Sample records for computational drug discovery

  1. Computational drug discovery

    PubMed Central

    Ou-Yang, Si-sheng; Lu, Jun-yan; Kong, Xiang-qian; Liang, Zhong-jie; Luo, Cheng; Jiang, Hualiang

    2012-01-01

    Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field. PMID:22922346

  2. Computer-aided drug discovery.

    PubMed

    Bajorath, Jürgen

    2015-01-01

    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.

  3. Computational methods in drug discovery

    PubMed Central

    Leelananda, Sumudu P

    2016-01-01

    The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery 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. PMID:28144341

  4. Computational methods in drug discovery.

    PubMed

    Leelananda, Sumudu P; Lindert, Steffen

    2016-01-01

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

  5. Computational Methods in Drug Discovery

    PubMed Central

    Sliwoski, Gregory; Kothiwale, Sandeepkumar; Meiler, Jens

    2014-01-01

    Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature. PMID:24381236

  6. Impact of computational structure-based methods on drug discovery.

    PubMed

    Reynolds, Charles H

    2014-01-01

    Structure-based drug design has become an indispensible tool in drug discovery. The emergence of structure-based design is due to gains in structural biology that have provided exponential growth in the number of protein crystal structures, new computational algorithms and approaches for modeling protein-ligand interactions, and the tremendous growth of raw computer power in the last 30 years. Computer modeling and simulation have made major contributions to the discovery of many groundbreaking drugs in recent years. Examples are presented that highlight the evolution of computational structure-based design methodology, and the impact of that methodology on drug discovery.

  7. Computational neuropharmacology: dynamical approaches in drug discovery.

    PubMed

    Aradi, Ildiko; Erdi, Péter

    2006-05-01

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

  8. Computational modeling in melanoma for novel drug discovery.

    PubMed

    Pennisi, Marzio; Russo, Giulia; Di Salvatore, Valentina; Candido, Saverio; Libra, Massimo; Pappalardo, Francesco

    2016-06-01

    There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches. This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials. Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.

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

    PubMed Central

    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

  10. Computational approaches for drug discovery.

    PubMed

    Hung, Che-Lun; Chen, Chi-Chun

    2014-09-01

    Cellular proteins are the mediators of multiple organism functions being involved in physiological mechanisms and disease. By discovering lead compounds that affect the function of target proteins, the target diseases or physiological mechanisms can be modulated. Based on knowledge of the ligand-receptor interaction, the chemical structures of leads can be modified to improve efficacy, selectivity and reduce side effects. One rational drug design technology, which enables drug discovery based on knowledge of target structures, functional properties and mechanisms, is computer-aided drug design (CADD). The application of CADD can be cost-effective using experiments to compare predicted and actual drug activity, the results from which can used iteratively to improve compound properties. The two major CADD-based approaches are structure-based drug design, where protein structures are required, and ligand-based drug design, where ligand and ligand activities can be used to design compounds interacting with the protein structure. Approaches in structure-based drug design include docking, de novo design, fragment-based drug discovery and structure-based pharmacophore modeling. Approaches in ligand-based drug design include quantitative structure-affinity relationship and pharmacophore modeling based on ligand properties. Based on whether the structure of the receptor and its interaction with the ligand are known, different design strategies can be seed. After lead compounds are generated, the rule of five can be used to assess whether these have drug-like properties. Several quality validation methods, such as cost function analysis, Fisher's cross-validation analysis and goodness of hit test, can be used to estimate the metrics of different drug design strategies. To further improve CADD performance, multi-computers and graphics processing units may be applied to reduce costs. © 2014 Wiley Periodicals, Inc.

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

    PubMed

    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.

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

    PubMed

    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.

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    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.

  15. Computer-Aided Drug Discovery: Molecular Docking of Diminazene Ligands to DNA Minor Groove

    ERIC Educational Resources Information Center

    Kholod, Yana; Hoag, Erin; Muratore, Katlynn; Kosenkov, Dmytro

    2018-01-01

    The reported project-based laboratory unit introduces upper-division undergraduate students to the basics of computer-aided drug discovery as a part of a computational chemistry laboratory course. The students learn to perform model binding of organic molecules (ligands) to the DNA minor groove with computer-aided drug discovery (CADD) tools. The…

  16. COMPUTER-AIDED DRUG DISCOVERY AND DEVELOPMENT (CADDD): in silico-chemico-biological approach

    PubMed Central

    Kapetanovic, I.M.

    2008-01-01

    It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3-D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure-activity and quantitative structure-property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve. PMID:17229415

  17. Successful applications of computer aided drug discovery: moving drugs from concept to the clinic.

    PubMed

    Talele, Tanaji T; Khedkar, Santosh A; Rigby, Alan C

    2010-01-01

    Drug discovery and development is an interdisciplinary, expensive and time-consuming process. Scientific advancements during the past two decades have changed the way pharmaceutical research generate novel bioactive molecules. Advances in computational techniques and in parallel hardware support have enabled in silico methods, and in particular structure-based drug design method, to speed up new target selection through the identification of hits to the optimization of lead compounds in the drug discovery process. This review is focused on the clinical status of experimental drugs that were discovered and/or optimized using computer-aided drug design. We have provided a historical account detailing the development of 12 small molecules (Captopril, Dorzolamide, Saquinavir, Zanamivir, Oseltamivir, Aliskiren, Boceprevir, Nolatrexed, TMI-005, LY-517717, Rupintrivir and NVP-AUY922) that are in clinical trial or have become approved for therapeutic use.

  18. Teach-Discover-Treat (TDT): Collaborative Computational Drug Discovery for Neglected Diseases

    PubMed Central

    Jansen, Johanna M.; Cornell, Wendy; Tseng, Y. Jane; Amaro, Rommie E.

    2012-01-01

    Teach – Discover – Treat (TDT) is an initiative to promote the development and sharing of computational tools solicited through a competition with the aim to impact education and collaborative drug discovery for neglected diseases. Collaboration, multidisciplinary integration, and innovation are essential for successful drug discovery. This requires a workforce that is trained in state-of-the-art workflows and equipped with the ability to collaborate on platforms that are accessible and free. The TDT competition solicits high quality computational workflows for neglected disease targets, using freely available, open access tools. PMID:23085175

  19. Enabling drug discovery project decisions with integrated computational chemistry and informatics

    NASA Astrophysics Data System (ADS)

    Tsui, Vickie; Ortwine, Daniel F.; Blaney, Jeffrey M.

    2017-03-01

    Computational chemistry/informatics scientists and software engineers in Genentech Small Molecule Drug Discovery collaborate with experimental scientists in a therapeutic project-centric environment. Our mission is to enable and improve pre-clinical drug discovery design and decisions. Our goal is to deliver timely data, analysis, and modeling to our therapeutic project teams using best-in-class software tools. We describe our strategy, the organization of our group, and our approaches to reach this goal. We conclude with a summary of the interdisciplinary skills required for computational scientists and recommendations for their training.

  20. Computational Tools for Allosteric Drug Discovery: Site Identification and Focus Library Design.

    PubMed

    Huang, Wenkang; Nussinov, Ruth; Zhang, Jian

    2017-01-01

    Allostery is an intrinsic phenomenon of biological macromolecules involving regulation and/or signal transduction induced by a ligand binding to an allosteric site distinct from a molecule's active site. Allosteric drugs are currently receiving increased attention in drug discovery because drugs that target allosteric sites can provide important advantages over the corresponding orthosteric drugs including specific subtype selectivity within receptor families. Consequently, targeting allosteric sites, instead of orthosteric sites, can reduce drug-related side effects and toxicity. On the down side, allosteric drug discovery can be more challenging than traditional orthosteric drug discovery due to difficulties associated with determining the locations of allosteric sites and designing drugs based on these sites and the need for the allosteric effects to propagate through the structure, reach the ligand binding site and elicit a conformational change. In this study, we present computational tools ranging from the identification of potential allosteric sites to the design of "allosteric-like" modulator libraries. These tools may be particularly useful for allosteric drug discovery.

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

    PubMed

    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.

  2. Virtual drug discovery: beyond computational chemistry?

    PubMed

    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.

  3. The importance of employing computational resources for the automation of drug discovery.

    PubMed

    Rosales-Hernández, Martha Cecilia; Correa-Basurto, José

    2015-03-01

    The application of computational tools to drug discovery helps researchers to design and evaluate new drugs swiftly with a reduce economic resources. To discover new potential drugs, computational chemistry incorporates automatization for obtaining biological data such as adsorption, distribution, metabolism, excretion and toxicity (ADMET), as well as drug mechanisms of action. This editorial looks at examples of these computational tools, including docking, molecular dynamics simulation, virtual screening, quantum chemistry, quantitative structural activity relationship, principal component analysis and drug screening workflow systems. The authors then provide their perspectives on the importance of these techniques for drug discovery. Computational tools help researchers to design and discover new drugs for the treatment of several human diseases without side effects, thus allowing for the evaluation of millions of compounds with a reduced cost in both time and economic resources. The problem is that operating each program is difficult; one is required to use several programs and understand each of the properties being tested. In the future, it is possible that a single computer and software program will be capable of evaluating the complete properties (mechanisms of action and ADMET properties) of ligands. It is also possible that after submitting one target, this computer-software will be capable of suggesting potential compounds along with ways to synthesize them, and presenting biological models for testing.

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

    PubMed

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

    2018-05-25

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

  5. Computational functional genomics-based approaches in analgesic drug discovery and repurposing.

    PubMed

    Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred; Lötsch, Jörn

    2018-06-01

    Persistent pain is a major healthcare problem affecting a fifth of adults worldwide with still limited treatment options. The search for new analgesics increasingly includes the novel research area of functional genomics, which combines data derived from various processes related to DNA sequence, gene expression or protein function and uses advanced methods of data mining and knowledge discovery with the goal of understanding the relationship between the genome and the phenotype. Its use in drug discovery and repurposing for analgesic indications has so far been performed using knowledge discovery in gene function and drug target-related databases; next-generation sequencing; and functional proteomics-based approaches. Here, we discuss recent efforts in functional genomics-based approaches to analgesic drug discovery and repurposing and highlight the potential of computational functional genomics in this field including a demonstration of the workflow using a novel R library 'dbtORA'.

  6. Computationally driven drug discovery meeting-3 - Verona (Italy): 4 - 6th of March 2014.

    PubMed

    Costantino, Gabriele

    2014-12-01

    The following article reports on the results and the outcome of a meeting organised at the Aptuit Auditorium in Verona (Italy), which highlighted the current applications of state-of-the-art computational science to drug design in Italy. The meeting, which had > 100 people in attendance, consisted of over 40 presentations and included keynote lectures given by world-renowned speakers. The topics included in the meeting are areas related to ligand and structure-based ligand design and library design and screening; it also provided discussion pertaining to chemometrics. The meeting also stressed the importance of public-private collaboration and reviewed the different approaches to computationally driven drug discovery taken within academia and industry. The meeting helped define the current position of state-of-the-art computational drug discovery in Italy, pointing out criticalities and assets. This kind of focused meeting is important in the sense that it lends the opportunity of a restricted yet representative community of fellow professionals to deeply discuss the current methodological approaches and provide future perspectives for computationally driven drug discovery.

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

    PubMed

    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).

  8. Applied metabolomics in drug discovery.

    PubMed

    Cuperlovic-Culf, M; Culf, A S

    2016-08-01

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

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

    PubMed

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

    2017-11-27

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

  10. A unified approach to computational drug discovery.

    PubMed

    Tseng, Chih-Yuan; Tuszynski, Jack

    2015-11-01

    It has been reported that a slowdown in the development of new medical therapies is affecting clinical outcomes. The FDA has thus initiated the Critical Path Initiative project investigating better approaches. We review the current strategies in drug discovery and focus on the advantages of the maximum entropy method being introduced in this area. The maximum entropy principle is derived from statistical thermodynamics and has been demonstrated to be an inductive inference tool. We propose a unified method to drug discovery that hinges on robust information processing using entropic inductive inference. Increasingly, applications of maximum entropy in drug discovery employ this unified approach and demonstrate the usefulness of the concept in the area of pharmaceutical sciences. Copyright © 2015. Published by Elsevier Ltd.

  11. Network-based drug discovery by integrating systems biology and computational technologies

    PubMed Central

    Leung, Elaine L.; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua

    2013-01-01

    Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network. However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple ‘-omics’ databases. The newly developed algorithm- or network-based computational models can tightly integrate ‘-omics’ databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various ‘-omics’ platforms and computational tools would accelerate development of network-based drug discovery and network medicine. PMID:22877768

  12. Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

    PubMed Central

    Ekins, Sean; Freundlich, Joel S.; Choi, Inhee; Sarker, Malabika; Talcott, Carolyn

    2010-01-01

    We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage this data in order to move from a hit to a lead to a clinical candidate and potentially a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are herein reviewed. We suggest these computational approaches should be more optimally integrated in a workflow with experimental approaches to accelerate TB drug discovery. PMID:21129975

  13. Bioinformatics in translational drug discovery.

    PubMed

    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).

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

    PubMed

    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.

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

    PubMed

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

    2011-01-01

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

  16. Designer drugs: the evolving science of drug discovery.

    PubMed

    Wanke, L A; DuBose, R F

    1998-07-01

    Drug discovery and design are fundamental to drug development. Until recently, most drugs were discovered through random screening or developed through molecular modification. New technologies are revolutionizing this phase of drug development. Rational drug design, using powerful computers and computational chemistry and employing X-ray crystallography, nuclear magnetic resonance spectroscopy, and three-dimensional quantitative structure activity relationship analysis, is creating highly specific, biologically active molecules by virtual reality modeling. Sophisticated screening technologies are eliminating all but the most active lead compounds. These new technologies promise more efficacious, safe, and cost-effective medications, while minimizing drug development time and maximizing profits.

  17. Genomics and transcriptomics in drug discovery.

    PubMed

    Dopazo, Joaquin

    2014-02-01

    The popularization of genomic high-throughput technologies is causing a revolution in biomedical research and, particularly, is transforming the field of drug discovery. Systems biology offers a framework to understand the extensive human genetic heterogeneity revealed by genomic sequencing in the context of the network of functional, regulatory and physical protein-drug interactions. Thus, approaches to find biomarkers and therapeutic targets will have to take into account the complex system nature of the relationships of the proteins with the disease. Pharmaceutical companies will have to reorient their drug discovery strategies considering the human genetic heterogeneity. Consequently, modeling and computational data analysis will have an increasingly important role in drug discovery. Copyright © 2013 Elsevier Ltd. All rights reserved.

  18. Modern drug discovery technologies: opportunities and challenges in lead discovery.

    PubMed

    Guido, Rafael V C; Oliva, Glaucius; Andricopulo, Adriano D

    2011-12-01

    The identification of promising hits and the generation of high quality leads are crucial steps in the early stages of drug discovery projects. The definition and assessment of both chemical and biological space have revitalized the screening process model and emphasized the importance of exploring the intrinsic complementary nature of classical and modern methods in drug research. In this context, the widespread use of combinatorial chemistry and sophisticated screening methods for the discovery of lead compounds has created a large demand for small organic molecules that act on specific drug targets. Modern drug discovery involves the employment of a wide variety of technologies and expertise in multidisciplinary research teams. The synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds emphasize the importance of the integration of advanced technologies in drug discovery programs. These technologies (VS, HTS, SBDD, LBDD, QSAR, and so on) are complementary in the sense that they have mutual goals, thereby the combination of both empirical and in silico efforts is feasible at many different levels of lead optimization and new chemical entity (NCE) discovery. This paper provides a brief perspective on the evolution and use of key drug design technologies, highlighting opportunities and challenges.

  19. Computer-Aided Drug Design in Epigenetics

    NASA Astrophysics Data System (ADS)

    Lu, Wenchao; Zhang, Rukang; Jiang, Hao; Zhang, Huimin; Luo, Cheng

    2018-03-01

    Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field.

  20. Computer-Aided Drug Design in Epigenetics

    PubMed Central

    Lu, Wenchao; Zhang, Rukang; Jiang, Hao; Zhang, Huimin; Luo, Cheng

    2018-01-01

    Epigenetic dysfunction has been widely implicated in several diseases especially cancers thus highlights the therapeutic potential for chemical interventions in this field. With rapid development of computational methodologies and high-performance computational resources, computer-aided drug design has emerged as a promising strategy to speed up epigenetic drug discovery. Herein, we make a brief overview of major computational methods reported in the literature including druggability prediction, virtual screening, homology modeling, scaffold hopping, pharmacophore modeling, molecular dynamics simulations, quantum chemistry calculation, and 3D quantitative structure activity relationship that have been successfully applied in the design and discovery of epi-drugs and epi-probes. Finally, we discuss about major limitations of current virtual drug design strategies in epigenetics drug discovery and future directions in this field. PMID:29594101

  1. Computational modeling approaches to quantitative structure-binding kinetics relationships in drug discovery.

    PubMed

    De Benedetti, Pier G; Fanelli, Francesca

    2018-03-21

    Simple comparative correlation analyses and quantitative structure-kinetics relationship (QSKR) models highlight the interplay of kinetic rates and binding affinity as an essential feature in drug design and discovery. The choice of the molecular series, and their structural variations, used in QSKR modeling is fundamental to understanding the mechanistic implications of ligand and/or drug-target binding and/or unbinding processes. Here, we discuss the implications of linear correlations between kinetic rates and binding affinity constants and the relevance of the computational approaches to QSKR modeling. Copyright © 2018 Elsevier Ltd. All rights reserved.

  2. Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis.

    PubMed

    Njogu, Peter M; Guantai, Eric M; Pavadai, Elumalai; Chibale, Kelly

    2016-01-08

    Despite the tremendous improvement in overall global health heralded by the adoption of the Millennium Declaration in the year 2000, tropical infections remain a major health problem in the developing world. Recent estimates indicate that the major tropical infectious diseases, namely, malaria, tuberculosis, trypanosomiasis, and leishmaniasis, account for more than 2.2 million deaths and a loss of approximately 85 million disability-adjusted life years annually. The crucial role of chemotherapy in curtailing the deleterious health and economic impacts of these infections has invigorated the search for new drugs against tropical infectious diseases. The research efforts have involved increased application of computational technologies in mainstream drug discovery programs at the hit identification, hit-to-lead, and lead optimization stages. This review highlights various computer-aided drug discovery approaches that have been utilized in efforts to identify novel antimalarial, antitubercular, antitrypanosomal, and antileishmanial agents. The focus is largely on developments over the past 5 years (2010-2014).

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

    PubMed

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

    2018-01-01

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

  4. Active-learning strategies in computer-assisted drug discovery.

    PubMed

    Reker, Daniel; Schneider, Gisbert

    2015-04-01

    High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the selection process by focusing on areas of chemical space that have the greatest chance of success while considering structural novelty. The core feature of these algorithms is their ability to adapt the structure-activity landscapes through feedback. Instead of full-deck screening, only focused subsets of compounds are tested, and the experimental readout is used to refine molecule selection for subsequent screening cycles. Once implemented, these techniques have the potential to reduce costs and save precious materials. Here, we provide a comprehensive overview of the various computational active-learning approaches and outline their potential for drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

    2016-01-01

    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. PMID:27074285

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

    PubMed

    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.

  8. Advancing Drug Discovery through Enhanced Free Energy Calculations.

    PubMed

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

    2017-07-18

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

  9. [Artificial Intelligence in Drug Discovery].

    PubMed

    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.

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

    PubMed

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

    2017-03-01

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

  11. Molecular dynamics-driven drug discovery: leaping forward with confidence.

    PubMed

    Ganesan, Aravindhan; Coote, Michelle L; Barakat, Khaled

    2017-02-01

    Given the significant time and financial costs of developing a commercial drug, it remains important to constantly reform the drug discovery pipeline with novel technologies that can narrow the candidates down to the most promising lead compounds for clinical testing. The past decade has witnessed tremendous growth in computational capabilities that enable in silico approaches to expedite drug discovery processes. Molecular dynamics (MD) has become a particularly important tool in drug design and discovery. From classical MD methods to more sophisticated hybrid classical/quantum mechanical (QM) approaches, MD simulations are now able to offer extraordinary insights into ligand-receptor interactions. In this review, we discuss how the applications of MD approaches are significantly transforming current drug discovery and development efforts. Copyright © 2016 Elsevier Ltd. All rights reserved.

  12. Modern approaches to accelerate discovery of new antischistosomal drugs.

    PubMed

    Neves, Bruno Junior; Muratov, Eugene; Machado, Renato Beilner; Andrade, Carolina Horta; Cravo, Pedro Vitor Lemos

    2016-06-01

    The almost exclusive use of only praziquantel for the treatment of schistosomiasis has raised concerns about the possible emergence of drug-resistant schistosomes. Consequently, there is an urgent need for new antischistosomal drugs. The identification of leads and the generation of high quality data are crucial steps in the early stages of schistosome drug discovery projects. Herein, the authors focus on the current developments in antischistosomal lead discovery, specifically referring to the use of automated in vitro target-based and whole-organism screens and virtual screening of chemical databases. They highlight the strengths and pitfalls of each of the above-mentioned approaches, and suggest possible roadmaps towards the integration of several strategies, which may contribute for optimizing research outputs and led to more successful and cost-effective drug discovery endeavors. Increasing partnerships and access to funding for drug discovery have strengthened the battle against schistosomiasis in recent years. However, the authors believe this battle also includes innovative strategies to overcome scientific challenges. In this context, significant advances of in vitro screening as well as computer-aided drug discovery have contributed to increase the success rate and reduce the costs of drug discovery campaigns. Although some of these approaches were already used in current antischistosomal lead discovery pipelines, the integration of these strategies in a solid workflow should allow the production of new treatments for schistosomiasis in the near future.

  13. Novel opportunities for computational biology and sociology in drug discovery☆

    PubMed Central

    Yao, Lixia; Evans, James A.; Rzhetsky, Andrey

    2013-01-01

    Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved in pharmaceutical development, 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 links 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:20349528

  14. Discovery and Development of ATP-Competitive mTOR Inhibitors Using Computational Approaches.

    PubMed

    Luo, Yao; Wang, Ling

    2017-11-16

    The mammalian target of rapamycin (mTOR) is a central controller of cell growth, proliferation, metabolism, and angiogenesis. This protein is an attractive target for new anticancer drug development. Significant progress has been made in hit discovery, lead optimization, drug candidate development and determination of the three-dimensional (3D) structure of mTOR. Computational methods have been applied to accelerate the discovery and development of mTOR inhibitors helping to model the structure of mTOR, screen compound databases, uncover structure-activity relationship (SAR) and optimize the hits, mine the privileged fragments and design focused libraries. Besides, computational approaches were also applied to study protein-ligand interactions mechanisms and in natural product-driven drug discovery. Herein, we survey the most recent progress on the application of computational approaches to advance the discovery and development of compounds targeting mTOR. Future directions in the discovery of new mTOR inhibitors using computational methods are also discussed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  15. Nexus Between Protein–Ligand Affinity Rank-Ordering, Biophysical Approaches, and Drug Discovery

    PubMed Central

    2013-01-01

    The confluence of computational and biophysical methods to accurately rank-order the binding affinities of small molecules and determine structures of macromolecular complexes is a potentially transformative advance in the work flow of drug discovery. This viewpoint explores the impact that advanced computational methods may have on the efficacy of small molecule drug discovery and optimization, particularly with respect to emerging fragment-based methods. PMID:24900579

  16. Systems biology impact on antiepileptic drug discovery.

    PubMed

    Margineanu, Doru Georg

    2012-02-01

    Systems biology (SB), a recent trend in bioscience research to consider the complex interactions in biological systems from a holistic perspective, sees the disease as a disturbed network of interactions, rather than alteration of single molecular component(s). SB-relying network pharmacology replaces the prevailing focus on specific drug-receptor interaction and the corollary of rational drug design of "magic bullets", by the search for multi-target drugs that would act on biological networks as "magic shotguns". Epilepsy being a multi-factorial, polygenic and dynamic pathology, SB approach appears particularly fit and promising for antiepileptic drug (AED) discovery. In fact, long before the advent of SB, AED discovery already involved some SB-like elements. A reported SB project aimed to find out new drug targets in epilepsy relies on a relational database that integrates clinical information, recordings from deep electrodes and 3D-brain imagery with histology and molecular biology data on modified expression of specific genes in the brain regions displaying spontaneous epileptic activity. Since hitting a single target does not treat complex diseases, a proper pharmacological promiscuity might impart on an AED the merit of being multi-potent. However, multi-target drug discovery entails the complicated task of optimizing multiple activities of compounds, while having to balance drug-like properties and to control unwanted effects. Specific design tools for this new approach in drug discovery barely emerge, but computational methods making reliable in silico predictions of poly-pharmacology did appear, and their progress might be quite rapid. The current move away from reductionism into network pharmacology allows expecting that a proper integration of the intrinsic complexity of epileptic pathology in AED discovery might result in literally anti-epileptic drugs. Copyright © 2011 Elsevier B.V. All rights reserved.

  17. Distributed Drug Discovery: Advancing Chemical Education through Contextualized Combinatorial Solid-Phase Organic Laboratories

    ERIC Educational Resources Information Center

    Scott, William L.; Denton, Ryan E.; Marrs, Kathleen A.; Durrant, Jacob D.; Samaritoni, J. Geno; Abraham, Milata M.; Brown, Stephen P.; Carnahan, Jon M.; Fischer, Lindsey G.; Glos, Courtney E.; Sempsrott, Peter J.; O'Donnell, Martin J.

    2015-01-01

    The Distributed Drug Discovery (D3) program trains students in three drug discovery disciplines (synthesis, computational analysis, and biological screening) while addressing the important challenge of discovering drug leads for neglected diseases. This article focuses on implementation of the synthesis component in the second-semester…

  18. The A-Z of Zika drug discovery.

    PubMed

    Mottin, Melina; Borba, Joyce V V B; Braga, Rodolpho C; Torres, Pedro H M; Martini, Matheus C; Proenca-Modena, Jose Luiz; Judice, Carla C; Costa, Fabio T M; Ekins, Sean; Perryman, Alexander L; Andrade, Carolina Horta

    2018-06-20

    Despite the recent outbreak of Zika virus (ZIKV), there are still no approved treatments, and early-stage compounds are probably many years away from approval. A comprehensive A-Z review of the recent advances in ZIKV drug discovery efforts is presented, highlighting drug repositioning and computationally guided compounds, including discovered viral and host cell inhibitors. Promising ZIKV molecular targets are also described and discussed, as well as targets belonging to the host cell, as new opportunities for ZIKV drug discovery. All this knowledge is not only crucial to advancing the fight against the Zika virus and other flaviviruses but also helps us prepare for the next emerging virus outbreak to which we will have to respond. Copyright © 2018. Published by Elsevier Ltd.

  19. Molecular dynamics simulations and novel drug discovery.

    PubMed

    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.

  20. Exploring the Role of Receptor Flexibility in Structure-Based Drug Discovery

    PubMed Central

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

    2015-01-01

    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. PMID:24332165

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

    PubMed

    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.

  2. Computer-aided drug design at Boehringer Ingelheim

    NASA Astrophysics Data System (ADS)

    Muegge, Ingo; Bergner, Andreas; Kriegl, Jan M.

    2017-03-01

    Computer-Aided Drug Design (CADD) is an integral part of the drug discovery endeavor at Boehringer Ingelheim (BI). CADD contributes to the evaluation of new therapeutic concepts, identifies small molecule starting points for drug discovery, and develops strategies for optimizing hit and lead compounds. The CADD scientists at BI benefit from the global use and development of both software platforms and computational services. A number of computational techniques developed in-house have significantly changed the way early drug discovery is carried out at BI. In particular, virtual screening in vast chemical spaces, which can be accessed by combinatorial chemistry, has added a new option for the identification of hits in many projects. Recently, a new framework has been implemented allowing fast, interactive predictions of relevant on and off target endpoints and other optimization parameters. In addition to the introduction of this new framework at BI, CADD has been focusing on the enablement of medicinal chemists to independently perform an increasing amount of molecular modeling and design work. This is made possible through the deployment of MOE as a global modeling platform, allowing computational and medicinal chemists to freely share ideas and modeling results. Furthermore, a central communication layer called the computational chemistry framework provides broad access to predictive models and other computational services.

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

    PubMed

    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.

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

    PubMed

    Barbault, Florent; Maurel, François

    2015-08-08

    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. Areas covered: 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. Expert opinion: 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.

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

    PubMed

    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.

  6. Cryo-EM in drug discovery: achievements, limitations and prospects.

    PubMed

    Renaud, Jean-Paul; Chari, Ashwin; Ciferri, Claudio; Liu, Wen-Ti; Rémigy, Hervé-William; Stark, Holger; Wiesmann, Christian

    2018-06-08

    Cryo-electron microscopy (cryo-EM) of non-crystalline single particles is a biophysical technique that can be used to determine the structure of biological macromolecules and assemblies. Historically, its potential for application in drug discovery has been heavily limited by two issues: the minimum size of the structures it can be used to study and the resolution of the images. However, recent technological advances - including the development of direct electron detectors and more effective computational image analysis techniques - are revolutionizing the utility of cryo-EM, leading to a burst of high-resolution structures of large macromolecular assemblies. These advances have raised hopes that single-particle cryo-EM might soon become an important tool for drug discovery, particularly if they could enable structural determination for 'intractable' targets that are still not accessible to X-ray crystallographic analysis. This article describes the recent advances in the field and critically assesses their relevance for drug discovery as well as discussing at what stages of the drug discovery pipeline cryo-EM can be useful today and what to expect in the near future.

  7. Advances in fragment-based drug discovery platforms.

    PubMed

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

    2009-11-01

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

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

    PubMed

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

    2018-05-30

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

  9. Modelling and enhanced molecular dynamics to steer structure-based drug discovery.

    PubMed

    Kalyaanamoorthy, Subha; Chen, Yi-Ping Phoebe

    2014-05-01

    The ever-increasing gap between the availabilities of the genome sequences and the crystal structures of proteins remains one of the significant challenges to the modern drug discovery efforts. The knowledge of structure-dynamics-functionalities of proteins is important in order to understand several key aspects of structure-based drug discovery, such as drug-protein interactions, drug binding and unbinding mechanisms and protein-protein interactions. This review presents a brief overview on the different state of the art computational approaches that are applied for protein structure modelling and molecular dynamics simulations of biological systems. We give an essence of how different enhanced sampling molecular dynamics approaches, together with regular molecular dynamics methods, assist in steering the structure based drug discovery processes. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

    PubMed

    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.

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

    PubMed

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

    2014-12-01

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

  12. Bioinformatics in protein kinases regulatory network and drug discovery.

    PubMed

    Chen, Qingfeng; Luo, Haiqiong; Zhang, Chengqi; Chen, Yi-Ping Phoebe

    2015-04-01

    Protein kinases have been implicated in a number of diseases, where kinases participate many aspects that control cell growth, movement and death. The deregulated kinase activities and the knowledge of these disorders are of great clinical interest of drug discovery. The most critical issue is the development of safe and efficient disease diagnosis and treatment for less cost and in less time. It is critical to develop innovative approaches that aim at the root cause of a disease, not just its symptoms. Bioinformatics including genetic, genomic, mathematics and computational technologies, has become the most promising option for effective drug discovery, and has showed its potential in early stage of drug-target identification and target validation. It is essential that these aspects are understood and integrated into new methods used in drug discovery for diseases arisen from deregulated kinase activity. This article reviews bioinformatics techniques for protein kinase data management and analysis, kinase pathways and drug targets and describes their potential application in pharma ceutical industry. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Toxins and drug discovery.

    PubMed

    Harvey, Alan L

    2014-12-15

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

  14. Computer aided drug design

    NASA Astrophysics Data System (ADS)

    Jain, A.

    2017-08-01

    Computer based method can help in discovery of leads and can potentially eliminate chemical synthesis and screening of many irrelevant compounds, and in this way, it save time as well as cost. Molecular modeling systems are powerful tools for building, visualizing, analyzing and storing models of complex molecular structure that can help to interpretate structure activity relationship. The use of various techniques of molecular mechanics and dynamics and software in Computer aided drug design along with statistics analysis is powerful tool for the medicinal chemistry to synthesis therapeutic and effective drugs with minimum side effect.

  15. The role of serendipity in drug discovery

    PubMed Central

    Ban, Thomas A.

    2006-01-01

    Serendipity is one of the many factors that may contribute to drug discovery. It has played a role in the discovery of prototype psychotropic drugs that led to modern pharmacological treatment in psychiatry. It has also played a role in the discovery of several drugs that have had an impact on the development of psychiatry, “Serendipity” in drug discovery implies the finding of one thing while looking for something else. This was the case in six of the twelve serendipitous discoveries reviewed in this paper, ie, aniline purple, penicillin, lysergic acid diethylamide, meprobamate, chlorpromazine, and imipramine, in the case of three drugs, ie, potassium bromide, chloral hydrate, and lithium, the discovery was serendipitous because an utterly false rationale led to correct empirical results; and in case of two others, ie, iproniazid and sildenafil, because valuable indications were found for these drugs which were not initially those sought. The discovery of one of the twelve drugs, chlordiazepoxide, was sheer luck. PMID:17117615

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

    PubMed

    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.

  17. Phenome-driven disease genetics prediction toward drug discovery.

    PubMed

    Chen, Yang; Li, Li; Zhang, Guo-Qiang; Xu, Rong

    2015-06-15

    Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data. To demonstrate our approach, we explored a new phenotype database from biomedical ontologies and constructed Disease Manifestation Network (DMN). We combined DMN with mimMiner, which was a widely used phenotype database in disease gene prediction studies. Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source. In the leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area under the curves of 90.7% and 90.3%, which are significantly higher than 84.2% (P < e(-4)) and 81.3% (P < e(-12)) for the baseline approach. We further demonstrated that our predicted genes have the translational potential in drug discovery. We used Crohn's disease as an example and ranked the candidate drugs based on the rank of drug targets. Our gene prediction approach prioritized druggable genes that are likely to be associated with Crohn's disease pathogenesis, and our rank of candidate drugs successfully prioritized the Food and Drug Administration-approved drugs for Crohn's disease. We also found literature evidence to support a number of drugs among the top 200 candidates. In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery. nlp. edu/public/data/DMN © The Author 2015. Published by Oxford University Press.

  18. Phenome-driven disease genetics prediction toward drug discovery

    PubMed Central

    Chen, Yang; Li, Li; Zhang, Guo-Qiang; Xu, Rong

    2015-01-01

    Motivation: Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data. Results: To demonstrate our approach, we explored a new phenotype database from biomedical ontologies and constructed Disease Manifestation Network (DMN). We combined DMN with mimMiner, which was a widely used phenotype database in disease gene prediction studies. Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source. In the leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area under the curves of 90.7% and 90.3%, which are significantly higher than 84.2% (P < e−4) and 81.3% (P < e−12) for the baseline approach. We further demonstrated that our predicted genes have the translational potential in drug discovery. We used Crohn’s disease as an example and ranked the candidate drugs based on the rank of drug targets. Our gene prediction approach prioritized druggable genes that are likely to be associated with Crohn’s disease pathogenesis, and our rank of candidate drugs successfully prioritized the Food and Drug Administration-approved drugs for Crohn’s disease. We also found literature evidence to support a number of drugs among the top 200 candidates. In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery. Availability and implementation: nlp

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

    PubMed Central

    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. Target assessment for antiparasitic drug discovery

    PubMed Central

    Frearson, Julie A.; Wyatt, Paul G.; Gilbert, Ian H.; Fairlamb, Alan H.

    2010-01-01

    Drug discovery is a high-risk, expensive and lengthy process taking at least 12 years and costing upwards of US$500 million per drug to reach the clinic. For neglected diseases, the drug discovery process is driven by medical need and guided by pre-defined target product profiles. Assessment and prioritisation of the most promising targets for entry into screening programmes is crucial for maximising chances of success. Here we describe criteria used in our drug discovery unit for target assessment and introduce the ‘traffic light’ system as a prioritisation and management tool. We hope this brief review will stimulate basic scientists to acquire additional information necessary for drug discovery. PMID:17962072

  1. Academic drug discovery: current status and prospects.

    PubMed

    Everett, Jeremy R

    2015-01-01

    The contraction in pharmaceutical drug discovery operations in the past decade has been counter-balanced by a significant rise in the number of academic drug discovery groups. In addition, pharmaceutical companies that used to operate in completely independent, vertically integrated operations for drug discovery, are now collaborating more with each other, and with academic groups. We are in a new era of drug discovery. This review provides an overview of the current status of academic drug discovery groups, their achievements and the challenges they face, together with perspectives on ways to achieve improved outcomes. Academic groups have made important contributions to drug discovery, from its earliest days and continue to do so today. However, modern drug discovery and development is exceedingly complex, and has high failure rates, principally because human biology is complex and poorly understood. Academic drug discovery groups need to play to their strengths and not just copy what has gone before. However, there are lessons to be learnt from the experiences of the industrial drug discoverers and four areas are highlighted for attention: i) increased validation of targets; ii) elimination of false hits from high throughput screening (HTS); iii) increasing the quality of molecular probes; and iv) investing in a high-quality informatics infrastructure.

  2. Mitigating risk in academic preclinical drug discovery.

    PubMed

    Dahlin, Jayme L; Inglese, James; Walters, Michael A

    2015-04-01

    The number of academic drug discovery centres has grown considerably in recent years, providing new opportunities to couple the curiosity-driven research culture in academia with rigorous preclinical drug discovery practices used in industry. To fully realize the potential of these opportunities, it is important that academic researchers understand the risks inherent in preclinical drug discovery, and that translational research programmes are effectively organized and supported at an institutional level. In this article, we discuss strategies to mitigate risks in several key aspects of preclinical drug discovery at academic drug discovery centres, including organization, target selection, assay design, medicinal chemistry and preclinical pharmacology.

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

    PubMed

    Frey, Jeremy G; Bird, Colin L

    2011-09-01

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

  4. Successes in drug discovery and design.

    PubMed

    2004-04-01

    The Society for Medicines Research (SMR) held a one-day meeting on case histories in drug discovery on December 4, 2003, at the National Heart and Lung Institute in London. These meetings have been organized by the SMR biannually for many years, and this latest meeting proved extremely popular, attracting a capacity audience of more than 130 registrants. The purpose of these meetings is educational; they allow those interested in drug discovery to hear key learnings from recent successful drug discovery programs. There was no overall linking theme between the talks, other than each success story has led to the introduction of a new and improved product of therapeutic use. The drug discovery stories covered in the meeting were extremely varied and, put together, they emphasized that each successful story is unique and special. This meeting is also special for the SMR because it presents the "SMR Award for Drug Discovery" in recognition of outstanding achievement and contribution in the area. It should be remembered that drug discovery is an extremely risky business and an extremely costly and complicated process in which the success rate is, at best, low. (c) 2004 Prous Science. All rights reserved.

  5. Advances in microfluidics for drug discovery.

    PubMed

    Lombardi, Dario; Dittrich, Petra S

    2010-11-01

    Microfluidics is considered as an enabling technology for the development of unconventional and innovative methods in the drug discovery process. The concept of micrometer-sized reaction systems in the form of continuous flow reactors, microdroplets or microchambers is intriguing, and the versatility of the technology perfectly fits with the requirements of drug synthesis, drug screening and drug testing. In this review article, we introduce key microfluidic approaches to the drug discovery process, highlighting the latest and promising achievements in this field, mainly from the years 2007 - 2010. Despite high expectations of microfluidic approaches to several stages of the drug discovery process, up to now microfluidic technology has not been able to significantly replace conventional drug discovery platforms. Our aim is to identify bottlenecks that have impeded the transfer of microfluidics into routine platforms for drug discovery and show some recent solutions to overcome these hurdles. Although most microfluidic approaches are still applied only for proof-of-concept studies, thanks to creative microfluidic research in the past years unprecedented novel capabilities of microdevices could be demonstrated, and general applicable, robust and reliable microfluidic platforms seem to be within reach.

  6. Drug discovery: lessons from evolution

    PubMed Central

    Warren, John

    2011-01-01

    A common view within the pharmaceutical industry is that there is a problem with drug discovery and we should do something about it. There is much sympathy for this from academics, regulators and politicians. In this article I propose that lessons learnt from evolution help identify those factors that favour successful drug discovery. This personal view is influenced by a decade spent reviewing drug development programmes submitted for European regulatory approval. During the prolonged gestation of a new medicine few candidate molecules survive. This process of elimination of many variants and the survival of so few has much in common with evolution, an analogy that encourages discussion of the forces that favour, and those that hinder, successful drug discovery. Imagining a world without vaccines, anaesthetics, contraception and anti-infectives reveals how medicines revolutionized humanity. How to manipulate conditions that favour such discoveries is worth consideration. PMID:21395642

  7. Mitigating risk in academic preclinical drug discovery

    PubMed Central

    Dahlin, Jayme L.; Inglese, James; Walters, Michael A.

    2018-01-01

    The number of academic drug discovery centres has grown considerably in recent years, providing new opportunities to couple the curiosity-driven research culture in academia with rigorous preclinical drug discovery practices used in industry. To fully realize the potential of these opportunities, it is important that academic researchers understand the risks inherent in preclinical drug discovery, and that translational research programmes are effectively organized and supported at an institutional level. In this article, we discuss strategies to mitigate risks in several key aspects of preclinical drug discovery at academic drug discovery centres, including organization, target selection, assay design, medicinal chemistry and preclinical pharmacology. PMID:25829283

  8. Balancing novelty with confined chemical space in modern drug discovery.

    PubMed

    Medina-Franco, José L; Martinez-Mayorga, Karina; Meurice, Nathalie

    2014-02-01

    The concept of chemical space has broad applications in drug discovery. In response to the needs of drug discovery campaigns, different approaches are followed to efficiently populate, mine and select relevant chemical spaces that overlap with biologically relevant chemical spaces. This paper reviews major trends in current drug discovery and their impact on the mining and population of chemical space. We also survey different approaches to develop screening libraries with confined chemical spaces balancing physicochemical properties. In this context, the confinement is guided by criteria that can be divided in two broad categories: i) library design focused on a relevant therapeutic target or disease and ii) library design focused on the chemistry or a desired molecular function. The design and development of chemical libraries should be associated with the specific purpose of the library and the project goals. The high complexity of drug discovery and the inherent imperfection of individual experimental and computational technologies prompt the integration of complementary library design and screening approaches to expedite the identification of new and better drugs. Library design approaches including diversity-oriented synthesis, biological-oriented synthesis or combinatorial library design, to name a few, and the design of focused libraries driven by target/disease, chemical structure or molecular function are more efficient if they are guided by multi-parameter optimization. In this context, consideration of pharmaceutically relevant properties is essential for balancing novelty with chemical space in drug discovery.

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

    PubMed

    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.

  10. Meeting report on the Alzheimer’s Drug Discovery Foundation 14th International Conference on Alzheimer’s Drug Discovery

    PubMed Central

    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.

  11. Advanced systems biology methods in drug discovery and translational biomedicine.

    PubMed

    Zou, Jun; Zheng, Ming-Wu; Li, Gen; Su, Zhi-Guang

    2013-01-01

    Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.

  12. Molecular Docking and Drug Discovery in β-Adrenergic Receptors.

    PubMed

    Vilar, Santiago; Sobarzo-Sanchez, Eduardo; Santana, Lourdes; Uriarte, Eugenio

    2017-01-01

    Evolution in computer engineering, availability of increasing amounts of data and the development of new and fast docking algorithms and software have led to improved molecular simulations with crucial applications in virtual high-throughput screening and drug discovery. Moreover, analysis of protein-ligand recognition through molecular docking has become a valuable tool in drug design. In this review, we focus on the applicability of molecular docking on a particular class of G protein-coupled receptors: the β-adrenergic receptors, which are relevant targets in clinic for the treatment of asthma and cardiovascular diseases. We describe the binding site in β-adrenergic receptors to understand key factors in ligand recognition along with the proteins activation process. Moreover, we focus on the discovery of new lead compounds that bind the receptors, on the evaluation of virtual screening using the active/ inactive binding site states, and on the structural optimization of known families of binders to improve β-adrenergic affinity. We also discussed strengths and challenges related to the applicability of molecular docking in β-adrenergic receptors. Molecular docking is a valuable technique in computational chemistry to deeply analyze ligand recognition and has led to important breakthroughs in drug discovery and design in the field of β-adrenergic receptors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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

    PubMed

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

    2016-07-08

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

  14. Trends in Modern Drug Discovery.

    PubMed

    Eder, Jörg; Herrling, Paul L

    2016-01-01

    Drugs discovered by the pharmaceutical industry over the past 100 years have dramatically changed the practice of medicine and impacted on many aspects of our culture. For many years, drug discovery was a target- and mechanism-agnostic approach that was based on ethnobotanical knowledge often fueled by serendipity. With the advent of modern molecular biology methods and based on knowledge of the human genome, drug discovery has now largely changed into a hypothesis-driven target-based approach, a development which was paralleled by significant environmental changes in the pharmaceutical industry. Laboratories became increasingly computerized and automated, and geographically dispersed research sites are now more and more clustered into large centers to capture technological and biological synergies. Today, academia, the regulatory agencies, and the pharmaceutical industry all contribute to drug discovery, and, in order to translate the basic science into new medical treatments for unmet medical needs, pharmaceutical companies have to have a critical mass of excellent scientists working in many therapeutic fields, disciplines, and technologies. The imperative for the pharmaceutical industry to discover breakthrough medicines is matched by the increasing numbers of first-in-class drugs approved in recent years and reflects the impact of modern drug discovery approaches, technologies, and genomics.

  15. Drug repurposing: translational pharmacology, chemistry, computers and the clinic.

    PubMed

    Issa, Naiem T; Byers, Stephen W; Dakshanamurthy, Sivanesan

    2013-01-01

    The process of discovering a pharmacological compound that elicits a desired clinical effect with minimal side effects is a challenge. Prior to the advent of high-performance computing and large-scale screening technologies, drug discovery was largely a serendipitous endeavor, as in the case of thalidomide for erythema nodosum leprosum or cancer drugs in general derived from flora located in far-reaching geographic locations. More recently, de novo drug discovery has become a more rationalized process where drug-target-effect hypotheses are formulated on the basis of already known compounds/protein targets and their structures. Although this approach is hypothesis-driven, the actual success has been very low, contributing to the soaring costs of research and development as well as the diminished pharmaceutical pipeline in the United States. In this review, we discuss the evolution in computational pharmacology as the next generation of successful drug discovery and implementation in the clinic where high-performance computing (HPC) is used to generate and validate drug-target-effect hypotheses completely in silico. The use of HPC would decrease development time and errors while increasing productivity prior to in vitro, animal and human testing. We highlight approaches in chemoinformatics, bioinformatics as well as network biopharmacology to illustrate potential avenues from which to design clinically efficacious drugs. We further discuss the implications of combining these approaches into an integrative methodology for high-accuracy computational predictions within the context of drug repositioning for the efficient streamlining of currently approved drugs back into clinical trials for possible new indications.

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

    PubMed

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

    2016-01-01

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

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

    PubMed

    Li, Yan; Kang, Congbao

    2017-08-23

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

  18. Effects of inductive bias on computational evaluations of ligand-based modeling and on drug discovery

    NASA Astrophysics Data System (ADS)

    Cleves, Ann E.; Jain, Ajay N.

    2008-03-01

    Inductive bias is the set of assumptions that a person or procedure makes in making a prediction based on data. Different methods for ligand-based predictive modeling have different inductive biases, with a particularly sharp contrast between 2D and 3D similarity methods. A unique aspect of ligand design is that the data that exist to test methodology have been largely man-made, and that this process of design involves prediction. By analyzing the molecular similarities of known drugs, we show that the inductive bias of the historic drug discovery process has a very strong 2D bias. In studying the performance of ligand-based modeling methods, it is critical to account for this issue in dataset preparation, use of computational controls, and in the interpretation of results. We propose specific strategies to explicitly address the problems posed by inductive bias considerations.

  19. Scaffold Repurposing of Old Drugs Towards New Cancer Drug Discovery.

    PubMed

    Chen, Haijun; Wu, Jianlei; Gao, Yu; Chen, Haiying; Zhou, Jia

    2016-01-01

    As commented by the Nobelist James Black that "The most fruitful basis of the discovery of a new drug is to start with an old drug", drug repurposing represents an attractive drug discovery strategy. Despite the success of several repurposed drugs on the market, the ultimate therapeutic potential of a large number of non-cancer drugs is hindered during their repositioning due to various issues including the limited efficacy and intellectual property. With the increasing knowledge about the pharmacological properties and newly identified targets, the scaffolds of the old drugs emerge as a great treasure-trove towards new cancer drug discovery. In this review, we summarize the recent advances in the development of novel small molecules for cancer therapy by scaffold repurposing with highlighted examples. The relevant strategies, advantages, challenges and future research directions associated with this approach are also discussed.

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

    PubMed Central

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

    2016-01-01

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

  1. TOXICOGENOMICS DRUG DISCOVERY AND THE PATHOLOGIST

    EPA Science Inventory

    Toxicogenomics, drug discovery, and pathologist.

    The field of toxicogenomics, which currently focuses on the application of large-scale differential gene expression (DGE) data to toxicology, is starting to influence drug discovery and development in the pharmaceutical indu...

  2. Serendipity in Cancer Drug Discovery: Rational or Coincidence?

    PubMed

    Prasad, Sahdeo; Gupta, Subash C; Aggarwal, Bharat B

    2016-06-01

    Novel drug development leading to final approval by the US FDA can cost as much as two billion dollars. Why the cost of novel drug discovery is so expensive is unclear, but high failure rates at the preclinical and clinical stages are major reasons. Although therapies targeting a given cell signaling pathway or a protein have become prominent in drug discovery, such treatments have done little in preventing or treating any disease alone because most chronic diseases have been found to be multigenic. A review of the discovery of numerous drugs currently being used for various diseases including cancer, diabetes, cardiovascular, pulmonary, and autoimmune diseases indicates that serendipity has played a major role in the discovery. In this review we provide evidence that rational drug discovery and targeted therapies have minimal roles in drug discovery, and that serendipity and coincidence have played and continue to play major roles. The primary focus in this review is on cancer-related drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Cheminformatics in Drug Discovery, an Industrial Perspective.

    PubMed

    Chen, Hongming; Kogej, Thierry; Engkvist, Ola

    2018-05-18

    Cheminformatics has established itself as a core discipline within large scale drug discovery operations. It would be impossible to handle the amount of data generated today in a small molecule drug discovery project without persons skilled in cheminformatics. In addition, due to increased emphasis on "Big Data", machine learning and artificial intelligence, not only in the society in general, but also in drug discovery, it is expected that the cheminformatics field will be even more important in the future. Traditional areas like virtual screening, library design and high-throughput screening analysis are highlighted in this review. Applying machine learning in drug discovery is an area that has become very important. Applications of machine learning in early drug discovery has been extended from predicting ADME properties and target activity to tasks like de novo molecular design and prediction of chemical reactions. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Tsai, Yingssu; Stanford University, 333 Campus Drive, Mudd Building, Stanford, CA 94305-5080; McPhillips, Scott E.

    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,more » 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

  5. Data Resources for the Computer-Guided Discovery of Bioactive Natural Products.

    PubMed

    Chen, Ya; de Bruyn Kops, Christina; Kirchmair, Johannes

    2017-09-25

    Natural products from plants, animals, marine life, fungi, bacteria, and other organisms are an important resource for modern drug discovery. Their biological relevance and structural diversity make natural products good starting points for drug design. Natural product-based drug discovery can benefit greatly from computational approaches, which are a valuable precursor or supplementary method to in vitro testing. We present an overview of 25 virtual and 31 physical natural product libraries that are useful for applications in cheminformatics, in particular virtual screening. The overview includes detailed information about each library, the extent of its structural information, and the overlap between different sources of natural products. In terms of chemical structures, there is a large overlap between freely available and commercial virtual natural product libraries. Of particular interest for drug discovery is that at least ten percent of known natural products are readily purchasable and many more natural products and derivatives are available through on-demand sourcing, extraction and synthesis services. Many of the readily purchasable natural products are of small size and hence of relevance to fragment-based drug discovery. There are also an increasing number of macrocyclic natural products and derivatives becoming available for screening.

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

    PubMed

    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.

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

    PubMed

    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.

  8. Discovery of novel drugs for promising targets.

    PubMed

    Martell, Robert E; Brooks, David G; Wang, Yan; Wilcoxen, Keith

    2013-09-01

    Once a promising drug target is identified, the steps to actually discover and optimize a drug are diverse and challenging. The goal of this study was to provide a road map to navigate drug discovery. Review general steps for drug discovery and provide illustrating references. A number of approaches are available to enhance and accelerate target identification and validation. Consideration of a variety of potential mechanisms of action of potential drugs can guide discovery efforts. The hit to lead stage may involve techniques such as high-throughput screening, fragment-based screening, and structure-based design, with informatics playing an ever-increasing role. Biologically relevant screening models are discussed, including cell lines, 3-dimensional culture, and in vivo screening. The process of enabling human studies for an investigational drug is also discussed. Drug discovery is a complex process that has significantly evolved in recent years. © 2013 Elsevier HS Journals, Inc. All rights reserved.

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

    PubMed

    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.

  10. The Tuberculosis Drug Discovery and Development Pipeline and Emerging Drug Targets

    PubMed Central

    Mdluli, Khisimuzi; Kaneko, Takushi; Upton, Anna

    2015-01-01

    The recent accelerated approval for use in extensively drug-resistant and multidrug-resistant-tuberculosis (MDR-TB) of two first-in-class TB drugs, bedaquiline and delamanid, has reinvigorated the TB drug discovery and development field. However, although several promising clinical development programs are ongoing to evaluate new TB drugs and regimens, the number of novel series represented is few. The global early-development pipeline is also woefully thin. To have a chance of achieving the goal of better, shorter, safer TB drug regimens with utility against drug-sensitive and drug-resistant disease, a robust and diverse global TB drug discovery pipeline is key, including innovative approaches that make use of recently acquired knowledge on the biology of TB. Fortunately, drug discovery for TB has resurged in recent years, generating compounds with varying potential for progression into developable leads. In parallel, advances have been made in understanding TB pathogenesis. It is now possible to apply the lessons learned from recent TB hit generation efforts and newly validated TB drug targets to generate the next wave of TB drug leads. Use of currently underexploited sources of chemical matter and lead-optimization strategies may also improve the efficiency of future TB drug discovery. Novel TB drug regimens with shorter treatment durations must target all subpopulations of Mycobacterium tuberculosis existing in an infection, including those responsible for the protracted TB treatment duration. This review summarizes the current TB drug development pipeline and proposes strategies for generating improved hits and leads in the discovery phase that could help achieve this goal. PMID:25635061

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

    PubMed

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

    2017-11-06

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

  12. FERN Ethnomedicinal Plant Database: Exploring Fern Ethnomedicinal Plants Knowledge for Computational Drug Discovery.

    PubMed

    Thakar, Sambhaji B; Ghorpade, Pradnya N; Kale, Manisha V; Sonawane, Kailas D

    2015-01-01

    Fern plants are known for their ethnomedicinal applications. Huge amount of fern medicinal plants information is scattered in the form of text. Hence, database development would be an appropriate endeavor to cope with the situation. So by looking at the importance of medicinally useful fern plants, we developed a web based database which contains information about several group of ferns, their medicinal uses, chemical constituents as well as protein/enzyme sequences isolated from different fern plants. Fern ethnomedicinal plant database is an all-embracing, content management web-based database system, used to retrieve collection of factual knowledge related to the ethnomedicinal fern species. Most of the protein/enzyme sequences have been extracted from NCBI Protein sequence database. The fern species, family name, identification, taxonomy ID from NCBI, geographical occurrence, trial for, plant parts used, ethnomedicinal importance, morphological characteristics, collected from various scientific literatures and journals available in the text form. NCBI's BLAST, InterPro, phylogeny, Clustal W web source has also been provided for the future comparative studies. So users can get information related to fern plants and their medicinal applications at one place. This Fern ethnomedicinal plant database includes information of 100 fern medicinal species. This web based database would be an advantageous to derive information specifically for computational drug discovery, botanists or botanical interested persons, pharmacologists, researchers, biochemists, plant biotechnologists, ayurvedic practitioners, doctors/pharmacists, traditional medicinal users, farmers, agricultural students and teachers from universities as well as colleges and finally fern plant lovers. This effort would be useful to provide essential knowledge for the users about the adventitious applications for drug discovery, applications, conservation of fern species around the world and finally to create

  13. Drug Discovery in Academia- the third way?

    PubMed Central

    Frearson, Julie; Wyatt, Paul

    2010-01-01

    As the pharmaceutical industry continues to re-strategise and focus on low-risk, relatively short term gains for the sake of survival, we need to re-invigorate the early stages of drug discovery and rebalance efforts towards novel modes of action therapeutics and neglected genetic and tropical diseases. Academic drug discovery is one model which offers the promise of new approaches and an alternative organisational culture for drug discovery as it attempts to apply academic innovation and thought processes to the challenge of discovering drugs to address real unmet need. PMID:20922062

  14. PhenoPredict: A disease phenome-wide drug repositioning approach towards schizophrenia drug discovery.

    PubMed

    Xu, Rong; Wang, QuanQiu

    2015-08-01

    Schizophrenia (SCZ) is a common complex disorder with poorly understood mechanisms and no effective drug treatments. Despite the high prevalence and vast unmet medical need represented by the disease, many drug companies have moved away from the development of drugs for SCZ. Therefore, alternative strategies are needed for the discovery of truly innovative drug treatments for SCZ. Here, we present a disease phenome-driven computational drug repositioning approach for SCZ. We developed a novel drug repositioning system, PhenoPredict, by inferring drug treatments for SCZ from diseases that are phenotypically related to SCZ. The key to PhenoPredict is the availability of a comprehensive drug treatment knowledge base that we recently constructed. PhenoPredict retrieved all 18 FDA-approved SCZ drugs and ranked them highly (recall=1.0, and average ranking of 8.49%). When compared to PREDICT, one of the most comprehensive drug repositioning systems currently available, in novel predictions, PhenoPredict represented clear improvements over PREDICT in Precision-Recall (PR) curves, with a significant 98.8% improvement in the area under curve (AUC) of the PR curves. In addition, we discovered many drug candidates with mechanisms of action fundamentally different from traditional antipsychotics, some of which had published literature evidence indicating their treatment benefits in SCZ patients. In summary, although the fundamental pathophysiological mechanisms of SCZ remain unknown, integrated systems approaches to studying phenotypic connections among diseases may facilitate the discovery of innovative SCZ drugs. Copyright © 2015 Elsevier Inc. All rights reserved.

  15. Introduction to fragment-based drug discovery.

    PubMed

    Erlanson, Daniel A

    2012-01-01

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

  16. Computer-Aided Drug Design Methods.

    PubMed

    Yu, Wenbo; MacKerell, Alexander D

    2017-01-01

    Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.

  17. Antisense oligonucleotide technologies in drug discovery.

    PubMed

    Aboul-Fadl, Tarek

    2006-09-01

    The principle of antisense oligonucleotide (AS-OD) technologies is based on the specific inhibition of unwanted gene expression by blocking mRNA activity. It has long appeared to be an ideal strategy to leverage new genomic knowledge for drug discovery and development. In recent years, AS-OD technologies have been widely used as potent and promising tools for this purpose. There is a rapid increase in the number of antisense molecules progressing in clinical trials. AS-OD technologies provide a simple and efficient approach for drug discovery and development and are expected to become a reality in the near future. This editorial describes the established and emerging AS-OD technologies in drug discovery.

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

    PubMed

    Cournia, Zoe; Allen, Bryce; Sherman, Woody

    2017-12-26

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

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

    PubMed

    Ekins, Sean; Perryman, Alexander L; Horta Andrade, Carolina

    2016-10-01

    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.

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

    PubMed

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

    2016-09-01

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

  1. Computer aided drug discovery of highly ligand efficient, low molecular weight imidazopyridine analogs as FLT3 inhibitors

    PubMed Central

    Frett, Brendan; McConnell, Nick; Smith, Catherine C.; Wang, Yuanxiang; Shah, Neil P.; Li, Hong-yu

    2015-01-01

    The FLT3 kinase represents an attractive target to effectively treat AML. Unfortunately, no FLT3 targeted therapeutic is currently approved. In line with our continued interests in treating kinase related disease for anti-FLT3 mutant activity, we utilized pioneering synthetic methodology in combination with computer aided drug discovery and identified low molecular weight, highly ligand efficient, FLT3 kinase inhibitors. Compounds were analyzed for biochemical inhibition, their ability to selectively inhibit cell proliferation, for FLT3 mutant activity, and preliminary aqueous solubility. Validated hits were discovered that can serve as starting platforms for lead candidates. PMID:25765758

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

    PubMed

    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.

  3. [Computational chemistry in structure-based drug design].

    PubMed

    Cao, Ran; Li, Wei; Sun, Han-Zi; Zhou, Yu; Huang, Niu

    2013-07-01

    Today, the understanding of the sequence and structure of biologically relevant targets is growing rapidly and researchers from many disciplines, physics and computational science in particular, are making significant contributions to modern biology and drug discovery. However, it remains challenging to rationally design small molecular ligands with desired biological characteristics based on the structural information of the drug targets, which demands more accurate calculation of ligand binding free-energy. With the rapid advances in computer power and extensive efforts in algorithm development, physics-based computational chemistry approaches have played more important roles in structure-based drug design. Here we reviewed the newly developed computational chemistry methods in structure-based drug design as well as the elegant applications, including binding-site druggability assessment, large scale virtual screening of chemical database, and lead compound optimization. Importantly, here we address the current bottlenecks and propose practical solutions.

  4. A renaissance of neural networks in drug discovery.

    PubMed

    Baskin, Igor I; Winkler, David; Tetko, Igor V

    2016-08-01

    Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach. In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characteristics of drug-delivery systems and virtual screening. Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine.

  5. Tissue constructs: platforms for basic research and drug discovery.

    PubMed

    Elson, Elliot L; Genin, Guy M

    2016-02-06

    The functions, form and mechanical properties of cells are inextricably linked to their extracellular environment. Cells from solid tissues change fundamentally when, isolated from this environment, they are cultured on rigid two-dimensional substrata. These changes limit the significance of mechanical measurements on cells in two-dimensional culture and motivate the development of constructs with cells embedded in three-dimensional matrices that mimic the natural tissue. While measurements of cell mechanics are difficult in natural tissues, they have proven effective in engineered tissue constructs, especially constructs that emphasize specific cell types and their functions, e.g. engineered heart tissues. Tissue constructs developed as models of disease also have been useful as platforms for drug discovery. Underlying the use of tissue constructs as platforms for basic research and drug discovery is integration of multiscale biomaterials measurement and computational modelling to dissect the distinguishable mechanical responses separately of cells and extracellular matrix from measurements on tissue constructs and to quantify the effects of drug treatment on these responses. These methods and their application are the main subjects of this review.

  6. Tissue constructs: platforms for basic research and drug discovery

    PubMed Central

    Elson, Elliot L.; Genin, Guy M.

    2016-01-01

    The functions, form and mechanical properties of cells are inextricably linked to their extracellular environment. Cells from solid tissues change fundamentally when, isolated from this environment, they are cultured on rigid two-dimensional substrata. These changes limit the significance of mechanical measurements on cells in two-dimensional culture and motivate the development of constructs with cells embedded in three-dimensional matrices that mimic the natural tissue. While measurements of cell mechanics are difficult in natural tissues, they have proven effective in engineered tissue constructs, especially constructs that emphasize specific cell types and their functions, e.g. engineered heart tissues. Tissue constructs developed as models of disease also have been useful as platforms for drug discovery. Underlying the use of tissue constructs as platforms for basic research and drug discovery is integration of multiscale biomaterials measurement and computational modelling to dissect the distinguishable mechanical responses separately of cells and extracellular matrix from measurements on tissue constructs and to quantify the effects of drug treatment on these responses. These methods and their application are the main subjects of this review. PMID:26855763

  7. CNS Anticancer Drug Discovery and Development Conference White Paper

    PubMed Central

    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.

    2015-01-01

    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. PMID:26403167

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

    PubMed Central

    Perryman, Alexander L.; Horta Andrade, Carolina

    2016-01-01

    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. PMID:27764115

  9. RAS - Screens & Assays - Drug Discovery

    Cancer.gov

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

  10. [Activity of NTDs Drug-discovery Research Consortium].

    PubMed

    Namatame, Ichiji

    2016-01-01

    Neglected tropical diseases (NTDs) are an extremely important issue facing global health care. To improve "access to health" where people are unable to access adequate medical care due to poverty and weak healthcare systems, we have established two consortiums: the NTD drug discovery research consortium, and the pediatric praziquantel consortium. The NTD drug discovery research consortium, which involves six institutions from industry, government, and academia, as well as an international non-profit organization, is committed to developing anti-protozoan active compounds for three NTDs (Leishmaniasis, Chagas disease, and African sleeping sickness). Each participating institute will contribute their efforts to accomplish the following: selection of drug targets based on information technology, and drug discovery by three different approaches (in silico drug discovery, "fragment evolution" which is a unique drug designing method of Astellas Pharma, and phenotypic screening with Astellas' compound library). The consortium has established a brand new database (Integrated Neglected Tropical Disease Database; iNTRODB), and has selected target proteins for the in silico and fragment evolution drug discovery approaches. Thus far, we have identified a number of promising compounds that inhibit the target protein, and we are currently trying to improve the anti-protozoan activity of these compounds. The pediatric praziquantel consortium was founded in July 2012 to develop and register a new praziquantel pediatric formulation for the treatment of schistosomiasis. Astellas Pharma has been a core member in this consortium since its establishment, and has provided expertise and technology in the area of pediatric formulation development and clinical development.

  11. Using glycome databases for drug discovery.

    PubMed

    Aoki-Kinoshita, Kiyoko F

    2008-08-01

    The glycomics field has made great advancements in the last decade due to technologies for their synthesis and analysis including carbohydrate microarrays. Accordingly, databases for glycomics research have also emerged and been made publicly available by many major institutions worldwide. This review introduces these and other useful databases on which new methods for drug discovery can be developed. The scope of this review covers current documented and accessible databases and resources pertaining to glycomics. These were selected with the expectation that they may be useful for drug discovery research. There is a plethora of glycomics databases that have much potential for drug discovery. This may seem daunting at first but this review helps to put some of these resources into perspective. Additionally, some thoughts on how to integrate these resources to allow more efficient research are presented.

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

    PubMed

    Rodrigues, Tiago

    2017-11-15

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

  13. Computer aided drug discovery of highly ligand efficient, low molecular weight imidazopyridine analogs as FLT3 inhibitors.

    PubMed

    Frett, Brendan; McConnell, Nick; Smith, Catherine C; Wang, Yuanxiang; Shah, Neil P; Li, Hong-yu

    2015-04-13

    The FLT3 kinase represents an attractive target to effectively treat AML. Unfortunately, no FLT3 targeted therapeutic is currently approved. In line with our continued interests in treating kinase related disease for anti-FLT3 mutant activity, we utilized pioneering synthetic methodology in combination with computer aided drug discovery and identified low molecular weight, highly ligand efficient, FLT3 kinase inhibitors. Compounds were analyzed for biochemical inhibition, their ability to selectively inhibit cell proliferation, for FLT3 mutant activity, and preliminary aqueous solubility. Validated hits were discovered that can serve as starting platforms for lead candidates. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  14. Drug discovery in jeopardy

    PubMed Central

    Cuatrecasas, Pedro

    2006-01-01

    Despite striking advances in the biomedical sciences, the flow of new drugs has slowed to a trickle, impairing therapeutic advances as well as the commercial success of drug companies. Reduced productivity in the drug industry is caused mainly by corporate policies that discourage innovation. This is compounded by various consequences of mega-mergers, the obsession for blockbuster drugs, the shift of control of research from scientists to marketers, the need for fast sales growth, and the discontinuation of development compounds for nontechnical reasons. Lessons from the past indicate that these problems can be overcome, and herein, new and improved directions for drug discovery are suggested. PMID:17080187

  15. Novel approaches to anticonvulsant drug discovery.

    PubMed

    Miziak, Barbara; Chrościńska-Krawczyk, Magdalena; Błaszczyk, Barbara; Radzik, Iwona; Czuczwar, Stanisław J

    2013-11-01

    The history of epilepsy dates back to 2000 BC. Yet, it was not until 1912 that the activity of the first antiepileptic, phenobarbital was discovered by accident. After this discovery, the next antiepileptic drugs to be discovered (phenytoin and primidone) were based on the phenobarbital's structure. Then, in 1960, carbamazepine was developed empirically, while in 1962, valproate demonstrated anticonvulsant activity against experimental seizures. The next antiepileptic drugs synthesized were either modifications of the existing drugs (such as oxcarbazepine and pregabalin) or completely novel chemical structures (lacosamide, perampanel and retigabine). The present paper briefly refers to the history of epilepsy and development of antiepileptic drugs. Further, the paper provides a discussion on the antiepileptogenic effects of antiepileptic drugs in terms of the constant percentage of epileptic patients with refractory seizures. The authors also review the likely factors involved in the false refractoriness (such as through the use of caffeine-containing beverages and smoking). Finally, the authors consider future directions in the search of novel antiepileptic drugs. In spite of the considerable number of newer antiepileptic drugs, the number of drug-resistant epileptic patients remains unchanged. This may be rather an indication of the suitability of the currently available discovery procedures for effective antiepileptic drugs in the whole population of epileptic patients. The authors, however, believe that it is likely that models of mimic chronic epilepsy will help bridge the gaps and aid in the discovery of novel antiepileptic drugs - ones that can effectively modify the course of the disease.

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

    PubMed

    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.

  17. Phenotypic screening in cancer drug discovery - past, present and future.

    PubMed

    Moffat, John G; Rudolph, Joachim; Bailey, David

    2014-08-01

    There has been a resurgence of interest in the use of phenotypic screens in drug discovery as an alternative to target-focused approaches. Given that oncology is currently the most active therapeutic area, and also one in which target-focused approaches have been particularly prominent in the past two decades, we investigated the contribution of phenotypic assays to oncology drug discovery by analysing the origins of all new small-molecule cancer drugs approved by the US Food and Drug Administration (FDA) over the past 15 years and those currently in clinical development. Although the majority of these drugs originated from target-based discovery, we identified a significant number whose discovery depended on phenotypic screening approaches. We postulate that the contribution of phenotypic screening to cancer drug discovery has been hampered by a reliance on 'classical' nonspecific drug effects such as cytotoxicity and mitotic arrest, exacerbated by a paucity of mechanistically defined cellular models for therapeutically translatable cancer phenotypes. However, technical and biological advances that enable such mechanistically informed phenotypic models have the potential to empower phenotypic drug discovery in oncology.

  18. How molecular profiling could revolutionize drug discovery.

    PubMed

    Stoughton, Roland B; Friend, Stephen H

    2005-04-01

    Information from genomic, proteomic and metabolomic measurements has already benefited target discovery and validation, assessment of efficacy and toxicity of compounds, identification of disease subgroups and the prediction of responses of individual patients. Greater benefits can be expected from the application of these technologies on a significantly larger scale; by simultaneously collecting diverse measurements from the same subjects or cell cultures; by exploiting the steadily improving quantitative accuracy of the technologies; and by interpreting the emerging data in the context of underlying biological models of increasing sophistication. The benefits of applying molecular profiling to drug discovery and development will include much lower failure rates at all stages of the drug development pipeline, faster progression from discovery through to clinical trials and more successful therapies for patient subgroups. Upheavals in existing organizational structures in the current 'conveyor belt' models of drug discovery might be required to take full advantage of these methods.

  19. Mining large heterogeneous data sets in drug discovery.

    PubMed

    Wild, David J

    2009-10-01

    Increasingly, effective drug discovery involves the searching and data mining of large volumes of information from many sources covering the domains of chemistry, biology and pharmacology amongst others. This has led to a proliferation of databases and data sources relevant to drug discovery. This paper provides a review of the publicly-available large-scale databases relevant to drug discovery, describes the kinds of data mining approaches that can be applied to them and discusses recent work in integrative data mining that looks for associations that pan multiple sources, including the use of Semantic Web techniques. The future of mining large data sets for drug discovery requires intelligent, semantic aggregation of information from all of the data sources described in this review, along with the application of advanced methods such as intelligent agents and inference engines in client applications.

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

    PubMed Central

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

    2017-01-01

    CNS Anticancer Drug Discovery and Development, 16-17 November 2016, Scottsdale, 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. PMID:28718326

  1. System-level multi-target drug discovery from natural products with applications to cardiovascular diseases.

    PubMed

    Zheng, Chunli; Wang, Jinan; Liu, Jianling; Pei, Mengjie; Huang, Chao; Wang, Yonghua

    2014-08-01

    The term systems pharmacology describes a field of study that uses computational and experimental approaches to broaden the view of drug actions rooted in molecular interactions and advance the process of drug discovery. The aim of this work is to stick out the role that the systems pharmacology plays across the multi-target drug discovery from natural products for cardiovascular diseases (CVDs). Firstly, based on network pharmacology methods, we reconstructed the drug-target and target-target networks to determine the putative protein target set of multi-target drugs for CVDs treatment. Secondly, we reintegrated a compound dataset of natural products and then obtained a multi-target compounds subset by virtual-screening process. Thirdly, a drug-likeness evaluation was applied to find the ADME-favorable compounds in this subset. Finally, we conducted in vitro experiments to evaluate the reliability of the selected chemicals and targets. We found that four of the five randomly selected natural molecules can effectively act on the target set for CVDs, indicating the reasonability of our systems-based method. This strategy may serve as a new model for multi-target drug discovery of complex diseases.

  2. New strategies in drug discovery.

    PubMed

    Ohlstein, Eliot H; Johnson, Anthony G; Elliott, John D; Romanic, Anne M

    2006-01-01

    Gene identification followed by determination of the expression of genes in a given disease and understanding of the function of the gene products is central to the drug discovery process. The ability to associate a specific gene with a disease can be attributed primarily to the extraordinary progress that has been made in the areas of gene sequencing and information technologies. Selection and validation of novel molecular targets have become of great importance in light of the abundance of new potential therapeutic drug targets that have emerged from human gene sequencing. In response to this revolution within the pharmaceutical industry, the development of high-throughput methods in both biology and chemistry has been necessitated. Further, the successful translation of basic scientific discoveries into clinical experimental medicine and novel therapeutics is an increasing challenge. As such, a new paradigm for drug discovery has emerged. This process involves the integration of clinical, genetic, genomic, and molecular phenotype data partnered with cheminformatics. Central to this process, the data generated are managed, collated, and interpreted with the use of informatics. This review addresses the use of new technologies that have arisen to deal with this new paradigm.

  3. The future of crystallography in drug discovery

    PubMed Central

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

    2014-01-01

    Introduction 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. Areas covered 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. Expert opinion 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

  4. The role of nanobiotechnology in drug discovery.

    PubMed

    Jain, Kewal K

    2009-01-01

    The potential applications of nanotechnology in life sciences, particularly nanobiotechnology, include those for drug discovery. This chapter shows how several of the nanotechnologies including nanoparticles and various nanodevices such as nanobiosensors and nanobiochips are being used to improve drug discovery. Nanoscale assays using nanoliter volumes contribute to cost saving. Some nanosubstances such as fullerenes are drug candidates. There are some safety concerns about the in vivo use of nanoparticles that are being investigated. However, future prospects for applications in healthcare of drugs discovered through nanotechnology and their role in the development of personalized medicine appear to be excellent.

  5. Open drug discovery for the Zika virus

    PubMed Central

    Ekins, Sean; Mietchen, Daniel; Coffee, Megan; Stratton, Thomas P; Freundlich, Joel S; Freitas-Junior, Lucio; Muratov, Eugene; Siqueira-Neto, Jair; Williams, Antony J; Andrade, Carolina

    2016-01-01

    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. PMID:27134728

  6. Open drug discovery for the Zika virus.

    PubMed

    Ekins, Sean; Mietchen, Daniel; Coffee, Megan; Stratton, Thomas P; Freundlich, Joel S; Freitas-Junior, Lucio; Muratov, Eugene; Siqueira-Neto, Jair; Williams, Antony J; Andrade, Carolina

    2016-01-01

    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.

  7. The future of quantum dots in drug discovery.

    PubMed

    Lin, Guimiao; Yin, Feng; Yong, Ken-Tye

    2014-09-01

    The rapid development of drug discovery today is inseparable from the interaction of advanced particle technologies and new drug synthesis protocols. Quantum dots (QDs) are regarded as a unique class of fluorescent labels, with unique optical properties such as high brightness and long-term colloidal and optical stability; these are suitable for optical imaging, drug delivery and optical tracking, fluorescence immunoassay and other medicinal applications. More importantly, QD possesses a rich surface chemistry property that is useful for incorporating various drug molecules, targeting ligands, and additional contrast agents (e.g., MRI, PET, etc.) onto the nanoparticle surface for achieving targeted and traceable drug delivery therapy at both cellular and systemic levels. In recent times, the advancement of QD technology has promoted the use of functionalized nanocrystals for in vivo applications. Such research is paving the way for drug discovery using various bioconjugated QD formulations. In this editorial, the authors highlight the current research progress and future applications of QDs in drug discovery.

  8. Drug Discovery Prospect from Untapped Species: Indications from Approved Natural Product Drugs

    PubMed Central

    Qin, Chu; Tao, Lin; Liu, Xin; Shi, Zhe; Zhang, Cun Long; Tan, Chun Yan; Chen, Yu Zong; Jiang, Yu Yang

    2012-01-01

    Due to extensive bioprospecting efforts of the past and technology factors, there have been questions about drug discovery prospect from untapped species. We analyzed recent trends of approved drugs derived from previously untapped species, which show no sign of untapped drug-productive species being near extinction and suggest high probability of deriving new drugs from new species in existing drug-productive species families and clusters. Case histories of recently approved drugs reveal useful strategies for deriving new drugs from the scaffolds and pharmacophores of the natural product leads of these untapped species. New technologies such as cryptic gene-cluster exploration may generate novel natural products with highly anticipated potential impact on drug discovery. PMID:22808057

  9. Innovative Methodology in the Discovery of Novel Drug Targets in the Free-Living Amoebae

    PubMed

    Baig, Abdul Mannan

    2018-04-25

    Despite advances in drug discovery and modifications in the chemotherapeutic regimens, human infections caused by free-living amoebae (FLA) have high mortality rates (~95%). The FLA that cause fatal human cerebral infections include Naegleria fowleri, Balamuthia mandrillaris and Acanthamoeba spp. Novel drug-target discovery remains the only viable option to tackle these central nervous system (CNS) infection in order to lower the mortality rates caused by the FLA. Of these FLA, N. fowleri causes primary amoebic meningoencephalitis (PAM), while the A. castellanii and B. Mandrillaris are known to cause granulomatous amoebic encephalitis (GAE). The infections caused by the FLA have been treated with drugs like Rifampin, Fluconazole, Amphotericin-B and Miltefosine. Miltefosine is an anti-leishmanial agent and an experimental anti-cancer drug. With only rare incidences of success, these drugs have remained unsuccessful to lower the mortality rates of the cerebral infection caused by FLA. Recently, with the help of bioinformatic computational tools and the discovered genomic data of the FLA, discovery of newer drug targets has become possible. These cellular targets are proteins that are either unique to the FLA or shared between the humans and these unicellular eukaryotes. The latter group of proteins has shown to be targets of some FDA approved drugs prescribed in non-infectious diseases. This review out-lines the bioinformatic methodologies that can be used in the discovery of such novel drug-targets, their chronicle by in-vitro assays done in the past and the translational value of such target discoveries in human diseases caused by FLA. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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

    PubMed

    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.

  11. Marinopyrroles: Unique Drug Discoveries Based on Marine Natural Products.

    PubMed

    Li, Rongshi

    2016-01-01

    Natural products provide a successful supply of new chemical entities (NCEs) for drug discovery to treat human diseases. Approximately half of the NCEs are based on natural products and their derivatives. Notably, marine natural products, a largely untapped resource, have contributed to drug discovery and development with eight drugs or cosmeceuticals approved by the U.S. Food and Drug Administration and European Medicines Agency, and ten candidates undergoing clinical trials. Collaborative efforts from drug developers, biologists, organic, medicinal, and natural product chemists have elevated drug discoveries to new levels. These efforts are expected to continue to improve the efficiency of natural product-based drugs. Marinopyrroles are examined here as a case study for potential anticancer and antibiotic agents. © 2015 Wiley Periodicals, Inc.

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

    PubMed

    Bodovitz, Steven

    2010-01-01

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

  13. Four disruptive strategies for removing drug discovery bottlenecks.

    PubMed

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

    2013-03-01

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

  14. Drug Discovery in Fish, Flies, and Worms

    PubMed Central

    Strange, Kevin

    2016-01-01

    Abstract Nonmammalian model organisms such as the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and the zebrafish Danio rerio provide numerous experimental advantages for drug discovery including genetic and molecular tractability, amenability to high-throughput screening methods and reduced experimental costs and increased experimental throughput compared to traditional mammalian models. An interdisciplinary approach that strategically combines the study of nonmammalian and mammalian animal models with diverse experimental tools has and will continue to provide deep molecular and genetic understanding of human disease and will significantly enhance the discovery and application of new therapies to treat those diseases. This review will provide an overview of C. elegans, Drosophila, and zebrafish biology and husbandry and will discuss how these models are being used for phenotype-based drug screening and for identification of drug targets and mechanisms of action. The review will also describe how these and other nonmammalian model organisms are uniquely suited for the discovery of drug-based regenerative medicine therapies. PMID:28053067

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

    PubMed

    Karthikeyan, Muthukumarasamy; Vyas, Renu

    2015-01-01

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

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

    PubMed

    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.

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

    PubMed

    Zhang, Aihua; Sun, Hui; Wang, Xijun

    2018-05-01

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

  18. Biophysical Discovery through the Lens of a Computational Microscope

    NASA Astrophysics Data System (ADS)

    Amaro, Rommie

    With exascale computing power on the horizon, improvements in the underlying algorithms and available structural experimental data are enabling new paradigms for chemical discovery. My work has provided key insights for the systematic incorporation of structural information resulting from state-of-the-art biophysical simulations into protocols for inhibitor and drug discovery. We have shown that many disease targets have druggable pockets that are otherwise ``hidden'' in high resolution x-ray structures, and that this is a common theme across a wide range of targets in different disease areas. We continue to push the limits of computational biophysical modeling by expanding the time and length scales accessible to molecular simulation. My sights are set on, ultimately, the development of detailed physical models of cells, as the fundamental unit of life, and two recent achievements highlight our efforts in this arena. First is the development of a molecular and Brownian dynamics multi-scale modeling framework, which allows us to investigate drug binding kinetics in addition to thermodynamics. In parallel, we have made significant progress developing new tools to extend molecular structure to cellular environments. Collectively, these achievements are enabling the investigation of the chemical and biophysical nature of cells at unprecedented scales.

  19. Growing PAINS in academic drug discovery.

    PubMed

    Whitty, Adrian

    2011-05-01

    In a recent article it was argued that compounds published as drug leads by academic laboratories commonly contain functionality that identifies them as nonspecific 'pan-assay interference compounds' (PAINS). The article raises broad questions about why best practices for hit and lead qualification that are well known in industry are not more widely employed in academia, as well as about the role of journals in publishing manuscripts that report drug leads of little potential value. Barriers to adoption of best practices for some academic drug-discovery researchers include knowledge gaps and infrastructure deficiencies, but they also arise from fundamental differences in how academic research is structured and how success is measured. Academic drug discovery should not seek to become identical to commercial pharmaceutical research, but we can do a better job of assessing and communicating the true potential of the drug leads we publish, thereby reducing the wastage of resources on nonviable compounds.

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

    PubMed

    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.

  1. Panacea, a semantic-enabled drug recommendations discovery framework.

    PubMed

    Doulaverakis, Charalampos; Nikolaidis, George; Kleontas, Athanasios; Kompatsiaris, Ioannis

    2014-03-06

    Personalized drug prescription can be benefited from the use of intelligent information management and sharing. International standard classifications and terminologies have been developed in order to provide unique and unambiguous information representation. Such standards can be used as the basis of automated decision support systems for providing drug-drug and drug-disease interaction discovery. Additionally, Semantic Web technologies have been proposed in earlier works, in order to support such systems. The paper presents Panacea, a semantic framework capable of offering drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standard classifications and terminologies, provide the backbone of the common representation of medical data while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Representation is based on a lightweight ontology. A layered reasoning approach is implemented where at the first layer ontological inference is used in order to discover underlying knowledge, while at the second layer a two-step rule selection strategy is followed resulting in a computationally efficient reasoning approach. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. Panacea is evaluated both in terms of quality of recommendations against real clinical data and performance. The quality recommendation gave useful insights regarding requirements for real world deployment and revealed several parameters that affected the recommendation results. Performance-wise, Panacea is compared to a previous published work by the authors, a service for drug recommendations named GalenOWL, and presents their differences in modeling and approach to the problem

  2. Machine-Learning Techniques Applied to Antibacterial Drug Discovery

    PubMed Central

    Durrant, Jacob D.; Amaro, Rommie E.

    2014-01-01

    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. 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 in an academic setting, leading to improved hit rates and faster transitions to pre-clinical 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. PMID:25521642

  3. Synthetic biology for pharmaceutical drug discovery

    PubMed Central

    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

  4. Chronicles in drug discovery.

    PubMed

    Davies, Shelley L; Ferrer, Elisa; Moral, Maria Angels

    2006-06-01

    Chronicles in Drug Discovery features special interest reports on advances in drug discovery. This month we highlight new options to prevent oral mucositis, a treatment-limiting adverse effect of chemotherapy. Studies are currently focusing on mechanism-based therapies to prevent or repair DNA damage to epithelial and submucosal cells and the cascade or events that follow to cause tissue damage or analgesics to ease the associated oral cavity pain. Therapeutic limitations also exist for the use of the highly effective antibiotic gentamicin, as it evokes acute renal failure. Mechanistic investigations have shed some light on potential targets: the kallikreins, peroxynitrite-related pathways, superoxide production and the accumulation of aminoglycosides. New antibiotic strategies for trachoma, the leading cause of preventable blindness, are also explored along with studies to aid the development of vaccine candidates. Finally, we discuss the utility of allosteric-potentiating ligands to modulate nicotinic acetylcholine receptors, mimicking the reward/addictive effects of nicotine, as potential strategies for smoking cessation. (c) 2006 Prous Science. All rights reserved.

  5. Applications of chemogenomic library screening in drug discovery.

    PubMed

    Jones, Lyn H; Bunnage, Mark E

    2017-04-01

    The allure of phenotypic screening, combined with the industry preference for target-based approaches, has prompted the development of innovative chemical biology technologies that facilitate the identification of new therapeutic targets for accelerated drug discovery. A chemogenomic library is a collection of selective small-molecule pharmacological agents, and a hit from such a set in a phenotypic screen suggests that the annotated target or targets of that pharmacological agent may be involved in perturbing the observable phenotype. In this Review, we describe opportunities for chemogenomic screening to considerably expedite the conversion of phenotypic screening projects into target-based drug discovery approaches. Other applications are explored, including drug repositioning, predictive toxicology and the discovery of novel pharmacological modalities.

  6. Recent advances in malaria drug discovery.

    PubMed

    Lanteri, Charlotte A; Johnson, Jacob D; Waters, Norman C

    2007-06-01

    Malaria is responsible for over 300 million clinical cases annually and claims the lives of approximately 1-2 million. With a disease that has plagued humanity throughout history, one would think that better control measures would be in place to decrease the mortality and morbidity associated with malaria. Due to malaria drug resistance, an increase in the number of clinical infections and deaths is soon likely to be observed. Therefore, there is a push to identify and introduce new drug entities for malaria treatment and prophylaxis. In an effort to develop new malaria drugs, several different approaches have been implemented. These include the use of drug combinations of either new or existing antimalarials, exploitation of natural products, identification of resistance reversal or sensitizing agents and the targeting of specific malarial enzymes. Past experience has shown that introduction of the same chemical entities, such as quinolines and antifolates, results in only limited efficacy with resistance developing rapidly within one year of introduction. New approaches to drug discovery should identify novel chemotypes which circumvent the parasite's disposition to drug resistance. This review summarizes current efforts in malaria drug discovery as uncovered in recent patent literature.

  7. Antibody-enabled small-molecule drug discovery.

    PubMed

    Lawson, Alastair D G

    2012-06-29

    Although antibody-based therapeutics have become firmly established as medicines for serious diseases, the value of antibodies as tools in the early stages of small-molecule drug discovery is only beginning to be realized. In particular, antibodies may provide information to reduce risk in small-molecule drug discovery by enabling the validation of targets and by providing insights into the design of small-molecule screening assays. Moreover, antibodies can act as guides in the quest for small molecules that have the ability to modulate protein-protein interactions, which have traditionally only been considered to be tractable targets for biological drugs. The development of small molecules that have similar therapeutic effects to current biologics has the potential to benefit a broader range of patients at earlier stages of disease.

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

    PubMed

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

    2017-11-08

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

  9. Three-Dimensional in Vitro Cell Culture Models in Drug Discovery and Drug Repositioning

    PubMed Central

    Langhans, Sigrid A.

    2018-01-01

    Drug development is a lengthy and costly process that proceeds through several stages from target identification to lead discovery and optimization, preclinical validation and clinical trials culminating in approval for clinical use. An important step in this process is high-throughput screening (HTS) of small compound libraries for lead identification. Currently, the majority of cell-based HTS is being carried out on cultured cells propagated in two-dimensions (2D) on plastic surfaces optimized for tissue culture. At the same time, compelling evidence suggests that cells cultured in these non-physiological conditions are not representative of cells residing in the complex microenvironment of a tissue. This discrepancy is thought to be a significant contributor to the high failure rate in drug discovery, where only a low percentage of drugs investigated ever make it through the gamut of testing and approval to the market. Thus, three-dimensional (3D) cell culture technologies that more closely resemble in vivo cell environments are now being pursued with intensity as they are expected to accommodate better precision in drug discovery. Here we will review common approaches to 3D culture, discuss the significance of 3D cultures in drug resistance and drug repositioning and address some of the challenges of applying 3D cell cultures to high-throughput drug discovery. PMID:29410625

  10. Protein Complex Production from the Drug Discovery Standpoint.

    PubMed

    Moarefi, Ismail

    2016-01-01

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

  11. Role of Molecular Dynamics and Related Methods in Drug Discovery.

    PubMed

    De Vivo, Marco; Masetti, Matteo; Bottegoni, Giovanni; Cavalli, Andrea

    2016-05-12

    Molecular dynamics (MD) and related methods are close to becoming routine computational tools for drug discovery. Their main advantage is in explicitly treating structural flexibility and entropic effects. This allows a more accurate estimate of the thermodynamics and kinetics associated with drug-target recognition and binding, as better algorithms and hardware architectures increase their use. Here, we review the theoretical background of MD and enhanced sampling methods, focusing on free-energy perturbation, metadynamics, steered MD, and other methods most consistently used to study drug-target binding. We discuss unbiased MD simulations that nowadays allow the observation of unsupervised ligand-target binding, assessing how these approaches help optimizing target affinity and drug residence time toward improved drug efficacy. Further issues discussed include allosteric modulation and the role of water molecules in ligand binding and optimization. We conclude by calling for more prospective studies to attest to these methods' utility in discovering novel drug candidates.

  12. Network-Based Approaches in Drug Discovery and Early Development

    PubMed Central

    Harrold, JM; Ramanathan, M; Mager, DE

    2015-01-01

    Identification of novel targets is a critical first step in the drug discovery and development process. Most diseases such as cancer, metabolic disorders, and neurological disorders are complex, and their pathogenesis involves multiple genetic and environmental factors. Finding a viable drug target–drug combination with high potential for yielding clinical success within the efficacy–toxicity spectrum is extremely challenging. Many examples are now available in which network-based approaches show potential for the identification of novel targets and for the repositioning of established targets. The objective of this article is to highlight network approaches for identifying novel targets with greater chances of gaining approved drugs with maximal efficacy and minimal side effects. Further enhancement of these approaches may emerge from effectively integrating computational systems biology with pharmacodynamic systems analysis. Coupling genomics, proteomics, and metabolomics databases with systems pharmacology modeling may aid in the development of disease-specific networks that can be further used to build confidence in target identification. PMID:24025802

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

    PubMed

    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.

  14. Drug discovery in prostate cancer mouse models.

    PubMed

    Valkenburg, Kenneth C; Pienta, Kenneth J

    2015-01-01

    The mouse is an important, though imperfect, organism with which to model human disease and to discover and test novel drugs in a preclinical setting. Many experimental strategies have been used to discover new biological and molecular targets in the mouse, with the hopes of translating these discoveries into novel drugs to treat prostate cancer in humans. Modeling prostate cancer in the mouse, however, has been challenging, and often drugs that work in mice have failed in human trials. The authors discuss the similarities and differences between mice and men; the types of mouse models that exist to model prostate cancer; practical questions one must ask when using a mouse as a model; and potential reasons that drugs do not often translate to humans. They also discuss the current value in using mouse models for drug discovery to treat prostate cancer and what needs are still unmet in field. With proper planning and following practical guidelines by the researcher, the mouse is a powerful experimental tool. The field lacks genetically engineered metastatic models, and xenograft models do not allow for the study of the immune system during the metastatic process. There remain several important limitations to discovering and testing novel drugs in mice for eventual human use, but these can often be overcome. Overall, mouse modeling is an essential part of prostate cancer research and drug discovery. Emerging technologies and better and ever-increasing forms of communication are moving the field in a hopeful direction.

  15. Computational biology for cardiovascular biomarker discovery.

    PubMed

    Azuaje, Francisco; Devaux, Yvan; Wagner, Daniel

    2009-07-01

    Computational biology is essential in the process of translating biological knowledge into clinical practice, as well as in the understanding of biological phenomena based on the resources and technologies originating from the clinical environment. One such key contribution of computational biology is the discovery of biomarkers for predicting clinical outcomes using 'omic' information. This process involves the predictive modelling and integration of different types of data and knowledge for screening, diagnostic or prognostic purposes. Moreover, this requires the design and combination of different methodologies based on statistical analysis and machine learning. This article introduces key computational approaches and applications to biomarker discovery based on different types of 'omic' data. Although we emphasize applications in cardiovascular research, the computational requirements and advances discussed here are also relevant to other domains. We will start by introducing some of the contributions of computational biology to translational research, followed by an overview of methods and technologies used for the identification of biomarkers with predictive or classification value. The main types of 'omic' approaches to biomarker discovery will be presented with specific examples from cardiovascular research. This will include a review of computational methodologies for single-source and integrative data applications. Major computational methods for model evaluation will be described together with recommendations for reporting models and results. We will present recent advances in cardiovascular biomarker discovery based on the combination of gene expression and functional network analyses. The review will conclude with a discussion of key challenges for computational biology, including perspectives from the biosciences and clinical areas.

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

    PubMed

    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.

  17. Cell and small animal models for phenotypic drug discovery.

    PubMed

    Szabo, Mihaly; Svensson Akusjärvi, Sara; Saxena, Ankur; Liu, Jianping; Chandrasekar, Gayathri; Kitambi, Satish S

    2017-01-01

    The phenotype-based drug discovery (PDD) approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s) or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery.

  18. Simple animal models for amyotrophic lateral sclerosis drug discovery.

    PubMed

    Patten, Shunmoogum A; Parker, J Alex; Wen, Xiao-Yan; Drapeau, Pierre

    2016-08-01

    Simple animal models have enabled great progress in uncovering the disease mechanisms of amyotrophic lateral sclerosis (ALS) and are helping in the selection of therapeutic compounds through chemical genetic approaches. Within this article, the authors provide a concise overview of simple model organisms, C. elegans, Drosophila and zebrafish, which have been employed to study ALS and discuss their value to ALS drug discovery. In particular, the authors focus on innovative chemical screens that have established simple organisms as important models for ALS drug discovery. There are several advantages of using simple animal model organisms to accelerate drug discovery for ALS. It is the authors' particular belief that the amenability of simple animal models to various genetic manipulations, the availability of a wide range of transgenic strains for labelling motoneurons and other cell types, combined with live imaging and chemical screens should allow for new detailed studies elucidating early pathological processes in ALS and subsequent drug and target discovery.

  19. Role of Academic Drug Discovery in the Quest for New CNS Therapeutics.

    PubMed

    Yokley, Brian H; Hartman, Matthew; Slusher, Barbara S

    2017-03-15

    There was a greater than 50% decline in central nervous system (CNS) drug discovery and development programs by major pharmaceutical companies from 2009 to 2014. This decline was paralleled by a rise in the number of university led drug discovery centers, many in the CNS area, and a growth in the number of public-private drug discovery partnerships. Diverse operating models have emerged as the academic drug discovery centers adapt to this changing ecosystem.

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

    PubMed

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

    2012-08-01

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

  1. Use of combinatorial chemistry to speed drug discovery.

    PubMed

    Rádl, S

    1998-10-01

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

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

    PubMed

    Vasaikar, Suhas; Bhatia, Pooja; Bhatia, Partap G; Chu Yaiw, Koon

    2016-11-21

    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. Rethinking 'academic' drug discovery: the Manchester Institute perspective.

    PubMed

    Jordan, Allan M; Waddell, Ian D; Ogilvie, Donald J

    2015-05-01

    The contraction in research within pharma has seen a renaissance in drug discovery within the academic setting. Often, groups grow organically from academic research laboratories, exploiting a particular area of novel biology or new technology. However, increasingly, new groups driven by industrial staff are emerging with demonstrable expertise in the delivery of medicines. As part of a strategic review by Cancer Research UK (CR-UK), the drug discovery team at the Manchester Institute was established to translate novel research from the Manchester cancer research community into drug discovery programmes. From a standing start, we have taken innovative approaches to solve key issues faced by similar groups, such as hit finding and target identification. Herein, we share our lessons learnt and successful strategies. Copyright © 2014 Elsevier Ltd. All rights reserved.

  4. Drug-Target Kinetics in Drug Discovery.

    PubMed

    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.

  5. A machine-learned computational functional genomics-based approach to drug classification.

    PubMed

    Lötsch, Jörn; Ultsch, Alfred

    2016-12-01

    The public accessibility of "big data" about the molecular targets of drugs and the biological functions of genes allows novel data science-based approaches to pharmacology that link drugs directly with their effects on pathophysiologic processes. This provides a phenotypic path to drug discovery and repurposing. This paper compares the performance of a functional genomics-based criterion to the traditional drug target-based classification. Knowledge discovery in the DrugBank and Gene Ontology databases allowed the construction of a "drug target versus biological process" matrix as a combination of "drug versus genes" and "genes versus biological processes" matrices. As a canonical example, such matrices were constructed for classical analgesic drugs. These matrices were projected onto a toroid grid of 50 × 82 artificial neurons using a self-organizing map (SOM). The distance, respectively, cluster structure of the high-dimensional feature space of the matrices was visualized on top of this SOM using a U-matrix. The cluster structure emerging on the U-matrix provided a correct classification of the analgesics into two main classes of opioid and non-opioid analgesics. The classification was flawless with both the functional genomics and the traditional target-based criterion. The functional genomics approach inherently included the drugs' modulatory effects on biological processes. The main pharmacological actions known from pharmacological science were captures, e.g., actions on lipid signaling for non-opioid analgesics that comprised many NSAIDs and actions on neuronal signal transmission for opioid analgesics. Using machine-learned techniques for computational drug classification in a comparative assessment, a functional genomics-based criterion was found to be similarly suitable for drug classification as the traditional target-based criterion. This supports a utility of functional genomics-based approaches to computational system pharmacology for drug

  6. Flow Cytometry: Impact On Early Drug Discovery

    PubMed Central

    Edwards, Bruce S.; Sklar, Larry A.

    2015-01-01

    Summary Modern flow cytometers can make optical measurements of 10 or more parameters per cell at tens-of-thousands of cells per second and over five orders of magnitude dynamic range. Although flow cytometry is used in most drug discovery stages, “sip-and-spit” sampling technology has restricted it to low sample throughput applications. The advent of HyperCyt sampling technology has recently made possible primary screening applications in which tens-of-thousands of compounds are analyzed per day. Target-multiplexing methodologies in combination with extended multi-parameter analyses enable profiling of lead candidates early in the discovery process, when the greatest numbers of candidates are available for evaluation. The ability to sample small volumes with negligible waste reduces reagent costs, compound usage and consumption of cells. Improved compound library formatting strategies can further extend primary screening opportunities when samples are scarce. Dozens of targets have been screened in 384- and 1536-well assay formats, predominantly in academic screening lab settings. In concert with commercial platform evolution and trending drug discovery strategies, HyperCyt-based systems are now finding their way into mainstream screening labs. Recent advances in flow-based imaging, mass spectrometry and parallel sample processing promise dramatically expanded single cell profiling capabilities to bolster systems level approaches to drug discovery. PMID:25805180

  7. Single-Cell Sequencing for Drug Discovery and Drug Development.

    PubMed

    Wu, Hongjin; Wang, Charles; Wu, Shixiu

    2017-01-01

    Next-generation sequencing (NGS), particularly single-cell sequencing, has revolutionized the scale and scope of genomic and biomedical research. Recent technological advances in NGS and singlecell studies have made the deep whole-genome (DNA-seq), whole epigenome and whole-transcriptome sequencing (RNA-seq) at single-cell level feasible. NGS at the single-cell level expands our view of genome, epigenome and transcriptome and allows the genome, epigenome and transcriptome of any organism to be explored without a priori assumptions and with unprecedented throughput. And it does so with single-nucleotide resolution. NGS is also a very powerful tool for drug discovery and drug development. In this review, we describe the current state of single-cell sequencing techniques, which can provide a new, more powerful and precise approach for analyzing effects of drugs on treated cells and tissues. Our review discusses single-cell whole genome/exome sequencing (scWGS/scWES), single-cell transcriptome sequencing (scRNA-seq), single-cell bisulfite sequencing (scBS), and multiple omics of single-cell sequencing. We also highlight the advantages and challenges of each of these approaches. Finally, we describe, elaborate and speculate the potential applications of single-cell sequencing for drug discovery and drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  8. Cancer drug discovery: recent innovative approaches to tumor modeling.

    PubMed

    Lovitt, Carrie J; Shelper, Todd B; Avery, Vicky M

    2016-09-01

    Cell culture models have been at the heart of anti-cancer drug discovery programs for over half a century. Advancements in cell culture techniques have seen the rapid evolution of more complex in vitro cell culture models investigated for use in drug discovery. Three-dimensional (3D) cell culture research has become a strong focal point, as this technique permits the recapitulation of the tumor microenvironment. Biologically relevant 3D cellular models have demonstrated significant promise in advancing cancer drug discovery, and will continue to play an increasing role in the future. In this review, recent advances in 3D cell culture techniques and their application in tumor modeling and anti-cancer drug discovery programs are discussed. The topics include selection of cancer cells, 3D cell culture assays (associated endpoint measurements and analysis), 3D microfluidic systems and 3D bio-printing. Although advanced cancer cell culture models and techniques are becoming commonplace in many research groups, the use of these approaches has yet to be fully embraced in anti-cancer drug applications. Furthermore, limitations associated with analyzing information-rich biological data remain unaddressed.

  9. Solid-Phase Biological Assays for Drug Discovery

    NASA Astrophysics Data System (ADS)

    Forsberg, Erica M.; Sicard, Clémence; Brennan, John D.

    2014-06-01

    In the past 30 years, there has been a significant growth in the use of solid-phase assays in the area of drug discovery, with a range of new assays being used for both soluble and membrane-bound targets. In this review, we provide some basic background to typical drug targets and immobilization protocols used in solid-phase biological assays (SPBAs) for drug discovery, with emphasis on particularly labile biomolecular targets such as kinases and membrane-bound receptors, and highlight some of the more recent approaches for producing protein microarrays, bioaffinity columns, and other devices that are central to small molecule screening by SPBA. We then discuss key applications of such assays to identify drug leads, with an emphasis on the screening of mixtures. We conclude by highlighting specific advantages and potential disadvantages of SPBAs, particularly as they relate to particular assay formats.

  10. Exploring Natural Products from the Biodiversity of Pakistan for Computational Drug Discovery Studies: Collection, Optimization, Design and Development of A Chemical Database (ChemDP).

    PubMed

    Mirza, Shaher Bano; Bokhari, Habib; Fatmi, Muhammad Qaiser

    2015-01-01

    Pakistan possesses a rich and vast source of natural products (NPs). Some of these secondary metabolites have been identified as potent therapeutic agents. However, the medicinal usage of most of these compounds has not yet been fully explored. The discoveries for new scaffolds of NPs as inhibitors of certain enzymes or receptors using advanced computational drug discovery approaches are also limited due to the unavailability of accurate 3D structures of NPs. An organized database incorporating all relevant information, therefore, can facilitate to explore the medicinal importance of the metabolites from Pakistani Biodiversity. The Chemical Database of Pakistan (ChemDP; release 01) is a fully-referenced, evolving, web-based, virtual database which has been designed and developed to introduce natural products (NPs) and their derivatives from the biodiversity of Pakistan to Global scientific communities. The prime aim is to provide quality structures of compounds with relevant information for computer-aided drug discovery studies. For this purpose, over 1000 NPs have been identified from more than 400 published articles, for which 2D and 3D molecular structures have been generated with a special focus on their stereochemistry, where applicable. The PM7 semiempirical quantum chemistry method has been used to energy optimize the 3D structure of NPs. The 2D and 3D structures can be downloaded as .sdf, .mol, .sybyl, .mol2, and .pdb files - readable formats by many chemoinformatics/bioinformatics software packages. Each entry in ChemDP contains over 100 data fields representing various molecular, biological, physico-chemical and pharmacological properties, which have been properly documented in the database for end users. These pieces of information have been either manually extracted from the literatures or computationally calculated using various computational tools. Cross referencing to a major data repository i.e. ChemSpider has been made available for overlapping

  11. Novel Directions for Diabetes Mellitus Drug Discovery

    PubMed Central

    Maiese, Kenneth; Chong, Zhao Zhong; Shang, Yan Chen; Wang, Shaohui

    2012-01-01

    Introduction Diabetes mellitus impacts almost 200 million individuals worldwide and leads to debilitating complications. New avenues of drug discovery must target the underlying cellular processes of oxidative stress, apoptosis, autophagy, and inflammation that can mediate multi-system pathology during diabetes mellitus. Areas Covered We examine novel directions for drug discovery that involve the β-nicotinamide adenine dinucleotide (NAD+) precursor nicotinamide, the cytokine erythropoietin, the NAD+-dependent protein histone deacetylase SIRT1, the serine/threonine-protein kinase mammalian target of rapamycin (mTOR), and the wingless pathway. Implications for the targeting of these pathways that oversee gluconeogenic genes, insulin signaling and resistance, fatty acid beta-oxidation, inflammation, and cellular survival are presented. Expert Opinion Nicotinamide, erythropoietin, and the downstram pathways of SIRT1, mTOR, forkhead transcription factors, and wingless signaling offer exciting prospects for novel directions of drug discovery for the treatment of metabolic disorders. Future investigations must dissect the complex relationship and fine modulation of these pathways for the successful translation of robust reparative and regenerative strategies against diabetes mellitus and the complications of this disorder. PMID:23092114

  12. Zebrafish models in neuropsychopharmacology and CNS drug discovery.

    PubMed

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

    2017-07-01

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

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

    PubMed

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

    2017-12-01

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

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

    PubMed

    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.

  15. Allosteric Tuning of Caspase-7: A Fragment-Based Drug Discovery Approach.

    PubMed

    Vance, Nicholas R; Gakhar, Lokesh; Spies, M Ashley

    2017-11-13

    The caspase family of cysteine proteases are highly sought-after drug targets owing to their essential roles in apoptosis, proliferation, and inflammation pathways. High-throughput screening efforts to discover inhibitors have gained little traction. Fragment-based screening has emerged as a powerful approach for the discovery of innovative drug leads. This method has become a central facet of drug discovery campaigns in the pharmaceutical industry and academia. A fragment-based drug discovery campaign against human caspase-7 resulted in the discovery of a novel series of allosteric inhibitors. An X-ray crystal structure of caspase-7 bound to a fragment hit and a thorough kinetic characterization of a zymogenic form of the enzyme were used to investigate the allosteric mechanism of inhibition. This work further advances our understanding of the mechanisms of allosteric control of this class of pharmaceutically relevant enzymes, and provides a new path forward for drug discovery efforts. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

  16. A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine

    PubMed Central

    Stern, Andrew M.; Schurdak, Mark E.; Bahar, Ivet; Berg, Jeremy M.; Taylor, D. Lansing

    2016-01-01

    Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)–driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point. PMID:26962875

  17. Novel Approaches to Pulmonary Arterial Hypertension Drug Discovery

    PubMed Central

    Sung, Yon K.; Yuan, Ke; de Jesus Perez, Vinicio A.

    2016-01-01

    Introduction Pulmonary arterial hypertension (PAH) is a rare disorder associated with abnormally elevated pulmonary pressures that, if untreated, leads to right heart failure and premature death. The goal of drug development for PAH is to develop effective therapies that halt, or ideally, reverse the obliterative vasculopathy that results in vessel loss and obstruction of blood flow to the lungs. Areas Covered This review summarizes the current approach to candidate discovery in PAH and discusses the currently available drug discovery methods that should be implemented to prioritize targets and obtain a comprehensive pharmacological profile of promising compounds with well-defined mechanisms. Expert opinion To improve the successful identification of leading drug candidates, it is necessary that traditional pre-clinical studies are combined with drug screening strategies that maximize the characterization of biological activity and identify relevant off-target effects that could hinder the clinical efficacy of the compound when tested in human subjects. A successful drug discovery strategy in PAH will require collaboration of clinician scientists with medicinal chemists and pharmacologists who can identify compounds with an adequate safety profile and biological activity against relevant disease mechanisms. PMID:26901465

  18. gene2drug: a computational tool for pathway-based rational drug repositioning.

    PubMed

    Napolitano, Francesco; Carrella, Diego; Mandriani, Barbara; Pisonero-Vaquero, Sandra; Sirci, Francesco; Medina, Diego L; Brunetti-Pierri, Nicola; di Bernardo, Diego

    2018-05-01

    Drug repositioning has been proposed as an effective shortcut to drug discovery. The availability of large collections of transcriptional responses to drugs enables computational approaches to drug repositioning directly based on measured molecular effects. We introduce a novel computational methodology for rational drug repositioning, which exploits the transcriptional responses following treatment with small molecule. Specifically, given a therapeutic target gene, a prioritization of potential effective drugs is obtained by assessing their impact on the transcription of genes in the pathway(s) including the target. We performed in silico validation and comparison with a state-of-art technique based on similar principles. We next performed experimental validation in two different real-case drug repositioning scenarios: (i) upregulation of the glutamate-pyruvate transaminase (GPT), which has been shown to induce reduction of oxalate levels in a mouse model of primary hyperoxaluria, and (ii) activation of the transcription factor TFEB, a master regulator of lysosomal biogenesis and autophagy, whose modulation may be beneficial in neurodegenerative disorders. A web tool for Gene2drug is freely available at http://gene2drug.tigem.it. An R package is under development and can be obtained from https://github.com/franapoli/gep2pep. dibernardo@tigem.it. Supplementary data are available at Bioinformatics online.

  19. Fragment-Based Drug Discovery Using NMR Spectroscopy

    PubMed Central

    Harner, Mary J.; Frank, Andreas O.; Fesik, Stephen W.

    2013-01-01

    Nuclear magnetic resonance (NMR) spectroscopy has evolved into a powerful tool for fragment-based drug discovery over the last two decades. While NMR has been traditionally used to elucidate the three-dimensional structures and dynamics of biomacromolecules and their interactions, it can also be a very valuable tool for the reliable identification of small molecules that bind to proteins and for hit-to-lead optimization. Here, we describe the use of NMR spectroscopy as a method for fragment-based drug discovery and how to most effectively utilize this approach for discovering novel therapeutics based on our experience. PMID:23686385

  20. Boesenbergia rotunda: From Ethnomedicine to Drug Discovery

    PubMed Central

    Eng-Chong, Tan; Yean-Kee, Lee; Chin-Fei, Chee; Choon-Han, Heh; Sher-Ming, Wong; Li-Ping, Christina Thio; Gen-Teck, Foo; Khalid, Norzulaani; Abd Rahman, Noorsaadah; Karsani, Saiful Anuar; Othman, Shatrah; Othman, Rozana; Yusof, Rohana

    2012-01-01

    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. PMID:23243448

  1. Targeting cysteine proteases in trypanosomatid disease drug discovery.

    PubMed

    Ferreira, Leonardo G; Andricopulo, Adriano D

    2017-12-01

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

  2. Metabolic stability for drug discovery and development: pharmacokinetic and biochemical challenges.

    PubMed

    Masimirembwa, Collen M; Bredberg, Ulf; Andersson, Tommy B

    2003-01-01

    Metabolic stability refers to the susceptibility of compounds to biotransformation in the context of selecting and/or designing drugs with favourable pharmacokinetic properties. Metabolic stability results are usually reported as measures of intrinsic clearance, from which secondary pharmacokinetic parameters such as bioavailability and half-life can be calculated when other data on volume of distribution and fraction absorbed are available. Since these parameters are very important in defining the pharmacological and toxicological profile of drugs as well as patient compliance, the pharmaceutical industry has a particular interest in optimising for metabolic stability during the drug discovery and development process. In the early phases of drug discovery, new chemical entities cannot be administered to humans; hence, predictions of these properties have to be made from in vivo animal, in vitro cellular/subcellular and computational systems. The utility of these systems to define the metabolic stability of compounds that is predictive of the human situation will be reviewed here. The timing of performing the studies in the discovery process and the impact of recent advances in research on drug absorption, distribution, metabolism and excretion (ADME) will be evaluated with respect to the scope and depth of metabolic stability issues. Quantitative prediction of in vivo clearance from in vitro metabolism data has, for many compounds, been shown to be poor in retrospective studies. One explanation for this may be that there are components used in the equations for scaling that are missing or uncertain and should be an area of more research. For example, as a result of increased biochemical understanding of drug metabolism, old assumptions (e.g. that the liver is the principal site of first-pass metabolism) need revision and new knowledge (e.g. the relationship between transporters and drug metabolising enzymes) needs to be incorporated into in vitro-in vivo

  3. Flow Cytometry: Impact on Early Drug Discovery.

    PubMed

    Edwards, Bruce S; Sklar, Larry A

    2015-07-01

    Modern flow cytometers can make optical measurements of 10 or more parameters per cell at tens of thousands of cells per second and more than five orders of magnitude dynamic range. Although flow cytometry is used in most drug discovery stages, "sip-and-spit" sampling technology has restricted it to low-sample-throughput applications. The advent of HyperCyt sampling technology has recently made possible primary screening applications in which tens of thousands of compounds are analyzed per day. Target-multiplexing methodologies in combination with extended multiparameter analyses enable profiling of lead candidates early in the discovery process, when the greatest numbers of candidates are available for evaluation. The ability to sample small volumes with negligible waste reduces reagent costs, compound usage, and consumption of cells. Improved compound library formatting strategies can further extend primary screening opportunities when samples are scarce. Dozens of targets have been screened in 384- and 1536-well assay formats, predominantly in academic screening lab settings. In concert with commercial platform evolution and trending drug discovery strategies, HyperCyt-based systems are now finding their way into mainstream screening labs. Recent advances in flow-based imaging, mass spectrometry, and parallel sample processing promise dramatically expanded single-cell profiling capabilities to bolster systems-level approaches to drug discovery. © 2015 Society for Laboratory Automation and Screening.

  4. Learning from the past for TB drug discovery in the future

    PubMed Central

    Mikušová, Katarína; Ekins, Sean

    2016-01-01

    Tuberculosis drug discovery has shifted in recent years from a primarily target-based approach to one that uses phenotypic high-throughput screens. As examples of this, through our EU-funded FP7 collaborations, New Medicines for Tuberculosis was target-based and our more-recent More Medicines for Tuberculosis project predominantly used phenotypic screening. From these projects we have examples of success (DprE1) and failure (PimA) going from drug to target and from target to drug, respectively. It is clear that we still have much to learn about the drug targets and the complex effects of the drugs on Mycobacterium tuberculosis. We propose a more integrated approach that learns from earlier drug discovery efforts that could help to move drug discovery forward. PMID:27717850

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

    PubMed

    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.

  6. Drug Discovery for Neglected Diseases: Molecular Target-Based and Phenotypic Approaches

    PubMed Central

    2013-01-01

    Drug discovery for neglected tropical diseases is carried out using both target-based and phenotypic approaches. In this paper, target-based approaches are discussed, with a particular focus on human African trypanosomiasis. Target-based drug discovery can be successful, but careful selection of targets is required. There are still very few fully validated drug targets in neglected diseases, and there is a high attrition rate in target-based drug discovery for these diseases. Phenotypic screening is a powerful method in both neglected and non-neglected diseases and has been very successfully used. Identification of molecular targets from phenotypic approaches can be a way to identify potential new drug targets. PMID:24015767

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

    PubMed

    Kumar, Ashutosh; Zhang, Kam Y J

    2015-01-01

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

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

    PubMed

    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.

  9. Organs-on-a-chip for drug discovery.

    PubMed

    Selimović, Seila; Dokmeci, Mehmet R; Khademhosseini, Ali

    2013-10-01

    The current drug discovery process is arduous and costly, and a majority of the drug candidates entering clinical trials fail to make it to the marketplace. The standard static well culture approaches, although useful, do not fully capture the intricate in vivo environment. By merging the advances in microfluidics with microfabrication technologies, novel platforms are being introduced that lead to the creation of organ functions on a single chip. Within these platforms, microengineering enables precise control over the cellular microenvironment, whereas microfluidics provides an ability to perfuse the constructs on a chip and to connect individual sections with each other. This approach results in microsystems that may better represent the in vivo environment. These organ-on-a-chip platforms can be utilized for developing disease models as well as for conducting drug testing studies. In this article, we highlight several key developments in these microscale platforms for drug discovery applications. Copyright © 2013 Elsevier Ltd. All rights reserved.

  10. Emergence of Chinese drug discovery research: impact of hit and lead identification.

    PubMed

    Zhou, Caihong; Zhou, Yan; Wang, Jia; Zhu, Yue; Deng, Jiejie; Wang, Ming-Wei

    2015-03-01

    The identification of hits and the generation of viable leads is an early and yet crucial step in drug discovery. In the West, the main players of drug discovery are pharmaceutical and biotechnology companies, while in China, academic institutions remain central in the field of drug discovery. There has been a tremendous amount of investment from the public as well as private sectors to support infrastructure buildup and expertise consolidation relative to drug discovery and development in the past two decades. A large-scale compound library has been established in China, and a series of high-impact discoveries of lead compounds have been made by integrating information obtained from different technology-based strategies. Natural products are a major source in China's drug discovery efforts. Knowledge has been enhanced via disruptive breakthroughs such as the discovery of Boc5 as a nonpeptidic agonist of glucagon-like peptide 1 receptor (GLP-1R), one of the class B G protein-coupled receptors (GPCRs). Most of the original hit identification and lead generation were carried out by academic institutions, including universities and specialized research institutes. The Chinese pharmaceutical industry is gradually transforming itself from manufacturing low-end generics and active pharmaceutical ingredients to inventing new drugs. © 2014 Society for Laboratory Automation and Screening.

  11. When drug discovery meets web search: Learning to Rank for ligand-based virtual screening.

    PubMed

    Zhang, Wei; Ji, Lijuan; Chen, Yanan; Tang, Kailin; Wang, Haiping; Zhu, Ruixin; Jia, Wei; Cao, Zhiwei; Liu, Qi

    2015-01-01

    The rapid increase in the emergence of novel chemical substances presents a substantial demands for more sophisticated computational methodologies for drug discovery. In this study, the idea of Learning to Rank in web search was presented in drug virtual screening, which has the following unique capabilities of 1). Applicable of identifying compounds on novel targets when there is not enough training data available for these targets, and 2). Integration of heterogeneous data when compound affinities are measured in different platforms. A standard pipeline was designed to carry out Learning to Rank in virtual screening. Six Learning to Rank algorithms were investigated based on two public datasets collected from Binding Database and the newly-published Community Structure-Activity Resource benchmark dataset. The results have demonstrated that Learning to rank is an efficient computational strategy for drug virtual screening, particularly due to its novel use in cross-target virtual screening and heterogeneous data integration. To the best of our knowledge, we have introduced here the first application of Learning to Rank in virtual screening. The experiment workflow and algorithm assessment designed in this study will provide a standard protocol for other similar studies. All the datasets as well as the implementations of Learning to Rank algorithms are available at http://www.tongji.edu.cn/~qiliu/lor_vs.html. Graphical AbstractThe analogy between web search and ligand-based drug discovery.

  12. Low Data Drug Discovery with One-Shot Learning

    PubMed Central

    2017-01-01

    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. Model.2015, 55, 263–27425635324). 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). PMID:28470045

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

    PubMed

    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).

  14. A Simple and Resource-efficient Setup for the Computer-aided Drug Design Laboratory.

    PubMed

    Moretti, Loris; Sartori, Luca

    2016-10-01

    Undertaking modelling investigations for Computer-Aided Drug Design (CADD) requires a proper environment. In principle, this could be done on a single computer, but the reality of a drug discovery program requires robustness and high-throughput computing (HTC) to efficiently support the research. Therefore, a more capable alternative is needed but its implementation has no widespread solution. Here, the realization of such a computing facility is discussed, from general layout to technical details all aspects are covered. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. Net present value approaches for drug discovery.

    PubMed

    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.

  16. Potential of agricultural fungicides for antifungal drug discovery.

    PubMed

    Jampilek, Josef

    2016-01-01

    While it is true that only a small fraction of fungal species are responsible for human mycoses, the increasing prevalence of fungal diseases has highlighted an urgent need to develop new antifungal drugs, especially for systemic administration. This contribution focuses on the similarities between agricultural fungicides and drugs. Inorganic, organometallic and organic compounds can be found amongst agricultural fungicides. Furthermore, fungicides are designed and developed in a similar fashion to drugs based on similar rules and guidelines, with fungicides also having to meet similar criteria of lead-likeness and/or drug-likeness. Modern approved specific-target fungicides are well-characterized entities with a proposed structure-activity relationships hypothesis and a defined mode of action. Extensive toxicological evaluation, including mammalian toxicology assays, is performed during the whole discovery and development process. Thus modern agrochemical research (design of modern agrochemicals) comes close to drug design, discovery and development. Therefore, modern specific-target fungicides represent excellent lead-like structures/models for novel drug design and development.

  17. A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine.

    PubMed

    Stern, Andrew M; Schurdak, Mark E; Bahar, Ivet; Berg, Jeremy M; Taylor, D Lansing

    2016-07-01

    Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)-driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point. © 2016 Society for Laboratory Automation and Screening.

  18. Medicinal chemistry inspired fragment-based drug discovery.

    PubMed

    Lanter, James; Zhang, Xuqing; Sui, Zhihua

    2011-01-01

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

  19. Biomimicry as a basis for drug discovery.

    PubMed

    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.

  20. Novel drug discovery for Chagas disease.

    PubMed

    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.

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

    PubMed

    Altmann, Karl-Heinz

    2017-10-25

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

  2. Discovery of FDA-Approved Drugs that Promote Retinal Cell Survival or Regeneration

    DTIC Science & Technology

    2015-10-01

    1 AD______________ AWARD NUMBER: W81XWH-14-1-0407 TITLE:Discovery of FDA-Approved Drugs that Promote Retinal Cell Survival or Regeneration...SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-14-1-0407Discovery of FDA-Approved Drugs that Promote Retinal Cell Survival or Regeneration 5c...vivo drug discovery platform named Automated Reporter Quantification in vivo (ARQiv). ARQiv quantifies reporter activity in transgenic zebrafish at

  3. Compound annotation with real time cellular activity profiles to improve drug discovery.

    PubMed

    Fang, Ye

    2016-01-01

    In the past decade, a range of innovative strategies have been developed to improve the productivity of pharmaceutical research and development. In particular, compound annotation, combined with informatics, has provided unprecedented opportunities for drug discovery. In this review, a literature search from 2000 to 2015 was conducted to provide an overview of the compound annotation approaches currently used in drug discovery. Based on this, a framework related to a compound annotation approach using real-time cellular activity profiles for probe, drug, and biology discovery is proposed. Compound annotation with chemical structure, drug-like properties, bioactivities, genome-wide effects, clinical phenotypes, and textural abstracts has received significant attention in early drug discovery. However, these annotations are mostly associated with endpoint results. Advances in assay techniques have made it possible to obtain real-time cellular activity profiles of drug molecules under different phenotypes, so it is possible to generate compound annotation with real-time cellular activity profiles. Combining compound annotation with informatics, such as similarity analysis, presents a good opportunity to improve the rate of discovery of novel drugs and probes, and enhance our understanding of the underlying biology.

  4. Drug discovery for neglected tropical diseases at the Sandler Center.

    PubMed

    Robertson, Stephanie A; Renslo, Adam R

    2011-08-01

    The Sandler Center's approach to target-based drug discovery for neglected tropical diseases is to focus on parasite targets that are homologous to human targets being actively investigated in the pharmaceutical industry. In this way we attempt to use both the know-how and actual chemical matter from other drug-development efforts to jump start the discovery process for neglected tropical diseases. Our approach is akin to drug repurposing, except that we seek to repurpose leads rather than drugs. Medicinal chemistry can then be applied to optimize the leads specifically for the desired antiparasitic indication.

  5. Postgenomic strategies in antibacterial drug discovery.

    PubMed

    Brötz-Oesterhelt, Heike; Sass, Peter

    2010-10-01

    During the last decade the field of antibacterial drug discovery has changed in many aspects including bacterial organisms of primary interest, discovery strategies applied and pharmaceutical companies involved. Target-based high-throughput screening had been disappointingly unsuccessful for antibiotic research. Understanding of this lack of success has increased substantially and the lessons learned refer to characteristics of targets, screening libraries and screening strategies. The 'genomics' approach was replaced by a diverse array of discovery strategies, for example, searching for new natural product leads among previously abandoned compounds or new microbial sources, screening for synthetic inhibitors by targeted approaches including structure-based design and analyses of focused libraries and designing resistance-breaking properties into antibiotics of established classes. Furthermore, alternative treatment options are being pursued including anti-virulence strategies and immunotherapeutic approaches. This article summarizes the lessons learned from the genomics era and describes discovery strategies resulting from that knowledge.

  6. Early Probe and Drug Discovery in Academia: A Minireview.

    PubMed

    Roy, Anuradha

    2018-02-09

    Drug discovery encompasses processes ranging from target selection and validation to the selection of a development candidate. While comprehensive drug discovery work flows are implemented predominantly in the big pharma domain, early discovery focus in academia serves to identify probe molecules that can serve as tools to study targets or pathways. Despite differences in the ultimate goals of the private and academic sectors, the same basic principles define the best practices in early discovery research. A successful early discovery program is built on strong target definition and validation using a diverse set of biochemical and cell-based assays with functional relevance to the biological system being studied. The chemicals identified as hits undergo extensive scaffold optimization and are characterized for their target specificity and off-target effects in in vitro and in animal models. While the active compounds from screening campaigns pass through highly stringent chemical and Absorption, Distribution, Metabolism, and Excretion (ADME) filters for lead identification, the probe discovery involves limited medicinal chemistry optimization. The goal of probe discovery is identification of a compound with sub-µM activity and reasonable selectivity in the context of the target being studied. The compounds identified from probe discovery can also serve as starting scaffolds for lead optimization studies.

  7. Oncology drug discovery: planning a turnaround.

    PubMed

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

    2014-04-01

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

  8. Accessing external innovation in drug discovery and development.

    PubMed

    Tufféry, Pierre

    2015-06-01

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

  9. Organic synthesis provides opportunities to transform drug discovery

    NASA Astrophysics Data System (ADS)

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

    2018-04-01

    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.

  10. High Throughput Screening for Anti–Trypanosoma cruzi Drug Discovery

    PubMed Central

    Alonso-Padilla, Julio; Rodríguez, Ana

    2014-01-01

    The discovery of new therapeutic options against Trypanosoma cruzi, the causative agent of Chagas disease, stands as a fundamental need. Currently, there are only two drugs available to treat this neglected disease, which represents a major public health problem in Latin America. Both available therapies, benznidazole and nifurtimox, have significant toxic side effects and their efficacy against the life-threatening symptomatic chronic stage of the disease is variable. Thus, there is an urgent need for new, improved anti–T. cruzi drugs. With the objective to reliably accelerate the drug discovery process against Chagas disease, several advances have been made in the last few years. Availability of engineered reporter gene expressing parasites triggered the development of phenotypic in vitro assays suitable for high throughput screening (HTS) as well as the establishment of new in vivo protocols that allow faster experimental outcomes. Recently, automated high content microscopy approaches have also been used to identify new parasitic inhibitors. These in vitro and in vivo early drug discovery approaches, which hopefully will contribute to bring better anti–T. cruzi drug entities in the near future, are reviewed here. PMID:25474364

  11. High throughput screening for anti-Trypanosoma cruzi drug discovery.

    PubMed

    Alonso-Padilla, Julio; Rodríguez, Ana

    2014-12-01

    The discovery of new therapeutic options against Trypanosoma cruzi, the causative agent of Chagas disease, stands as a fundamental need. Currently, there are only two drugs available to treat this neglected disease, which represents a major public health problem in Latin America. Both available therapies, benznidazole and nifurtimox, have significant toxic side effects and their efficacy against the life-threatening symptomatic chronic stage of the disease is variable. Thus, there is an urgent need for new, improved anti-T. cruzi drugs. With the objective to reliably accelerate the drug discovery process against Chagas disease, several advances have been made in the last few years. Availability of engineered reporter gene expressing parasites triggered the development of phenotypic in vitro assays suitable for high throughput screening (HTS) as well as the establishment of new in vivo protocols that allow faster experimental outcomes. Recently, automated high content microscopy approaches have also been used to identify new parasitic inhibitors. These in vitro and in vivo early drug discovery approaches, which hopefully will contribute to bring better anti-T. cruzi drug entities in the near future, are reviewed here.

  12. In vitro screening techniques for reactive metabolites for minimizing bioactivation potential in drug discovery.

    PubMed

    Prakash, Chandra; Sharma, Raman; Gleave, Michelle; Nedderman, Angus

    2008-11-01

    Drug induced toxicity remains one of the major reasons for failures of new pharmaceuticals, and for the withdrawal of approved drugs from the market. Efforts are being made to reduce attrition of drug candidates, and to minimize their bioactivation potential in the early stages of drug discovery in order to bring safer compounds to the market. Therefore, in addition to potency and selectivity; drug candidates are now selected on the basis of acceptable metabolism/toxicology profiles in preclinical species. To support this, new approaches have been developed, which include extensive in vitro methods using human and animal hepatic cellular and subcellular systems, recombinant human drug metabolizing enzymes, increased automation for higher-throughput screens, sensitive analytical technologies and in silico computational models to assess the metabolism aspects of the new chemical entities. By using these approaches many compounds that might have serious adverse reactions associated with them are effectively eliminated before reaching clinical trials, however some toxicities such as those caused by idiosyncratic responses, are not detected until a drug is in late stages of clinical trials or has become available to the market. One of the proposed mechanisms for the development of idiosyncratic drug toxicity is the bioactivation of drugs to form reactive metabolites by drug metabolizing enzymes. This review discusses the different approaches to, and benefits of using existing in vitro techniques, for the detection of reactive intermediates in order to minimize bioactivation potential in drug discovery.

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

    PubMed

    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.

  14. CRISPR/Cas9: From Genome Engineering to Cancer Drug Discovery

    PubMed Central

    Luo, Ji

    2016-01-01

    Advances in translational research are often driven by new technologies. The advent of microarrays, next-generation sequencing, proteomics and RNA interference (RNAi) have led to breakthroughs in our understanding of the mechanisms of cancer and the discovery of new cancer drug targets. The discovery of the bacterial clustered regularly interspaced palindromic repeat (CRISPR) system and its subsequent adaptation as a tool for mammalian genome engineering has opened up new avenues for functional genomics studies. This review will focus on the utility of CRISPR in the context of cancer drug target discovery. PMID:28603775

  15. ADDME – Avoiding Drug Development Mistakes Early: central nervous system drug discovery perspective

    PubMed Central

    Tsaioun, Katya; Bottlaender, Michel; Mabondzo, Aloise

    2009-01-01

    The advent of early absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening has increased the attrition rate of weak drug candidates early in the drug-discovery process, and decreased the proportion of compounds failing in clinical trials for ADMET reasons. This paper reviews the history of ADMET screening and its place in pharmaceutical development, and central nervous system drug discovery in particular. Assays that have been developed in response to specific needs and improvements in technology that result in higher throughput and greater accuracy of prediction of human mechanisms of absorption and toxicity are discussed. The paper concludes with the authors' forecast of new models that will better predict human efficacy and toxicity. PMID:19534730

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

    PubMed

    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.

  17. Rational, computer-enabled peptide drug design: principles, methods, applications and future directions.

    PubMed

    Diller, David J; Swanson, Jon; Bayden, Alexander S; Jarosinski, Mark; Audie, Joseph

    2015-01-01

    Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

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

    PubMed

    Komnatnyy, Vitaly V; Nielsen, Thomas E; Qvortrup, Katrine

    2018-06-11

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

  19. Scientific workflows as productivity tools for drug discovery.

    PubMed

    Shon, John; Ohkawa, Hitomi; Hammer, Juergen

    2008-05-01

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

  20. Drug discovery for neglected tropical diseases at the Sandler Center

    PubMed Central

    Robertson, Stephanie A; Renslo, Adam R

    2011-01-01

    The Sandler Center’s approach to target-based drug discovery for neglected tropical diseases is to focus on parasite targets that are homologous to human targets being actively investigated in the pharmaceutical industry. In this way we attempt to use both the know-how and actual chemical matter from other drug-development efforts to jump start the discovery process for neglected tropical diseases. Our approach is akin to drug repurposing, except that we seek to repurpose leads rather than drugs. Medicinal chemistry can then be applied to optimize the leads specifically for the desired antiparasitic indication. PMID:21859302

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

    PubMed

    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.

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

    PubMed Central

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

    2015-01-01

    Introduction 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. Areas covered 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. Expert opinion 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. PMID:25662177

  3. From bench to patient: model systems in drug discovery

    PubMed Central

    Breyer, Matthew D.; Look, A. Thomas; Cifra, Alessandra

    2015-01-01

    ABSTRACT Model systems, including laboratory animals, microorganisms, and cell- and tissue-based systems, are central to the discovery and development of new and better drugs for the treatment of human disease. In this issue, Disease Models & Mechanisms launches a Special Collection that illustrates the contribution of model systems to drug discovery and optimisation across multiple disease areas. This collection includes reviews, Editorials, interviews with leading scientists with a foot in both academia and industry, and original research articles reporting new and important insights into disease therapeutics. This Editorial provides a summary of the collection's current contents, highlighting the impact of multiple model systems in moving new discoveries from the laboratory bench to the patients' bedsides. PMID:26438689

  4. A computational chemistry perspective on the current status and future direction of hepatitis B antiviral drug discovery.

    PubMed

    Morgnanesi, Dante; Heinrichs, Eric J; Mele, Anthony R; Wilkinson, Sean; Zhou, Suzanne; Kulp, John L

    2015-11-01

    Computational chemical biology, applied to research on hepatitis B virus (HBV), has two major branches: bioinformatics (statistical models) and first-principle methods (molecular physics). While bioinformatics focuses on statistical tools and biological databases, molecular physics uses mathematics and chemical theory to study the interactions of biomolecules. Three computational techniques most commonly used in HBV research are homology modeling, molecular docking, and molecular dynamics. Homology modeling is a computational simulation to predict protein structure and has been used to construct conformers of the viral polymerase (reverse transcriptase domain and RNase H domain) and the HBV X protein. Molecular docking is used to predict the most likely orientation of a ligand when it is bound to a protein, as well as determining an energy score of the docked conformation. Molecular dynamics is a simulation that analyzes biomolecule motions and determines conformation and stability patterns. All of these modeling techniques have aided in the understanding of resistance mutations on HBV non-nucleos(t)ide reverse-transcriptase inhibitor binding. Finally, bioinformatics can be used to study the DNA and RNA protein sequences of viruses to both analyze drug resistance and to genotype the viral genomes. Overall, with these techniques, and others, computational chemical biology is becoming more and more necessary in hepatitis B research. This article forms part of a symposium in Antiviral Research on "An unfinished story: from the discovery of the Australia antigen to the development of new curative therapies for hepatitis B." Copyright © 2015 Elsevier B.V. All rights reserved.

  5. The Significance of Acid/Base Properties in Drug Discovery

    PubMed Central

    Manallack, David T.; Prankerd, Richard J.; Yuriev, Elizabeth; Oprea, Tudor I.; Chalmers, David K.

    2013-01-01

    While drug discovery scientists take heed of various guidelines concerning drug-like character, the influence of acid/base properties often remains under-scrutinised. Ionisation constants (pKa values) are fundamental to the variability of the biopharmaceutical characteristics of drugs and to underlying parameters such as logD and solubility. pKa values affect physicochemical properties such as aqueous solubility, which in turn influences drug formulation approaches. More importantly, absorption, distribution, metabolism, excretion and toxicity (ADMET) are profoundly affected by the charge state of compounds under varying pH conditions. Consideration of pKa values in conjunction with other molecular properties is of great significance and has the potential to be used to further improve the efficiency of drug discovery. Given the recent low annual output of new drugs from pharmaceutical companies, this review will provide a timely reminder of an important molecular property that influences clinical success. PMID:23099561

  6. Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

    PubMed Central

    Ekins, Sean; Spektor, Anna Coulon; Clark, Alex M.; Dole, Krishna; Bunin, Barry A.

    2016-01-01

    Neglected disease drug discovery is generally poorly funded compared with major diseases and hence there is an increasing focus on collaboration and precompetitive efforts such as public–private partnerships (PPPs). The More Medicines for Tuberculosis (MM4TB) project is one such collaboration funded by the EU with the goal of discovering new drugs for tuberculosis. Collaborative Drug Discovery has provided a commercial web-based platform called CDD Vault which is a hosted collaborative solution for securely sharing diverse chemistry and biology data. Using CDD Vault alongside other commercial and free cheminformatics tools has enabled support of this and other large collaborative projects, aiding drug discovery efforts and fostering collaboration. We will describe CDD's efforts in assisting with the MM4TB project. PMID:27884746

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

    PubMed

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

    2008-11-01

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

  8. Tackling the conformational sampling of larger flexible compounds and macrocycles in pharmacology and drug discovery.

    PubMed

    Chen, I-Jen; Foloppe, Nicolas

    2013-12-15

    Computational conformational sampling underpins much of molecular modeling and design in pharmaceutical work. The sampling of smaller drug-like compounds has been an active area of research. However, few studies have tested in details the sampling of larger more flexible compounds, which are also relevant to drug discovery, including therapeutic peptides, macrocycles, and inhibitors of protein-protein interactions. Here, we investigate extensively mainstream conformational sampling methods on three carefully curated compound sets, namely the 'Drug-like', larger 'Flexible', and 'Macrocycle' compounds. These test molecules are chemically diverse with reliable X-ray protein-bound bioactive structures. The compared sampling methods include Stochastic Search and the recent LowModeMD from MOE, all the low-mode based approaches from MacroModel, and MD/LLMOD recently developed for macrocycles. In addition to default settings, key parameters of the sampling protocols were explored. The performance of the computational protocols was assessed via (i) the reproduction of the X-ray bioactive structures, (ii) the size, coverage and diversity of the output conformational ensembles, (iii) the compactness/extendedness of the conformers, and (iv) the ability to locate the global energy minimum. The influence of the stochastic nature of the searches on the results was also examined. Much better results were obtained by adopting search parameters enhanced over the default settings, while maintaining computational tractability. In MOE, the recent LowModeMD emerged as the method of choice. Mixed torsional/low-mode from MacroModel performed as well as LowModeMD, and MD/LLMOD performed well for macrocycles. The low-mode based approaches yielded very encouraging results with the flexible and macrocycle sets. Thus, one can productively tackle the computational conformational search of larger flexible compounds for drug discovery, including macrocycles. Copyright © 2013 Elsevier Ltd. All

  9. Building Cognition: The Construction of Computational Representations for Scientific Discovery

    ERIC Educational Resources Information Center

    Chandrasekharan, Sanjay; Nersessian, Nancy J.

    2015-01-01

    Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a…

  10. The discovery of drug-induced illness.

    PubMed

    Jick, H

    1977-03-03

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

  11. Discovery of Anthelmintic Drug Targets and Drugs Using Chokepoints in Nematode Metabolic Pathways

    PubMed Central

    Taylor, Christina M.; Wang, Qi; Rosa, Bruce A.; Huang, Stanley Ching-Cheng; Powell, Kerrie; Schedl, Tim; Pearce, Edward J.; Abubucker, Sahar; Mitreva, Makedonka

    2013-01-01

    Parasitic roundworm infections plague more than 2 billion people (1/3 of humanity) and cause drastic losses in crops and livestock. New anthelmintic drugs are urgently needed as new drug resistance and environmental concerns arise. A “chokepoint reaction” is defined as a reaction that either consumes a unique substrate or produces a unique product. A chokepoint analysis provides a systematic method of identifying novel potential drug targets. Chokepoint enzymes were identified in the genomes of 10 nematode species, and the intersection and union of all chokepoint enzymes were found. By studying and experimentally testing available compounds known to target proteins orthologous to nematode chokepoint proteins in public databases, this study uncovers features of chokepoints that make them successful drug targets. Chemogenomic screening was performed on drug-like compounds from public drug databases to find existing compounds that target homologs of nematode chokepoints. The compounds were prioritized based on chemical properties frequently found in successful drugs and were experimentally tested using Caenorhabditis elegans. Several drugs that are already known anthelmintic drugs and novel candidate targets were identified. Seven of the compounds were tested in Caenorhabditis elegans and three yielded a detrimental phenotype. One of these three drug-like compounds, Perhexiline, also yielded a deleterious effect in Haemonchus contortus and Onchocerca lienalis, two nematodes with divergent forms of parasitism. Perhexiline, known to affect the fatty acid oxidation pathway in mammals, caused a reduction in oxygen consumption rates in C. elegans and genome-wide gene expression profiles provided an additional confirmation of its mode of action. Computational modeling of Perhexiline and its target provided structural insights regarding its binding mode and specificity. Our lists of prioritized drug targets and drug-like compounds have potential to expedite the discovery

  12. Can Untargeted Metabolomics Be Utilized in Drug Discovery/Development?

    PubMed

    Caldwell, Gary W; Leo, Gregory C

    2017-01-01

    Untargeted metabolomics is a promising approach for reducing the significant attrition rate for discovering and developing drugs in the pharmaceutical industry. This review aims to highlight the practical decision-making value of untargeted metabolomics for the advancement of drug candidates in drug discovery/development including potentially identifying and validating novel therapeutic targets, creating alternative screening paradigms, facilitating the selection of specific and translational metabolite biomarkers, identifying metabolite signatures for the drug efficacy mechanism of action, and understanding potential drug-induced toxicity. The review provides an overview of the pharmaceutical process workflow to discover and develop new small molecule drugs followed by the metabolomics process workflow that is involved in conducting metabolomics studies. The pros and cons of the major components of the pharmaceutical and metabolomics workflows are reviewed and discussed. Finally, selected untargeted metabolomics literature examples, from primarily 2010 to 2016, are used to illustrate why, how, and where untargeted metabolomics can be integrated into the drug discovery/preclinical drug development process. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  13. Component architecture in drug discovery informatics.

    PubMed

    Smith, Peter M

    2002-05-01

    This paper reviews the characteristics of a new model of computing that has been spurred on by the Internet, known as Netcentric computing. Developments in this model led to distributed component architectures, which, although not new ideas, are now realizable with modern tools such as Enterprise Java. The application of this approach to scientific computing, particularly in pharmaceutical discovery research, is discussed and highlighted by a particular case involving the management of biological assay data.

  14. Application of chemical biology in target identification and drug discovery.

    PubMed

    Zhu, Yue; Xiao, Ting; Lei, Saifei; Zhou, Fulai; Wang, Ming-Wei

    2015-09-01

    Drug discovery and development is vital to the well-being of mankind and sustainability of the pharmaceutical industry. Using chemical biology approaches to discover drug leads has become a widely accepted path partially because of the completion of the Human Genome Project. Chemical biology mainly solves biological problems through searching previously unknown targets for pharmacologically active small molecules or finding ligands for well-defined drug targets. It is a powerful tool to study how these small molecules interact with their respective targets, as well as their roles in signal transduction, molecular recognition and cell functions. There have been an increasing number of new therapeutic targets being identified and subsequently validated as a result of advances in functional genomics, which in turn led to the discovery of numerous active small molecules via a variety of high-throughput screening initiatives. In this review, we highlight some applications of chemical biology in the context of drug discovery.

  15. From bench to patient: model systems in drug discovery.

    PubMed

    Breyer, Matthew D; Look, A Thomas; Cifra, Alessandra

    2015-10-01

    Model systems, including laboratory animals, microorganisms, and cell- and tissue-based systems, are central to the discovery and development of new and better drugs for the treatment of human disease. In this issue, Disease Models & Mechanisms launches a Special Collection that illustrates the contribution of model systems to drug discovery and optimisation across multiple disease areas. This collection includes reviews, Editorials, interviews with leading scientists with a foot in both academia and industry, and original research articles reporting new and important insights into disease therapeutics. This Editorial provides a summary of the collection's current contents, highlighting the impact of multiple model systems in moving new discoveries from the laboratory bench to the patients' bedsides. © 2015. Published by The Company of Biologists Ltd.

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

    PubMed

    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.

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

    PubMed

    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.

  18. Zebrafish as tools for drug discovery.

    PubMed

    MacRae, Calum A; Peterson, Randall T

    2015-10-01

    The zebrafish has become a prominent vertebrate model for disease and has already contributed to several examples of successful phenotype-based drug discovery. For the zebrafish to become useful in drug development more broadly, key hurdles must be overcome, including a more comprehensive elucidation of the similarities and differences between human and zebrafish biology. Recent studies have begun to establish the capabilities and limitations of zebrafish for disease modelling, drug screening, target identification, pharmacology, and toxicology. As our understanding increases and as the technologies for manipulating zebrafish improve, it is hoped that the zebrafish will have a key role in accelerating the emergence of precision medicine.

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

    PubMed

    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.

  20. Computational chemistry at Janssen

    NASA Astrophysics Data System (ADS)

    van Vlijmen, Herman; Desjarlais, Renee L.; Mirzadegan, Tara

    2017-03-01

    Computer-aided drug discovery activities at Janssen are carried out by scientists in the Computational Chemistry group of the Discovery Sciences organization. This perspective gives an overview of the organizational and operational structure, the science, internal and external collaborations, and the impact of the group on Drug Discovery at Janssen.

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

    PubMed

    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.

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

    Cancer.gov

    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.

  3. Scientific Prediction and Prophetic Patenting in Drug Discovery.

    PubMed

    Curry, Stephen H; Schneiderman, Anne M

    2015-01-01

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

  4. Strategies to support drug discovery through integration of systems and data.

    PubMed

    Waller, Chris L; Shah, Ajay; Nolte, Matthias

    2007-08-01

    Much progress has been made over the past several years to provide technologies for the integration of drug discovery software applications and the underlying data bits. Integration at the application layer has focused primarily on developing and delivering applications that support specific workflows within the drug discovery arena. A fine balance between creating behemoth applications and providing business value must be maintained. Heterogeneous data sources have typically been integrated at the data level in an effort to provide a more holistic view of the data packages supporting key decision points. This review will highlight past attempts, current status, and potential future directions for systems and data integration strategies in support of drug discovery efforts.

  5. Modern Natural Products Drug Discovery and its Relevance to Biodiversity Conservation†

    PubMed Central

    Kingston, David G. I.

    2010-01-01

    Natural products continue to provide a diverse and unique source of bioactive lead compounds for drug discovery, but maintaining their continued eminence as source compounds is challenging in the face of the changing face of the pharmaceutical industry and the changing nature of biodiversity prospecting brought about by the Convention of Biodiversity. This review provides an overview of some of these challenges, and suggests ways in which they can be addressed so that natural products research can remain a viable and productive route to drug discovery. Results from International Cooperative Biodiversity Groups (ICBGs) working in Madagascar, Panama, and Suriname are used as examples of what can be achieved when biodiversity conservation is linked to drug discovery. PMID:21138324

  6. Improving drug safety: From adverse drug reaction knowledge discovery to clinical implementation.

    PubMed

    Tan, Yuxiang; Hu, Yong; Liu, Xiaoxiao; Yin, Zhinan; Chen, Xue-Wen; Liu, Mei

    2016-11-01

    Adverse drug reactions (ADRs) are a major public health concern, causing over 100,000 fatalities in the United States every year with an annual cost of $136 billion. Early detection and accurate prediction of ADRs is thus vital for drug development and patient safety. Multiple scientific disciplines, namely pharmacology, pharmacovigilance, and pharmacoinformatics, have been addressing the ADR problem from different perspectives. With the same goal of improving drug safety, this article summarizes and links the research efforts in the multiple disciplines into a single framework from comprehensive understanding of the interactions between drugs and biological system and the identification of genetic and phenotypic predispositions of patients susceptible to higher ADR risks and finally to the current state of implementation of medication-related decision support systems. We start by describing available computational resources for building drug-target interaction networks with biological annotations, which provides a fundamental knowledge for ADR prediction. Databases are classified by functions to help users in selection. Post-marketing surveillance is then introduced where data-driven approach can not only enhance the prediction accuracy of ADRs but also enables the discovery of genetic and phenotypic risk factors of ADRs. Understanding genetic risk factors for ADR requires well organized patient genetics information and analysis by pharmacogenomic approaches. Finally, current state of clinical decision support systems is presented and described how clinicians can be assisted with the integrated knowledgebase to minimize the risk of ADR. This review ends with a discussion of existing challenges in each of disciplines with potential solutions and future directions. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Customizing microarrays for neuroscience drug discovery.

    PubMed

    Girgenti, Matthew J; Newton, Samuel S

    2007-08-01

    Microarray-based gene profiling has become the centerpiece of gene expression studies in the biological sciences. The ability to now interrogate the entire genome using a single chip demonstrates the progress in technology and instrumentation that has been made over the last two decades. Although this unbiased approach provides researchers with an immense quantity of data, obtaining meaningful insight is not possible without intensive data analysis and processing. Custom developed arrays have emerged as a viable and attractive alternative that can take advantage of this robust technology and tailor it to suit the needs and requirements of individual investigations. The ability to simplify data analysis, reduce noise and carefully optimize experimental conditions makes it a suitable tool that can be effectively utilized in neuroscience drug discovery efforts. Furthermore, incorporating recent advancements in fine focusing gene profiling to include specific cellular phenotypes can help resolve the complex cellular heterogeneity of the brain. This review surveys the use of microarray technology in neuroscience paying special attention to customized arrays and their potential in drug discovery. Novel applications of microarrays and ancillary techniques, such as laser microdissection, FAC sorting and RNA amplification, have also been discussed. The notion that a hypothesis-driven approach can be integrated into drug development programs is highlighted.

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

    PubMed

    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.

  9. Towards microfluidic technology-based MALDI-MS platforms for drug discovery: a review.

    PubMed

    Winkle, Richard F; Nagy, Judit M; Cass, Anthony Eg; Sharma, Sanjiv

    2008-11-01

    Microfluidic methods have found applications in various disciplines. It has been predicted that the microfluidic technology would be useful in performing routine steps in drug discovery ranging from target identification to lead optimisation in which the number of compounds evaluated in this regard determines the success of combinatorial screening. The sheer size of the parameter space that can be explored often poses an enormous challenge. We set out to find how close we are towards the use of integrated matrix-assisted laser desorption/ionisation mass spectrometry (MALDI-MS) microfluidic systems for drug discovery. In this article we review the latest applications of microfluidic technology in the area of MALDI-MS and drug discovery. Our literature survey revealed microfluidic technologies-based approaches for various stages of drug discovery; however, they are in still in developmental stages. Furthermore, we speculate on how these technologies could be used in the future.

  10. Cancer epigenetics drug discovery and development: the challenge of hitting the mark

    PubMed Central

    Campbell, Robert M.; Tummino, Peter J.

    2014-01-01

    Over the past several years, there has been rapidly expanding evidence of epigenetic dysregulation in cancer, in which histone and DNA modification play a critical role in tumor growth and survival. These findings have gained the attention of the drug discovery and development community, and offer the potential for a second generation of cancer epigenetic agents for patients following the approved “first generation” of DNA methylation (e.g., Dacogen, Vidaza) and broad-spectrum HDAC inhibitors (e.g., Vorinostat, Romidepsin). This Review provides an analysis of prospects for discovery and development of novel cancer agents that target epigenetic proteins. We will examine key examples of epigenetic dysregulation in tumors as well as challenges to epigenetic drug discovery with emerging biology and novel classes of drug targets. We will also highlight recent successes in cancer epigenetics drug discovery and consider important factors for clinical success in this burgeoning area. PMID:24382391

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

    PubMed

    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.

  12. Can Functional Magnetic Resonance Imaging Improve Success Rates in CNS Drug Discovery?

    PubMed Central

    Borsook, David; Hargreaves, Richard; Becerra, Lino

    2011-01-01

    Introduction The bar for developing new treatments for CNS disease is getting progressively higher and fewer novel mechanisms are being discovered, validated and developed. The high costs of drug discovery necessitate early decisions to ensure the best molecules and hypotheses are tested in expensive late stage clinical trials. The discovery of brain imaging biomarkers that can bridge preclinical to clinical CNS drug discovery and provide a ‘language of translation’ affords the opportunity to improve the objectivity of decision-making. Areas Covered This review discusses the benefits, challenges and potential issues of using a science based biomarker strategy to change the paradigm of CNS drug development and increase success rates in the discovery of new medicines. The authors have summarized PubMed and Google Scholar based publication searches to identify recent advances in functional, structural and chemical brain imaging and have discussed how these techniques may be useful in defining CNS disease state and drug effects during drug development. Expert opinion The use of novel brain imaging biomarkers holds the bold promise of making neuroscience drug discovery smarter by increasing the objectivity of decision making thereby improving the probability of success of identifying useful drugs to treat CNS diseases. Functional imaging holds the promise to: (1) define pharmacodynamic markers as an index of target engagement (2) improve translational medicine paradigms to predict efficacy; (3) evaluate CNS efficacy and safety based on brain activation; (4) determine brain activity drug dose-response relationships and (5) provide an objective evaluation of symptom response and disease modification. PMID:21765857

  13. Microfluidics for Drug Discovery and Development: From Target Selection to Product Lifecycle Management

    PubMed Central

    Kang, Lifeng; Chung, Bong Geun; Langer, Robert; Khademhosseini, Ali

    2009-01-01

    Microfluidic technologies’ ability to miniaturize assays and increase experimental throughput have generated significant interest in the drug discovery and development domain. These characteristics make microfluidic systems a potentially valuable tool for many drug discovery and development applications. Here, we review the recent advances of microfluidic devices for drug discovery and development and highlight their applications in different stages of the process, including target selection, lead identification, preclinical tests, clinical trials, chemical synthesis, formulations studies, and product management. PMID:18190858

  14. Potential biological targets for bioassay development in drug discovery of Sturge-Weber syndrome.

    PubMed

    Mohammadipanah, Fatemeh; Salimi, Fatemeh

    2018-02-01

    Sturge-Weber Syndrome (SWS) is a neurocutaneous disease with clinical manifestations including ocular (glaucoma), cutaneous (port-wine birthmark), neurologic (seizures), and vascular problems. Molecular mechanisms of SWS pathogenesis are initiated by the somatic mutation in GNAQ. Therefore, no definite treatments exist for SWS and treatment options only mitigate the intensity of its clinical manifestations. Biological assay design for drug discovery against this syndrome demands comprehensive knowledge on mechanisms which are involved in its pathogenesis. By analysis of the interrelated molecular targets of SWS, some in vitro bioassay systems can be allotted for drug screening against its progression. Development of such platforms of bioassay can bring along the implementation of high-throughput screening of natural or synthetic compounds in drug discovery programs. Regarding the fact that study of molecular targets and their integration in biological assay design can facilitate the process of effective drug discovery; some potential biological targets and their respective biological assay for SWS drug discovery are propounded in this review. For this purpose, some biological targets for SWS drug discovery such as acetylcholinesterase, alkaline phosphatase, GABAergic receptors, Hypoxia-Inducible Factor (HIF)-1α and 2α are suggested. © 2017 John Wiley & Sons A/S.

  15. Building Cognition: The Construction of Computational Representations for Scientific Discovery.

    PubMed

    Chandrasekharan, Sanjay; Nersessian, Nancy J

    2015-11-01

    Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model-building by an engineer that led to a remarkable discovery in basic bioscience. Accounting for such discoveries requires a distributed cognition (DC) analysis, as DC focuses on the roles played by external representations in cognitive processes. However, DC analyses by and large have not examined scientific discovery, and they mostly focus on memory offloading, particularly how the use of existing external representations changes the nature of cognitive tasks. In contrast, we study discovery processes and argue that discoveries emerge from the processes of building the computational representation. The building process integrates manipulations in imagination and in the representation, creating a coupled cognitive system of model and modeler, where the model is incorporated into the modeler's imagination. This account extends DC significantly, and we present some of the theoretical and application implications of this extended account. Copyright © 2014 Cognitive Science Society, Inc.

  16. Antitrypanosomatid drug discovery: an ongoing challenge and a continuing need

    PubMed Central

    Field, Mark C.; Horn, David; Fairlamb, Alan H.; Ferguson, Michael A. J.; Gray, David W.; Read, Kevin D.; De Rycker, Manu; Torrie, Leah S.; Wyatt, Paul G.; Wyllie, Susan; Gilbert, Ian H.

    2017-01-01

    The World Health Organization recognizes human African trypanosomiasis, Chagas’ disease and the leishmaniases as neglected tropical diseases. These diseases are caused by parasitic trypanosomatids and range in severity from mild and self-curing to near invariably fatal. Public health advances have substantially decreased the impact of these diseases in recent decades, but alone will not eliminate these diseases. Here we discuss why new drugs against trypanosomatids are needed, approaches that are under investigation to develop new drugs and why the drug discovery pipeline remains essentially unfilled. Additionally, we consider the important challenges to drug discovery strategies and the new technologies that can address them. The combination of new drugs, new technologies and public health initiatives are essential for the management and hopefully eventual elimination of trypanosomatid diseases from the human population. PMID:28239154

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

    PubMed

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

    2018-06-01

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

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

    PubMed Central

    2017-01-01

    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. PMID:29392184

  19. Strategies for bringing drug delivery tools into discovery.

    PubMed

    Kwong, Elizabeth; Higgins, John; Templeton, Allen C

    2011-06-30

    The past decade has yielded a significant body of literature discussing approaches for development and discovery collaboration in the pharmaceutical industry. As a result, collaborations between discovery groups and development scientists have increased considerably. The productivity of pharma companies to deliver new drugs to the market, however, has not increased and development costs continue to rise. Inability to predict clinical and toxicological response underlies the high attrition rate of leads at every step of drug development. A partial solution to this high attrition rate could be provided by better preclinical pharmacokinetics measurements that inform PD response based on key pathways that drive disease progression and therapeutic response. A critical link between these key pharmacology, pharmacokinetics and toxicology studies is the formulation. The challenges in pre-clinical formulation development include limited availability of compounds, rapid turn-around requirements and the frequent un-optimized physical properties of the lead compounds. Despite these challenges, this paper illustrates some successes resulting from close collaboration between formulation scientists and discovery teams. This close collaboration has resulted in development of formulations that meet biopharmaceutical needs from early stage preclinical in vivo model development through toxicity testing and development risk assessment of pre-clinical drug candidates. Published by Elsevier B.V.

  20. Organs-on-chips at the frontiers of drug discovery

    PubMed Central

    Esch, Eric W.; Bahinski, Anthony; Huh, Dongeun

    2016-01-01

    Improving the effectiveness of preclinical predictions of human drug responses is critical to reducing costly failures in clinical trials. Recent advances in cell biology, microfabrication and microfluidics have enabled the development of microengineered models of the functional units of human organs — known as organs-on-chips — that could provide the basis for preclinical assays with greater predictive power. Here, we examine the new opportunities for the application of organ-on-chip technologies in a range of areas in preclinical drug discovery, such as target identification and validation, target-based screening, and phenotypic screening. We also discuss emerging drug discovery opportunities enabled by organs-on-chips, as well as important challenges in realizing the full potential of this technology. PMID:25792263

  1. Drug Discovery and Development of Antimalarial Agents: Recent Advances.

    PubMed

    Thota, Sreekanth; Yerra, Rajeshwar

    2016-01-01

    Malaria, a deadly infectious parasitic disease, is a major issue of public health in the world today and already produces serious economic constraints in the endemic countries. Most of the malarial infections and deaths are due to Plasmodium falciparum and Plasmodium vivax species. The recent emergence of resistance necessitates the search for new antimalarial drugs, which overcome the resistance and act through new mechanisms. Although much effort has been directed towards the discovery of novel antimalarial drugs. 4-anilino quinolone triazines as potent antimalarial agents, their in silico modelling and bioevaluation as Plasmodium falciparum transketolase and β-hematin inhibitors has been reported. This review is primarily focused on the drug discovery of the recent advances in the development of antimalarial agents and their mechanism of action.

  2. Computationally guided discovery of thermoelectric materials

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Gorai, Prashun; Stevanović, Vladan; Toberer, Eric S.

    The potential for advances in thermoelectric materials, and thus solid-state refrigeration and power generation, is immense. Progress so far has been limited by both the breadth and diversity of the chemical space and the serial nature of experimental work. In this Review, we discuss how recent computational advances are revolutionizing our ability to predict electron and phonon transport and scattering, as well as materials dopability, and we examine efficient approaches to calculating critical transport properties across large chemical spaces. When coupled with experimental feedback, these high-throughput approaches can stimulate the discovery of new classes of thermoelectric materials. Within smaller materialsmore » subsets, computations can guide the optimal chemical and structural tailoring to enhance materials performance and provide insight into the underlying transport physics. Beyond perfect materials, computations can be used for the rational design of structural and chemical modifications (such as defects, interfaces, dopants and alloys) to provide additional control on transport properties to optimize performance. Through computational predictions for both materials searches and design, a new paradigm in thermoelectric materials discovery is emerging.« less

  3. Computationally guided discovery of thermoelectric materials

    DOE PAGES

    Gorai, Prashun; Stevanović, Vladan; Toberer, Eric S.

    2017-08-22

    The potential for advances in thermoelectric materials, and thus solid-state refrigeration and power generation, is immense. Progress so far has been limited by both the breadth and diversity of the chemical space and the serial nature of experimental work. In this Review, we discuss how recent computational advances are revolutionizing our ability to predict electron and phonon transport and scattering, as well as materials dopability, and we examine efficient approaches to calculating critical transport properties across large chemical spaces. When coupled with experimental feedback, these high-throughput approaches can stimulate the discovery of new classes of thermoelectric materials. Within smaller materialsmore » subsets, computations can guide the optimal chemical and structural tailoring to enhance materials performance and provide insight into the underlying transport physics. Beyond perfect materials, computations can be used for the rational design of structural and chemical modifications (such as defects, interfaces, dopants and alloys) to provide additional control on transport properties to optimize performance. Through computational predictions for both materials searches and design, a new paradigm in thermoelectric materials discovery is emerging.« less

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

    PubMed

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

    2014-03-26

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

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

    PubMed

    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.

  6. Multitarget drug discovery projects in CNS diseases: quantitative systems pharmacology as a possible path forward.

    PubMed

    Geerts, Hugo; Kennis, Ludo

    2014-01-01

    Clinical development in brain diseases has one of the lowest success rates in the pharmaceutical industry, and many promising rationally designed single-target R&D projects fail in expensive Phase III trials. By contrast, successful older CNS drugs do have a rich pharmacology. This article will provide arguments suggesting that highly selective single-target drugs are not sufficiently powerful to restore complex neuronal circuit homeostasis. A rationally designed multitarget project can be derisked by dialing in an additional symptomatic treatment effect on top of a disease modification target. Alternatively, we expand upon a hypothetical workflow example using a humanized computer-based quantitative systems pharmacology platform. The hope is that incorporating rationally multipharmacology drug discovery could potentially lead to more impactful polypharmacy drugs.

  7. Potential biological targets for bioassay development in drug discovery of Sturge-Weber syndrome.

    PubMed

    Mohammadipanah, Fatemeh; Salimi, Fatemeh

    2017-04-29

    Sturge-Weber Syndrome (SWS) is among the neurocutaneous diseases, which has several clinical manifestations of ocular (glaucoma), cutaneous (port-wine stain), neurological (seizures) and vascular problems. Molecular mechanisms of SWS pathogenesis are initiated by the somatic mutation in GNAQ. Therefore, no definite treatments exist for the SWS and treatment options only mitigate the intensity of its clinical manifestations. Biological assay design for drug discovery against this syndrome demands comprehensive knowledge on mechanisms which are involved in its pathogenesis. By analysis of the interrelated molecular targets of SWS, some in vitro bioassay systems can be allotted for drug screening against this syndrome. Development of such platforms of bioassay can bring along the implementation of high throughput screening of natural or synthetic compounds in drug discovery programs. Regarding the fact that study of biological targets and their integration in biological assay design can facilitate the process of effective drug discovery; some potential biological targets and their respective biological assay for SWS drug discovery are propounded in this review. For this purpose, some biological targets for SWS drug discovery such as acetylcholine esterase, alkaline phosphatase, gamma-aminobutyricacidergic, Hypoxia-Inducible Factor (HIF) -1α and 2α are suggested. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  8. Antibacterial Drug Discovery: Some Assembly Required.

    PubMed

    Tommasi, Rubén; Iyer, Ramkumar; Miller, Alita A

    2018-05-11

    Our limited understanding of the molecular basis for compound entry into and efflux out of Gram-negative bacteria is now recognized as a key bottleneck for the rational discovery of novel antibacterial compounds. Traditional, large-scale biochemical or target-agnostic phenotypic antibacterial screening efforts have, as a result, not been very fruitful. A main driver of this knowledge gap has been the historical lack of predictive cellular assays, tools, and models that provide structure-activity relationships to inform optimization of compound accumulation. A variety of recent approaches has recently been described to address this conundrum. This Perspective explores these approaches and considers ways in which their integration could successfully redirect antibacterial drug discovery efforts.

  9. Portfolio management in early stage drug discovery - a traveler's guide through uncharted territory.

    PubMed

    Betz, Ulrich A K

    2011-07-01

    Portfolio management in drug development has become a best practice in the pharmaceutical industry. By contrast, early on in the value chain - the discovery phase - portfolio management is still in its infancy. Nevertheless, owing to the attrition of R&D projects from phase to phase and the cost of capital involved, these early phases of drug discovery play a significant part for the overall cost of bringing new, innovative drugs to the market. This paper describes various approaches to manage a portfolio of projects in early-stage drug discovery and provides crucial factors that determine the success of such an approach. Copyright © 2011 Elsevier Ltd. All rights reserved.

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

    PubMed

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

    2017-02-01

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

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

    PubMed

    Fraietta, Ivan; Gasparri, Fabio

    2016-01-01

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

  12. Optimizing Oral Bioavailability in Drug Discovery: An Overview of Design and Testing Strategies and Formulation Options.

    PubMed

    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.

  13. The application of absolute quantitative (1)H NMR spectroscopy in drug discovery and development.

    PubMed

    Singh, Suruchi; Roy, Raja

    2016-07-01

    The identification of a drug candidate and its structural determination is the most important step in the process of the drug discovery and for this, nuclear magnetic resonance (NMR) is one of the most selective analytical techniques. The present review illustrates the various perspectives of absolute quantitative (1)H NMR spectroscopy in drug discovery and development. It deals with the fundamentals of quantitative NMR (qNMR), the physiochemical properties affecting qNMR, and the latest referencing techniques used for quantification. The precise application of qNMR during various stages of drug discovery and development, namely natural product research, drug quantitation in dosage forms, drug metabolism studies, impurity profiling and solubility measurements is elaborated. To achieve this, the authors explore the literature of NMR in drug discovery and development between 1963 and 2015. It also takes into account several other reviews on the subject. qNMR experiments are used for drug discovery and development processes as it is a non-destructive, versatile and robust technique with high intra and interpersonal variability. However, there are several limitations also. qNMR of complex biological samples is incorporated with peak overlap and a low limit of quantification and this can be overcome by using hyphenated chromatographic techniques in addition to NMR.

  14. Fragment-based drug discovery using rational design.

    PubMed

    Jhoti, H

    2007-01-01

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

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

    PubMed

    Tanaka, Daisuke

    2010-03-01

    Fragment-Based Drug Discovery (FBDD) has been recognized as a newly emerging lead discovery methodology that involves biophysical fragment screening and chemistry-driven fragment-to-lead stages. Although fragments, defined as structurally simple and small compounds (typically <300 Da), have not been employed in conventional high-throughput screening (HTS), the recent significant progress in the biophysical screening methods enables fragment screening at a practical level. The intention of FBDD primarily turns our attention to weakly but specifically binding fragments (hit fragments) as the starting point of medicinal chemistry. Hit fragments are then promoted to more potent lead compounds through linking or merging with another hit fragment and/or attaching functional groups. Another positive aspect of FBDD is ligand efficiency. Ligand efficiency is a useful guide in screening hit selection and hit-to-lead phases to achieve lead-likeness. Owing to these features, a number of successful applications of FBDD to "undruggable targets" (where HTS and other lead identification methods failed to identify useful lead compounds) have been reported. As a result, FBDD is now expected to complement more conventional methodologies. This review, as an introduction of the following articles, will summarize the fundamental concepts of FBDD and will discuss its advantages over other conventional drug discovery approaches.

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

    PubMed

    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.

  17. Drug discovery and development for rare genetic disorders.

    PubMed

    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.

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

    PubMed Central

    Clematis, Andrea; Quarati, Alfonso; Cesini, Daniele; Milanesi, Luciano; Merelli, Ivan

    2013-01-01

    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. PMID:24106693

  19. Sphingosine 1-Phosphate Receptor Modulators and Drug Discovery

    PubMed Central

    Park, Soo-Jin; Im, Dong-Soon

    2017-01-01

    Initial discovery on sphingosine 1-phosphate (S1P) as an intracellular second messenger was faced unexpectedly with roles of S1P as a first messenger, which subsequently resulted in cloning of its G protein-coupled receptors, S1P1–5. The molecular identification of S1P receptors opened up a new avenue for pathophysiological research on this lipid mediator. Cellular and molecular in vitro studies and in vivo studies on gene deficient mice have elucidated cellular signaling pathways and the pathophysiological meanings of S1P receptors. Another unexpected finding that fingolimod (FTY720) modulates S1P receptors accelerated drug discovery in this field. Fingolimod was approved as a first-in-class, orally active drug for relapsing multiple sclerosis in 2010, and its applications in other disease conditions are currently under clinical trials. In addition, more selective S1P receptor modulators with better pharmacokinetic profiles and fewer side effects are under development. Some of them are being clinically tested in the contexts of multiple sclerosis and other autoimmune and inflammatory disorders, such as, psoriasis, Crohn’s disease, ulcerative colitis, polymyositis, dermatomyositis, liver failure, renal failure, acute stroke, and transplant rejection. In this review, the authors discuss the state of the art regarding the status of drug discovery efforts targeting S1P receptors and place emphasis on potential clinical applications. PMID:28035084

  20. Perspectives on NMR in drug discovery: a technique comes of age

    PubMed Central

    Pellecchia, Maurizio; Bertini, Ivano; Cowburn, David; Dalvit, Claudio; Giralt, Ernest; Jahnke, Wolfgang; James, Thomas L.; Homans, Steve W.; Kessler, Horst; Luchinat, Claudio; Meyer, Bernd; Oschkinat, Hartmut; Peng, Jeff; Schwalbe, Harald; Siegal, Gregg

    2009-01-01

    In the past decade, the potential of harnessing the ability of nuclear magnetic resonance (NMR) spectroscopy to monitor intermolecular interactions as a tool for drug discovery has been increasingly appreciated in academia and industry. In this Perspective, we highlight some of the major applications of NMR in drug discovery, focusing on hit and lead generation, and provide a critical analysis of its current and potential utility. PMID:19172689

  1. Three-Dimensional Cell Cultures in Drug Discovery and Development

    PubMed Central

    Fang, Ye; Eglen, Richard M.

    2017-01-01

    The past decades have witnessed significant efforts toward the development of three-dimensional (3D) cell cultures as systems that better mimic in vivo physiology. Today, 3D cell cultures are emerging, not only as a new tool in early drug discovery but also as potential therapeutics to treat disease. In this review, we assess leading 3D cell culture technologies and their impact on drug discovery, including spheroids, organoids, scaffolds, hydrogels, organs-on-chips, and 3D bioprinting. We also discuss the implementation of these technologies in compound identification, screening, and development, ranging from disease modeling to assessment of efficacy and safety profiles. PMID:28520521

  2. The reductionist paradox: are the laws of chemistry and physics sufficient for the discovery of new drugs?

    PubMed

    Maggiora, Gerald M

    2011-08-01

    Reductionism is alive and well in drug-discovery research. In that tradition, we continually improve experimental and computational methods for studying smaller and smaller aspects of biological systems. Although significant improvements continue to be made, are our efforts too narrowly focused? Suppose all error could be removed from these methods, would we then understand biological systems sufficiently well to design effective drugs? Currently, almost all drug research focuses on single targets. Should the process be expanded to include multiple targets? Recent efforts in this direction have lead to the emerging field of polypharmacology. This appears to be a move in the right direction, but how much polypharmacology is enough? As the complexity of the processes underlying polypharmacology increase will we be able to understand them and their inter-relationships? Is "new" mathematics unfamiliar in much of physics and chemistry research needed to accomplish this task? A number of these questions will be addressed in this paper, which focuses on issues and questions not answers to the drug-discovery conundrum.

  3. Experiences in fragment-based drug discovery.

    PubMed

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

    2012-05-01

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

  4. Stem cell technology for drug discovery and development.

    PubMed

    Hook, Lilian A

    2012-04-01

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

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

    PubMed

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

    2017-08-24

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

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

    PubMed

    Huryn, Donna M

    2013-03-14

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

  7. Drug discovery strategies to outer membrane targets in Gram-negative pathogens.

    PubMed

    Brown, Dean G

    2016-12-15

    This review will cover selected recent examples of drug discovery strategies which target the outer membrane (OM) of Gram-negative bacteria either by disruption of outer membrane function or by inhibition of essential gene products necessary for outer membrane assembly. Significant advances in pathway elucidation, structural biology and molecular inhibitor designs have created new opportunities for drug discovery within this target-class space. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Large-scale computational drug repositioning to find treatments for rare diseases.

    PubMed

    Govindaraj, Rajiv Gandhi; Naderi, Misagh; Singha, Manali; Lemoine, Jeffrey; Brylinski, Michal

    2018-01-01

    Rare, or orphan, diseases are conditions afflicting a small subset of people in a population. Although these disorders collectively pose significant health care problems, drug companies require government incentives to develop drugs for rare diseases due to extremely limited individual markets. Computer-aided drug repositioning, i.e., finding new indications for existing drugs, is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Structure-based matching of drug-binding pockets is among the most promising computational techniques to inform drug repositioning. In order to find new targets for known drugs ultimately leading to drug repositioning, we recently developed e MatchSite, a new computer program to compare drug-binding sites. In this study, e MatchSite is combined with virtual screening to systematically explore opportunities to reposition known drugs to proteins associated with rare diseases. The effectiveness of this integrated approach is demonstrated for a kinase inhibitor, which is a confirmed candidate for repositioning to synapsin Ia. The resulting dataset comprises 31,142 putative drug-target complexes linked to 980 orphan diseases. The modeling accuracy is evaluated against the structural data recently released for tyrosine-protein kinase HCK. To illustrate how potential therapeutics for rare diseases can be identified, we discuss a possibility to repurpose a steroidal aromatase inhibitor to treat Niemann-Pick disease type C. Overall, the exhaustive exploration of the drug repositioning space exposes new opportunities to combat orphan diseases with existing drugs. DrugBank/Orphanet repositioning data are freely available to research community at https://osf.io/qdjup/.

  9. Financing drug discovery for orphan diseases.

    PubMed

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

    2014-05-01

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

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

    PubMed

    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.

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

    PubMed

    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.

  12. Advanced cell culture techniques for cancer drug discovery.

    PubMed

    Lovitt, Carrie J; Shelper, Todd B; Avery, Vicky M

    2014-05-30

    Human cancer cell lines are an integral part of drug discovery practices. However, modeling the complexity of cancer utilizing these cell lines on standard plastic substrata, does not accurately represent the tumor microenvironment. Research into developing advanced tumor cell culture models in a three-dimensional (3D) architecture that more prescisely characterizes the disease state have been undertaken by a number of laboratories around the world. These 3D cell culture models are particularly beneficial for investigating mechanistic processes and drug resistance in tumor cells. In addition, a range of molecular mechanisms deconstructed by studying cancer cells in 3D models suggest that tumor cells cultured in two-dimensional monolayer conditions do not respond to cancer therapeutics/compounds in a similar manner. Recent studies have demonstrated the potential of utilizing 3D cell culture models in drug discovery programs; however, it is evident that further research is required for the development of more complex models that incorporate the majority of the cellular and physical properties of a tumor.

  13. Advanced Cell Culture Techniques for Cancer Drug Discovery

    PubMed Central

    Lovitt, Carrie J.; Shelper, Todd B.; Avery, Vicky M.

    2014-01-01

    Human cancer cell lines are an integral part of drug discovery practices. However, modeling the complexity of cancer utilizing these cell lines on standard plastic substrata, does not accurately represent the tumor microenvironment. Research into developing advanced tumor cell culture models in a three-dimensional (3D) architecture that more prescisely characterizes the disease state have been undertaken by a number of laboratories around the world. These 3D cell culture models are particularly beneficial for investigating mechanistic processes and drug resistance in tumor cells. In addition, a range of molecular mechanisms deconstructed by studying cancer cells in 3D models suggest that tumor cells cultured in two-dimensional monolayer conditions do not respond to cancer therapeutics/compounds in a similar manner. Recent studies have demonstrated the potential of utilizing 3D cell culture models in drug discovery programs; however, it is evident that further research is required for the development of more complex models that incorporate the majority of the cellular and physical properties of a tumor. PMID:24887773

  14. Do drug metabolism and pharmacokinetic departments make any contribution to drug discovery?

    PubMed

    Smith, Dennis; Schmid, Esther; Jones, Barry

    2002-01-01

    The alignment of drug metabolism and pharmacokinetic departments with drug discovery has not produced a radical improvement in the pharmacokinetic properties of new chemical entities. The reason for this is complex, reflecting in part the difficulty of combining potency, selectivity, water solubility, metabolic stability and membrane permeability into a single molecule. This combination becomes increasingly problematic as the drug targets become more distant from aminergic seven-transmembrane-spanning receptors (7-TMs). The leads available for aminergic 7-TMs, like the natural agonists, are invariably small molecular weight, water soluble and potent. Even moving to 7-TMs for which the agonist is a peptide invariably produces lead matter that is less drug-like (higher molecular weight and lipophilic). The role of drug metabolism departments, therefore, has been to guide chemistry to obtaining adequate, rather than optimal, pharmacokinetic properties for these 'difficult' drug targets. A consistent belief of many researchers is that a high value is placed on optimal, rather than adequate, pharmacokinetic properties. One measure of value is market sales, and when these are examined no clear pattern emerges. Part of the success of amlodipine in the calcium channel antagonist sector must be due to its excellent pharmacokinetic profile, but the best-selling drugs among the angiotensin antagonists and beta-blockers have a much greater market share than other agents with better pharmacokinetic properties. Clearly, many other factors are important in the successful launch of a medicine, some reflected in the manner the compound is developed and the subsequent structure of the labelling. Overall, therefore the presence of drug metabolism in drug discovery has probably contributed most by allowing 'difficult' drug targets to be prosecuted, rather than by guiding medicinal chemists to optimal pharmacokinetics. These 'difficult' target candidates become successful drugs when

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

    PubMed

    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).

  16. Distribution and licensing of drug discovery tools – NIH perspectives

    PubMed Central

    Kim, J. P.

    2009-01-01

    Now, more than ever, drug discovery conducted at industrial or academic facilities requires rapid access to state-of-the-art research tools. Unreasonable restrictions or delays in the distribution or use of such tools can stifle new discoveries, thus limiting the development of future biomedical products. In grants and its own research programs the National Institutes of Health (NIH) is implementing its new policy to facilitate the exchanges of these tools for research discoveries and product development. PMID:12546842

  17. Natural products and drug discovery: a survey of stakeholders in industry and academia.

    PubMed

    Amirkia, Vafa; Heinrich, Michael

    2015-01-01

    In recent decades, natural products have undisputedly played a leading role in the development of novel medicines. Yet, trends in the pharmaceutical industry at the level of research investments indicate that natural product research is neither prioritized nor perceived as fruitful in drug discovery programmes as compared with incremental structural modifications and large volume HTS screening of synthetics. We seek to understand this phenomenon through insights from highly experienced natural product experts in industry and academia. We conducted a survey including a series of qualitative and quantitative questions related to current insights and prospective developments in natural product drug development. The survey was completed by a cross-section of 52 respondents in industry and academia. One recurrent theme is the dissonance between the perceived high potential of NP as drug leads among individuals and the survey participants' assessment of the overall industry and/or company level strategies and their success. The study's industry and academic respondents did not perceive current discovery efforts as more effective as compared with previous decades, yet industry contacts perceived higher hit rates in HTS efforts as compared with academic respondents. Surprisingly, many industry contacts were highly critical to prevalent company and industry-wide drug discovery strategies indicating a high level of dissatisfaction within the industry. These findings support the notion that there is an increasing gap in perception between the effectiveness of well established, commercially widespread drug discovery strategies between those working in industry and academic experts. This research seeks to shed light on this gap and aid in furthering natural product discovery endeavors through an analysis of current bottlenecks in industry drug discovery programmes.

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

    PubMed

    Raza, Mohsin

    2006-04-06

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

  19. Applications of SHAPES screening in drug discovery.

    PubMed

    Lepre, Christopher A; Peng, Jeffrey; Fejzo, Jasna; Abdul-Manan, Norzehan; Pocas, Jennifer; Jacobs, Marc; Xie, Xiaoling; Moore, Jonathan M

    2002-12-01

    The SHAPES strategy combines nuclear magnetic resonance (NMR) screening of a library of small drug-like molecules with a variety of complementary methods, such as virtual screening, high throughput enzymatic assays, combinatorial chemistry, X-ray crystallography, and molecular modeling, in a directed search for new medicinal chemistry leads. In the past few years, the SHAPES strategy has found widespread utility in pharmaceutical research. To illustrate a variety of different implementations of the method, we will focus in this review on recent applications of the SHAPES strategy in several drug discovery programs at Vertex Pharmaceuticals.

  20. Novel drug discovery approaches for treating arenavirus infections.

    PubMed

    Pasquato, Antonella; Kunz, Stefan

    2016-01-01

    Arenaviruses are enveloped negative stranded viruses endemic in Africa, Europe and the Americas. Several arenaviruses cause severe viral hemorrhagic fever with high mortality in humans and pose serious public health threats. So far, there are no FDA-approved vaccines and therapeutic options are restricted to the off-label use of ribavirin. The major human pathogenic arenaviruses are classified as Category A agents and require biosafety level (BSL)-4 containment. Herein, the authors cover the recent progress in the development of BSL2 surrogate systems that recapitulate the entire or specific steps of the arenavirus life cycle and are serving as powerful platforms for drug discovery. Furthermore, they highlight the identification of selected novel drugs that target individual steps of arenavirus multiplication describing their discovery, their targets, and mode of action. The lack of effective drugs against arenaviruses is an unmatched challenge in current medical virology. Novel technologies have provided important insights into the basic biology of arenaviruses and the mechanisms underlying virus-host cell interaction. Significant progress of our understanding of how the virus invades the host cell paved the way to develop powerful novel screening platforms. Recent efforts have provided a range of promising drug candidates currently under evaluation for therapeutic intervention in vivo.

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

    PubMed

    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.

  2. Fragment informatics and computational fragment-based drug design: an overview and update.

    PubMed

    Sheng, Chunquan; Zhang, Wannian

    2013-05-01

    Fragment-based drug design (FBDD) is a promising approach for the discovery and optimization of lead compounds. Despite its successes, FBDD also faces some internal limitations and challenges. FBDD requires a high quality of target protein and good solubility of fragments. Biophysical techniques for fragment screening necessitate expensive detection equipment and the strategies for evolving fragment hits to leads remain to be improved. Regardless, FBDD is necessary for investigating larger chemical space and can be applied to challenging biological targets. In this scenario, cheminformatics and computational chemistry can be used as alternative approaches that can significantly improve the efficiency and success rate of lead discovery and optimization. Cheminformatics and computational tools assist FBDD in a very flexible manner. Computational FBDD can be used independently or in parallel with experimental FBDD for efficiently generating and optimizing leads. Computational FBDD can also be integrated into each step of experimental FBDD and help to play a synergistic role by maximizing its performance. This review will provide critical analysis of the complementarity between computational and experimental FBDD and highlight recent advances in new algorithms and successful examples of their applications. In particular, fragment-based cheminformatics tools, high-throughput fragment docking, and fragment-based de novo drug design will provide the focus of this review. We will also discuss the advantages and limitations of different methods and the trends in new developments that should inspire future research. © 2012 Wiley Periodicals, Inc.

  3. Discovery of peptide drug carrier candidates for targeted multi-drug delivery into prostate cancer cells.

    PubMed

    Bashari, O; Redko, B; Cohen, A; Luboshits, G; Gellerman, G; Firer, M A

    2017-11-01

    Metastatic castration-resistant prostate cancer (mCRPC) remains essentially incurable. Targeted Drug Delivery (TDD) systems may overcome the limitations of current mCRPC therapies. We describe the use of strict criteria to isolate novel prostate cancer cell targeting peptides that specifically deliver drugs into target cells. Phage from a libraries displaying 7mer peptides were exposed to PC-3 cells and only internalized phage were recovered. The ability of these phage to internalize into other prostate cancer cells (LNCaP, DU-145) was validated. The displayed peptides of selected phage clones were synthesized and their specificity for target cells was validated in vitro and in vivo. One peptide (P12) which specifically targeted PC-3 tumors in vivo was incorporated into mono-drug (Chlorambucil, Combretastatin or Camptothecin) and dual-drug (Chlorambucil/Combretastatin or Chlorambucil/Camptothecin) PDCs and the cytotoxic efficacy of these conjugates for target cells was tested. Conjugation of P12 into dual-drug PDCs allowed discovery of new drug combinations with synergistic effects. The use of strict selection criteria can lead to discovery of novel peptides for use as drug carriers for TDD. PDCs represent an effective alternative to current modes of free drug chemotherapy for prostate cancer. Copyright © 2017. Published by Elsevier B.V.

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

    PubMed

    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.

  5. Drug discovery in the next millennium.

    PubMed

    Ohlstein, E H; Ruffolo, R R; Elliott, J D

    2000-01-01

    Selection and validation of novel molecular targets have become of paramount importance in light of the plethora of new potential therapeutic drug targets that have emerged from human gene sequencing. In response to this revolution within the pharmaceutical industry, the development of high-throughput methods in both biology and chemistry has been necessitated. This review addresses these technological advances as well as several new areas that have been created by necessity to deal with this new paradigm, such as bioinformatics, cheminformatics, and functional genomics. With many of these key components of future drug discovery now in place, it is possible to map out a critical path for this process that will be used into the new millennium.

  6. Cryptosporidiosis Drug Discovery: Opportunities and Challenges.

    PubMed

    Manjunatha, Ujjini H; Chao, Alexander T; Leong, F Joel; Diagana, Thierry T

    2016-08-12

    The apicomplexan parasite Cryptosporidium is the second most important diarrheal pathogen causing life-threatening diarrhea in children, which is also associated with long-term growth faltering and cognitive deficiency. Cryptosporidiosis is a parasitic disease of public health concern caused by Cryptosporidium parvum and Cryptosporidium hominis. Currently, nitazoxanide is the only approved treatment for cryptosporidium infections. Unfortunately, it has limited efficacy in the most vulnerable patients, thus there is an urgent need for a safe and efficacious cryptosporidiosis drug. In this work, we present our current perspectives on the target product profile for novel cryptosporidiosis therapies and the perceived challenges and possible mitigation plans at different stages in the cryptosporidiosis drug discovery process.

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

    PubMed

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

    2015-11-01

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

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

    PubMed

    Davey, John

    2004-04-01

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

  9. The re-emergence of natural products for drug discovery in the genomics era.

    PubMed

    Harvey, Alan L; Edrada-Ebel, RuAngelie; Quinn, Ronald J

    2015-02-01

    Natural products have been a rich source of compounds for drug discovery. However, their use has diminished in the past two decades, in part because of technical barriers to screening natural products in high-throughput assays against molecular targets. Here, we review strategies for natural product screening that harness the recent technical advances that have reduced these barriers. We also assess the use of genomic and metabolomic approaches to augment traditional methods of studying natural products, and highlight recent examples of natural products in antimicrobial drug discovery and as inhibitors of protein-protein interactions. The growing appreciation of functional assays and phenotypic screens may further contribute to a revival of interest in natural products for drug discovery.

  10. Open Source Drug Discovery in Practice: A Case Study

    PubMed Central

    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

  11. A Kernel for Open Source Drug Discovery in Tropical Diseases

    PubMed Central

    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

    Background 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”. Methodology/Principal Findings 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. Conclusions/Significance 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. PMID:19381286

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

    PubMed

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

    2015-12-01

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

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

    PubMed

    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.

  14. Toxicophore exploration as a screening technology for drug design and discovery: techniques, scope and limitations.

    PubMed

    Singh, Pankaj Kumar; Negi, Arvind; Gupta, Pawan Kumar; Chauhan, Monika; Kumar, Raj

    2016-08-01

    Toxicity is a common drawback of newly designed chemotherapeutic agents. With the exception of pharmacophore-induced toxicity (lack of selectivity at higher concentrations of a drug), the toxicity due to chemotherapeutic agents is based on the toxicophore moiety present in the drug. To date, methodologies implemented to determine toxicophores may be broadly classified into biological, bioanalytical and computational approaches. The biological approach involves analysis of bioactivated metabolites, whereas the computational approach involves a QSAR-based method, mapping techniques, an inverse docking technique and a few toxicophore identification/estimation tools. Being one of the major steps in drug discovery process, toxicophore identification has proven to be an essential screening step in drug design and development. The paper is first of its kind, attempting to cover and compare different methodologies employed in predicting and determining toxicophores with an emphasis on their scope and limitations. Such information may prove vital in the appropriate selection of methodology and can be used as screening technology by researchers to discover the toxicophoric potentials of their designed and synthesized moieties. Additionally, it can be utilized in the manipulation of molecules containing toxicophores in such a manner that their toxicities might be eliminated or removed.

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

    PubMed

    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

  16. Discovery of Bioactive Compounds by the UIC-ICBG Drug Discovery Program in the 18 Years Since 1998.

    PubMed

    Zhang, Hong-Jie; Li, Wan-Fei; Fong, Harry H S; Soejarto, Djaja Doel

    2016-10-31

    The International Cooperative Biodiversity Groups (ICBG) Program based at the University of Illinois at Chicago (UIC) is a program aimed to address the interdependent issues of inventory and conservation of biodiversity, drug discovery and sustained economic growth in both developing and developed countries. It is an interdisciplinary program involving the extensive synergies and collaborative efforts of botanists, chemists and biologists in the countries of Vietnam, Laos and the USA. The UIC-ICBG drug discovery efforts over the past 18 years have resulted in the collection of a cumulative total of more than 5500 plant samples (representing more than 2000 species), that were evaluated for their potential biological effects against cancer, HIV, bird flu, tuberculosis and malaria. The bioassay-guided fractionation and separation of the bioactive plant leads resulted in the isolation of approximately 300 compounds of varying degrees of structural complexity and/or biological activity. The present paper summarizes the significant drug discovery achievements made by the UIC-ICBG team of multidisciplinary collaborators in the project over the period of 1998-2012 and the projects carried on in the subsequent years by involving the researchers in Hong Kong.

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

    PubMed

    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.

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

    PubMed

    Dobchev, Dimitar; Karelson, Mati

    2016-07-01

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

  19. Chemical Informatics and the Drug Discovery Knowledge Pyramid

    PubMed Central

    Lushington, Gerald H.; Dong, Yinghua; Theertham, Bhargav

    2012-01-01

    The magnitude of the challenges in preclinical drug discovery is evident in the large amount of capital invested in such efforts in pursuit of a small static number of eventually successful marketable therapeutics. An explosion in the availability of potentially drug-like compounds and chemical biology data on these molecules can provide us with the means to improve the eventual success rates for compounds being considered at the preclinical level, but only if the community is able to access available information in an efficient and meaningful way. Thus, chemical database resources are critical to any serious drug discovery effort. This paper explores the basic principles underlying the development and implementation of chemical databases, and examines key issues of how molecular information may be encoded within these databases so as to enhance the likelihood that users will be able to extract meaningful information from data queries. In addition to a broad survey of conventional data representation and query strategies, key enabling technologies such as new context-sensitive chemical similarity measures and chemical cartridges are examined, with recommendations on how such resources may be integrated into a practical database environment. PMID:23782037

  20. From flamingo dance to (desirable) drug discovery: a nature-inspired approach.

    PubMed

    Sánchez-Rodríguez, Aminael; Pérez-Castillo, Yunierkis; Schürer, Stephan C; Nicolotti, Orazio; Mangiatordi, Giuseppe Felice; Borges, Fernanda; Cordeiro, M Natalia D S; Tejera, Eduardo; Medina-Franco, José L; Cruz-Monteagudo, Maykel

    2017-10-01

    The therapeutic effects of drugs are well known to result from their interaction with multiple intracellular targets. Accordingly, the pharma industry is currently moving from a reductionist approach based on a 'one-target fixation' to a holistic multitarget approach. However, many drug discovery practices are still procedural abstractions resulting from the attempt to understand and address the action of biologically active compounds while preventing adverse effects. Here, we discuss how drug discovery can benefit from the principles of evolutionary biology and report two real-life case studies. We do so by focusing on the desirability principle, and its many features and applications, such as machine learning-based multicriteria virtual screening. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. The Screening Compound Collection: A Key Asset for Drug Discovery.

    PubMed

    Boss, Christoph; Hazemann, Julien; Kimmerlin, Thierry; von Korff, Modest; Lüthi, Urs; Peter, Oliver; Sander, Thomas; Siegrist, Romain

    2017-10-25

    In this case study on an essential instrument of modern drug discovery, we summarize our successful efforts in the last four years toward enhancing the Actelion screening compound collection. A key organizational step was the establishment of the Compound Library Committee (CLC) in September 2013. This cross-functional team consisting of computational scientists, medicinal chemists and a biologist was endowed with a significant annual budget for regular new compound purchases. Based on an initial library analysis performed in 2013, the CLC developed a New Library Strategy. The established continuous library turn-over mode, and the screening library size of 300'000 compounds were maintained, while the structural library quality was increased. This was achieved by shifting the selection criteria from 'druglike' to 'leadlike' structures, enriching for non-flat structures, aiming for compound novelty, and increasing the ratio of higher cost 'Premium Compounds'. Novel chemical space was gained by adding natural compounds, macrocycles, designed and focused libraries to the collection, and through mutual exchanges of proprietary compounds with agrochemical companies. A comparative analysis in 2016 provided evidence for the positive impact of these measures. Screening the improved library has provided several highly promising hits, including a macrocyclic compound, that are currently followed up in different Hit-to-Lead and Lead Optimization programs. It is important to state that the goal of the CLC was not to achieve higher HTS hit rates, but to increase the chances of identified hits to serve as the basis of successful early drug discovery programs. The experience gathered so far legitimates the New Library Strategy.

  2. [Caenorhabditis elegans: a powerful tool for drug discovery].

    PubMed

    Jia, Xi-Hua; Cao, Cheng

    2009-07-01

    A simple model organism Caenorhabditis elegans has contributed substantially to the fundamental researches in biology. In an era of functional genomics, nematode worm has been developed into a multi-purpose tool that can be exploited to identify disease-causing or disease-associated genes, validate potential drug targets. This, coupled with its genetic amenability, low cost experimental manipulation and compatibility with high throughput screening in an intact physiological condition, makes the model organism into an effective toolbox for drug discovery. This review shows the unique features of C. elegans, how it can play a valuable role in our understanding of the molecular mechanism of human diseases and finding drug leads in drug development process.

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

    PubMed

    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.

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

    PubMed

    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.

  5. Emerging principles in protease-based drug discovery

    PubMed Central

    Drag, Marcin; Salvesen, Guy S.

    2010-01-01

    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. PMID:20811381

  6. Anticancer drug discovery from the marine environment.

    PubMed

    Nastrucci, Candida; Cesario, Alfredo; Russo, Patrizia

    2012-05-01

    Discovery, isolation, biochemical/pharmacological characterization, pre-clinical and clinical trials of drugs derived from the marine environment are continuously developing and increasing. One of the most promising area is cancer therapy. Currently, there are two drugs approved by the Food and Drug Administration (FDA) and European Agency for the Evaluation of Medicinal Products (EMA) in cancer treatment, namely Cytarabine (Cytosar-U1®) and Eribulin (E7389 or Halaven®). Trabectedin (ET-743 or Yondelis1®), approved by EMA, is completing key Phase III studies in the U.S. for final approval. It was estimated that 118 marine natural products (MNPs) are currently in preclinical trials, 22 MNPs in clinical trials and 3 MNPs on the market. The characteristics and selectivity profiles of new drugs for cancer therapy, as well as drugs disclosed in related patent applications, will be the focus of this review, providing a brief and ready to use reference.

  7. Can formulation and drug delivery reduce attrition during drug discovery and development—review of feasibility, benefits and challenges

    PubMed Central

    Basavaraj, S; Betageri, Guru V.

    2014-01-01

    Drug discovery and development has become longer and costlier process. The fear of failure and stringent regulatory review process is driving pharmaceutical companies towards “me too” drugs and improved generics (505(b) (2)) fillings. The discontinuance of molecules at late stage clinical trials is common these years. The molecules are withdrawn at various stages of discovery and development process for reasons such as poor ADME properties, lack of efficacy and safety reasons. Hence this review focuses on possible applications of formulation and drug delivery to salvage molecules and improve the drugability. The formulation and drug delivery technologies are suitable for addressing various issues contributing to attrition are discussed in detail. PMID:26579359

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

    PubMed

    Weaver, Ian N; Weaver, Donald F

    2013-10-01

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

  9. Bayesian Models Leveraging Bioactivity and Cytotoxicity Information for Drug Discovery

    PubMed Central

    Ekins, Sean; Reynolds, Robert C.; Kim, Hiyun; Koo, Mi-Sun; Ekonomidis, Marilyn; Talaue, Meliza; Paget, Steve D.; Woolhiser, Lisa K.; Lenaerts, Anne J.; Bunin, Barry A.; Connell, Nancy; Freundlich, Joel S.

    2013-01-01

    SUMMARY Identification of unique leads represents a significant challenge in drug discovery. This hurdle is magnified in neglected diseases such as tuberculosis. We have leveraged public high-throughput screening (HTS) data, to experimentally validate virtual screening approach employing Bayesian models built with bioactivity information (single-event model) as well as bioactivity and cytotoxicity information (dual-event model). We virtually screen a commercial library and experimentally confirm actives with hit rates exceeding typical HTS results by 1-2 orders of magnitude. The first dual-event Bayesian model identified compounds with antitubercular whole-cell activity and low mammalian cell cytotoxicity from a published set of antimalarials. The most potent hit exhibits the in vitro activity and in vitro/in vivo safety profile of a drug lead. These Bayesian models offer significant economies in time and cost to drug discovery. PMID:23521795

  10. 3D bioprinting for drug discovery and development in pharmaceutics.

    PubMed

    Peng, Weijie; Datta, Pallab; Ayan, Bugra; Ozbolat, Veli; Sosnoski, Donna; Ozbolat, Ibrahim T

    2017-07-15

    Successful launch of a commercial drug requires significant investment of time and financial resources wherein late-stage failures become a reason for catastrophic failures in drug discovery. This calls for infusing constant innovations in technologies, which can give reliable prediction of efficacy, and more importantly, toxicology of the compound early in the drug discovery process before clinical trials. Though computational advances have resulted in more rationale in silico designing, in vitro experimental studies still require gaining industry confidence and improving in vitro-in vivo correlations. In this quest, due to their ability to mimic the spatial and chemical attributes of native tissues, three-dimensional (3D) tissue models have now proven to provide better results for drug screening compared to traditional two-dimensional (2D) models. However, in vitro fabrication of living tissues has remained a bottleneck in realizing the full potential of 3D models. Recent advances in bioprinting provide a valuable tool to fabricate biomimetic constructs, which can be applied in different stages of drug discovery research. This paper presents the first comprehensive review of bioprinting techniques applied for fabrication of 3D tissue models for pharmaceutical studies. A comparative evaluation of different bioprinting modalities is performed to assess the performance and ability of fabricating 3D tissue models for pharmaceutical use as the critical selection of bioprinting modalities indeed plays a crucial role in efficacy and toxicology testing of drugs and accelerates the drug development cycle. In addition, limitations with current tissue models are discussed thoroughly and future prospects of the role of bioprinting in pharmaceutics are provided to the reader. Present advances in tissue biofabrication have crucial role to play in aiding the pharmaceutical development process achieve its objectives. Advent of three-dimensional (3D) models, in particular, is

  11. Successful generation of structural information for fragment-based drug discovery.

    PubMed

    Öster, Linda; Tapani, Sofia; Xue, Yafeng; Käck, Helena

    2015-09-01

    Fragment-based drug discovery relies upon structural information for efficient compound progression, yet it is often challenging to generate structures with bound fragments. A summary of recent literature reveals that a wide repertoire of experimental procedures is employed to generate ligand-bound crystal structures successfully. We share in-house experience from setting up and executing fragment crystallography in a project that resulted in 55 complex structures. The ligands span five orders of magnitude in affinity and the resulting structures are made available to be of use, for example, for development of computational methods. Analysis of the results revealed that ligand properties such as potency, ligand efficiency (LE) and, to some degree, clogP influence the success of complex structure generation. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

    PubMed

    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.

  13. Drug–Target Kinetics in Drug Discovery

    PubMed Central

    2017-01-01

    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. PMID:28640596

  14. Microfluidic Devices for Automation of Assays on Drosophila Melanogaster for Applications in Drug Discovery and Biological Studies.

    PubMed

    Ghaemi, Reza; Selvaganapathy, Ponnambalam R

    Drug discovery is a long and expensive process, which usually takes 12-15 years and could cost up to ~$1 billion. Conventional drug discovery process starts with high throughput screening and selection of drug candidates that bind to specific target associated with a disease condition. However, this process does not consider whether the chosen candidate is optimal not only for binding but also for ease of administration, distribution in the body, effect of metabolism and associated toxicity if any. A holistic approach, using model organisms early in the drug discovery process to select drug candidates that are optimal not only in binding but also suitable for administration, distribution and are not toxic is now considered as a viable way for lowering the cost and time associated with the drug discovery process. However, the conventional drug discovery assays using Drosophila are manual and required skill operator, which makes them expensive and not suitable for high-throughput screening. Recently, microfluidics has been used to automate many of the operations (e.g. sorting, positioning, drug delivery) associated with the Drosophila drug discovery assays and thereby increase their throughput. This review highlights recent microfluidic devices that have been developed for Drosophila assays with primary application towards drug discovery for human diseases. The microfluidic devices that have been reviewed in this paper are categorized based on the stage of the Drosophila that have been used. In each category, the microfluidic technologies behind each device are described and their potential biological applications are discussed.

  15. Alkaloids as Cyclooxygenase Inhibitors in Anticancer Drug Discovery.

    PubMed

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

    2018-01-01

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

  16. Microwave and continuous flow technologies in drug discovery.

    PubMed

    Sadler, Sara; Moeller, Alexander R; Jones, Graham B

    2012-12-01

    Microwave and continuous flow microreactors have become mainstream heating sources in contemporary pharmaceutical company laboratories. Such technologies will continue to benefit from design and engineering improvements, and now play a key role in the drug discovery process. The authors review the applications of flow- and microwave-mediated heating in library, combinatorial, solid-phase, metal-assisted, and protein chemistries. Additionally, the authors provide a description of the combination of microwave and continuous flow platforms, with applications in the preparation of radiopharmaceuticals and in drug candidate development. Literature reviewed is chiefly 2000 - 2012, plus key citations from earlier reports. With the advent of microwave irradiation, reactions that normally took days to complete can now be performed in a matter of minutes. Coupled with the introduction of continuous flow microreactors, pharmaceutical companies have an easy way to improve the greenness and efficiency of many synthetic operations. The combined force of these technologies offers the potential to revolutionize discovery and manufacturing processes.

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

    PubMed

    Kitambi, Satish Srinivas; Chandrasekar, Gayathri

    2011-01-01

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

  18. Imaging in Central Nervous System Drug Discovery.

    PubMed

    Gunn, Roger N; Rabiner, Eugenii A

    2017-01-01

    The discovery and development of central nervous system (CNS) drugs is an extremely challenging process requiring large resources, timelines, and associated costs. The high risk of failure leads to high levels of risk. Over the past couple of decades PET imaging has become a central component of the CNS drug-development process, enabling decision-making in phase I studies, where early discharge of risk provides increased confidence to progress a candidate to more costly later phase testing at the right dose level or alternatively to kill a compound through failure to meet key criteria. The so called "3 pillars" of drug survival, namely; tissue exposure, target engagement, and pharmacologic activity, are particularly well suited for evaluation by PET imaging. This review introduces the process of CNS drug development before considering how PET imaging of the "3 pillars" has advanced to provide valuable tools for decision-making on the critical path of CNS drug development. Finally, we review the advances in PET science of biomarker development and analysis that enable sophisticated drug-development studies in man. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    PubMed

    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.

  20. The Production of 3D Tumor Spheroids for Cancer Drug Discovery

    PubMed Central

    Sant, Shilpa; Johnston, Paul A.

    2017-01-01

    New cancer drug approval rates are ≤ 5% despite significant investments in cancer research, drug discovery and development. One strategy to improve the rate of success of new cancer drugs transitioning into the clinic would be to more closely align the cellular models used in the early lead discovery with pre-clinical animal models and patient tumors. For solid tumors, this would mandate the development and implementation of three dimensional (3D) in vitro tumor models that more accurately recapitulate human solid tumor architecture and biology. Recent advances in tissue engineering and regenerative medicine have provided new techniques for 3D spheroid generation and a variety of in vitro 3D cancer models are being explored for cancer drug discovery. Although homogeneous assay methods and high content imaging approaches to assess tumor spheroid morphology, growth and viability have been developed, the implementation of 3D models in HTS remains challenging due to reasons that we discuss in this review. Perhaps the biggest obstacle to achieve acceptable HTS assay performance metrics occurs in 3D tumor models that produce spheroids with highly variable morphologies and/or sizes. We highlight two methods that produce uniform size-controlled 3D multicellular tumor spheroids that are compatible with cancer drug research and HTS; tumor spheroids formed in ultra-low attachment microplates, or in polyethylene glycol dimethacrylate hydrogel microwell arrays. PMID:28647083

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

    PubMed Central

    2011-01-01

    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. PMID:21955914

  2. Is there a best strategy for drug discovery?--SMR Meeting. 13 March 2003, London, UK.

    PubMed

    Lunec, Anna

    2003-05-01

    This gathering of members from academia and industry allowed the sharing of ideas and techniques or the acceleration of drug discovery, and it was clear that there is a need for a more streamlined approach to discovery and development. Clearly, new technologies will aid in the discovery process, but the abilities of the human brain to analyze and interpret data should not be overlooked, as many discoveries have been made by chance or as the result of a hunch, and it would be a shame if the advent of artificial intelligence quashed that inquisitive aspect of drug discovery.

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

    PubMed

    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

  4. Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases

    PubMed Central

    Frijters, Raoul; van Vugt, Marianne; Smeets, Ruben; van Schaik, René; de Vlieg, Jacob; Alkema, Wynand

    2010-01-01

    The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs. PMID:20885778

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

    NASA Astrophysics Data System (ADS)

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

    2009-08-01

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

  6. Capacity building in anthelmintic drug discovery.

    PubMed

    Kron, Michael; Yousif, Fouad; Ramirez, Bernadette

    2007-10-01

    International collaboration in anthelmintic drug discovery holds special challenges compared with local or national discovery projects, and at the same time presents the opportunity to build capacity, forge long lasting inter-institutional relationships and strengthen infrastructure in multinational priority areas. This chapter discusses important issues that should be considered in the context of anthelmintic screening centre development and will give examples (Philippines and Egypt) of the productivity of developing country based screening centres. The positive outcomes of infrastructure building is realised in greater capacities for anthelmintic screening at institutions in the countries where the parasitic diseases are endemic and allows for optimum use of specialised resources for public health priority diseases that may be different from those in Western countries. Support for developing country based screening centres also can help countries optimise product development procedures and policies and can facilitate diffusion of desirable technology in corresponding global regions around the world.

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

    PubMed

    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.

  8. Simulating the drug discovery pipeline: a Monte Carlo approach

    PubMed Central

    2012-01-01

    Background The early drug discovery phase in pharmaceutical research and development marks the beginning of a long, complex and costly process of bringing a new molecular entity to market. As such, it plays a critical role in helping to maintain a robust downstream clinical development pipeline. Despite its importance, however, to our knowledge there are no published in silico models to simulate the progression of discrete virtual projects through a discovery milestone system. Results Multiple variables were tested and their impact on productivity metrics examined. Simulations predict that there is an optimum number of scientists for a given drug discovery portfolio, beyond which output in the form of preclinical candidates per year will remain flat. The model further predicts that the frequency of compounds to successfully pass the candidate selection milestone as a function of time will be irregular, with projects entering preclinical development in clusters marked by periods of low apparent productivity. Conclusions The model may be useful as a tool to facilitate analysis of historical growth and achievement over time, help gauge current working group progress against future performance expectations, and provide the basis for dialogue regarding working group best practices and resource deployment strategies. PMID:23186040

  9. 50 years of hurdles and hope in anxiolytic drug discovery

    PubMed Central

    Griebel, Guy; Holmes, Andrew

    2014-01-01

    Anxiety disorders are the most prevalent group of psychiatric diseases, and have high personal and societal costs. The search for novel pharmacological treatments for these conditions is driven by the growing medical need to improve on the effectiveness and the side effect profile of existing drugs. A huge volume of data has been generated by anxiolytic drug discovery studies, which has led to the progression of numerous new molecules into clinical trials. However, the clinical outcome of these efforts has been disappointing, as promising results with novel agents in rodent studies have very rarely translated into effectiveness in humans. Here, we analyse the major trends from preclinical studies over the past 50 years conducted in the search for new drugs beyond those that target the prototypical anxiety-associated GABA (γ-aminobutyric acid)–benzodiazepine system, which have focused most intensively on the serotonin, neuropeptide, glutamate and endocannabinoid systems. We highlight various key issues that may have hampered progress in the field, and offer recommendations for how anxiolytic drug discovery can be more effective in the future. PMID:23989795

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

    PubMed

    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 .

  11. Molecular targets for flavivirus drug discovery

    PubMed Central

    Sampath, Aruna; Padmanabhan, R.

    2009-01-01

    Flaviviruses are a major cause of infectious disease in humans. Dengue virus causes an estimated 50 million cases of febrile illness each year, including an increasing number of cases of hemorrhagic fever. West Nile virus, which recently spread from the Mediterranean basin to the Western Hemisphere, now causes thousands of sporadic cases of encephalitis annually. Despite the existence of licensed vaccines, yellow fever, Japanese encephalitis and tick-borne encephalitis also claim many thousands of victims each year across their vast endemic areas. Antiviral therapy could potentially reduce morbidity and mortality from flavivirus infections, but no effective drugs are currently available. This article introduces a collection of papers in Antiviral Research on molecular targets for flavivirus antiviral drug design and murine models of dengue virus disease that aims to encourage drug development efforts. After reviewing the flavivirus replication cycle, we discuss the envelope glycoprotein, NS3 protease, NS3 helicase, NS5 methyltransferase and NS5 RNA-dependent RNA polymerase as potential drug targets, with special attention being given to the viral protease. The other viral proteins are the subject of individual articles in the journal. Together, these papers highlight current status of drug discovery efforts for flavivirus diseases and suggest promising areas for further research. PMID:18796313

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

    PubMed

    Benson, Neil

    2015-08-01

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

  13. Structural Genomics and Drug Discovery for Infectious Diseases

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Anderson, W.F.

    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,more » 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.« less

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

    PubMed

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

    2017-07-13

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

  15. Strategic incorporation of fluorine in the drug discovery of new-generation antitubercular agents targeting bacterial cell division protein FtsZ⋆

    PubMed Central

    Ojima, Iwao; Awasthi, Divya; Wei, Longfei; Haranahalli, Krupanandan

    2016-01-01

    This article presents an account of our research on the discovery and development of new-generation fluorine-containing antibacterial agents against drug-resistant tuberculosis, targeting FtsZ. FtsZ is an essential protein for bacterial cell division and a highly promising therapeutic target for antibacterial drug discovery. Through design, synthesis and semi-HTP screening of libraries of novel benzimidazoles, followed by SAR studies, we identified highly potent lead compounds. However, these lead compounds were found to lack sufficient metabolic and plasma stabilities. Accordingly, we have performed extensive study on the strategic incorporation of fluorine into lead compounds to improve pharmacological properties. This study has led to the development of highly efficacious fluorine-containing benzimidazoles as potential drug candidates. We have also performed computational docking analysis of these novel FtsZ inhibitors to identify their putative binding site. Based on the structural data and docking analysis, a plausible mode-of-action for this novel class of FtsZ inhibitors is proposed. PMID:28555087

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

    PubMed

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

    2018-05-07

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

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

    PubMed Central

    Hall, David R.; Vajda, Sandor

    2015-01-01

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

  18. Scaling predictive modeling in drug development with cloud computing.

    PubMed

    Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

    2015-01-26

    Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

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

    PubMed Central

    Ribas, João; Sadeghi, Hossein; Manbachi, Amir; Leijten, Jeroen; Brinegar, Katelyn; Zhang, Yu Shrike; Ferreira, Lino

    2016-01-01

    Abstract 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 vasculature have become common causes for preclinical project closures, and preclinical models do not fully recapitulate human in vivo dynamics. Recently, organs-on-a-chip technologies have been proposed to mimic the dynamic conditions of the cardiovascular system—in particular, heart and general vasculature. These systems pay particular attention to mimicking structural organization, shear stress, transmural pressure, mechanical stretching, and electrical stimulation. Heart- and vasculature-on-a-chip platforms have been successfully generated to study a variety of physiological phenomena, model diseases, and probe the effects of drugs. Here, we review and discuss recent breakthroughs in the development of cardiovascular organs-on-a-chip platforms, and their current and future applications in the area of drug discovery and development. PMID:28971113

  20. Computational Discovery of Materials Using the Firefly Algorithm

    NASA Astrophysics Data System (ADS)

    Avendaño-Franco, Guillermo; Romero, Aldo

    Our current ability to model physical phenomena accurately, the increase computational power and better algorithms are the driving forces behind the computational discovery and design of novel materials, allowing for virtual characterization before their realization in the laboratory. We present the implementation of a novel firefly algorithm, a population-based algorithm for global optimization for searching the structure/composition space. This novel computation-intensive approach naturally take advantage of concurrency, targeted exploration and still keeping enough diversity. We apply the new method in both periodic and non-periodic structures and we present the implementation challenges and solutions to improve efficiency. The implementation makes use of computational materials databases and network analysis to optimize the search and get insights about the geometric structure of local minima on the energy landscape. The method has been implemented in our software PyChemia, an open-source package for materials discovery. We acknowledge the support of DMREF-NSF 1434897 and the Donors of the American Chemical Society Petroleum Research Fund for partial support of this research under Contract 54075-ND10.

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

    PubMed

    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.

  2. Open Access High Throughput Drug Discovery in the Public Domain: A Mount Everest in the Making

    PubMed Central

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

    2013-01-01

    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. PMID:20809896

  3. Using ChEMBL web services for building applications and data processing workflows relevant to drug discovery.

    PubMed

    Nowotka, Michał M; Gaulton, Anna; Mendez, David; Bento, A Patricia; Hersey, Anne; Leach, Andrew

    2017-08-01

    ChEMBL is a manually curated database of bioactivity data on small drug-like molecules, used by drug discovery scientists. Among many access methods, a REST API provides programmatic access, allowing the remote retrieval of ChEMBL data and its integration into other applications. This approach allows scientists to move from a world where they go to the ChEMBL web site to search for relevant data, to one where ChEMBL data can be simply integrated into their everyday tools and work environment. Areas covered: This review highlights some of the audiences who may benefit from using the ChEMBL API, and the goals they can address, through the description of several use cases. The examples cover a team communication tool (Slack), a data analytics platform (KNIME), batch job management software (Luigi) and Rich Internet Applications. Expert opinion: The advent of web technologies, cloud computing and micro services oriented architectures have made REST APIs an essential ingredient of modern software development models. The widespread availability of tools consuming RESTful resources have made them useful for many groups of users. The ChEMBL API is a valuable resource of drug discovery bioactivity data for professional chemists, chemistry students, data scientists, scientific and web developers.

  4. Use of Natural Products as Chemical Library for Drug Discovery and Network Pharmacology

    PubMed Central

    Gu, Jiangyong; Gui, Yuanshen; Chen, Lirong; Yuan, Gu; Lu, Hui-Zhe; Xu, Xiaojie

    2013-01-01

    Background Natural products have been an important source of lead compounds for drug discovery. How to find and evaluate bioactive natural products is critical to the achievement of drug/lead discovery from natural products. Methodology We collected 19,7201 natural products structures, reported biological activities and virtual screening results. Principal component analysis was employed to explore the chemical space, and we found that there was a large portion of overlap between natural products and FDA-approved drugs in the chemical space, which indicated that natural products had large quantity of potential lead compounds. We also explored the network properties of natural product-target networks and found that polypharmacology was greatly enriched to those compounds with large degree and high betweenness centrality. In order to make up for a lack of experimental data, high throughput virtual screening was employed. All natural products were docked to 332 target proteins of FDA-approved drugs. The most potential natural products for drug discovery and their indications were predicted based on a docking score-weighted prediction model. Conclusions Analysis of molecular descriptors, distribution in chemical space and biological activities of natural products was conducted in this article. Natural products have vast chemical diversity, good drug-like properties and can interact with multiple cellular target proteins. PMID:23638153

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

    PubMed

    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.

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

    PubMed

    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.

  7. Computational methods for a three-dimensional model of the petroleum-discovery process

    USGS Publications Warehouse

    Schuenemeyer, J.H.; Bawiec, W.J.; Drew, L.J.

    1980-01-01

    A discovery-process model devised by Drew, Schuenemeyer, and Root can be used to predict the amount of petroleum to be discovered in a basin from some future level of exploratory effort: the predictions are based on historical drilling and discovery data. Because marginal costs of discovery and production are a function of field size, the model can be used to make estimates of future discoveries within deposit size classes. The modeling approach is a geometric one in which the area searched is a function of the size and shape of the targets being sought. A high correlation is assumed between the surface-projection area of the fields and the volume of petroleum. To predict how much oil remains to be found, the area searched must be computed, and the basin size and discovery efficiency must be estimated. The basin is assumed to be explored randomly rather than by pattern drilling. The model may be used to compute independent estimates of future oil at different depth intervals for a play involving multiple producing horizons. We have written FORTRAN computer programs that are used with Drew, Schuenemeyer, and Root's model to merge the discovery and drilling information and perform the necessary computations to estimate undiscovered petroleum. These program may be modified easily for the estimation of remaining quantities of commodities other than petroleum. ?? 1980.

  8. Distributed Drug Discovery, Part 2: Global Rehearsal of Alkylating Agents for the Synthesis of Resin-Bound Unnatural Amino Acids and Virtual D3 Catalog Construction

    PubMed Central

    2008-01-01

    Distributed Drug Discovery (D3) proposes solving large drug discovery problems by breaking them into smaller units for processing at multiple sites. A key component of the synthetic and computational stages of D3 is the global rehearsal of prospective reagents and their subsequent use in the creation of virtual catalogs of molecules accessible by simple, inexpensive combinatorial chemistry. The first section of this article documents the feasibility of the synthetic component of Distributed Drug Discovery. Twenty-four alkylating agents were rehearsed in the United States, Poland, Russia, and Spain, for their utility in the synthesis of resin-bound unnatural amino acids 1, key intermediates in many combinatorial chemistry procedures. This global reagent rehearsal, coupled to virtual library generation, increases the likelihood that any member of that virtual library can be made. It facilitates the realistic integration of worldwide virtual D3 catalog computational analysis with synthesis. The second part of this article describes the creation of the first virtual D3 catalog. It reports the enumeration of 24 416 acylated unnatural amino acids 5, assembled from lists of either rehearsed or well-precedented alkylating and acylating reagents, and describes how the resulting catalog can be freely accessed, searched, and downloaded by the scientific community. PMID:19105725

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

    PubMed

    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.

  10. Pulsar discovery by global volunteer computing.

    PubMed

    Knispel, B; Allen, B; Cordes, J M; Deneva, J S; Anderson, D; Aulbert, C; Bhat, N D R; Bock, O; Bogdanov, S; Brazier, A; Camilo, F; Champion, D J; Chatterjee, S; Crawford, F; Demorest, P B; Fehrmann, H; Freire, P C C; Gonzalez, M E; Hammer, D; Hessels, J W T; Jenet, F A; Kasian, L; Kaspi, V M; Kramer, M; Lazarus, P; van Leeuwen, J; Lorimer, D R; Lyne, A G; Machenschalk, B; McLaughlin, M A; Messenger, C; Nice, D J; Papa, M A; Pletsch, H J; Prix, R; Ransom, S M; Siemens, X; Stairs, I H; Stappers, B W; Stovall, K; Venkataraman, A

    2010-09-10

    Einstein@Home aggregates the computer power of hundreds of thousands of volunteers from 192 countries to mine large data sets. It has now found a 40.8-hertz isolated pulsar in radio survey data from the Arecibo Observatory taken in February 2007. Additional timing observations indicate that this pulsar is likely a disrupted recycled pulsar. PSR J2007+2722's pulse profile is remarkably wide with emission over almost the entire spin period; the pulsar likely has closely aligned magnetic and spin axes. The massive computing power provided by volunteers should enable many more such discoveries.

  11. Aging Biology and Novel Targets for Drug Discovery

    PubMed Central

    McLachlan, Andrew J.; Quinn, Ronald J.; Simpson, Stephen J.; de Cabo, Rafael

    2012-01-01

    Despite remarkable technological advances in genetics and drug screening, the discovery of new pharmacotherapies has slowed and new approaches to drug development are needed. Research into the biology of aging is generating many novel targets for drug development that may delay all age-related diseases and be used long term by the entire population. Drugs that successfully delay the aging process will clearly become “blockbusters.” To date, the most promising leads have come from studies of the cellular pathways mediating the longevity effects of caloric restriction (CR), particularly target of rapamycin and the sirtuins. Similar research into pathways governing other hormetic responses that influence aging is likely to yield even more targets. As aging becomes a more attractive target for drug development, there will be increasing demand to develop biomarkers of aging as surrogate outcomes for the testing of the effects of new agents on the aging process. PMID:21693687

  12. Drug discovery: phosphinolactone, in vivo bioisostere of the lactol group.

    PubMed

    Volle, Jean-Noël; Filippini, Damien; Krawczy, Bartlomiej; Kaloyanov, Nikolay; Van der Lee, Arie; Maurice, Tangui; Pirat, Jean-Luc; Virieux, David

    2010-03-21

    In drug discovery, structural modifications over the lead molecule are often crucial for the development of a drug. Herein, we reported the first in vivo bioisosteric effect of phosphinolactone function in relation to the lactol group constituting the bioactive molecule: Hydroxybupropion. The preparation of phosphinolactone analogues and their antidepressant evaluation towards forced swimming test in mice showed that biological activity was regained and even strengthen.

  13. Natural Products as a Foundation for Drug Discovery

    PubMed Central

    Beutler, John A.

    2009-01-01

    Natural products have contributed to the development of many drugs for diverse indications. While most U.S. pharmaceutical companies have reduced or eliminated their in-house natural product groups, new paradigms and new enterprises have evolved to carry on a role for natural products in the pharmaceutical industry. Many of the reasons for the decline in popularity of natural products are being addressed by the development of new techniques for screening and production. This overview aims to inform pharmacologists of current strategies and techniques that make natural products a viable strategic choice for inclusion in drug discovery programs. PMID:20161632

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

    PubMed

    Nikapitiya, Chamilani

    2012-01-01

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

  15. Protein interactions in 3D: from interface evolution to drug discovery.

    PubMed

    Winter, Christof; Henschel, Andreas; Tuukkanen, Anne; Schroeder, Michael

    2012-09-01

    Over the past 10years, much research has been dedicated to the understanding of protein interactions. Large-scale experiments to elucidate the global structure of protein interaction networks have been complemented by detailed studies of protein interaction interfaces. Understanding the evolution of interfaces allows one to identify convergently evolved interfaces which are evolutionary unrelated but share a few key residues and hence have common binding partners. Understanding interaction interfaces and their evolution is an important basis for pharmaceutical applications in drug discovery. Here, we review the algorithms and databases on 3D protein interactions and discuss in detail applications in interface evolution, drug discovery, and interface prediction. Copyright © 2012 Elsevier Inc. All rights reserved.

  16. Competitive intelligence and patent analysis in drug discovery.

    PubMed

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

    2005-01-01

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

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

    PubMed

    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.

  18. Killing the hypnozoite--drug discovery approaches to prevent relapse in Plasmodium vivax.

    PubMed

    Campo, Brice; Vandal, Omar; Wesche, David L; Burrows, Jeremy N

    2015-05-01

    The eradication of malaria will only be possible if effective, well-tolerated medicines kill hypnozoites in vivax and ovale malaria, and thus prevent relapses in patients. Despite progress in the 8-aminoquinoline series, with tafenoquine in Phase III showing clear benefits over primaquine, the drug discovery challenge to identify hypnozoiticidal or hypnozoite-activating compounds has been hampered by the dearth of biological tools and assays, which in turn has been limited by the immense scientific and logistical challenges associated with accessing relevant human tissue and sporozoites. This review summarises the existing drug discovery series and approaches concerning the goal to block relapse.

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

    PubMed

    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.

  20. Ligand-based receptor tyrosine kinase partial agonists: New paradigm for cancer drug discovery?

    PubMed

    Riese, David J

    2011-02-01

    INTRODUCTION: Receptor tyrosine kinases (RTKs) are validated targets for oncology drug discovery and several RTK antagonists have been approved for the treatment of human malignancies. Nonetheless, the discovery and development of RTK antagonists has lagged behind the discovery and development of agents that target G-protein coupled receptors. In part, this is because it has been difficult to discover analogs of naturally-occurring RTK agonists that function as antagonists. AREAS COVERED: Here we describe ligands of ErbB receptors that function as partial agonists for these receptors, thereby enabling these ligands to antagonize the activity of full agonists for these receptors. We provide insights into the mechanisms by which these ligands function as antagonists. We discuss how information concerning these mechanisms can be translated into screens for novel small molecule- and antibody-based antagonists of ErbB receptors and how such antagonists hold great potential as targeted cancer chemotherapeutics. EXPERT OPINION: While there have been a number of important key findings into this field, the identification of the structural basis of ligand functional specificity is still of the greatest importance. While it is true that, with some notable exceptions, peptide hormones and growth factors have not proven to be good platforms for oncology drug discovery; addressing the fundamental issues of antagonistic partial agonists for receptor tyrosine kinases has the potential to steer oncology drug discovery in new directions. Mechanism based approaches are now emerging to enable the discovery of RTK partial agonists that may antagonize both agonist-dependent and -independent RTK signaling and may hold tremendous promise as targeted cancer chemotherapeutics.

  1. Compound Libraries: Recent Advances and Their Applications in Drug Discovery.

    PubMed

    Gong, Zhen; Hu, Guoping; Li, Qiang; Liu, Zhiguo; Wang, Fei; Zhang, Xuejin; Xiong, Jian; Li, Peng; Xu, Yan; Ma, Rujian; Chen, Shuhui; Li, Jian

    2017-01-01

    Hit identification is the starting point of small-molecule drug discovery and is therefore very important to the pharmaceutical industry. One of the most important approaches to identify a new hit is to screen a compound library using an in vitro assay. High-throughput screening has made great contributions to drug discovery since the 1990s but requires expensive equipment and facilities, and its success depends on the size of the compound library. Recent progress in the development of compound libraries has provided more efficient ways to identify new hits for novel drug targets, thereby helping to promote the development of the pharmaceutical industry, especially for firstin- class drugs. A multistage and systematic research of articles published between 1986 and 2017 has been performed, which was organized into 5 sections and discussed in detail. In this review, the sources and classification of compound libraries are summarized. The progress made in combinatorial libraries and DNA-encoded libraries is reviewed. Library design methods, especially for focused libraries, are introduced in detail. In the final part, the status of the compound libraries at WuXi is reported. The progress related to compound libraries, especially drug template libraries, DELs, and focused libraries, will help to identify better hits for novel drug targets and promote the development of the pharmaceutical industry. Moreover, these libraries can facilitate hit identification, which benefits most research organizations, including academics and small companies. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  2. Prediction of intracellular exposure bridges the gap between target- and cell-based drug discovery

    PubMed Central

    Gordon, Laurie J.; Wayne, Gareth J.; Almqvist, Helena; Axelsson, Hanna; Seashore-Ludlow, Brinton; Treyer, Andrea; Lundbäck, Thomas; West, Andy; Hann, Michael M.; Artursson, Per

    2017-01-01

    Inadequate target exposure is a major cause of high attrition in drug discovery. Here, we show that a label-free method for quantifying the intracellular bioavailability (Fic) of drug molecules predicts drug access to intracellular targets and hence, pharmacological effect. We determined Fic in multiple cellular assays and cell types representing different targets from a number of therapeutic areas, including cancer, inflammation, and dementia. Both cytosolic targets and targets localized in subcellular compartments were investigated. Fic gives insights on membrane-permeable compounds in terms of cellular potency and intracellular target engagement, compared with biochemical potency measurements alone. Knowledge of the amount of drug that is locally available to bind intracellular targets provides a powerful tool for compound selection in early drug discovery. PMID:28701380

  3. Integrated Computational Analysis of Genes Associated with Human Hereditary Insensitivity to Pain. A Drug Repurposing Perspective

    PubMed Central

    Lötsch, Jörn; Lippmann, Catharina; Kringel, Dario; Ultsch, Alfred

    2017-01-01

    Genes causally involved in human insensitivity to pain provide a unique molecular source of studying the pathophysiology of pain and the development of novel analgesic drugs. The increasing availability of “big data” enables novel research approaches to chronic pain while also requiring novel techniques for data mining and knowledge discovery. We used machine learning to combine the knowledge about n = 20 genes causally involved in human hereditary insensitivity to pain with the knowledge about the functions of thousands of genes. An integrated computational analysis proposed that among the functions of this set of genes, the processes related to nervous system development and to ceramide and sphingosine signaling pathways are particularly important. This is in line with earlier suggestions to use these pathways as therapeutic target in pain. Following identification of the biological processes characterizing hereditary insensitivity to pain, the biological processes were used for a similarity analysis with the functions of n = 4,834 database-queried drugs. Using emergent self-organizing maps, a cluster of n = 22 drugs was identified sharing important functional features with hereditary insensitivity to pain. Several members of this cluster had been implicated in pain in preclinical experiments. Thus, the present concept of machine-learned knowledge discovery for pain research provides biologically plausible results and seems to be suitable for drug discovery by identifying a narrow choice of repurposing candidates, demonstrating that contemporary machine-learned methods offer innovative approaches to knowledge discovery from available evidence. PMID:28848388

  4. Potential insight for drug discovery from high fidelity receptor-mediated transduction mechanisms in insects

    PubMed Central

    Raffa, Robert B.; Raffa, Kenneth F.

    2011-01-01

    Introduction There is a pervasive and growing concern about the small number of new pharmaceutical agents. There are many proposed explanations for this trend that do not involve the drug-discovery process per se, but the discovery process itself has also come under scrutiny. If the current paradigms are indeed not working, where are novel ideas to come from? Perhaps it is time to look to novel sources. Areas covered The receptor-signaling and 2nd-messenger transduction processes present in insects are quite similar to those in mammals (involving G proteins, ion channels, etc.). However, a review of these systems reveals an unprecedented degree of high potency and receptor selectivity to an extent greater than that modeled in most current drug-discovery approaches. Expert opinion A better understanding of insect receptor pharmacology could stimulate novel theoretical and practical ideas in mammalian pharmacology (drug discovery) and, conversely, the application of pharmacology and medicinal chemistry principles could stimulate novel advances in entomology (safer and more targeted control of pest species). PMID:21984882

  5. Postmarket Drug Surveillance Without Trial Costs: Discovery of Adverse Drug Reactions Through Large-Scale Analysis of Web Search Queries

    PubMed Central

    Gabrilovich, Evgeniy

    2013-01-01

    Background Postmarket drug safety surveillance largely depends on spontaneous reports by patients and health care providers; hence, less common adverse drug reactions—especially those caused by long-term exposure, multidrug treatments, or those specific to special populations—often elude discovery. Objective Here we propose a low cost, fully automated method for continuous monitoring of adverse drug reactions in single drugs and in combinations thereof, and demonstrate the discovery of heretofore-unknown ones. Methods We used aggregated search data of large populations of Internet users to extract information related to drugs and adverse reactions to them, and correlated these data over time. We further extended our method to identify adverse reactions to combinations of drugs. Results We validated our method by showing high correlations of our findings with known adverse drug reactions (ADRs). However, although acute early-onset drug reactions are more likely to be reported to regulatory agencies, we show that less acute later-onset ones are better captured in Web search queries. Conclusions Our method is advantageous in identifying previously unknown adverse drug reactions. These ADRs should be considered as candidates for further scrutiny by medical regulatory authorities, for example, through phase 4 trials. PMID:23778053

  6. Drug Discovery Algorithm for Cutaneous Leishmaniasis

    PubMed Central

    Grogl, Max; Hickman, Mark; Ellis, William; Hudson, Thomas; Lazo, John S.; Sharlow, Elizabeth R.; Johnson, Jacob; Berman, Jonathan; Sciotti, Richard J.

    2013-01-01

    Cutaneous leishmaniasis is clinically widespread but lacks treatments that are effective and well tolerated. Because all present drugs have been grandfathered into clinical use, there are no examples of a pre-clinical product evaluation scheme that lead to new candidates for formal development. To provide oral agents for development targeting cutaneous leishmaniasis, we have implemented a discovery scheme that incorporates in vitro and in vivo testing of efficacy, toxicity, and pharmacokinetics/metabolism. Particular emphasis is placed on in vivo testing, progression from higher-throughput models to those with most clinical relevance, and efficient use of resources. PMID:23390221

  7. A bioinformatics knowledge discovery in text application for grid computing

    PubMed Central

    Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco

    2009-01-01

    Background A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. Methods The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. Results A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. Conclusion In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and

  8. A bioinformatics knowledge discovery in text application for grid computing.

    PubMed

    Castellano, Marcello; Mastronardi, Giuseppe; Bellotti, Roberto; Tarricone, Gianfranco

    2009-06-16

    A fundamental activity in biomedical research is Knowledge Discovery which has the ability to search through large amounts of biomedical information such as documents and data. High performance computational infrastructures, such as Grid technologies, are emerging as a possible infrastructure to tackle the intensive use of Information and Communication resources in life science. The goal of this work was to develop a software middleware solution in order to exploit the many knowledge discovery applications on scalable and distributed computing systems to achieve intensive use of ICT resources. The development of a grid application for Knowledge Discovery in Text using a middleware solution based methodology is presented. The system must be able to: perform a user application model, process the jobs with the aim of creating many parallel jobs to distribute on the computational nodes. Finally, the system must be aware of the computational resources available, their status and must be able to monitor the execution of parallel jobs. These operative requirements lead to design a middleware to be specialized using user application modules. It included a graphical user interface in order to access to a node search system, a load balancing system and a transfer optimizer to reduce communication costs. A middleware solution prototype and the performance evaluation of it in terms of the speed-up factor is shown. It was written in JAVA on Globus Toolkit 4 to build the grid infrastructure based on GNU/Linux computer grid nodes. A test was carried out and the results are shown for the named entity recognition search of symptoms and pathologies. The search was applied to a collection of 5,000 scientific documents taken from PubMed. In this paper we discuss the development of a grid application based on a middleware solution. It has been tested on a knowledge discovery in text process to extract new and useful information about symptoms and pathologies from a large collection of

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

    PubMed

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

    2018-05-01

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

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

    PubMed

    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.

  11. Biophysical interactions with model lipid membranes: applications in drug discovery and drug delivery

    PubMed Central

    Peetla, Chiranjeevi; Stine, Andrew; Labhasetwar, Vinod

    2009-01-01

    The transport of drugs or drug delivery systems across the cell membrane is a complex biological process, often difficult to understand because of its dynamic nature. In this regard, model lipid membranes, which mimic many aspects of cell-membrane lipids, have been very useful in helping investigators to discern the roles of lipids in cellular interactions. One can use drug-lipid interactions to predict pharmacokinetic properties of drugs, such as their transport, biodistribution, accumulation, and hence efficacy. These interactions can also be used to study the mechanisms of transport, based on the structure and hydrophilicity/hydrophobicity of drug molecules. In recent years, model lipid membranes have also been explored to understand their mechanisms of interactions with peptides, polymers, and nanocarriers. These interaction studies can be used to design and develop efficient drug delivery systems. Changes in the lipid composition of cells and tissue in certain disease conditions may alter biophysical interactions, which could be explored to develop target-specific drugs and drug delivery systems. In this review, we discuss different model membranes, drug-lipid interactions and their significance, studies of model membrane interactions with nanocarriers, and how biophysical interaction studies with lipid model membranes could play an important role in drug discovery and drug delivery. PMID:19432455

  12. Manipulation of the mouse genome: a multiple impact resource for drug discovery and development.

    PubMed

    Prosser, Haydn; Rastan, Sohaila

    2003-05-01

    Few would deny that the pharmaceutical industry's investment in genomics throughout the 1990s has yet to deliver in terms of drugs on the market. The reasons are complex and beyond the scope of this review. The unique ability to manipulate the mouse genome, however, has already had a positive impact on all stages of the drug discovery process and, increasingly, on the drug development process too. We give an overview of some recent applications of so-called 'transgenic' mouse technology in pharmaceutical research and development. We show how genetic manipulation in the mouse can be employed at multiple points in the drug discovery and development process, providing new solutions to old problems.

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

    PubMed

    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.

  14. Novel Pieces for the Emerging Picture of Sulfoximines in Drug Discovery: Synthesis and Evaluation of Sulfoximine Analogues of Marketed Drugs and Advanced Clinical Candidates.

    PubMed

    Sirvent, Juan Alberto; Lücking, Ulrich

    2017-04-06

    Sulfoximines have gained considerable recognition as an important structural motif in drug discovery of late. In particular, the clinical kinase inhibitors for the treatment of cancer, roniciclib (pan-CDK inhibitor), BAY 1143572 (P-TEFb inhibitor), and AZD 6738 (ATR inhibitor), have recently drawn considerable attention. Whilst the interest in this underrepresented functional group in drug discovery is clearly on the rise, there remains an incomplete understanding of the medicinal-chemistry-relevant properties of sulfoximines. Herein we report the synthesis and in vitro characterization of a variety of sulfoximine analogues of marketed drugs and advanced clinical candidates to gain a better understanding of this neglected functional group and its potential in drug discovery. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  15. iDrug: a web-accessible and interactive drug discovery and design platform

    PubMed Central

    2014-01-01

    Background The progress in computer-aided drug design (CADD) approaches over the past decades accelerated the early-stage pharmaceutical research. Many powerful standalone tools for CADD have been developed in academia. As programs are developed by various research groups, a consistent user-friendly online graphical working environment, combining computational techniques such as pharmacophore mapping, similarity calculation, scoring, and target identification is needed. Results We presented a versatile, user-friendly, and efficient online tool for computer-aided drug design based on pharmacophore and 3D molecular similarity searching. The web interface enables binding sites detection, virtual screening hits identification, and drug targets prediction in an interactive manner through a seamless interface to all adapted packages (e.g., Cavity, PocketV.2, PharmMapper, SHAFTS). Several commercially available compound databases for hit identification and a well-annotated pharmacophore database for drug targets prediction were integrated in iDrug as well. The web interface provides tools for real-time molecular building/editing, converting, displaying, and analyzing. All the customized configurations of the functional modules can be accessed through featured session files provided, which can be saved to the local disk and uploaded to resume or update the history work. Conclusions iDrug is easy to use, and provides a novel, fast and reliable tool for conducting drug design experiments. By using iDrug, various molecular design processing tasks can be submitted and visualized simply in one browser without installing locally any standalone modeling softwares. iDrug is accessible free of charge at http://lilab.ecust.edu.cn/idrug. PMID:24955134

  16. An ion channel library for drug discovery and safety screening on automated platforms.

    PubMed

    Wible, Barbara A; Kuryshev, Yuri A; Smith, Stephen S; Liu, Zhiqi; Brown, Arthur M

    2008-12-01

    Ion channels represent the third largest class of targets in drug discovery after G-protein coupled receptors and kinases. In spite of this ranking, ion channels continue to be under exploited as drug targets compared with the other two groups for several reasons. First, with 400 ion channel genes and an even greater number of functional channels due to mixing and matching of individual subunits, a systematic collection of ion channel-expressing cell lines for drug discovery and safety screening has not been available. Second, the lack of high-throughput functional assays for ion channels has limited their use as drug targets. Now that automated electrophysiology has come of age and provided the technology to assay ion channels at medium to high throughput, we have addressed the need for a library of ion channel cell lines by constructing the Ion Channel Panel (ChanTest Corp., Cleveland, OH). From 400 ion channel genes, a collection of 82 of the most relevant human ion channels for drug discovery, safety, and human disease has been assembled.Each channel has been stably overexpressed in human embryonic kidney 293 or Chinese hamster ovary cells. Cell lines have been selected and validated on automated electrophysiology systems to facilitate cost-effective screening for safe and selective compounds at earlier stages in the drug development process. The screening and validation processes as well as the relative advantages of different screening platforms are discussed.

  17. Discovery of novel drug targets and their functions using phenotypic screening of natural products.

    PubMed

    Chang, Junghwa; Kwon, Ho Jeong

    2016-03-01

    Natural products are valuable resources that provide a variety of bioactive compounds and natural pharmacophores in modern drug discovery. Discovery of biologically active natural products and unraveling their target proteins to understand their mode of action have always been critical hurdles for their development into clinical drugs. For effective discovery and development of bioactive natural products into novel therapeutic drugs, comprehensive screening and identification of target proteins are indispensable. In this review, a systematic approach to understanding the mode of action of natural products isolated using phenotypic screening involving chemical proteomics-based target identification is introduced. This review highlights three natural products recently discovered via phenotypic screening, namely glucopiericidin A, ecumicin, and terpestacin, as representative case studies to revisit the pivotal role of natural products as powerful tools in discovering the novel functions and druggability of targets in biological systems and pathological diseases of interest.

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

    PubMed

    Clark, Alex M; Dole, Krishna; Coulon-Spektor, Anna; McNutt, Andrew; Grass, George; Freundlich, Joel S; Reynolds, Robert C; Ekins, Sean

    2015-06-22

    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.

  19. Discovery of Antimalarial Drugs from Streptomycetes Metabolites Using a Metabolomic Approach

    PubMed Central

    Baba, Mohd Shukri

    2017-01-01

    Natural products continue to play an important role as a source of biologically active substances for the development of new drug. Streptomyces, Gram-positive bacteria which are widely distributed in nature, are one of the most popular sources of natural antibiotics. Recently, by using a bioassay-guided fractionation, an antimalarial compound, Gancidin-W, has been discovered from these bacteria. However, this classical method in identifying potentially novel bioactive compounds from the natural products requires considerable effort and is a time-consuming process. Metabolomics is an emerging “omics” technology in systems biology study which integrated in process of discovering drug from natural products. Metabolomics approach in finding novel therapeutics agent for malaria offers dereplication step in screening phase to shorten the process. The highly sensitive instruments, such as Liquid Chromatography-Mass Spectrophotometry (LC-MS), Gas Chromatography-Mass Spectrophotometry (GC-MS), and Nuclear Magnetic Resonance (1H-NMR) spectroscopy, provide a wide range of information in the identification of potentially bioactive compounds. The current paper reviews concepts of metabolomics and its application in drug discovery of malaria treatment as well as assessing the antimalarial activity from natural products. Metabolomics approach in malaria drug discovery is still new and needs to be initiated, especially for drug research in Malaysia. PMID:29123551

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

    PubMed

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

    2017-06-19

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

  1. Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches.

    PubMed

    Lee, Jonathan A; Berg, Ellen L

    2013-12-01

    Innovation and new molecular entity production by the pharmaceutical industry has been below expectations. Surprisingly, more first-in-class small-molecule drugs approved by the U.S. Food and Drug Administration (FDA) between 1999 and 2008 were identified by functional phenotypic lead generation strategies reminiscent of pre-genomics pharmacology than contemporary molecular targeted strategies that encompass the vast majority of lead generation efforts. This observation, in conjunction with the difficulty in validating molecular targets for drug discovery, has diminished the impact of the "genomics revolution" and has led to a growing grassroots movement and now broader trend in pharma to reconsider the use of modern physiology-based or phenotypic drug discovery (PDD) strategies. This "From the Guest Editors" column provides an introduction and overview of the two-part special issues of Journal of Biomolecular Screening on PDD. Terminology and the business case for use of PDD are defined. Key issues such as assay performance, chemical optimization, target identification, and challenges to the organization and implementation of PDD are discussed. Possible solutions for these challenges and a new neoclassic vision for PDD that combines phenotypic and functional approaches with technology innovations resulting from the genomics-driven era of target-based drug discovery (TDD) are also described. Finally, an overview of the manuscripts in this special edition is provided.

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

    PubMed

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

    2018-02-02

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

  3. A Computational Approach to Finding Novel Targets for Existing Drugs

    PubMed Central

    Li, Yvonne Y.; An, Jianghong; Jones, Steven J. M.

    2011-01-01

    Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects. PMID:21909252

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

    PubMed

    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. Tiered analytics for purity assessment of macrocyclic peptides in drug discovery: Analytical consideration and method development.

    PubMed

    Qian Cutrone, Jingfang Jenny; Huang, Xiaohua Stella; Kozlowski, Edward S; Bao, Ye; Wang, Yingzi; Poronsky, Christopher S; Drexler, Dieter M; Tymiak, Adrienne A

    2017-05-10

    Synthetic macrocyclic peptides with natural and unnatural amino acids have gained considerable attention from a number of pharmaceutical/biopharmaceutical companies in recent years as a promising approach to drug discovery, particularly for targets involving protein-protein or protein-peptide interactions. Analytical scientists charged with characterizing these leads face multiple challenges including dealing with a class of complex molecules with the potential for multiple isomers and variable charge states and no established standards for acceptable analytical characterization of materials used in drug discovery. In addition, due to the lack of intermediate purification during solid phase peptide synthesis, the final products usually contain a complex profile of impurities. In this paper, practical analytical strategies and methodologies were developed to address these challenges, including a tiered approach to assessing the purity of macrocyclic peptides at different stages of drug discovery. Our results also showed that successful progression and characterization of a new drug discovery modality benefited from active analytical engagement, focusing on fit-for-purpose analyses and leveraging a broad palette of analytical technologies and resources. Copyright © 2017. Published by Elsevier B.V.

  6. The role of chromatographic and chiroptical spectroscopic techniques and methodologies in support of drug discovery for atropisomeric drug inhibitors of Bruton's tyrosine kinase.

    PubMed

    Dai, Jun; Wang, Chunlei; Traeger, Sarah C; Discenza, Lorell; Obermeier, Mary T; Tymiak, Adrienne A; Zhang, Yingru

    2017-03-03

    Atropisomers are stereoisomers resulting from hindered bond rotation. From synthesis of pure atropisomers, characterization of their interconversion thermodynamics to investigation of biological stereoselectivity, the evaluation of drug candidates subject to atropisomerism creates special challenges and can be complicated in both early drug discovery and later drug development. In this paper, we demonstrate an array of analytical techniques and systematic approaches to study the atropisomerism of drug molecules to meet these challenges. Using a case study of Bruton's tyrosine kinase (BTK) inhibitor drug candidates at Bristol-Myers Squibb, we present the analytical strategies and methodologies used during drug discovery including the detection of atropisomers, the determination of their relative composition, the identification of relative chirality, the isolation of individual atropisomers, the evaluation of interconversion kinetics, and the characterization of chiral stability in the solid state and in solution. In vivo and in vitro stereo-stability and stereo-selectivity were investigated as well as the pharmacological significance of any changes in atropisomer ratios. Techniques applied in these studies include analytical and preparative enantioselective supercritical fluid chromatography (SFC), enantioselective high performance liquid chromatography (HPLC), circular dichroism (CD), and mass spectrometry (MS). Our experience illustrates how atropisomerism can be a very complicated issue in drug discovery and why a thorough understanding of this phenomenon is necessary to provide guidance for pharmaceutical development. Analytical techniques and methodologies facilitate key decisions during the discovery of atropisomeric drug candidates by characterizing time-dependent physicochemical properties that can have significant biological implications and relevance to pharmaceutical development plans. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. GalenOWL: Ontology-based drug recommendations discovery

    PubMed Central

    2012-01-01

    Background Identification of drug-drug and drug-diseases interactions can pose a difficult problem to cope with, as the increasingly large number of available drugs coupled with the ongoing research activities in the pharmaceutical domain, make the task of discovering relevant information difficult. Although international standards, such as the ICD-10 classification and the UNII registration, have been developed in order to enable efficient knowledge sharing, medical staff needs to be constantly updated in order to effectively discover drug interactions before prescription. The use of Semantic Web technologies has been proposed in earlier works, in order to tackle this problem. Results This work presents a semantic-enabled online service, named GalenOWL, capable of offering real time drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standards such as the aforementioned ICD-10 and UNII, provide the backbone of the common representation of medical data, while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. A comparison of the developed ontology-based system with a similar system developed using a traditional business logic rule engine is performed, giving insights on the advantages and drawbacks of both implementations. Conclusions The use of Semantic Web technologies has been found to be a good match for developing drug recommendation systems. Ontologies can effectively encapsulate medical knowledge and rule-based reasoning can capture and encode the drug interactions knowledge. PMID:23256945

  8. Drug Discovery Using Chemical Systems Biology: Repositioning the Safe Medicine Comtan to Treat Multi-Drug and Extensively Drug Resistant Tuberculosis

    PubMed Central

    Tonge, Peter J.; Xie, Lei; Bourne, Philip E.

    2009-01-01

    The rise of multi-drug resistant (MDR) and extensively drug resistant (XDR) tuberculosis around the world, including in industrialized nations, poses a great threat to human health and defines a need to develop new, effective and inexpensive anti-tubercular agents. Previously we developed a chemical systems biology approach to identify off-targets of major pharmaceuticals on a proteome-wide scale. In this paper we further demonstrate the value of this approach through the discovery that existing commercially available drugs, prescribed for the treatment of Parkinson's disease, have the potential to treat MDR and XDR tuberculosis. These drugs, entacapone and tolcapone, are predicted to bind to the enzyme InhA and directly inhibit substrate binding. The prediction is validated by in vitro and InhA kinetic assays using tablets of Comtan, whose active component is entacapone. The minimal inhibition concentration (MIC99) of entacapone for Mycobacterium tuberculosis (M.tuberculosis) is approximately 260.0 µM, well below the toxicity concentration determined by an in vitro cytotoxicity model using a human neuroblastoma cell line. Moreover, kinetic assays indicate that Comtan inhibits InhA activity by 47.0% at an entacapone concentration of approximately 80 µM. Thus the active component in Comtan represents a promising lead compound for developing a new class of anti-tubercular therapeutics with excellent safety profiles. More generally, the protocol described in this paper can be included in a drug discovery pipeline in an effort to discover novel drug leads with desired safety profiles, and therefore accelerate the development of new drugs. PMID:19578428

  9. Towards the discovery of drug-like RNA ligands?

    PubMed

    Foloppe, Nicolas; Matassova, Natalia; Aboul-Ela, Fareed

    2006-11-01

    Targeting RNA with small molecule drugs is an area of great potential for therapeutic treatment of infections and possibly genetic and autoimmune diseases. However, a mature set of precedents and established methodology is lacking. The physicochemical properties of RNA raise specific issues and obstacles to development, and contribute to explain the distinct characteristics of natural RNA ligands, including antibiotics. Yet, RNA-targeting strategies are being implemented to reinvigorate antibacterial discovery by using the ribosomal X-ray structures to modify known antibiotics. To exploit further these structures, we suggest the use of existing protein kinase-directed libraries of drug-like compounds to target the A-site of the bacterial ribosome, on the basis of a specific structural hypothesis.

  10. Physicochemical descriptors of aromatic character and their use in drug discovery.

    PubMed

    Ritchie, Timothy J; Macdonald, Simon J F

    2014-09-11

    Published physicochemical descriptors of molecules that convey aromaticity-related character are reviewed in the context of drug design and discovery. Studies that have employed aromatic descriptors are discussed, and several descriptors are compared and contrasted.

  11. Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

    PubMed Central

    Lionta, Evanthia; Spyrou, George; Vassilatis, Demetrios K.; Cournia, Zoe

    2014-01-01

    Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process. PMID:25262799

  12. 3D in vitro technology for drug discovery.

    PubMed

    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.

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

    PubMed

    Williams, Antony J; Wilbanks, John; Ekins, Sean

    2012-01-01

    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.

  14. Why Open Drug Discovery Needs Four Simple Rules for Licensing Data and Models

    PubMed Central

    Williams, Antony J.; Wilbanks, John; Ekins, Sean

    2012-01-01

    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. PMID:23028298

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

    PubMed

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

    2018-04-11

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

  16. The rise of fragment-based drug discovery.

    PubMed

    Murray, Christopher W; Rees, David C

    2009-06-01

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

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

    PubMed

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

    2015-07-16

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

  18. Use of Computational Functional Genomics in Drug Discovery and Repurposing for Analgesic Indications.

    PubMed

    Lötsch, Jörn; Kringel, Dario

    2018-06-01

    The novel research area of functional genomics investigates biochemical, cellular, or physiological properties of gene products with the goal of understanding the relationship between the genome and the phenotype. These developments have made analgesic drug research a data-rich discipline mastered only by making use of parallel developments in computer science, including the establishment of knowledge bases, mining methods for big data, machine-learning, and artificial intelligence, (Table ) which will be exemplarily introduced in the following. © 2018 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

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

    PubMed Central

    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

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

    PubMed

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

    2011-06-01

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

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

    PubMed Central

    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

  2. The role of public-sector research in the discovery of drugs and vaccines.

    PubMed

    Stevens, Ashley J; Jensen, Jonathan J; Wyller, Katrine; Kilgore, Patrick C; Chatterjee, Sabarni; Rohrbaugh, Mark L

    2011-02-10

    Historically, public-sector researchers have performed the upstream, basic research that elucidated the underlying mechanisms of disease and identified promising points of intervention, whereas corporate researchers have performed the downstream, applied research resulting in the discovery of drugs for the treatment of diseases and have carried out development activities to bring them to market. However, the boundaries between the roles of the public and private sectors have shifted substantially since the dawn of the biotechnology era, and the public sector now has a much more direct role in the applied-research phase of drug discovery. We identified new drugs and vaccines approved by the Food and Drug Administration (FDA) that were discovered by public-sector research institutions (PSRIs) and classified them according to their therapeutic category and potential therapeutic effect. We found that during the past 40 years, 153 new FDA-approved drugs, vaccines, or new indications for existing drugs were discovered through research carried out in PSRIs. These drugs included 93 small-molecule drugs, 36 biologic agents, 15 vaccines, 8 in vivo diagnostic materials, and 1 over-the-counter drug. More than half of these drugs have been used in the treatment or prevention of cancer or infectious diseases. PSRI-discovered drugs are expected to have a disproportionately large therapeutic effect. Public-sector research has had a more immediate effect on improving public health than was previously realized.

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

    PubMed Central

    2017-01-01

    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. PMID:28875019

  4. The Significance of G Protein-Coupled Receptor Crystallography for Drug Discovery

    PubMed Central

    Salon, John A.; Lodowski, David T.

    2011-01-01

    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. PMID:21969326

  5. Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

    PubMed

    Alaimo, Salvatore; Giugno, Rosalba; Pulvirenti, Alfredo

    2016-01-01

    The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

  6. Drug discovery for the treatment of leishmaniasis, African sleeping sickness and Chagas disease.

    PubMed

    2013-10-01

    The trypanosomatid protozoa Leishmania, Trypanosoma brucei and Trypanosoma cruzi are the caustive agents of the human diseases respectively, leishmaniasis, African sleeping sickness and Chagas disease. Among the 17 'neglected tropical diseases' highlighted by WHO, progress towards the treatment of these diseases has improved in recent decades, as a result of increased awareness, the emergence of public-private research partnerships and advances in drug-discovery technologies and techniques. Despite this, the current therapies for these diseases have serious shortcomings and, as such, the need to develop novel drugs, improve diagnosis and control the spread of disease is of paramount importance. Future Medicinal Chemistry invited leading experts in the field to share their thoughts and opinions on the changing face of drug discovery in the pursuit of treatments for trypanosomatid-based diseases.

  7. Preparation, characterization, drug release and computational modelling studies of antibiotics loaded amorphous chitin nanoparticles.

    PubMed

    Gayathri, N K; Aparna, V; Maya, S; Biswas, Raja; Jayakumar, R; Mohan, C Gopi

    2017-12-01

    We present a computational investigation of binding affinity of different types of drugs with chitin nanocarriers. Understanding the chitn polymer-drug interaction is important to design and optimize the chitin based drug delivery systems. The binding affinity of three different types of anti-bacterial drugs Ethionamide (ETA) Methacycline (MET) and Rifampicin (RIF) with amorphous chitin nanoparticles (AC-NPs) were studied by integrating computational and experimental techniques. The binding energies (BE) of hydrophobic ETA, hydrophilic MET and hydrophobic RIF were -7.3kcal/mol, -5.1kcal/mol and -8.1kcal/mol respectively, with respect to AC-NPs, using molecular docking studies. This theoretical result was in good correlation with the experimental studies of AC-drug loading and drug entrapment efficiencies of MET (3.5±0.1 and 25± 2%), ETA (5.6±0.02 and 45±4%) and RIF (8.9±0.20 and 53±5%) drugs respectively. Stability studies of the drug encapsulated nanoparticles showed stable values of size, zeta and polydispersity index at 6°C temperature. The correlation between computational BE and experimental drug entrapment efficiencies of RIF, ETA and MET drugs with four AC-NPs strands were 0.999 respectively, while that of the drug loading efficiencies were 0.854 respectively. Further, the molecular docking results predict the atomic level details derived from the electrostatic, hydrogen bonding and hydrophobic interactions of the drug and nanoparticle for its encapsulation and loading in the chitin-based host-guest nanosystems. The present results thus revealed the drug loading and drug delivery insights and has the potential of reducing the time and cost of processing new antibiotic drug delivery nanosystem optimization, development and discovery. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Computational discovery of picomolar Q(o) site inhibitors of cytochrome bc1 complex.

    PubMed

    Hao, Ge-Fei; Wang, Fu; Li, Hui; Zhu, Xiao-Lei; Yang, Wen-Chao; Huang, Li-Shar; Wu, Jia-Wei; Berry, Edward A; Yang, Guang-Fu

    2012-07-11

    A critical challenge to the fragment-based drug discovery (FBDD) is its low-throughput nature due to the necessity of biophysical method-based fragment screening. Herein, a method of pharmacophore-linked fragment virtual screening (PFVS) was successfully developed. Its application yielded the first picomolar-range Q(o) site inhibitors of the cytochrome bc(1) complex, an important membrane protein for drug and fungicide discovery. Compared with the original hit compound 4 (K(i) = 881.80 nM, porcine bc(1)), the most potent compound 4f displayed 20 507-fold improved binding affinity (K(i) = 43.00 pM). Compound 4f was proved to be a noncompetitive inhibitor with respect to the substrate cytochrome c, but a competitive inhibitor with respect to the substrate ubiquinol. Additionally, we determined the crystal structure of compound 4e (K(i) = 83.00 pM) bound to the chicken bc(1) at 2.70 Å resolution, providing a molecular basis for understanding its ultrapotency. To our knowledge, this study is the first application of the FBDD method in the discovery of picomolar inhibitors of a membrane protein. This work demonstrates that the novel PFVS approach is a high-throughput drug discovery method, independent of biophysical screening techniques.

  9. West Nile Virus Drug Discovery

    PubMed Central

    Lim, Siew Pheng; Shi, Pei-Yong

    2013-01-01

    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. PMID:24300672

  10. West Nile virus drug discovery.

    PubMed

    Lim, Siew Pheng; Shi, Pei-Yong

    2013-12-03

    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. Understanding G Protein-Coupled Receptor Allostery via Molecular Dynamics Simulations: Implications for Drug Discovery.

    PubMed

    Basith, Shaherin; Lee, Yoonji; Choi, Sun

    2018-01-01

    Unraveling the mystery of protein allostery has been one of the greatest challenges in both structural and computational biology. However, recent advances in computational methods, particularly molecular dynamics (MD) simulations, have led to its utility as a powerful and popular tool for the study of protein allostery. By capturing the motions of a protein's constituent atoms, simulations can enable the discovery of allosteric hot spots and the determination of the mechanistic basis for allostery. These structural and dynamic studies can provide a foundation for a wide range of applications, including rational drug design and protein engineering. In our laboratory, the use of MD simulations and network analysis assisted in the elucidation of the allosteric hotspots and intracellular signal transduction of G protein-coupled receptors (GPCRs), primarily on one of the adenosine receptor subtypes, A 2A adenosine receptor (A 2A AR). In this chapter, we describe a method for calculating the map of allosteric signal flow in different GPCR conformational states and illustrate how these concepts have been utilized in understanding the mechanism of GPCR allostery. These structural studies will provide valuable insights into the allosteric and orthosteric modulations that would be of great help to design novel drugs targeting GPCRs in pathological states.

  12. State-of-the-art and dissemination of computational tools for drug-design purposes: a survey among Italian academics and industrial institutions.

    PubMed

    Artese, Anna; Alcaro, Stefano; Moraca, Federica; Reina, Rocco; Ventura, Marzia; Costantino, Gabriele; Beccari, Andrea R; Ortuso, Francesco

    2013-05-01

    During the first edition of the Computationally Driven Drug Discovery meeting, held in November 2011 at Dompé Pharma (L'Aquila, Italy), a questionnaire regarding the diffusion and the use of computational tools for drug-design purposes in both academia and industry was distributed among all participants. This is a follow-up of a previously reported investigation carried out among a few companies in 2007. The new questionnaire implemented five sections dedicated to: research group identification and classification; 18 different computational techniques; software information; hardware data; and economical business considerations. In this article, together with a detailed history of the different computational methods, a statistical analysis of the survey results that enabled the identification of the prevalent computational techniques adopted in drug-design projects is reported and a profile of the computational medicinal chemist currently working in academia and pharmaceutical companies in Italy is highlighted.

  13. Targeting α-synuclein oligomers by protein-fragment complementation for drug discovery in synucleinopathies.

    PubMed

    Moussaud, Simon; Malany, Siobhan; Mehta, Alka; Vasile, Stefan; Smith, Layton H; McLean, Pamela J

    2015-05-01

    Reducing the burden of α-synuclein oligomeric species represents a promising approach for disease-modifying therapies against synucleinopathies such as Parkinson's disease and dementia with Lewy bodies. However, the lack of efficient drug discovery strategies that specifically target α-synuclein oligomers has been a limitation to drug discovery programs. Here we describe an innovative strategy that harnesses the power of bimolecular protein-fragment complementation to monitor synuclein-synuclein interactions. We have developed two robust models to monitor α-synuclein oligomerization by generating novel stable cell lines expressing α-synuclein fusion proteins for either fluorescent or bioluminescent protein-fragment complementation under the tetracycline-controlled transcriptional activation system. A pilot screen was performed resulting in the identification of two potential hits, a p38 MAPK inhibitor and a casein kinase 2 inhibitor, thereby demonstrating the suitability of our protein-fragment complementation assay for the measurement of α-synuclein oligomerization in living cells at high throughput. The application of the strategy described herein to monitor α-synuclein oligomer formation in living cells with high throughput will facilitate drug discovery efforts for disease-modifying therapies against synucleinopathies and other proteinopathies.

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

    PubMed

    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.

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

    PubMed

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

    PubMed

    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.

  17. Killing the hypnozoite – drug discovery approaches to prevent relapse in Plasmodium vivax

    PubMed Central

    Campo, Brice; Vandal, Omar; Wesche, David L.; Burrows, Jeremy N.

    2015-01-01

    The eradication of malaria will only be possible if effective, well-tolerated medicines kill hypnozoites in vivax and ovale malaria, and thus prevent relapses in patients. Despite progress in the 8-aminoquinoline series, with tafenoquine in Phase III showing clear benefits over primaquine, the drug discovery challenge to identify hypnozoiticidal or hypnozoite-activating compounds has been hampered by the dearth of biological tools and assays, which in turn has been limited by the immense scientific and logistical challenges associated with accessing relevant human tissue and sporozoites. This review summarises the existing drug discovery series and approaches concerning the goal to block relapse. PMID:25891812

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

    PubMed Central

    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

  19. Ten years of dengue drug discovery: progress and prospects.

    PubMed

    Lim, Siew Pheng; Wang, Qing-Yin; Noble, Christian G; Chen, Yen-Liang; Dong, Hongping; Zou, Bin; Yokokawa, Fumiaki; Nilar, Shahul; Smith, Paul; Beer, David; Lescar, Julien; Shi, Pei-Yong

    2013-11-01

    To combat neglected diseases, the Novartis Institute of Tropical Diseases (NITD) was founded in 2002 through private-public funding from Novartis and the Singapore Economic Development Board. One of NITD's missions is to develop antivirals for dengue virus (DENV), the most prevalent mosquito-borne viral pathogen. Neither vaccine nor antiviral is currently available for DENV. Here we review the progress in dengue drug discovery made at NITD as well as the major discoveries made by academia and other companies. Four strategies have been pursued to identify inhibitors of DENV through targeting both viral and host proteins: (i) HTS (high-throughput screening) using virus replication assays; (ii) HTS using viral enzyme assays; (iii) structure-based in silico docking and rational design; (iv) repurposing hepatitis C virus inhibitors for DENV. Along the developmental process from hit finding to clinical candidate, many inhibitors did not advance beyond the stage of hit-to-lead optimization, due to their poor selectivity, physiochemical or pharmacokinetic properties. Only a few compounds showed efficacy in the AG129 DENV mouse model. Two nucleoside analogs, NITD-008 and Balapiravir, entered preclinical animal safety study and clinic trial, but both were terminated due to toxicity and lack of potency, respectively. Celgosivir, a host alpha-glucosidase inhibitor, is currently under clinical trial; its clinical efficacy remains to be determined. The knowledge accumulated during the past decade has provided a better rationale for ongoing dengue drug discovery. Though challenging, we are optimistic that this continuous, concerted effort will lead to an effective dengue therapy. Copyright © 2013 Elsevier B.V. All rights reserved.

  20. Spotting and designing promiscuous ligands for drug discovery.

    PubMed

    Schneider, P; Röthlisberger, M; Reker, D; Schneider, G

    2016-01-21

    The promiscuous binding behavior of bioactive compounds forms a mechanistic basis for understanding polypharmacological drug action. We present the development and prospective application of a computational tool for identifying potential promiscuous drug-like ligands. In combination with computational target prediction methods, the approach provides a working concept for rationally designing such molecular structures. We could confirm the multi-target binding of a de novo generated compound in a proof-of-concept study relying on the new method.

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

    PubMed

    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.

  2. Antimalarial natural products drug discovery in Panama.

    PubMed

    Calderón, Angela I; Simithy-Williams, Johayra; Gupta, Mahabir P

    2012-01-01

    Malaria is still a major public health problem. The biodiversity of the tropics is extremely rich and represents an invaluable source of novel bioactive molecules. For screening of this diversity more sensitive and economical in vitro methods are needed, Flora of Panama has been studied based on ethnomedical uses for discovering antimalarial compounds. This review aims to provide an overview of in vitro screening methodologies for antimalarial drug discovery and to present results of this effort in Panama during the last quarter century. A literature search in SciFinder and PubMed and original publications of Panamanian scientists was performed to gather all the information on antimalarial drug discovery from the Panamanian flora and in vitro screening methods. A variety of colorimetric, staining, fluorometric, and mass spectrometry and radioactivity-based methods have been provided. The advantages and limitations of these methods are also discussed. Plants used in ethnomedicine for symptoms of malaria by three native Panamanian groups of Amerindians, Kuna, Ngöbe Buglé and Teribes are provided. Seven most active plants with IC(50) values < 10 μg/mL were identified Talisia nervosa Radlk. (Sapindaceae), Topobea parasitica Aubl.(Melastomataceae), Monochaetum myrtoideum Naudin (Melastomataceae), Bourreria spathulata (Miers) Hemsl.(Boraginaceae), Polygonum acuminatum Kunth (Polygonaceae), Clematis campestris A. St.-Hil. (Ranunculaceae) and Terminalia triflora (Griseb.) Lillo (Combretaceae). Thirty bioactive compounds belonging to a variety of chemical classes such as spermine and isoquinoline alkaloids, glycosylflavones, phenylethanoid glycosides, ecdysteroids, quercetin arabinofuranosides, clerodane-type diterpenoids, sipandinolid, galloylquercetin derivatives, gallates, oleamide and mangiferin derivatives.

  3. Computational discovery of extremal microstructure families

    PubMed Central

    Chen, Desai; Skouras, Mélina; Zhu, Bo; Matusik, Wojciech

    2018-01-01

    Modern fabrication techniques, such as additive manufacturing, can be used to create materials with complex custom internal structures. These engineered materials exhibit a much broader range of bulk properties than their base materials and are typically referred to as metamaterials or microstructures. Although metamaterials with extraordinary properties have many applications, designing them is very difficult and is generally done by hand. We propose a computational approach to discover families of microstructures with extremal macroscale properties automatically. Using efficient simulation and sampling techniques, we compute the space of mechanical properties covered by physically realizable microstructures. Our system then clusters microstructures with common topologies into families. Parameterized templates are eventually extracted from families to generate new microstructure designs. We demonstrate these capabilities on the computational design of mechanical metamaterials and present five auxetic microstructure families with extremal elastic material properties. Our study opens the way for the completely automated discovery of extremal microstructures across multiple domains of physics, including applications reliant on thermal, electrical, and magnetic properties. PMID:29376124

  4. Connecting the virtual world of computers to the real world of medicinal chemistry.

    PubMed

    Glen, Robert C

    2011-03-01

    Drug discovery involves the simultaneous optimization of chemical and biological properties, usually in a single small molecule, which modulates one of nature's most complex systems: the balance between human health and disease. The increased use of computer-aided methods is having a significant impact on all aspects of the drug-discovery and development process and with improved methods and ever faster computers, computer-aided molecular design will be ever more central to the discovery process.

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

    PubMed

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

    2014-01-01

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

  6. Computer-Assisted Drug Formulation Design: Novel Approach in Drug Delivery.

    PubMed

    Metwally, Abdelkader A; Hathout, Rania M

    2015-08-03

    We hypothesize that, by using several chemo/bio informatics tools and statistical computational methods, we can study and then predict the behavior of several drugs in model nanoparticulate lipid and polymeric systems. Accordingly, two different matrices comprising tripalmitin, a core component of solid lipid nanoparticles (SLN), and PLGA were first modeled using molecular dynamics simulation, and then the interaction of drugs with these systems was studied by means of computing the free energy of binding using the molecular docking technique. These binding energies were hence correlated with the loadings of these drugs in the nanoparticles obtained experimentally from the available literature. The obtained relations were verified experimentally in our laboratory using curcumin as a model drug. Artificial neural networks were then used to establish the effect of the drugs' molecular descriptors on the binding energies and hence on the drug loading. The results showed that the used soft computing methods can provide an accurate method for in silico prediction of drug loading in tripalmitin-based and PLGA nanoparticulate systems. These results have the prospective of being applied to other nano drug-carrier systems, and this integrated statistical and chemo/bio informatics approach offers a new toolbox to the formulation science by proposing what we present as computer-assisted drug formulation design (CADFD).

  7. Drug discovery chemistry: a primer for the non-specialist.

    PubMed

    Jordan, Allan M; Roughley, Stephen D

    2009-08-01

    Like all scientific disciplines, drug discovery chemistry is rife with terminology and methodology that can seem intractable to those outside the sphere of synthetic chemistry. Derived from a successful in-house workshop, this Foundation Review aims to demystify some of this inherent terminology, providing the non-specialist with a general insight into the nomenclature, terminology and workflow of medicinal chemists within the pharmaceutical industry.

  8. Computer-Aided Structure Based Drug Design Approaches for the Discovery of New Anti-CHIKV Agents.

    PubMed

    Jadav, Surender Singh; Sinha, Barij Nayan; Hilgenfeld, Rolf; Jayaprakash, Venkatesan

    2017-11-10

    Chikungunya is a viral infection caused by Chikungunya virus (CHIKV), an arbovirus transmitted through mosquito (Aedes aegypti and Aedes albopictus) bite. The virus from sylvatic cycle in Africa mutated to new vector adaptation and became one of the major emerging and re-emerging viral infections in the past decade, affecting more than 40 countries. Efforts are being made by many researches to develop means to prevent and control the infection through vaccines and vector control strategy. On the other hand, search for novel chemotherapeutic agents for the treatment of infected patients is on. Approach of repurposed drug is one way of identifying an existing drug for the treatment of CHIKV infection. Review the history of CHIKV nsp2 protease inhibitors derived through structure-based computer-aided drug design along with phytochemicals identified as anti-CHIKV agents. A survey on CHIKV inhibitors reported till date has been carriedout. The data obtained were organized and discussed under natural substances and synthetic derivatives obtained as result of rational design. The review provides a well organized content in chronological order that has highly significant information for medicinal chemist who wish to explore the area of Anti-CHIKV drug design and development. Natural compounds with different scaffolds provides an opportunity to explore Ligand based drug design (LBDD), while rational drug design approaches provides opportunity to explore the Structure based drug design. From the presented mini-review, readers can understand that this area is less explored and has lots of potential in anti-CHIKVviral drug design & development. of reported literature inferred that, unlike other viral proteases, the nsP2 protease can be targeted for CHIKV viral inhibition. The HTVS process for the identification of anti-CHIK agents provided a few successive validated lead compounds against CHIKV infections. Copyright© Bentham Science Publishers; For any queries, please email

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

    PubMed

    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.

  10. Community-Reviewed Biological Network Models for Toxicology and Drug Discovery Applications

    PubMed Central

    Namasivayam, Aishwarya Alex; Morales, Alejandro Ferreiro; Lacave, Ángela María Fajardo; Tallam, Aravind; Simovic, Borislav; Alfaro, David Garrido; Bobbili, Dheeraj Reddy; Martin, Florian; Androsova, Ganna; Shvydchenko, Irina; Park, Jennifer; Calvo, Jorge Val; Hoeng, Julia; Peitsch, Manuel C.; Racero, Manuel González Vélez; Biryukov, Maria; Talikka, Marja; Pérez, Modesto Berraquero; Rohatgi, Neha; Díaz-Díaz, Noberto; Mandarapu, Rajesh; Ruiz, Rubén Amián; Davidyan, Sergey; Narayanasamy, Shaman; Boué, Stéphanie; Guryanova, Svetlana; Arbas, Susana Martínez; Menon, Swapna; Xiang, Yang

    2016-01-01

    Biological network models offer a framework for understanding disease by describing the relationships between the mechanisms involved in the regulation of biological processes. Crowdsourcing can efficiently gather feedback from a wide audience with varying expertise. In the Network Verification Challenge, scientists verified and enhanced a set of 46 biological networks relevant to lung and chronic obstructive pulmonary disease. The networks were built using Biological Expression Language and contain detailed information for each node and edge, including supporting evidence from the literature. Network scoring of public transcriptomics data inferred perturbation of a subset of mechanisms and networks that matched the measured outcomes. These results, based on a computable network approach, can be used to identify novel mechanisms activated in disease, quantitatively compare different treatments and time points, and allow for assessment of data with low signal. These networks are periodically verified by the crowd to maintain an up-to-date suite of networks for toxicology and drug discovery applications. PMID:27429547

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

    PubMed

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

    2011-02-01

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

  12. The reproducibility issue and preclinical academic drug discovery: educational and institutional initiatives fostering translation success.

    PubMed

    Janero, David R

    2016-09-01

    Drug discovery depends critically upon published results from the academy. The reproducibility of preclinical research findings reported by academia in the peer-reviewed literature has been called into question, seriously jeopardizing the value of academic science for inventing therapeutics. The corrosive effects of the reproducibility issue on drug discovery are considered. Purported correctives imposed upon academia from the outside deal mainly with expunging fraudulent literature and imposing punitive sanctions on the responsible authors. The salutary influence of such post facto actions on the reproducibility of discovery-relevant preclinical research data from academia appears limited. Rather, intentional doctoral-scientist education focused on data replicability and translationally-meaningful science and active participation of university entities charged with research innovation and asset commercialization toward ensuring data quality are advocated as key academic initiatives for addressing the reproducibility issue. A mindset shift on the part of both senior university faculty and the academy to take responsibility for the data reproducibility crisis and commit proactively to positive educational, incentivization, and risk- and reward-sharing practices will be fundamental for improving the value of published preclinical academic research to drug discovery.

  13. Drug discovery based on genetic and metabolic findings in schizophrenia.

    PubMed

    Dwyer, Donard S; Weeks, Kathrine; Aamodt, Eric J

    2008-11-01

    Recent progress in the genetics of schizophrenia provides the rationale for re-evaluating causative factors and therapeutic strategies for this disease. Here, we review the major candidate susceptibility genes and relate the aberrant function of these genes to defective regulation of energy metabolism in the schizophrenic brain. Disturbances in energy metabolism potentially lead to neurodevelopmental deficits, impaired function of the mature nervous system and failure to maintain neurites/dendrites and synaptic connections. Current antipsychotic drugs do not specifically address these underlying deficits; therefore, a new generation of more effective medications is urgently needed. Novel targets for future drug discovery are identified in this review. The coordinated application of structure-based drug design, systems biology and research on model organisms may greatly facilitate the search for next-generation antipsychotic drugs.

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

    PubMed

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

    2011-06-01

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

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

    PubMed Central

    Sykes, Melissa L.; Jones, Amy J.; Shelper, Todd B.; Simpson, Moana; Lang, Rebecca; Poulsen, Sally-Ann; Sleebs, Brad E.

    2017-01-01

    ABSTRACT 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. PMID:28674055

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

    PubMed

    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.

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

    PubMed

    Ishii, Masaru

    2015-06-01

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

  18. Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage.

    PubMed

    Gantner, Melisa E; Peroni, Roxana N; Morales, Juan F; Villalba, María L; Ruiz, María E; Talevi, Alan

    2017-08-28

    Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter linked to the multidrug resistance phenomenon in many diseases such as epilepsy and cancer and a potential source of drug interactions. For these reasons, the early identification of substrates and nonsubstrates of this transporter during the drug discovery stage is of great interest. We have developed a computational nonlinear model ensemble based on conformational independent molecular descriptors using a combined strategy of genetic algorithms, J48 decision tree classifiers, and data fusion. The best model ensemble consists in averaging the ranking of the 12 decision trees that showed the best performance on the training set, which also demonstrated a good performance for the test set. It was experimentally validated using the ex vivo everted rat intestinal sac model. Five anticonvulsant drugs classified as nonsubstrates for BRCP by the model ensemble were experimentally evaluated, and none of them proved to be a BCRP substrate under the experimental conditions used, thus confirming the predictive ability of the model ensemble. The model ensemble reported here is a potentially valuable tool to be used as an in silico ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug-drug interactions.

  19. A new approach to the rationale discovery of polymeric biomaterials

    PubMed Central

    Kohn, Joachim; Welsh, William J.; Knight, Doyle

    2007-01-01

    This paper attempts to illustrate both the need for new approaches to biomaterials discovery as well as the significant promise inherent in the use of combinatorial and computational design strategies. The key observation of this Leading Opinion Paper is that the biomaterials community has been slow to embrace advanced biomaterials discovery tools such as combinatorial methods, high throughput experimentation, and computational modeling in spite of the significant promise shown by these discovery tools in materials science, medicinal chemistry and the pharmaceutical industry. It seems that the complexity of living cells and their interactions with biomaterials has been a conceptual as well as a practical barrier to the use of advanced discovery tools in biomaterials science. However, with the continued increase in computer power, the goal of predicting the biological response of cells in contact with biomaterials surfaces is within reach. Once combinatorial synthesis, high throughput experimentation, and computational modeling are integrated into the biomaterials discovery process, a significant acceleration is possible in the pace of development of improved medical implants, tissue regeneration scaffolds, and gene/drug delivery systems. PMID:17644176

  20. The Role of HTS in Drug Discovery at the University of Michigan

    PubMed Central

    Larsen, Martha J.; Larsen, Scott D.; Fribley, Andrew; Grembecka, Jolanta; Homan, Kristoff; Mapp, Anna; Haak, Andrew; Nikolovska-Coleska, Zaneta; Stuckey, Jeanne A.; Sun, Duxin

    2014-01-01

    High throughput screening (HTS) is an integral part of a highly collaborative approach to drug discovery at the University of Michigan. The HTS lab is one of four core centers that provide services to identify, produce, screen and follow-up on biomedical targets for faculty. Key features of this system are: protein cloning and purification, protein crystallography, small molecule and siRNA HTS, medicinal chemistry and pharmacokinetics. Therapeutic areas that have been targeted include anti-bacterial, metabolic, neurodegenerative, cardiovascular, anti-cancer and anti-viral. The centers work in a coordinated, interactive environment to affordably provide academic investigators with the technology, informatics and expertise necessary for successful drug discovery. This review provides an overview of these centers at the University of Michigan, along with case examples of successful collaborations with faculty. PMID:24409957

  1. The value of plants used in traditional medicine for drug discovery.

    PubMed Central

    Fabricant, D S; Farnsworth, N R

    2001-01-01

    In this review we describe and discuss several approaches to selecting higher plants as candidates for drug development with the greatest possibility of success. We emphasize the role of information derived from various systems of traditional medicine (ethnomedicine) and its utility for drug discovery purposes. We have identified 122 compounds of defined structure, obtained from only 94 species of plants, that are used globally as drugs and demonstrate that 80% of these have had an ethnomedical use identical or related to the current use of the active elements of the plant. We identify and discuss advantages and disadvantages of using plants as starting points for drug development, specifically those used in traditional medicine. PMID:11250806

  2. Software and resources for computational medicinal chemistry

    PubMed Central

    Liao, Chenzhong; Sitzmann, Markus; Pugliese, Angelo; Nicklaus, Marc C

    2011-01-01

    Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools. PMID:21707404

  3. Computational Identification of the Paralogs and Orthologs of Human Cytochrome P450 Superfamily and the Implication in Drug Discovery

    PubMed Central

    Pan, Shu-Ting; Xue, Danfeng; Li, Zhi-Ling; Zhou, Zhi-Wei; He, Zhi-Xu; Yang, Yinxue; Yang, Tianxin; Qiu, Jia-Xuan; Zhou, Shu-Feng

    2016-01-01

    The human cytochrome P450 (CYP) superfamily consisting of 57 functional genes is the most important group of Phase I drug metabolizing enzymes that oxidize a large number of xenobiotics and endogenous compounds, including therapeutic drugs and environmental toxicants. The CYP superfamily has been shown to expand itself through gene duplication, and some of them become pseudogenes due to gene mutations. Orthologs and paralogs are homologous genes resulting from speciation or duplication, respectively. To explore the evolutionary and functional relationships of human CYPs, we conducted this bioinformatic study to identify their corresponding paralogs, homologs, and orthologs. The functional implications and implications in drug discovery and evolutionary biology were then discussed. GeneCards and Ensembl were used to identify the paralogs of human CYPs. We have used a panel of online databases to identify the orthologs of human CYP genes: NCBI, Ensembl Compara, GeneCards, OMA (“Orthologous MAtrix”) Browser, PATHER, TreeFam, EggNOG, and Roundup. The results show that each human CYP has various numbers of paralogs and orthologs using GeneCards and Ensembl. For example, the paralogs of CYP2A6 include CYP2A7, 2A13, 2B6, 2C8, 2C9, 2C18, 2C19, 2D6, 2E1, 2F1, 2J2, 2R1, 2S1, 2U1, and 2W1; CYP11A1 has 6 paralogs including CYP11B1, 11B2, 24A1, 27A1, 27B1, and 27C1; CYP51A1 has only three paralogs: CYP26A1, 26B1, and 26C1; while CYP20A1 has no paralog. The majority of human CYPs are well conserved from plants, amphibians, fishes, or mammals to humans due to their important functions in physiology and xenobiotic disposition. The data from different approaches are also cross-validated and validated when experimental data are available. These findings facilitate our understanding of the evolutionary relationships and functional implications of the human CYP superfamily in drug discovery. PMID:27367670

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

    PubMed

    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.

  5. Minimizing DILI risk in drug discovery - A screening tool for drug candidates.

    PubMed

    Schadt, S; Simon, S; Kustermann, S; Boess, F; McGinnis, C; Brink, A; Lieven, R; Fowler, S; Youdim, K; Ullah, M; Marschmann, M; Zihlmann, C; Siegrist, Y M; Cascais, A C; Di Lenarda, E; Durr, E; Schaub, N; Ang, X; Starke, V; Singer, T; Alvarez-Sanchez, R; Roth, A B; Schuler, F; Funk, C

    2015-12-25

    Drug-induced liver injury (DILI) is a leading cause of acute hepatic failure and a major reason for market withdrawal of drugs. Idiosyncratic DILI is multifactorial, with unclear dose-dependency and poor predictability since the underlying patient-related susceptibilities are not sufficiently understood. Because of these limitations, a pharmaceutical research option would be to reduce the compound-related risk factors in the drug-discovery process. Here we describe the development and validation of a methodology for the assessment of DILI risk of drug candidates. As a training set, 81 marketed or withdrawn compounds with differing DILI rates - according to the FDA categorization - were tested in a combination of assays covering different mechanisms and endpoints contributing to human DILI. These include the generation of reactive metabolites (CYP3A4 time-dependent inhibition and glutathione adduct formation), inhibition of the human bile salt export pump (BSEP), mitochondrial toxicity and cytotoxicity (fibroblasts and human hepatocytes). Different approaches for dose- and exposure-based calibrations were assessed and the same parameters applied to a test set of 39 different compounds. We achieved a similar performance to the training set with an overall accuracy of 79% correctly predicted, a sensitivity of 76% and a specificity of 82%. This test system may be applied in a prospective manner to reduce the risk of idiosyncratic DILI of drug candidates. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. A Network-Biology Informed Computational Drug Repositioning Strategy to Target Disease Risk Trajectories and Comorbidities of Peripheral Artery Disease.

    PubMed

    Shameer, Khader; Dow, Garrett; Glicksberg, Benjamin S; Johnson, Kipp W; Ze, Yi; Tomlinson, Max S; Readhead, Ben; Dudley, Joel T; Kullo, Iftikhar J

    2018-01-01

    Currently, drug discovery approaches focus on the design of therapies that alleviate an index symptom by reengineering the underlying biological mechanism in agonistic or antagonistic fashion. For example, medicines are routinely developed to target an essential gene that drives the disease mechanism. Therapeutic overloading where patients get multiple medications to reduce the primary and secondary side effect burden is standard practice. This single-symptom based approach may not be scalable, as we understand that diseases are more connected than random and molecular interactions drive disease comorbidities. In this work, we present a proof-of-concept drug discovery strategy by combining network biology, disease comorbidity estimates, and computational drug repositioning, by targeting the risk factors and comorbidities of peripheral artery disease, a vascular disease associated with high morbidity and mortality. Individualized risk estimation and recommending disease sequelae based therapies may help to lower the mortality and morbidity of peripheral artery disease.

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

    PubMed Central

    2017-01-01

    Conspectus 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

  8. Fruitful research: drug target discovery for neurodegenerative diseases in Drosophila.

    PubMed

    Konsolaki, Mary

    2013-12-01

    Although vertebrate model systems have obvious advantages in the study of human disease, invertebrate organisms have contributed enormously to this field as well. The conservation of genome structure and physiology among organisms poses unexpected peculiarities, and the redundancy in certain gene families or the presence of polymorphisms that can slightly alter gene expression can, in certain instances, bring invertebrate systems, such as Drosophila, closer to humans than mice and vice versa. This necessitates the analysis of disease pathways in multiple model organisms. The author highlights findings from Drosophila models of neurodegenerative diseases that have occurred in the past few years. She also highlights and discusses various molecular, genetic and genomic tools used in flies, as well as methods for generating disease models. Finally, the author describes Drosophila models of Alzheimer's, Parkinson's tri-nucleotide repeat diseases, and Fragile X syndrome and summarizes insights in disease mechanisms that have been discovered directly in fly models. Full genome genetic screens in Drosophila can lead to the rapid identification of drug target candidates that can be subsequently validated in a vertebrate system. In addition, the Drosophila models of neurodegeneration may often show disease phenotypes that are absent in equivalent mouse models. The author believes that the extensive contribution of Drosophila to both new disease drug target discovery, in addition to target validation, makes them indispensible to drug discovery and development.

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

    PubMed

    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

    2014-02-20

    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). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ∼10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes--to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

  10. Current perspectives in drug discovery against tuberculosis from natural products.

    PubMed

    Nguta, Joseph Mwanzia; Appiah-Opong, Regina; Nyarko, Alexander K; Yeboah-Manu, Dorothy; Addo, Phyllis G A

    2015-09-01

    Currently, one third of the world's population is latently infected with Mycobacterium tuberculosis (MTB), while 8.9-9.9 million new and relapse cases of tuberculosis (TB) are reported yearly. The renewed research interests in natural products in the hope of discovering new and novel antitubercular leads have been driven partly by the increased incidence of multidrug-resistant strains of MTB and the adverse effects associated with the first- and second-line antitubercular drugs. Natural products have been, and will continue to be a rich source of new drugs against many diseases. The depth and breadth of therapeutic agents that have their origins in the secondary metabolites produced by living organisms cannot be compared with any other source of therapeutic agents. Discovery of new chemical molecules against active and latent TB from natural products requires an interdisciplinary approach, which is a major challenge facing scientists in this field. In order to overcome this challenge, cutting edge techniques in mycobacteriology and innovative natural product chemistry tools need to be developed and used in tandem. The present review provides a cross-linkage to the most recent literature in both fields and their potential to impact the early phase of drug discovery against TB if seamlessly combined. Copyright © 2015 Asian African Society for Mycobacteriology. Published by Elsevier Ltd. All rights reserved.

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

    PubMed

    Janero, David R

    2014-08-01

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

  12. Something old, something new: revisiting natural products in antibiotic drug discovery.

    PubMed

    Wright, Gerard D

    2014-03-01

    Antibiotic discovery is in crisis. Despite a growing need for new drugs resulting from the increasing number of multi-antibiotic-resistant pathogens, there have been only a handful of new antibiotics approved for clinical use in the past 2 decades. Faced with scientific, economic, and regulatory challenges, the pharmaceutical sector seems unable to respond to what has been called an "apocalyptic" threat. Natural products produced by bacteria and fungi are genetically encoded products of natural selection that have been the mainstay sources of the antibiotics in current clinical use. The pharmaceutical industry has largely abandoned these compounds in favor of large libraries of synthetic molecules because of difficulties in identifying new natural product antibiotics scaffolds. Advances in next-generation genome sequencing, bioinformatics, and analytical chemistry are combining to overcome barriers to natural products. Coupled with new strategies in antibiotic discovery, including inhibition of resistance, novel drug combinations, and new targets, natural products are poised for a renaissance to address what is a pressing health care crisis.

  13. Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery.

    PubMed

    Jia, Zhilong; Liu, Ying; Guan, Naiyang; Bo, Xiaochen; Luo, Zhigang; Barnes, Michael R

    2016-05-27

    Drug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the "black box" nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context. We demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena . In conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and

  14. Methodologies Related to Computational models in View of Developing Anti-Alzheimer Drugs: An Overview.

    PubMed

    Baheti, Kirtee; Kale, Mayura Ajay

    2018-04-17

    Since last two decades, there has been more focus on the development strategies related to Anti-Alzheimer's drug research. This may be attributed to the fact that most of the Alzheimer's cases are still mostly unknown except for a few cases, where genetic differences have been identified. With the progress of the disease, the symptoms involve intellectual deterioration, memory impairment, abnormal personality and behavioural patterns, confusion, aggression, mood swings, irritability Current therapies available for this disease give only symptomatic relief and do not focus on manipulations of biololecular processes. Nearly all the therapies to treat Alzheimer's disease, target to change the amyloid cascade which is considered to be an important in AD pathogenesis. New drug regimens are not able to keep pace with the ever-increasing understanding about dementia at molecular level. Looking into these aggravated problems, we though to put forth molecular modeling as a drug discovery approach for developing novel drugs to treat Alzheimer disease. The disease is incurable and it gets worst as it advances and finally causes death. Due to this, the design of drugs to treat this disease has become an utmost priority for research. One of the most important emerging technologies applied for this has been Computer-assisted drug design (CADD). It is a research tool that employs large scale computing strategies in an attempt to develop a model receptor site which can be used for designing of an anti-Alzheimer drug. The various models of amyloid-based calcium channels have been computationally optimized. Docking and De novo evolution are used to design the compounds. These are further subjected to absorption, distribution, metabolism, excretion and toxicity (ADMET) studies to finally bring about active compounds that are able to cross BBB. Many novel compounds have been designed which might be promising ones for the treatment of AD. The present review describes the research

  15. High-Throughput Nano-Biofilm Microarray for Antifungal Drug Discovery

    DTIC Science & Technology

    2013-06-25

    High-Throughput Nano-Biofilm Microarray for Antifungal Drug Discovery Anand Srinivasan,a, c Kai P. Leung,d Jose L. Lopez-Ribot,b, c Anand K...Ramasubramaniana, c Departments of Biomedical Engineeringa and Biologyb and South Texas Center for Emerging Infectious Diseases, c The University of Texas at San...of the opportunistic fungal pathogen Candida albicans on a microarray platform. The mi- croarray consists of 1,200 individual cultures of 30 nl of C

  16. Big Data in Drug Discovery.

    PubMed

    Brown, Nathan; Cambruzzi, Jean; Cox, Peter J; Davies, Mark; Dunbar, James; Plumbley, Dean; Sellwood, Matthew A; Sim, Aaron; Williams-Jones, Bryn I; Zwierzyna, Magdalena; Sheppard, David W

    2018-01-01

    Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation. © 2018 Elsevier B.V. All rights reserved.

  17. Strategies for Utilizing Neuroimaging Biomarkers in CNS Drug Discovery and Development: CINP/JSNP Working Group Report.

    PubMed

    Suhara, Tetsuya; Chaki, Shigeyuki; Kimura, Haruhide; Furusawa, Makoto; Matsumoto, Mitsuyuki; Ogura, Hiroo; Negishi, Takaaki; Saijo, Takeaki; Higuchi, Makoto; Omura, Tomohiro; Watanabe, Rira; Miyoshi, Sosuke; Nakatani, Noriaki; Yamamoto, Noboru; Liou, Shyh-Yuh; Takado, Yuhei; Maeda, Jun; Okamoto, Yasumasa; Okubo, Yoshiaki; Yamada, Makiko; Ito, Hiroshi; Walton, Noah M; Yamawaki, Shigeto

    2017-04-01

    Despite large unmet medical needs in the field for several decades, CNS drug discovery and development has been largely unsuccessful. Biomarkers, particularly those utilizing neuroimaging, have played important roles in aiding CNS drug development, including dosing determination of investigational new drugs (INDs). A recent working group was organized jointly by CINP and Japanese Society of Neuropsychopharmacology (JSNP) to discuss the utility of biomarkers as tools to overcome issues of CNS drug development.The consensus statement from the working group aimed at creating more nuanced criteria for employing biomarkers as tools to overcome issues surrounding CNS drug development. To accomplish this, a reverse engineering approach was adopted, in which criteria for the utilization of biomarkers were created in response to current challenges in the processes of drug discovery and development for CNS disorders. Based on this analysis, we propose a new paradigm containing 5 distinct tiers to further clarify the use of biomarkers and establish new strategies for decision-making in the context of CNS drug development. Specifically, we discuss more rational ways to incorporate biomarker data to determine optimal dosing for INDs with novel mechanisms and targets, and propose additional categorization criteria to further the use of biomarkers in patient stratification and clinical efficacy prediction. Finally, we propose validation and development of new neuroimaging biomarkers through public-private partnerships to further facilitate drug discovery and development for CNS disorders. © The Author 2016. Published by Oxford University Press on behalf of CINP.

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

    PubMed

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

    2016-06-15

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

  19. Decaprenyl-phosphoryl-ribose 2'-epimerase (DprE1): challenging target for antitubercular drug discovery.

    PubMed

    Gawad, Jineetkumar; Bonde, Chandrakant

    2018-06-23

    Tuberculosis has proved harmful to the entire history of mankind from past several decades. Decaprenyl-phosphoryl-ribose 2'-epimerase (DprE1) is a recent target which was identified in 2009 but unfortunately it is neither explored nor crossed phase II. In past several decades few targets were identified for effective antitubercular drug discovery. Resistance is the major problem for effective antitubercular drug discovery. Arabinose is constituent of mycobacterium cell wall. Biosynthesis of arabinose is FAD dependant two step epimerisation reaction which is catalysed by DprE1 and DprE2 flavoprotein enzymes. The current review is mainly emphases on DprE1 as a perspective challenge for further research.

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

    PubMed

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

    2018-05-24

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