped to create the field of tissue engineering. In addition, new paradigms for creating biomaterials, early work on nanotechnology in medicine and intelligent drug delivery systems are discussed. Copyright © 2013 Wiley Periodicals, Inc.

  8. 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.

  9. Transcriptome inference and systems approaches to polypharmacology and drug discovery in herbal medicine.

    Science.gov (United States)

    Li, Peng; Chen, Jianxin; Zhang, Wuxia; Fu, Bangze; Wang, Wei

    2017-01-04

    Herbal medicine is a concoction of numerous chemical ingredients, and it exhibits polypharmacological effects to act on multiple pharmacological targets, regulating different biological mechanisms and treating a variety of diseases. Thus, this complexity is impossible to deconvolute by the reductionist method of extracting one active ingredient acting on one biological target. To dissect the polypharmacological effects of herbal medicines and their underling pharmacological targets as well as their corresponding active ingredients. We propose a system-biology strategy that combines omics and bioinformatical methodologies for exploring the polypharmacology of herbal mixtures. The myocardial ischemia model was induced by Ameroid constriction of the left anterior descending coronary in Ba-Ma miniature pigs. RNA-seq analysis was utilized to find the differential genes induced by myocardial ischemia in pigs treated with formula QSKL. A transcriptome-based inference method was used to find the landmark drugs with similar mechanisms to QSKL. Gene-level analysis of RNA-seq data in QSKL-treated cases versus control animals yields 279 differential genes. Transcriptome-based inference methods identified 80 landmark drugs that covered nearly all drug classes. Then, based on the landmark drugs, 155 potential pharmacological targets and 57 indications were identified for QSKL. Our results demonstrate the power of a combined approach for exploring the pharmacological target and chemical space of herbal medicines. We hope that our method could enhance our understanding of the molecular mechanisms of herbal systems and further accelerate the exploration of the value of traditional herbal medicine systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  10. 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.

  11. 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....

  12. Building New Bridges between In Vitro and In Vivo in Early Drug Discovery: Where Molecular Modeling Meets Systems Biology.

    Science.gov (United States)

    Pearlstein, Robert A; McKay, Daniel J J; Hornak, Viktor; Dickson, Callum; Golosov, Andrei; Harrison, Tyler; Velez-Vega, Camilo; Duca, José

    2017-01-01

    Cellular drug targets exist within networked function-generating systems whose constituent molecular species undergo dynamic interdependent non-equilibrium state transitions in response to specific perturbations (i.e.. inputs). Cellular phenotypic behaviors are manifested through the integrated behaviors of such networks. However, in vitro data are frequently measured and/or interpreted with empirical equilibrium or steady state models (e.g. Hill, Michaelis-Menten, Briggs-Haldane) relevant to isolated target populations. We propose that cells act as analog computers, "solving" sets of coupled "molecular differential equations" (i.e. represented by populations of interacting species)via "integration" of the dynamic state probability distributions among those populations. Disconnects between biochemical and functional/phenotypic assays (cellular/in vivo) may arise with targetcontaining systems that operate far from equilibrium, and/or when coupled contributions (including target-cognate partner binding and drug pharmacokinetics) are neglected in the analysis of biochemical results. The transformation of drug discovery from a trial-and-error endeavor to one based on reliable design criteria depends on improved understanding of the dynamic mechanisms powering cellular function/dysfunction at the systems level. Here, we address the general mechanisms of molecular and cellular function and pharmacological modulation thereof. We outline a first principles theory on the mechanisms by which free energy is stored and transduced into biological function, and by which biological function is modulated by drug-target binding. We propose that cellular function depends on dynamic counter-balanced molecular systems necessitated by the exponential behavior of molecular state transitions under non-equilibrium conditions, including positive versus negative mass action kinetics and solute-induced perturbations to the hydrogen bonds of solvating water versus kT. Copyright© Bentham

  13. 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.

  14. The role of the serotonergic and GABA system in translational approaches in drug discovery for anxiety disorders

    Directory of Open Access Journals (Sweden)

    Jocelien DA Olivier

    2013-06-01

    Full Text Available There is ample evidence that genetic factors play an important role in anxiety disorders. In support, human genome-wide association studies have implicated several novel candidate genes. However, illumination of such genetic factors involved in anxiety disorders has not resulted in novel drugs over the past decades. A complicating factor is the heterogeneous classification of anxiety disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR and diverging operationalization of anxiety used in preclinical and clinical studies. Currently, there is an increasing focus on the gene-environment interaction in anxiety as genes do not operate in isolation and environmental factors have been found to significantly contribute to the development of anxiety disorders in at-risk individuals. Nevertheless, extensive research on gene-environment mechanisms in anxiety has not resulted in major breakthroughs in drug discovery. Modification of individual genes in rodent models has enabled the specific study of anxiety in preclinical studies. In this context, two extensively studied neurotransmitters involved in anxiety are the GABA and 5-HT system. In this review, we illustrate the complex interplay between genes and environment in anxiety processes by reviewing preclinical and clinical studies on the serotonin transporter (5-HTT, 5-HT1A receptor, 5-HT2 receptor and GABAA receptor. Even though targets from the serotonin and GABA system have yielded drugs with known anxiolytic efficacy, the relation between the genetic background of these targets and anxiety symptoms and development of anxiety disorders is largely unknown. The aim of this review is to show the vast complexity of genetic and environmental factors in anxiety disorders. In light of the difficulty with which common genetic variants are identified in anxiety disorders, animal models with translational validity may aid in elucidating the neurobiological background of these genes

  15. A guide to drug discovery. Protecting your inventions: the patent system.

    Science.gov (United States)

    Webber, Philip M

    2003-10-01

    Whether you work for a multi-national pharmaceutical company, a biotech start-up or a university, a knowledge of the patent system is essential for understanding how best to protect the fruits of your research. The aim of this article is to give an overview of what a patent is, how you might get one and the rights that a patent confers.

  16. The oxytocin system in drug discovery for autism: Animal models and novel therapeutic strategies

    Science.gov (United States)

    Modi, Meera E.; Young, Larry J.

    2012-01-01

    Animal models and behavioral paradigms are critical for elucidating the neural mechanism involved in complex behaviors, including social cognition. Both genotype and phenotype based models have implicated the neuropeptide oxytocin (OT) in the regulation of social behavior. Based on the findings in animal models, alteration of the OT system has been hypothesized to play a role in the social deficits associated with autism and other neuropsychiatric disorders. While the evidence linking the peptide to the etiology of the disorder is not yet conclusive, evidence from multiple animal models suggest modulation of the OT system may be a viable strategy for the pharmacological treatment of social deficits. In this review, we will discuss how animal models have been utilized to understand the role of OT in social cognition and how those findings can be applied to the conceptualization and treatment of the social impairments in ASD. Animal models with genetic alterations of the OT system, like the OT, OT receptor and CD38 knock-out mice, and those with phenotypic variation in social behavior, like BTBR inbred mice and prairie voles, coupled with behavioral paradigms with face and construct validity may prove to have predictive validity for identifying the most efficacious methods of stimulating the OT system to enhance social cognition in humans. The widespread use of strong animal models of social cognition has the potential yield pharmacological, interventions for the treatment social impairments psychiatric disorders. This article is part of a Special Issue entitled Oxytocin, Vasopressin, and Social Behavior. PMID:22206823

  17. 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.

  18. The role of the serotonergic and GABA system in translational approaches in drug discovery for anxiety disorders

    Science.gov (United States)

    Olivier, Jocelien D. A.; Vinkers, Christiaan H.; Olivier, Berend

    2013-01-01

    There is ample evidence that genetic factors play an important role in anxiety disorders. In support, human genome-wide association studies have implicated several novel candidate genes. However, illumination of such genetic factors involved in anxiety disorders has not resulted in novel drugs over the past decades. A complicating factor is the heterogeneous classification of anxiety disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) and diverging operationalization of anxiety used in preclinical and clinical studies. Currently, there is an increasing focus on the gene × environment (G × E) interaction in anxiety as genes do not operate in isolation and environmental factors have been found to significantly contribute to the development of anxiety disorders in at-risk individuals. Nevertheless, extensive research on G × E mechanisms in anxiety has not resulted in major breakthroughs in drug discovery. Modification of individual genes in rodent models has enabled the specific study of anxiety in preclinical studies. In this context, two extensively studied neurotransmitters involved in anxiety are the gamma-aminobutyric acid (GABA) and 5-HT (5-hydroxytryptamine) system. In this review, we illustrate the complex interplay between genes and environment in anxiety processes by reviewing preclinical and clinical studies on the serotonin transporter (5-HTT), 5-HT1A receptor, 5-HT2 receptor, and GABAA receptor. Even though targets from the serotonin and GABA system have yielded drugs with known anxiolytic efficacy, the relation between the genetic background of these targets and anxiety symptoms and development of anxiety disorders is largely unknown. The aim of this review is to show the vast complexity of genetic and environmental factors in anxiety disorders. In light of the difficulty with which common genetic variants are identified in anxiety disorders, animal models with translational validity may aid in elucidating the

  19. Compound Data Mining for Drug Discovery.

    Science.gov (United States)

    Bajorath, Jürgen

    2017-01-01

    In recent years, there has been unprecedented growth in compound activity data in the public domain. These compound data provide an indispensable resource for drug discovery in academic environments as well as in the pharmaceutical industry. To handle large volumes of heterogeneous and complex compound data and extract discovery-relevant knowledge from these data, advanced computational mining approaches are required. Herein, major public compound data repositories are introduced, data confidence criteria reviewed, and selected data mining approaches discussed.

  20. 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.

  1. Discovery Approaches for Novel Dyslipidemia Drugs.

    Science.gov (United States)

    Maqbool, Faheem; Safavi, Malihe; Bahadar, Haji; Rahimifard, Mahban; Niaz, Kamal; Abdollahi, Mohammad

    2015-01-01

    Dyslipidemia is increased fasting level of total cholesterol (TC), LDL cholesterol (LDL-C), and triglycerides (TG), along with decreased levels of HDL cholesterol (HDL-C). Owing to effect on the cardiovascular system and increased chances of metabolic diseases, it is needed to review novel under development drugs and new approaches in drug discovery for dyslipidemia. This article reviews all phases I to IV clinical trials and preclinical trials with results associated with novel treatment of dyslipidemia. Drug discovery for dyslipidemia, toward newer targets has been addressed. Statins are, currently available, best choice of drugs for treating dyslipidemia and coronary diseases. In addition to this, lipid lowering drugs support treatment to a great extent, either as monotherapy or in combinations with other groups. Pravastatin used in combination with cholesteryl ester, transfers protein inhibitors (CETP) to produce efficient results. Peroxisome proliferator-activated receptor agonists (PPAR) like muraglitazar, aleglitazar and tesaglitazar are PPAR α/γ receptor agonist, dual in action performs better in phase 3 clinical study and reduces renal and cardiovascular events. By targeting both receptors, a better treatment for cardiovascular and diabetic problems can be achieved. Proprotein convertase subtilisin/kexin type 9 (PCSK-9) inhibitors like humanized monoclonal antibodies, are newly discovered inhibitors that reduce the risk of cardiovascular diseases. During the past few years, nucleic acid-based therapies targeting lipid and lipoprotein metabolism, such as microsomal TG transfer protein (MTP) may be a promising therapeutic approach to treat vascular diseases. Gene regulating transcription factors involved in bile acids and cholesterol metabolism can be controlled by FXR agonists in dyslipidemia. To overcome these drawbacks, many thyroid hormone analogues have been developed to lower down cholesterol level by targeting specifically thyroid hormone

  2. Drug Discovery, Design and Delivery

    Science.gov (United States)

    2012-06-28

    benzoate) nanoparticles by nanoprecipitation with and without a lipophilic probe ( coumarin ) to study stability and drug release. The advantage of this...degradation. Addition of a lipophilic molecule such as coumarin 6 to the media allows for up to 1.6% of the polymer weight to be entrapped during...indicate that 78% of the coumarin 6 was encapsulated within the polymer matrix of the nanoparticle, and the residual surface-bond coumarin 6 was quickly

  3. Boesenbergia rotunda: From Ethnomedicine to Drug Discovery

    Directory of Open Access Journals (Sweden)

    Tan Eng-Chong

    2012-01-01

    Full Text Available Boesenbergia rotunda is a herb from the Boesenbergia genera under the Zingiberaceae family. B. rotunda is widely found in Asian countries where it is commonly used as a food ingredient and in ethnomedicinal preparations. The popularity of its ethnomedicinal usage has drawn the attention of scientists worldwide to further investigate its medicinal properties. Advancement in drug design and discovery research has led to the development of synthetic drugs from B. rotunda metabolites via bioinformatics and medicinal chemistry studies. Furthermore, with the advent of genomics, transcriptomics, proteomics, and metabolomics, new insights on the biosynthetic pathways of B. rotunda metabolites can be elucidated, enabling researchers to predict the potential bioactive compounds responsible for the medicinal properties of the plant. The vast biological activities exhibited by the compounds obtained from B. rotunda warrant further investigation through studies such as drug discovery, polypharmacology, and drug delivery using nanotechnology.

  4. 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.

  5. Computational functional group mapping for drug discovery

    OpenAIRE

    Guvench, Olgun

    2016-01-01

    Computational functional group mapping (cFGM) is emerging as a high-impact complement to existing widely used experimental and computational structure-based drug discovery methods. cFGM provides comprehensive atomic-resolution 3D maps of the affinity of functional groups that can constitute drug-like molecules for a given target, typically a protein. These 3D maps can be intuitively and interactively visualized by medicinal chemists to rapidly design synthetically accessible ligands. Given th...

  6. Protein chemical synthesis in drug discovery.

    Science.gov (United States)

    Liu, Fa; Mayer, John P

    2015-01-01

    The discovery of novel therapeutics to combat human disease has traditionally been among the most important goals of research chemists. After a century of innovation, state-of-the-art chemical protein synthesis is now capable of efficiently assembling proteins of up to several hundred residues in length from individual amino acids. By virtue of its unique ability to incorporate non-native structural elements, chemical protein synthesis has been seminal in the recent development of several novel drug discovery technologies. In this chapter, we review the key advances in peptide and protein chemistry which have enabled our current synthetic capabilities. We also discuss the synthesis of D-proteins and their applications in mirror image phage-display and racemic protein crystallography, the synthesis of enzymes for structure-based drug discovery, and the direct synthesis of homogenous protein pharmaceuticals.

  7. Communication in Drug Development: "Translating" Scientific Discovery.

    Science.gov (United States)

    Settleman, Jeff; Cohen, Robert L

    2016-03-10

    The discovery and development of new medicines that promote human health and potentially extend natural life remains a remarkably challenging endeavor. In this Commentary, we identify key elements of communication required to successfully translate promising biological findings to novel approved drug therapies and discuss the attendant challenges and opportunities. Copyright © 2016 Elsevier Inc. All rights reserved.

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

    African Journals Online (AJOL)

    Abstracts prsented at the "Translational Science 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 ...

  9. Pathways to new drug discovery in neuropsychiatry.

    Science.gov (United States)

    Berk, Michael

    2012-11-29

    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.

  10. 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

  11. Molecular dynamics simulations and novel drug discovery.

    Science.gov (United States)

    Liu, Xuewei; Shi, Danfeng; Zhou, Shuangyan; Liu, Hongli; Liu, Huanxiang; Yao, Xiaojun

    2018-01-01

    Molecular dynamics (MD) simulations can provide not only plentiful dynamical structural information on biomacromolecules but also a wealth of energetic information about protein and ligand interactions. Such information is very important to understanding the structure-function relationship of the target and the essence of protein-ligand interactions and to guiding the drug discovery and design process. Thus, MD simulations have been applied widely and successfully in each step of modern drug discovery. Areas covered: In this review, the authors review the applications of MD simulations in novel drug discovery, including the pathogenic mechanisms of amyloidosis diseases, virtual screening and the interaction mechanisms between drugs and targets. Expert opinion: MD simulations have been used widely in investigating the pathogenic mechanisms of diseases caused by protein misfolding, in virtual screening, and in investigating drug resistance mechanisms caused by mutations of the target. These issues are very difficult to solve by experimental methods alone. Thus, in the future, MD simulations will have wider application with the further improvement of computational capacity and the development of better sampling methods and more accurate force fields together with more efficient analysis methods.

  12. Antibacterial drug discovery in the resistance era.

    Science.gov (United States)

    Brown, Eric D; Wright, Gerard D

    2016-01-21

    The looming antibiotic-resistance crisis has penetrated the consciousness of clinicians, researchers, policymakers, politicians and the public at large. The evolution and widespread distribution of antibiotic-resistance elements in bacterial pathogens has made diseases that were once easily treatable deadly again. Unfortunately, accompanying the rise in global resistance is a failure in antibacterial drug discovery. Lessons from the history of antibiotic discovery and fresh understanding of antibiotic action and the cell biology of microorganisms have the potential to deliver twenty-first century medicines that are able to control infection in the resistance era.

  13. 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).

  14. Synthetic biology for pharmaceutical drug discovery

    Science.gov (United States)

    Trosset, Jean-Yves; Carbonell, Pablo

    2015-01-01

    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. PMID:26673570

  15. The National Institutes of Health Microphysiological Systems Program focuses on a critical challenge in the drug discovery pipeline.

    Science.gov (United States)

    Sutherland, Margaret L; Fabre, Kristin M; Tagle, Danilo A

    2013-01-01

    The National Institutes of Health has partnered with the US Food and Drug Administration and the Defense Advanced Research Projects Agency to accelerate the development of human microphysiological systems (MPS) that address challenges faced in predictive toxicity assessment and efficacy analysis of new molecular entities during the preclinical phase of drug development. Use of human MPS could provide better models for predicting the efficacy of new molecular entities in clinical trials. It is also anticipated that improvements in predicting drug toxicities early in the drug development process through the use of MPS or human organs-on-a-chip will decrease the need to withdraw new therapies from the market and minimize or eliminate deaths due to unidentified drug toxicities.

  16. Novel drug discovery for Chagas disease.

    Science.gov (United States)

    Moraes, Carolina B; Franco, Caio H

    2016-01-01

    Chagas disease is a chronic infection associated with long-term morbidity. Increased funding and advocacy for drug discovery for neglected diseases have prompted the introduction of several important technological advances, and Chagas disease is among the neglected conditions that has mostly benefited from technological developments. A number of screening campaigns, and the development of new and improved in vitro and in vivo assays, has led to advances in the field of drug discovery. This review highlights the major advances in Chagas disease drug screening, and how these are being used not only to discover novel chemical entities and drug candidates, but also increase our knowledge about the disease and the parasite. Different methodologies used for compound screening and prioritization are discussed, as well as novel techniques for the investigation of these targets. The molecular mechanism of action is also discussed. Technological advances have been executed with scientific rigour for the development of new in vitro cell-based assays and in vivo animal models, to bring about novel and better drugs for Chagas disease, as well as to increase our understanding of what are the necessary properties for a compound to be successful in the clinic. The gained knowledge, combined with new exciting approaches toward target deconvolution, will help identifying new targets for Chagas disease chemotherapy in the future.

  17. Developing doctoral scientists for drug discovery: pluridimensional education required.

    Science.gov (United States)

    Janero, David R

    2013-02-01

    Research universities continue to produce new scientists capable of generating knowledge with the potential to inform disease etiology and treatment. Mounting interest of doctoral-level experimental science students in therapeutics-related research careers is discordant with the widespread lack of direct drug-discovery and development experience, let alone commercialization success, among university faculty and administrators. Likewise, the archetypical publication- and grant-fueled, principal investigator (PI)-focused academic system ("PI-stan") risks commoditization of science students pursuing their doctorates as a labor source, rendering them ill-prepared for career options related to therapeutics innovation by marginalizing their development of "beyond-the-bench" professional skills foundational to modern drug-discovery campaigns and career fluency. To militate against professionalization deficits in doctoral drug-discovery researchers, the author--a scientist-administrator-consultant with decades of discovery research and development (R&D), business, and educator experience in commercial and university settings--posits a critical need for pluridimensionality in graduate education and mentorship that extends well beyond thesis-related scientific domains/laboratory techniques to instill transferable operational-intelligence, project/people-management, and communication competencies. Specific initiatives are advocated to help enhance the doctoral science student's market competitiveness, adaptability, and navigation of the significant research, commercial, and occupational challenges associated with contemporary preclinical drug-discovery R&D.

  18. New paradigms in GPCR drug discovery.

    Science.gov (United States)

    Jacobson, Kenneth A

    2015-12-15

    G protein-coupled receptors (GPCRs) remain a major domain of pharmaceutical discovery. The identification of GPCR lead compounds and their optimization are now structure-based, thanks to advances in X-ray crystallography, molecular modeling, protein engineering and biophysical techniques. In silico screening provides useful hit molecules. New pharmacological approaches to tuning the pleotropic action of GPCRs include: allosteric modulators, biased ligands, GPCR heterodimer-targeted compounds, manipulation of polypharmacology, receptor antibodies and tailoring of drug molecules to fit GPCR pharmacogenomics. Measurements of kinetics and drug efficacy are factors influencing clinical success. With the exception of inhibitors of GPCR kinases, targeting of intracellular GPCR signaling or receptor cycling for therapeutic purposes remains a futuristic concept. New assay approaches are more efficient and multidimensional: cell-based, label-free, fluorescence-based assays, and biosensors. Tailoring GPCR drugs to a patient's genetic background is now being considered. Chemoinformatic tools can predict ADME-tox properties. New imaging technology visualizes drug action in vivo. Thus, there is reason to be optimistic that new technology for GPCR ligand discovery will help reverse the current narrowing of the pharmaceutical pipeline. Published by Elsevier Inc.

  19. Meeting report on the Alzheimer?s Drug Discovery Foundation 14th International Conference on Alzheimer?s Drug Discovery

    OpenAIRE

    Friedman, Lauren G; Price, Katherine; Lane, Rachel F; Carman, Aaron J; Dacks, Penny A; Shineman, Diana W; Fillit, Howard M

    2014-01-01

    The Alzheimer?s Drug Discovery Foundation?s 14th International Conference on Alzheimer?s Drug Discovery was held on 9 and 10 September in Jersey City, NJ, USA. This annual meeting highlights novel therapeutic approaches supported by the Alzheimer?s Drug Discovery Foundation in development for Alzheimer?s disease and related dementias.

  20. The future of crystallography in drug discovery.

    Science.gov (United States)

    Zheng, Heping; Hou, Jing; Zimmerman, Matthew D; Wlodawer, Alexander; Minor, Wladek

    2014-02-01

    X-ray crystallography plays an important role in structure-based drug design (SBDD), and accurate analysis of crystal structures of target macromolecules and macromolecule-ligand complexes is critical at all stages. However, whereas there has been significant progress in improving methods of structural biology, particularly in X-ray crystallography, corresponding progress in the development of computational methods (such as in silico high-throughput screening) is still on the horizon. Crystal structures can be overinterpreted and thus bias hypotheses and follow-up experiments. As in any experimental science, the models of macromolecular structures derived from X-ray diffraction data have their limitations, which need to be critically evaluated and well understood for structure-based drug discovery. This review describes how the validity, accuracy and precision of a protein or nucleic acid structure determined by X-ray crystallography can be evaluated from three different perspectives: i) the nature of the diffraction experiment; ii) the interpretation of an electron density map; and iii) the interpretation of the structural model in terms of function and mechanism. The strategies to optimally exploit a macromolecular structure are also discussed in the context of 'Big Data' analysis, biochemical experimental design and structure-based drug discovery. Although X-ray crystallography is one of the most detailed 'microscopes' available today for examining macromolecular structures, the authors would like to re-emphasize that such structures are only simplified models of the target macromolecules. The authors also wish to reinforce the idea that a structure should not be thought of as a set of precise coordinates but rather as a framework for generating hypotheses to be explored. Numerous biochemical and biophysical experiments, including new diffraction experiments, can and should be performed to verify or falsify these hypotheses. X-ray crystallography will find its

  1. Computational functional group mapping for drug discovery.

    Science.gov (United States)

    Guvench, Olgun

    2016-12-01

    Computational functional group mapping (cFGM) is emerging as a high-impact complement to existing widely used experimental and computational structure-based drug discovery methods. cFGM provides comprehensive atomic-resolution 3D maps of the affinity of functional groups that can constitute drug-like molecules for a given target, typically a protein. These 3D maps can be intuitively and interactively visualized by medicinal chemists to rapidly design synthetically accessible ligands. Given that the maps can inform selection of functional groups for affinity, specificity, and pharmacokinetic properties, they are of utility for both the optimization of existing drug candidates and creating novel ones. Here, I review recent advances in cFGM with emphasis on the unique information content in the approach that offers the potential of broadly facilitating structure-based ligand design. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. 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.

  3. 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.

  4. Targeted drug discovery for pediatric leukemia

    Directory of Open Access Journals (Sweden)

    Andrew D Napper

    2013-07-01

    Full Text Available Despite dramatic advances in the treatment of pediatric leukemia over the past 50 years, there remain subsets of patients who respond poorly to treatment. Many of the high-risk cases of childhood leukemia with the poorest prognosis have been found to harbor specific genetic signatures, often resulting from chromosomal rearrangements. With increased understanding of the genetic and epigenetic makeup of high-risk pediatric leukemia has come the opportunity to develop targeted therapies that promise to be both more effective and less toxic than current chemotherapy. Of particular importance is an understanding of the interconnections between different targets within the same cancer, and observations of synergy between two different targeted therapies or between a targeted drug and conventional chemotherapy. It has become clear that many cancers are able to circumvent a single specific blockade, and pediatric leukemias are no exception in this regard. This review highlights the most promising approaches to new drugs and drug combinations for high-risk pediatric leukemia. Key biological evidence supporting selection of molecular targets is presented, together with a critical survey of recent progress towards the discovery, pre-clinical development, and clinical study of novel molecular therapeutics.

  5. Functional genomics and cancer drug target discovery.

    Science.gov (United States)

    Moody, Susan E; Boehm, Jesse S; Barbie, David A; Hahn, William C

    2010-06-01

    The recent development of technologies for whole-genome sequencing, copy number analysis and expression profiling enables the generation of comprehensive descriptions of cancer genomes. However, although the structural analysis and expression profiling of tumors and cancer cell lines can allow the identification of candidate molecules that are altered in the malignant state, functional analyses are necessary to confirm such genes as oncogenes or tumor suppressors. Moreover, recent research suggests that tumor cells also depend on synthetic lethal targets, which are not mutated or amplified in cancer genomes; functional genomics screening can facilitate the discovery of such targets. This review provides an overview of the tools available for the study of functional genomics, and discusses recent research involving the use of these tools to identify potential novel drug targets in cancer.

  6. Translational paradigms in pharmacology and drug discovery.

    Science.gov (United States)

    Mullane, Kevin; Winquist, Raymond J; Williams, Michael

    2014-01-01

    The translational sciences represent the core element in enabling and utilizing the output from the biomedical sciences and to improving drug discovery metrics by reducing the attrition rate as compounds move from preclinical research to clinical proof of concept. Key to understanding the basis of disease causality and to developing therapeutics is an ability to accurately diagnose the disease and to identify and develop safe and effective therapeutics for its treatment. The former requires validated biomarkers and the latter, qualified targets. Progress has been hampered by semantic issues, specifically those that define the end product, and by scientific issues that include data reliability, an overt reductionistic cultural focus and a lack of hierarchically integrated data gathering and systematic analysis. A necessary framework for these activities is represented by the discipline of pharmacology, efforts and training in which require recognition and revitalization. Copyright © 2013 Elsevier Inc. All rights reserved.

  7. The Elements of Antifungal Drug Discovery

    DEFF Research Database (Denmark)

    Kjellerup, Lasse

    compounds (ZACs). Zinc is an important micronutrient and the immune system is known to operate with a similar mechanism to the ZACs by scavenging zinc from the site of infection, thus preventing the growth of pathogens through zinc starvation. In addition to the observations made about the ZAC compounds......In this PhD thesis I will explore the development of antifungal drugs. Fungal infections are estimated to cause the death of 1.5 million patients each year. There is currently a need for new antifungal drugs as the existing drugs are hampered by lack of broad-spectrum antifungal activity...

  8. Discovery and Characterization of Novel Anti-schistosomal Properties of the Anti-anginal Drug, Perhexiline and Its Impact on Schistosoma mansoni Male and Female Reproductive Systems.

    Directory of Open Access Journals (Sweden)

    Alessandra Guidi

    2016-08-01

    Full Text Available Schistosomiasis, one of the world's greatest human neglected tropical diseases, is caused by parasitic trematodes of the genus Schistosoma. A unique feature of schistosome biology is that the induction of sexual maturation as well as the maintenance of the differentiation status of female reproductive organs and egg production, necessary for both disease transmission and pathogenesis, are strictly dependent on the male. The treatment and most control initiatives of schistosomiasis rely today on the long-term application of a single drug, praziquantel (PZQ, mostly by campaigns of mass drug administration. PZQ, while very active on adult parasites, has much lower activity against juvenile worms. Monotherapy also favors the selection of drug resistance and, therefore, new drugs are urgently needed.Following the screening of a small compound library with an ATP-based luminescent assay on Schistosoma mansoni schistosomula, we here report the identification and characterization of novel antischistosomal properties of the anti-anginal drug perhexiline maleate (PHX. By phenotypic worm survival assays and confocal microscopy studies we show that PHX, in vitro, has a marked lethal effect on all S. mansoni parasite life stages (newly transformed schistosomula, juvenile and adult worms of the definitive host. We further demonstrate that sub-lethal doses of PHX significantly impair egg production and lipid depletion within the vitellarium of adult female worms. Moreover, we highlighted tegumental damage in adult male worms and remarkable reproductive system alterations in both female and male adult parasites. The in vivo study in S. mansoni-patent mice showed a notable variability of worm burdens in the individual experiments, with an overall minimal schistosomicidal effect upon PHX treatment. The short PHX half-life in mice, together with its very high rodent plasma proteins binding could be the cause of the modest efficacy of PHX in the schistosomiasis

  9. 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.

  10. The application of nuclear medicine technologies to drug discovery

    International Nuclear Information System (INIS)

    Gibson, R.E.; Burns, H.D.; Solomon, H.F.

    1990-01-01

    Radiopharmaceutical chemistry and nuclear medicine technology offer unique tools for drug discovery which yield data on test animals non-invasively or provide data on the pharmacology of a drug under physiological conditions in relevant models, including man. Imaging modalities also allow one to use an animal as its own control for subsequent drug tests. Specific examples in which the authors have used these technologies are (1) the examination of a drug delivery system for a lipophilic drug labeled with C-11; (2) the determination of drug delivery to the CNS in a stroke model using a C-11 labeled analogue of a drug with potential of reducing damage to stroke victims; and (3) evaluation of a drug effect on physiological parameters such as oxygen metabolism in the CNS. Drug candidates can be studied via isotopic substitution with isotopes such as C-11; analogues which are labeled with gamma-emitting isotopes may be used to provide information on relevant disease processes, e.g. changes in receptor number and/or function, or provide useful data on drug interactions using in vivo competition studies

  11. 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

  12. Recent lab-on-chip developments for novel drug discovery.

    Science.gov (United States)

    Khalid, Nauman; Kobayashi, Isao; Nakajima, Mitsutoshi

    2017-07-01

    Microelectromechanical systems (MEMS) and micro total analysis systems (μTAS) revolutionized the biochemical and electronic industries, and this miniaturization process became a key driver for many markets. Now, it is a driving force for innovations in life sciences, diagnostics, analytical sciences, and chemistry, which are called 'lab-on-a-chip, (LOC)' devices. The use of these devices allows the development of fast, portable, and easy-to-use systems with a high level of functional integration for applications such as point-of-care diagnostics, forensics, the analysis of biomolecules, environmental or food analysis, and drug development. In this review, we report on the latest developments in fabrication methods and production methodologies to tailor LOC devices. A brief overview of scale-up strategies is also presented together with their potential applications in drug delivery and discovery. The impact of LOC devices on drug development and discovery has been extensively reviewed in the past. The current research focuses on fast and accurate detection of genomics, cell mutations and analysis, drug delivery, and discovery. The current research also differentiates the LOC devices into new terminology of microengineering, like organ-on-a-chip, stem cells-on-a-chip, human-on-a-chip, and body-on-a-chip. Key challenges will be the transfer of fabricated LOC devices from lab-scale to industrial large-scale production. Moreover, extensive toxicological studies are needed to justify the use of microfabricated drug delivery vehicles in biological systems. It will also be challenging to transfer the in vitro findings to suitable and promising in vivo models. WIREs Syst Biol Med 2017, 9:e1381. doi: 10.1002/wsbm.1381 For further resources related to this article, please visit the WIREs website. © 2017 Wiley Periodicals, Inc.

  13. Pharmacognosy: Science of natural products in drug discovery.

    Science.gov (United States)

    Orhan, Ilkay Erdogan

    2014-01-01

    Pharmacognosy deals with the natural drugs obtained from organisms such as most plants, microbes, and animals. Up to date, many important drugs including morphine, atropine, galanthamine, etc. have originated from natural sources which continue to be good model molecules in drug discovery. Traditional medicine is also a part of pharmacognosy and most of the third world countries still depend on the use of herbal medicines. Consequently, pharmacognosy always keeps its popularity in pharmaceutical sciences and plays a critical role in drug discovery.

  14. Orphan nuclear receptors, excellent targets of drug discovery.

    Science.gov (United States)

    Shi, Yanhong

    2006-11-01

    To date, the pharmaceutical industry has placed a considerable amount of interest in the discovery of drug targets and diagnostics. One of the most challenging areas of drug discovery today is the search for novel receptor-ligand pairs. Nuclear receptors comprise a large superfamily of ligand-dependent transcription factors that regulate the expression of genes critical for a variety of biological processes, including development, growth, differentiation, and homeostasis. Orphan nuclear receptors, for which the ligands are not yet identified, represent the most ancient component of the nuclear receptor superfamily. Orphan nuclear receptors not only offer a unique system to uncover novel signaling pathways that impact human health, but also provide excellent targets of drug discoveries for a variety of human diseases. This review highlights advances made on ligand identification for orphan nuclear receptors using transgenic mouse models, cell-based screening, direct binding, structure-based assays, and computer-aided virtual screening. With rapid advances in combinatorial chemistry and high throughput screening, along with other modern technologies, this field promises a bountiful harvest.

  15. 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.

  16. Optical imaging for the new grammar of drug discovery.

    Science.gov (United States)

    Krucker, Thomas; Sandanaraj, Britto S

    2011-11-28

    Optical technologies used in biomedical research have undergone tremendous development in the last decade and enabled important insight into biochemical, cellular and physiological phenomena at the microscopic and macroscopic level. Historically in drug discovery, to increase throughput in screening, or increase efficiency through automation of image acquisition and analysis in pathology, efforts in imaging were focused on the reengineering of established microscopy solutions. However, with the emergence of the new grammar for drug discovery, other requirements and expectations have created unique opportunities for optical imaging. The new grammar of drug discovery provides rules for translating the wealth of genomic and proteomic information into targeted medicines with a focus on complex interactions of proteins. This paradigm shift requires highly specific and quantitative imaging at the molecular level with tools that can be used in cellular assays, animals and finally translated into patients. The development of fluorescent targeted and activatable 'smart' probes, fluorescent proteins and new reporter gene systems as functional and dynamic markers of molecular events in vitro and in vivo is therefore playing a pivotal role. An enabling optical imaging platform will combine optical hardware refinement with a strong emphasis on creating and validating highly specific chemical and biological tools.

  17. Zebrafish xenograft models of cancer and metastasis for drug discovery.

    Science.gov (United States)

    Brown, Hannah K; Schiavone, Kristina; Tazzyman, Simon; Heymann, Dominique; Chico, Timothy Ja

    2017-04-01

    Patients with metastatic cancer suffer the highest rate of cancer-related death, but existing animal models of metastasis have disadvantages that limit our ability to understand this process. The zebrafish is increasingly used for cancer modelling, particularly xenografting of human cancer cell lines, and drug discovery, and may provide novel scientific and therapeutic insights. However, this model system remains underexploited. Areas covered: The authors discuss the advantages and disadvantages of the zebrafish xenograft model for the study of cancer, metastasis and drug discovery. They summarise previous work investigating the metastatic cascade, such as tumour-induced angiogenesis, intravasation, extravasation, dissemination and homing, invasion at secondary sites, assessing metastatic potential and evaluation of cancer stem cells in zebrafish. Expert opinion: The practical advantages of zebrafish for basic biological study and drug discovery are indisputable. However, their ability to sufficiently reproduce and predict the behaviour of human cancer and metastasis remains unproven. For this to be resolved, novel mechanisms must to be discovered in zebrafish that are subsequently validated in humans, and for therapeutic interventions that modulate cancer favourably in zebrafish to successfully translate to human clinical studies. In the meantime, more work is required to establish the most informative methods in zebrafish.

  18. 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.

  19. Brivaracetam: a rational drug discovery success story

    Science.gov (United States)

    Rogawski, M A

    2008-01-01

    Levetiracetam, the α-ethyl analogue of the nootropic piracetam, is a widely used antiepileptic drug (AED) that provides protection against partial seizures and is also effective in the treatment of primary generalized seizure syndromes including juvenile myoclonic epilepsy. Levetiracetam was discovered in 1992 through screening in audiogenic seizure susceptible mice and, 3 years later, was reported to exhibit saturable, stereospecific binding in brain to a ∼90 kDa protein, later identified as the ubiquitous synaptic vesicle glycoprotein SV2A. A large-scale screening effort to optimize binding affinity identified the 4-n-propyl analogue, brivaracetam, as having greater potency and a broadened spectrum of activity in animal seizure models. Recent phase II clinical trials demonstrating that brivaracetam is efficacious and well tolerated in the treatment of partial onset seizures have validated the strategy of the discovery programme. Brivaracetam is among the first clinically effective AEDs to be discovered by optimization of pharmacodynamic activity at a molecular target. PMID:18552880

  20. CNS Anticancer Drug Discovery and Development: 2016 conference insights.

    Science.gov (United States)

    Levin, Victor A; Abrey, Lauren E; Heffron, Timothy P; Tonge, Peter J; Dar, Arvin C; Weiss, William A; Gallo, James M

    2017-07-18

    CNS Anticancer Drug Discovery and Development November 2016, AZ, USA The 2016 second CNS Anticancer Drug Discovery and Development Conference addressed diverse viewpoints about why new drug discovery/development focused on CNS cancers has been sorely lacking. Despite more than 70,000 individuals in the USA being diagnosed with a primary brain malignancy and 151,669-286,486 suffering from metastatic CNS cancer, in 1999, temozolomide was the last drug approved by the US FDA as an anticancer agent for high-grade gliomas. Among the topics discussed were economic factors and pharmaceutical risk assessments, regulatory constraints and perceptions and the need for improved imaging surrogates of drug activity. Included were modeling tumor growth and drug effects in a medical environment in which direct tumor sampling for biological effects can be problematic, potential new drugs under investigation and targets for drug discovery and development. The long trajectory and diverse impediments to novel drug discovery, and expectation that more than one drug will be needed to adequately inhibit critical intracellular tumor pathways were viewed as major disincentives for most pharmaceutical/biotechnology companies. While there were a few unanimities, one consensus is the need for continued and focused discussion among academic and industry scientists and clinicians to address tumor targets, new drug chemistry, and more time- and cost-efficient clinical trials based on surrogate end points.

  1. 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).

  2. Complementary Approaches to Existing Target Based Drug Discovery for Identifying Novel Drug Targets

    Directory of Open Access Journals (Sweden)

    Suhas Vasaikar

    2016-11-01

    Full Text Available In the past decade, it was observed that the relationship between the emerging New Molecular Entities and the quantum of R&D investment has not been favorable. There might be numerous reasons but few studies stress the introduction of target based drug discovery approach as one of the factors. Although a number of drugs have been developed with an emphasis on a single protein target, yet identification of valid target is complex. The approach focuses on an in vitro single target, which overlooks the complexity of cell and makes process of validation drug targets uncertain. Thus, it is imperative to search for alternatives rather than looking at success stories of target-based drug discovery. It would be beneficial if the drugs were developed to target multiple components. New approaches like reverse engineering and translational research need to take into account both system and target-based approach. This review evaluates the strengths and limitations of known drug discovery approaches and proposes alternative approaches for increasing efficiency against treatment.

  3. CNS Anticancer Drug Discovery and Development Conference White Paper.

    Science.gov (United States)

    Levin, Victor A; Tonge, Peter J; Gallo, James M; Birtwistle, Marc R; Dar, Arvin C; Iavarone, Antonio; Paddison, Patrick J; Heffron, Timothy P; Elmquist, William F; Lachowicz, Jean E; Johnson, Ted W; White, Forest M; Sul, Joohee; Smith, Quentin R; Shen, Wang; Sarkaria, Jann N; Samala, Ramakrishna; Wen, Patrick Y; Berry, Donald A; Petter, Russell C

    2015-11-01

    Following the first CNS Anticancer Drug Discovery and Development Conference, the speakers from the first 4 sessions and organizers of the conference created this White Paper hoping to stimulate more and better CNS anticancer drug discovery and development. The first part of the White Paper reviews, comments, and, in some cases, expands on the 4 session areas critical to new drug development: pharmacological challenges, recent drug approaches, drug targets and discovery, and clinical paths. Following this concise review of the science and clinical aspects of new CNS anticancer drug discovery and development, we discuss, under the rubric "Accelerating Drug Discovery and Development for Brain Tumors," further reasons why the pharmaceutical industry and academia have failed to develop new anticancer drugs for CNS malignancies and what it will take to change the current status quo and develop the drugs so desperately needed by our patients with malignant CNS tumors. While this White Paper is not a formal roadmap to that end, it should be an educational guide to clinicians and scientists to help move a stagnant field forward. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. Drug discovery and design: medical aspects

    National Research Council Canada - National Science Library

    Matsoukas, J; Mavromoustakos, T

    2002-01-01

    ... the role of membranes in drug activity and formulation. The book continues with the third part that covers the conformational analysis of bioactive drugs. The final part mainly touches aspects of the molecular targets and drug design. This part is actually broader in scope and covers biological aspects of medicinal chemistry. The logic of classificatio...

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

    Science.gov (United States)

    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

    2013-05-01

    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 demonstrated. This workflow was run once on the same 96 samples that the group had examined manually and the workflow cycled successfully through all of the samples, collected data from the same samples that were selected manually and located the same peaks of unmodeled density in the resulting difference Fourier

  10. 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.

  11. Epigenetic drug discovery for Alzheimer's disease.

    Science.gov (United States)

    Cacabelos, Ramón; Torrellas, Clara

    2014-09-01

    It is assumed that epigenetic modifications are reversible and could potentially be targeted by pharmacological and dietary interventions. Epigenetic drugs are gaining particular interest as potential candidates for the treatment of Alzheimer's disease (AD). This article covers relevant information from over 50 different epigenetic drugs including: DNA methyltransferase inhibitors; histone deacetylase inhibitors; histone acetyltransferase modulators; histone methyltransferase inhibitors; histone demethylase inhibitors; non-coding RNAs (microRNAs) and dietary regimes. The authors also review the pharmacoepigenomics and the pharmacogenomics of epigenetic drugs. The readers will gain insight into i) the classification of epigenetic drugs; ii) the mechanisms by which these drugs might be useful in AD; iii) the pharmacological properties of selected epigenetic drugs; iv) pharmacoepigenomics and the influence of epigenetic drugs on genes encoding CYP enzymes, transporters and nuclear receptors; and v) the genes associated with the pharmacogenomics of anti-dementia drugs. Epigenetic drugs reverse epigenetic changes in gene expression and might open future avenues in AD therapeutics. Unfortunately, clinical trials with this category of drugs are lacking in AD. The authors highlight the need for pharmacogenetic and pharmacoepigenetic studies to properly evaluate any efficacy and safety issues.

  12. 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.

  13. 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.

  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. 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.

  16. 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

  17. Minireview: Targeting GPCR Activated ERK Pathways for Drug Discovery.

    Science.gov (United States)

    Eishingdrelo, Haifeng; Kongsamut, Sathapana

    2013-01-01

    It has become clear in recent years that multiple signal transduction pathways are employed upon GPCR activation. One of the major cellular effectors activated by GPCRs is extracellular signal-regulated kinase (ERK). Both G-protein and β-arrestin mediated signaling pathways can lead to ERK activation. However, depending on activation pathway, the subcellular destination of activated ERK1/2 may be different. G-protein -dependent ERK activation results in the translocation of active ERK to the nucleus, whereas ERK activated via an arrestin-dependent mechanism remains largely in the cytoplasm. The subcellular location of activated ERK1/2 determines the downstream signaling cascade. Many substrates of ERK1/2 are found in the nucleus: nuclear transcription factors that participate in gene transcription, cell proliferation and differentiation. ERK1/2 substrates are also found in cytosol and other cellular organelles: they may play roles in translation, mitosis, apoptosis and cross-talk with other signaling pathways. Therefore, determining specific subcellular locations of activated ERK1/2 mediated by GPCR ligands would be important in correlating signaling pathways with cellular physiological functions. While GPCR-stimulated selective ERK pathway activation has been studied in several receptor systems, exploitation of these different signaling cascades for therapeutics has not yet been seriously pursued. Many old drug candidates were identified from screens based on G-protein signaling assays, and their activity on β-arrestin signaling pathways being mostly unknown, especially regarding their subcellular ERK pathways. With today's knowledge of complicated GPCR signaling pathways, drug discovery can no longer rely on single-pathway approaches. Since ERK activation is an important signaling pathway and associated with many physiological functions, targeting the ERK pathway, especially specific subcellular activation pathways should provide new avenues for GPCR drug

  18. 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.

  19. Locked nucleic acid: modality, diversity, and drug discovery

    DEFF Research Database (Denmark)

    Hagedorn, Peter H.; Persson, Robert; Funder, Erik D.

    2017-01-01

    Over the past 20 years, the field of RNA-targeted therapeutics has advanced based on discoveries of modified oligonucleotide chemistries, and an ever-increasing understanding of how to apply cellular assays to identify oligonucleotides with pharmacological properties in vivo. Locked nucleic acid ...... structural differences between oligonucleotides can often lead to substantial differences in their pharmacological properties. Here, we outline new principles for drug discovery exploiting oligonucleotide diversity to identify rare molecules with unique pharmacological properties....

  20. Leveraging big data to transform target selection and drug discovery.

    Science.gov (United States)

    Chen, B; Butte, A J

    2016-03-01

    The advances of genomics, sequencing, and high throughput technologies have led to the creation of large volumes of diverse datasets for drug discovery. Analyzing these datasets to better understand disease and discover new drugs is becoming more common. Recent open data initiatives in basic and clinical research have dramatically increased the types of data available to the public. The past few years have witnessed successful use of big data in many sectors across the whole drug discovery pipeline. In this review, we will highlight the state of the art in leveraging big data to identify new targets, drug indications, and drug response biomarkers in this era of precision medicine. © 2015 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

  1. Molecular topology as a novel approach for drug discovery.

    Science.gov (United States)

    Gálvez, Jorge; Gálvez-Llompart, María; García-Domenech, Ramón

    2012-02-01

    Molecular topology (MT) has emerged in recent years as a powerful approach for the in silico generation of new drugs. One key part of MT is that, in the process of drug design/discovery, there is no need for an explicit knowledge of a drug's mechanism of action unlike other drug discovery methods. In this review, the authors introduce the topic by explaining briefly the most common methodology used today in drug design/discovery and address the most important concepts of MT and the methodology followed (QSAR equations, LDA, etc.). Furthermore, the significant results achieved, from this approach, are outlined and discussed. The results outlined herein can be explained by considering that MT represents a new paradigm in the field of drug design. This means that it is not only an alternative method to the conventional methods, but it is also independent, that is, it represents a pathway to connect directly molecular structure with the experimental properties of the compounds (particularly drugs). Moreover, the process can be realized also in the reverse pathway, that is, designing new molecules from their topological pattern, what opens almost limitless expectations in new drugs development, given that the virtual universe of molecules is much greater than that of the existing ones.

  2. "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.

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

    African Journals Online (AJOL)

    Abstract: Infectious and parasitic diseases continue to threaten the health of million of people throughout the world, with the major burden being in developing countries. Many of the currently available drugs for the treatment of these diseases face setbacks such as insufficient efficacy, increasing loss of effectiveness due to ...

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

    African Journals Online (AJOL)

    Infectious and parasitic diseases continue to threaten the health of million of people throughout the world, with the major burden being in developing countries. Many of the currently available drugs for the treatment of these diseases face setbacks such as insufficient efficacy, increasing loss of effectiveness due to ...

  5. (Some) current concepts in antibacterial drug discovery.

    Science.gov (United States)

    van Geelen, Lasse; Meier, Dieter; Rehberg, Nidja; Kalscheuer, Rainer

    2018-04-01

    The rise of multidrug resistance in bacteria rendering pathogens unresponsive to many clinical drugs is widely acknowledged and considered a critical global healthcare issue. There is broad consensus that novel antibacterial chemotherapeutic options are extremely urgently needed. However, the development pipeline of new antibacterial drug lead structures is poorly filled and not commensurate with the scale of the problem since the pharmaceutical industry has shown reduced interest in antibiotic development in the past decades due to high economic risks and low profit expectations. Therefore, academic research institutions have a special responsibility in finding novel treatment options for the future. In this mini review, we want to provide a broad overview of the different approaches and concepts that are currently pursued in this research field.

  6. Special Issue: Novel Antifungal Drug Discovery

    Directory of Open Access Journals (Sweden)

    Maurizio Del Poeta

    2016-12-01

    Full Text Available This Special Issue is designed to highlight the latest research and development on new antifungal compounds with mechanisms of action different from the ones of polyenes, azoles, and echinocandins. The papers presented here highlight new pathways and targets that could be exploited for the future development of new antifungal agents to be used alone or in combination with existing antifungals. A computational model for better predicting antifungal drug resistance is also presented.

  7. 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.

  8. Barriers to Alzheimer disease drug discovery and development in academia.

    Science.gov (United States)

    Van Eldik, Linda J; Koppal, Tanuja; Watterson, D Martin

    2002-01-01

    The drug discovery and the drug development processes represent a continuum of recursive activities that range from initial drug target identification to final Food and Drug Administration approval and marketing of a new therapeutic. Drug discovery, as its name implies, is more exploratory and less focused in many cases, whereas drug development has a clinically defined endpoint and a specific disease goal. Academia has historically made major contributions to this process at the early discovery phases. However, current trends in the organization of the pharmaceutical industry suggest an expanded role for academia in the near future. Megamergers among major pharmaceutical corporations indicate their movement toward a focus on end-stage clinical trials, manufacturing, and marketing. There has been a parallel increase in outsourcing of intermediate steps to specialty small pharmaceutical, biotechnology, and contract service companies. The new paradigm suggests that academia will play an increasingly important role at the proof-of-principle stage of basic and clinical drug discovery research, in training the future skilled work force, and in close partnerships with small pharmaceutical and biotechnology companies. However, academic drug discovery research faces a set of barriers to progress, the relative importance of which varies with the home institution and the details of the research area. These barriers fall into four general categories: (1) the historical administrative structure and environment of academia; (2) the structure and emphasis of peer review panels that control research funding by government and private agencies; (3) the organization and operation of the academic infrastructure; and (4) the structure and availability of specialized resources and information management. Selected examples of barriers to drug discovery and drug development research and training in academia are presented, as are some specific recommendations designed to minimize or

  9. Recent Advances in Drug Discovery from South African Marine Invertebrates

    OpenAIRE

    Davies-Coleman, Michael T.; Veale, Clinton G. L.

    2015-01-01

    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-resist...

  10. 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.

  11. 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.

  12. Patient-derived tumour xenografts for breast cancer drug discovery.

    Science.gov (United States)

    Cassidy, John W; Batra, Ankita S; Greenwood, Wendy; Bruna, Alejandra

    2016-12-01

    Despite remarkable advances in our understanding of the drivers of human malignancies, new targeted therapies often fail to show sufficient efficacy in clinical trials. Indeed, the cost of bringing a new agent to market has risen substantially in the last several decades, in part fuelled by extensive reliance on preclinical models that fail to accurately reflect tumour heterogeneity. To halt unsustainable rates of attrition in the drug discovery process, we must develop a new generation of preclinical models capable of reflecting the heterogeneity of varying degrees of complexity found in human cancers. Patient-derived tumour xenograft (PDTX) models prevail as arguably the most powerful in this regard because they capture cancer's heterogeneous nature. Herein, we review current breast cancer models and their use in the drug discovery process, before discussing best practices for developing a highly annotated cohort of PDTX models. We describe the importance of extensive multidimensional molecular and functional characterisation of models and combination drug-drug screens to identify complex biomarkers of drug resistance and response. We reflect on our own experiences and propose the use of a cost-effective intermediate pharmacogenomic platform (the PDTX-PDTC platform) for breast cancer drug and biomarker discovery. We discuss the limitations and unanswered questions of PDTX models; yet, still strongly envision that their use in basic and translational research will dramatically change our understanding of breast cancer biology and how to more effectively treat it. © 2016 The authors.

  13. Open data in drug discovery and development: lessons from malaria.

    Science.gov (United States)

    Wells, Timothy N C; Willis, Paul; Burrows, Jeremy N; Hooft van Huijsduijnen, Rob

    2016-10-01

    There is a growing consensus that drug discovery thrives in an open environment. Here, we describe how the malaria community has embraced four levels of open data - open science, open innovation, open access and open source - to catalyse the development of new medicines, and consider principles that could enable open data approaches to be applied to other disease areas.

  14. 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...

  15. Genetics of rheumatoid arthritis contributes to biology and drug discovery

    NARCIS (Netherlands)

    Okada, Yukinori; Wu, Di; Trynka, Gosia; Raj, Towfique; Terao, Chikashi; Ikari, Katsunori; Kochi, Yuta; Ohmura, Koichiro; Suzuki, Akari; Yoshida, Shinji; Graham, Robert R.; Manoharan, Arun; Ortmann, Ward; Bhangale, Tushar; Denny, Joshua C.; Carroll, Robert J.; Eyler, Anne E.; Greenberg, Jeffrey D.; Kremer, Joel M.; Pappas, Dimitrios A.; Jiang, Lei; Yin, Jian; Ye, Lingying; Su, Ding-Feng; Yang, Jian; Xie, Gang; Keystone, Ed; Westra, Harm-Jan; Esko, Tõnu; Metspalu, Andres; Zhou, Xuezhong; Gupta, Namrata; Mirel, Daniel; Stahl, Eli A.; Diogo, Dorothée; Cui, Jing; Liao, Katherine; Guo, Michael H.; Myouzen, Keiko; Kawaguchi, Takahisa; Coenen, Marieke J. H.; van Riel, Piet L. C. M.; van de Laar, Mart A. F. J.; Guchelaar, Henk-Jan; Huizinga, Tom W. J.; Dieudé, Philippe; Mariette, Xavier; Bridges, S. Louis; Zhernakova, Alexandra; Toes, Rene E. M.; Tak, Paul P.; Miceli-Richard, Corinne; Bang, So-Young; Lee, Hye-Soon; Martin, Javier; Gonzalez-Gay, Miguel A.; Rodriguez-Rodriguez, Luis; Rantapää-Dahlqvist, Solbritt; Arlestig, Lisbeth; Choi, Hyon K.; Kamatani, Yoichiro; Galan, Pilar; Lathrop, Mark; Eyre, Steve; Bowes, John; Barton, Anne; de Vries, Niek; Moreland, Larry W.; Criswell, Lindsey A.; Karlson, Elizabeth W.; Taniguchi, Atsuo; Yamada, Ryo; Kubo, Michiaki; Liu, Jun S.; Bae, Sang-Cheol; Worthington, Jane; Padyukov, Leonid; Klareskog, Lars; Gregersen, Peter K.; Raychaudhuri, Soumya; Stranger, Barbara E.; de Jager, Philip L.; Franke, Lude; Visscher, Peter M.; Brown, Matthew A.; Yamanaka, Hisashi; Mimori, Tsuneyo; Takahashi, Atsushi; Xu, Huji; Behrens, Timothy W.; Siminovitch, Katherine A.; Momohara, Shigeki; Matsuda, Fumihiko; Yamamoto, Kazuhiko; Plenge, Robert M.; Lee, Annette; Martin, Paul; Stahl, Eli; Viatte, Sebastien; McAllister, Kate; Amos, Christopher I.; Wijmenga, Cisca; Alfredsson, Lars; Hu, Xinli; Sandor, Cynthia; de Bakker, Paul I. W.; Davila, Sonia; Khor, Chiea Chuen; Heng, Khai Koon; Andrews, Robert; Edkins, Sarah; Hunt, Sarah E.; Langford, Cordelia; Symmons, Deborah; Concannon, Pat; Onengut-Gumuscu, Suna; Rich, Stephen S.; Deloukas, Panos; Ärlsetig, Lisbeth; Kurreman, Fina; Nishida, Nao; Ohmiya, Hiroko; Takahashi, Meiko; Sawada, Tetsuji; Nishioka, Yuichi; Yukioka, Masao; Matsubara, Tsukasa; Wakitani, Shigeyuki; Teshima, Ryota; Tohma, Shigeto; Takasugi, Kiyoshi; Shimada, Kota; Murasawa, Akira; Honjo, Shigeru; Matsuo, Keitaro; Tanaka, Hideo; Tajima, Kazuo; Suzuki, Taku; Iwamoto, Takuji; Kawamura, Yoshiya; Tanii, Hisashi; Okazaki, Yuji; Sasaki, Tsukasa; Tokunaga, Katsushi; Nakamura, Yusuke; Kamatani, Naoyuki

    2014-01-01

    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

  16. Stabilization of protein-protein interactions in drug discovery.

    Science.gov (United States)

    Andrei, Sebastian A; Sijbesma, Eline; Hann, Michael; Davis, Jeremy; O'Mahony, Gavin; Perry, Matthew W D; Karawajczyk, Anna; Eickhoff, Jan; Brunsveld, Luc; Doveston, Richard G; Milroy, Lech-Gustav; Ottmann, Christian

    2017-09-01

    PPIs are involved in every disease and specific modulation of these PPIs with small molecules would significantly improve our prospects of developing therapeutic agents. Both industry and academia have engaged in the identification and use of PPI inhibitors. However in comparison, the opposite strategy of employing small-molecule stabilizers of PPIs is underrepresented in drug discovery. Areas covered: PPI stabilization has not been exploited in a systematic manner. Rather, this concept validated by a number of therapeutically used natural products like rapamycin and paclitaxel has been shown retrospectively to be the basis of the activity of synthetic molecules originating from drug discovery projects among them lenalidomide and tafamidis. Here, the authors cover the growing number of synthetic small-molecule PPI stabilizers to advocate for a stronger consideration of this as a drug discovery approach. Expert opinion: Both the natural products and the growing number of synthetic molecules show that PPI stabilization is a viable strategy for drug discovery. There is certainly a significant challenge to adapt compound libraries, screening techniques and downstream methodologies to identify, characterize and optimize PPI stabilizers, but the examples of molecules reviewed here in our opinion justify these efforts.

  17. Advances in capillary electrophoresis and the implications for drug discovery.

    Science.gov (United States)

    Ouimet, Claire M; D'amico, Cara I; Kennedy, Robert T

    2017-02-01

    Many screening platforms are prone to assay interferences that can be avoided by directly measuring the target or enzymatic product. Capillary electrophoresis (CE) and microchip electrophoresis (MCE) have been applied in a variety of formats to drug discovery. CE provides direct detection of the product allowing for the identification of some forms of assay interference. The high efficiency, rapid separations, and low volume requirements make CE amenable to drug discovery. Areas covered: This article describes advances in capillary electrophoresis throughput, sample introduction, and target assays as they pertain to drug discovery and screening. Instrumental advances discussed include integrated droplet microfluidics platforms and multiplexed arrays. Applications of CE to assays of diverse drug discovery targets, including enzymes and affinity interactions are also described. Expert opinion: Current screening with CE does not fully take advantage of the throughputs or low sample volumes possible with CE and is most suitable as a secondary screening method or for screens that are inaccessible with more common platforms. With further development, droplet microfluidics coupled to MCE could take advantage of the low sample requirements by performing assays on the nanoliter scale at high throughput.

  18. Drug-drug interaction discovery and demystification using Semantic Web technologies.

    Science.gov (United States)

    Noor, Adeeb; Assiri, Abdullah; Ayvaz, Serkan; Clark, Connor; Dumontier, Michel

    2017-05-01

    To develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs. A mechanism-based DDI knowledge base was constructed by integrating knowledge from several existing sources at the pharmacokinetic, pharmacodynamic, pharmacogenetic, and multipathway interaction levels. A query-based framework was then created to utilize this integrated knowledge base in conjunction with 9 inference rules to infer mechanistic explanations for asserted DDIs and deduce potential DDIs. The drug-drug interactions discovery and demystification (D3) system achieved an overall 85% recall rate in terms of inferring mechanistic explanations for the DDIs integrated into its knowledge base, while demonstrating a 61% precision rate in terms of the inference or lack of inference of mechanistic explanations for a balanced, randomly selected collection of interacting and noninteracting drug pairs. The successful demonstration of the D3 system's ability to confirm interactions involving well-studied drugs enhances confidence in its ability to deduce interactions involving less-studied drugs. In its demonstration, the D3 system infers putative explanations for most of its integrated DDIs. Further enhancements to this work in the future might include ranking interaction mechanisms based on likelihood of applicability, determining the likelihood of deduced DDIs, and making the framework publicly available. The D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy.

  19. Systems biology and biomarker discovery

    Energy Technology Data Exchange (ETDEWEB)

    Rodland, Karin D.

    2010-12-01

    Medical practitioners have always relied on surrogate markers of inaccessible biological processes to make their diagnosis, whether it was the pallor of shock, the flush of inflammation, or the jaundice of liver failure. Obviously, the current implementation of biomarkers for disease is far more sophisticated, relying on highly reproducible, quantitative measurements of molecules that are often mechanistically associated with the disease in question, as in glycated hemoglobin for the diagnosis of diabetes [1] or the presence of cardiac troponins in the blood for confirmation of myocardial infarcts [2]. In cancer, where the initial symptoms are often subtle and the consequences of delayed diagnosis often drastic for disease management, the impetus to discover readily accessible, reliable, and accurate biomarkers for early detection is compelling. Yet despite years of intense activity, the stable of clinically validated, cost-effective biomarkers for early detection of cancer is pathetically small and still dominated by a handful of markers (CA-125, CEA, PSA) first discovered decades ago. It is time, one could argue, for a fresh approach to the discovery and validation of disease biomarkers, one that takes full advantage of the revolution in genomic technologies and in the development of computational tools for the analysis of large complex datasets. This issue of Disease Markers is dedicated to one such new approach, loosely termed the 'Systems Biology of Biomarkers'. What sets the Systems Biology approach apart from other, more traditional approaches, is both the types of data used, and the tools used for data analysis - and both reflect the revolution in high throughput analytical methods and high throughput computing that has characterized the start of the twenty first century.

  20. High-throughput protein crystallography and drug discovery.

    Science.gov (United States)

    Tickle, Ian; Sharff, Andrew; Vinkovic, Mladen; Yon, Jeff; Jhoti, Harren

    2004-10-20

    Single crystal X-ray diffraction is the technique of choice for studying the interactions of small organic molecules with proteins by determining their three-dimensional structures; however the requirement for highly purified protein and lack of process automation have traditionally limited its use in this field. Despite these shortcomings, the use of crystal structures of therapeutically relevant drug targets in pharmaceutical research has increased significantly over the last decade. The application of structure-based drug design has resulted in several marketed drugs and is now an established discipline in most pharmaceutical companies. Furthermore, the recently published full genome sequences of Homo sapiens and a number of micro-organisms have provided a plethora of new potential drug targets that could be utilised in structure-based drug design programs. In order to take maximum advantage of this explosion of information, techniques have been developed to automate and speed up the various procedures required to obtain protein crystals of suitable quality, to collect and process the raw X-ray diffraction data into usable structural information, and to use three-dimensional protein structure as a basis for drug discovery and lead optimisation. This tutorial review covers the various technologies involved in the process pipeline for high-throughput protein crystallography as it is currently being applied to drug discovery. It is aimed at synthetic and computational chemists, as well as structural biologists, in both academia and industry, who are interested in structure-based drug design.

  1. 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.

  2. 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.

  3. 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 .

  4. 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.

  5. A look at ligand binding thermodynamics in drug discovery.

    Science.gov (United States)

    Claveria-Gimeno, Rafael; Vega, Sonia; Abian, Olga; Velazquez-Campoy, Adrian

    2017-04-01

    Drug discovery is a challenging endeavor requiring the interplay of many different research areas. Gathering information on ligand binding thermodynamics may help considerably in reducing the risk within a high uncertainty scenario, allowing early rejection of flawed compounds and pushing forward optimal candidates. In particular, the free energy, the enthalpy, and the entropy of binding provide fundamental information on the intermolecular forces driving such interaction. Areas covered: The authors review the current status and recent developments in the application of ligand binding thermodynamics in drug discovery. The thermodynamic binding profile (Gibbs energy, enthalpy, and entropy of binding) can be used for lead selection and optimization (binding enthalpy, selectivity, and adaptability). Expert opinion: Binding thermodynamics provides fundamental information on the forces driving the formation of the drug-target complex. It has been widely accepted that binding thermodynamics may be used as a decision criterion along the ligand optimization process in drug discovery and development. In particular, the binding enthalpy may be used as a guide when selecting and optimizing compounds over a set of potential candidates. However, this has been recently called into question by arguing certain difficulties and in the light of certain experimental examples.

  6. 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.

  7. 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.

  8. Latest advances in molecular topology applications for drug discovery.

    Science.gov (United States)

    Zanni, Riccardo; Galvez-Llompart, Maria; García-Domenech, Ramón; Galvez, Jorge

    2015-01-01

    Molecular topology (MT) has emerged in recent years as a powerful approach for the in silico generation of new drugs. In the last decade, its application has become more and more popular among the leading research groups in the field of quantitative structure-activity relationships (QSAR) and drug design. This has, in turn, contributed to the rapid development of new techniques and applications of MT in QSAR studies, as well as the introduction of new topological indices. This review collates the main innovative techniques in the field of MT and provides a description of the novel topological indices recently introduced, through an exhaustive recompilation of the most significant works carried out by the leading research groups in the field of drug design and discovery. The objective is to show the importance of MT methods combined with the effectiveness of the descriptors. Recent years have witnessed a remarkable rise in QSAR methods based on MT and its application to drug design. New methodologies have been introduced in the area such as QSAR multi-target, Markov networks or perturbation methods. Moreover, novel topological indices, such as Bourgas' descriptors and other new concepts as the derivative of a graph or cliques capable to distinguish between conformers, have also been introduced. New drugs have also been discovered, including anticonvulsants, anineoplastics, antimalarials or antiallergics, just to name a few. In the authors' opinion, MT and QSAR have moved from an attractive possibility to representing a foundation stone in the process of drug discovery.

  9. A Historical Overview of Natural Products in Drug Discovery

    Directory of Open Access Journals (Sweden)

    Daniel A. Dias

    2012-04-01

    Full Text Available Historically, natural products have been used since ancient times and in folklore for the treatment of many diseases and illnesses. Classical natural product chemistry methodologies enabled a vast array of bioactive secondary metabolites from terrestrial and marine sources to be discovered. Many of these natural products have gone on to become current drug candidates. This brief review aims to highlight historically significant bioactive marine and terrestrial natural products, their use in folklore and dereplication techniques to rapidly facilitate their discovery. Furthermore a discussion of how natural product chemistry has resulted in the identification of many drug candidates; the application of advanced hyphenated spectroscopic techniques to aid in their discovery, the future of natural product chemistry and finally adopting metabolomic profiling and dereplication approaches for the comprehensive study of natural product extracts will be discussed.

  10. Drug Discovery of Therapies for Duchenne Muscular Dystrophy.

    Science.gov (United States)

    Blat, Yuval; Blat, Shachar

    2015-12-01

    Duchenne muscular dystrophy (DMD) is a genetic, lethal, muscle disorder caused by the loss of the muscle protein, dystrophin, leading to progressive loss of muscle fibers and muscle weakness. Drug discovery efforts targeting DMD have used two main approaches: (1) the restoration of dystrophin expression or the expression of a compensatory protein, and (2) the mitigation of downstream pathological mechanisms, including dysregulated calcium homeostasis, oxidative stress, inflammation, fibrosis, and muscle ischemia. The aim of this review is to introduce the disease, its pathophysiology, and the available research tools to a drug discovery audience. This review will also detail the most promising therapies that are currently being tested in clinical trials or in advanced preclinical models. © 2015 Society for Laboratory Automation and Screening.

  11. The Evolving Role of Chemical Synthesis in Antibacterial Drug Discovery

    Science.gov (United States)

    Wright, Peter M.; Seiple, Ian B.; Myers, Andrew G.

    2015-01-01

    The discovery and implementation of antibiotics in the early twentieth century transformed human health and wellbeing. Chemical synthesis enabled the development of the first antibacterial substances, organoarsenicals and sulfa drugs, but these were soon outshone by a host of more powerful and vastly more complex antibiotics from nature: penicillin, streptomycin, tetracycline, and erythromycin, among others. These primary defences are now significantly less effective as an unavoidable consequence of rapid evolution of resistance within pathogenic bacteria, made worse by widespread misuse of antibiotics. For decades medicinal chemists replenished the arsenal of antibiotics by semisynthetic and to a lesser degree fully synthetic routes, but economic factors have led to a subsidence of this effort, which places society on the precipice of a disaster. We believe that the strategic application of modern chemical synthesis to antibacterial drug discovery must play a critical role if a crisis of global proportions is to be averted. PMID:24990531

  12. The Role of Target Binding Kinetics in Drug Discovery.

    Science.gov (United States)

    Guo, Dong; Heitman, Laura H; IJzerman, Adriaan P

    2015-11-01

    Traditionally structure-activity/affinity relationships (SAR) have dominated research in medicinal chemistry. However, structure-kinetics relationships (SKR) can be very informative too. In this viewpoint we explore the molecular determinants of binding kinetics and discuss challenges for future binding kinetics studies. A scheme for future kinetics-directed drug design and discovery is also proposed. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. 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.

  14. Catecholamine receptors: prototypes for GPCR-based drug discovery.

    Science.gov (United States)

    Emery, Andrew C

    2013-01-01

    Drugs acting at G protein-coupled receptors (GPCRs) constitute ~40% of those in current clinical use. GPCR-based drug discovery remains at the forefront of drug development, especially for new treatments for psychiatric illness and neurological disease. Here, the basic framework of GPCR signaling learned through the elucidation of catecholamine receptor signaling through G proteins and β-arrestins, and X-ray crystallographic structure determination is reviewed. In silico docking studies developed in tandem with confirmatory empirical data gathering from binding and signaling experiments have allowed this basic framework to be expanded to drug hunting through predictive in silico searching as well as high-throughput and high-content screening approaches. For efforts moving forward for the deployment of new GPCR-acting drugs, collaborative efforts between industry and government/academic research in target validation at the molecular and cellular levels have become progressively more common. Polypharmacological approaches have become increasingly available for learning more about the mechanisms of GPCR-targeted drugs, based on interaction not with a single, but with a wide range of GPCR targets. These approaches are likely to aid in drug repurposing efforts, yield valuable insight on the side effects of currently employed drugs, and allow for a clearer picture of the actual targets of "atypical" drugs used in a variety of therapeutic contexts. © 2013 Elsevier Inc. All rights reserved.

  15. 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.

  16. Recent Advances in Drug Discovery from South African Marine Invertebrates.

    Science.gov (United States)

    Davies-Coleman, Michael T; Veale, Clinton G L

    2015-10-14

    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.

  17. 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.

  18. Low Data Drug Discovery with One-Shot Learning.

    Science.gov (United States)

    Altae-Tran, Han; Ramsundar, Bharath; Pappu, Aneesh S; Pande, Vijay

    2017-04-26

    Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds (Ma, J. et al. J. Chem. Inf. 2015, 55, 263-274). However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the iterative refinement long short-term memory, that, when combined with graph convolutional neural networks, significantly improves learning of meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery (Ramsundar, B. deepchem.io. https://github.com/deepchem/deepchem, 2016).

  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. Mouse Models of Type 2 Diabetes Mellitus in Drug Discovery.

    Science.gov (United States)

    Baribault, Helene

    2016-01-01

    Type 2 diabetes is a fast-growing epidemic in industrialized countries, associated with obesity, lack of physical exercise, aging, family history, and ethnic background. Diagnostic criteria are elevated fasting or postprandial blood glucose levels, a consequence of insulin resistance. Early intervention can help patients to revert the progression of the disease together with lifestyle changes or monotherapy. Systemic glucose toxicity can have devastating effects leading to pancreatic beta cell failure, blindness, nephropathy, and neuropathy, progressing to limb ulceration or even amputation. Existing treatments have numerous side effects and demonstrate variability in individual patient responsiveness. However, several emerging areas of discovery research are showing promises with the development of novel classes of antidiabetic drugs.The mouse has proven to be a reliable model for discovering and validating new treatments for type 2 diabetes mellitus. We review here commonly used methods to measure endpoints relevant to glucose metabolism which show good translatability to the diagnostic of type 2 diabetes in humans: baseline fasting glucose and insulin, glucose tolerance test, insulin sensitivity index, and body type composition. Improvements on these clinical values are essential for the progression of a novel potential therapeutic molecule through a preclinical and clinical pipeline.

  1. GLIDA: GPCR-ligand database for chemical genomic drug discovery.

    Science.gov (United States)

    Okuno, Yasushi; Yang, Jiyoon; Taneishi, Kei; Yabuuchi, Hiroaki; Tsujimoto, Gozoh

    2006-01-01

    G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GPCR-LIgand DAtabase (GLIDA) is a novel public GPCR-related chemical genomic database that is primarily focused on the correlation of information between GPCRs and their ligands. It provides correlation data between GPCRs and their ligands, along with chemical information on the ligands, as well as access information to the various web databases regarding GPCRs. These data are connected with each other in a relational database, allowing users in the field of GPCR-related drug discovery to easily retrieve such information from either biological or chemical starting points. GLIDA includes structure similarity search functions for the GPCRs and for their ligands. Thus, GLIDA can provide correlation maps linking the searched homologous GPCRs (or ligands) with their ligands (or GPCRs). By analyzing the correlation patterns between GPCRs and ligands, we can gain more detailed knowledge about their interactions and improve drug design efforts by focusing on inferred candidates for GPCR-specific drugs. GLIDA is publicly available at http://gdds.pharm.kyoto-u.ac.jp:8081/glida. We hope that it will prove very useful for chemical genomic research and GPCR-related drug discovery.

  2. 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.

  3. Drug discovery and development for rare genetic disorders.

    Science.gov (United States)

    Sun, Wei; Zheng, Wei; Simeonov, Anton

    2017-09-01

    Approximately 7,000 rare diseases affect millions of individuals in the United States. Although rare diseases taken together have an enormous impact, there is a significant gap between basic research and clinical interventions. Opportunities now exist to accelerate drug development for the treatment of rare diseases. Disease foundations and research centers worldwide focus on better understanding rare disorders. Here, the state-of-the-art drug discovery strategies for small molecules and biological approaches for orphan diseases are reviewed. Rare diseases are usually genetic diseases; hence, employing pharmacogenetics to develop treatments and using whole genome sequencing to identify the etiologies for such diseases are appropriate strategies to exploit. Beginning with high throughput screening of small molecules, the benefits and challenges of target-based and phenotypic screens are discussed. Explanations and examples of drug repurposing are given; drug repurposing as an approach to quickly move programs to clinical trials is evaluated. Consideration is given to the category of biologics which include gene therapy, recombinant proteins, and autologous transplants. Disease models, including animal models and induced pluripotent stem cells (iPSCs) derived from patients, are surveyed. Finally, the role of biomarkers in drug discovery and development, as well as clinical trials, is elucidated. © 2017 Wiley Periodicals, Inc.

  4. Synthesis of Chiral Building Blocks for Use in Drug Discovery

    Directory of Open Access Journals (Sweden)

    Rustum S. Boyce

    2004-05-01

    Full Text Available In the past decade there has been a significant growth in the sales of pharmaceutical drugs worldwide, but more importantly there has been a dramatic growth in the sales of single enantiomer drugs. The pharmaceutical industry has a rising demand for chiral intermediates and research reagents because of the continuing imperative to improve drug efficacy. This in turn impacts on researchers involved in preclinical discovery work. Besides traditional chiral pool and resolution of racemates as sources of chiral building blocks, many new synthetic methods including a great variety of catalytic reactions have been developed which facilitate the production of complex chiral drug candidates for clinical trials. The most ambitious technique is to synthesise homochiral compounds from non-chiral starting materials using chiral metal catalysts and related chemistry. Examples of the synthesis of chiral building blocks from achiral materials utilizing asymmetric hydrogenation and asymmetric epoxidation are presented.

  5. 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.

  6. An introduction to web scale discovery systems.

    Science.gov (United States)

    Hoy, Matthew B

    2012-01-01

    This article explores the basic principles of web-scale discovery systems and how they are being implemented in libraries. "Web scale discovery" refers to a class of products that index a vast number of resources in a wide variety formats and allow users to search for content in the physical collection, print and electronic journal collections, and other resources from a single search box. Search results are displayed in a manner similar to Internet searches, in a relevance ranked list with links to online content. The advantages and disadvantages of these systems are discussed, and a list of popular discovery products is provided. A list of library websites with discovery systems currently implemented is also provided.

  7. [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.

  8. Understanding mechanisms of toxicity: Insights from drug discovery research

    International Nuclear Information System (INIS)

    Houck, Keith A.; Kavlock, Robert J.

    2008-01-01

    Toxicology continues to rely heavily on use of animal testing for prediction of potential for toxicity in humans. Where mechanisms of toxicity have been elucidated, for example endocrine disruption by xenoestrogens binding to the estrogen receptor, in vitro assays have been developed as surrogate assays for toxicity prediction. This mechanistic information can be combined with other data such as exposure levels to inform a risk assessment for the chemical. However, there remains a paucity of such mechanistic assays due at least in part to lack of methods to determine specific mechanisms of toxicity for many toxicants. A means to address this deficiency lies in utilization of a vast repertoire of tools developed by the drug discovery industry for interrogating the bioactivity of chemicals. This review describes the application of high-throughput screening assays as experimental tools for profiling chemicals for potential for toxicity and understanding underlying mechanisms. The accessibility of broad panels of assays covering an array of protein families permits evaluation of chemicals for their ability to directly modulate many potential targets of toxicity. In addition, advances in cell-based screening have yielded tools capable of reporting the effects of chemicals on numerous critical cell signaling pathways and cell health parameters. Novel, more complex cellular systems are being used to model mammalian tissues and the consequences of compound treatment. Finally, high-throughput technology is being applied to model organism screens to understand mechanisms of toxicity. However, a number of formidable challenges to these methods remain to be overcome before they are widely applicable. Integration of successful approaches will contribute towards building a systems approach to toxicology that will provide mechanistic understanding of the effects of chemicals on biological systems and aid in rationale risk assessments

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

    DEFF Research Database (Denmark)

    Lin, Yu; Mehta, Saurabh; Küçük-McGinty, Hande

    2017-01-01

    characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein...... domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models......BACKGROUND: 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...

  10. 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.

  11. Structural Genomics and Drug Discovery for Infectious Diseases

    Energy Technology Data Exchange (ETDEWEB)

    Anderson, W.F.

    2010-09-03

    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.

  12. Modern advances in heterocyclic chemistry in drug discovery.

    Science.gov (United States)

    Taylor, Alexandria P; Robinson, Ralph P; Fobian, Yvette M; Blakemore, David C; Jones, Lyn H; Fadeyi, Olugbeminiyi

    2016-07-12

    New advances in synthetic methodologies that allow rapid access to a wide variety of functionalized heterocyclic compounds are of critical importance to the medicinal chemist as it provides the ability to expand the available drug-like chemical space and drive more efficient delivery of drug discovery programs. Furthermore, the development of robust synthetic routes that can readily generate bulk quantities of a desired compound help to accelerate the drug development process. While established synthetic methodologies are commonly utilized during the course of a drug discovery program, the development of innovative heterocyclic syntheses that allow for different bond forming strategies are having a significant impact in the pharmaceutical industry. This review will focus on recent applications of new methodologies in C-H activation, photoredox chemistry, borrowing hydrogen catalysis, multicomponent reactions, regio- and stereoselective syntheses, as well as other new, innovative general syntheses for the formation and functionalization of heterocycles that have helped drive project delivery. Additionally, the importance and value of collaborations between industry and academia in shaping the development of innovative synthetic approaches to functionalized heterocycles that are of greatest interest to the pharmaceutical industry will be highlighted.

  13. In silico drug combination discovery for personalized cancer therapy.

    Science.gov (United States)

    Jeon, Minji; Kim, Sunkyu; Park, Sungjoon; Lee, Heewon; Kang, Jaewoo

    2018-03-19

    Drug combination therapy, which is considered as an alternative to single drug therapy, can potentially reduce resistance and toxicity, and have synergistic efficacy. As drug combination therapies are widely used in the clinic for hypertension, asthma, and AIDS, they have also been proposed for the treatment of cancer. However, it is difficult to select and experimentally evaluate effective combinations because not only is the number of cancer drug combinations extremely large but also the effectiveness of drug combinations varies depending on the genetic variation of cancer patients. A computational approach that prioritizes the best drug combinations considering the genetic information of a cancer patient is necessary to reduce the search space. We propose an in-silico method for personalized drug combination therapy discovery. We predict the synergy between two drugs and a cell line using genomic information, targets of drugs, and pharmacological information. We calculate and predict the synergy scores of 583 drug combinations for 31 cancer cell lines. For feature dimension reduction, we select the mutations or expression levels of the genes in cancer-related pathways. We also used various machine learning models. Extremely Randomized Trees (ERT), a tree-based ensemble model, achieved the best performance in the synergy score prediction regression task. The correlation coefficient between the synergy scores predicted by ERT and the actual observations is 0.738. To compare with an existing drug combination synergy classification model, we reformulate the problem as a binary classification problem by thresholding the synergy scores. ERT achieved an F1 score of 0.954 when synergy scores of 20 and -20 were used as the threshold, which is 8.7% higher than that obtained by the state-of-the-art baseline model. Moreover, the model correctly predicts the most synergistic combination, from approximately 100 candidate drug combinations, as the top choice for 15 out of the

  14. Emerging principles in protease-based drug discovery.

    Science.gov (United States)

    Drag, Marcin; Salvesen, Guy S

    2010-09-01

    Proteases have an important role in many signalling pathways, and represent potential drug targets for diseases ranging from cardiovascular disorders to cancer, as well as for combating many parasites and viruses. Although inhibitors of well-established protease targets such as angiotensin-converting enzyme and HIV protease have shown substantial therapeutic success, developing drugs for new protease targets has proved challenging in recent years. This in part could be due to issues such as the difficulty of achieving selectivity when targeting protease active sites. This Perspective discusses the general principles in protease-based drug discovery, highlighting the lessons learned and the emerging strategies, such as targeting allosteric sites, which could help harness the therapeutic potential of new protease targets.

  15. 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.

  16. Coactivators in assay design for nuclear hormone receptor drug discovery.

    Science.gov (United States)

    Chen, Taosheng; Xie, Wen; Agler, Michele; Banks, Martyn

    2003-12-01

    Nuclear hormone receptors (NHRs) represent one of the most important drug targets in terms of therapeutic applications. The NHR superfamily consists of a family of DNA binding transcription factors whose function can be controlled by small molecules (steroids or organic acids). Therefore, NHRs are suitable protein targets for the therapies of human diseases. Some of the current marketed drugs, including several anticancer and antidiabetic drugs, are known to target NHRs. Examples include the anticancer and retinoid X receptor-targeting Targretin and the antidiabetic and peroxisome proliferative-activated receptor-gamma-targeting thiaozolidinediones. More NHR-targeting drugs are expected in the coming years. Identification of specific NHR modulators, as well as identification of ligands for orphan NHRs, will lead to new therapies for many human diseases. Many pharmaceutical companies are investing in NHR-targeted drugs, which are estimated to be 10-15% of the US dollars 400 billion global pharmaceutical market. This minireview discusses various aspects of NHR drug discovery, with a focus on the application of NHR coactivators in assay design for NHR ligand identification.

  17. 'Big data' approaches for novel anti-cancer drug discovery.

    Science.gov (United States)

    Benstead-Hume, Graeme; Wooller, Sarah K; Pearl, Frances M G

    2017-06-01

    The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Recent advances in platform technologies and the increasing availability of biological 'big data' are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. The discoveries made using these new technologies may lead to novel therapeutic interventions. Areas covered: The authors discuss the current approaches that use 'big data' to identify cancer drivers. These approaches include the analysis of genomic sequencing data, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. They review how big data is being used to identify novel drug targets. The authors then provide an overview of the available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs.

  18. 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.

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

    DEFF Research Database (Denmark)

    Hauser, Alexander Sebastian; Gloriam, David E.; Attwood, Misty M.

    2017-01-01

    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...

  20. Biomolecular Network-Based Synergistic Drug Combination Discovery

    Directory of Open Access Journals (Sweden)

    Xiangyi Li

    2016-01-01

    Full Text Available Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

  1. 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.

  2. 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.

  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. 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).

  5. Leveraging human genetics to guide drug target discovery.

    Science.gov (United States)

    Stitziel, Nathan O; Kathiresan, Sekar

    2017-07-01

    Identifying appropriate molecular targets is a critical step in drug development. Despite many advantages, the traditional tools of observational epidemiology and cellular or animal models of disease can be misleading in identifying causal pathways likely to lead to successful therapeutics. Here, we review some favorable aspects of human genetics studies that have the potential to accelerate drug target discovery. These include using genetic studies to identify pathways relevant to human disease, leveraging human genetics to discern causal relationships between biomarkers and disease, and studying genetic variation in humans to predict the potential efficacy and safety of inhibitory compounds aimed at molecular targets. We present some examples taken from studies of plasma lipids and coronary artery disease to highlight how human genetics can accelerate therapeutics development. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. 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.

  7. 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

  8. Biased agonism: An emerging paradigm in GPCR drug discovery.

    Science.gov (United States)

    Rankovic, Zoran; Brust, Tarsis F; Bohn, Laura M

    2016-01-15

    G protein coupled receptors have historically been one of the most druggable classes of cellular proteins. The members of this large receptor gene family couple to primary effectors, G proteins, that have built in mechanisms for regeneration and amplification of signaling with each engagement of receptor and ligand, a kinetic event in itself. In recent years GPCRs, have been found to interact with arrestin proteins to initiate signal propagation in the absence of G protein interactions. This pinnacle observation has changed a previously held notion of the linear spectrum of GPCR efficacy and uncovered a new paradigm in GPCR research and drug discovery that relies on multidimensionality of GPCR signaling. Ligands were found that selectively confer activity in one pathway over another, and this phenomenon has been referred to as 'biased agonism' or 'functional selectivity'. While great strides in the understanding of this phenomenon have been made in recent years, two critical questions still dominate the field: How can we rationally design biased GPCR ligands, and ultimately, which physiological responses are due to G protein versus arrestin interactions? This review will discuss the current understanding of some of the key aspects of biased signaling that are related to these questions, including mechanistic insights in the nature of biased signaling and methods for measuring ligand bias, as well as relevant examples of drug discovery applications and medicinal chemistry strategies that highlight the challenges and opportunities in this rapidly evolving field. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Agreement of drug discovery data with Benford's law.

    Science.gov (United States)

    Orita, Masaya; Hagiwara, Yosuke; Moritomo, Ayako; Tsunoyama, Kazuhisa; Watanabe, Toshihiro; Ohno, Kazuki

    2013-01-01

    The ever-increasing rate of drug discovery data has complicated data analysis and potentially compromised data quality due to factors such as data handling errors. Parallel to this concern is the rise in blatant scientific misconduct. Combined, these problems highlight the importance of developing a method that can be used to systematically assess data quality. Benford's law has been used to discover data manipulation and data fabrication in various fields. In the authors' previous studies, it was demonstrated that the distribution of the corresponding activity and solubility data followed Benford's law distribution. It was also shown that too intense a selection of training data sets of regression model can disrupt Benford's law. Here, the authors present the application of Benford's law to a wider range of drug discovery data such as microarray and sequence data. They also suggest that Benford's law could also be applied to model building and reliability for structure-activity relationship study. Finally, the authors propose a protocol based on Benford's law which will provide researchers with an efficient method for data quality assessment. However, multifaceted quality control such as combinatorial use with data visualization may also be needed to further improve its reliability.

  10. The beautiful cell: high-content screening in drug discovery.

    Science.gov (United States)

    Bickle, Marc

    2010-09-01

    The term "high-content screening" has become synonymous with imaging screens using automated microscopes and automated image analysis. The term was coined a little over 10 years ago. Since then the technology has evolved considerably and has established itself firmly in the drug discovery and development industry. Both the instruments and the software controlling the instruments and analyzing the data have come to maturity, so the full benefits of high-content screening can now be realized. Those benefits are the capability of carrying out phenotypic multiparametric cellular assays in an unbiased, fully automated, and quantitative fashion. Automated microscopes and automated image analysis are being applied at all stages of the drug discovery and development pipeline. All major pharmaceutical companies have adopted the technology and it is in the process of being embraced broadly by the academic community. This review aims at describing the current capabilities and limits of the technology as well as highlighting necessary developments that are required to exploit fully the potential of high-content screening and analysis.

  11. Exploiting complexity and the robustness of network architecture for drug discovery.

    Science.gov (United States)

    Hellerstein, Marc K

    2008-04-01

    The issue of complexity stands at the center of contemporary drug discovery and development. The central problem in drug development today is attrition of drug candidates identified by the modern molecular target-based discovery approach, due to two related features of complex metabolic networks: their fundamentally unpredictable response to targeted interventions and their "robustness" (tendency to maintain stable function in the face of internal or external perturbations). Complexity and adaptations are, therefore, generally seen as obstacles to drug discovery. Here, the converse proposition is presented-that the complexity and adaptive responses of highly interconnected metabolic networks can be exploited for therapeutic discovery. Unanticipated connectivity relationships may result in "off-target" changes in metabolic fluxes, leading to unexpected therapeutic actions of agents. Exploiting this approach requires that fully assembled living systems (in vivo models) be studied and that informative in vivo biomarkers of the activity of biochemical pathways responsible for disease be available. These biomarkers should be sensitive, predictive of functional endpoints, and have high enough throughput for efficient screening of large numbers of agents. To the extent that such biomarkers unambiguously reflect the activity of pathways that mediate disease or therapeutic response (i.e., are "authentic"), their utility will be increased. Examples are presented of pathway-based screening of approved drugs for unexpected actions. Results support the principle that agents that have one action typically have many actions, including unanticipated actions, reflecting connectivity relationships of complex networks. Pathway-based screening in vivo represents an alternative to the high attrition of the molecular target-based discovery paradigm.

  12. Open-access and Structured Data in Drug Discovery

    Directory of Open Access Journals (Sweden)

    Yixin Zhang

    2015-01-01

    Full Text Available A data journal in the biomedical field is an innovative and interesting task with potential benefits to the scientific society, the pharmaceutical and biotechnology industrials, as well as the medical institutions and authorities. It can serve as a source to stimulate, using computational modeling and bioinformatics, the bridging of biomedicine and basic biology researches and connecting of pharmaceutical and biotechnology industrials with academy. In the era of various high throughput technologies and big data, it could become a powerful driving force to combine expertise to understand the complexity of life and to catalyze new innovative therapeutics and diagnosis. The developments of various high-throughput and/or high-content drug-screening techniques are aiming not only to probe a large number of chemical compounds, but also to obtain deep insights into the molecule/molecule interaction associated with the diversity of chemical space, the structure-activity relationship, as well as the pharmacological mechanism. Moreover, the cell-based drug-screening approaches can also shed new light on the dynamics and regulation of proteins and genes associated with various physiological and pathological states. To reflect on these developments, the BMDJ Editorial Board announed a Call for Papers for a special issue on datasets in the field of drug screening and discovery: http://biomed-data.eu/calls-for-papers/high-throughput-drug-screening

  13. Inositol Polyphosphate Kinases, Fungal Virulence and Drug Discovery.

    Science.gov (United States)

    Li, Cecilia; Lev, Sophie; Saiardi, Adolfo; Desmarini, Desmarini; Sorrell, Tania C; Djordjevic, Julianne T

    2016-09-06

    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.

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. Open source drug discovery in practice: a case study.

    Directory of Open Access Journals (Sweden)

    Christine Årdal

    Full Text Available 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

  19. Biomarkers: in medicine, drug discovery, and environmental health

    National Research Council Canada - National Science Library

    Vaidya, Vishal S; Bonventre, Joseph V

    2010-01-01

    ... Identification Using Mass Spectrometry Sample Preparation Protein Quantitation Examples of Biomarker Discovery and Evaluation Challenges in Proteomic Biomarker Discovery The Road Forward: Targeted ...

  20. 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.

  1. Early drug discovery and the rise of pharmaceutical chemistry.

    Science.gov (United States)

    Jones, Alan Wayne

    2011-06-01

    Studies in the field of forensic pharmacology and toxicology would not be complete without some knowledge of the history of drug discovery, the various personalities involved, and the events leading to the development and introduction of new therapeutic agents. The first medicinal drugs came from natural sources and existed in the form of herbs, plants, roots, vines and fungi. Until the mid-nineteenth century nature's pharmaceuticals were all that were available to relieve man's pain and suffering. The first synthetic drug, chloral hydrate, was discovered in 1869 and introduced as a sedative-hypnotic; it is still available today in some countries. The first pharmaceutical companies were spin-offs from the textiles and synthetic dye industry and owe much to the rich source of organic chemicals derived from the distillation of coal (coal-tar). The first analgesics and antipyretics, exemplified by phenacetin and acetanilide, were simple chemical derivatives of aniline and p-nitrophenol, both of which were byproducts from coal-tar. An extract from the bark of the white willow tree had been used for centuries to treat various fevers and inflammation. The active principle in white willow, salicin or salicylic acid, had a bitter taste and irritated the gastric mucosa, but a simple chemical modification was much more palatable. This was acetylsalicylic acid, better known as Aspirin®, the first blockbuster drug. At the start of the twentieth century, the first of the barbiturate family of drugs entered the pharmacopoeia and the rest, as they say, is history. Copyright © 2011 John Wiley & Sons, Ltd.

  2. The significance of G protein-coupled receptor crystallography for drug discovery.

    Science.gov (United States)

    Salon, John A; Lodowski, David T; Palczewski, Krzysztof

    2011-12-01

    Crucial as molecular sensors for many vital physiological processes, seven-transmembrane domain G protein-coupled receptors (GPCRs) comprise the largest family of proteins targeted by drug discovery. Together with structures of the prototypical GPCR rhodopsin, solved structures of other liganded GPCRs promise to provide insights into the structural basis of the superfamily's biochemical functions and assist in the development of new therapeutic modalities and drugs. One of the greatest technical and theoretical challenges to elucidating and exploiting structure-function relationships in these systems is the emerging concept of GPCR conformational flexibility and its cause-effect relationship for receptor-receptor and receptor-effector interactions. Such conformational changes can be subtle and triggered by relatively small binding energy effects, leading to full or partial efficacy in the activation or inactivation of the receptor system at large. Pharmacological dogma generally dictates that these changes manifest themselves through kinetic modulation of the receptor's G protein partners. Atomic resolution information derived from increasingly available receptor structures provides an entrée to the understanding of these events and practically applying it to drug design. Supported by structure-activity relationship information arising from empirical screening, a unified structural model of GPCR activation/inactivation promises to both accelerate drug discovery in this field and improve our fundamental understanding of structure-based drug design in general. This review discusses fundamental problems that persist in drug design and GPCR structural determination.

  3. Successes and future outlook for microfluidics-based cardiovascular drug discovery.

    Science.gov (United States)

    Skommer, Joanna; Wlodkowic, Donald

    2015-03-01

    The greatest advantage of using microfluidics as a platform for the assessment of cardiovascular drug action is its ability to finely regulate fluid flow conditions, including flow rate, shear stress and pulsatile flow. At the same time, microfluidics provide means for modifying the vessel geometry (bifurcations, stenoses, complex networks), the type of surface of the vessel walls, and for patterning cells in 3D tissue-like architecture, including generation of lumen walls lined with cells and heart-on-a-chip structures for mimicking ventricular cardiomyocyte physiology. In addition, owing to the small volume of required specimens, microfluidics is ideally suited to clinical situations whereby monitoring of drug dosing or efficacy needs to be coupled with minimal phlebotomy-related drug loss. In this review, the authors highlight potential applications for the currently existing and emerging technologies and offer several suggestions on how to close the development cycle of microfluidic devices for cardiovascular drug discovery. The ultimate goal in microfluidics research for drug discovery is to develop 'human-on-a-chip' systems, whereby several organ cultures, including the vasculature and the heart, can mimic complex interactions between the organs and body systems. This would provide in vivo-like pharmacokinetics and pharmacodynamics for drug ADMET assessment. At present, however, the great variety of available designs does not go hand in hand with their use by the pharmaceutical community.

  4. Use of "big data" in drug discovery and clinical trials.

    Science.gov (United States)

    Taglang, Guillaume; Jackson, David B

    2016-04-01

    Oncology is undergoing a data-driven metamorphosis. Armed with new and ever more efficient molecular and information technologies, we have entered an era where data is helping us spearhead the fight against cancer. This technology driven data explosion, often referred to as "big data", is not only expediting biomedical discovery, but it is also rapidly transforming the practice of oncology into an information science. This evolution is critical, as results to-date have revealed the immense complexity and genetic heterogeneity of patients and their tumors, a sobering reminder of the challenge facing every patient and their oncologist. This can only be addressed through development of clinico-molecular data analytics that provide a deeper understanding of the mechanisms controlling the biological and clinical response to available therapeutic options. Beyond the exciting implications for improved patient care, such advancements in predictive and evidence-based analytics stand to profoundly affect the processes of cancer drug discovery and associated clinical trials. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. 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.

  6. Animal models of pain and migraine in drug discovery

    DEFF Research Database (Denmark)

    Munro, Gordon; Jansen-Olesen, Inger; Olesen, Jes

    2017-01-01

    Preclinical research activities in relation to pain typically involve the 'holy trinity' of nociceptive, inflammatory and neuropathic pain for purposes of target validation and defining target product profiles of novel analgesic compounds. For some reason it seems that headache or migraine...... are rarely considered as additional entities to explore. Frontline medications used in the treatment of, for example, inflammatory pain, neuropathic pain and migraine (NSAIDs versus pregabalin/duloxetine versus triptans) reveal distinct differences in pathophysiology that partially explain this approach....... Nevertheless, for many patients enduring chronic pain, regardless of aetiology, high unmet needs remain. By focusing more on commonalities shared between neuropathic pain and headache disorders such as migraine, drug discovery efforts could be spread more efficiently across a larger indication area. Here, some...

  7. Current NMR Techniques for Structure-Based Drug Discovery.

    Science.gov (United States)

    Sugiki, Toshihiko; Furuita, Kyoko; Fujiwara, Toshimichi; Kojima, Chojiro

    2018-01-12

    A variety of nuclear magnetic resonance (NMR) applications have been developed for structure-based drug discovery (SBDD). NMR provides many advantages over other methods, such as the ability to directly observe chemical compounds and target biomolecules, and to be used for ligand-based and protein-based approaches. NMR can also provide important information about the interactions in a protein-ligand complex, such as structure, dynamics, and affinity, even when the interaction is too weak to be detected by ELISA or fluorescence resonance energy transfer (FRET)-based high-throughput screening (HTS) or to be crystalized. In this study, we reviewed current NMR techniques. We focused on recent progress in NMR measurement and sample preparation techniques that have expanded the potential of NMR-based SBDD, such as fluorine NMR ( 19 F-NMR) screening, structure modeling of weak complexes, and site-specific isotope labeling of challenging targets.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. Epigenetics and cancer: implications for drug discovery and safety assessment

    International Nuclear Information System (INIS)

    Moggs, Jonathan G.; Goodman, Jay I.; Trosko, James E.; Roberts, Ruth A.

    2004-01-01

    It is necessary to determine whether chemicals or drugs have the potential to pose a threat to human health. Research conducted over the last two decades has led to the paradigm that chemicals can cause cancer either by damaging DNA or by altering cellular growth, probably via receptor-mediated changes in gene expression. However, recent evidence suggests that gene expression can be altered markedly via several diverse epigenetic mechanisms that can lead to permanent or reversible changes in cellular behavior. Key molecular events underlying these mechanisms include the alteration of DNA methylation and chromatin, and changes in the function of cell surface molecules. Thus, for example, DNA methyltransferase enzymes together with chromatin-associated proteins such as histone modifying enzymes and remodelling factors can modify the genetic code and contribute to the establishment and maintenance of altered epigenetic states. This is relevant to many types of toxicity including but not limited to cancer. In this paper, we describe the potential for interplay between genetic alteration and epigenetic changes in cell growth regulation and discuss the implications for drug discovery and safety assessment

  13. 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.

  14. The drug discovery portal: a computational platform for identifying drug leads from academia.

    Science.gov (United States)

    Clark, Rachel L; Johnston, Blair F; Mackay, Simon P; Breslin, Catherine J; Robertson, Murray N; Sutcliffe, Oliver B; Dufton, Mark J; Harvey, Alan L

    2010-05-01

    The Drug Discovery Portal (DDP) is a research initiative based at the University of Strathclyde in Glasgow, Scotland. It was initiated in 2007 by a group of researchers with expertise in virtual screening. Academic research groups in the university working in drug discovery programmes estimated there was a historical collection of physical compounds going back 50 years that had never been adequately catalogued. This invaluable resource has been harnessed to form the basis of the DDP library, and has attracted a high-percentage uptake from the Universities and Research Groups internationally. Its unique attributes include the diversity of the academic database, sourced from synthetic, medicinal and phytochemists working an academic laboratories and the ability to link biologists with appropriate chemical expertise through a target-matching virtual screening approach, and has resulted in seven emerging hit development programmes between international contributors.

  15. 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.

  16. 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.

  17. Research & market strategy: how choice of drug discovery approach can affect market position.

    Science.gov (United States)

    Sams-Dodd, Frank

    2007-04-01

    In principal, drug discovery approaches can be grouped into target- and function-based, with the respective aims of developing either a target-selective drug or a drug that produces a specific biological effect irrespective of its mode of action. Most analyses of drug discovery approaches focus on productivity, whereas the strategic implications of the choice of drug discovery approach on market position and ability to maintain market exclusivity are rarely considered. However, a comparison of approaches from the perspective of market position indicates that the functional approach is superior for the development of novel, innovative treatments.

  18. Communicating Our Science to Our Customers: Drug Discovery in Five Simple Experiments.

    Science.gov (United States)

    Pearson, Lesley-Anne; Foley, David William

    2017-02-09

    The complexities of modern drug discovery-an interdisciplinary process that often takes years and costs billions-can be extremely challenging to explain to a public audience. We present details of a 30 minute demonstrative lecture that uses well-known experiments to illustrate key concepts in drug discovery including synthesis, assay and metabolism.

  19. Zika virus NS5 protein potential inhibitors: an enhanced in silico approach in drug discovery.

    Science.gov (United States)

    Ramharack, Pritika; Soliman, Mahmoud E S

    2018-04-01

    The re-emerging Zika virus (ZIKV) is an arthropod-borne virus that has been described to have explosive potential as a worldwide pandemic. The initial transmission of the virus was through a mosquito vector, however, evolving modes of transmission has allowed the spread of the disease over continents. The virus has already been linked to irreversible chronic central nervous system conditions. The concerns of the scientific and clinical community are the consequences of Zika viral mutations, thus suggesting the urgent need for viral inhibitors. There have been large strides in vaccine development against the virus but there are still no FDA approved drugs available. Rapid rational drug design and discovery research is fundamental in the production of potent inhibitors against the virus that will not just mask the virus, but destroy it completely. In silico drug design allows for this prompt screening of potential leads, thus decreasing the consumption of precious time and resources. This study demonstrates an optimized and proven screening technique in the discovery of two potential small molecule inhibitors of ZIKV Methyltransferase and RNA dependent RNA polymerase. This in silico 'per-residue energy decomposition pharmacophore' virtual screening approach will be critical in aiding scientists in the discovery of not only effective inhibitors of Zika viral targets, but also a wide range of anti-viral agents.

  20. 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.

  1. Supersaturating drug delivery systems

    DEFF Research Database (Denmark)

    Laitinen, Riikka; Löbmann, Korbinian; Grohganz, Holger

    2017-01-01

    Amorphous solid dispersions (ASDs) are probably the most common and important supersaturating drug delivery systems for the formulation of poorly water-soluble compounds. These delivery systems are able to achieve and maintain a sustained drug supersaturation which enables improvement...... of the bioavailability of poorly water-soluble drugs by increasing the driving force for drug absorption. However, ASDs often require a high weight percentage of carrier (usually a hydrophilic polymer) to ensure molecular mixing of the drug in the carrier and stabilization of the supersaturated state, often leading...... strategy for poorly-soluble drugs. While the current research on co-amorphous formulations is focused on preparation and characterization of these systems, more detailed research on their supersaturation and precipitation behavior and the effect of co-formers on nucleation and crystal growth inhibition...

  2. 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 .

  3. 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.

  4. Evolving towards a human-cell based and multiscale approach to drug discovery for CNS disorders

    Directory of Open Access Journals (Sweden)

    Eric eSchadt

    2014-12-01

    Full Text Available A disruptive approach to therapeutic discovery and development is required in order to significantly improve the success rate of drug discovery for central nervous system (CNS disorders. In this review, we first assess the key factors contributing to the frequent clinical failures for novel drugs. Second, we discuss cancer translational research paradigms that addressed key issues in drug discovery and development and have resulted in delivering drugs with significantly improved outcomes for patients. Finally, we discuss two emerging technologies that could improve the success rate of CNS therapies: human induced pluripotent stem cell (hiPSC-based studies and multiscale biology models. Coincident with advances in cellular technologies that enable the generation of hiPSCs directly from patient blood or skin cells, together with methods to differentiate these hiPSC lines into specific neural cell types relevant to neurological disease, it is also now possible to combine data from large-scale forward genetics and post-mortem global epigenetic and expression studies in order to generate novel predictive models. The application of systems biology approaches to account for the multiscale nature of different data types, from genetic to molecular and cellular to clinical, can lead to new insights into human diseases that are emergent properties of biological networks, not the result of changes to single genes. Such studies have demonstrated the heterogeneity in etiological pathways and the need for studies on model systems that are patient-derived and thereby recapitulate neurological disease pathways with higher fidelity. In the context of two common and presumably representative neurological diseases, the neurodegenerative disease Alzheimer’s Disease (AD, and the psychiatric disorder schizophrenia (SZ, we propose the need for, and exemplify the impact of, a multiscale biology approach that can integrate panomic, clinical, imaging, and literature

  5. A Taxonomy of Self-configuring Service Discovery Systems

    NARCIS (Netherlands)

    Sundramoorthy, V.; Hartel, Pieter H.; Scholten, Johan

    2007-01-01

    We analyze the fundamental concepts and issues in service discovery. This analysis places service discovery in the context of distributed systems by describing service discovery as a third generation naming system. We also describe the essential architectures and the functionalities in service

  6. Solar System Moons Discovery and Mythology

    CERN Document Server

    Blunck, Jürgen

    2010-01-01

    Starting from Mars outward this concise handbook provides thorough information on the satellites of the planets in the solar system. Each chapter begins with a section on the discovery and the naming of the planet's satellites or rings. This is followed by a section presenting the historic sources of those names. The book contains tables with the orbital and physical parameters of all satellites and is illustrated throughout with modern photos of the planets and their moons as well as historical and mythological drawings. The Cyrillic transcriptions of the satellite names are provided in a register. Readers interested in the history of astronomy and its mythological backgrounds will enjoy this beautiful volume.

  7. Microphysiological Human Brain and Neural Systems-on-a-Chip: Potential Alternatives to Small Animal Models and Emerging Platforms for Drug Discovery and Personalized Medicine.

    Science.gov (United States)

    Haring, Alexander P; Sontheimer, Harald; Johnson, Blake N

    2017-06-01

    Translational challenges associated with reductionist modeling approaches, as well as ethical concerns and economic implications of small animal testing, drive the need for developing microphysiological neural systems for modeling human neurological diseases, disorders, and injuries. Here, we provide a comprehensive review of microphysiological brain and neural systems-on-a-chip (NSCs) for modeling higher order trajectories in the human nervous system. Societal, economic, and national security impacts of neurological diseases, disorders, and injuries are highlighted to identify critical NSC application spaces. Hierarchical design and manufacturing of NSCs are discussed with distinction for surface- and bulk-based systems. Three broad NSC classes are identified and reviewed: microfluidic NSCs, compartmentalized NSCs, and hydrogel NSCs. Emerging areas and future directions are highlighted, including the application of 3D printing to design and manufacturing of next-generation NSCs, the use of stem cells for constructing patient-specific NSCs, and the application of human NSCs to 'personalized neurology'. Technical hurdles and remaining challenges are discussed. This review identifies the state-of-the-art design methodologies, manufacturing approaches, and performance capabilities of NSCs. This work suggests NSCs appear poised to revolutionize the modeling of human neurological diseases, disorders, and injuries.

  8. A novel in silico approach to drug discovery via computational intelligence.

    Science.gov (United States)

    Hecht, David; Fogel, Gary B

    2009-04-01

    A computational intelligence drug discovery platform is introduced as an innovative technology designed to accelerate high-throughput drug screening for generalized protein-targeted drug discovery. This technology results in collections of novel small molecule compounds that bind to protein targets as well as details on predicted binding modes and molecular interactions. The approach was tested on dihydrofolate reductase (DHFR) for novel antimalarial drug discovery; however, the methods developed can be applied broadly in early stage drug discovery and development. For this purpose, an initial fragment library was defined, and an automated fragment assembly algorithm was generated. These were combined with a computational intelligence screening tool for prescreening of compounds relative to DHFR inhibition. The entire method was assayed relative to spaces of known DHFR inhibitors and with chemical feasibility in mind, leading to experimental validation in future studies.

  9. Accelerating drug discovery through tight integration of expert molecular design and predictive scoring.

    Science.gov (United States)

    Abel, Robert; Mondal, Sayan; Masse, Craig; Greenwood, Jeremy; Harriman, Geraldine; Ashwell, Mark A; Bhat, Sathesh; Wester, Ronald; Frye, Leah; Kapeller, Rosana; Friesner, Richard A

    2017-04-01

    Modeling protein-ligand interactions has been a central goal of computational chemistry for many years. We here review recent progress toward this goal, and highlight the role free energy calculation methods and computational solvent analysis techniques are now having in drug discovery. We further describe recent use of these methodologies to advance two separate drug discovery programs targeting acetyl-CoA carboxylase and tyrosine kinase 2. These examples suggest that tight integration of sophisticated chemistry teams with state-of-the-art computational methods can dramatically improve the efficiency of small molecule drug discovery. Copyright © 2016. Published by Elsevier Ltd.

  10. Drug discovery in the era of Facebook--new tools for scientific networking.

    Science.gov (United States)

    Bailey, David S; Zanders, Edward D

    2008-10-01

    Social networking is beginning to make an impact on the drug discovery process. While bioinformatics and chemoinformatics underpin research at a scientific level, rapid communication between individual researchers across continents now allows the global exchange of ideas, tools and technologies. Networking at this level of speed and reach is quite a recent phenomenon. It facilitates the development of common interests, accelerates technology transfer and increases cooperative and competitive behaviour. In this review, we critically evaluate different web based networking approaches as effective resources for the drug discovery scientist. We also ask whether social networking sites will evolve into serious and credible resources for the drug discovery community.

  11. Structural Analysis Of CD59 Of Chinese Tree Shrew: A New Reference Molecule For Human Immune System Specific CD59 Drug Discovery.

    Science.gov (United States)

    Panda, Subhamay; Kumari, Leena; Panda, Santamay

    2017-11-17

    Chinese tree shrews (Tupaia belangeri chinensis) bear several characteristics that are considered to be very crucial for utilizing in animal experimental models in biomedical research. Subsequent to the identification of key aspects and signaling pathways in nervous and immune systems, it is revealed that tree shrews acquires shared common as well as unique characteristics, and hence offers a genetic basis for employing this animal as a prospective model for biomedical research. CD59 glycoprotein, commonly referred to as MAC-inhibitory protein (MAC-IP), membrane inhibitor of reactive lysis (MIRL), or protectin, is encoded by the CD59 gene in human beings. It is the member of the LY6/uPAR/alpha-neurotoxin protein family. With this initial point the objective of this study was to determine a comparative composite based structure of CD59 of Chinese tree shrew. The additional objective of this study was to examine the distribution of negatively and positively charged amino acid over molecular modeled structure, distribution of secondary structural elements, hydrophobicity molecular surface analysis and electrostatic potential analysis with the assistance of several bioinformatical analytical tools. CD59 Amino acid sequence of Chinese tree shrew collected from the online database system of National Centre for Biotechnology Information. SignalP 4.0 online server was employed for detection of signal peptide instance within the protein sequence of CD59. Molecular model structure of CD59 protein was generated by the Iterative Threading ASSEmbly Refinement (I-TASSER) suite. The confirmation for three-dimensional structural model was evaluated by structure validation tools. Location of negatively and positively charged amino acid over molecular modeled structure, distribution of secondary structural elements, and hydrophobicity molecular surface analysis was performed with the help of Chimera tool. Electrostatic potential analysis was carried out with the adaptive Poisson

  12. 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.

  13. An integrated data management framework for drug discovery--from data capturing to decision support.

    Science.gov (United States)

    Cedeño, Walter; Alex, Simson; Jaeger, Edward P; Agrafiotis, Dimitris K; Lobanov, Victor S

    2012-01-01

    Drug discovery is a highly complex process requiring scientists from wide-ranging disciplines to work together in a well-coordinated and streamlined fashion. While the process can be compartmentalized into well-defined functional domains, the success of the entire enterprise rests on the ability to exchange data conveniently between these domains, and integrate it in meaningful ways to support the design, execution and interpretation of experiments aimed at optimizing the efficacy and safety of new drugs. This, in turn, requires information management systems that can support many different types of scientific technologies generating data of imposing complexity, diversity and volume. Here, we describe the key components of our Advanced Biological and Chemical Discovery (ABCD), a software platform designed at Johnson & Johnson to bring coherence in the way discovery data is collected, annotated, organized, integrated, mined and visualized. Unlike the Gordian knot of one-off solutions built to serve a single purpose for a single set of users that one typically encounters in the pharmaceutical industry, we sought to develop a framework that could be extended and leveraged across different application domains, and offer a consistent user experience marked by superior performance and usability. In this work, several major components of ABCD are highlighted, ranging from operational subsystems for managing reagents, reactions, compounds, and assays, to advanced data mining and visualization tools for SAR analysis and interpretation. All these capabilities are delivered through a common application front-end called Third Dimension Explorer (3DX), a modular, multifunctional and extensible platform designed to be the "Swiss-army knife" of the discovery scientist.

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

    OpenAIRE

    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 algori...

  15. QSAR methods for the discovery of new inflammatory bowel disease drugs.

    Science.gov (United States)

    García-Domenech, Ramón; Gálvez-Llompart, María; Zanni, Riccardo; Recio, María C; Gálvez, Jorge

    2013-08-01

    Inflammatory bowel disease (IBD) represents an important class of chronic gastrointestinal tract disease. And although there are already several useful treatments to reduce and control the symptoms, there is still no cure. One drug discovery technique used is the computer-aided (in silico) discovery approach which has largely demonstrated efficacy. Computational techniques, when used in combination with traditional drug discovery methodology, greatly increase the chance of drug discovery in a sustainable and economical fashion. This review aims to provide the most recent and important advances of in silico IBD drug discovery. While this review is mainly focused on QSAR methods, especially those based on molecular topology (MT), additional topics, such as docking or comparative field analysis are also addressed. IBD is a worldwide growing health concern that can only be currently treated in symptomatic and palliative way; thus, the search for new drugs is imperative. Computer-aided methods, which focus on the drug-receptor interaction, are essential tool in this regard. It is noted, however that a major problem is that although there are many known receptors associated with IBD, none of these have yet been found essential. The use of other approaches, including QSAR methodology, is certainly a complementary and attractive alternative; especially QSAR methods based on MT, which has proven successful in other drug discovery.

  16. 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

  17. 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.

  18. Endophytes : Exploiting biodiversity for the improvement of natural product-based drug discovery

    NARCIS (Netherlands)

    Staniek, Agata; Woerdenbag, Herman J.; Kayser, Oliver

    2008-01-01

    Endophytes, microorganisms that colonize internal tissues of all plant species, create a huge biodiversity with yet unknown novel natural products, presumed to push forward the frontiers of drug discovery. Next to the clinically acknowledged antineoplastic agent, paclitaxel, endophyte research has

  19. Open access high throughput drug discovery in the public domain: a Mount Everest in the making.

    Science.gov (United States)

    Roy, Anuradha; McDonald, Peter R; Sittampalam, Sitta; Chaguturu, Rathnam

    2010-11-01

    High throughput screening (HTS) facilitates screening large numbers of compounds against a biochemical target of interest using validated biological or biophysical assays. In recent years, a significant number of drugs in clinical trails originated from HTS campaigns, validating HTS as a bona fide mechanism for hit finding. In the current drug discovery landscape, the pharmaceutical industry is embracing open innovation strategies with academia to maximize their research capabilities and to feed their drug discovery pipeline. The goals of academic research have therefore expanded from target identification and validation to probe discovery, chemical genomics, and compound library screening. This trend is reflected in the emergence of HTS centers in the public domain over the past decade, ranging in size from modestly equipped academic screening centers to well endowed Molecular Libraries Probe Centers Network (MLPCN) centers funded by the NIH Roadmap initiative. These centers facilitate a comprehensive approach to probe discovery in academia and utilize both classical and cutting-edge assay technologies for executing primary and secondary screening campaigns. The various facets of academic HTS centers as well as their implications on technology transfer and drug discovery are discussed, and a roadmap for successful drug discovery in the public domain is presented. New lead discovery against therapeutic targets, especially those involving the rare and neglected diseases, is indeed a Mount Everestonian size task, and requires diligent implementation of pharmaceutical industry's best practices for a successful outcome.

  20. 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

  1. “Omics”-Informed Drug and Biomarker Discovery: Opportunities, Challenges and Future Perspectives

    Directory of Open Access Journals (Sweden)

    Holly Matthews

    2016-09-01

    Full Text Available The pharmaceutical industry faces unsustainable program failure despite significant increases in investment. Dwindling discovery pipelines, rapidly expanding R&D budgets and increasing regulatory control, predict significant gaps in the future drug markets. The cumulative duration of discovery from concept to commercialisation is unacceptably lengthy, and adds to the deepening crisis. Existing animal models predicting clinical translations are simplistic, highly reductionist and, therefore, not fit for purpose. The catastrophic consequences of ever-increasing attrition rates are most likely to be felt in the developing world, where resistance acquisition by killer diseases like malaria, tuberculosis and HIV have paced far ahead of new drug discovery. The coming of age of Omics-based applications makes available a formidable technological resource to further expand our knowledge of the complexities of human disease. The standardisation, analysis and comprehensive collation of the “data-heavy” outputs of these sciences are indeed challenging. A renewed focus on increasing reproducibility by understanding inherent biological, methodological, technical and analytical variables is crucial if reliable and useful inferences with potential for translation are to be achieved. The individual Omics sciences—genomics, transcriptomics, proteomics and metabolomics—have the singular advantage of being complimentary for cross validation, and together could potentially enable a much-needed systems biology perspective of the perturbations underlying disease processes. If current adverse trends are to be reversed, it is imperative that a shift in the R&D focus from speed to quality is achieved. In this review, we discuss the potential implications of recent Omics-based advances for the drug development process.

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    Røttingen John-Arne

    2011-09-01

    Full Text Available Abstract Background 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. Methods 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. Results 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. Conclusions 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.

  4. 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.

  5. Drug discovery of neurodegenerative disease through network pharmacology approach in herbs.

    Science.gov (United States)

    Ke, Zhipeng; Zhang, Xinzhuang; Cao, Zeyu; Ding, Yue; Li, Na; Cao, Liang; Wang, Tuanjie; Zhang, Chenfeng; Ding, Gang; Wang, Zhenzhong; Xu, Xiaojie; Xiao, Wei

    2016-03-01

    Neurodegenerative diseases, referring to as the progressive loss of structure and function of neurons, constitute one of the major challenges of modern medicine. Traditional Chinese herbs have been used as a major preventive and therapeutic strategy against disease for thousands years. The numerous species of medicinal herbs and Traditional Chinese Medicine (TCM) compound formulas in nervous system disease therapy make it a large chemical resource library for drug discovery. In this work, we collected 7362 kinds of herbs and 58,147 Traditional Chinese medicinal compounds (Tcmcs). The predicted active compounds in herbs have good oral bioavailability and central nervous system (CNS) permeability. The molecular docking and network analysis were employed to analyze the effects of herbs on neurodegenerative diseases. In order to evaluate the predicted efficacy of herbs, automated text mining was utilized to exhaustively search in PubMed by some related keywords. After that, receiver operator characteristic (ROC) curves was used to estimate the accuracy of predictions. Our study suggested that most herbs were distributed in family of Asteraceae, Fabaceae, Lamiaceae and Apocynaceae. The predictive model yielded good sensitivity and specificity with the AUC values above 0.800. At last, 504 kinds of herbs were obtained by using the optimal cutoff values in ROC curves. These 504 herbs would be the most potential herb resources for neurodegenerative diseases treatment. This study would give us an opportunity to use these herbs as a chemical resource library for drug discovery of anti-neurodegenerative disease. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  6. Mucoadhesive drug delivery systems

    Directory of Open Access Journals (Sweden)

    Rahamatullah Shaikh

    2011-01-01

    Full Text Available Mucoadhesion is commonly defined as the adhesion between two materials, at least one of which is a mucosal surface. Over the past few decades, mucosal drug delivery has received a great deal of attention. Mucoadhesive dosage forms may be designed to enable prolonged retention at the site of application, providing a controlled rate of drug release for improved therapeutic outcome. Application of dosage forms to mucosal surfaces may be of benefit to drug molecules not amenable to the oral route, such as those that undergo acid degradation or extensive first-pass metabolism. The mucoadhesive ability of a dosage form is dependent upon a variety of factors, including the nature of the mucosal tissue and the physicochemical properties of the polymeric formulation. This review article aims to provide an overview of the various aspects of mucoadhesion, mucoadhesive materials, factors affecting mucoadhesion, evaluating methods, and finally various mucoadhesive drug delivery systems (buccal, nasal, ocular, gastro, vaginal, and rectal.

  7. Live Cell in Vitro and in Vivo Imaging Applications: Accelerating Drug Discovery

    Science.gov (United States)

    Isherwood, Beverley; Timpson, Paul; McGhee, Ewan J; Anderson, Kurt I; Canel, Marta; Serrels, Alan; Brunton, Valerie G; Carragher, Neil O

    2011-01-01

    Dynamic regulation of specific molecular processes and cellular phenotypes in live cell systems reveal unique insights into cell fate and drug pharmacology that are not gained from traditional fixed endpoint assays. Recent advances in microscopic imaging platform technology combined with the development of novel optical biosensors and sophisticated image analysis solutions have increased the scope of live cell imaging applications in drug discovery. We highlight recent literature examples where live cell imaging has uncovered novel insight into biological mechanism or drug mode-of-action. We survey distinct types of optical biosensors and associated analytical methods for monitoring molecular dynamics, in vitro and in vivo. We describe the recent expansion of live cell imaging into automated target validation and drug screening activities through the development of dedicated brightfield and fluorescence kinetic imaging platforms. We provide specific examples of how temporal profiling of phenotypic response signatures using such kinetic imaging platforms can increase the value of in vitro high-content screening. Finally, we offer a prospective view of how further application and development of live cell imaging technology and reagents can accelerate preclinical lead optimization cycles and enhance the in vitro to in vivo translation of drug candidates. PMID:24310493

  8. Computer-aided drug discovery research at a global contract research organization.

    Science.gov (United States)

    Kitchen, Douglas B

    2017-03-01

    Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20 years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.

  9. 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.

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

    African Journals Online (AJOL)

    SAM

    2014-06-04

    tuberculosis research. Key words: ... by the Food and Drug Administration (FDA), as a component of a combination therapy for the .... clinical trials, to establish its efficacy and safety, before it is accepted as a drug. These processes take ...

  11. Advancing cancer drug discovery towards more agile development of targeted combination therapies.

    Science.gov (United States)

    Carragher, Neil O; Unciti-Broceta, Asier; Cameron, David A

    2012-01-01

    Current drug-discovery strategies are typically 'target-centric' and are based upon high-throughput screening of large chemical libraries against nominated targets and a selection of lead compounds with optimized 'on-target' potency and selectivity profiles. However, high attrition of targeted agents in clinical development suggest that combinations of targeted agents will be most effective in treating solid tumors if the biological networks that permit cancer cells to subvert monotherapies are identified and retargeted. Conventional drug-discovery and development strategies are suboptimal for the rational design and development of novel drug combinations. In this article, we highlight a series of emerging technologies supporting a less reductionist, more agile, drug-discovery and development approach for the rational design, validation, prioritization and clinical development of novel drug combinations.

  12. 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.

  13. X-ray crystallography over the past decade for novel drug discovery - where are we heading next?

    Science.gov (United States)

    Zheng, Heping; Handing, Katarzyna B; Zimmerman, Matthew D; Shabalin, Ivan G; Almo, Steven C; Minor, Wladek

    2015-01-01

    Macromolecular X-ray crystallography has been the primary methodology for determining the three-dimensional structures of proteins, nucleic acids and viruses. Structural information has paved the way for structure-guided drug discovery and laid the foundations for structural bioinformatics. However, X-ray crystallography still has a few fundamental limitations, some of which may be overcome and complemented using emerging methods and technologies in other areas of structural biology. This review describes how structural knowledge gained from X-ray crystallography has been used to advance other biophysical methods for structure determination (and vice versa). This article also covers current practices for integrating data generated by other biochemical and biophysical methods with those obtained from X-ray crystallography. Finally, the authors articulate their vision about how a combination of structural and biochemical/biophysical methods may improve our understanding of biological processes and interactions. X-ray crystallography has been, and will continue to serve as, the central source of experimental structural biology data used in the discovery of new drugs. However, other structural biology techniques are useful not only to overcome the major limitation of X-ray crystallography, but also to provide complementary structural data that is useful in drug discovery. The use of recent advancements in biochemical, spectroscopy and bioinformatics methods may revolutionize drug discovery, albeit only when these data are combined and analyzed with effective data management systems. Accurate and complete data management is crucial for developing experimental procedures that are robust and reproducible.

  14. 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

    2018-02-16

    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.

  15. 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.

  16. Understanding sleep-wake mechanisms and drug discovery.

    Science.gov (United States)

    Equihua-Benítez, Ana Clementina; Guzmán-Vásquez, Khalil; Drucker-Colín, René

    2017-07-01

    Although not discernible at first glance, sleep is a highly active and regulated brain state. Although we spend practically one third of our lifetimes in this stage, its importance is often taken for granted. Sleep loss can lead to disease, error and economic loss. Our understanding of how sleep is achieved has greatly advanced in recent years, and with that, the management of sleep disorders has improved. There is still room for improvement and recently many new compounds have reached clinical trials with a few being approved for commercial use. Areas covered: In this review, the authors make the case of sleep disorders as a matter of public health. The mechanisms of sleep transition are discussed emphasizing the wake and sleep promoting interaction of different brain regions. Finally, advances in pharmacotherapy are examined in the context of chronic insomnia and narcolepsy. Expert opinion: The orexinergic system is an example of a breakthrough in sleep medicine that has catalyzed drug development. Nevertheless, sleep is a topic still with many unanswered questions. That being said, the melanin-concentrating hormone system is becoming increasingly relevant and we speculate it will be the next target of sleep medication.

  17. Therapeutic Approaches to Genetic Ion Channelopathies and Perspectives in Drug Discovery

    Science.gov (United States)

    Imbrici, Paola; Liantonio, Antonella; Camerino, Giulia M.; De Bellis, Michela; Camerino, Claudia; Mele, Antonietta; Giustino, Arcangela; Pierno, Sabata; De Luca, Annamaria; Tricarico, Domenico; Desaphy, Jean-Francois; Conte, Diana

    2016-01-01

    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. PMID:27242528

  18. 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.

  19. Design and Implementation of an Interdisciplinary Elective Course in Drug Discovery, Development, and Commercialization

    Directory of Open Access Journals (Sweden)

    William S. Ettouati

    2013-01-01

    Full Text Available Objective: To describe the design and implementation of an elective course in drug discovery, development, and commercialization for pharmacy, medical, biomedical graduate, business, and law students. Case Study: This course included didactic lectures, student group discussions, a longitudinal assignment, and a question and answer panel session. A 9-item instrument using a 5-point response scale was used for course evaluation. The longitudinal assignment was the creation and presentation of a product lifecycle strategic plan (PLSP. Respondents rated 'agree' and 'strongly agree' in the course providing useful information on drug discovery (39% and 53%, drug development (39% and 60%, and drug commercialization (33% and 60%. The majority of student-reported overall understanding of the drug discovery and drug development process was rated 'very good' (49% and 46%, while the drug commercialization process was rated 'good' (46%. Conclusions: An elective course on drug discovery, development, and commercialization included enrollment of students with diverse educational training. The course provided useful information and improved overall student understanding.   Type: Case Study

  20. Cardiovascular Organ-on-a-Chip Platforms for Drug Discovery and Development

    NARCIS (Netherlands)

    Ribas, J.; Sadeghi, H.; Manbachi, A.; Leijten, Jeroen Christianus Hermanus; Brinegar, K.; Zhang, Y.S.; Ferreira, L.; Khademhosseini, A.

    2016-01-01

    Cardiovascular diseases are prevalent worldwide and are the most frequent causes of death in the United States. Although spending in drug discovery/development has increased, the amount of drug approvals has seen a progressive decline. Particularly, adverse side effects to the heart and general

  1. Established and Emerging Trends in Computational Drug Discovery in the Structural Genomics Era

    DEFF Research Database (Denmark)

    Taboureau, Olivier; Baell, Jonathan B.; Fernández-Recio, Juan

    2012-01-01

    combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery...

  2. 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.

  3. Structure-guided, target-based drug discovery - exploiting genome information from HIV to mycobacterial infections.

    Science.gov (United States)

    Malhotra, Sony; Thomas, Sherine E; Ochoa Montano, Bernardo; Blundell, Tom L

    The use of protein crystallography in structure-guided drug discovery allows identification of potential inhibitor-binding sites and optimisation of interactions of hits and lead compounds with a target protein. An early example of this approach was the use of the structure of HIV protease in designing AIDS antivirals. More recently, use of structure-guided design with fragment-based drug discovery, which reduces the size of screening libraries by decreasing complexity, has improved ligand efficiency in drug design. Here, we discuss the use of structure-guided target identification and lead optimisation using fragment-based approaches in the development of new antimicrobials for mycobacterial infections.

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

    African Journals Online (AJOL)

    The global resurgence of TB and the development of multidrug-resistant tuberculosis (MDR TB) and extensively drug-resistant tuberculosis (XDR-TB), call for the development of new anti-tuberculosis drugs to combat this disease. Plant natural products have a proven global history of treating diseases and ailments.

  5. The major impacts of James Black's drug discoveries on medicine and pharmacology.

    Science.gov (United States)

    Walker, Michael J A

    2011-04-01

    James Black has many claims to pharmacological fame as the creator of two new classes of drugs (beta-blockers and H2 antihistamines) and as a tireless innovator in drug discovery strategies and analytical procedures. The latter attributes in particular assisted Black in the invention of the prototypes for the two major classes of drugs for which he is best known, propranolol and cimetidine. The clinical impact of these drugs on both morbidity and mortality has been profound. In addition, the application of his analytical approach to drug discovery and pharmacology led others in the field to create many other new classes of drugs. Shortly before he died in 2010, Black wrote a retrospective review of his research career that provides insight into his innovative thinking and career success. This overview affords readers a very personal picture of the man, his ideas and his contributions. Copyright © 2011 Elsevier Ltd. All rights reserved.

  6. 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.

  7. Quantitative structure-activity relationship: promising advances in drug discovery platforms.

    Science.gov (United States)

    Wang, Tao; Wu, Mian-Bin; Lin, Jian-Ping; Yang, Li-Rong

    2015-12-01

    Quantitative structure-activity relationship (QSAR) modeling is one of the most popular computer-aided tools employed in medicinal chemistry for drug discovery and lead optimization. It is especially powerful in the absence of 3D structures of specific drug targets. QSAR methods have been shown to draw public attention since they were first introduced. In this review, the authors provide a brief discussion of the basic principles of QSAR, model development and model validation. They also highlight the current applications of QSAR in different fields, particularly in virtual screening, rational drug design and multi-target QSAR. Finally, in view of recent controversies, the authors detail the challenges faced by QSAR modeling and the relevant solutions. The aim of this review is to show how QSAR modeling can be applied in novel drug discovery, design and lead optimization. QSAR should intentionally be used as a powerful tool for fragment-based drug design platforms in the field of drug discovery and design. Although there have been an increasing number of experimentally determined protein structures in recent years, a great number of protein structures cannot be easily obtained (i.e., membrane transport proteins and G-protein coupled receptors). Fragment-based drug discovery, such as QSAR, could be applied further and have a significant role in dealing with these problems. Moreover, along with the development of computer software and hardware, it is believed that QSAR will be increasingly important.

  8. Exploring the role of receptor flexibility in structure-based drug discovery.

    Science.gov (United States)

    Feixas, Ferran; Lindert, Steffen; Sinko, William; McCammon, J Andrew

    2014-02-01

    The proper understanding of biomolecular recognition mechanisms that take place in a drug target is of paramount importance to improve the efficiency of drug discovery and development. The intrinsic dynamic character of proteins has a strong influence on biomolecular recognition mechanisms and models such as conformational selection have been widely used to account for this dynamic association process. However, conformational changes occurring in the receptor prior and upon association with other molecules are diverse and not obvious to predict when only a few structures of the receptor are available. In view of the prominent role of protein flexibility in ligand binding and its implications for drug discovery, it is of great interest to identify receptor conformations that play a major role in biomolecular recognition before starting rational drug design efforts. In this review, we discuss a number of recent advances in computer-aided drug discovery techniques that have been proposed to incorporate receptor flexibility into structure-based drug design. The allowance for receptor flexibility provided by computational techniques such as molecular dynamics simulations or enhanced sampling techniques helps to improve the accuracy of methods used to estimate binding affinities and, thus, such methods can contribute to the discovery of novel drug leads. Copyright © 2013 Elsevier B.V. All rights reserved.

  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. 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

  11. Machine-Learning Techniques Applied to Antibacterial Drug Discovery

    OpenAIRE

    Durrant, Jacob D.; Amaro, Rommie E.

    2015-01-01

    The emergence of drug-resistant bacteria threatens to catapult humanity back to the pre-antibiotic era. Even now, multi-drug-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 resulting vacuum.

  12. Improving attrition rates in Ebola virus drug discovery.

    Science.gov (United States)

    Glisic, Sanja; Paessler, Slobodan; Veljkovic, Nevena; Perovic, Vladimir R; Prljic, Jelena; Veljkovic, Veljko

    2015-01-01

    The Ebola 2014/2015 outbreak has had devastating effects on the people living in West Africa. The spread of the disease in endemic countries and the potential introduction of sporadic cases in other continents points out the global health threat of Ebola virus disease (EVD). Despite the urgent need for treating EVD, there are no approved treatments. Given the lack of treatments available, alternative therapeutic strategies have had to be used. This article summarizes the unregistered therapeutics that were used to treat patients during the Ebola 2014/2015 outbreak, in addition to approaches used for the selection of candidate drugs. The article also proposes potential theoretical criterion for use in virtual screening of molecular libraries for candidate Ebola drugs. In the absence of approved therapeutics for EVD, experimental drugs have had to be used. The repurposing of approved drugs for the treatment of EVD, as an alternative therapeutic strategy, has also been suggested. Screening in vitro- and in silico-approved drugs revealed several promising candidates but further testing is required to test their efficacy. All these therapeutic approaches are, however, only short-term solutions and there is still an urgent need for the development of specific drugs for the current and future outbreaks.

  13. Predicting changes in cardiac myocyte contractility during early drug discovery with in vitro assays

    Energy Technology Data Exchange (ETDEWEB)

    Morton, M.J., E-mail: michael.morton@astrazeneca.com [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Armstrong, D.; Abi Gerges, N. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Bridgland-Taylor, M. [Discovery Sciences, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom); Pollard, C.E.; Bowes, J.; Valentin, J.-P. [Drug Safety and Metabolism, AstraZeneca, Macclesfield, Cheshire SK10 4TG (United Kingdom)

    2014-09-01

    Cardiovascular-related adverse drug effects are a major concern for the pharmaceutical industry. Activity of an investigational drug at the L-type calcium channel could manifest in a number of ways, including changes in cardiac contractility. The aim of this study was to define which of the two assay technologies – radioligand-binding or automated electrophysiology – was most predictive of contractility effects in an in vitro myocyte contractility assay. The activity of reference and proprietary compounds at the L-type calcium channel was measured by radioligand-binding assays, conventional patch-clamp, automated electrophysiology, and by measurement of contractility in canine isolated cardiac myocytes. Activity in the radioligand-binding assay at the L-type Ca channel phenylalkylamine binding site was most predictive of an inotropic effect in the canine cardiac myocyte assay. The sensitivity was 73%, specificity 83% and predictivity 78%. The radioligand-binding assay may be run at a single test concentration and potency estimated. The least predictive assay was automated electrophysiology which showed a significant bias when compared with other assay formats. Given the importance of the L-type calcium channel, not just in cardiac function, but also in other organ systems, a screening strategy emerges whereby single concentration ligand-binding can be performed early in the discovery process with sufficient predictivity, throughput and turnaround time to influence chemical design and address a significant safety-related liability, at relatively low cost. - Highlights: • The L-type calcium channel is a significant safety liability during drug discovery. • Radioligand-binding to the L-type calcium channel can be measured in vitro. • The assay can be run at a single test concentration as part of a screening cascade. • This measurement is highly predictive of changes in cardiac myocyte contractility.

  14. Mass spectrometry innovations in drug discovery and development.

    Science.gov (United States)

    Papac, D I; Shahrokh, Z

    2001-02-01

    This review highlights the many roles mass spectrometry plays in the discovery and development of new therapeutics by both the pharmaceutical and the biotechnology industries. Innovations in mass spectrometer source design, improvements to mass accuracy, and implementation of computer-controlled automation have accelerated the purification and characterization of compounds derived from combinatorial libraries, as well as the throughput of pharmacokinetics studies. The use of accelerator mass spectrometry, chemical reaction interface-mass spectrometry and continuous flow-isotope ratio mass spectrometry are promising alternatives for conducting mass balance studies in man. To meet the technical challenges of proteomics, discovery groups in biotechnology companies have led the way to development of instruments with greater sensitivity and mass accuracy (e.g., MALDI-TOF, ESI-Q-TOF, Ion Trap), the miniaturization of separation techniques and ion sources (e.g., capillary HPLC and nanospray), and the utilization of bioinformatics. Affinity-based methods coupled to mass spectrometry are allowing rapid and selective identification of both synthetic and biological molecules. With decreasing instrument cost and size and increasing reliability, mass spectrometers are penetrating both the manufacturing and the quality control arenas. The next generation of technologies to simplify the investigation of the complex fate of novel pharmaceutical entities in vitro and in vivo will be chip-based approaches coupled with mass spectrometry.

  15. 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.

  16. Small diverse antioxidant functionalities for oxidative stress disease drug discovery.

    Science.gov (United States)

    Sikazwe, D; Grillo, A; Ramsinghani, S; Davis, J; McQuiston, K; Ablordeppey, S Y

    2012-07-01

    There is an up-surge of interest in antioxidants because of their potential use in mitigating a wide array of oxidative stress mediated diseases. In the course of our literature search for diverse functional groups, with utility in the design of potential drugs for preventing oxidative stress related cell injury, we have collected a small literature library of core structures or moieties possessing antioxidant activities. These functional groups can be re-configured into robust antioxidants drug molecules, in their own right, or incorporated into drug structures where the antioxidant capability is required. The lack of single papers presenting a collection of diverse small molecule antioxidant moieties as potential design leads prompted us to write this short review of twenty five such functionalities.

  17. Fungal biofilm composition and opportunities in drug discovery.

    Science.gov (United States)

    Reichhardt, Courtney; Stevens, David A; Cegelski, Lynette

    2016-08-01

    Biofilm infections are exceptionally recalcitrant to antimicrobial treatment or clearance by host immune responses. Within biofilms, microbes form adherent multicellular communities that are embedded in an extracellular matrix. Many prescribed antifungal drugs are not effective against biofilm infections owing to several protective factors including poor diffusion of drugs through biofilms as well as specific drug-matrix interactions. Despite the key roles that biofilms play in infections, there is little quantitative information about their composition and structural complexity because of the analytical challenge of studying these dense networks using traditional techniques. Within this review, recent work to elucidate fungal biofilm composition is discussed, with particular attention given to the challenges of annotation and quantification of matrix composition.

  18. 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.

  19. Translational Prospects and Challenges in Human Induced Pluripotent Stem Cell Research in Drug Discovery

    Directory of Open Access Journals (Sweden)

    Masaki Hosoya

    2016-12-01

    Full Text Available Despite continuous efforts to improve the process of drug discovery and development, achieving success at the clinical stage remains challenging because of a persistent translational gap between the preclinical and clinical settings. Under these circumstances, the discovery of human induced pluripotent stem (iPS cells has brought new hope to the drug discovery field because they enable scientists to humanize a variety of pharmacological and toxicological models in vitro. The availability of human iPS cell-derived cells, particularly as an alternative for difficult-to-access tissues and organs, is increasing steadily; however, their use in the field of translational medicine remains challenging. Biomarkers are an essential part of the translational effort to shift new discoveries from bench to bedside as they provide a measurable indicator with which to evaluate pharmacological and toxicological effects in both the preclinical and clinical settings. In general, during the preclinical stage of the drug development process, in vitro models that are established to recapitulate human diseases are validated by using a set of biomarkers; however, their translatability to a clinical setting remains problematic. This review provides an overview of current strategies for human iPS cell-based drug discovery from the perspective of translational research, and discusses the importance of early consideration of clinically relevant biomarkers.

  20. Discovery of a new method for potent drug development using power function of stoichiometry of homomeric biocomplexes or biological nanomotors.

    Science.gov (United States)

    Pi, Fengmei; Vieweger, Mario; Zhao, Zhengyi; Wang, Shaoying; Guo, Peixuan

    2016-01-01

    Multidrug resistance and the appearance of incurable diseases inspire the quest for potent therapeutics. We review a new methodology in designing potent drugs by targeting multi-subunit homomeric biological motors, machines or complexes with Z > 1 and K = 1, where Z is the stoichiometry of the target, and K is the number of drugged subunits required to block the function of the complex. The condition is similar to a series electrical circuit of Christmas decorations: failure of one light bulb causes the entire lighting system to lose power. In most multi-subunit, homomeric biological systems, a sequential coordination or cooperative action mechanism is utilized, thus K equals 1. Drug inhibition depends on the ratio of drugged to non-drugged complexes. When K = 1, and Z > 1, the inhibition effect follows a power law with respect to Z, leading to enhanced drug potency. The hypothesis that the potency of drug inhibition depends on the stoichiometry of the targeted biological complexes was recently quantified by Yang-Hui's Triangle (or binomial distribution), and proved using a highly sensitive in vitro phi29 viral DNA packaging system. Examples of targeting homomeric bio-complexes with high stoichiometry for potent drug discovery are discussed. Biomotors with multiple subunits are widespread in viruses, bacteria and cells, making this approach generally applicable in the development of inhibition drugs with high efficiency.

  1. Drug discovery via human-derived stem cell organoids

    Directory of Open Access Journals (Sweden)

    Fangkun Liu

    2016-09-01

    Full Text Available Patient-derived cell lines and animal models have proven invaluable for the understanding of human intestinal diseases and for drug development although both inherently comprise disadvantages and caveats. Many genetically determined intestinal diseases occur in specific tissue microenvironments that are not adequately modeled by monolayer cell culture. Likewise, animal models incompletely recapitulate the complex pathologies of intestinal diseases of humans and fall short in predicting the effects of candidate drugs. Patient-derived stem cell organoids are new and effective models for the development of novel targeted therapies. With the use of intestinal organoids from patients with inherited diseases, the potency and toxicity of drug candidates can be evaluated better. Moreover, owing to the novel CRISPR/Cas9 genome-editing technologies, researchers can use organoids to precisely modulate human genetic status and identify pathogenesis-related genes of intestinal diseases. Therefore, here we discuss how patient-derived organoids should be grown and how advanced genome-editing tools may be applied to research on modeling of cancer and infectious diseases. We also highlight practical applications of organoids ranging from basic studies to drug screening and precision medicine.

  2. Strategies to address low drug solubility in discovery and development.

    Science.gov (United States)

    Williams, Hywel D; Trevaskis, Natalie L; Charman, Susan A; Shanker, Ravi M; Charman, William N; Pouton, Colin W; Porter, Christopher J H

    2013-01-01

    Drugs with low water solubility are predisposed to low and variable oral bioavailability and, therefore, to variability in clinical response. Despite significant efforts to "design in" acceptable developability properties (including aqueous solubility) during lead optimization, approximately 40% of currently marketed compounds and most current drug development candidates remain poorly water-soluble. The fact that so many drug candidates of this type are advanced into development and clinical assessment is testament to an increasingly sophisticated understanding of the approaches that can be taken to promote apparent solubility in the gastrointestinal tract and to support drug exposure after oral administration. Here we provide a detailed commentary on the major challenges to the progression of a poorly water-soluble lead or development candidate and review the approaches and strategies that can be taken to facilitate compound progression. In particular, we address the fundamental principles that underpin the use of strategies, including pH adjustment and salt-form selection, polymorphs, cocrystals, cosolvents, surfactants, cyclodextrins, particle size reduction, amorphous solid dispersions, and lipid-based formulations. In each case, the theoretical basis for utility is described along with a detailed review of recent advances in the field. The article provides an integrated and contemporary discussion of current approaches to solubility and dissolution enhancement but has been deliberately structured as a series of stand-alone sections to allow also directed access to a specific technology (e.g., solid dispersions, lipid-based formulations, or salt forms) where required.

  3. Virtual target screening to rapidly identify potential protein targets of natural products in drug discovery

    Directory of Open Access Journals (Sweden)

    Yuri Pevzner

    2015-08-01

    Full Text Available Inherent biological viability and diversity of natural products make them a potentially rich source for new therapeutics. However, identification of bioactive compounds with desired therapeutic effects and identification of their protein targets is a laborious, expensive process. Extracts from organism samples may show desired activity in phenotypic assays but specific bioactive compounds must be isolated through further separation methods and protein targets must be identified by more specific phenotypic and in vitro experimental assays. Still, questions remain as to whether all relevant protein targets for a compound have been identified. The desire is to understand breadth of purposing for the compound to maximize its use and intellectual property, and to avoid further development of compounds with insurmountable adverse effects. Previously we developed a Virtual Target Screening system that computationally screens one or more compounds against a collection of virtual protein structures. By scoring each compound-protein interaction, we can compare against averaged scores of synthetic drug-like compounds to determine if a particular protein would be a potential target of a compound of interest. Here we provide examples of natural products screened through our system as we assess advantages and shortcomings of our current system in regards to natural product drug discovery.

  4. Virtual target screening to rapidly identify potential protein targets of natural products in drug discovery

    Directory of Open Access Journals (Sweden)

    Yuri Pevzner

    2014-05-01

    Full Text Available Inherent biological viability and diversity of natural products make them a potentially rich source for new therapeutics. However, identification of bioactive compounds with desired therapeutic effects and identification of their protein targets is a laborious, expensive process. Extracts from organism samples may show desired activity in phenotypic assays but specific bioactive compounds must be isolated through further separation methods and protein targets must be identified by more specific phenotypic and in vitro experimental assays. Still, questions remain as to whether all relevant protein targets for a compound have been identified. The desire is to understand breadth of purposing for the compound to maximize its use and intellectual property, and to avoid further development of compounds with insurmountable adverse effects. Previously we developed a Virtual Target Screening system that computationally screens one or more compounds against a collection of virtual protein structures. By scoring each compound-protein interaction, we can compare against averaged scores of synthetic drug-like compounds to determine if a particular protein would be a potential target of a compound of interest. Here we provide examples of natural products screened through our system as we assess advantages and shortcomings of our current system in regards to natural product drug discovery.

  5. From bench to bedside: ways and steps of drug discovery

    Directory of Open Access Journals (Sweden)

    Ş.Ş. Alkan

    2011-12-01

    Full Text Available For the public two things are not easy to understand about drugs: a Why drugs are so expensive? b Why there are only so few new drugs discovered despite higher investments every year. Usually it takes 10- 15 years to develop a drug. Here, I will guide you through all stages of classical drug development so that you can understand the reasons of the current situation. Although I am not an expert in hematological diseases, the lessons I have learned during my 30 years of experience in the pharmaceutical industry will hopefully prepare you to find novel ways for the treatment of thalassemia. 关于药品,公众有两个疑问: a 为什么药品如此昂贵?b尽管每年投入高额投资,为什么只研发出少量的新药? 通常,研发一种药品需要10到15年时间。 在此,我会介绍典型药品研发的所有步骤,帮助你了解造成现状的原因。 虽然我不是地中海贫血病的专家,但是凭借在我在医药行业30年的经验教训,希望能帮助你找到治疗地中海贫血的新方法。

  6. 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.

  7. 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

  8. Alkaloids from Marine Invertebrates as Important Leads for Anticancer Drugs Discovery and Development

    Directory of Open Access Journals (Sweden)

    Concetta Imperatore

    2014-12-01

    Full Text Available The present review describes research on novel natural antitumor alkaloids isolated from marine invertebrates. The structure, origin, and confirmed cytotoxic activity of more than 130 novel alkaloids belonging to several structural families (indoles, pyrroles, pyrazines, quinolines, and pyridoacridines, together with some of their synthetic analogs, are illustrated. Recent discoveries concerning the current state of the potential and/or development of some of them as new drugs, as well as the current knowledge regarding their modes of action, are also summarized. A special emphasis is given to the role of marine invertebrate alkaloids as an important source of leads for anticancer drug discovery.

  9. The chemistry-biology-medicine continuum and the drug discovery and development process in academia.

    Science.gov (United States)

    Nicolaou, K C

    2014-09-18

    Admirable as it is, the drug discovery and development process is continuously undergoing changes and adjustments in search of further improvements in efficiency, productivity, and profitability. Recent trends in academic-industrial partnerships promise to provide new opportunities for advancements of this process through transdisciplinary collaborations along the entire spectrum of activities involved in this complex process. This perspective discusses ways to promote the emerging academic paradigm of the chemistry-biology-medicine continuum as a means to advance the drug discovery and development process. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. pH-Dependent solubility and permeability criteria for provisional biopharmaceutics classification (BCS and BDDCS) in early drug discovery.

    Science.gov (United States)

    Varma, Manthena V; Gardner, Iain; Steyn, Stefanus J; Nkansah, Paul; Rotter, Charles J; Whitney-Pickett, Carrie; Zhang, Hui; Di, Li; Cram, Michael; Fenner, Katherine S; El-Kattan, Ayman F

    2012-05-07

    The Biopharmaceutics Classification System (BCS) is a scientific framework that provides a basis for predicting the oral absorption of drugs. These concepts have been extended in the Biopharmaceutics Drug Disposition Classification System (BDDCS) to explain the potential mechanism of drug clearance and understand the effects of uptake and efflux transporters on absorption, distribution, metabolism, and elimination. The objective of present work is to establish criteria for provisional biopharmaceutics classification using pH-dependent passive permeability and aqueous solubility data generated from high throughput screening methodologies in drug discovery settings. The apparent permeability across monolayers of clonal cell line of Madin-Darby canine kidney cells, selected for low endogenous efflux transporter expression, was measured for a set of 105 drugs, with known BCS and BDDCS class. The permeability at apical pH 6.5 for acidic drugs and at pH 7.4 for nonacidic drugs showed a good correlation with the fraction absorbed in human (Fa). Receiver operating characteristic (ROC) curve analysis was utilized to define the permeability class boundary. At permeability ≥ 5 × 10(-6) cm/s, the accuracy of predicting Fa of ≥ 0.90 was 87%. Also, this cutoff showed more than 80% sensitivity and specificity in predicting the literature permeability classes (BCS), and the metabolism classes (BDDCS). The equilibrium solubility of a subset of 49 drugs was measured in pH 1.2 medium, pH 6.5 phosphate buffer, and in FaSSIF medium (pH 6.5). Although dose was not considered, good concordance of the measured solubility with BCS and BDDCS solubility class was achieved, when solubility at pH 1.2 was used for acidic compounds and FaSSIF solubility was used for basic, neutral, and zwitterionic compounds. Using a cutoff of 200 μg/mL, the data set suggested a 93% sensitivity and 86% specificity in predicting both the BCS and BDDCS solubility classes. In conclusion, this study identified

  11. 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

  12. ROR nuclear receptors: structures, related diseases, and drug discovery.

    Science.gov (United States)

    Zhang, Yan; Luo, Xiao-yu; Wu, Dong-hai; Xu, Yong

    2015-01-01

    Nuclear receptors (NRs) are ligand-regulated transcription factors that regulate metabolism, development and immunity. The NR superfamily is one of the major classes of drug targets for human diseases. Retinoic acid receptor-related orphan receptor (ROR) α, β and γ belong to the NR superfamily, and these receptors are still considered as 'orphan' receptors because the identification of their endogenous ligands has been controversial. Recent studies have demonstrated that these receptors are regulated by synthetic ligands, thus emerge as important drug targets for the treatment of multiple sclerosis, rheumatoid arthritis, psoriasis, etc. Studying the structural basis and ligand development of RORs will pave the way for a better understanding of the roles of these receptors in human diseases. Here, we review the structural basis, disease relevance, strategies for ligand identification, and current status of development of therapeutic ligands for RORs.

  13. Industrial natural product chemistry for drug discovery and development.

    Science.gov (United States)

    Bauer, Armin; Brönstrup, Mark

    2014-01-01

    Covering: up to March 2013. In addition to their prominent role in basic biological and chemical research, natural products are a rich source of commercial products for the pharmaceutical and other industries. Industrial natural product chemistry is of fundamental importance for successful product development, as the vast majority (ca. 80%) of commercial drugs derived from natural products require synthetic efforts, either to enable economical access to bulk material, and/or to optimize drug properties through structural modifications. This review aims to illustrate issues on the pathway from lead to product, and how they have been successfully addressed by modern natural product chemistry. It is focused on natural products of current relevance that are, or are intended to be, used as pharmaceuticals.

  14. Hydrophobicity – Shake Flasks, Protein Folding and Drug Discovery

    Science.gov (United States)

    Sarkar, Aurijit; Kellogg, Glen E.

    2009-01-01

    Hydrophobic interactions are some of the most important interactions in nature. They are the primary driving force in a number of phenomena. This is mostly an entropic effect and can account for a number of biophysical events such as protein-protein or protein-ligand binding that are of immense importance in drug design. The earliest studies on this phenomenon can be dated back to the end of the 19th century when Meyer and Overton independently correlated the hydrophobic nature of gases to their anesthetic potency. Since then, significant progress has been made in this realm of science. This review briefly traces the history of hydrophobicity research along with the theoretical estimation of partition coefficients. Finally, the application of hydrophobicity estimation methods in the field of drug design and protein folding is discussed. PMID:19929828

  15. GLIDA: GPCR-ligand database for chemical genomic drug discovery

    OpenAIRE

    Okuno, Yasushi; Yang, Jiyoon; Taneishi, Kei; Yabuuchi, Hiroaki; Tsujimoto, Gozoh

    2005-01-01

    G-protein coupled receptors (GPCRs) represent one of the most important families of drug targets in pharmaceutical development. GPCR-LIgand DAtabase (GLIDA) is a novel public GPCR-related chemical genomic database that is primarily focused on the correlation of information between GPCRs and their ligands. It provides correlation data between GPCRs and their ligands, along with chemical information on the ligands, as well as access information to the various web databases regarding GPCRs. Thes...

  16. The impact of natural products upon modern drug discovery.

    Science.gov (United States)

    Ganesan, A

    2008-06-01

    In the period 1970-2006, a total of 24 unique natural products were discovered that led to an approved drug. We analyze these successful leads in terms of drug-like properties, and show that they can be divided into two equal subsets. The first falls in the 'Lipinski universe' and complies with the Rule of Five. The second is a 'parallel universe' that violates the rules. Nevertheless, the latter compounds remain largely compliant in terms of logP and H-bond donors, highlighting the importance of these two metrics in predicting bioavailability. Natural products are often cited as an exception to Lipinski's rules. We believe this is because nature has learned to maintain low hydrophobicity and intermolecular H-bond donating potential when it needs to make biologically active compounds with high molecular weight and large numbers of rotatable bonds. In addition, natural products are more likely than purely synthetic compounds to resemble biosynthetic intermediates or endogenous metabolites, and hence take advantage of active transport mechanisms. Interestingly, the natural product leads in the Lipinski and parallel universe had an identical success rate (50%) in delivering an oral drug.

  17. Commentary: Why Pharmaceutical Scientists in Early Drug Discovery Are Critical for Influencing the Design and Selection of Optimal Drug Candidates.

    Science.gov (United States)

    Landis, Margaret S; Bhattachar, Shobha; Yazdanian, Mehran; Morrison, John

    2018-01-01

    This commentary reflects the collective view of pharmaceutical scientists from four different organizations with extensive experience in the field of drug discovery support. Herein, engaging discussion is presented on the current and future approaches for the selection of the most optimal and developable drug candidates. Over the past two decades, developability assessment programs have been implemented with the intention of improving physicochemical and metabolic properties. However, the complexity of both new drug targets and non-traditional drug candidates provides continuing challenges for developing formulations for optimal drug delivery. The need for more enabled technologies to deliver drug candidates has necessitated an even more active role for pharmaceutical scientists to influence many key molecular parameters during compound optimization and selection. This enhanced role begins at the early in vitro screening stages, where key learnings regarding the interplay of molecular structure and pharmaceutical property relationships can be derived. Performance of the drug candidates in formulations intended to support key in vivo studies provides important information on chemotype-formulation compatibility relationships. Structure modifications to support the selection of the solid form are also important to consider, and predictive in silico models are being rapidly developed in this area. Ultimately, the role of pharmaceutical scientists in drug discovery now extends beyond rapid solubility screening, early form assessment, and data delivery. This multidisciplinary role has evolved to include the practice of proactively taking part in the molecular design to better align solid form and formulation requirements to enhance developability potential.

  18. 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.

  19. 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.

  20. DNA demethylases: a new epigenetic frontier in drug discovery.

    Science.gov (United States)

    Carey, Nessa; Marques, C Joana; Reik, Wolf

    2011-08-01

    DNA methylation is one of the most extensively studied, and one of the most stable, of all epigenetic modifications. Two drugs that target DNA methyltransferase enzymes are licensed for clinical use in oncology but relatively little attention has focused on the enzymatic pathways by which DNA methylation can be reversed. Recent breakthroughs have identified at least two classes of enzymes that can achieve functional reversal. This review discusses the significance of DNA demethylation in a range of human diseases, the candidate proteins that mediate the demethylation and the opportunities and challenges in targeting these candidates to develop new therapeutics. Copyright © 2011 Elsevier Ltd. All rights reserved.

  1. Recent Advances in Prostate Cancer Treatment and Drug Discovery

    Directory of Open Access Journals (Sweden)

    Ekaterina Nevedomskaya

    2018-05-01

    Full Text Available Novel drugs, drug sequences and combinations have improved the outcome of prostate cancer in recent years. The latest approvals include abiraterone acetate, enzalutamide and apalutamide which target androgen receptor (AR signaling, radium-223 dichloride for reduction of bone metastases, sipuleucel-T immunotherapy and taxane-based chemotherapy. Adding abiraterone acetate to androgen deprivation therapy (ADT in order to achieve complete androgen blockade has proven highly beneficial for treatment of locally advanced prostate cancer and metastatic hormone-sensitive prostate cancer (mHSPC. Also, ADT together with docetaxel treatment showed significant benefit in mHSPC. Ongoing clinical trials for different subgroups of prostate cancer patients include the evaluation of the second-generation AR antagonists enzalutamide, apalutamide and darolutamide, of inhibitors of the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K pathway, of inhibitors of DNA damage response, of targeted alpha therapy and of prostate-specific membrane antigen (PSMA targeting approaches. Advanced clinical studies with immune checkpoint inhibitors have shown limited benefits in prostate cancer and more trials are needed to demonstrate efficacy. The identification of improved, personalized treatments will be much supported by the major progress recently made in the molecular characterization of early- and late-stage prostate cancer using “omics” technologies. This has already led to novel classifications of prostate tumors based on gene expression profiles and mutation status, and should greatly help in the choice of novel targeted therapies best tailored to the needs of patients.

  2. 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

  3. 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.

  4. Reverse Pharmacognosy and Reverse Pharmacology; Two Closely Related Approaches for Drug Discovery Development.

    Science.gov (United States)

    Saeidnia, Soodabeh; Gohari, Ahmad R; Manayi, Azadeh

    Pharmacognosy is a science, which study natural products as a source of new drug leads and effective drug development. Rational and economic search for novel lead structures could maximize the speed of drug discovery by using powerful high technology methods. Reverse pharmacognosy, a complementary to pharmacognosy, couples the high throughput screening (HTS), virtual screening and databases along with the knowledge of traditional medicines. These strategies lead to identification of numerous in vitro active and selective hits enhancing the speed of drug discovery from natural sources. Besides, reverse pharmacology is a target base drug discovery approach; in the first step, a hypothesis is made that the alteration of specific protein activity will produce beneficial curative effects. Both, reverse pharmacognosy and reverse pharmacology take advantages of high technology methods to accomplish their particular purposes. Moreover, reverse pharmacognosy effectively utilize traditional medicines and natural products as promising sources to provide new drug leads as well as promote the rational use of them by using valuable information like protein structure databases and chemical libraries which prepare pharmacological profile of traditional medicine, plant extract or natural compounds.

  5. Open drug discovery for the Zika virus [version 1; referees: 3 approved

    Directory of Open Access Journals (Sweden)

    Sean Ekins

    2016-02-01

    Full Text Available The Zika virus (ZIKV outbreak in the Americas has caused global concern that we may be on the brink of a healthcare crisis. The lack of research on ZIKV in the over 60 years that we have known about it has left us with little in the way of starting points for drug discovery. Our response can build on previous efforts with virus outbreaks and lean heavily on work done on other flaviviruses such as dengue virus. We provide some suggestions of what might be possible and propose an open drug discovery effort that mobilizes global science efforts and provides leadership, which thus far has been lacking. We also provide a listing of potential resources and molecules that could be prioritized for testing as in vitro assays for ZIKV are developed. We propose also that in order to incentivize drug discovery, a neglected disease priority review voucher should be available to those who successfully develop an FDA approved treatment. Learning from the response to the ZIKV, the approaches to drug discovery used and the success and failures will be critical for future infectious disease outbreaks.

  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. Constellation pharmacology: a new paradigm for drug discovery.

    Science.gov (United States)

    Teichert, Russell W; Schmidt, Eric W; Olivera, Baldomero M

    2015-01-01

    Constellation pharmacology is a cell-based high-content phenotypic-screening platform that utilizes subtype-selective pharmacological agents to elucidate the cell-specific combinations (constellations) of key signaling proteins that define specific cell types. Heterogeneous populations of native cells, in which the different individual cell types have been identified and characterized, are the foundation for this screening platform. Constellation pharmacology is useful for screening small molecules or for deconvoluting complex mixtures of biologically active natural products. This platform has been used to purify natural products and discover their molecular mechanisms. In the ongoing development of constellation pharmacology, there is a positive feedback loop between the pharmacological characterization of cell types and screening for new drug candidates. As constellation pharmacology is used to discover compounds with novel targeting-selectivity profiles, those new compounds then further help to elucidate the constellations of specific cell types, thereby increasing the content of this high-content platform.

  8. Isotope chemistry; a useful tool in the drug discovery arsenal.

    Science.gov (United States)

    Elmore, Charles S; Bragg, Ryan A

    2015-01-15

    As Medicinal Chemists are responsible for the synthesis and optimization of compounds, they often provide intermediates for use by isotope chemistry. Nevertheless, there is generally an incomplete understanding of the critical factors involved in the labeling of compounds. The remit of an Isotope Chemistry group varies from company to company, but often includes the synthesis of compounds labeled with radioisotopes, especially H-3 and C-14 and occasionally I-125, and stable isotopes, especially H-2, C-13, and N-15. Often the remit will also include the synthesis of drug metabolites. The methods used to prepare radiolabeled compounds by Isotope Chemists have been reviewed relatively recently. However, the organization and utilization of Isotope Chemistry has not been discussed recently and will be reviewed herein. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Bioenergetics of Mycobacterium: An Emerging Landscape for Drug Discovery

    Directory of Open Access Journals (Sweden)

    Iram Khan Iqbal

    2018-02-01

    Full Text Available Mycobacterium tuberculosis (Mtb exhibits remarkable metabolic flexibility that enables it to survive a plethora of host environments during its life cycle. With the advent of bedaquiline for treatment of multidrug-resistant tuberculosis, oxidative phosphorylation has been validated as an important target and a vulnerable component of mycobacterial metabolism. Exploiting the dependence of Mtb on oxidative phosphorylation for energy production, several components of this pathway have been targeted for the development of new antimycobacterial agents. This includes targeting NADH dehydrogenase by phenothiazine derivatives, menaquinone biosynthesis by DG70 and other compounds, terminal oxidase by imidazopyridine amides and ATP synthase by diarylquinolines. Importantly, oxidative phosphorylation also plays a critical role in the survival of persisters. Thus, inhibitors of oxidative phosphorylation can synergize with frontline TB drugs to shorten the course of treatment. In this review, we discuss the oxidative phosphorylation pathway and development of its inhibitors in detail.

  10. Applicability of bioanalysis of multiple analytes in drug discovery and development: review of select case studies including assay development considerations.

    Science.gov (United States)

    Srinivas, Nuggehally R

    2006-05-01

    The development of sound bioanalytical method(s) is of paramount importance during the process of drug discovery and development culminating in a marketing approval. Although the bioanalytical procedure(s) originally developed during the discovery stage may not necessarily be fit to support the drug development scenario, they may be suitably modified and validated, as deemed necessary. Several reviews have appeared over the years describing analytical approaches including various techniques, detection systems, automation tools that are available for an effective separation, enhanced selectivity and sensitivity for quantitation of many analytes. The intention of this review is to cover various key areas where analytical method development becomes necessary during different stages of drug discovery research and development process. The key areas covered in this article with relevant case studies include: (a) simultaneous assay for parent compound and metabolites that are purported to display pharmacological activity; (b) bioanalytical procedures for determination of multiple drugs in combating a disease; (c) analytical measurement of chirality aspects in the pharmacokinetics, metabolism and biotransformation investigations; (d) drug monitoring for therapeutic benefits and/or occupational hazard; (e) analysis of drugs from complex and/or less frequently used matrices; (f) analytical determination during in vitro experiments (metabolism and permeability related) and in situ intestinal perfusion experiments; (g) determination of a major metabolite as a surrogate for the parent molecule; (h) analytical approaches for universal determination of CYP450 probe substrates and metabolites; (i) analytical applicability to prodrug evaluations-simultaneous determination of prodrug, parent and metabolites; (j) quantitative determination of parent compound and/or phase II metabolite(s) via direct or indirect approaches; (k) applicability in analysis of multiple compounds in select

  11. Plasmonic ruler on field-effect devices for kinase drug discovery applications.

    Science.gov (United States)

    Bhalla, Nikhil; Formisano, Nello; Miodek, Anna; Jain, Aditya; Di Lorenzo, Mirella; Pula, Giordano; Estrela, Pedro

    2015-09-15

    Protein kinases are cellular switches that mediate phosphorylation of proteins. Abnormal phosphorylation of proteins is associated with lethal diseases such as cancer. In the pharmaceutical industry, protein kinases have become an important class of drug targets. This study reports a versatile approach for the detection of protein phosphorylation. The change in charge of the myelin basic protein upon phosphorylation by the protein kinase C-alpha (PKC-α) in the presence of adenosine 5'-[γ-thio] triphosphate (ATP-S) was detected on gold metal-insulator-semiconductor (Au-MIS) capacitor structures. Gold nanoparticles (AuNPs) can then be attached to the thio-phosphorylated proteins, forming a Au-film/AuNP plasmonic couple. This was detected by a localized surface plasmon resonance (LSPR) technique alongside MIS capacitance. All reactions were validated using surface plasmon resonance technique and the interaction of AuNPs with the thio-phosphorylated proteins quantified by quartz crystal microbalance. The plasmonic coupling was also visualized by simulations using finite element analysis. The use of this approach in drug discovery applications was demonstrated by evaluating the response in the presence of a known inhibitor of PKC-α kinase. LSPR and MIS on a single platform act as a cross check mechanism for validating kinase activity and make the system robust to test novel inhibitors. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. X-ray crystallography over the past decade for novel drug discovery – where are we heading next?

    Science.gov (United States)

    Zheng, Heping; Handing, Katarzyna B; Zimmerman, Matthew D; Shabalin, Ivan G; Almo, Steven C; Minor, Wladek

    2015-01-01

    Introduction Macromolecular X-ray crystallography has been the primary methodology for determining the three-dimensional structures of proteins, nucleic acids and viruses. Structural information has paved the way for structure-guided drug discovery and laid the foundations for structural bioinformatics. However, X-ray crystallography still has a few fundamental limitations, some of which may be overcome and complemented using emerging methods and technologies in other areas of structural biology. Areas covered This review describes how structural knowledge gained from X-ray crystallography has been used to advance other biophysical methods for structure determination (and vice versa). This article also covers current practices for integrating data generated by other biochemical and biophysical methods with those obtained from X-ray crystallography. Finally, the authors articulate their vision about how a combination of structural and biochemical/biophysical methods may improve our understanding of biological processes and interactions. Expert opinion X-ray crystallography has been, and will continue to serve as, the central source of experimental structural biology data used in the discovery of new drugs. However, other structural biology techniques are useful not only to overcome the major limitation of X-ray crystallography, but also to provide complementary structural data that is useful in drug discovery. The use of recent advancements in biochemical, spectroscopy and bioinformatics methods may revolutionize drug discovery, albeit only when these data are combined and analyzed with effective data management systems. Accurate and complete data management is crucial for developing experimental procedures that are robust and reproducible. PMID:26177814

  13. 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.

  14. 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.

  15. 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.

  16. Structure-Based Drug Discovery for Prion Disease Using a Novel Binding Simulation

    Directory of Open Access Journals (Sweden)

    Daisuke Ishibashi

    2016-07-01

    Full Text Available The accumulation of abnormal prion protein (PrPSc converted from the normal cellular isoform of PrP (PrPC is assumed to induce pathogenesis in prion diseases. Therefore, drug discovery studies for these diseases have focused on the protein conversion process. We used a structure-based drug discovery algorithm (termed Nagasaki University Docking Engine: NUDE that ran on an intensive supercomputer with a graphic-processing unit to identify several compounds with anti-prion effects. Among the candidates showing a high-binding score, the compounds exhibited direct interaction with recombinant PrP in vitro, and drastically reduced PrPSc and protein-aggresomes in the prion-infected cells. The fragment molecular orbital calculation showed that the van der Waals interaction played a key role in PrPC binding as the intermolecular interaction mode. Furthermore, PrPSc accumulation and microgliosis were significantly reduced in the brains of treated mice, suggesting that the drug candidates provided protection from prion disease, although further in vivo tests are needed to confirm these findings. This NUDE-based structure-based drug discovery for normal protein structures is likely useful for the development of drugs to treat other conformational disorders, such as Alzheimer's disease.

  17. Platelet-activating factor podoplanin: from discovery to drug development.

    Science.gov (United States)

    Takemoto, Ai; Miyata, Kenichi; Fujita, Naoya

    2017-06-01

    Tumor cell-induced platelet aggregation facilitates hematogenous metastasis by promoting tumor embolization, preventing immunological assaults and shear stress, and the platelet-releasing growth factors support tumor growth and invasion. Podoplanin, also known as Aggrus, is a type I transmembrane mucin-like glycoprotein and is expressed on wide range of tumor cells. Podoplanin has a role in platelet aggregation and metastasis formation through the binding to its platelet receptor, C-type lectin-like receptor 2 (CLEC-2). The podoplanin research was originally started from the cloning of highly metastatic NL-17 subclone from mouse colon 26 cancer cell line and from the establishment of 8F11 monoclonal antibody (mAb) that could neutralize NL-17-induced platelet aggregation and hematogenous metastasis. Later on, podoplanin was identified as the antigen of 8F11 mAb, and its ectopic expression brought to cells the platelet-aggregating abilities and hematogenous metastasis phenotypes. From the 8F11 mAb recognition epitopes, podoplanin is found to contain tandemly repeated, highly conserved motifs, designated platelet aggregation-stimulating (PLAG) domains. Series of analyses using the cells expressing the mutants and the established neutralizing anti-podoplanin mAbs uncovered that both PLAG3 and PLAG4 domains are associated with the CLEC-2 binding. The neutralizing mAbs targeting PLAG3 or PLAG4 could suppress podoplanin-induced platelet aggregation and hematogenous metastasis through inhibiting the podoplanin-CLEC-2 binding. Therefore, these domains are certainly functional in podoplanin-mediated metastasis through its platelet-aggregating activity. This review summarizes the platelet functions in metastasis formation, the role of platelet aggregation-inducing factor podoplanin in pathological and physiological situations, and the possibility to develop podoplanin-targeting drugs in the future.

  18. 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.

  19. 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.

    2015-01-01

    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. PMID:25883136

  20. 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.

  1. OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery.

    Directory of Open Access Journals (Sweden)

    Sean Ekins

    2016-10-01

    Full Text Available The Zika virus outbreak in the Americas has caused global concern. To help accelerate this fight against Zika, we launched the OpenZika project. OpenZika is an IBM World Community Grid Project that uses distributed computing on millions of computers and Android devices to run docking experiments, in order to dock tens of millions of drug-like compounds against crystal structures and homology models of Zika proteins (and other related flavivirus targets. This will enable the identification of new candidates that can then be tested in vitro, to advance the discovery and development of new antiviral drugs against the Zika virus. The docking data is being made openly accessible so that all members of the global research community can use it to further advance drug discovery studies against Zika and other related flaviviruses.

  2. canSAR: an updated cancer research and drug discovery knowledgebase.

    Science.gov (United States)

    Tym, Joseph E; Mitsopoulos, Costas; Coker, Elizabeth A; Razaz, Parisa; Schierz, Amanda C; Antolin, Albert A; Al-Lazikani, Bissan

    2016-01-04

    canSAR (http://cansar.icr.ac.uk) is a publicly available, multidisciplinary, cancer-focused knowledgebase developed to support cancer translational research and drug discovery. canSAR integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and druggability data. canSAR is widely used to rapidly access information and help interpret experimental data in a translational and drug discovery context. Here we describe major enhancements to canSAR including new data, improved search and browsing capabilities, new disease and cancer cell line summaries and new and enhanced batch analysis tools. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  3. Behavioral studies on anxiety and depression in a drug discovery environment: keys to a successful future.

    Science.gov (United States)

    Bouwknecht, J Adriaan

    2015-04-15

    The review describes a personal journey through 25 years of animal research with a focus on the contribution of rodent models for anxiety and depression to the development of new medicines in a drug discovery environment. Several classic acute models for mood disorders are briefly described as well as chronic stress and disease-induction models. The paper highlights a variety of factors that influence the quality and consistency of behavioral data in a laboratory setting. The importance of meta-analysis techniques for study validation (tolerance interval) and assay sensitivity (Monte Carlo modeling) are demonstrated by examples that use historic data. It is essential for successful discovery of new potential drugs to maintain a high level of control in animal research and to bridge knowledge across in silico modeling, and in vitro and in vivo assays. Today, drug discovery is a highly dynamic environment in search of new types of treatments and new animal models which should be guided by enhanced two-way translation between bench and bed. Although productivity has been disappointing in the search of new and better medicines in psychiatry over the past decades, there has been and will always be an important role for in vivo models in-between preclinical discovery and clinical development. The right balance between good science and proper judgment versus a decent level of innovation, assay development and two-way translation will open the doors to a very bright future. Copyright © 2014 Elsevier B.V. All rights reserved.

  4. Natural products used as a chemical library for protein-protein interaction targeted drug discovery.

    Science.gov (United States)

    Jin, Xuemei; Lee, Kyungro; Kim, Nam Hee; Kim, Hyun Sil; Yook, Jong In; Choi, Jiwon; No, Kyoung Tai

    2018-01-01

    Protein-protein interactions (PPIs), which are essential for cellular processes, have been recognized as attractive therapeutic targets. Therefore, the construction of a PPI-focused chemical library is an inevitable necessity for future drug discovery. Natural products have been used as traditional medicines to treat human diseases for millennia; in addition, their molecular scaffolds have been used in diverse approved drugs and drug candidates. The recent discovery of the ability of natural products to inhibit PPIs led us to use natural products as a chemical library for PPI-targeted drug discovery. In this study, we collected natural products (NPDB) from non-commercial and in-house databases to analyze their similarities to small-molecule PPI inhibitors (iPPIs) and FDA-approved drugs by using eight molecular descriptors. Then, we evaluated the distribution of NPDB and iPPIs in the chemical space, represented by the molecular fingerprint and molecular scaffolds, to identify the promising scaffolds, which could interfere with PPIs. To investigate the ability of natural products to inhibit PPI targets, molecular docking was used. Then, we predicted a set of high-potency natural products by using the iPPI-likeness score based on a docking score-weighted model. These selected natural products showed high binding affinities to the PPI target, namely XIAP, which were validated in an in vitro experiment. In addition, the natural products with novel scaffolds might provide a promising starting point for further medicinal chemistry developments. Overall, our study shows the potency of natural products in targeting PPIs, which might help in the design of a PPI-focused chemical library for future drug discovery. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Using directed information for influence discovery in interconnected dynamical systems

    Science.gov (United States)

    Rao, Arvind; Hero, Alfred O.; States, David J.; Engel, James Douglas

    2008-08-01

    Structure discovery in non-linear dynamical systems is an important and challenging problem that arises in various applications such as computational neuroscience, econometrics, and biological network discovery. Each of these systems have multiple interacting variables and the key problem is the inference of the underlying structure of the systems (which variables are connected to which others) based on the output observations (such as multiple time trajectories of the variables). Since such applications demand the inference of directed relationships among variables in these non-linear systems, current methods that have a linear assumption on structure or yield undirected variable dependencies are insufficient. Hence, in this work, we present a methodology for structure discovery using an information-theoretic metric called directed time information (DTI). Using both synthetic dynamical systems as well as true biological datasets (kidney development and T-cell data), we demonstrate the utility of DTI in such problems.

  6. Application of genefishing discovery system on differential gene ...

    African Journals Online (AJOL)

    GREGORY

    2010-08-30

    Aug 30, 2010 ... this discovery system for a prokaryotic system by modifying the eukaryotic protocol using the poly (A)- ... eukaryotic system mainly in humans, screening of ... RNA isolation. Total RNA extraction from the bacterial cells was performed at room temperature using RNeasy® Mini Kit (Qiagen). Initially, the cells.

  7. A Drug Discovery Partnership for Personalized Breast Cancer Therapy

    Science.gov (United States)

    2015-09-01

    efficacy.4,5 Employing flavone and coumarin cores as rigid moieties, the long conjugated system-modified ceramide 4   analogs were designed. The...we successfully synthesized 11 pyranoflavone and 4 furano flavone/ coumarin building blocks for the preparation of fluorescent ceramide analogs...Year 3, we successfully synthesized 11 pyrano-, furano-, dioxolo-, and pyridino-flavone/ coumarin building blocks (Figure 7). We have established an

  8. Facilitating drug discovery: an automated high-content inflammation assay in zebrafish.

    Science.gov (United States)

    Wittmann, Christine; Reischl, Markus; Shah, Asmi H; Mikut, Ralf; Liebel, Urban; Grabher, Clemens

    2012-07-16

    Zebrafish larvae are particularly amenable to whole animal small molecule screens due to their small size and relative ease of manipulation and observation, as well as the fact that compounds can simply be added to the bathing water and are readily absorbed when administered in a Introduction of the chemically induced inflammation (ChIn) assay eliminated these obstacles. Since wounding is inflicted chemically the number of embryos that can be treated simultaneously is virtually unlimited. Temporary treatment of zebrafish larvae with copper sulfate selectively induces cell death in hair cells of the lateral line system and results in rapid granulocyte recruitment to injured neuromasts. The inflammatory response can be followed in real-time by using compound transgenic cldnB::GFP/lysC::DsRED2 zebrafish larvae that express a green fluorescent protein in neuromast cells, as well as a red fluorescent protein labeling granulocytes. In order to devise a screening strategy that would allow both high-content and high-throughput analyses we introduced robotic liquid handling and combined automated microscopy with a custom developed software script. This script enables automated quantification of the inflammatory response by scoring the percent area occupied by red fluorescent leukocytes within an empirically defined area surrounding injured green fluorescent neuromasts. Furthermore, we automated data processing, handling, visualization, and storage all based on custom developed MATLAB and Python scripts. In brief, we introduce an automated HC/HT screen that allows testing of chemical compounds for their effect on initiation, progression or resolution of a granulocytic inflammatory response. This protocol serves a good starting point for more in-depth analyses of drug mechanisms and pathways involved in the orchestration of an innate immune response. In the future, it may help identifying intolerable toxic or off-target effects at earlier phases of drug discovery and thereby

  9. 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.

  10. Collation and data-mining of literature bioactivity data for drug discovery.

    Science.gov (United States)

    Bellis, Louisa J; Akhtar, Ruth; Al-Lazikani, Bissan; Atkinson, Francis; Bento, A Patricia; Chambers, Jon; Davies, Mark; Gaulton, Anna; Hersey, Anne; Ikeda, Kazuyoshi; Krüger, Felix A; Light, Yvonne; McGlinchey, Shaun; Santos, Rita; Stauch, Benjamin; Overington, John P

    2011-10-01

    The challenge of translating the huge amount of genomic and biochemical data into new drugs is a costly and challenging task. Historically, there has been comparatively little focus on linking the biochemical and chemical worlds. To address this need, we have developed ChEMBL, an online resource of small-molecule SAR (structure-activity relationship) data, which can be used to support chemical biology, lead discovery and target selection in drug discovery. The database contains the abstracted structures, properties and biological activities for over 700000 distinct compounds and in excess of more than 3 million bioactivity records abstracted from over 40000 publications. Additional public domain resources can be readily integrated into the same data model (e.g. PubChem BioAssay data). The compounds in ChEMBL are largely extracted from the primary medicinal chemistry literature, and are therefore usually 'drug-like' or 'lead-like' small molecules with full experimental context. The data cover a significant fraction of the discovery of modern drugs, and are useful in a wide range of drug design and discovery tasks. In addition to the compound data, ChEMBL also contains information for over 8000 protein, cell line and whole-organism 'targets', with over 4000 of those being proteins linked to their underlying genes. The database is searchable both chemically, using an interactive compound sketch tool, protein sequences, family hierarchies, SMILES strings, compound research codes and key words, and biologically, using a variety of gene identifiers, protein sequence similarity and protein families. The information retrieved can then be readily filtered and downloaded into various formats. ChEMBL can be accessed online at https://www.ebi.ac.uk/chembldb.

  11. Drug nanocrystals for the formulation of poorly soluble drugs and its application as a potential drug delivery system

    International Nuclear Information System (INIS)

    Gao Lei; Zhang Dianrui; Chen Minghui

    2008-01-01

    Formulation of poorly soluble drugs is a general intractable problem in pharmaceutical field, especially those compounds poorly soluble in both aqueous and organic media. It is difficult to resolve this problem using conventional formulation approaches, so many drugs are abandoned early in discovery. Nanocrystals, a new carrier-free colloidal drug delivery system with a particle size ranging from 100 to 1000 nm, is thought as a viable drug delivery strategy to develop the poorly soluble drugs, because of their simplicity in preparation and general applicability. In this article, the product techniques of the nanocrystals were reviewed and compared, the special features of drug nanocrystals were discussed. The researches on the application of the drug nanocrystals to various administration routes were described in detail. In addition, as introduced later, the nanocrystals could be easily scaled up, which was the prerequisite to the development of a delivery system as a market product

  12. The role of machine learning in neuroimaging for drug discovery and development.

    Science.gov (United States)

    Doyle, Orla M; Mehta, Mitul A; Brammer, Michael J

    2015-11-01

    Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging can provide insight into drug penetration and distribution, target engagement, pharmacodynamics, mechanistic action and potential indicators of clinical efficacy. In this review, we focus on machine learning approaches for neuroimaging which enable us to make predictions at the individual level based on the distributed effects across the whole brain. Crucially, these approaches can be trained on data from one study and applied to an independent study and, unlike group-level statistics, can be readily use to assess the generalisability to unseen data. In this review, we present examples and suggestions for how machine learning could help answer fundamental questions spanning the drug discovery pipeline: (1) Who should I recruit for this study? (2) What should I measure and when should I measure it? (3) How does the pharmacological agent behave using an experimental medicine model?, and (4) How does a compound differ from and/or resemble existing compounds? Specifically, we present studies from the literature and we suggest areas for the focus of future development. Further refinement and tailoring of machine learning techniques may help realise their tremendous potential for drug discovery and drug validation.

  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. The role of big data and advanced analytics in drug discovery, development, and commercialization.

    Science.gov (United States)

    Szlezák, N; Evers, M; Wang, J; Pérez, L

    2014-05-01

    In recent years, few ideas have captured the imagination of health-care practitioners as much as the advent of "big data" and the advanced analytical methods and technologies used to interpret it-it is a trend seen as having the potential to revolutionize biology, medicine, and health care.(1,2,3) As new types of data and tools become available, a unique opportunity is emerging for smarter and more effective discovery, development, and commercialization of innovative biopharmaceutical drugs.

  15. Induced Pluripotent Stem Cells as a Model for Accelerated Patient- and Disease-specific Drug Discovery

    OpenAIRE

    Gunaseeli, I.; Doss, M.X.; Antzelevitch, C.; Hescheler, J.; Sachinidis, A.

    2010-01-01

    Human induced pluripotent stem (iPS) cells hold great promise for therapy of a number of degenerative diseases such as ischemic heart failure, Parkinson’s disease, Alzheimer’s disease, diabetes mellitus, sickle cell anemia and Huntington disease. They also have the potential to accelerate drug discovery in 3 ways. The first involves the delineation of chemical components for efficient reprogramming of patient’s blood cells or cells from biopsies, obviating the need for cellular delivery of re...

  16. 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.

  17. A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment.

    Science.gov (United States)

    Schuler, James; Hudson, Matthew L; Schwartz, Diane; Samudrala, Ram

    2017-10-20

    Ebola virus disease (EVD) is a deadly global public health threat, with no currently approved treatments. Traditional drug discovery and development is too expensive and inefficient to react quickly to the threat. We review published research studies that utilize computational approaches to find or develop drugs that target the Ebola virus and synthesize its results. A variety of hypothesized and/or novel treatments are reported to have potential anti-Ebola activity. Approaches that utilize multi-targeting/polypharmacology have the most promise in treating EVD.

  18. A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment

    Directory of Open Access Journals (Sweden)

    James Schuler

    2017-10-01

    Full Text Available Ebola virus disease (EVD is a deadly global public health threat, with no currently approved treatments. Traditional drug discovery and development is too expensive and inefficient to react quickly to the threat. We review published research studies that utilize computational approaches to find or develop drugs that target the Ebola virus and synthesize its results. A variety of hypothesized and/or novel treatments are reported to have potential anti-Ebola activity. Approaches that utilize multi-targeting/polypharmacology have the most promise in treating EVD.

  19. 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

  20. Recent advances in Entamoeba biology: RNA interference, drug discovery, and gut microbiome

    Science.gov (United States)

    Singh, Upinder

    2016-01-01

    In recent years, substantial progress has been made in understanding the molecular and cell biology of the human parasite Entamoeba histolytica, an important pathogen with significant global impact. This review outlines some recent advances in the Entamoeba field in the last five years, focusing on areas that have not recently been discussed in detail: (i) molecular mechanisms regulating parasite gene expression, (ii) new efforts at drug discovery using high-throughput drug screens, and (iii) the effect of gut microbiota on amoebiasis. PMID:27853522

  1. 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.

  2. 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.

  3. PROTACs: An Emerging Targeting Technique for Protein Degradation in Drug Discovery.

    Science.gov (United States)

    Gu, Shanshan; Cui, Danrui; Chen, Xiaoyu; Xiong, Xiufang; Zhao, Yongchao

    2018-04-01

    Proteolysis-targeting chimeric molecules (PROTACs) represent an emerging technique that is receiving much attention for therapeutic intervention. The mechanism is based on the inhibition of protein function by hijacking a ubiquitin E3 ligase for protein degradation. The hetero-bifunctional PROTACs contain a ligand for recruiting an E3 ligase, a linker, and another ligand to bind with the protein targeted for degradation. Thus, PROTACs have profound potential to eliminate "undruggable" protein targets, such as transcription factors and non-enzymatic proteins, which are not limited to physiological substrates of the ubiquitin-proteasome system. These findings indicate great prospects for PROTACs in the development of therapeutics. However, there are several limitations related to poor stability, biodistribution, and penetrability in vivo. This review provides an overview of the main PROTAC-based approaches that have been developed and discusses the promising opportunities and considerations for the application of this technology in therapies and drug discovery. © 2018 The Authors. BioEssays Published by Wiley Periodicals, Inc.

  4. Potential of Glutamate-Based Drug Discovery for Next Generation Antidepressants

    Directory of Open Access Journals (Sweden)

    Shigeyuki Chaki

    2015-09-01

    Full Text Available Recently, ketamine has been demonstrated to exert rapid-acting antidepressant effects in patients with depression, including those with treatment-resistant depression, and this discovery has been regarded as the most significant advance in drug development for the treatment of depression in over 50 years. To overcome unwanted side effects of ketamine, numerous approaches targeting glutamatergic systems have been vigorously investigated. For example, among agents targeting the NMDA receptor, the efficacies of selective GluN2B receptor antagonists and a low-trapping antagonist, as well as glycine site modulators such as GLYX-13 and sarcosine have been demonstrated clinically. Moreover, agents acting on metabotropic glutamate receptors, such as mGlu2/3 and mGlu5 receptors, have been proposed as useful approaches to mimicking the antidepressant effects of ketamine. Neural and synaptic mechanisms mediated through the antidepressant effects of ketamine have been being delineated, most of which indicate that ketamine improves abnormalities in synaptic transmission and connectivity observed in depressive states via the AMPA receptor and brain-derived neurotrophic factor-dependent mechanisms. Interestingly, some of the above agents may share some neural and synaptic mechanisms with ketamine. These studies should provide important insights for the development of superior pharmacotherapies for depression with more potent and faster onsets of actions.

  5. Opportunities and challenges provided by cloud repositories for bioinformatics-enabled drug discovery.

    Science.gov (United States)

    Dalpé, Gratien; Joly, Yann

    2014-09-01

    Healthcare-related bioinformatics databases are increasingly offering the possibility to maintain, organize, and distribute DNA sequencing data. Different national and international institutions are currently hosting such databases that offer researchers website platforms where they can obtain sequencing data on which they can perform different types of analysis. Until recently, this process remained mostly one-dimensional, with most analysis concentrated on a limited amount of data. However, newer genome sequencing technology is producing a huge amount of data that current computer facilities are unable to handle. An alternative approach has been to start adopting cloud computing services for combining the information embedded in genomic and model system biology data, patient healthcare records, and clinical trials' data. In this new technological paradigm, researchers use virtual space and computing power from existing commercial or not-for-profit cloud service providers to access, store, and analyze data via different application programming interfaces. Cloud services are an alternative to the need of larger data storage; however, they raise different ethical, legal, and social issues. The purpose of this Commentary is to summarize how cloud computing can contribute to bioinformatics-based drug discovery and to highlight some of the outstanding legal, ethical, and social issues that are inherent in the use of cloud services. © 2014 Wiley Periodicals, Inc.

  6. 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.

  7. Advances in Drug Discovery of New Antitubercular Multidrug-Resistant Compounds

    Directory of Open Access Journals (Sweden)

    Guilherme Felipe dos Santos Fernandes

    2017-06-01

    Full Text Available Tuberculosis (TB, a disease caused mainly by the Mycobacterium tuberculosis (Mtb, is according to the World Health Organization (WHO the infectious disease responsible for the highest number of deaths worldwide. The increased number of multidrug-resistant (MDR-TB and extensively drug-resistant (XDR-TB strains, and the ineffectiveness of the current treatment against latent tuberculosis are challenges to be overcome in the coming years. The scenario of drug discovery becomes alarming when it is considered that the number of new drugs does not increase proportionally to the emergence of drug resistance. In this review, we will demonstrate the current advances in antitubercular drug discovery, focusing on the research of compounds with potent antituberculosis activity against MDR-TB strains. Herein, active compounds against MDR-TB with minimum inhibitory concentrations (MICs less than 11 µM and low toxicity published in the last 4 years in the databases PubMed, Web of Science and Scopus will be presented and discussed.

  8. A Brief Review of Drug Discovery Research for Human African Trypanosomiasis.

    Science.gov (United States)

    Cullen, Danica R; Mocerino, Mauro

    2017-01-01

    Human African Trypanosomiasis (HAT), a neglected disease endemic in Sub- Saharan Africa, is usually fatal if left untreated. It is caused by the parasite Trypanosoma brucei, and is spread by the tsetse fly. The drugs currently available to treat HAT are few, and limited in efficacy. Furthermore, resistance towards these drugs is beginning to grow. In the last 25 years, only one advance has been made into HAT treatment and consequently, there is an increasing need for new drugs to be sought that are able to effectively treat this disease. This review provides a brief overview of drug discovery research for HAT, focusing on research published in the last four years, identifying new molecules with the potential to be developed into anti-HAT agents. The methods of drug discovery have been grouped into three key areas; new molecules inspired by known antitrypanosomal agents, target-based screening, and phenotypic screening. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  9. Natural product drug discovery: the successful optimization of ISP-1 and halichondrin B.

    Science.gov (United States)

    Yeung, Bryan K S

    2011-08-01

    The concept that natural products provide excellent leads for drug discovery, ultimately producing viable drugs, is a widely accepted view. Natural products embody inherent structural complexity and biological activity which often leads to new targets, pathways, or modes of action. The challenge lies in identifying quality natural product scaffolds that can ultimately result in a drug. Two recently approved drugs originating from unlikely natural product leads, ISP-1 and halichondrin B, were examples of such high quality scaffolds. In initial testing, both compounds displayed excellent in vitro potency, but more importantly were amenable to chemical optimization. This combination of unique biological activity plus the generation of structural activity relationships (SAR) may be early indicators of a high quality natural product scaffold worthy of additional studies. Copyright © 2011 Elsevier Ltd. All rights reserved.

  10. 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

  11. An in vivo-like tumor stem cell-related glioblastoma in vitro model for drug discovery

    DEFF Research Database (Denmark)

    Jensen, Stine Skov; Aaberg-Jessen, Charlotte; Nørregaard, Annette

    the effects of new drugs on tumor cells including tumor stem cells. Implantation of glioblastoma cells into organotypic brain slice cultures has previously been published as a model system, but not using a stem cell favourable environment. Organotypic corticostriatal rat brain slice cultures were prepared...... and cultured in a serum containing medium replaced after three days with a serum-free stem cell medium. Thereafter fluorescent DiI labelled glioblastoma spheroids from the cell line U87 and the tumor stem cell line SJ-1 established in our laboratory were implanted into the brain slices between cortex......The discovery of tumor stem cells being highly resistant against therapy makes new demands to model systems suitable for evaluation of the effects of new drugs on tumor stem cells. The aim of the present study was therefore to develop an in vivo-like in vitro glioblastoma model for testing...

  12. 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.

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

    Science.gov (United States)

    Patrizii, Michele; Bartucci, Monica; Pine, Sharon R; Sabaawy, Hatem E

    2018-01-01

    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.

  14. 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

  15. 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

  16. Impact of a Discovery System on Interlibrary Loan

    Science.gov (United States)

    Musser, Linda R.; Coopey, Barbara M.

    2016-01-01

    Web-scale discovery services such as Summon (Serial Solutions), WorldCat Local (OCLC), EDS (EBSCO), and Primo (Ex Libris) are often touted as a single search solution to connect users to library-owned and -licensed content, improving discoverability and retrieval of resources. Assessing how well these systems achieve this goal can be challenging,…

  17. On Building a Search Interface Discovery System

    Science.gov (United States)

    Shestakov, Denis

    A huge portion of the Web known as the deep Web is accessible via search interfaces to myriads of databases on the Web. While relatively good approaches for querying the contents of web databases have been recently proposed, one cannot fully utilize them having most search interfaces unlocated. Thus, the automatic recognition of search interfaces to online databases is crucial for any application accessing the deep Web. This paper describes the architecture of the I-Crawler, a system for finding and classifying search interfaces. The I-Crawler is intentionally designed to be used in the deep web characterization surveys and for constructing directories of deep web resources.

  18. 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.

  19. 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.

  20. Tango assay for ligand-induced GPCR-β-arrestin2 interaction: Application in drug discovery.

    Science.gov (United States)

    Dogra, Shalini; Sona, Chandan; Kumar, Ajeet; Yadav, Prem N

    2016-01-01

    G protein-coupled receptors (GPCRs) are widely known to modulate almost all physiological functions and have been demonstrated over the time as therapeutic targets for wide gamut of diseases. The design and implementation of high-throughput GPCR-based assays that permit the efficient screening of large compound libraries to discover novel drug candidates are essential for a successful drug discovery endeavor. Usually, GPCR-based functional assays depend primarily on the measurement of G protein-mediated second messenger generation. However, with advent of advanced molecular biology tools and increased understanding of GPCR signal transduction, many G protein-independent pathways such as β-arrestin translocation are being utilized to detect the activity of GPCRs. These assays provide additional information on functional selectivity (also known as biased agonism) of compounds that could be harnessed to develop pathway-selective drug candidates to reduce the adverse effects associated with given GPCR target. In this chapter, we describe the basic principle, detailed methodologies and assay setup, result analysis and data interpretations of the β-arrestin2 Tango assay, and its comparison with cell-based G protein-dependent GPCR assays, which could be employed in a simple academic setup to facilitate GPCR-based drug discovery. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Peptide deformylase: a new target in antibacterial, antimalarial and anticancer drug discovery.

    Science.gov (United States)

    Sangshetti, Jaiprakash N; Khan, Firoz A Kalam; Shinde, Devanand B

    2015-01-01

    Peptide deformylase (PDF) is a class of metalloenzyme responsible for catalyzing the removal of the N-formyl group from N-terminal methionine following translation. PDF inhibitors are moving into new phase of drug development. Initially, PDF was considered as an important target in antibacterial drug discovery; however genome database searches have revealed PDF-like sequences in parasites (P. falciparum) and human, widening the utility of this target in antimalarial and anticancer drug discovery along with antibacterial. Using structural and mechanistic information together with high throughput screening, several types of chemical classes of PDF inhibitors with improved efficacy and specificity have been identified. Various drugs like, GSK-1322322 (Phase II), BB-83698 (Phase I), and LBM-415 (Phase I) have entered into clinical developments. Developments in the field have prompted us to review the current aspects of PDFs, especially their structures, different classes of PDF inhibitors, and molecular modeling studies. In nut shell, this review enlightens PDF as a versatile target along with its inhibitors and future perspectives of different PDF inhibitors.

  2. 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...

  3. Venoms, toxins and derivatives from the Brazilian fauna: valuable sources for drug discovery.

    Science.gov (United States)

    De Marco Almeida, Flávia; de Castro Pimenta, Adriano Monteiro; Oliveira, Mônica Cristina; De Lima, Maria Elena

    2015-06-25

    Animal venoms have been widely investigated throughout the world. The great number of biotechnological articles as well as patent applications in the field of drug discovery based on these compounds indicates how important the source is. This review presents a list of the most studied Brazilian venomous animal species and shows the most recent patent applications filed from 2000 to 2013, which comprise Brazilian venoms, toxins and derivatives. We analyze the data according to the species, the type of products claimed and the nationality of the inventors. Fifty-five patent applications were found, involving 8 genera. Crotalus, Lachesis, Bothrops and Loxosceles represented 78% of the patent applications. The other 22% were represented by Phoneutria, Tityus, Acanthoscurria and Phyllomedusa. Most of the inventions (42%) involved anticancer, immunomodulator or antimicrobial drugs, while 13% involved anti-venoms and vaccines, 11% involved hypotensive compositions, 9% involved antinociceptive and/or anti-inflammatory compositions, and the other 25% involved methods, kits or compositions for various purposes. Brazilian inventors filed 49% of the patent applications, but other countries, mainly the United States of America, Germany, Russia and France, also filed patent applications claiming products comprising venoms, toxins and/or derivatives from the Brazilian fauna. Brazil holds an important number of patent applications which mostly belong to universities and research institutes, but the pharmaceutical industry in this field is still weak in Brazil. Although, Brazilian venomous animal species have been reported in drug discovery throughout the world, many species remain to be explored as valuable and promising tools for drug discovery and development.

  4. Gene signature critical to cancer phenotype as a paradigm for anticancer drug discovery.

    Science.gov (United States)

    Sampson, E R; McMurray, H R; Hassane, D C; Newman, L; Salzman, P; Jordan, C T; Land, H

    2013-08-15

    Malignant cell transformation commonly results in the deregulation of thousands of cellular genes, an observation that suggests a complex biological process and an inherently challenging scenario for the development of effective cancer interventions. To better define the genes/pathways essential to regulating the malignant phenotype, we recently described a novel strategy based on the cooperative nature of carcinogenesis that focuses on genes synergistically deregulated in response to cooperating oncogenic mutations. These so-called 'cooperation response genes' (CRGs) are highly enriched for genes critical for the cancer phenotype, thereby suggesting their causal role in the malignant state. Here, we show that CRGs have an essential role in drug-mediated anticancer activity and that anticancer agents can be identified through their ability to antagonize the CRG expression profile. These findings provide proof-of-concept for the use of the CRG signature as a novel means of drug discovery with relevance to underlying anticancer drug mechanisms.

  5. Small-molecule stabilization of protein-protein interactions: an underestimated concept in drug discovery?

    Science.gov (United States)

    Thiel, Philipp; Kaiser, Markus; Ottmann, Christian

    2012-02-27

    The modulation of protein-protein interactions (PPIs) has been recognized as one of the most challenging tasks in drug discovery. While their systematic development has long been considered as intractable, this view has changed over the last years, with the first drug candidates undergoing clinical studies. To date, the vast majority of PPI modulators are interaction inhibitors. However, in many biological contexts a prolonged lifespan of a PPI might be desirable, calling for the complementary approach of PPI stabilization. In fact, nature offers impressive examples of this concept and some PPI-stabilizing natural products have already found application as important drugs. Moreover, directed small-molecule stabilization has recently been demonstrated. Therefore, it is time to take a closer look at the constructive side of modulating PPIs. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. 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.

  7. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks.

    Science.gov (United States)

    Segler, Marwin H S; Kogej, Thierry; Tyrchan, Christian; Waller, Mark P

    2018-01-24

    In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus , the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.

  8. 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

    New, safe and effective drugs are urgently needed to treat and control malaria and tuberculosis, which affect millions of people annually. However, financial return on investment in the poor settings where these diseases are mostly prevalent is very minimal to support market-driven drug discovery and development. Moreover, the imminent loss of therapeutic lifespan of existing therapies due to evolution and spread of drug resistance further compounds the urgency to identify novel effective drugs. However, the advent of new public-private partnerships focused on tropical diseases and the recent release of large data sets by pharmaceutical companies on antimalarial and antituberculosis compounds derived from phenotypic whole cell high throughput screening have spurred renewed interest and opened new frontiers in malaria and tuberculosis drug discovery. This Account recaps the existing challenges facing antimalarial and antituberculosis drug discovery, including limitations associated with experimental animal models as well as biological complexities intrinsic to the causative pathogens. We enlist various highlights from a body of work within our research group aimed at identifying and characterizing new chemical leads, and navigating these challenges to contribute toward the global drug discovery and development pipeline in malaria and tuberculosis. We describe a catalogue of in-house efforts toward deriving safe and efficacious preclinical drug development candidates via cell-based medicinal chemistry optimization of phenotypic whole-cell medium and high throughput screening hits sourced from various small molecule chemical libraries. We also provide an appraisal of target-based screening, as invoked in our laboratory for mechanistic evaluation of the hits generated, with particular focus on the enzymes within the de novo pyrimidine biosynthetic and hemoglobin degradation pathways, the latter constituting a heme detoxification process and an associated cysteine

  9. 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.

  10. Radioligand binding assays in the drug discovery process: potential pitfalls of high throughput screenings.

    Science.gov (United States)

    Noël, F; Mendonça-Silva, D L; Quintas, L E

    2001-02-01

    Radioligand binding assays evaluating directly the ability of a drug to interact with a defined molecular target is part of the drug discovery process. The need for a high throughput rate in screening drugs is actually leading to simplified experimental schemes that increase the probability of false negative results. Special concern involves voltage-gated ion channel drug discovery where a great care is required in designing assays because of frequent multiplicity of (interacting) binding sites. To clearly illustrate this situation, three different assays used in the academic drug discovery program of the authors were selected because they are rich of intrinsic artifacts: (I) (20 mmol/l caffeine almost duplicated [3H]ryanodine binding (89% higher than control) to rat heart microsomes at 0.3 mumol/l free calcium but did not exert any effect when using a high (107 mumol/l) free calcium, as mostly used in ryanodine binding assays; (II) An agonist for the ionotropic glutamate receptor of the kainate type can distinctly affect [3H]kainate binding to chicken cerebellum membranes depending on its concentration: unlabelled kainic acid per se either stimulated about 30% (at 50-100 nmol/l), had no effect (at 200 nmol/l) or even progressively decreased (at 0.3-2 mumol/l) the binding of 5 nmol/l [3H]kainate, emphasizing the risk of using a single concentration for screening a drug; (III) in a classical [3H]flunitrazepam binding assay, the stimulatory effect of a GABA (gamma-aminobutyric acid) agonist was only observed when using extensively washed rat brain synaptosomes (10 mumol/l GABA increased flunitrazepam binding by 90%). On the other hand, the inhibitory effect of a GABA antagonist was only observed when using crude synaptosomes (10 mumol/l bicuculine reduced [3H]flunitrazepam binding by 40%). It can be concluded that carefully designed radioligand assays which can be performed in an academic laboratory are appropriate for screening a small number of drugs, especially if

  11. 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.

  12. 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.

  13. 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.

  14. Documenting and harnessing the biological potential of molecules in Distributed Drug Discovery (D3) virtual catalogs.

    Science.gov (United States)

    Abraham, Milata M; Denton, Ryan E; Harper, Richard W; Scott, William L; O'Donnell, Martin J; Durrant, Jacob D

    2017-11-01

    Virtual molecular catalogs have limited utility if member compounds are (i) difficult to synthesize or (ii) unlikely to have biological activity. The Distributed Drug Discovery (D3) program addresses the synthesis challenge by providing scientists with a free virtual D3 catalog of 73,024 easy-to-synthesize N-acyl unnatural α-amino acids, their methyl esters, and primary amides. The remaining challenge is to document and exploit the bioactivity potential of these compounds. In the current work, a search process is described that retrospectively identifies all virtual D3 compounds classified as bioactive hits in PubChem-cataloged experimental assays. The results provide insight into the broad range of drug-target classes amenable to inhibition and/or agonism by D3-accessible molecules. To encourage computer-aided drug discovery centered on these compounds, a publicly available virtual database of D3 molecules prepared for use with popular computer docking programs is also presented. © 2017 John Wiley & Sons A/S.

  15. 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.

  16. Exploring open innovation with a patient focus in drug discovery: an evolving paradigm of patient engagement.

    Science.gov (United States)

    Allarakhia, Minna

    2015-06-01

    It is suggested in this article that patient engagement should occur further upstream during the drug discovery stage. 'Lead patients', namely those patients who are proactive with respect to their health, possess knowledge of their disease and resulting symptoms. They are also well informed about the conventional as well as non-conventional treatments for disease management; and so can provide a nuanced perspective to drug design. Understanding how patients view the management of their diseases and how they view the use of conventional versus non-conventional interventions is of imperative importance to researchers. Indeed, this can provide insight into how conventional treatments might be designed from the outset to encourage compliance and positive health outcomes. Consequently, a continuum of lead patient engagement is employed that focuses on drug discovery processes ranging from participative, informative to collaborative engagement. This article looks at a variety of open innovation models that are currently employed across this engagement spectrum. It is no longer sufficient for industry stakeholders to consider conventional therapies as the only mechanisms being sought after by patients. Without patient engagement, the industry risks being re-prioritized in terms of its role in the patient journey towards not only recovery of health, but also sustained health and wellness before disease onset.

  17. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Cowan-Jacob, Sandra W., E-mail: sandra.jacob@novartis.com; Fendrich, Gabriele; Floersheimer, Andreas; Furet, Pascal; Liebetanz, Janis; Rummel, Gabriele; Rheinberger, Paul; Centeleghe, Mario; Fabbro, Doriano; Manley, Paul W. [Novartis Institutes for Biomedical Research, Basel (Switzerland)

    2007-01-01

    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.

  19. 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

    cooperative 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

  20. Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure.

    Science.gov (United States)

    P Tafti, Ahmad; Badger, Jonathan; LaRose, Eric; Shirzadi, Ehsan; Mahnke, Andrea; Mayer, John; Ye, Zhan; Page, David; Peissig, Peggy

    2017-12-08

    The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis. ©Ahmad P Tafti, Jonathan Badger, Eric LaRose, Ehsan Shirzadi, Andrea Mahnke, John Mayer, Zhan Ye, David Page, Peggy Peissig. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.12.2017.

  1. ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery

    Science.gov (United States)

    Partl, Christian; Lex, Alexander; Streit, Marc; Strobelt, Hendrik; Wassermann, Anne-Mai; Pfister, Hanspeter; Schmalstieg, Dieter

    2016-01-01

    Large scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms. PMID:26356902

  2. Microfabricated injectable drug delivery system

    Science.gov (United States)

    Krulevitch, Peter A.; Wang, Amy W.

    2002-01-01

    A microfabricated, fully integrated drug delivery system capable of secreting controlled dosages of multiple drugs over long periods of time (up to a year). The device includes a long and narrow shaped implant with a sharp leading edge for implantation under the skin of a human in a manner analogous to a sliver. The implant includes: 1) one or more micromachined, integrated, zero power, high and constant pressure generating osmotic engine; 2) low power addressable one-shot shape memory polymer (SMP) valves for switching on the osmotic engine, and for opening drug outlet ports; 3) microfabricated polymer pistons for isolating the pressure source from drug-filled microchannels; 4) multiple drug/multiple dosage capacity, and 5) anisotropically-etched, atomically-sharp silicon leading edge for penetrating the skin during implantation. The device includes an externally mounted controller for controlling on-board electronics which activates the SMP microvalves, etc. of the implant.

  3. 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.

  4. 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

  5. A novel compact mass detection platform for the open access (OA) environment in drug discovery and early development.

    Science.gov (United States)

    Gao, Junling; Ceglia, Scott S; Jones, Michael D; Simeone, Jennifer; Antwerp, John Van; Zhang, Li-Kang; Ross, Charles W; Helmy, Roy

    2016-04-15

    A new 'compact mass detector' co-developed with an instrument manufacturer (Waters Corporation) as an interface for liquid chromatography (LC), specifically Ultra-high performance LC(®) (UPLC(®) or UHPLC) analysis was evaluated as a potential new Open Access (OA) LC-MS platform in the Drug Discovery and Early Development space. This new compact mass detector based platform was envisioned to provide increased reliability and speed while exhibiting significant cost, noise, and footprint reductions. The new detector was evaluated in batch mode (typically 1-3 samples per run) to monitor reactions and check purity, as well as in High Throughput Screening (HTS) mode to run 24, 48, and 96 well plates. The latter workflows focused on screening catalysis conditions, process optimization, and library work. The objective of this investigation was to assess the performance, reliability, and flexibility of the compact mass detector in the OA setting for a variety of applications. The compact mass detector results were compared to those obtained by current OA LC-MS systems, and the capabilities and benefits of the compact mass detector in the open access setting for chemists in the drug discovery and development space are demonstrated. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. 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.

  7. Amazonian Plant Natural Products: Perspectives for Discovery of New Antimalarial Drug Leads

    Directory of Open Access Journals (Sweden)

    Lucio H. Freitas-Junior

    2013-08-01

    Full Text Available Plasmodium falciparum and P. vivax malaria parasites are now resistant, or showing signs of resistance, to most drugs used in therapy. Novel chemical entities that exhibit new mechanisms of antiplasmodial action are needed. New antimalarials that block transmission of Plasmodium spp. from humans to Anopheles mosquito vectors are key to malaria eradication efforts. Although P. vivax causes a considerable number of malaria cases, its importance has for long been neglected. Vivax malaria can cause severe manifestations and death; hence there is a need for P. vivax-directed research. Plants used in traditional medicine, namely Artemisia annua and Cinchona spp. are the sources of the antimalarial natural products artemisinin and quinine, respectively. Based on these compounds, semi-synthetic artemisinin-derivatives and synthetic quinoline antimalarials have been developed and are the most important drugs in the current therapeutic arsenal for combating malaria. In the Amazon region, where P. vivax predominates, there is a local tradition of using plant-derived preparations to treat malaria. Here, we review the current P. falciparum and P. vivax drug-sensitivity assays, focusing on challenges and perspectives of drug discovery for P. vivax, including tests against hypnozoites. We also present the latest findings of our group and others on the antiplasmodial and antimalarial chemical components from Amazonian plants that may be potential drug leads against malaria.

  8. Cyclodextrins in drug carrier systems.

    Science.gov (United States)

    Uekama, K; Otagiri, M

    1987-01-01

    One of the important characteristics of cyclodextrins is the formation of an inclusion complex with a variety of drug molecules in solution and in the solid state. As a consequence of intensive basic research, exhaustive toxic studies, and realization of industrial production during the past decade, there seem to be no more barriers for the practical application of natural cyclodextrins in the biomedical field. Recently, a number of cyclodextrin derivatives and cyclodextrin polymers have been prepared to obtain better inclusion abilities than parent cyclodextrins. The natural cyclodextrins and their synthetic derivatives have been successfully utilized to improve various drug properties, such as solubility, dissolution and release rates, stability, or bioavailability. In addition, the enhancement of drug activity, selective transfer, or the reduction of side effects has been achieved by means of inclusion complexation. The drug-cyclodextrin complex is generally formed outside of the body and, after administration, it dissociates, releasing the drug into the organism in a fast and nearly uniform manner. In the biomedical application of cyclodextrins, therefore, particular attention should be directed to the magnitude of the stability constant of the inclusion complex. In the case of parenteral application, a rather limited amount of work has been done because the cyclodextrins in the drug carrier systems have to be more effectively designed to compete with various biological components in the circulatory system. However, the works published thus far apparently indicate that the inclusion phenomena of cyclodextrin analogs may allow the rational design of drug formulation and that the combination of molecular encapsulation with other carrier systems will become a very effective and valuable method for the development of a new drug delivery system in the near future.

  9. MyoScreen, a High-Throughput Phenotypic Screening Platform Enabling Muscle Drug Discovery.

    Science.gov (United States)

    Young, Joanne; Margaron, Yoran; Fernandes, Mathieu; Duchemin-Pelletier, Eve; Michaud, Joris; Flaender, Mélanie; Lorintiu, Oana; Degot, Sébastien; Poydenot, Pauline

    2018-03-01

    Despite the need for more effective drug treatments to address muscle atrophy and disease, physiologically accurate in vitro screening models and higher information content preclinical assays that aid in the discovery and development of novel therapies are lacking. To this end, MyoScreen was developed: a robust and versatile high-throughput high-content screening (HT/HCS) platform that integrates a physiologically and pharmacologically relevant micropatterned human primary skeletal muscle model with a panel of pertinent phenotypic and functional assays. MyoScreen myotubes form aligned, striated myofibers, and they show nerve-independent accumulation of acetylcholine receptors (AChRs), excitation-contraction coupling (ECC) properties characteristic of adult skeletal muscle and contraction in response to chemical stimulation. Reproducibility and sensitivity of the fully automated MyoScreen platform are highlighted in assays that quantitatively measure myogenesis, hypertrophy and atrophy, AChR clusterization, and intracellular calcium release dynamics, as well as integrating contractility data. A primary screen of 2560 compounds to identify stimulators of myofiber regeneration and repair, followed by further biological characterization of two hits, validates MyoScreen for the discovery and testing of novel therapeutics. MyoScreen is an improvement of current in vitro muscle models, enabling a more predictive screening strategy for preclinical selection of the most efficacious new chemical entities earlier in the discovery pipeline process.

  10. 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.

  11. The opportunities of mining historical and collective data in drug discovery.

    Science.gov (United States)

    Wassermann, Anne Mai; Lounkine, Eugen; Davies, John W; Glick, Meir; Camargo, L Miguel

    2015-04-01

    Vast amounts of bioactivity data have been generated for small molecules across public and corporate domains. Biological signatures, either derived from systematic profiling efforts or from existing historical assay data, have been successfully employed for small molecule mechanism-of-action elucidation, drug repositioning, hit expansion and screening subset design. This article reviews different types of biological descriptors and applications, and we demonstrate how biological data can outlive the original purpose or project for which it was generated. By comparing 150 HTS campaigns run at Novartis over the past decade on the basis of their active and inactive chemical matter, we highlight the opportunities and challenges associated with cross-project learning in drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. A Fluorescence Displacement Assay for Antidepressant Drug Discovery Based on Ligand-Conjugated Quantum Dots

    Energy Technology Data Exchange (ETDEWEB)

    Chang, Jerry [Vanderbilt University; Tomlinson, Ian [Oak Ridge National Laboratory (ORNL); Warnement, Michael [Vanderbilt University; Iwamoto, Hideki [Vanderbilt University

    2011-01-01

    The serotonin (5-hydroxytryptamine, 5-HT) transporter (SERT) protein plays a central role in terminating 5-HT neurotransmission and is the most important therapeutic target for the treatment of major depression and anxiety disorders. We report an innovative, versatile, and target-selective quantum dot (QD) labeling approach for SERT in single Xenopus oocytes that can be adopted as a drug-screening platform. Our labeling approach employs a custom-made, QD-tagged indoleamine derivative ligand, IDT318, that is structurally similar to 5-HT and accesses the primary binding site with enhanced human SERT selectivity. Incubating QD-labeled oocytes with paroxetine (Paxil), a high-affinity SERT-specific inhibitor, showed a concentration- and time-dependent decrease in QD fluorescence, demonstrating the utility of our approach for the identification of SERT modulators. Furthermore, with the development of ligands aimed at other pharmacologically relevant targets, our approach may potentially form the basis for a multitarget drug discovery platform.

  13. Clostridium difficile Drug Pipeline: Challenges in Discovery and Development of New Agents

    Science.gov (United States)

    2015-01-01

    In the past decade Clostridium difficile has become a bacterial pathogen of global significance. Epidemic strains have spread throughout hospitals, while community acquired infections and other sources ensure a constant inoculation of spores into hospitals. In response to the increasing medical burden, a new C. difficile antibiotic, fidaxomicin, was approved in 2011 for the treatment of C. difficile-associated diarrhea. Rudimentary fecal transplants are also being trialed as effective treatments. Despite these advances, therapies that are more effective against C. difficile spores and less damaging to the resident gastrointestinal microbiome and that reduce recurrent disease are still desperately needed. However, bringing a new treatment for C. difficile infection to market involves particular challenges. This review covers the current drug discovery pipeline, including both small molecule and biologic therapies, and highlights the challenges associated with in vitro and in vivo models of C. difficile infection for drug screening and lead optimization. PMID:25760275

  14. Hit and lead criteria in drug discovery for infectious diseases of the developing world.

    Science.gov (United States)

    Katsuno, Kei; Burrows, Jeremy N; Duncan, Ken; Hooft van Huijsduijnen, Rob; Kaneko, Takushi; Kita, Kiyoshi; Mowbray, Charles E; Schmatz, Dennis; Warner, Peter; Slingsby, B T

    2015-11-01

    Reducing the burden of infectious diseases that affect people in the developing world requires sustained collaborative drug discovery efforts. The quality of the chemical starting points for such projects is a key factor in improving the likelihood of clinical success, and so it is important to set clear go/no-go criteria for the progression of hit and lead compounds. With this in mind, the Japanese Global Health Innovative Technology (GHIT) Fund convened with experts from the Medicines for Malaria Venture, the Drugs for Neglected Diseases initiative and the TB Alliance, together with representatives from the Bill &Melinda Gates Foundation, to set disease-specific criteria for hits and leads for malaria, tuberculosis, visceral leishmaniasis and Chagas disease. Here, we present the agreed criteria and discuss the underlying rationale.

  15. Pharmacognosy and reverse pharmacognosy: a new concept for accelerating natural drug discovery.

    Science.gov (United States)

    Do, Quoc-Tuan; Bernard, Philippe

    2004-11-01

    Combinatorial chemistry and high-throughput screening (HTS) have led to the identification of numerous agents that are active and selective in vitro. Identifying drugs that are active in vivo, however, remains a challenge. Traditional medicinal cures based on natural materials have proven useful for many populations worldwide, representing huge and disperse tracts of knowledge that are sometimes neglected in Western research due to differences in the concepts of illness. In this review we introduce a new approach, termed 'reverse pharmacognosy' (from diverse molecules to plants), which can be coupled with pharmacognosy (from biodiverse plants to molecules). Reverse pharmacognosy utilizes new techniques, such as HTS, virtual screening and a knowledge database containing the traditional uses of plants. Integrating pharmacognosy and reverse pharmacognosy in the research process may provide an efficient and rapid tool for natural drug discovery.

  16. Quality not Quantity: The Role of Marine Natural Products in Drug Discovery and Reverse Chemical Proteomics

    Directory of Open Access Journals (Sweden)

    Andrew M. Piggott

    2005-06-01

    Full Text Available Reverse chemical proteomics combines affinity chromatography with phage display and promises to be a powerful new platform technology for the isolation of natural product receptors, facilitating the drug discovery process by rapidly linking biologically active small molecules to their cellular receptors and the receptors’ genes. In this paper we review chemical proteomics and reverse chemical proteomics and show how these techniques can add value to natural products research. We also report on techniques for the derivatisation of polystyrene microtitre plates with cleavable linkers and marine natural products that can be used in chemical proteomics or reverse chemical proteomics. Specifically, we have derivatised polystyrene with palau’amine and used reverse chemical proteomics to try and isolate the human receptors for this potent anticancer marine drug.

  17. Academic-Pharma drug discovery alliances: seeking ways to eliminate the valley of death.

    Science.gov (United States)

    Hammonds, Tim

    2015-01-01

    Industrial pharmaceutical companies (Pharma) share a common goal with academic scientists (Academia) in that they wish to create an environment in which patients are treated for diseases with ever more effective therapies. As disease biology has proven to be ever more complex and money and new drugs are becoming more elusive, Pharma and Academia are reaching toward each other with ever greater collaborative intent. There are a growing number of collaboration models that allow scientists to work together and profit from the creation of new drugs. Here I give a personal view of how we came to where we are, present an overview of a number of these models and look to the future in terms of running successful discovery alliances.

  18. Zika antiviral chemotherapy: identification of drugs and promising starting points for drug discovery from an FDA-approved library [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Bruno S. Pascoalino

    2016-10-01

    Full Text Available Background The recent epidemics of Zika virus (ZIKV implicated it as the cause of serious and potentially lethal congenital conditions such microcephaly and other central nervous system defects, as well as the development of the Guillain-Barré syndrome in otherwise healthy patients. Recent findings showed that anti-Dengue antibodies are capable of amplifying ZIKV infection by a mechanism similar to antibody-dependent enhancement, increasing the severity of the disease. This scenario becomes potentially catastrophic when the global burden of Dengue and the advent of the newly approved anti-Dengue vaccines in the near future are taken into account. Thus, antiviral chemotherapy should be pursued as a priority strategy to control the spread of the virus and prevent the complications associated with Zika. Methods Here we describe a fast and reliable cell-based, high-content screening assay for discovery of anti-ZIKV compounds. This methodology has been used to screen the National Institute of Health Clinical Collection compound library, a small collection of FDA-approved drugs. Results and conclusion From 725 FDA-approved compounds triaged, 29 (4% were found to have anti-Zika virus activity, of which 22 had confirmed (76% of confirmation by dose-response curves. Five candidates presented selective activity against ZIKV infection and replication in a human cell line. These hits have abroad spectrum of chemotypes and therapeutic uses, offering valuable opportunities for selection of leads for antiviral drug discovery.

  19. 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

  20. An evaluation of the utility of physiologically based models of pharmacokinetics in early drug discovery.

    Science.gov (United States)

    Parrott, Neil; Paquereau, Nicolas; Coassolo, Philippe; Lavé, Thierry

    2005-10-01

    Generic physiologically-based models of pharmacokinetics were evaluated for early drug discovery. Plasma profiles after intravenous and oral dosing were simulated in rat for 68 compounds from six chemical classes. Input data consisted of structure based predictions of lipophilicity, ionization, and protein binding plus intrinsic clearance measured in rat hepatocytes, single measured values of aqueous solubility, and artificial membrane permeability. LogP of compounds was high with a mean of 3.9 while free fraction in plasma (mean 9%) and solubility (mean 37 microg/mL) were low. Predicted and observed clearance and volume showed mean fold-error and R2 of 1.8, 0.56, and 1.9, 0.25 respectively. Predicted bioavailability showed strong bias to under prediction correlated to very low aqueous solubility and a theoretical correction for bile salt solubilization in vivo brought some improvement in average prediction error (to 31%). Overall, this evaluation shows that generic simulation may be applicable for typical drug-like compounds to predict differences in pharmacokinetic parameters of more than twofold based upon minimal measured input data. However verification of the simulations with in vivo data for a few compounds of each compound class is recommended since recent discovery compounds may have properties beyond the scope of the current generic models. Copyright (c) 2005 Wiley-Liss, Inc. and the American Pharmacists Association

  1. Bigger data, collaborative tools and the future of predictive drug discovery.

    Science.gov (United States)

    Ekins, Sean; Clark, Alex M; Swamidass, S Joshua; Litterman, Nadia; Williams, Antony J

    2014-10-01

    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 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.

  2. 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

  3. Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

    Science.gov (United States)

    2015-01-01

    On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery. PMID:25994950

  4. Transient Receptor Potential (TRP Channels in Drug Discovery: Old Concepts & New Thoughts

    Directory of Open Access Journals (Sweden)

    Susan Huang

    2017-07-01

    Full Text Available 2017 marks the 20th anniversary of the molecular cloning by David Julius and colleagues (1997 of the long sought-after capsaicin receptor, now known as TRPV1 (Transient Receptor Potential Vanilloid 1 [1]. This seminal discovery has opened up a “hot” new field of basic research and launched drug discovery efforts into the large family (by the latest count 28 mammalian members, 27 in humans of TRP ion channels [2]. Indeed, it took less than a decade for the first potent, small molecule TRPV1 antagonists to enter phase 1 clinical trials [3]. Yet, despite the large amount of resources that has been invested in TRPV1 research, there are currently no TRPV1-targeted drugs in phase 3 clinical trials. In this special issue of Pharmaceuticals, we aim to capture the progress in the TRP channel field over the past twenty years, with 15 articles covering a variety of TRP channels and potential relevant disease states and applications.

  5. Bigger data, collaborative tools and the future of predictive drug discovery

    Science.gov (United States)

    Ekins, Sean; Clark, Alex M.; Swamidass, S. Joshua; Litterman, Nadia; Williams, Antony J.

    2014-10-01

    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 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.

  6. Challenges with in vitro and in vivo experimental models of urinary bladder cancer for novel drug discovery.

    Science.gov (United States)

    Oliveira, P A; Gil da Costa, R M; Vasconcelos-Nóbrega, C; Arantes-Rodrigues, R; Pinto-Leite, R

    2016-06-01

    Urinary bladder cancer (UBC) is the second most frequent malignancy of the urinary system and the ninth most common cancer worldwide, affecting individuals over the age of 65. Several investigations have embarked on advancing knowledge of the mechanisms underlying urothelial carcinogenesis, understanding the mechanisms of antineoplastic drugs resistance and discovering new antineoplastic drugs. In vitro and in vivo models are crucial for providing additional insights into the mechanisms of urothelial carcinogenesis. With these models, various molecular pathways involved in urothelial carcinogenesis have been discovered, allowing therapeutic manipulation. This paper provides critical information on existing in vitro and in vivo models to screen the efficacy and toxicity of innovative UBC therapies and point out the challenges for new and improved models. In our opinion, results obtained with in vitro and in vivo models should be interpreted together, as a set of delicate biological tools that can be used at different stages in the drug discovery process, to address specific questions. With the development of new technologies, new assays and biomarkers are going to play an important role in the study of UBC. The molecular diagnostics and genomic revolution will not only help to develop new drug therapies, but also to achieve tailored therapies.

  7. Computer Aided Drug Design Studies in the Discovery of Secondary Metabolites Targeted Against Age-Related Neurodegenerative Diseases.

    Science.gov (United States)

    Scotti, Luciana; Scotti, Marcus Tullius

    2015-01-01

    Secondary metabolites are plant products that occur usually in differentiated cells, generally not being necessary for the cells themselves, but likely useful for the plant as a whole. Neurodegeneration can be found in many different levels in the neurons, it always begins at the molecular level and progresses toward the systemic levels. Usually, alterations are observed such as decreasing cholinergic impulse, toxicity related to reactive oxygen species (ROS, inflammatory "amyloid plaque" related processes, catecholamine disequilibrium, etc. Computer aided drug design (CADD has become relevant in the drug discovery process; technological advances in the areas of molecular structure characterization, computational science, and molecular biology have contributed to the planning of new drugs against neurodegenerative diseases. This review discusses scientific CADD studies of the secondary metabolites. Flavonoids, alkaloids, and xanthone compounds have been studied by various researchers (as inhibitory ligands in molecular docking; mainly with three enzymes: acetylcholinesterase (AChE; EC 3.1.1.7, butyrylcholinesterase (BChE; EC 3.1.1.8, and monoamine oxidase (MAO; EC 1.4.3.4. In addition, we have applied ligand-based-virtual screening (using Random Forest, associated with structure-based- virtual screening (docking of a small dataset of 469 alkaloids of the Apocynaceae family from an in-house data bank to select structures with potential inhibitory activity against human AChE. This computer-aided drug design study selected certain alkaloids that might be useful in further studies for the treatment of neurological disorders such as Alzheimer's and Parkinson's disease.

  8. Transferosomes - A vesicular transdermal delivery system for enhanced drug permeation

    Directory of Open Access Journals (Sweden)

    Reshmy Rajan

    2011-01-01

    Full Text Available Transdermal administration of drugs is generally limited by the barrier function of the skin. Vesicular systems are one of the most controversial methods for transdermal delivery of active substances. The interest in designing transdermal delivery systems was relaunched after the discovery of elastic vesicles like transferosomes, ethosomes, cubosomes, phytosomes, etc. This paper presents the composition, mechanisms of penetration, manufacturing and characterization methods of transferosomes as transdermal delivery systems of active substances. For a drug to be absorbed and distributed into organs and tissues and eliminated from the body, it must pass through one or more biological membranes/barriers at various locations. Such a movement of drug across the membrane is called as drug transport. For the drugs to be delivered to the body, they should cross the membranous barrier. The concept of these delivery systems was designed in an attempt to concentrate the drug in the tissues of interest, while reducing the amount of drug in the remaining tissues. Hence, surrounding tissues are not affected by the drug. In addition, loss of drug does not happen due to localization of drug, leading to get maximum efficacy of the medication. Therefore, the phospholipid based carrier systems are of considerable interest in this era.

  9. Protein Reporter Bioassay Systems for the Phenotypic Screening of Candidate Drugs: A Mouse Platform for Anti-Aging Drug Screening

    Directory of Open Access Journals (Sweden)

    Isao Shimokawa

    2012-02-01

    Full Text Available Recent drug discovery efforts have utilized high throughput screening (HTS of large chemical libraries to identify compounds that modify the activity of discrete molecular targets. The molecular target approach to drug screening is widely used in the pharmaceutical and biotechnology industries, because of the amount of knowledge now available regarding protein structure that has been obtained by computer simulation. The molecular target approach requires that the structure of target molecules, and an understanding of their physiological functions, is known. This approach to drug discovery may, however, limit the identification of novel drugs. As an alternative, the phenotypic- or pathway-screening approach to drug discovery is gaining popularity, particularly in the academic sector. This approach not only provides the opportunity to identify promising drug candidates, but also enables novel information regarding biological pathways to be unveiled. Reporter assays are a powerful tool for the phenotypic screening of compound libraries. Of the various reporter genes that can be used in such assays, those encoding secreted proteins enable the screening of hit molecules in both living cells and animals. Cell- and animal-based screens enable simultaneous evaluation of drug metabolism or toxicity with biological activity. Therefore, drug candidates identified in these screens may have increased biological efficacy and a lower risk of side effects in humans. In this article, we review the reporter bioassay systems available for phenotypic drug discovery.

  10. Highlights from SelectBio 2015: Academic Drug Discovery Conference, Cambridge, UK, 19-20 May 2015.

    Science.gov (United States)

    Spencer, John; Coaker, Hannah

    2015-01-01

    The SelectBio 2015: Academic Drug Discovery Conference was held in Cambridge, UK, on 19-20 May 2015. Building on the success of academic drug discovery events in the USA, this conference aimed to showcase the exciting new research emerging from academic drug discovery and to help bridge the gap between basic research and commercial application. At the event the authors heard from a number of speakers on a broad array of topics, from partnering models for academia and industry to novel drug discovery approaches across various therapeutic areas, with a few talks, such as those by Susanne Muller-Knapp (Structure Genomics Consortium, Oxford University, Oxford, UK) and Julian Blagg (Institute of Cancer Research, UK), covering both remits, by highlighting a number of such partnerships and then delving into some case studies. The conference concluded with a heated debate on whether phenotypic discovery should be favored over targeted discovery in academia and pharma, in a panel discussion chaired by Roland Wolkowicz (San Diego State University, USA).

  11. Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

    Science.gov (United States)

    Jing, Yankang; Bian, Yuemin; Hu, Ziheng; Wang, Lirong; Xie, Xiang-Qun Sean

    2018-03-30

    Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.

  12. Novel approaches to drug discovery for the treatment of type 2 diabetes.

    Science.gov (United States)

    Xu, Xing; Wang, Gaihong; Zhou, Tingting; Chen, Lili; Chen, Jing; Shen, Xu

    2014-09-01

    Type 2 diabetes mellitus (T2DM) is a chronic, complex and multifactorial metabolic disorder, which has become a serious global health problem. The side effects of known drugs and the deficiency of long-term safety data, in addition to the already determined adverse effects for the current preclinical drugs against T2DM, have largely called upon the urgent exploration of novel therapeutic and preventative strategies against this disease. The authors highlight the potential approaches for anti-T2DM drug discovery by focusing on: the restoration of pancreatic β-cell mass, the promotion of insulin secretion, the regulation of oxidative stress and endoplasmic reticulum (ER) stress and the modulation of autophagy. T2DM is based on the gradual development of insulin resistance and β-cell dysfunction. Thus, the restoration of β-cell function is considered as one of the promising therapeutic strategies against T2DM. The stress factors, such as oxidative stress, ER stress and autophagy, play potent roles in the regulation of β-cell apoptosis, insulin secretion and sensitivity in the development of T2DM involving complicated cross-talks. Based on multiplex stress-involved regulatory networks, more and more novel potential targets have been discovered and the multi-targeted drug leads are expected to help develop more effective clinical agents for the treatment of T2DM.

  13. 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

  14. 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.

  15. Drug discovery strategies in the field of tumor energy metabolism: Limitations by metabolic flexibility and metabolic resistance to chemotherapy.

    Science.gov (United States)

    Amoedo, N D; Obre, E; Rossignol, R

    2017-08-01

    The search for new drugs capable of blocking the metabolic vulnerabilities of human tumors has now entered the clinical evaluation stage, but several projects already failed in phase I or phase II. In particular, very promising in vitro studies could not be translated in vivo at preclinical stage and beyond. This was the case for most glycolysis inhibitors that demonstrated systemic toxicity. A more recent example is the inhibition of glutamine catabolism in lung adenocarcinoma that failed in vivo despite a strong addiction of several cancer cell lines to glutamine in vitro. Such contradictory findings raised several questions concerning the optimization of drug discovery strategies in the field of cancer metabolism. For instance, the cell culture models in 2D or 3D might already show strong limitations to mimic the tumor micro- and macro-environment. The microenvironment of tumors is composed of cancer cells of variegated metabolic profiles, supporting local metabolic exchanges and symbiosis, but also of immune cells and stroma that further interact with and reshape cancer cell metabolism. The macroenvironment includes the different tissues of the organism, capable of exchanging signals and fueling the tumor 'a distance'. Moreover, most metabolic targets were identified from their increased expression in tumor transcriptomic studies, or from targeted analyses looking at the metabolic impact of particular oncogenes or tumor suppressors on selected metabolic pathways. Still, very few targets were identified from in vivo analyses of tumor metabolism in patients because such studies are difficult and adequate imaging methods are only currently being developed for that purpose. For instance, perfusion of patients with [ 13 C]-glucose allows deciphering the metabolomics of tumors and opens a new area in the search for effective targets. Metabolic imaging with positron emission tomography and other techniques that do not involve [ 13 C] can also be used to evaluate tumor

  16. 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.

  17. A Complex Systems Approach to Causal Discovery in Psychiatry.

    Directory of Open Access Journals (Sweden)

    Glenn N Saxe

    Full Text Available Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach--the Complex Systems-Causal Network (CS-CN method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study. Next, it was applied to a much larger dataset of traumatized children (replication study. Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment. The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro and high-level (macro insights and thus represents a promising approach for complex systems-oriented research in psychiatry.

  18. MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development | Office of Cancer Genomics

    Science.gov (United States)

    Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology.

  19. Discovery of a low order drug-cell response surface for applications in personalized medicine

    International Nuclear Information System (INIS)

    Ding, Xianting; Liu, Wenjia; Li, Yiyang; Weiss, Andrea; Van den Bergh, Hubert; Nowak-Sliwinska, Patrycja; Wong, Ieong; Ho, Chih-Ming; Griffioen, Arjan W; Xu, Hongquan

    2014-01-01

    The cell is a complex system involving numerous components, which may often interact in a non-linear dynamic manner. Diseases at the cellular level are thus likely to involve multiple cellular constituents and pathways. As some drugs, or drug combinations, may act synergistically on these multiple pathways, they might be more effective than the respective single target agents. Optimizing a drug mixture for a given disease in a particular patient is particularly challenging due to both the difficulty in the selection of the drug mixture components to start out with, and the all-important doses of these drugs to be applied. For n concentrations of m drugs, in principle, n m combinations will have to be tested. As this may lead to a costly and time-consuming investigation for each individual patient, we have developed a Feedback System Control (FSC) technique which can rapidly select the optimal drug–dose combination from the often millions of possible combinations. By testing this FSC technique in a number of experimental systems representing different disease states, we found that the response of cells to multiple drugs is well described by a low order, rather smooth, drug-mixture-input/drug-effect-output multidimensional surface. The main consequences of this are that optimal drug combinations can be found in a surprisingly small number of tests, and that translation from in vitro to in vivo is simplified. This points to the possibility of personalized optimal drug mixtures in the near future. This unexpectedly simple input–output relationship may also lead to a simple solution for handling the issue of human diversity in cancer therapeutics. (paper)

  20. Whole animal automated platform for drug discovery against multi-drug resistant Staphylococcus aureus.

    Directory of Open Access Journals (Sweden)

    Rajmohan Rajamuthiah

    Full Text Available Staphylococcus aureus, the leading cause of hospital-acquired infections in the United States, is also pathogenic to the model nematode Caenorhabditis elegans. The C. elegans-S. aureus infection model was previously carried out on solid agar plates where the bacteriovorous C. elegans feeds on a lawn of S. aureus. However, agar-based assays are not amenable to large scale screens for antibacterial compounds. We have developed a high throughput liquid screening assay that uses robotic instrumentation to dispense a precise amount of methicillin resistant S. aureus (MRSA and worms in 384-well assay plates, followed by automated microscopy and image analysis. In validation of the liquid assay, an MRSA cell wall defective mutant, MW2ΔtarO, which is attenuated for killing in the agar-based assay, was found to be less virulent in the liquid assay. This robust assay with a Z'-factor consistently greater than 0.5 was utilized to screen the Biomol 4 compound library consisting of 640 small molecules with well characterized bioactivities. As proof of principle, 27 of the 30 clinically used antibiotics present in the library conferred increased C. elegans survival and were identified as hits in the screen. Surprisingly, the antihelminthic drug closantel was also identified as a hit in the screen. In further studies, we confirmed the anti-staphylococcal activity of closantel against vancomycin-resistant S. aureus isolates and other Gram-positive bacteria. The liquid C. elegans-S. aureus assay described here allows screening for anti-staphylococcal compounds that are not toxic to the host.

  1. Whole animal automated platform for drug discovery against multi-drug resistant Staphylococcus aureus.

    Science.gov (United States)

    Rajamuthiah, Rajmohan; Fuchs, Beth Burgwyn; Jayamani, Elamparithi; Kim, Younghoon; Larkins-Ford, Jonah; Conery, Annie; Ausubel, Frederick M; Mylonakis, Eleftherios

    2014-01-01

    Staphylococcus aureus, the leading cause of hospital-acquired infections in the United States, is also pathogenic to the model nematode Caenorhabditis elegans. The C. elegans-S. aureus infection model was previously carried out on solid agar plates where the bacteriovorous C. elegans feeds on a lawn of S. aureus. However, agar-based assays are not amenable to large scale screens for antibacterial compounds. We have developed a high throughput liquid screening assay that uses robotic instrumentation to dispense a precise amount of methicillin resistant S. aureus (MRSA) and worms in 384-well assay plates, followed by automated microscopy and image analysis. In validation of the liquid assay, an MRSA cell wall defective mutant, MW2ΔtarO, which is attenuated for killing in the agar-based assay, was found to be less virulent in the liquid assay. This robust assay with a Z'-factor consistently greater than 0.5 was utilized to screen the Biomol 4 compound library consisting of 640 small molecules with well characterized bioactivities. As proof of principle, 27 of the 30 clinically used antibiotics present in the library conferred increased C. elegans survival and were identified as hits in the screen. Surprisingly, the antihelminthic drug closantel was also identified as a hit in the screen. In further studies, we confirmed the anti-staphylococcal activity of closantel against vancomycin-resistant S. aureus isolates and other Gram-positive bacteria. The liquid C. elegans-S. aureus assay described here allows screening for anti-staphylococcal compounds that are not toxic to the host.

  2. Towards an in vitro model of Plasmodium hypnozoites suitable for drug discovery.

    Science.gov (United States)

    Dembele, Laurent; Gego, Audrey; Zeeman, Anne-Marie; Franetich, Jean-François; Silvie, Olivier; Rametti, Armelle; Le Grand, Roger; Dereuddre-Bosquet, Nathalie; Sauerwein, Robert; van Gemert, Geert-Jan; Vaillant, Jean-Christophe; Thomas, Alan W; Snounou, Georges; Kocken, Clemens H M; Mazier, Dominique

    2011-03-31

    Amongst the Plasmodium species in humans, only P. vivax and P. ovale produce latent hepatic stages called hypnozoites, which are responsible for malaria episodes long after a mosquito bite. Relapses contribute to increased morbidity, and complicate malaria elimination programs. A single drug effective against hypnozoites, primaquine, is available, but its deployment is curtailed by its haemolytic potential in glucose-6-phosphate dehydrogenase deficient persons. Novel compounds are thus urgently needed to replace primaquine. Discovery of compounds active against hypnozoites is restricted to the in vivo P. cynomolgi-rhesus monkey model. Slow growing hepatic parasites reminiscent of hypnozoites had been noted in cultured P. vivax-infected hepatoma cells, but similar forms are also observed in vitro by other species including P. falciparum that do not produce hypnozoites. P. falciparum or P. cynomolgi sporozoites were used to infect human or Macaca fascicularis primary hepatocytes, respectively. The susceptibility of the slow and normally growing hepatic forms obtained in vitro to three antimalarial drugs, one active against hepatic forms including hypnozoites and two only against the growing forms, was measured. The non-dividing slow growing P. cynomolgi hepatic forms, observed in vitro in primary hepatocytes from the natural host Macaca fascicularis, can be distinguished from similar forms seen in P. falciparum-infected human primary hepatocytes by the differential action of selected anti-malarial drugs. Whereas atovaquone and pyrimethamine are active on all the dividing hepatic forms observed, the P. cynomolgi slow growing forms are highly resistant to treatment by these drugs, but remain susceptible to primaquine. Resistance of the non-dividing P. cynomolgi forms to atovaquone and pyrimethamine, which do not prevent relapses, strongly suggests that these slow growing forms are hypnozoites. This represents a first step towards the development of a practical medium

  3. Towards an in vitro model of Plasmodium hypnozoites suitable for drug discovery.

    Directory of Open Access Journals (Sweden)

    Laurent Dembele

    2011-03-01

    Full Text Available Amongst the Plasmodium species in humans, only P. vivax and P. ovale produce latent hepatic stages called hypnozoites, which are responsible for malaria episodes long after a mosquito bite. Relapses contribute to increased morbidity, and complicate malaria elimination programs. A single drug effective against hypnozoites, primaquine, is available, but its deployment is curtailed by its haemolytic potential in glucose-6-phosphate dehydrogenase deficient persons. Novel compounds are thus urgently needed to replace primaquine. Discovery of compounds active against hypnozoites is restricted to the in vivo P. cynomolgi-rhesus monkey model. Slow growing hepatic parasites reminiscent of hypnozoites had been noted in cultured P. vivax-infected hepatoma cells, but similar forms are also observed in vitro by other species including P. falciparum that do not produce hypnozoites.P. falciparum or P. cynomolgi sporozoites were used to infect human or Macaca fascicularis primary hepatocytes, respectively. The susceptibility of the slow and normally growing hepatic forms obtained in vitro to three antimalarial drugs, one active against hepatic forms including hypnozoites and two only against the growing forms, was measured.The non-dividing slow growing P. cynomolgi hepatic forms, observed in vitro in primary hepatocytes from the natural host Macaca fascicularis, can be distinguished from similar forms seen in P. falciparum-infected human primary hepatocytes by the differential action of selected anti-malarial drugs. Whereas atovaquone and pyrimethamine are active on all the dividing hepatic forms observed, the P. cynomolgi slow growing forms are highly resistant to treatment by these drugs, but remain susceptible to primaquine.Resistance of the non-dividing P. cynomolgi forms to atovaquone and pyrimethamine, which do not prevent relapses, strongly suggests that these slow growing forms are hypnozoites. This represents a first step towards the development of a

  4. Reverse engineering systems models of regulation: discovery, prediction and mechanisms.

    Science.gov (United States)

    Ashworth, Justin; Wurtmann, Elisabeth J; Baliga, Nitin S

    2012-08-01

    Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down 'reverse engineering' approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties. Copyright © 2011 Elsevier Ltd. All rights reserved.

  5. BONSAI Garden: Parallel knowledge discovery system for amino acid sequences

    Energy Technology Data Exchange (ETDEWEB)

    Shoudai, T.; Miyano, S.; Shinohara, A.; Okazaki, T.; Arikawa, S. [Kyushu Univ., Fukuoka (Japan)] [and others

    1995-12-31

    We have developed a machine discovery system BON-SAI which receives positive and negative examples as inputs and produces as a hypothesis a pair of a decision tree over regular patterns and an alphabet indexing. This system has succeeded in discovering reasonable knowledge on transmembrane domain sequences and signal peptide sequences by computer experiments. However, when several kinds of sequences axe mixed in the data, it does not seem reasonable for a single BONSAI system to find a hypothesis of a reasonably small size with high accuracy. For this purpose, we have designed a system BONSAI Garden, in which several BONSAI`s and a program called Gardener run over a network in parallel, to partition the data into some number of classes together with hypotheses explaining these classes accurately.

  6. BONSAI Garden: parallel knowledge discovery system for amino acid sequences.

    Science.gov (United States)

    Shoudai, T; Lappe, M; Miyano, S; Shinohara, A; Okazaki, T; Arikawa, S; Uchida, T; Shimozono, S; Shinohara, T; Kuhara, S

    1995-01-01

    We have developed a machine discovery system BONSAI which receives positive and negative examples as inputs and produces as a hypothesis a pair of a decision tree over regular patterns and an alphabet indexing. This system has succeeded in discovering reasonable knowledge on transmembrane domain sequences and signal peptide sequences by computer experiments. However, when several kinds of sequences are mixed in the data, it does not seem reasonable for a single BONSAI system to find a hypothesis of a reasonably small size with high accuracy. For this purpose, we have designed a system BONSAI Garden, in which several BONSAI's and a program called Gardener run over a network in parallel, to partition the data into some number of classes together with hypotheses explaining these classes accurately.

  7. Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models.

    Directory of Open Access Journals (Sweden)

    Sean Ekins

    Full Text Available High-throughput screening (HTS in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb for further development towards new tuberculosis (TB drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15-28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.

  8. Artificial Neural Network Methods Applied to Drug Discovery for Neglected Diseases.

    Science.gov (United States)

    Scotti, Luciana; Ishiki, Hamilton; Mendonça Júnior, Francisco J B; da Silva, Marcelo S; Scotti, Marcus T

    2015-01-01

    Among the chemometric tools used in rational drug design, we find artificial neural network methods (ANNs), a statistical learning algorithm similar to the human brain, to be quite powerful. Some ANN applications use biological and molecular data of the training series that are inserted to ensure the machine learning, and to generate robust and predictive models. In drug discovery, researchers use this methodology, looking to find new chemotherapeutic agents for various diseases. The neglected diseases are a group of tropical parasitic diseases that primarily affect poor countries in Africa, Asia, and South America. Current drugs against these diseases cause side effects, are ineffective during the chronic stages of the disease, and are often not available to the needy population, have relative high toxicity, and face developing resistance. Faced with so many problems, new chemotherapeutic agents to treat these infections are much needed. The present review reports on neural network research, which studies new ligands against Chagas' disease, sleeping sickness, malaria, tuberculosis, and leishmaniasis; a few of the neglected diseases.

  9. An overview of hydrogen deuterium exchange mass spectrometry (HDX-MS) in drug discovery.

    Science.gov (United States)

    Masson, Glenn R; Jenkins, Meredith L; Burke, John E

    2017-10-01

    Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a powerful methodology to study protein dynamics, protein folding, protein-protein interactions, and protein small molecule interactions. The development of novel methodologies and technical advancements in mass spectrometers has greatly expanded the accessibility and acceptance of this technique within both academia and industry. Areas covered: This review examines the theoretical basis of how amide exchange occurs, how different mass spectrometer approaches can be used for HDX-MS experiments, as well as the use of HDX-MS in drug development, specifically focusing on how HDX-MS is used to characterize bio-therapeutics, and its use in examining protein-protein and protein small molecule interactions. Expert opinion: HDX-MS has been widely accepted within the pharmaceutical industry for the characterization of bio-therapeutics as well as in the mapping of antibody drug epitopes. However, there is room for this technique to be more widely used in the drug discovery process. This is particularly true in the use of HDX-MS as a complement to other high-resolution structural approaches, as well as in the development of small molecule therapeutics that can target both active-site and allosteric binding sites.

  10. Human pluripotent stem cells as tools for neurodegenerative and neurodevelopmental disease modeling and drug discovery.

    Science.gov (United States)

    Corti, Stefania; Faravelli, Irene; Cardano, Marina; Conti, Luciano

    2015-06-01

    Although intensive efforts have been made, effective treatments for neurodegenerative and neurodevelopmental diseases have not been yet discovered. Possible reasons for this include the lack of appropriate disease models of human neurons and a limited understanding of the etiological and neurobiological mechanisms. Recent advances in pluripotent stem cell (PSC) research have now opened the path to the generation of induced pluripotent stem cells (iPSCs) starting from somatic cells, thus offering an unlimited source of patient-specific disease-relevant neuronal cells. In this review, the authors focus on the use of human PSC-derived cells in modeling neurological disorders and discovering of new drugs and provide their expert perspectives on the field. The advent of human iPSC-based disease models has fuelled renewed enthusiasm and enormous expectations for insights of disease mechanisms and identification of more disease-relevant and novel molecular targets. Human PSCs offer a unique tool that is being profitably exploited for high-throughput screening (HTS) platforms. This process can lead to the identification and optimization of molecules/drugs and thus move forward new pharmacological therapies for a wide range of neurodegenerative and neurodevelopmental conditions. It is predicted that improvements in the production of mature neuronal subtypes, from patient-specific human-induced pluripotent stem cells and their adaptation to culture, to HTS platforms will allow the increased exploitation of human pluripotent stem cells in drug discovery programs.

  11. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

    Directory of Open Access Journals (Sweden)

    Marwin H. S. Segler

    2017-12-01

    Full Text Available In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria, it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.

  12. SPME as a promising tool in translational medicine and drug discovery: From bench to bedside.

    Science.gov (United States)

    Goryński, Krzysztof; Goryńska, Paulina; Górska, Agnieszka; Harężlak, Tomasz; Jaroch, Alina; Jaroch, Karol; Lendor, Sofia; Skobowiat, Cezary; Bojko, Barbara

    2016-10-25

    Solid phase microextraction (SPME) is a technology where a small amount of an extracting phase dispersed on a solid support is exposed to the sample for a well-defined period of time. The open-bed geometry and biocompatibility of the materials used for manufacturing of the devices makes it very convenient tool for direct extraction from complex biological matrices. The flexibility of the formats permits tailoring the method according the needs of the particular application. Number of studies concerning monitoring of drugs and their metabolites, analysis of metabolome of volatile as well as non-volatile compounds, determination of ligand-protein binding, permeability and compound toxicity was already reported. All these applications were performed in different matrices including biological fluids and tissues, cell cultures, and in living animals. The low invasiveness of in vivo SPME, ability of using very small sample volumes and analysis of cell cultures permits to address the rule of 3R, which is currently acknowledged ethical standard in R&D labs. In the current review systematic evaluation of the applicability of SPME to studies required to be conduct at different stages of drug discovery and development and translational medicine is presented. The advantages and challenges are discussed based on the examples directly showing given experimental design or on the studies, which could be translated to the models routinely used in drug development process. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. Institutional profile: the national Swedish academic drug discovery & development platform at SciLifeLab.

    Science.gov (United States)

    Arvidsson, Per I; Sandberg, Kristian; Sakariassen, Kjell S

    2017-06-01

    The Science for Life Laboratory Drug Discovery and Development Platform (SciLifeLab DDD) was established in Stockholm and Uppsala, Sweden, in 2014. It is one of ten platforms of the Swedish national SciLifeLab which support projects run by Swedish academic researchers with large-scale technologies for molecular biosciences with a focus on health and environment. SciLifeLab was created by the coordinated effort of four universities in Stockholm and Uppsala: Stockholm University, Karolinska Institutet, KTH Royal Institute of Technology and Uppsala University, and has recently expanded to other Swedish university locations. The primary goal of the SciLifeLab DDD is to support selected academic discovery and development research projects with tools and resources to discover novel lead therapeutics, either molecules or human antibodies. Intellectual property developed with the help of SciLifeLab DDD is wholly owned by the academic research group. The bulk of SciLifeLab DDD's research and service activities are funded from the Swedish state, with only consumables paid by the academic research group through individual grants.

  14. MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development.

    Science.gov (United States)

    Harati, Sahar; Cooper, Lee A D; Moran, Josue D; Giuste, Felipe O; Du, Yuhong; Ivanov, Andrei A; Johns, Margaret A; Khuri, Fadlo R; Fu, Haian; Moreno, Carlos S

    2017-01-01

    Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal.

  15. Induced Pluripotent Stem Cells for Disease Modeling and Drug Discovery in Neurodegenerative Diseases.

    Science.gov (United States)

    Cao, Lei; Tan, Lan; Jiang, Teng; Zhu, Xi-Chen; Yu, Jin-Tai

    2015-08-01

    Although most neurodegenerative diseases have been closely related to aberrant accumulation of aggregation-prone proteins in neurons, understanding their pathogenesis remains incomplete, and there is no treatment to delay the onset or slow the progression of many neurodegenerative diseases. The availability of induced pluripotent stem cells (iPSCs) in recapitulating the phenotypes of several late-onset neurodegenerative diseases marks the new era in in vitro modeling. The iPSC collection represents a unique and well-characterized resource to elucidate disease mechanisms in these diseases and provides a novel human stem cell platform for screening new candidate therapeutics. Modeling human diseases using iPSCs has created novel opportunities for both mechanistic studies as well as for the discovery of new disease therapies. In this review, we introduce iPSC-based disease modeling in neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. In addition, we discuss the implementation of iPSCs in drug discovery associated with some new techniques.

  16. Scientific Knowledge Discovery in Complex Semantic Networks of Geophysical Systems

    Science.gov (United States)

    Fox, P.

    2012-04-01

    The vast majority of explorations of the Earth's systems are limited in their ability to effectively explore the most important (often most difficult) problems because they are forced to interconnect at the data-element, or syntactic, level rather than at a higher scientific, or semantic, level. Recent successes in the application of complex network theory and algorithms to climate data, raise expectations that more general graph-based approaches offer the opportunity for new discoveries. In the past ~ 5 years in the natural sciences there has substantial progress in providing both specialists and non-specialists the ability to describe in machine readable form, geophysical quantities and relations among them in meaningful and natural ways, effectively breaking the prior syntax barrier. The corresponding open-world semantics and reasoning provide higher-level interconnections. That is, semantics provided around the data structures, using semantically-equipped tools, and semantically aware interfaces between science application components allowing for discovery at the knowledge level. More recently, formal semantic approaches to continuous and aggregate physical processes are beginning to show promise and are soon likely to be ready to apply to geoscientific systems. To illustrate these opportunities, this presentation presents two application examples featuring domain vocabulary (ontology) and property relations (named and typed edges in the graphs). First, a climate knowledge discovery pilot encoding and exploration of CMIP5 catalog information with the eventual goal to encode and explore CMIP5 data. Second, a multi-stakeholder knowledge network for integrated assessments in marine ecosystems, where the data is highly inter-disciplinary.

  17. Introducing Telescoping Process to Synthesis of a Key Intermediate of Drug Discoveries Using Design of Experiment.

    Science.gov (United States)

    Nishimura, Koichiro; Saitoh, Toshikazu

    2016-07-01

    The 5-bromo-2-methylamino-8-methoxyquinazoline (1) is a key intermediate in our drug discoveries. Compound 1 bears a monomethylamino group at the 2-position of the quinazoline ring. This compound has been synthesized from 6-bromo-2-fluoro-3-methoxybenzaldehyde by a synthetic route including a total of four isolation processes in the medicinal chemistry laboratories. Our process chemistry laboratories successfully improved the original synthetic route by introducing the telescoping process. We successfully reduced the isolation processes from four to two processes by using information extracted through design of experiment. The total yield of compound 1 increased by 18%, while maintaining the purity of compound 1 of the original synthetic route. Accordingly, we contributed to the quick supply of compound 1 to the medicinal laboratories.

  18. Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery.

    Science.gov (United States)

    Reutlinger, Michael; Schneider, Gisbert

    2012-04-01

    Visualization of 'chemical space' and compound distributions has received much attraction by medicinal chemists as it may help to intuitively comprehend pharmaceutically relevant molecular features. It has been realized that for meaningful feature extraction from complex multivariate chemical data, such as compound libraries represented by many molecular descriptors, nonlinear projection techniques are required. Recent advances in machine-learning and artificial intelligence have resulted in a transfer of such methods to chemistry. We provide an overview of prominent visualization methods based on nonlinear dimensionality reduction, and highlight applications in drug discovery. Emphasis is on neural network techniques, kernel methods and stochastic embedding approaches, which have been successfully used for ligand-based virtual screening, SAR landscape analysis, combinatorial library design, and screening compound selection. Copyright © 2011 Elsevier Inc. All rights reserved.

  19. Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery.

    Science.gov (United States)

    Barnes, Michael R; Harland, Lee; Foord, Steven M; Hall, Matthew D; Dix, Ian; Thomas, Scott; Williams-Jones, Bryn I; Brouwer, Cory R

    2009-09-01

    Pharmaceutical research and development is facing substantial challenges that have prompted the industry to shift funding from early- to late-stage projects. Among the effects is a major change in the attitude of many companies to their internal bioinformatics resources: the focus has moved from the vigorous pursuit of intellectual property towards exploration of pre-competitive cross-industry collaborations and engagement with the public domain. High-quality, open and accessible data are the foundation of pre-competitive research, and strong public-private partnerships have considerable potential to enhance public data resources, which would benefit everyone engaged in drug discovery. In this article, we discuss the background to these changes and propose new areas of collaboration in computational biology and chemistry between the public domain and the pharmaceutical industry.

  20. PyMine: a PyMOL plugin to integrate and visualize data for drug discovery.

    Science.gov (United States)

    Chaudhari, Rajan; Li, Zhijun

    2015-10-01

    Tremendous amount of chemical and biological data are being generated by various high-throughput biotechnologies that could facilitate modern drug discovery. However, lack of integration makes it very challenging for individual scientists to access and understand all the data related to a specific protein of interest. To overcome this challenge, we developed PyMine, a PyMOL plugin that retrieves chemical, structural, pathway and other related biological data of a receptor and small molecules from a variety of high-quality databases and presents them in a graphic and uniformed way. Developed as an interactive and user-friendly tool, PyMine can be used as a central data-hub for users to access and visualize multiple types of data and to generate new ideas intuitively for structure-based molecule design.

  1. Integrated Approaches to Drug Discovery for Oxidative Stress-Related Retinal Diseases.

    Science.gov (United States)

    Nishimura, Yuhei; Hara, Hideaki

    2016-01-01

    Excessive oxidative stress induces dysregulation of functional networks in the retina, resulting in retinal diseases such as glaucoma, age-related macular degeneration, and diabetic retinopathy. Although various therapies have been developed to reduce oxidative stress in retinal diseases, most have failed to show efficacy in clinical trials. This may be due to oversimplification of target selection for such a complex network as oxidative stress. Recent advances in high-throughput technologies have facilitated the collection of multilevel omics data, which has driven growth in public databases and in the development of bioinformatics tools. Integration of the knowledge gained from omics databases can be used to generate disease-related biological networks and to identify potential therapeutic targets within the networks. Here, we provide an overview of integrative approaches in the drug discovery process and provide simple examples of how the approaches can be exploited to identify oxidative stress-related targets for retinal diseases.

  2. Differential gene expression analysis in ageing muscle and drug discovery perspectives.

    Science.gov (United States)

    Melouane, Aicha; Ghanemi, Abdelaziz; Aubé, Simon; Yoshioka, Mayumi; St-Amand, Jonny

    2018-01-01

    Identifying therapeutic target genes represents the key step in functional genomics-based therapies. Within this context, the disease heterogeneity, the exogenous factors and the complexity of genomic structure and function represent important challenges. The functional genomics aims to overcome such obstacles via identifying the gene functions and therefore highlight disease-causing genes as therapeutic targets. Genomic technologies promise to reshape the research on ageing muscle, exercise response and drug discovery. Herein, we describe the functional genomics strategies, mainly differential gene expression methods microarray, serial analysis of gene expression (SAGE), massively parallel signature sequence (MPSS), RNA sequencing (RNA seq), representational difference analysis (RDA), and suppression subtractive hybridization (SSH). Furthermore, we review these illustrative approaches that have been used to discover new therapeutic targets for some complex diseases along with the application of these tools to study the modulation of the skeletal muscle transcriptome. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Why open drug discovery needs four simple rules for licensing data and models.

    Directory of Open Access Journals (Sweden)

    Antony J Williams

    Full Text Available When we look at the rapid growth of scientific databases on the Internet in the past decade, we tend to take the accessibility and provenance of the data for granted. As we see a future of increased database integration, the licensing of the data may be a hurdle that hampers progress and usability. We have formulated four rules for licensing data for open drug discovery, which we propose as a starting point for consideration by databases and for their ultimate adoption. This work could also be extended to the computational models derived from such data. We suggest that scientists in the future will need to consider data licensing before they embark upon re-using such content in databases they construct themselves.

  4. Cloud Infrastructures for In Silico Drug Discovery: Economic and Practical Aspects

    Directory of Open Access Journals (Sweden)

    Daniele D'Agostino

    2013-01-01

    Full Text Available Cloud computing opens new perspectives for small-medium biotechnology laboratories that need to perform bioinformatics analysis in a flexible and effective way. This seems particularly true for hybrid clouds that couple the scalability offered by general-purpose public clouds with the greater control and ad hoc customizations supplied by the private ones. A hybrid cloud broker, acting as an intermediary between users and public providers, can support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality. This paper analyses some economic and practical aspects of exploiting cloud computing in a real research scenario for the in silico drug discovery in terms of requirements, costs, and computational load based on the number of expected users. In particular, our work is aimed at supporting both the researchers and the cloud broker delivering an IaaS cloud infrastructure for biotechnology laboratories exposing different levels of nonfunctional requirements.

  5. Early phase drug discovery: cheminformatics and computational techniques in identifying lead series.

    Science.gov (United States)

    Duffy, Bryan C; Zhu, Lei; Decornez, Hélène; Kitchen, Douglas B

    2012-09-15

    Early drug discovery processes rely on hit finding procedures followed by extensive experimental confirmation in order to select high priority hit series which then undergo further scrutiny in hit-to-lead studies. The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds. We describe the steps in typical hit identification and hit-to-lead programs and then describe how cheminformatic analysis assists this process. In particular, scaffold analysis, clustering and property calculations assist in the design of high-throughput screening libraries, the early analysis of hits and then organizing compounds into series for their progression from hits to leads. Additionally, these computational tools can be used in virtual screening to design hit-finding libraries and as procedures to help with early SAR exploration. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. High Throughput Screening in Duchenne Muscular Dystrophy: From Drug Discovery to Functional Genomics

    Directory of Open Access Journals (Sweden)

    Thomas J.J. Gintjee

    2014-11-01

    Full Text Available Centers for the screening of biologically active compounds and genomic libraries are becoming common in the academic setting and have enabled researchers devoted to developing strategies for the treatment of diseases or interested in studying a biological phenomenon to have unprecedented access to libraries that, until few years ago, were accessible only by pharmaceutical companies. As a result, new drugs and genetic targets have now been identified for the treatment of Duchenne muscular dystrophy (DMD, the most prominent of the neuromuscular disorders affecting children. Although the work is still at an early stage, the results obtained to date are encouraging and demonstrate the importance that these centers may have in advancing therapeutic strategies for DMD as well as other diseases. This review will provide a summary of the status and progress made toward the development of a cure for this disorder and implementing high-throughput screening (HTS technologies as the main source of discovery. As more academic institutions are gaining access to HTS as a valuable discovery tool, the identification of new biologically active molecules is likely to grow larger. In addition, the presence in the academic setting of experts in different aspects of the disease will offer the opportunity to develop novel assays capable of identifying new targets to be pursued as potential therapeutic options. These assays will represent an excellent source to be used by pharmaceutical companies for the screening of larger libraries providing the opportunity to establish strong collaborations between the private and academic sectors and maximizing the chances of bringing into the clinic new drugs for the treatment of DMD.

  7. Clinical Pharmacokinetics of Systemically Administered Antileishmanial Drugs

    NARCIS (Netherlands)

    Kip, Anke E; Schellens, Jan H M; Beijnen, Jos H; Dorlo, Thomas P C

    This review describes the pharmacokinetic properties of the systemically administered antileishmanial drugs pentavalent antimony, paromomycin, pentamidine, miltefosine and amphotericin B (AMB), including their absorption, distribution, metabolism and excretion and potential drug-drug interactions.

  8. When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis.

    Directory of Open Access Journals (Sweden)

    Jack W Scannell

    Full Text Available A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a to provide quantitatively and historically plausible explanations of the contrast; and (b identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc. within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc. whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development, changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man can offset large (e.g., 10 fold, even 100 fold changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated

  9. Advancing Drug Discovery and Development from Active Constituents of Yinchenhao Tang, a Famous Traditional Chinese Medicine Formula

    Directory of Open Access Journals (Sweden)

    Aihua Zhang

    2013-01-01

    Full Text Available Traditional Chinese medicine (TCM formula has been playing a very important role in health protection and disease control for thousands of years. Guided by TCM syndrome theories, formula are designed to contain a combination of various kinds of crude drugs that, when combined, will achieve synergistic efficacy. However, the precise mechanism of synergistic action remains poorly understood. One example is a famous TCM formula Yinchenhao Tang (YCHT, whose efficacy in treating hepatic injury (HI and Jaundice syndrome, has recently been well established as a case study. We also conducted a systematic analysis of synergistic effects of the principal compound using biochemistry, pharmacokinetics and systems biology, to explore the key molecular mechanisms. We had found that the three component (6,7-dimethylesculetin (D, geniposide (G, and rhein (R combination exerts a more robust synergistic effect than any one or two of the three individual compounds by hitting multiple targets. They can regulate molecular networks through activating both intrinsic and extrinsic pathways to synergistically cause intensified therapeutic effects. This paper provides an overview of the recent and potential developments of chemical fingerprinting coupled with systems biology advancing drug discovery towards more agile development of targeted combination therapies for the YCHT.

  10. Enabling Open Research Data Discovery through a Recommender System

    Science.gov (United States)

    Devaraju, Anusuriya; Jayasinghe, Gaya; Klump, Jens; Hogan, Dominic

    2017-04-01

    Government agencies, universities, research and nonprofit organizations are increasingly publishing their datasets to promote transparency, induce new research and generate economic value through the development of new products or services. The datasets may be downloaded from various data portals (data repositories) which are general or domain-specific. The Registry of Research Data Repository (re3data.org) lists more than 2500 such data repositories from around the globe. Data portals allow keyword search and faceted navigation to facilitate discovery of research datasets. However, the volume and variety of datasets have made finding relevant datasets more difficult. Common dataset search mechanisms may be time consuming, may produce irrelevant results and are primarily suitable for users who are familiar with the general structure and contents of the respective database. Therefore, we need new approaches to support research data discovery. Recommender systems offer new possibilities for users to find datasets that are relevant to their research interests. This study presents a recommender system developed for the CSIRO Data Access Portal (DAP, http://data.csiro.au). The datasets hosted on the portal are diverse, published by researchers from 13 business units in the organisation. The goal of the study is not to replace the current search mechanisms on the data portal, but rather to extend the data discovery through an exploratory search, in this case by building a recommender system. We adopted a hybrid recommendation approach, comprising content-based filtering and item-item collaborative filtering. The content-based filtering computes similarities between datasets based on metadata such as title, keywords, descriptions, fields of research, location, contributors, etc. The collaborative filtering utilizes user search behaviour and download patterns derived from the server logs to determine similar datasets. Similarities above are then combined with different

  11. Data quality in drug discovery: the role of analytical performance in ligand binding assays.

    Science.gov (United States)

    Wätzig, Hermann; Oltmann-Norden, Imke; Steinicke, Franziska; Alhazmi, Hassan A; Nachbar, Markus; El-Hady, Deia Abd; Albishri, Hassan M; Baumann, Knut; Exner, Thomas; Böckler, Frank M; El Deeb, Sami

    2015-09-01

    Despite its importance and all the considerable efforts made, the progress in drug discovery is limited. One main reason for this is the partly questionable data quality. Models relating biological activity and structures and in silico predictions rely on precisely and accurately measured binding data. However, these data vary so strongly, such that only variations by orders of magnitude are considered as unreliable. This can certainly be improved considering the high analytical performance in pharmaceutical quality control. Thus the principles, properties and performances of biochemical and cell-based assays are revisited and evaluated. In the part of biochemical assays immunoassays, fluorescence assays, surface plasmon resonance, isothermal calorimetry, nuclear magnetic resonance and affinity capillary electrophoresis are discussed in details, in addition radiation-based ligand binding assays, mass spectrometry, atomic force microscopy and microscale thermophoresis are briefly evaluated. In addition, general sources of error, such as solvent, dilution, sample pretreatment and the quality of reagents and reference materials are discussed. Biochemical assays can be optimized to provide good accuracy and precision (e.g. percental relative standard deviation data quality are still advancing and will further advance the progress in drug development.

  12. 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.

  13. Peptide Phage Display as a Tool for Drug Discovery: Targeting Membrane Receptors

    Directory of Open Access Journals (Sweden)

    Tomaz Bratkovic

    2011-01-01

    Full Text Available Ligands selected from phage-displayed random peptide libraries tend to be directed to biologically relevant sites on the surface of the target protein. Consequently, peptides derived from library screenings often modulate the target protein’s activity in vitro and in vivo and can be used as lead compounds in drug design and as alternatives to antibodies for target validation in both genomics and drug discovery. This review discusses the use of phage display to identify membrane receptor modulators with agonistic or antagonistic activities. Because isolating or producing recombinant membrane proteins for use as target molecules in library screening is often impossible, innovative selection strategies such as panning against whole cells or tissues, recombinant receptor ectodomains, or neutralizing antibodies to endogenous binding partners were devised. Prominent examples from a two-decade history of peptide phage display will be presented, focusing on the design of affinity selection experiments, methods for improving the initial hits, and applications of the identified peptides.

  14. Integrity, standards, and QC-related issues with big data in pre-clinical drug discovery.

    Science.gov (United States)

    Brothers, John F; Ung, Matthew; Escalante-Chong, Renan; Ross, Jermaine; Zhang, Jenny; Cha, Yoonjeong; Lysaght, Andrew; Funt, Jason; Kusko, Rebecca

    2018-03-15

    The tremendous expansion of data analytics and public and private big datasets presents an important opportunity for pre-clinical drug discovery and development. In the field of life sciences, the growth of genetic, genomic, transcriptomic and proteomic data is partly driven by a rapid decline in experimental costs as biotechnology improves throughput, scalability, and speed. Yet far too many researchers tend to underestimate the challenges and consequences involving data integrity and quality standards. Given the effect of data integrity on scientific interpretation, these issues have significant implications during preclinical drug development. We describe standardized approaches for maximizing the utility of publicly available or privately generated biological data and address some of the common pitfalls. We also discuss the increasing interest to integrate and interpret cross-platform data. Principles outlined here should serve as a useful broad guide for existing analytical practices and pipelines and as a tool for developing additional insights into therapeutics using big data. Copyright © 2018 Elsevier Inc. All rights reserved.

  15. Therapeutic Potential of Plants as Anti-Microbials for Drug Discovery

    Directory of Open Access Journals (Sweden)

    Ramar Perumal Samy

    2010-01-01

    Full Text Available The uses of traditional medicinal plants for primary health care have steadily increased worldwide in recent years. Scientists are in search of new phytochemicals that could be developed as useful anti-microbials for treatment of infectious diseases. Currently, out of 80% of pharmaceuticals derived from plants, very few are now being used as anti-microbials. Plants are rich in a wide variety of secondary metabolites that have found anti-microbial properties. This review highlights the current status of traditional medicine, its contribution to modern medicine, recent trends in the evaluation of anti-microbials with a special emphasis upon some tribal medicine, in vitro and in vivo experimental design for screening, and therapeutic efficacy in safety and human clinical trails for commercial outlet. Many of these commercially available compounds are crude preparations administered without performing human clinical trials. Recent methods are useful to standardize the extraction for scientific investigation of new phytochemicals and anti-microbials of traditionally used plants. It is concluded that once the local ethnomedical preparations of traditional sources are scientifically evaluated before dispensing they should replace existing drugs commonly used for the therapeutic treatment of infection. This method should be put into practice for future investigations in the field of ethnopharmacology, phytochemistry, ethnobotany and other biological fields for drug discovery.

  16. Early identification of hERG liability in drug discovery programs by automated patch clamp.

    Science.gov (United States)

    Danker, Timm; Möller, Clemens

    2014-01-01

    Blockade of the cardiac ion channel coded by human ether-à-gogo-related gene (hERG) can lead to cardiac arrhythmia, which has become a major concern in drug discovery and development. Automated electrophysiological patch clamp allows assessment of hERG channel effects early in drug development to aid medicinal chemistry programs and has become routine in pharmaceutical companies. However, a number of potential sources of errors in setting up hERG channel assays by automated patch clamp can lead to misinterpretation of data or false effects being reported. This article describes protocols for automated electrophysiology screening of compound effects on the hERG channel current. Protocol details and the translation of criteria known from manual patch clamp experiments to automated patch clamp experiments to achieve good quality data are emphasized. Typical pitfalls and artifacts that may lead to misinterpretation of data are discussed. While this article focuses on hERG channel recordings using the QPatch (Sophion A/S, Copenhagen, Denmark) technology, many of the assay and protocol details given in this article can be transferred for setting up different ion channel assays by automated patch clamp and are similar on other planar patch clamp platforms.

  17. Charting, navigating, and populating natural product chemical space for drug discovery.

    Science.gov (United States)

    Lachance, Hugo; Wetzel, Stefan; Kumar, Kamal; Waldmann, Herbert

    2012-07-12

    Natural products are a heterogeneous group of compounds with diverse, yet particular molecular properties compared to synthetic compounds and drugs. All relevant analyses show that natural products indeed occupy parts of chemical space not explored by available screening collections while at the same time largely adhering to the rule-of-five. This renders them a valuable, unique, and necessary component of screening libraries used in drug discovery. With ChemGPS-NP on the Web and Scaffold Hunter two tools are available to the scientific community to guide exploration of biologically relevant NP chemical space in a focused and targeted fashion with a view to guide novel synthesis approaches. Several of the examples given illustrate the possibility of bridging the gap between computational methods and compound library synthesis and the possibility of integrating cheminformatics and chemical space analyses with synthetic chemistry and biochemistry to successfully explore chemical space for the identification of novel small molecule modulators of protein function.The examples also illustrate the synergistic potential of the chemical space concept and modern chemical synthesis for biomedical research and drug discovery. Chemical space analysis can map under explored biologically relevant parts of chemical space and identify the structure types occupying these parts. Modern synthetic methodology can then be applied to efficiently fill this “virtual space” with real compounds.From a cheminformatics perspective, there is a clear demand for open-source and easy to use tools that can be readily applied by educated nonspecialist chemists and biologists in their daily research. This will include further development of Scaffold Hunter, ChemGPS-NP, and related approaches on the Web. Such a “cheminformatics toolbox” would enable chemists and biologists to mine their own data in an intuitive and highly interactive process and without the need for specialized computer

  18. Riboswitches: discovery of drugs that target bacterial gene-regulatory RNAs

    Science.gov (United States)

    Deigan, Katherine E.; Ferré-D’Amaré, Adrian R.

    2011-01-01

    Conspectus Riboswitches, which were discovered in the first years of the XXI century, are gene-regulatory mRNA domains that respond to the intracellular concentration of a variety of metabolites and second messengers. They control essential genes in many pathogenic bacteria, and represent a new class of biomolecular target for the development of antibiotics and chemical-biological tools. Five mechanisms of gene regulation are known for riboswitches. Most bacterial riboswitches modulate transcription termination or translation initiation in response to ligand binding. All known examples of eukaryotic riboswitches and some bacterial riboswitches control gene expression by alternative splicing. The glmS riboswitch, widespread in Gram-positive bacteria, is a catalytic RNA activated by ligand binding. Its self-cleavage destabilizes the mRNA of which it is part. Finally, one example of trans-acting riboswitch is known. Three-dimensional (3D) structures have been determined of representatives of thirteen structurally distinct riboswitch classes, providing atomic-level insight into their mechanisms of ligand recognition. While cellular and viral RNAs in general have attracted interest as potential drug targets, riboswitches show special promise due to the diversity and sophistication of small molecule recognition strategies on display in their ligand binding pockets. Moreover, uniquely among known structured RNA domains, riboswitches evolved to recognize small molecule ligands. Structural and biochemical advances in the study of riboswitches provide an impetus for the development of methods for the discovery of novel riboswitch activators and inhibitors. Recent rational drug design efforts focused on select riboswitch classes have yielded a small number of candidate antibiotic compounds, including one active in a mouse model of Staphylococcus aureus infection. The development of high-throughput methods suitable for riboswitch-specific drug discovery is ongoing. A fragment

  19. SOLID SELF NANOEMULSIFYING DRUG DELIVERY SYSTEM (SNEDDS) DEVOLOPMENT, APPLICATIONS AND FUTURE PERSPECTIVE: A REVIEW

    OpenAIRE

    Febi S Kuruvila*, Flowerlet Mathew ,S Kuppuswamy

    2017-01-01

    Developments in recent drug discovery programs, yields a large proportion of novel pharmacologically active molecules that are lipophilic and poorly soluble ,which is a major challenge for pharmaceutical researchers to enhance the oral bioavailability of such drug molecules. Compared to conventional oral dosage forms, Self nanoemulsifying drug delivery systems (SNEDDS) possesses potential advantages like ease of manufacture and scale up, quick onset of action, reduction in drug dose, reductio...

  20. The discovery and development of proteomic safety biomarkers for the detection of drug-induced liver toxicity

    International Nuclear Information System (INIS)

    Amacher, David E.

    2010-01-01

    Biomarkers are biometric measurements that provide critical quantitative information about the biological condition of the animal or individual being tested. In drug safety studies, established toxicity biomarkers are used along with other conventional study data to determine dose-limiting organ toxicity, and to define species sensitivity for new chemical entities intended for possible use as human medicines. A continuing goal of drug safety scientists in the pharmaceutical industry is to discover and develop better trans-species biomarkers that can be used to determine target organ toxicities for preclinical species in short-term studies at dose levels that are some multiple of the intended human dose and again later in full development for monitoring clinical trials at lower therapeutic doses. Of particular value are early, predictive, noninvasive biomarkers that have in vitro, in vivo, and clinical transferability. Such translational biomarkers bridge animal testing used in preclinical science and human studies that are part of subsequent clinical testing. Although suitable for in vivo preclinical regulatory studies, conventional hepatic safety biomarkers are basically confirmatory markers because they signal organ toxicity after some pathological damage has occurred, and are therefore not well-suited for short-term, predictive screening assays early in the discovery-to-development progression of new chemical entities (NCEs) available in limited quantities. Efforts between regulatory agencies and the pharmaceutical industry are underway for the coordinated discovery, qualification, verification and validation of early predictive toxicity biomarkers. Early predictive safety biomarkers are those that are detectable and quantifiable prior to the onset of irreversible tissue injury and which are associated with a mechanism of action relevant to a specific type of potential hepatic injury. Potential drug toxicity biomarkers are typically endogenous macromolecules in

  1. 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

  2. A high-throughput lab-on-a-chip interface for zebrafish embryo tests in drug discovery and ecotoxicology

    Science.gov (United States)

    Zhu, Feng; Akagi, Jin; Hall, Chris J.; Crosier, Kathryn E.; Crosier, Philip S.; Delaage, Pierre; Wlodkowic, Donald

    2013-12-01

    Drug discovery screenings performed on zebrafish embryos mirror with a high level of accuracy. The tests usually performed on mammalian animal models, and the fish embryo toxicity assay (FET) is one of the most promising alternative approaches to acute ecotoxicity testing with adult fish. Notwithstanding this, conventional methods utilising 96-well microtiter plates and manual dispensing of fish embryos are very time-consuming. They rely on laborious and iterative manual pipetting that is a main source of analytical errors and low throughput. In this work, we present development of a miniaturised and high-throughput Lab-on-a-Chip (LOC) platform for automation of FET assays. The 3D high-density LOC array was fabricated in poly-methyl methacrylate (PMMA) transparent thermoplastic using infrared laser micromachining while the off-chip interfaces were fabricated using additive manufacturing processes (FDM and SLA). The system's design facilitates rapid loading and immobilization of a large number of embryos in predefined clusters of traps during continuous microperfusion of drugs/toxins. It has been conceptually designed to seamlessly interface with both upright and inverted fluorescent imaging systems and also to directly interface with conventional microtiter plate readers that accept 96-well plates. We also present proof-of-concept interfacing with a high-speed imaging cytometer Plate RUNNER HD® capable of multispectral image acquisition with resolution of up to 8192 x 8192 pixels and depth of field of about 40 μm. Furthermore, we developed a miniaturized and self-contained analytical device interfaced with a miniaturized USB microscope. This system modification is capable of performing rapid imaging of multiple embryos at a low resolution for drug toxicity analysis.

  3. Pharmacokinetics in Drug Discovery: An Exposure-Centred Approach to Optimising and Predicting Drug Efficacy and Safety.

    Science.gov (United States)

    Reichel, Andreas; Lienau, Philip

    2016-01-01

    The role of pharmacokinetics (PK) in drug discovery is to support the optimisation of the absorption, distribution, metabolism and excretion (ADME) properties of lead compounds with the ultimate goal to attain a clinical candidate which achieves a concentration-time profile in the body that is adequate for the desired efficacy and safety profile. A thorough characterisation of the lead compounds aiming at the identification of the inherent PK liabilities also includes an early generation of PK/PD relationships linking in vitro potency and target exposure/engagement with expression of pharmacological activity (mode-of-action) and efficacy in animal studies. The chapter describes an exposure-centred approach to lead generation, lead optimisation and candidate selection and profiling that focuses on a stepwise generation of an understanding between PK/exposure and PD/efficacy relationships by capturing target exposure or surrogates thereof and cellular mode-of-action readouts in vivo. Once robust PK/PD relationship in animal PD models has been constructed, it is translated to anticipate the pharmacologically active plasma concentrations in patients and the human therapeutic dose and dosing schedule which is also based on the prediction of the PK behaviour in human as described herein. The chapter outlines how the level of confidence in the predictions increases with the level of understanding of both the PK and the PK/PD of the new chemical entities (NCE) in relation to the disease hypothesis and the ability to propose safe and efficacious doses and dosing schedules in responsive patient populations. A sound identification of potential drug metabolism and pharmacokinetics (DMPK)-related development risks allows proposing of an effective de-risking strategy for the progression of the project that is able to reduce uncertainties and to increase the probability of success during preclinical and clinical development.

  4. Drug discovery opportunities and challenges at G protein coupled receptors for long chain free fatty acids

    Directory of Open Access Journals (Sweden)

    Nicholas D Holliday

    2012-01-01

    Full Text Available Discovery of G protein coupled receptors for long chain free fatty acids (FFAs, FFA1 (GPR40 and GPR120, has expanded our understanding of these nutrients as signalling molecules. These receptors have emerged as important sensors for FFA levels in the circulation or the gut lumen, based on evidence from in vitro and rodent models, and an increasing number of human studies. Here we consider their promise as therapeutic targets for metabolic disease, including type 2 diabetes and obesity. FFA1 directly mediates acute FFA-induced glucose-stimulated insulin secretion in pancreatic beta-cells, while GPR120 and FFA1 trigger release of incretins from intestinal endocrine cells, and so indirectly enhance insulin secretion and promote satiety. GPR120 signalling in adipocytes and macrophages also results in insulin sensitizing and beneficial anti-inflammatory effects. Drug discovery has focussed on agonists to replicate acute benefits of FFA receptor signalling, with promising early results for FFA1 agonists in man. Controversy surrounding chronic effects of FFA1 on beta-cells illustrates that long term benefits of antagonists also need exploring. It has proved challenging to generate highly selective potent ligands for FFA1 or GPR120 subtypes, given that both receptors have hydrophobic orthosteric binding sites, which are not completely defined and have modest ligand affinity. Structure activity relationships are also reliant on functional read outs, in the absence of robust binding assays to provide direct affinity estimates. Nevertheless synthetic ligands have already helped dissect specific contributions of FFA1 and GPR120 signalling from the many possible cellular effects of FFAs. Approaches including use of fluorescent ligand binding assays, and targeting allosteric receptor sites, may improve further preclinical ligand development at these receptors, to exploit their unique potential to target multiple facets of diabetes.

  5. Signal Transducers and Activators of Transcription (STAT Regulatory Networks in Marine Organisms: From Physiological Observations towards Marine Drug Discovery

    Directory of Open Access Journals (Sweden)

    Jin-Young Lee

    2015-08-01

    Full Text Available Part of our ocean’s richness comes from its extensive history of supporting life, resulting in a highly diverse ecological system. To date, over 250,000 species of marine organisms have been identified, but it is speculated that the actual number of marine species exceeds one million, including several hundreds of millions of species of marine microorganisms. Past studies suggest that approximately 70% of all deep-sea microorganisms, gorgonians, and sea sponges produce secondary metabolites with anti-cancer activities. Recently, novel FDA-approved drugs derived from marine sponges have been shown to reduce metastatic breast cancer, malignant lymphoma, and Hodgkin’s disease. Despite the fact that many marine natural products have been shown to possess a good inhibition potential against most of the cancer-related cell signaling pathways, only a few marine natural products have been shown to target JAK/STAT signaling. In the present paper, we describe the JAK/STAT signaling pathways found in marine organisms, before elaborating on the recent advances in the field of STAT inhibition by marine natural products and the potential application in anti-cancer drug discovery.

  6. Advanced In Silico Approaches for Drug Discovery: Mining Information from Multiple Biological and Chemical Data Through mtk- QSBER and pt-QSPR Strategies.

    Science.gov (United States)

    Speck-Planche, Alejandro; Cordeiro, Maria Natália Dias Soeiro

    2017-01-01

    The last decade has been seeing an increase of public-private partnerships in drug discovery, mostly driven by factors such as the decline in productivity, the high costs, time, and resources needed, along with the requirements of regulatory agencies. In this context, traditional computer-aided drug discovery techniques have been playing an important role, enabling the identification of new molecular entities at early stages. However, recent advances in chemoinformatics and systems pharmacology, alongside with a growing body of high quality, publicly accessible medicinal chemistry data, have led to the emergence of novel in silico approaches. These novel approaches are able to integrate a vast amount of multiple chemical and biological data into a single modeling equation. The present review analyzes two main kinds of such cutting-edge in silico approaches. In the first subsection, we discuss the updates on multitasking models for quantitative structure-biological effect relationships (mtk- QSBER), whose applications have been significantly increasing in the past years. In the second subsection, we provide detailed information regarding a novel approach that combines perturbation theory with quantitative structure-property relationships modeling tools (pt- QSPR). Finally, and most importantly, we show that the joint use of mtk-QSBER and pt- QSPR modeling tools are apt to guide drug discovery through its multiple stages: from in vitro assays to preclinical studies and clinical trials. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  7. Multi-Step Usage of in Vivo Models During Rational Drug Design and Discovery

    Directory of Open Access Journals (Sweden)

    Charles H. Williams

    2011-04-01

    Full Text Available In this article we propose a systematic development method for rational drug design while reviewing paradigms in industry, emerging techniques and technologies in the field. Although the process of drug development today has been accelerated by emergence of computational methodologies, it is a herculean challenge requiring exorbitant resources; and often fails to yield clinically viable results. The current paradigm of target based drug design is often misguided and tends to yield compounds that have poor absorption, distribution, metabolism, and excretion, toxicology (ADMET properties. Therefore, an in vivo organism based approach allowing for a multidisciplinary inquiry into potent and selective molecules is an excellent place to begin rational drug design. We will review how organisms like the zebrafish and Caenorhabditis elegans can not only be starting points, but can be used at various steps of the drug development process from target identification to pre-clinical trial models. This systems biology based approach paired with the power of computational biology; genetics and developmental biology provide a methodological framework to avoid the pitfalls of traditional target based drug design.

  8. A REVIEW ON OSMOTIC DRUG DELIVERY SYSTEM

    OpenAIRE

    Harnish Patel; Upendra Patel; Hiren Kadikar; Bhavin Bhimani; Dhiren Daslaniya; Ghanshyam Patel

    2012-01-01

    Conventional oral drug delivery systems supply an instantaneous release of drug, which cannot control the release of the drug and effective concentration at the target site. This kind of dosing pattern may result in constantly changing, unpredictable plasma concentrations. Drugs can be delivered in a controlled pattern over a long period of time by the process of osmosis. Osmotic devices are the most promising strategy based systems for controlled drug delivery. They are the most reliable con...

  9. Understanding Self-healing in Service-Discovery Systems

    Science.gov (United States)

    2002-11-01

    service manager (SM) and service user (SU). Figure 1 shows a two-party architecture deployed in a six-component topology: one SM and five SUs. A...sufficient number of SCMs. Upon cessation of aggressive discovery, a component Service Manager Service User Service Cache Manager Aggressive-Discovery... Manager UPnP Multicast Group Unicast Links Figure 2. Three-party service-discovery architecture with five service users (SUs), a service manager (SM), a

  10. Anti-amyloid aggregation activity of novel carotenoids: implications for Alzheimer’s drug discovery

    Directory of Open Access Journals (Sweden)

    Lakey-Beitia J

    2017-05-01

    Full Text Available Johant Lakey-Beitia,1,2 Deborah Doens,2,3 D Jagadeesh Kumar,4 Enrique Murillo,5 Patricia L Fernandez,3 KS Rao,6 Armando A Durant-Archibold1,5 1Center for Biodiversity and Drug Discovery, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP, Panama, Republic of Panama; 2Department of Biotechnology, Acharya Nagarjuna University, Guntur, India; 3Center for Molecular and Cellular Biology of Diseases, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP, Panama, Republic of Panama; 4Department of Biotechnology, Sir M Visvesvaraya Institute of Technology, Bangalore, India; 5Department of Biochemistry, College of Natural, Exact Sciences and Technology, University of Panama, Panama, Republic of Panama; 6Center for Neuroscience, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP, Panama, Republic of Panama Abstract: Alzheimer’s disease (AD is the leading cause of dementia, affecting approximately 33.5 million people worldwide. Aging is the main risk factor associated with AD. Drug discovery based on nutraceutical molecules for prevention and treatment of AD is a growing topic. In this sense, carotenoids are phytochemicals present mainly in fruits and vegetables with reported benefits for human health. In this research, the anti-amyloidogenic activity of three carotenoids, cryptocapsin, cryptocapsin-5,6-epoxide, and zeaxanthin, was assessed. Cryptocapsin showed the highest bioactivity, while cryptocapsin-5,6-epoxide and zeaxanthin exhibited similar activity on anti-aggregation assays. Molecular modeling analysis revealed that the evaluated carotenoids might follow two mechanisms for inhibiting Aβ aggregation: by preventing the formation of the fibril and through disruption of the Aβ aggregates. Our studies provided evidence that cryptocapsin, cryptocapsin-5,6-epoxide, and zeaxanthin have anti-amyloidogenic potential and could be used for

  11. 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.

  12. Crystallographic analysis of TPP riboswitch binding by small-molecule ligands discovered through fragment-based drug discovery approaches.

    Science.gov (United States)

    Warner, Katherine Deigan; Ferré-D'Amaré, Adrian R

    2014-01-01

    Riboswitches are structured mRNA elements that regulate gene expression in response to metabolite or second-messenger binding and are promising targets for drug discovery. Fragment-based drug discovery methods have identified weakly binding small molecule "fragments" that bind a thiamine pyrophosphate (TPP) riboswitch. However, these fragments require substantial chemical elaboration into more potent, drug-like molecules. Structure determination of the fragments bound to the riboswitch is the necessary next step. In this chapter, we describe the methods for co-crystallization and structure determination of fragment-bound TPP riboswitch structures. We focus on considerations for screening crystallization conditions across multiple crystal forms and provide guidance for building the fragment into the refined crystallographic model. These methods are broadly applicable for crystallographic analyses of any small molecules that bind structured RNAs.

  13. Drug Discovery Targeting Serotonin G Protein-Coupled Receptors in the Treatment of Neuropsychiatric Disorders

    Science.gov (United States)

    Felsing, Daniel E.

    Clinical data show that activation of 5-HT2C G protein-coupled receptors (GPCRs) can treat obesity (lorcaserin/BelviqRTM) and psychotic disorders (aripiprazole/Abilify.), including schizophrenia. 5-HT2C GPCRs are members of the 5-HT2 sub-family of 5-HT GPCRs, which include 5-HT2A, 5-HT2B, and 5-HT 2C GPCRs. 5-HT2C is structurally similar to 5-HT2A and 5-HT2B GPCRs, but activation of 5-HT2A and/or 5-HT 2B causes deleterious effects, including hallucinations and cardiac valvulopathy. Thus, there is a challenge to develop drugs that selectively activate only 5-HT2C. Prolonged activation of GPCRs by agonists reduces their function via a regulatory process called desensitization. This has clinical relevance, as 45% of drugs approved by the FDA target GPCRs, and agonist drugs (e.g., morphine) typically lose efficacy over time due to desensitization, which invites tolerance. Agonists that cause less desensitization may show extended clinical efficacy as well as a more acceptable clinical dose range. We hypothesized that structurally distinct agonists of the 5-HT2C receptor may cause varying degrees of desensitization by stabilizing unique 5-HT2C receptor conformations. Discovery of 5-HT2C agonists that exhibit minimal desensitization is therapeutically relevant for the pharmacotherapeutic treatment of chronic diseases such as obesity and psychotic disorders. The 5-HT7 receptor has recently been discovered as a druggable target, and selective activation of the 5-HT7 receptor has been shown to alleviate locomotor deficits in mouse models of Rett Syndrome. Additionally, buspirone has been shown to display therapeutically relevant affinity at 5-HT 1A and is currently in phase II clinical trials to treat stereotypy in children with autism. The 5-PAT chemical scaffold shows high affinity towards the 5-HT7 and 5-HT1A receptors. Modulations around the 5-phenyl moiety were able to improve selectivity in binding towards the 5-HT 7 receptor, whereas modulations of the alkyl chains

  14. Candidiasis and the impact of flow cytometry on antifungal drug discovery.

    Science.gov (United States)

    Ku, Tsun Sheng N; Bernardo, Stella; Walraven, Carla J; Lee, Samuel A

    2017-11-01

    Invasive candidiasis continues to be associated with significant morbidity and mortality as well as substantial health care costs nationally and globally. One of the contributing factors is the development of resistance to antifungal agents that are already in clinical use. Moreover, there are known treatment limitations with all of the available antifungal agents. Since traditional techniques in novel drug discovery are time consuming, high-throughput screening using flow cytometry presents as a potential tool to identify new antifungal agents that would be useful in the management of these patients. Areas covered: In this review, the authors discuss the use of automated high-throughput screening assays based upon flow cytometry to identify potential antifungals from a library comprised of a large number of bioactive compounds. They also review studies that employed the use of this research methodology that has identified compounds with antifungal activity. Expert opinion: High-throughput screening using flow cytometry has substantially decreased the processing time necessary for screening thousands of compounds, and has helped enhance our understanding of fungal pathogenesis. Indeed, the authors see this technology as a powerful tool to help scientists identify new antifungal agents that can be added to the clinician's arsenal in their fight against invasive candidiasis.

  15. An overview of Ca2+mobilization assays in GPCR drug discovery.

    Science.gov (United States)

    Ma, Qiang; Ye, Lingyan; Liu, Hongxia; Shi, Ying; Zhou, Naiming

    2017-05-01

    Calcium ions (Ca 2+ ) serve as a second messenger or universal signal transducer implicated in the regulation of a wide range of physiological processes. A change in the concentration of intracellular Ca 2+ is an important step in intracellular signal transduction. G protein-coupled receptors (GPCRs), the largest and most versatile group of cell surface receptors, transduce extracellular signals into intracellular responses via their coupling to heterotrimeric G proteins. Since Ca 2+ plays a crucial role in GPCR-induced signaling, measurement of intracellular Ca 2+ has attracted more and more attention in GPCR-targeted drug discovery. Areas covered: This review focuses on the most popular functional assays measuring GPCRs-induced intracellular Ca 2+ signaling. These include photoprotein-based, synthetic fluorescent indicator-based and genetically encoded calcium indicator (GECI)-based Ca 2+ mobilization assays. A brief discussion of the design strategy of fluorescent probes in GPCR studies is also presented. Expert opinion: GPCR-mediated intracellular signaling is multidimensional. There is an urgent need for the development of multiple-readout screening assays capable of simultaneous detection of biased signaling and screening of both agonists and antagonists in the same assay. It is also necessary to develop GECIs offering low cost and consistent assays suitable for investigating GPCR activation in vivo.

  16. The European Lead Factory: A Blueprint for Public-Private Partnerships in Early Drug Discovery.

    Science.gov (United States)

    Karawajczyk, Anna; Orrling, Kristina M; de Vlieger, Jon S B; Rijnders, Ton; Tzalis, Dimitrios

    2016-01-01

    The European Lead Factory (ELF) is a public-private partnership (PPP) that provides researchers in Europe with a unique platform for translation of innovative biology and chemistry into high-quality starting points for drug discovery. It combines an exceptional collection of small molecules, high-throughput screening (HTS) infrastructure, and hit follow-up capabilities to advance research projects from both private companies and publicly funded researchers. By active interactions with the wider European life science community, ELF connects and unites bright ideas, talent, and experience from several disciplines. As a result, ELF is a unique, collaborative lead generation engine that has so far resulted in >4,500 hit compounds with a defined biological activity from 83 successfully completed HTS and hit evaluation campaigns. The PPP has also produced more than 120,000 novel innovative library compounds that complement the 327,000 compounds contributed by the participating pharmaceutical companies. Intrinsic to its setup, ELF enables breakthroughs in areas with unmet medical and societal needs, where no individual entity would be able to create a comparable impact in such a short time.

  17. Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

    Science.gov (United States)

    McCall, Laura-Isobel; Sarker, Malabika; Yadav, Maneesh; Ponder, Elizabeth L.; Kallel, E. Adam; Kellar, Danielle; Chen, Steven; Arkin, Michelle; Bunin, Barry A.; McKerrow, James H.; Talcott, Carolyn

    2015-01-01

    Background Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity. Methodology/Principal Findings In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC50 discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs. PMID:26114876

  18. Myxobacterial natural products: An under-valued source of products for drug discovery for neurological disorders.

    Science.gov (United States)

    Dehhaghi, Mona; Mohammadipanah, Fatemeh; Guillemin, Gilles J

    2018-03-02

    Age-related disorders impose noticeable financial and emotional burdens on society. This impact is becoming more prevalent with the increasing incidence of neurodegenerative diseases and is causing critical concerns for treatment of patients worldwide. Parkinson's disease, Alzheimer's disease, multiple sclerosis and motor neuron disease are the most prevalent and the most expensive to treat neurodegenerative diseases globally. Therefore, exploring effective therapies to overcome these disorders is a necessity. Natural products and their derivatives have increasingly attracted attention in drug discovery programs that have identified microorganisms which produce a large range of metabolites with bioactive properties. Myxobacteria, a group of Gram-negative bacteria with large genome size, produce a wide range of secondary metabolites with significant chemical structures and a variety of biological effects. They are potent natural product producers. In this review paper, we attempt to overview some secondary metabolites synthesized by myxobacteria with neuroprotective activity through known mechanisms including production of polyunsaturated fatty acids, reduction of apoptosis, immunomodulation, stress reduction of endoplasmic reticulum, stabilization of microtubules, enzyme inhibition and serotonin receptor modulation. Copyright © 2018 Elsevier B.V. All rights reserved.

  19. The discovery of antidepressant drugs by computer-analyzed human cerebral bio-electrical potentials (CEEG).

    Science.gov (United States)

    Itil, T M

    1983-01-01

    Antidepressant properties of six compounds were predicted based on their computer-analyzed human electroencephalographical (CEEG) profiles. The clinical investigations with mianserin (GB-94) confirmed the CEEG prediction. This compound has now been marketed as the first antidepressant of which the clinical effects were discovered solely by the quantitative pharmaco-EEG method. As predicted by the CEEG, clinical antidepressant properties of GC-46, mesterolone, and estradiol valerate were observed in preliminary investigations. No extensive studies with definite statistical results were yet carried out with these compounds. No systematic large studies could be conducted with cyclozocine and cyproterone acetate because of the intolerable side effects with these compounds. The optical isomers of mianserin, GF-59 and GF-60, both predicted as antidepressant by the computer EEG data base, have not yet been tested in depressive patients. None of these compounds possess the "typical" pharmacological and/or biochemical profiles of marketed antidepressants. Thus, the discovery of the established antidepressant properties of mianserin (GB-94) by computer analyzed EEG method challenges the well-known biochemical hypotheses of depression and the "classical" development of antidepressant drugs.

  20. State of the Art Review and Report of New Tool for Drug Discovery.

    Science.gov (United States)

    Martinez-Lopez, Yoan; Caballero, Yaile; Barigye, Stephen J; Marrero-Ponce, Yovani; Millan-Cabrera, Reisel; Madera, Julio; Torrens, Francisco; Castillo-Garit, Juan A

    2017-01-01

    There are a great number of tools that can be used in QSAR/QSPR studies; they are implemented in several programs that are reviewed in this report. The usefulness of new tools can be proved through comparison, with previously published approaches. In order to perform the comparison, the most usual is the use of several benchmark datasets such as DRAGON and Sutherland's datasets. Here, an exploratory study of Atomic Weighted Vectors (AWVs), a new tool useful for drug discovery using different datasets, is presented. In order to evaluate the performance of the new tool, several statistics and QSAR/QSPR experiments are performed. Variability analyses are used to quantify the information content of the AWVs obtained from the tool, by means of an information theory-based algorithm. Principal components analysis is used to analyze the orthogonality of these descriptors, for which the new MDs from AWVs provide different information from those codified by DRAGON descriptors (0-2D). The QSAR models are obtained for every Sutherland's dataset, according to the original division into training/test sets, by means of the multiple linear regression with genetic algorithm (MLR-GA). These models have been validated and compared favorably to several previously published approaches, using the same benchmark datasets. The obtained results show that this tool should be a useful strategy for the QSAR/QSPR studies, despite its simplicity. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  1. The dengue virus NS5 protein as a target for drug discovery.

    Science.gov (United States)

    Lim, Siew Pheng; Noble, Christian G; Shi, Pei-Yong

    2015-07-01

    The non-structural protein 5 (NS5) of flaviviruses is the most conserved amongst the viral proteins. It is about 900 kDa and bears enzymatic activities that play vital roles in virus replication. Its N-terminal domain encodes dual N7 and 2'-O methyltransferase activities (MTase), and possibly guanylyltransferase (GTase) involved in RNA cap formation. The C-terminal region comprises a RNA-dependent RNA polymerase (RdRp) required for viral RNA synthesis. Both MTase and RdRp activities of dengue virus NS5 are well characterized, structurally and functionally. Numerous crystal structures of the flavivirus MTase and RdRp domains have been solved. Inhibitors of both functions have been identified through screening activities using biochemical and cell-based assays, as well as via rational design approaches. This review summaries the current knowledge as well as prospective views on these aspects. This article forms part of a symposium on flavivirus drug discovery in Antiviral Research. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. Mesua beccariana (Clusiaceae, A Source of Potential Anti-cancer Lead Compounds in Drug Discovery

    Directory of Open Access Journals (Sweden)

    Soek Sin Teh

    2012-09-01

    Full Text Available An investigation on biologically active secondary metabolites from the stem bark of Mesua beccariana was carried out. A new cyclodione, mesuadione (1, along with several known constituents which are beccamarin (2, 2,5-dihydroxy-1,3,4-trimethoxy anthraquinone (3, 4-methoxy-1,3,5-trihydroxyanthraquinone (4, betulinic acid (5 and stigmasterol (6 were obtained from this ongoing research. Structures of these compounds were elucidated by extensive spectroscopic methods, including 1D and 2D-NMR, GC-MS, IR and UV techniques. Preliminary tests of the in vitro cytotoxic activities of all the isolated metabolites against a panel of human cancer cell lines Raji (lymphoma, SNU-1 (gastric carcinoma, K562 (erythroleukemia cells, LS-174T (colorectal adenocarcinoma, HeLa (cervical cells, SK-MEL-28 (malignant melanoma cells, NCI-H23 (lung adenocarcinoma, IMR-32 (neuroblastoma and Hep-G2 (hepatocellular liver carcinoma were carried out using an MTT assay. Mesuadione (1, beccamarin (2, betulinic acid (5 and stigmasterol (6 displayed strong inhibition of Raji cell proliferation, while the proliferation rate of SK-MEL-28 and HeLa were strongly inhibited by stigmasterol (6 and beccamarin (2, indicating these secondary metabolites could be anti-cancer lead compounds in drug discovery.

  3. Emerging Glycolysis Targeting and Drug Discovery from Chinese Medicine in Cancer Therapy

    Directory of Open Access Journals (Sweden)

    Zhiyu Wang

    2012-01-01

    Full Text Available Molecular-targeted therapy has been developed for cancer chemoprevention and treatment. Cancer cells have different metabolic properties from normal cells. Normal cells mostly rely upon the process of mitochondrial oxidative phosphorylation to produce energy whereas cancer cells have developed an altered metabolism that allows them to sustain higher proliferation rates. Cancer cells could predominantly produce energy by glycolysis even in the presence of oxygen. This alternative metabolic characteristic is known as the “Warburg Effect.” Although the exact mechanisms underlying the Warburg effect are unclear, recent progress indicates that glycolytic pathway of cancer cells could be a critical target for drug discovery. With a long history in cancer treatment, traditional Chinese medicine (TCM is recognized as a valuable source for seeking bioactive anticancer compounds. A great progress has been made to identify active compounds from herbal medicine targeting on glycolysis for cancer treatment. Herein, we provide an overall picture of the current understanding of the molecular targets in the cancer glycolytic pathway and reviewed active compounds from Chinese herbal medicine with the potentials to inhibit the metabolic targets for cancer treatment. Combination of TCM with conventional therapies will provide an attractive strategy for improving clinical outcome in cancer treatment.

  4. Discovery of Peptidomimetic Antibody-Drug Conjugate Linkers with Enhanced Protease Specificity.

    Science.gov (United States)

    Wei, BinQing; Gunzner-Toste, Janet; Yao, Hui; Wang, Tao; Wang, Jing; Xu, Zijin; Chen, Jinhua; Wai, John; Nonomiya, Jim; Tsai, Siao Ping; Chuh, Josefa; Kozak, Katherine R; Liu, Yichin; Yu, Shang-Fan; Lau, Jeff; Li, Guangmin; Phillips, Gail D; Leipold, Doug; Kamath, Amrita; Su, Dian; Xu, Keyang; Eigenbrot, Charles; Steinbacher, Stefan; Ohri, Rachana; Raab, Helga; Staben, Leanna R; Zhao, Guiling; Flygare, John A; Pillow, Thomas H; Verma, Vishal; Masterson, Luke A; Howard, Philip W; Safina, Brian

    2018-02-08

    Antibody-drug conjugates (ADCs) have become an important therapeutic modality for oncology, with three approved by the FDA and over 60 others in clinical trials. Despite the progress, improvements in ADC therapeutic index are desired. Peptide-based ADC linkers that are cleaved by lysosomal proteases have shown sufficient stability in serum and effective payload-release in targeted cells. If the linker can be preferentially hydrolyzed by tumor-specific proteases, safety margin may improve. However, the use of peptide-based linkers limits our ability to modulate protease specificity. Here we report the structure-guided discovery of novel, nonpeptidic ADC linkers. We show that a cyclobutane-1,1-dicarboxamide-containing linker is hydrolyzed predominantly by cathepsin B while the valine-citrulline dipeptide linker is not. ADCs bearing the nonpeptidic linker are as efficacious and stable in vivo as those with the dipeptide linker. Our results strongly support the application of the peptidomimetic linker and present new opportunities for improving the selectivity of ADCs.

  5. 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.

  6. Dystrophin-deficient cardiomyocytes derived from human urine: New biologic reagents for drug discovery

    Directory of Open Access Journals (Sweden)

    Xuan Guan

    2014-03-01

    Full Text Available The ability to extract somatic cells from a patient and reprogram them to pluripotency opens up new possibilities for personalized medicine. Induced pluripotent stem cells (iPSCs have been employed to generate beating cardiomyocytes from a patient's skin or blood cells. Here, iPSC methods were used to generate cardiomyocytes starting from the urine of a patient with Duchenne muscular dystrophy (DMD. Urine was chosen as a starting material because it contains adult stem cells called urine-derived stem cells (USCs. USCs express the canonical reprogramming factors c-myc and klf4, and possess high telomerase activity. Pluripotency of urine-derived iPSC clones was confirmed by immunocytochemistry, RT-PCR and teratoma formation. Urine-derived iPSC clones generated from healthy volunteers and a DMD patient were differentiated into beating cardiomyocytes using a series of small molecules in monolayer culture. Results indicate that cardiomyocytes retain the DMD patient's dystrophin mutation. Physiological assays suggest that dystrophin-deficient cardiomyocytes possess phenotypic differences from normal cardiomyocytes. These results demonstrate the feasibility of generating cardiomyocytes from a urine sample and that urine-derived cardiomyocytes retain characteristic features that might be further exploited for mechanistic studies and drug discovery.

  7. Muscular dystrophy in a dish: engineered human skeletal muscle mimetics for disease modeling and drug discovery

    Science.gov (United States)

    Smith, Alec S.T.; Davis, Jennifer; Lee, Gabsang; Mack, David L.

    2016-01-01

    Engineered in vitro models using human cells, particularly patient-derived induced pluripotent stem cells (iPSCs), offer a potential solution to issues associated with the use of animals for studying disease pathology and drug efficacy. Given the prevalence of muscle diseases in human populations, an engineered tissue model of human skeletal muscle could provide a biologically accurate platform to study basic muscle physiology, disease progression, and drug efficacy and/or toxicity. Such platforms could be used as phenotypic drug screens to identify compounds capable of alleviating or reversing congenital myopathies, such as Duchene muscular dystrophy (DMD). Here, we review current skeletal muscle modeling technologies with a specific focus on efforts to generate biomimetic systems for investigating the pathophysiology of dystrophic muscle. PMID:27109386

  8. Systems and methods for knowledge discovery in spatial data

    Science.gov (United States)

    Obradovic, Zoran; Fiez, Timothy E.; Vucetic, Slobodan; Lazarevic, Aleksandar; Pokrajac, Dragoljub; Hoskinson, Reed L.

    2005-03-08

    Systems and methods are provided for knowledge discovery in spatial data as well as to systems and methods for optimizing recipes used in spatial environments such as may be found in precision agriculture. A spatial data analysis and modeling module is provided which allows users to interactively and flexibly analyze and mine spatial data. The spatial data analysis and modeling module applies spatial data mining algorithms through a number of steps. The data loading and generation module obtains or generates spatial data and allows for basic partitioning. The inspection module provides basic statistical analysis. The preprocessing module smoothes and cleans the data and allows for basic manipulation of the data. The partitioning module provides for more advanced data partitioning. The prediction module applies regression and classification algorithms on the spatial data. The integration module enhances prediction methods by combining and integrating models. The recommendation module provides the user with site-specific recommendations as to how to optimize a recipe for a spatial environment such as a fertilizer recipe for an agricultural field.

  9. Induced pluripotent stem cells: applications in regenerative medicine, disease modeling, and drug discovery

    Science.gov (United States)

    Singh, Vimal K.; Kalsan, Manisha; Kumar, Neeraj; Saini, Abhishek; Chandra, Ramesh

    2015-01-01

    such as animal models. Many toxic compounds (different chemical compounds, pharmaceutical drugs, other hazardous chemicals, or environmental conditions) which are encountered by humans and newly designed drugs may be evaluated for toxicity and effects by using iPSCs. Thus, the applications of iPSCs in regenerative medicine, disease modeling, and drug discovery are enormous and should be explored in a more comprehensive manner. PMID:25699255

  10. 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.

    Fungal plasma membrane H+-ATPase (Pma1) has recently emerged as a potential target for the discovery of new antifungal agents. This p-type pump which localized on the surface of fungal cells plays a crucial role in many physiol. functions and processes inside the cell. Esp., by pumping proton......-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...

  11. A method for systematic discovery of adverse drug events from clinical notes.

    Science.gov (United States)

    Wang, Guan; Jung, Kenneth; Winnenburg, Rainer; Shah, Nigam H

    2015-11-01

    Adverse drug events (ADEs) are undesired harmful effects resulting from use of a medication, and occur in 30% of hospitalized patients. The authors have developed a data-mining method for systematic, automated detection of ADEs from electronic medical records. This method uses the text from 9.5 million clinical notes, along with prior knowledge of drug usages and known ADEs, as inputs. These inputs are further processed into statistics used by a discriminative classifier which outputs the probability that a given drug-disorder pair represents a valid ADE association. Putative ADEs identified by the classifier are further filtered for positive support in 2 independent, complementary data sources. The authors evaluate this method by assessing support for the predictions in other curated data sources, including a manually curated, time-indexed reference standard of label change events. This method uses a classifier that achieves an area under the curve of 0.94 on a held out test set. The classifier is used on 2,362,950 possible drug-disorder pairs comprised of 1602 unique drugs and 1475 unique disorders for which we had data, resulting in 240 high-confidence, well-supported drug-AE associations. Eighty-seven of them (36%) are supported in at least one of the resources that have information that was not available to the classifier. This method demonstrates the feasibility of systematic post-marketing surveillance for ADEs using electronic medical records, a key component of the learning healthcare system. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  12. Collagen macromolecular drug delivery systems

    International Nuclear Information System (INIS)

    Gilbert, D.L.

    1988-01-01

    The objective of this study was to examine collagen for use as a macromolecular drug delivery system by determining the mechanism of release through a matrix. Collagen membranes varying in porosity, crosslinking density, structure and crosslinker were fabricated. Collagen characterized by infrared spectroscopy and solution viscosity was determined to be pure and native. The collagen membranes were determined to possess native vs. non-native quaternary structure and porous vs. dense aggregate membranes by electron microscopy. Collagen monolithic devices containing a model macromolecule (inulin) were fabricated. In vitro release rates were found to be linear with respect to t 1/2 and were affected by crosslinking density, crosslinker and structure. The biodegradation of the collagen matrix was also examined. In vivo biocompatibility, degradation and 14 C-inulin release rates were evaluated subcutaneously in rats

  13. Navigating the Future of Cardiovascular Drug Development-Leveraging Novel Approaches to Drive Innovation and Drug Discovery: Summary of Findings from the Novel Cardiovascular Therapeutics Conference.

    Science.gov (United States)

    Povsic, Thomas J; Scott, Rob; Mahaffey, Kenneth W; Blaustein, Robert; Edelberg, Jay M; Lefkowitz, Martin P; Solomon, Scott D; Fox, Jonathan C; Healy, Kevin E; Khakoo, Aarif Y; Losordo, Douglas W; Malik, Fady I; Monia, Brett P; Montgomery, Rusty L; Riesmeyer, Jeffrey; Schwartz, Gregory G; Zelenkofske, Steven L; Wu, Joseph C; Wasserman, Scott M; Roe, Matthew T

    2017-08-01

    The need for novel approaches to cardiovascular drug development served as the impetus to convene an open meeting of experts from the pharmaceutical industry and academia to assess the challenges and develop solutions for drug discovery in cardiovascular disease. The Novel Cardiovascular Therapeutics Summit first reviewed recent examples of ongoing or recently completed programs translating basic science observations to targeted drug development, highlighting successes (protein convertase sutilisin/kexin type 9 [PCSK9] and neprilysin inhibition) and targets still under evaluation (cholesteryl ester transfer protein [CETP] inhibition), with the hope of gleaning key lessons to successful drug development in the current era. Participants then reviewed the use of innovative approaches being explored to facilitate rapid and more cost-efficient evaluations of drug candidates in a short timeframe. We summarize observations gleaned from this summit and offer insight into future cardiovascular drug development. The rapid development in genetic and high-throughput drug evaluation technologies, coupled with new approaches to rapidly evaluate potential cardiovascular therapies with in vitro techniques, offer opportunities to identify new drug targets for cardiovascular disease, study new therapies with better efficiency and higher throughput in the preclinical setting, and more rapidly bring the most promising therapies to human testing. However, there must be a critical interface between industry and academia to guide the future of cardiovascular drug development. The shared interest among academic institutions and pharmaceutical companies in developing promising therapies to address unmet clinical needs for patients with cardiovascular disease underlies and guides innovation and discovery platforms that are significantly altering the landscape of cardiovascular drug development.

  14. 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.

  15. Applications of Dynamic Clamp to Cardiac Arrhythmia Research: Role in Drug Target Discovery and Safety Pharmacology Testing.

    Science.gov (United States)

    Ortega, Francis A; Grandi, Eleonora; Krogh-Madsen, Trine; Christini, David J

    2017-01-01

    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.

  16. Systems Biology Modeling of the Radiation Sensitivity Network: A Biomarker Discovery Platform

    International Nuclear Information System (INIS)

    Eschrich, Steven; Zhang Hongling; Zhao Haiyan; Boulware, David; Lee, Ji-Hyun; Bloom, Gregory; Torres-Roca, Javier F.

    2009-01-01

    Purpose: The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying radiation-specific biomarkers. Methods and Materials: Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt). Results: The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers. Conclusion: We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.

  17. Kidney–targeted drug delivery systems

    Directory of Open Access Journals (Sweden)

    Peng Zhou

    2014-02-01

    Full Text Available Kidney-targeted drug delivery systems represent a promising technology to improve drug efficacy and safety in the treatment of renal diseases. In this review, we summarize the strategies that have been employed to develop kidney-targeted drug delivery systems. We also describe how macromolecular carriers and prodrugs play crucial roles in targeting drugs to particular target cells in the kidney. New technologies render it possible to create renal targeting conjugates and other delivery systems including nanoparticles and liposomes present promising strategies to achieve the goal of targeting drugs to the kidney.

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

    OpenAIRE

    Differding, Edmond

    2017-01-01

    Abstract 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 ac...

  19. Recent advances in Entamoeba biology: RNA interference, drug discovery, and gut microbiome [version 1; referees: 4 approved

    Directory of Open Access Journals (Sweden)

    Pedro Morgado

    2016-10-01

    Full Text Available In recent years, substantial progress has been made in understanding the molecular and cell biology of the human parasite Entamoeba histolytica, an important pathogen with significant global impact. This review outlines some recent advances in the Entamoeba field in the last five years, focusing on areas that have not recently been discussed in detail: (i molecular mechanisms regulating parasite gene expression, (ii new efforts at drug discovery using high-throughput drug screens, and (iii the effect of gut microbiota on amoebiasis.

  20. Discovery and therapeutic potential of drugs that shift energy metabolism from mitochondrial respiration to glycolysis

    Science.gov (United States)

    Gohil, Vishal M.; Sheth, Sunil A.; Nilsson, Roland; Wojtovich, Andrew P.; Lee, Jeong Hyun; Perocchi, Fabiana; Chen, William; Clish, Clary B.; Ayata, Cenk; Brookes, Paul S.; Mootha, Vamsi K.

    2010-01-01

    Most cells can dynamically shift their relative reliance on glycolytic versus oxidative metabolism in response to nutrient availability, during development, and in disease. Studies in model systems have shown that re-directing energy metabolism from respiration to glycolysis can suppress oxidative damage and cell death in ischemic injury. At present we have a limited set of drugs that safely toggle energy metabolism in humans. Here, we introduce a quantitative, nutrient sensitized screening strategy that can identify such compounds based on their ability to selectively impair growth and viability of cells grown in galactose versus glucose. We identify several FDA approved agents never before linked to energy metabolism, including meclizine, which blunts cellular respiration via a mechanism distinct from canonical inhibitors. We further show that meclizine pretreatment confers cardioprotection and neuroprotection against ischemia-reperfusion injury in murine models. Nutrient-sensitized screening may offer a useful framework for understanding gene function and drug action within the context of energy metabolism. PMID:20160716