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.

Virtual Screening with AutoDock: Theory and Practice

PubMed Central

Cosconati, Sandro; Forli, Stefano; Perryman, Alex L.; Harris, Rodney; Goodsell, David S.; Olson, Arthur J.

2011-01-01

Importance to the field Virtual screening is a computer-based technique for identifying promising compounds to bind to a target molecule of known structure. Given the rapidly increasing number of protein and nucleic acid structures, virtual screening continues to grow as an effective method for the discovery of new inhibitors and drug molecules. Areas covered in this review We describe virtual screening methods that are available in the AutoDock suite of programs, and several of our successes in using AutoDock virtual screening in pharmaceutical lead discovery. What the reader will gain A general overview of the challenges of virtual screening is presented, along with the tools available in the AutoDock suite of programs for addressing these challenges. Take home message Virtual screening is an effective tool for the discovery of compounds for use as leads in drug discovery, and the free, open source program AutoDock is an effective tool for virtual screening. PMID:21532931

Collaborative pre-competitive preclinical drug discovery with academics and pharma/biotech partners at Sanford|Burnham: infrastructure, capabilities & operational models.

PubMed

Chung, Thomas D Y

2014-03-01

There has been increased concern that the current "blockbuster" model of drug discovery and development practiced by "Big Pharma" are unsustainable in terms of cost (> $1 billion/approved drug) and time to market (10 - 15 years). The recent mergers and acquisitions (M&A), shuttering of internal research programs, closure of "redundant" sites of operations, senior management turnover and continued workforce reductions among the top 10 major pharmaceutical companies reflect draconian responses to reduce costs. However, the resultant exodus of intellectual capital, loss in motivation and momentum, and exit from early stage discovery programs by pharmaceutical companies has contributed to an "innovation deficit". Disease advocacy groups, investment communities and the government are calling for new innovative business models to address this deficit. In particular they are looking towards academia and clinical trials centers to catalyze new innovations in translational research. Indeed over the last decade many academic institutions have launched drug discovery centers largely comprising high-throughput screening (HTS) to accelerate "translational" research. A major impetus for this "open innovation" effort has been the National Institutes of Health (NIH) "Roadmap" and Molecular Libraries Initiative/Program (MLI/MLP), which is in its last year, and will be transitioned into the National Center for the Advancement of Translational Sciences (NCATS). With the end of Roadmap funding, general reduction in Federal government funding and its recent sequestration, academic drug discovery centers are being challenged to become selfsustaining, adding financial value, while remaining aligned with the missions of their respective academic non-profit institutions. We describe herein, a brief history of our bi-coastal Conrad Prebys Center for Chemical Genomics (Prebys Center) at the Sanford|Burnham Medical Research Institute (SBMRI), the key components of its infrastructure, core competencies of its fully integrated drug discovery expertise, best practices adopted in our day-to-day operations, and finally some of our current funding and collaboration and/or strategic alliance models for pre-competitive drug discovery with other academic/clinical partners, other governmental agencies, and with pharmaceutical and biotechnology companies.

Drugging the gut microbiome.

PubMed

Garber, Ken

2015-03-01

Using conventional drug discovery and novel synthetic biology approaches, some investigators and companies are mining our resident microbes and their metabolites for targets in small-molecule drug programs. Ken Garber reports.

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.

PREVENT Cancer Preclinical Drug Development Program (PREVENT) | Division of Cancer Prevention

Cancer.gov

The PREVENT program provides a structure for the introduction of new agents, drugs and vaccines to inhibit, retard or reverse the cancer process. The program was designed to optimize translational opportunities from discovery to the clinic, and provide a mechanism to identify and study efficacy and pharmacodynamics biomarkers that will help in phase II trials to evaluate drug

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.

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

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.

PharMillenium '99--the second world pharmaceutical congress and exhibition. Accelerating the pipeline: from drug discovery to market. 1-3 February 1999, Washington DC, USA.

PubMed

Fernandes, M

1999-04-01

This highly interactive meeting effectively covered critical issues on every transaction from drug discovery through to development and commercialization. The program included company-specific descriptions of new discovery products, together with seminars by clinical research and site management organizations on the acceleration of development, pharmaco-economics, branding of products, direct-to-consumer advertising, global marketing, management, information technology and business strategy. There were approximately 50 sessions covered by 70 speakers.

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

PubMed

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

2016-09-22

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

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 ".

Key aspects of the Novartis compound collection enhancement project for the compilation of a comprehensive chemogenomics drug discovery screening collection.

PubMed

Jacoby, Edgar; Schuffenhauer, Ansgar; Popov, Maxim; Azzaoui, Kamal; Havill, Benjamin; Schopfer, Ulrich; Engeloch, Caroline; Stanek, Jaroslav; Acklin, Pierre; Rigollier, Pascal; Stoll, Friederike; Koch, Guido; Meier, Peter; Orain, David; Giger, Rudolph; Hinrichs, Jürgen; Malagu, Karine; Zimmermann, Jürg; Roth, Hans-Joerg

2005-01-01

The NIBR (Novartis Institutes for BioMedical Research) compound collection enrichment and enhancement project integrates corporate internal combinatorial compound synthesis and external compound acquisition activities in order to build up a comprehensive screening collection for a modern drug discovery organization. The main purpose of the screening collection is to supply the Novartis drug discovery pipeline with hit-to-lead compounds for today's and the future's portfolio of drug discovery programs, and to provide tool compounds for the chemogenomics investigation of novel biological pathways and circuits. As such, it integrates designed focused and diversity-based compound sets from the synthetic and natural paradigms able to cope with druggable and currently deemed undruggable targets and molecular interaction modes. Herein, we will summarize together with new trends published in the literature, scientific challenges faced and key approaches taken at NIBR to match the chemical and biological spaces.

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

PubMed

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

2011-05-17

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

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

PubMed Central

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

2011-01-01

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

Spotlight on Fluorescent Biosensors—Tools for Diagnostics and Drug Discovery

PubMed Central

2013-01-01

Fluorescent biosensors constitute potent tools for probing biomolecules in their natural environment and for visualizing dynamic processes in complex biological samples, living cells, and organisms. They are well suited for highlighting molecular alterations associated with pathological disorders, thereby offering means of implementing sensitive and alternative technologies for diagnostic purposes. They constitute attractive tools for drug discovery programs, from high throughput screening assays to preclinical studies. PMID:24900780

The University of New Mexico Center for Molecular Discovery

PubMed Central

Edwards, Bruce S.; Gouveia, Kristine; Oprea, Tudor I.; Sklar, Larry A.

2015-01-01

The University of New Mexico Center for Molecular Discovery (UNMCMD) is an academic research center that specializes in discovery using high throughput flow cytometry (HTFC) integrated with virtual screening, as well as knowledge mining and drug informatics. With a primary focus on identifying small molecules that can be used as chemical probes and as leads for drug discovery, it is a central core resource for research and translational activities at UNM that supports implementation and management of funded screening projects as well as “up-front” services such as consulting for project design and implementation, assistance in assay development and generation of preliminary data for pilot projects in support of competitive grant applications. The HTFC platform in current use represents advanced, proprietary technology developed at UNM that is now routinely capable of processing bioassays arrayed in 96-, 384- and 1536-well formats at throughputs of 60,000 or more wells per day. Key programs at UNMCMD include screening of research targets submitted by the international community through NIH’s Molecular Libraries Program; a multi-year effort involving translational partnerships at UNM directed towards drug repurposing - identifying new uses for clinically approved drugs; and a recently established personalized medicine initiative for advancing cancer therapy by the application of “smart” oncology drugs in selected patients based on response patterns of their cancer cells in vitro. UNMCMD discoveries, innovation, and translation have contributed to a wealth of inventions, patents, licenses and publications, as well as startup companies, clinical trials and a multiplicity of domestic and international collaborative partnerships to further the research enterprise. PMID:24409953

The University of New Mexico Center for Molecular Discovery.

PubMed

Edwards, Bruce S; Gouveia, Kristine; Oprea, Tudor I; Sklar, Larry A

2014-03-01

The University of New Mexico Center for Molecular Discovery (UNMCMD) is an academic research center that specializes in discovery using high throughput flow cytometry (HTFC) integrated with virtual screening, as well as knowledge mining and drug informatics. With a primary focus on identifying small molecules that can be used as chemical probes and as leads for drug discovery, it is a central core resource for research and translational activities at UNM that supports implementation and management of funded screening projects as well as "up-front" services such as consulting for project design and implementation, assistance in assay development and generation of preliminary data for pilot projects in support of competitive grant applications. The HTFC platform in current use represents advanced, proprietary technology developed at UNM that is now routinely capable of processing bioassays arrayed in 96-, 384- and 1536-well formats at throughputs of 60,000 or more wells per day. Key programs at UNMCMD include screening of research targets submitted by the international community through NIH's Molecular Libraries Program; a multi-year effort involving translational partnerships at UNM directed towards drug repurposing - identifying new uses for clinically approved drugs; and a recently established personalized medicine initiative for advancing cancer therapy by the application of "smart" oncology drugs in selected patients based on response patterns of their cancer cells in vitro. UNMCMD discoveries, innovation, and translation have contributed to a wealth of inventions, patents, licenses and publications, as well as startup companies, clinical trials and a multiplicity of domestic and international collaborative partnerships to further the research enterprise.

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.

Targeted drug discovery and development, from molecular signaling to the global market: an educational program at New York University, 5-year metrics

PubMed Central

Lee, Gloria; Plaksin, Joseph; Ramasamy, Ravichandran; Gold-von Simson, Gabrielle

2018-01-01

Drug discovery and development (DDD) is a collaborative, dynamic process of great interest to researchers, but an area where there is a lack of formal training. The Drug Development Educational Program (DDEP) at New York University was created in 2012 to stimulate an improved, multidisciplinary DDD workforce by educating early stage scientists as well as a variety of other like-minded students. The first course of the program emphasizes post-compounding aspects of DDD; the second course focuses on molecular signaling pathways. In five years, 196 students (candidates for PhD, MD, Master’s degree, and post-doctoral MD/PhD) from different schools (Medicine, Biomedical Sciences, Dentistry, Engineering, Business, and Education) completed the course(s). Pre/post surveys demonstrate knowledge gain across all course topics. 26 students were granted career development awards (73% women, 23% underrepresented minorities). Some graduates of their respective degree-granting/post-doctoral programs embarked on DDD related careers. This program serves as a framework for other academic institutions to develop compatible programs designed to train a more informed DDD workforce. PMID:29657854

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.

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.

Leveraging model-informed approaches for drug discovery and development in the cardiovascular space.

PubMed

Dockendorf, Marissa F; Vargo, Ryan C; Gheyas, Ferdous; Chain, Anne S Y; Chatterjee, Manash S; Wenning, Larissa A

2018-06-01

Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.

Cheminformatic comparison of approved drugs from natural product versus synthetic origins.

PubMed

Stratton, Christopher F; Newman, David J; Tan, Derek S

2015-11-01

Despite the recent decline of natural product discovery programs in the pharmaceutical industry, approximately half of all new drug approvals still trace their structural origins to a natural product. Herein, we use principal component analysis to compare the structural and physicochemical features of drugs from natural product-based versus completely synthetic origins that were approved between 1981 and 2010. Drugs based on natural product structures display greater chemical diversity and occupy larger regions of chemical space than drugs from completely synthetic origins. Notably, synthetic drugs based on natural product pharmacophores also exhibit lower hydrophobicity and greater stereochemical content than drugs from completely synthetic origins. These results illustrate that structural features found in natural products can be successfully incorporated into synthetic drugs, thereby increasing the chemical diversity available for small-molecule drug discovery. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

Report on the 10th anniversary of international drug discovery science and technology conference, 8 - 10 november 2012, nanjing, china.

PubMed

Everett, Jeremy R

2013-03-01

The 10th Anniversary of International Drug Discovery Science and Technology (IDDST) Conference was held in Nanjing, China from 8 to 10 November 2012. The conference ran in parallel with the 2nd Annual Symposium of Drug Delivery Systems. Over 400 delegates from both conferences came together for the Opening Ceremony and Keynote Addresses but otherwise pursued separate paths in the huge facilities of the Nanjing International Expo Centre. The IDDST was arranged into 19 separate Chapters covering drug discovery biology, target validation, chemistry, rational drug design, pharmacology and toxicology, drug screening technology, 'omics' technologies, analytical, automation and enabling technologies, informatics, stem cells and regenerative medicine, bioprocessing, generics, biosimilars and biologicals and seven disease areas: cancer, CNS, respiratory and inflammation, autoimmune, emerging infectious, bone and orphan diseases. There were also two sessions of a 'Bench to Bedside to Business' Program and a Chinese Scientist programme. In each period of the IDDST conference, up to seven sessions were running in parallel. This Meeting Highlight samples just a fraction of the content of this large meeting. The talks included have as a link, the use of new approaches to drug discovery. Many other excellent talks could have been highlighted and the author has necessarily had to be selective.

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

PubMed

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

2017-09-01

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

A rapid alternative to X-ray crystallography for chiral determination: case studies of vibrational circular dichroism (VCD) to advance drug discovery projects.

PubMed

Wesolowski, Steven S; Pivonka, Don E

2013-07-15

The absolute stereochemistry of chiral drugs is usually established via X-ray crystallography. However, vibrational circular dichroism (VCD) spectroscopy coupled with quantum mechanics simulations offers a rapid alternative to crystallography and is readily applied to both crystalline and non-crystalline samples. VCD is an effective complement to X-ray analysis of drug candidates, and it can be used as a high-throughput means of assessing absolute stereochemistry at all phases of the discovery process (hundreds of assignments per year). The practical implementation (or fee-for-service outsourcing) of VCD and selected case studies are illustrated with an emphasis on providing utility and impact to pharmaceutical discovery programs. Copyright © 2013 Elsevier Ltd. All rights reserved.

Therapeutics discovery: From bench to first in-human trials*

PubMed Central

Al-Hujaily, Ensaf M.; Khatlani, Tanvir; Alehaideb, Zeyad; Ali, Rizwan; Almuzaini, Bader; Alrfaei, Bahauddeen M; Iqbal, Jahangir; Islam, Imadul; Malik, Shuja; Marwani, Bader A; Massadeh, Salam; Nehdi, Atef; Alsomaie, Barrak; Debasi, Bader; Bushnak, Ibraheem; Noibi, Saeed; Hussain, Syed; Wajid, Wahid Abdul; Armand, Jean-Pierre; Gul, Sheraz; Oyarzabal, Julen; Rais, Rana; Bountra, Chas; Alaskar, Ahmed; Knawy, Bander Al; Boudjelal, Mohamed

2018-01-01

The ‘Therapeutics discovery: From bench to first in-human trials’ conference, held at the King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard Health Affairs (MNGHA), Kingdom of Saudi Arabia (KSA) from October 10–12, 2017, provided a unique opportunity for experts worldwide to discuss advances in drug discovery and development, focusing on phase I clinical trials. It was the first event of its kind to be hosted at the new research center, which was constructed to boost drug discovery and development in the KSA in collaboration with institutions, such as the Academic Drug Discovery Consortium in the United States of America (USA), Structural Genomics Consortium of the University of Oxford in the United Kingdom (UK), and Institute of Materia Medica of the Chinese Academy of Medical Sciences in China. The program was divided into two parts. A pre-symposium day took place on October 10, during which courses were conducted on clinical trials, preclinical drug discovery, molecular biology and nanofiber research. The attendees had the opportunity for one-to-one meetings with international experts to exchange information and foster collaborations. In the second part of the conference, which took place on October 11 and 12, the clinical trials pipeline, design and recruitment of volunteers, and economic impact of clinical trials were discussed. The Saudi Food and Drug Administration presented the regulations governing clinical trials in the KSA. The process of preclinical drug discovery from small molecules, cellular and immunologic therapies, and approaches to identifying new targets were also presented. The recommendation of the conference was that researchers in the KSA must invest more fund, talents and infrastructure to lead the region in phase I clinical trials and preclinical drug discovery. Diseases affecting the local population, such as Middle East Respiratory Syndrome and resistant bacterial infections, represent the optimal starting point. PMID:29564125

Therapeutics discovery: From bench to first in-human trials.

PubMed

Al-Hujaily, Ensaf M; Khatlani, Tanvir; Alehaideb, Zeyad; Ali, Rizwan; Almuzaini, Bader; Alrfaei, Bahauddeen M; Iqbal, Jahangir; Islam, Imadul; Malik, Shuja; Marwani, Bader A; Massadeh, Salam; Nehdi, Atef; Alsomaie, Barrak; Debasi, Bader; Bushnak, Ibraheem; Noibi, Saeed; Hussain, Syed; Wajid, Wahid Abdul; Armand, Jean-Pierre; Gul, Sheraz; Oyarzabal, Julen; Rais, Rana; Bountra, Chas; Alaskar, Ahmed; Knawy, Bander Al; Boudjelal, Mohamed

2018-03-01

The 'Therapeutics discovery: From bench to first in-human trials' conference, held at the King Abdullah International Medical Research Center (KAIMRC), Ministry of National Guard Health Affairs (MNGHA), Kingdom of Saudi Arabia (KSA) from October 10-12, 2017, provided a unique opportunity for experts worldwide to discuss advances in drug discovery and development, focusing on phase I clinical trials. It was the first event of its kind to be hosted at the new research center, which was constructed to boost drug discovery and development in the KSA in collaboration with institutions, such as the Academic Drug Discovery Consortium in the United States of America (USA), Structural Genomics Consortium of the University of Oxford in the United Kingdom (UK), and Institute of Materia Medica of the Chinese Academy of Medical Sciences in China. The program was divided into two parts. A pre-symposium day took place on October 10, during which courses were conducted on clinical trials, preclinical drug discovery, molecular biology and nanofiber research. The attendees had the opportunity for one-to-one meetings with international experts to exchange information and foster collaborations. In the second part of the conference, which took place on October 11 and 12, the clinical trials pipeline, design and recruitment of volunteers, and economic impact of clinical trials were discussed. The Saudi Food and Drug Administration presented the regulations governing clinical trials in the KSA. The process of preclinical drug discovery from small molecules, cellular and immunologic therapies, and approaches to identifying new targets were also presented. The recommendation of the conference was that researchers in the KSA must invest more fund, talents and infrastructure to lead the region in phase I clinical trials and preclinical drug discovery. Diseases affecting the local population, such as Middle East Respiratory Syndrome and resistant bacterial infections, represent the optimal starting point.

[Challenges and strategies of drug innovation].

PubMed

Guo, Zong-Ru; Zhao, Hong-Yu

2013-07-01

Drug research involves scientific discovery, technological inventions and product development. This multiple dimensional effort embodies both high risk and high reward and is considered one of the most complicated human activities. Prior to the initiation of a program, an in-depth analysis of "what to do" and "how to do it" must be conducted. On the macro level, market prospects, capital required, risk assessment, necessary human resources, etc. need to be evaluated critically. For execution, drug candidates need to be optimized in multiple properties such as potency, selectivity, pharmacokinetics, safety, formulation, etc., all with the constraint of finite amount of time and resources, to maximize the probability of success in clinical development. Drug discovery is enormously complicated, both in terms of technological innovation and organizing capital and other resources. A deep understanding of the complexity of drug research and our competitive edge is critical for success. Our unique government-enterprise-academia system represents a distinct advantage. As a new player, we have not heavily invested in any particular discovery paradigm, which allows us to select the optimal approach with little organizational burden. Virtue R&D model using CROs has gained momentum lately and China is a global leader in CRO market. Essentially all technological support for drug discovery can be found in China, which greatly enables domestic R&D efforts. The information technology revolution ensures the globalization of drug discovery knowledge, which has bridged much of the gap between China and the developed countries. The blockbuster model and the target-centric drug discovery paradigm have overlooked the research in several important fields such as injectable drugs, orphan drugs, and following high quality therapeutic leads, etc. Prejudice against covalent ligands, prodrugs, nondrug-like ligands can also be taken advantage of to find novel medicines. This article will discuss the current challenges and future opportunities for drug innovation in China.

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

PubMed

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

2016-09-20

Computational methods for docking small molecules to proteins are prominent in drug discovery. There are hundreds, if not thousands, of documented examples-and several pertinent cases within our research program. Fifteen years ago, our first docking-guided drug design project yielded nanomolar metalloproteinase inhibitors and illustrated the potential of structure-based drug design. Subsequent applications of docking programs to the design of integrin antagonists, BACE-1 inhibitors, and aminoglycosides binding to bacterial RNA demonstrated that available docking programs needed significant improvement. At that time, docking programs primarily considered flexible ligands and rigid proteins. We demonstrated that accounting for protein flexibility, employing displaceable water molecules, and using ligand-based pharmacophores improved the docking accuracy of existing methods-enabling the design of bioactive molecules. The success prompted the development of our own program, Fitted, implementing all of these aspects. The primary motivation has always been to respond to the needs of drug design studies; the majority of the concepts behind the evolution of Fitted are rooted in medicinal chemistry projects and collaborations. Several examples follow: (1) Searching for HDAC inhibitors led us to develop methods considering drug-zinc coordination and its effect on the pKa of surrounding residues. (2) Targeting covalent prolyl oligopeptidase (POP) inhibitors prompted an update to Fitted to identify reactive groups and form bonds with a given residue (e.g., a catalytic residue) when the geometry allows it. Fitted-the first fully automated covalent docking program-was successfully applied to the discovery of four new classes of covalent POP inhibitors. As a result, efficient stereoselective syntheses of a few screening hits were prioritized rather than synthesizing large chemical libraries-yielding nanomolar inhibitors. (3) In order to study the metabolism of POP inhibitors by cytochrome P450 enzymes (CYPs)-for toxicology studies-the program Impacts was derived from Fitted and helped us to reveal a complex metabolism with unforeseen stereocenter isomerizations. These efforts, combined with those of other docking software developers, have strengthened our understanding of the complex drug-protein binding process while providing the medicinal chemistry community with useful tools that have led to drug discoveries. In this Account, we describe our contributions over the past 15 years-within their historical context-to the design of drug candidates, including BACE-1 inhibitors, POP covalent inhibitors, G-quadruplex binders, and aminoglycosides binding to nucleic acids. We also remark the necessary developments of docking programs, specifically Fitted, that enabled structure-based design to flourish and yielded multiple fruitful, rational medicinal chemistry campaigns.

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.

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.

Expansion of chemical space for collaborative lead generation and drug discovery: the European Lead Factory Perspective.

PubMed

Karawajczyk, Anna; Giordanetto, Fabrizio; Benningshof, Jorg; Hamza, Daniel; Kalliokoski, Tuomo; Pouwer, Kees; Morgentin, Remy; Nelson, Adam; Müller, Gerhard; Piechot, Alexander; Tzalis, Dimitrios

2015-11-01

High-throughput screening (HTS) represents a major cornerstone of drug discovery. The availability of an innovative, relevant and high-quality compound collection to be screened often dictates the final fate of a drug discovery campaign. Given that the chemical space to be sampled in research programs is practically infinite and sparsely populated, significant efforts and resources need to be invested in the generation and maintenance of a competitive compound collection. The European Lead Factory (ELF) project is addressing this challenge by leveraging the diverse experience and know-how of academic groups and small and medium enterprises (SMEs) engaged in synthetic and/or medicinal chemistry. Here, we describe the novelty, diversity, structural complexity, physicochemical characteristics and overall attractiveness of this first batch of ELF compounds for HTS purposes. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Opportunities for natural products in 21st century antibiotic discovery.

PubMed

Wright, Gerard D

2017-07-01

Natural products and their derivatives are mainstays of our antibiotic drugs, but they are increasingly in peril. The combination of widespread multidrug resistance in once susceptible bacterial pathogens, disenchantment with natural products as sources of new drugs, lack of success using synthetic compounds and target-based discovery methods, along with shifting economic and regulatory issues, conspire to move investment in research and development away from the antibiotics arena. The result is a growing crisis in antibiotic drug discovery that threatens modern medicine. 21 st century natural product research is perfectly positioned to fill the antibiotic discovery gap and bring new drug candidates to the clinic. Innovations in genomics and techniques to explore new sources of antimicrobial chemical matter are revealing new chemistry. Increasing appreciation of the value of narrow-spectrum drugs and re-examination of once discarded chemical scaffolds coupled with synthetic biology methods to generate new compounds and improve yields offer new strategies to revitalize once moribund natural product programs. The increasing awareness that the combination of antibiotics with adjuvants, non-antibiotic compounds that overcome resistance and enhance drug activity, can rescue older chemical scaffolds, and concepts such as blocking pathogen virulence present orthogonal strategies to traditional antibiotics. In all these areas, natural products offer chemical matter, shaped by natural selection, that is privileged in this therapeutic area. Natural product research is poised to regain prominence in delivering new drugs to solve the antibiotic crisis.

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).

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

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.

Screening the receptorome to discover the molecular targets for plant-derived psychoactive compounds: a novel approach for CNS drug discovery.

PubMed

Roth, Bryan L; Lopez, Estela; Beischel, Scott; Westkaemper, Richard B; Evans, Jon M

2004-05-01

Because psychoactive plants exert profound effects on human perception, emotion, and cognition, discovering the molecular mechanisms responsible for psychoactive plant actions will likely yield insights into the molecular underpinnings of human consciousness. Additionally, it is likely that elucidation of the molecular targets responsible for psychoactive drug actions will yield validated targets for CNS drug discovery. This review article focuses on an unbiased, discovery-based approach aimed at uncovering the molecular targets responsible for psychoactive drug actions wherein the main active ingredients of psychoactive plants are screened at the "receptorome" (that portion of the proteome encoding receptors). An overview of the receptorome is given and various in silico, public-domain resources are described. Newly developed tools for the in silico mining of data derived from the National Institute of Mental Health Psychoactive Drug Screening Program's (NIMH-PDSP) K(i) Database (K(i) DB) are described in detail. Additionally, three case studies aimed at discovering the molecular targets responsible for Hypericum perforatum, Salvia divinorum, and Ephedra sinica actions are presented. Finally, recommendations are made for future studies.

Distributed Drug Discovery, Part 3: Using D3 Methodology to Synthesize Analogs of an Anti-Melanoma Compound

PubMed Central

2008-01-01

For the successful implementation of Distributed Drug Discovery (D3) (outlined in the accompanying Perspective), students, in the course of their educational laboratories, must be able to reproducibly make new, high quality, molecules with potential for biological activity. This article reports the successful achievement of this goal. Using previously rehearsed alkylating agents, students in a second semester organic chemistry laboratory performed a solid-phase combinatorial chemistry experiment in which they made 38 new analogs of the most potent member of a class of antimelanoma compounds. All compounds were made in duplicate, purified by silica gel chromatography, and characterized by NMR and LC/MS. As a continuing part of the Distributed Drug Discovery program, a virtual D3 catalog based on this work was then enumerated and is made freely available to the global scientific community. PMID:19105723

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.

Sources for Leads: Natural Products and Libraries.

PubMed

van Herwerden, Eric F; Süssmuth, Roderich D

2016-01-01

Natural products have traditionally been a major source of leads in the drug discovery process. However, the development of high-throughput screening led to an increased interest in synthetic methods that enabled the rapid construction of large libraries of molecules. This resulted in the termination or downscaling of many natural product research programs, but the chemical libraries did not necessarily produce a larger amount of drug leads. On one hand, this chapter explores the current state of natural product research within the drug discovery process. On the other hand it evaluates the efforts made to increase the amount of leads generated from chemical libraries and considers what role natural products could play here.

Cardiovascular drug discovery in the academic setting: building infrastructure, harnessing strengths, and seeking synergies.

PubMed

Gardell, Stephen J; Roth, Gregory P; Kelly, Daniel P

2010-10-01

The flow of innovative, effective, and safe new drugs from pharmaceutical laboratories for the treatment and prevention of cardiovascular disease has slowed to a trickle. While the need for breakthrough cardiovascular disease drugs is still paramount, the incentive to develop these agents has been blunted by burgeoning clinical development costs coupled with a heightened risk of failure due to the unprecedented nature of the emerging drug targets and increasingly challenging regulatory environment. A fuller understanding of the drug targets and employing novel biomarker strategies in clinical trials should serve to mitigate the risk. In any event, these current challenges have evoked changing trends in the pharmaceutical industry, which have created an opportunity for non-profit biomedical research institutions to play a pivotal partnering role in early stage drug discovery. The obvious strengths of academic research institutions is the breadth of their scientific programs and the ability and motivation to "go deep" to identify and characterize new target pathways that unlock the specific mysteries of cardiovascular diseases--leading to a bounty of novel therapeutic targets and prescient biomarkers. However, success in the drug discovery arena within the academic environment is contingent upon assembling the requisite infrastructure, annexing the talent to interrogate and validate the drug targets, and building translational bridges with pharmaceutical organizations and patient-oriented researchers.

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.

CREB and the discovery of cognitive enhancers.

PubMed

Scott, Roderick; Bourtchuladze, Rusiko; Gossweiler, Scott; Dubnau, Josh; Tully, Tim

2002-01-01

In the past few years, a series of molecular-genetic, biochemical, cellular and behavioral studies in fruit flies, sea slugs and mice have confirmed a long-standing notion that long-term memory formation depends on the synthesis of new proteins. Experiments focused on the cAMP-responsive transcription factor, CREB, have established that neural activity-induced regulation of gene transcription promotes a synaptic growth process that strengthens the connections among active neurons. This process constitutes a physical basis for the engram--and CREB is a "molecular switch" to produce the engram. Helicon Therapeutics has been formed to identify drug compounds that enhance memory formation via augmentation of CREB biochemistry. Candidate compounds have been identified from a high throughput cell-based screen and are being evaluated in animal models of memory formation. A gene discovery program also seeks to identify new genes, which function downstream of CREB during memory formation, as a source for new drug discoveries in the future. Together, these drug and gene discovery efforts promise new class of pharmaceutical therapies for the treatment of various forms of cognitive dysfunction.

75 FR 33268 - Technology Innovation Program (TIP) Notice of Availability of Funds; Amendment

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-11

... military/ weaponry applications (e.g. warhead manufacture, chemical/biological warfare materials production.... production of biofuels or small molecule drugs); Projects that primarily focus on drug discovery or design of... design that are not a part of the manufacturing of engineered tissues; and Projects that do not have a...

Nature's Medicines: Traditional Knowledge and Intellectual Property Management. Case Studies from the National Institutes of Health (NIH), USA

PubMed Central

Gupta, Ranjan; Gabrielsen, Bjarne; Ferguson, Steven M.

2009-01-01

With the emergence and re-emergence of infectious diseases and development of multi-drug resistance, there is a dire need to find newer cures and to produce more drugs and vaccines in the pipeline. To meet these increasing demands biomedical researchers and pharmaceutical companies are combining advanced methods of drug discovery, such as combinatorial chemistry, high-throughput screening and genomics, with conventional approaches using natural products and traditional knowledge. However, such approaches require much international cooperation and understanding of international laws and conventions as well as local customs and traditions. This article reviews the forty years of cumulative experience at the National Institutes of Health (initiated by the National Cancer Institute) in natural products drug discovery. It presents (1) three major cooperative programs (2) the legal mechanisms for cooperation and (3) illustrative case studies from these programs. We hope that these discussions and our lessons learned would be helpful to others seeking to develop their own models of cooperation for the benefit of global health. PMID:16475917

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.

KCa 3.1-a microglial target ready for drug repurposing?

PubMed

Dale, Elena; Staal, Roland G W; Eder, Claudia; Möller, Thomas

2016-10-01

Over the past decade, glial cells have attracted attention for harboring unexploited targets for drug discovery. Several glial targets have attracted de novo drug discovery programs, as highlighted in this GLIA Special Issue. Drug repurposing, which has the objective of utilizing existing drugs as well as abandoned, failed, or not yet pursued clinical development candidates for new indications, might provide a faster opportunity to bring drugs for glial targets to patients with unmet needs. Here, we review the potential of the intermediate-conductance calcium-activated potassium channels KCa 3.1 as the target for such a repurposing effort. We discuss the data on KCa 3.1 expression on microglia in vitro and in vivo and review the relevant literature on the two KCa 3.1 inhibitors TRAM-34 and Senicapoc. Finally, we provide an outlook of what it might take to harness the potential of KCa 3.1 as a bona fide microglial drug target. GLIA 2016;64:1733-1741. © 2016 Wiley Periodicals, Inc.

Overcoming the challenges of drug discovery for neglected tropical diseases: the A·WOL experience.

PubMed

Johnston, Kelly L; Ford, Louise; Taylor, Mark J

2014-03-01

Neglected tropical diseases (NTDs) are a group of 17 diseases that typically affect poor people in tropical countries. Each has been neglected for decades in terms of funding, research, and policy, but the recent grouping of them into one unit, which can be targeted using integrated control measures, together with increased advocacy has helped to place them on the global health agenda. The World Health Organization has set ambitious goals to control or eliminate 10 NTDs by 2020 and launched a roadmap in January 2012 to guide this global plan. The result of the launch meeting, which brought together representatives from the pharmaceutical industry, donors, and politicians, was the London Declaration: a series of commitments to provide more drugs, research, and funds to achieve the 2020 goals. Drug discovery and development for these diseases are extremely challenging, and this article highlights these challenges in the context of the London Declaration, before focusing on an example of a drug discovery and development program for the NTDs onchocerciasis and lymphatic filariasis (the anti-Wolbachia consortium, A·WOL).

The roots of modern oncology: from discovery of new antitumor anthracyclines to their clinical use.

PubMed

Cassinelli, Giuseppe

2016-06-02

In May 1960, the Farmitalia CEO Dr. Bertini and the director of the Istituto Nazionale dei Tumori of Milan Prof. Bucalossi (talent scout and city's Mayor) signed a research agreement for the discovery and development up to clinical trials of new natural antitumor agents. This agreement can be considered as a pioneering and fruitful example of a translational discovery program with relevant transatlantic connections. Owing to an eclectic Streptomyces, found near Castel del Monte (Apulia), and to the skilled and motivated participants of both institutions, a new natural antitumor drug, daunomycin, was ready for clinical trials within 3 years. Patent interference by the Farmitalia French partner was overcome by the good quality of the Italian drug and by the cooperation between Prof. Di Marco, director of the Istituto Ricerche Farmitalia Research Laboratories for Microbiology and Chemotherapy, and Prof. Karnofsky, head of the Sloan-Kettering Cancer Institute of New York, leading to the first transatlantic clinical trials. The search for daunomycin's sister anthracyclines led to the discovery and development of adriamycin, one of the best drugs born in Milan. This was the second act prologue of the history of Italian antitumor discovery and clinical oncology, which started in July 1969 when Prof. Di Marco sent Prof. Bonadonna the first vials of adriamycin (doxorubicin) to be tested in clinical trials. This article reviews the Milan scene in the 1960s, a city admired and noted for the outstanding scientific achievements of its private and public institutions in drugs and industrial product discovery.

Measuring the effectiveness and impact of an open innovation platform.

PubMed

Carroll, Glenn P; Srivastava, Sanjay; Volini, Adam S; Piñeiro-Núñez, Marta M; Vetman, Tatiana

2017-05-01

Today, most pharmaceutical companies complement their traditional R&D models with some variation on the Open Innovation (OI) approach in an effort to better access global scientific talent, ideas and hypotheses. Traditional performance indicators that measure economic returns from R&D through commercialization are often not applicable to the practical assessment of these OI approaches, particularly within the context of early drug discovery. This leaves OI programs focused on early R&D without a standard assessment framework from which to evaluate overall performance. This paper proposes a practical dashboard for such assessment, encompassing quantitative and qualitative elements, to enable decision-making and improvement of future performance. The use of this dashboard is illustrated using real-time data from the Lilly Open Innovation Drug Discovery (OIDD) program. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

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.

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

"Partners in Science": A Model Cooperative Program Introducing High School Teachers and Students to Leading-Edge Pharmaceutical Science

ERIC Educational Resources Information Center

Woska, Joseph R., Jr.; Collins, Danielle M.; Canney, Brian J.; Arcario, Erin L.; Reilly, Patricia L.

2005-01-01

"Partners in Science" is a cooperative program between Boehringer Ingelheim Pharmaceuticals, Inc. and area high schools in the community surrounding our Connecticut campus. It is a two-phase program that introduces high school students and teachers to the world of drug discovery and leading-edge pharmaceutical research. Phase 1 involves…

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.

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

PubMed

Luise, Nicola; Wyatt, Paul

2018-05-07

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

Direction Discovery: A Science Enrichment Program for High School Students

ERIC Educational Resources Information Center

Sikes, Suzanne S.; Schwartz-Bloom, Rochelle D.

2009-01-01

Launch into education about pharmacology (LEAP) is an inquiry-based science enrichment program designed to enhance competence in biology and chemistry and foster interest in science careers especially among under-represented minorities. The study of how drugs work, how they enter cells, alter body chemistry, and exit the body engages students to…

Rediscovering natural products as a source of new drugs.

PubMed

Koehn, Frank E; Carter, Guy T

2005-04-01

Extract: Since the very beginnings of human medicine, physicians have relied on chemical compounds produced by animals, plants and microorganisms, so-called natural products, to treat diseases. Natural products are directly or indirectly responsible for roughly one-half of all drugs currently in use. Of the 877 small-molecule new drug molecules introduced between 1981 and 2002, 49% were natural products or natural product analogs. Despite the great success of the 70s and 80s, the pharmaceutical industry de-emphasized natural products research during the following decade. In this article, we examine the underlying reasons for the decline, and assess future prospects for natural products research in drug discovery. In the 1990s, major pharmaceutical companies moved to a lead-finding strategy based on High Throughput Screening (HTS) of very large collections (libraries) of synthetic compounds. The move arose from the belief that techniques such as combinatorial chemistry could produce larger, more cost-effective libraries with improved hit rates and quality. Additionally, advances in molecular biology, cellular biology and genomics dramatically increased the number of molecular targets, prompting shorter drug discovery timelines. In today's drug discovery environment, rapid screening and identification of potential drug molecules is essential for success. This puts traditional natural products-based programs, with their reliance on the lengthy processes of the screening of extracts library, bioassay-guided isolation of the active components, structure elucidation and subsequent production scale-up, at a competitive disadvantage.

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

PubMed Central

Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

2016-01-01

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design. PMID:27958331

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem.

PubMed

Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

2016-12-13

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

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.

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.

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

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.

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.

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

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.

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

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.

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.

Unified Software Solution for Efficient SPR Data Analysis in Drug Research

PubMed Central

Dahl, Göran; Steigele, Stephan; Hillertz, Per; Tigerström, Anna; Egnéus, Anders; Mehrle, Alexander; Ginkel, Martin; Edfeldt, Fredrik; Holdgate, Geoff; O’Connell, Nichole; Kappler, Bernd; Brodte, Annette; Rawlins, Philip B.; Davies, Gareth; Westberg, Eva-Lotta; Folmer, Rutger H. A.; Heyse, Stephan

2016-01-01

Surface plasmon resonance (SPR) is a powerful method for obtaining detailed molecular interaction parameters. Modern instrumentation with its increased throughput has enabled routine screening by SPR in hit-to-lead and lead optimization programs, and SPR has become a mainstream drug discovery technology. However, the processing and reporting of SPR data in drug discovery are typically performed manually, which is both time-consuming and tedious. Here, we present the workflow concept, design and experiences with a software module relying on a single, browser-based software platform for the processing, analysis, and reporting of SPR data. The efficiency of this concept lies in the immediate availability of end results: data are processed and analyzed upon loading the raw data file, allowing the user to immediately quality control the results. Once completed, the user can automatically report those results to data repositories for corporate access and quickly generate printed reports or documents. The software module has resulted in a very efficient and effective workflow through saved time and improved quality control. We discuss these benefits and show how this process defines a new benchmark in the drug discovery industry for the handling, interpretation, visualization, and sharing of SPR data. PMID:27789754

Genomes2Drugs: Identifies Target Proteins and Lead Drugs from Proteome Data

PubMed Central

Toomey, David; Hoppe, Heinrich C.; Brennan, Marian P.; Nolan, Kevin B.; Chubb, Anthony J.

2009-01-01

Background Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials. Methodology/Principal Findings To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins. Conclusions/Significance Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under ‘change-of-application’ patents. PMID:19593435

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

PubMed

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

2018-01-01

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

Discovery of cryptophycin-1 and BCN-183577: examples of strategies and problems in the detection of antitumor activity in mice.

PubMed

Corbett, T H; Valeriote, F A; Demchik, L; Lowichik, N; Polin, L; Panchapor, C; Pugh, S; White, K; Kushner, J; Rake, J; Wentland, M; Golakoti, T; Hetzel, C; Ogino, J; Patterson, G; Moore, R

1997-01-01

Historically, many new anticancer agents were first detected in a prescreen; usually consisting of a molecular/biochemical target or a cellular cytotoxicity assay. The agent then progressed to in vivo evaluation against transplanted human or mouse tumors. If the investigator had a large drug supply and ample resources, multiple tests were possible, with variations in tumor models, tumor and drug routes, dose-decrements, dose-schedules, number of groups, etc. However, in most large programs involving several hundred in vivo tests yearly, resource limitations and drug supply limitations have usually dictated a single trial. Under such restrictive conditions, we have implemented a flexible in vivo testing protocol. With this strategy, the tumor model is dictated by in vitro cellular sensitivity; drug route by water solubility (with water soluble agents injected intravenously); dosage decrement by drug supply, dose-schedule by toxicities encountered, etc. In this flexible design, many treatment parameters can be changed during the course of treatment (e.g., dose and schedule). The discovery of two active agents are presented (Cryptophycin-1, and Thioxanthone BCN 183577). Both were discovered by the intravenous route of administration. Both would have been missed if they were tested intraperitoneally, the usual drug route used in discovery protocols. It is also likely that they would have been missed with an easy to execute fixed protocol design, even if injected i.v.

Managing Innovation to Maximize Value Along the Discovery-Translation-Application Continuum.

PubMed

Waldman, S A; Terzic, A

2017-01-01

Success in pharmaceutical development led to a record 51 drugs approved in the past year, surpassing every previous year since 1950. Technology innovation enabled identification and exploitation of increasingly precise disease targets ensuring next generation diagnostic and therapeutic products for patient management. The expanding biopharmaceutical portfolio stands, however, in contradistinction to the unsustainable costs that reflect remarkable challenges of clinical development programs. This annual Therapeutic Innovations issue juxtaposes advances in translating molecular breakthroughs into transformative therapies with essential considerations for lowering attrition and improving the cost-effectiveness of the drug-development paradigm. Realizing the discovery-translation-application continuum mandates a congruent approval, adoption, and access triad. © 2016 ASCPT.

Managing Innovation to Maximize Value Along the Discovery-Translation-Application Continuum

PubMed Central

Waldman, SA; Terzic, A

2017-01-01

Success in pharmaceutical development led to a record 51 drug approved in the past year, surpassing every previous year since 1950. Technology innovation enabled identification and exploitation of increasingly precise disease targets ensuring a next generation diagnostic and therapeutic products for patient management. The expanding biopharmaceutical portfolio stands however in contradistinction to the unsustainable costs that reflect remarkable challenges of clinical development programs. This annual Therapeutic Innovations issue juxtaposes advances in translating molecular breakthroughs into transformative therapies with essential considerations for lowering attrition and improving the cost-effectiveness of the drug development paradigm. Realizing the discovery-translation-application continuum mandates a congruent approval, adoption and access triad. PMID:27869291

Discovery and Characterization of a Water-Soluble Prodrug of a Dual Inhibitor of Bacterial DNA Gyrase and Topoisomerase IV.

PubMed

O'Dowd, Hardwin; Shannon, Dean E; Chandupatla, Kishan R; Dixit, Vaishali; Engtrakul, Juntyma J; Ye, Zhengqi; Jones, Steven M; O'Brien, Colleen F; Nicolau, David P; Tessier, Pamela R; Crandon, Jared L; Song, Bin; Macikenas, Dainius; Hanzelka, Brian L; Le Tiran, Arnaud; Bennani, Youssef L; Charifson, Paul S; Grillot, Anne-Laure

2015-07-09

Benzimidazole 1 is the lead compound resulting from an antibacterial program targeting dual inhibitors of bacterial DNA gyrase and topoisomerase IV. With the goal of improving key drug-like properties, namely, the solubility and the formulability of 1, an effort to identify prodrugs was undertaken. This has led to the discovery of a phosphate ester prodrug 2. This prodrug is rapidly cleaved to the parent drug molecule upon both oral and intravenous administration. The prodrug achieved equivalent exposure of 1 compared to dosing the parent in multiple species. The prodrug 2 has improved aqueous solubility, simplifying both intravenous and oral formulation.

Ayurgenomics for stratified medicine: TRISUTRA consortium initiative across ethnically and geographically diverse Indian populations.

PubMed

Prasher, Bhavana; Varma, Binuja; Kumar, Arvind; Khuntia, Bharat Krushna; Pandey, Rajesh; Narang, Ankita; Tiwari, Pradeep; Kutum, Rintu; Guin, Debleena; Kukreti, Ritushree; Dash, Debasis; Mukerji, Mitali

2017-02-02

Genetic differences in the target proteins, metabolizing enzymes and transporters that contribute to inter-individual differences in drug response are not integrated in contemporary drug development programs. Ayurveda, that has propelled many drug discovery programs albeit for the search of new chemical entities incorporates inter-individual variability "Prakriti" in development and administration of drug in an individualized manner. Prakriti of an individual largely determines responsiveness to external environment including drugs as well as susceptibility to diseases. Prakriti has also been shown to have molecular and genomic correlates. We highlight how integration of Prakriti concepts can augment the efficiency of drug discovery and development programs through a unique initiative of Ayurgenomics TRISUTRA consortium. Five aspects that have been carried out are (1) analysis of variability in FDA approved pharmacogenomics genes/SNPs in exomes of 72 healthy individuals including predominant Prakriti types and matched controls from a North Indian Indo-European cohort (2) establishment of a consortium network and development of five genetically homogeneous cohorts from diverse ethnic and geo-climatic background (3) identification of parameters and development of uniform standard protocols for objective assessment of Prakriti types (4) development of protocols for Prakriti evaluation and its application in more than 7500 individuals in the five cohorts (5) Development of data and sample repository and integrative omics pipelines for identification of genomic correlates. Highlight of the study are (1) Exome sequencing revealed significant differences between Prakriti types in 28 SNPs of 11 FDA approved genes of pharmacogenomics relevance viz. CYP2C19, CYP2B6, ESR1, F2, PGR, HLA-B, HLA-DQA1, HLA-DRB1, LDLR, CFTR, CPS1. These variations are polymorphic in diverse Indian and world populations included in 1000 genomes project. (2) Based on the phenotypic attributes of Prakriti we identified anthropometry for anatomical features, biophysical parameters for skin types, HRV for autonomic function tests, spirometry for vital capacity and gustometry for taste thresholds as objective parameters. (3) Comparison of Prakriti phenotypes across different ethnic, age and gender groups led to identification of invariant features as well as some that require weighted considerations across the cohorts. Considering the molecular and genomics differences underlying Prakriti and relevance in disease pharmacogenomics studies, this novel integrative platform would help in identification of differently susceptible and drug responsive population. Additionally, integrated analysis of phenomic and genomic variations would not only allow identification of clinical and genomic markers of Prakriti for application in personalized medicine but also its integration in drug discovery and development programs. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Development of a high-throughput brain slice method for studying drug distribution in the central nervous system.

PubMed

Fridén, Markus; Ducrozet, Frederic; Middleton, Brian; Antonsson, Madeleine; Bredberg, Ulf; Hammarlund-Udenaes, Margareta

2009-06-01

New, more efficient methods of estimating unbound drug concentrations in the central nervous system (CNS) combine the amount of drug in whole brain tissue samples measured by conventional methods with in vitro estimates of the unbound brain volume of distribution (V(u,brain)). Although the brain slice method is the most reliable in vitro method for measuring V(u,brain), it has not previously been adapted for the needs of drug discovery research. The aim of this study was to increase the throughput and optimize the experimental conditions of this method. Equilibrium of drug between the buffer and the brain slice within the 4 to 5 h of incubation is a fundamental requirement. However, it is difficult to meet this requirement for many of the extensively binding, lipophilic compounds in drug discovery programs. In this study, the dimensions of the incubation vessel and mode of stirring influenced the equilibration time, as did the amount of brain tissue per unit of buffer volume. The use of cassette experiments for investigating V(u,brain) in a linear drug concentration range increased the throughput of the method. The V(u,brain) for the model compounds ranged from 4 to 3000 ml . g brain(-1), and the sources of variability are discussed. The optimized setup of the brain slice method allows precise, robust estimation of V(u,brain) for drugs with diverse properties, including highly lipophilic compounds. This is a critical step forward for the implementation of relevant measurements of CNS exposure in the drug discovery setting.

The First AIDS Drugs | Center for Cancer Research

Cancer.gov

Faced with the burgeoning HIV/AIDS epidemic in the 1980s, NCI’s intramural program developed the first therapies to effectively treat the disease. These discoveries helped transform a fatal diagnosis to the manageable condition it is for many today.

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework.

PubMed

Chen, Yi-An; Tripathi, Lokesh P; Mizuguchi, Kenji

2016-01-01

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org. © The Author(s) 2016. Published by Oxford University Press.

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework

PubMed Central

Chen, Yi-An; Tripathi, Lokesh P.; Mizuguchi, Kenji

2016-01-01

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format. Database URL: http://targetmine.mizuguchilab.org PMID:26989145

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).

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.

The UIC ICBG (University of Illinois at Chicago International Cooperative Biodiversity Group) Memorandum of Agreement: a model of benefit-sharing arrangement in natural products drug discovery and development.

PubMed

Soejarto, D D; Gyllenhaal, C; Fong, H H S; Xuan, L T; Hiep, N T; Hung, N V; Bich, T Q; Southavong, B; Sydara, K; Pezzuto, J M

2004-02-01

The Convention on Biodiversity mandates a new approach to the discovery of natural product drugs, one that incorporates concepts of national ownership of genetic resources, intellectual property rights in traditional knowledge, and sharing of economic benefits with countries that are the source of new natural products. The International Cooperative Biodiversity Group (ICBG) program was established to support experimentation in implementation of the Convention through development and execution of international agreements for bioprospecting. The agreement of one such ICBG program, between the University of Illinois at Chicago and institutions in Vietnam and Laos, is presented here. The core elements contained in the single, five-way Memorandum of Agreement are the arrangements for intellectual property rights, treatment of informed consent, and plans for benefit-sharing (including the sharing of short- and long-term royalty benefits, capacity building, and community reciprocity). Program participants were able to develop a practical and flexible agreement that satisfies the wishes of all institutions that are parties to it.

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

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).

Bridging the translational gap: collaborative drug development and dispelling the stigma of commercialization.

PubMed

Yu, Helen W H

2016-02-01

The current drug discovery and development process is stalling the translation of basic science into lifesaving products. Known as the 'Valley of Death', the traditional technology transfer model fails to bridge the gap between early-stage discoveries and preclinical research to advance innovations beyond the discovery phase. In addition, the stigma associated with 'commercialization' detracts from the importance of efficient translation of basic research. Here, I introduce a drug discovery model whereby the respective expertise of academia and industry are brought together to take promising discoveries through to proof of concept as a way to derisk the drug discovery and development process. Known as the 'integrated drug discovery model', I examine here the extent to which existing legal frameworks support this model. Copyright © 2015 Elsevier Ltd. All rights reserved.

The impact of data integrity on decision making in early lead discovery

NASA Astrophysics Data System (ADS)

Beck, Bernd; Seeliger, Daniel; Kriegl, Jan M.

2015-09-01

Data driven decision making is a key element of today's pharmaceutical research, including early drug discovery. It comprises questions like which target to pursue, which chemical series to pursue, which compound to make next, or which compound to select for advanced profiling and promotion to pre-clinical development. In the following paper we will exemplify how data integrity, i.e. the context data is generated in and auxiliary information that is provided for individual result records, can influence decision making in early lead discovery programs. In addition we will describe some approaches which we pursue at Boehringer Ingelheim to reduce the risk for getting misguided.

The Blood Brain Barrier and its Role in Alzheimer's Therapy: An Overview.

PubMed

Jakki, Satya Lavanya; Senthil, V; Yasam, Venkata Ramesh; Chandrasekar, M J N; Vijayaraghavan, C

2018-01-01

Alzheimer's disease (AD) is the most frequent age related neurodegenerative disorder. It represents 70% of all dementia. Millions of people have been affected by AD worldwide. It is a complex illness characterized pathologically by accumulation of protein aggregates of amyloid and neurofibrillary tangles containing hyperphosphorylated neuronal tau protein. AD requires drugs that can circumvent the blood-brain barrier (BBB) which is not a simple physical barrier between blood and brain, but acts as an iron curtain, allowing only selective molecules to enter the brain. Unfortunately, this dynamic barrier restricts transport of drugs to the brain; due to which, currently very few drugs are available for AD treatment. The present review focuses mainly on strategies used for administration of drug to the CNS by-passing BBB for the treatment of AD. Many studies have proved to be effective in overcoming BBB and targeting drugs to CNS by using different strategies. Here we have discussed some of the most important drug permeability and drug targeting approaches. In conclusion, concentrating solely in development of drug discovery programs is not enough but it is important to maintain balance between the drug discovery and drug delivery systems that are more specific and effective in targeting CNS of AD patients. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

The Application of the Open Pharmacological Concepts Triple Store (Open PHACTS) to Support Drug Discovery Research

PubMed Central

Ratnam, Joseline; Zdrazil, Barbara; Digles, Daniela; Cuadrado-Rodriguez, Emiliano; Neefs, Jean-Marc; Tipney, Hannah; Siebes, Ronald; Waagmeester, Andra; Bradley, Glyn; Chau, Chau Han; Richter, Lars; Brea, Jose; Evelo, Chris T.; Jacoby, Edgar; Senger, Stefan; Loza, Maria Isabel; Ecker, Gerhard F.; Chichester, Christine

2014-01-01

Integration of open access, curated, high-quality information from multiple disciplines in the Life and Biomedical Sciences provides a holistic understanding of the domain. Additionally, the effective linking of diverse data sources can unearth hidden relationships and guide potential research strategies. However, given the lack of consistency between descriptors and identifiers used in different resources and the absence of a simple mechanism to link them, gathering and combining relevant, comprehensive information from diverse databases remains a challenge. The Open Pharmacological Concepts Triple Store (Open PHACTS) is an Innovative Medicines Initiative project that uses semantic web technology approaches to enable scientists to easily access and process data from multiple sources to solve real-world drug discovery problems. The project draws together sources of publicly-available pharmacological, physicochemical and biomolecular data, represents it in a stable infrastructure and provides well-defined information exploration and retrieval methods. Here, we highlight the utility of this platform in conjunction with workflow tools to solve pharmacological research questions that require interoperability between target, compound, and pathway data. Use cases presented herein cover 1) the comprehensive identification of chemical matter for a dopamine receptor drug discovery program 2) the identification of compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease and 3) the evaluation of established targets in the Vitamin D metabolism pathway to aid novel Vitamin D analogue design. The example workflows presented illustrate how the Open PHACTS Discovery Platform can be used to exploit existing knowledge and generate new hypotheses in the process of drug discovery. PMID:25522365

Found in translation: how preclinical research is guiding the clinical development of the BCL-2-selective inhibitor venetoclax

PubMed Central

Leverson, Joel D.; Sampath, Deepak; Souers, Andrew J.; Rosenberg, Saul H.; Fairbrother, Wayne J.; Amiot, Martine; Konopleva, Marina; Letai, Anthony

2017-01-01

Since the discovery of apoptosis as a form of programmed cell death, targeting the apoptosis pathway to induce cancer cell death has been a high priority goal for cancer therapy. After decades of effort, drug discovery scientists have succeeded in generating small-molecule inhibitors of antiapoptotic BCL-2 family proteins. Innovative medicinal chemistry and structure-based drug design, coupled with a strong fundamental understanding of BCL-2 biology, were essential to the development of BH3 mimetics such as the BCL-2-selective inhibitor venetoclax. We review a number of preclinical studies that have deepened our understanding of BCL-2 biology and facilitated the clinical development of venetoclax. PMID:29146569

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

End of inevitability: programming and reprogramming.

PubMed

Turksen, Kursad

2013-08-01

Stem cell commitment and differentiation leading to functional cell types and organs has generally been considered unidirectional and deterministic. Starting first with a landmark study 50 years ago, and now with more recent observations, this paradigm has been challenged, necessitating a rethink of what constitutes both programming and reprogramming processes, and how we can use this new understanding for new approaches to drug discovery and regenerative medicine.

Joint Service Chemical and Biological Defense Program: FY 06-07 Overview

DTIC Science & Technology

2006-01-01

Performers Molecular model of human plasma-derived butyryl Electronmicrograph of bacillus spores adhering to cell membrane processes 38866_BATT_TX 11...agents, and radioactive fallout. CPS is integrated with the ship’s Heating, Ventilation, and Air-Conditioning ( HVAC ) systems and provides filtered air...molecules for intervention against protein NTA. • Identify and evaluate effectiveness of spore germination inhibitors. • Expand drug discovery program

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.

Nitro drugs for the treatment of trypanosomatid diseases: past, present, and future prospects

PubMed Central

Patterson, Stephen; Wyllie, Susan

2014-01-01

There is an urgent need for new, safer, and effective treatments for the diseases caused by the protozoan parasites Trypanosoma brucei, Trypanosoma cruzi, and Leishmania spp. In the search for more effective drugs to treat these ‘neglected diseases’ researchers have chosen to reassess the therapeutic value of nitroaromatic compounds. Previously avoided in drug discovery programs owing to potential toxicity issues, a nitro drug is now being used successfully as part of a combination therapy for human African trypanosomiasis. We describe here the rehabilitation of nitro drugs for the treatment of trypanosomatid diseases and discuss the future prospects for this compound class. PMID:24776300

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.

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.

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.

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

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.

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.

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.

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...

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

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.

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.

77 FR 59198 - Center for Scientific Review; Notice of Closed Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-26

....gov . Name of Committee: Center for Scientific Review Special Emphasis Panel; Drug Discovery for the... DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health Center for Scientific Review... personal privacy. Name of Committee: Center for Scientific Review Special Emphasis Panel; Program Projects...

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.

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.

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.

Found in Translation: How Preclinical Research Is Guiding the Clinical Development of the BCL2-Selective Inhibitor Venetoclax.

PubMed

Leverson, Joel D; Sampath, Deepak; Souers, Andrew J; Rosenberg, Saul H; Fairbrother, Wayne J; Amiot, Martine; Konopleva, Marina; Letai, Anthony

2017-12-01

Since the discovery of apoptosis as a form of programmed cell death, targeting the apoptosis pathway to induce cancer cell death has been a high-priority goal for cancer therapy. After decades of effort, drug-discovery scientists have succeeded in generating small-molecule inhibitors of antiapoptotic BCL2 family proteins. Innovative medicinal chemistry and structure-based drug design, coupled with a strong fundamental understanding of BCL2 biology, were essential to the development of BH3 mimetics such as the BCL2-selective inhibitor venetoclax. We review a number of preclinical studies that have deepened our understanding of BCL2 biology and facilitated the clinical development of venetoclax. Significance: Basic research into the pathways governing programmed cell death have paved the way for the discovery of apoptosis-inducing agents such as venetoclax, a BCL2-selective inhibitor that was recently approved by the FDA and the European Medicines Agency. Preclinical studies aimed at identifying BCL2-dependent tumor types have translated well into the clinic thus far and will likely continue to inform the clinical development of venetoclax and other BCL2 family inhibitors. Cancer Discov; 7(12); 1376-93. ©2017 AACR. ©2017 American Association for Cancer Research.

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.

Developing the Biomolecular Screening Facility at the EPFL into the Chemical Biology Screening Platform for Switzerland.

PubMed

Turcatti, Gerardo

2014-05-01

The Biomolecular Screening Facility (BSF) is a multidisciplinary laboratory created in 2006 at the Ecole Polytechnique Federale de Lausanne (EPFL) to perform medium and high throughput screening in life sciences-related projects. The BSF was conceived and developed to meet the needs of a wide range of researchers, without privileging a particular biological discipline or therapeutic area. The facility has the necessary infrastructure, multidisciplinary expertise and flexibility to perform large screening programs using small interfering RNAs (siRNAs) and chemical collections in the areas of chemical biology, systems biology and drug discovery. In the framework of the National Centres of Competence in Research (NCCR) Chemical Biology, the BSF is hosting 'ACCESS', the Academic Chemical Screening Platform of Switzerland that provides the scientific community with chemical diversity, screening facilities and know-how in chemical genetics. In addition, the BSF started its own applied research axes that are driven by innovation in thematic areas related to preclinical drug discovery and discovery of bioactive probes.

State of the Art in African Trypanosome Drug Discovery

PubMed Central

Jacobs, Robert T.; Nare, Bakela; Phillips, Margaret A.

2011-01-01

African sleeping sickness is endemic in sub-Saharan Africa where the WHO estimates that 60 million people are at risk for the disease. Human African trypanosomiasis (HAT) is 100% fatal if untreated and the current drug therapies have significant limitations due to toxicity and difficult treatment regimes. No new chemical agents have been approved since eflornithine in 1990. The pentamidine analog DB289, which was in late stage clinical trials for the treatment of early stage HAT recently failed due to toxicity issues. A new protocol for the treatment of late-stage T. brucei gambiense that uses combination nifurtomox/eflornithine (NECT) was recently shown to have better safety and efficacy than eflornithine alone, while being easier to administer. This breakthrough represents the only new therapy for HAT since the approval of eflornithine. A number of research programs are on going to exploit the unusual biochemical pathways in the parasite to identify new targets for target based drug discovery programs. HTS efforts are also underway to discover new chemical entities through whole organism screening approaches. A number of inhibitors with anti-trypanosomal activity have been identified by both approaches, but none of the programs are yet at the stage of identifying a preclinical candidate. This dire situation underscores the need for continued effort to identify new chemical agents for the treatment of HAT. PMID:21401507

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

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.

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.

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.

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.

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.

Updates on Managing Type 2 Diabetes Mellitus with Natural Products: Towards Antidiabetic Drug Development.

PubMed

Alam, Fahmida; Islam, Md Asiful; Kamal, M A; Gan, Siew Hua

2016-08-13

Over the years, natural products have shown success as antidiabetics in vitro, in vivo and in clinical trials. Because natural product-derived drugs are more affordable and effective with fewer side-effects compared to conventional therapies, pharmaceutical research is increasingly leaning towards the discovery of new antidiabetic drugs from natural products targeting pathways or components associated with type 2 diabetes mellitus (T2DM) pathophysiology. However, the drug discovery process is very lengthy and costly with significant challenges. Therefore, various techniques are currently being developed for the preclinical research phase of drug discovery with the aim of drug development with less time and efforts from natural products. In this review, we have provided an update on natural products including fruits, vegetables, spices, nuts, beverages and mushrooms with potential antidiabetic activities from in vivo, in vitro and clinical studies. Synergistic interactions between natural products and antidiabetic drugs; and potential antidiabetic active compounds from natural products are also documented to pave the way for combination treatment and new drug discovery, respectively. Additionally, a brief idea of the drug discovery process along with the challenges that arise during drug development from natural products and the methods to conquer those challenges are discussed to create a more convenient future drug discovery process.

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.

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.

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.

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.

A Collaborative Web-Based Architecture For Sharing ToxCast Data

EPA Science Inventory

Collaborative Drug Discovery (CDD) has created a scalable platform that combines traditional drug discovery informatics with Web2.0 features. Traditional drug discovery capabilities include substructure, similarity searching and export to excel or sdf formats. Web2.0 features inc...

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

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

New Perspectives on How to Discover Drugs from Herbal Medicines: CAM's Outstanding Contribution to Modern Therapeutics.

PubMed

Pan, Si-Yuan; Zhou, Shu-Feng; Gao, Si-Hua; Yu, Zhi-Ling; Zhang, Shuo-Feng; Tang, Min-Ke; Sun, Jian-Ning; Ma, Dik-Lung; Han, Yi-Fan; Fong, Wang-Fun; Ko, Kam-Ming

2013-01-01

With tens of thousands of plant species on earth, we are endowed with an enormous wealth of medicinal remedies from Mother Nature. Natural products and their derivatives represent more than 50% of all the drugs in modern therapeutics. Because of the low success rate and huge capital investment need, the research and development of conventional drugs are very costly and difficult. Over the past few decades, researchers have focused on drug discovery from herbal medicines or botanical sources, an important group of complementary and alternative medicine (CAM) therapy. With a long history of herbal usage for the clinical management of a variety of diseases in indigenous cultures, the success rate of developing a new drug from herbal medicinal preparations should, in theory, be higher than that from chemical synthesis. While the endeavor for drug discovery from herbal medicines is "experience driven," the search for a therapeutically useful synthetic drug, like "looking for a needle in a haystack," is a daunting task. In this paper, we first illustrated various approaches of drug discovery from herbal medicines. Typical examples of successful drug discovery from botanical sources were given. In addition, problems in drug discovery from herbal medicines were described and possible solutions were proposed. The prospect of drug discovery from herbal medicines in the postgenomic era was made with the provision of future directions in this area of drug development.

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.

Is the GAIN Act a turning point in new antibiotic discovery?

PubMed

Brown, Eric D

2013-03-01

The United States GAIN (Generating Antibiotic Incentives Now) Act is a call to action for new antibiotic discovery and development that arises from a ground swell of concern over declining activity in this therapeutic area in the pharmaceutical sector. The GAIN Act aims to provide economic incentives for antibiotic drug discovery in the form of market exclusivity and accelerated drug approval processes. The legislation comes on the heels of nearly two decades of failure using the tools of modern drug discovery to find new antibiotic drugs. The lessons of failure are examined herein as are the prospects for a renewed effort in antibiotic drug discovery and development stimulated by new investments in both the public and private sector.

High-field MRS in clinical drug development.

PubMed

Ross, Brian D

2013-07-01

Magnetic resonance spectroscopy (MRS) will continue to play an ever increasing role in drug discovery because MRS does readily define biomarkers for several hundreds of clinically distinct diseases. Published evidence based medicine (EBM) surveys, which generally conclude the opposite, are seriously flawed and do a disservice to the field of drug discovery. This article presents MRS and how it has guided several hundreds of practical human 'drug discovery' endeavors since its development. Specifically, the author looks at the process of 'reverse-translation' and its influence in the expansion of the number of preclinical drug discoveries from in vivo MRS. The author also provides a structured approach of eight criteria, including EBM acceptance, which could potentially re-open the field of MRS for productive exploration of existing and repurposed drugs and cost-effective drug-discovery. MRS-guided drug discovery is poised for future expansion. The cost of clinical trials has escalated and the use of biomarkers has become increasingly useful in improving patient selection for drug trials. Clinical MRS has uncovered a treasure-trove of novel biomarkers and clinical MRS itself has become better standardized and more widely available on 'routine' clinical MRI scanners. When combined with available new MRI sequences, MRS can provide a 'one stop shop' with multiple potential outcome measures for the disease and the drug in question.

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.

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).

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.

In silico fragment-based drug design.

PubMed

Konteatis, Zenon D

2010-11-01

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

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.

Adapting High-Throughput Screening Methods and Assays for Biocontainment Laboratories

PubMed Central

Tigabu, Bersabeh; White, E. Lucile; Bostwick, Robert; Tower, Nichole; Bukreyev, Alexander; Rockx, Barry; LeDuc, James W.; Noah, James W.

2015-01-01

Abstract High-throughput screening (HTS) has been integrated into the drug discovery process, and multiple assay formats have been widely used in many different disease areas but with limited focus on infectious agents. In recent years, there has been an increase in the number of HTS campaigns using infectious wild-type pathogens rather than surrogates or biochemical pathogen-derived targets. Concurrently, enhanced emerging pathogen surveillance and increased human mobility have resulted in an increase in the emergence and dissemination of infectious human pathogens with serious public health, economic, and social implications at global levels. Adapting the HTS drug discovery process to biocontainment laboratories to develop new drugs for these previously uncharacterized and highly pathogenic agents is now feasible, but HTS at higher biosafety levels (BSL) presents a number of unique challenges. HTS has been conducted with multiple bacterial and viral pathogens at both BSL-2 and BSL-3, and pilot screens have recently been extended to BSL-4 environments for both Nipah and Ebola viruses. These recent successful efforts demonstrate that HTS can be safely conducted at the highest levels of biological containment. This review outlines the specific issues that must be considered in the execution of an HTS drug discovery program for high-containment pathogens. We present an overview of the requirements for HTS in high-level biocontainment laboratories. PMID:25710545

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.

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

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 discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

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

ATOM - Accelerating therapeutics through opportunities in medicine

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

Mcmahon, Benjamin Hamilton; Dotson, Paul Jeffrey

Create a new paradigm of drug discovery that would reduce the time from an identified drug target to clinical candidate from the current ~6 years to just 12 months. ATOM will develop, test, and validate a multidisciplinary approach to drug discovery in which modern science, technology and engineering, supercomputing, simulations, data science, and artificial intelligence are highly integrated into a single drug-discovery platform that can ultimately be shared with the drug development community at-large.

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.

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.

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.

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.

A Fragment-Based Method of Creating Small-Molecule Libraries to Target the Aggregation of Intrinsically Disordered Proteins.

PubMed

Joshi, Priyanka; Chia, Sean; Habchi, Johnny; Knowles, Tuomas P J; Dobson, Christopher M; Vendruscolo, Michele

2016-03-14

The aggregation process of intrinsically disordered proteins (IDPs) has been associated with a wide range of neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. Currently, however, no drug in clinical use targets IDP aggregation. To facilitate drug discovery programs in this important and challenging area, we describe a fragment-based approach of generating small-molecule libraries that target specific IDPs. The method is based on the use of molecular fragments extracted from compounds reported in the literature to inhibit of the aggregation of IDPs. These fragments are used to screen existing large generic libraries of small molecules to form smaller libraries specific for given IDPs. We illustrate this approach by describing three distinct small-molecule libraries to target, Aβ, tau, and α-synuclein, which are three IDPs implicated in Alzheimer's and Parkinson's diseases. The strategy described here offers novel opportunities for the identification of effective molecular scaffolds for drug discovery for neurodegenerative disorders and to provide insights into the mechanism of small-molecule binding to IDPs.

Big data to smart data in Alzheimer's disease: The brain health modeling initiative to foster actionable knowledge.

PubMed

Geerts, Hugo; Dacks, Penny A; Devanarayan, Viswanath; Haas, Magali; Khachaturian, Zaven S; Gordon, Mark Forrest; Maudsley, Stuart; Romero, Klaus; Stephenson, Diane

2016-09-01

Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These "big-data" databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Identification of shikimate kinase inhibitors among anti-Mycobacterium tuberculosis compounds by LC-MS.

PubMed

Simithy, Johayra; Reeve, Nathaniel; Hobrath, Judith V; Reynolds, Robert C; Calderón, Angela I

2014-03-01

Increasing drug resistance has challenged the control and treatment of tuberculosis, sparking recent interest in finding new antitubercular agents with different chemical scaffolds and mechanisms of action. Mycobacterium tuberculosis shikimate kinase (MtSK), an enzyme present in the shikimate pathway in bacteria, is essential for the survival of the tubercle bacillus, representing an ideal target for therapeutic intervention given its absence in mammals. In this study, a small library of 404 synthetic antimycobacterial compounds identified and supplied through the NIH Tuberculosis Antimicrobial Acquisition and Coordinating Facility (TAACF) high throughput screening program against whole cell M. tuberculosis H37Rv was further screened using a mass spectrometry-based functional assay in order to identify a potential enzymatic target. Fourteen compounds containing an oxadiazole-amide or a 2-aminobenzothiazole core scaffold showed MtSK inhibitory activity at 50 μM, with the lowest giving an IC50 of 1.94 μM. Induced fit docking studies suggested that the scaffolds shared by these compounds fit well in the shikimate binding pocket of MtSK. In summary, we report new early discovery stage lead scaffolds targeting the essential protein MtSK that can be further pursued in a rational drug design program for the discovery of more selective antitubercular drugs. Copyright © 2014 Elsevier Ltd. All rights reserved.

New Perspectives on How to Discover Drugs from Herbal Medicines: CAM's Outstanding Contribution to Modern Therapeutics

PubMed Central

Pan, Si-Yuan; Zhou, Shu-Feng; Gao, Si-Hua; Yu, Zhi-Ling; Zhang, Shuo-Feng; Tang, Min-Ke; Sun, Jian-Ning; Han, Yi-Fan; Fong, Wang-Fun; Ko, Kam-Ming

2013-01-01

With tens of thousands of plant species on earth, we are endowed with an enormous wealth of medicinal remedies from Mother Nature. Natural products and their derivatives represent more than 50% of all the drugs in modern therapeutics. Because of the low success rate and huge capital investment need, the research and development of conventional drugs are very costly and difficult. Over the past few decades, researchers have focused on drug discovery from herbal medicines or botanical sources, an important group of complementary and alternative medicine (CAM) therapy. With a long history of herbal usage for the clinical management of a variety of diseases in indigenous cultures, the success rate of developing a new drug from herbal medicinal preparations should, in theory, be higher than that from chemical synthesis. While the endeavor for drug discovery from herbal medicines is “experience driven,” the search for a therapeutically useful synthetic drug, like “looking for a needle in a haystack,” is a daunting task. In this paper, we first illustrated various approaches of drug discovery from herbal medicines. Typical examples of successful drug discovery from botanical sources were given. In addition, problems in drug discovery from herbal medicines were described and possible solutions were proposed. The prospect of drug discovery from herbal medicines in the postgenomic era was made with the provision of future directions in this area of drug development. PMID:23634172

Accessible high-throughput virtual screening molecular docking software for students and educators.

PubMed

Jacob, Reed B; Andersen, Tim; McDougal, Owen M

2012-05-01

We survey low cost high-throughput virtual screening (HTVS) computer programs for instructors who wish to demonstrate molecular docking in their courses. Since HTVS programs are a useful adjunct to the time consuming and expensive wet bench experiments necessary to discover new drug therapies, the topic of molecular docking is core to the instruction of biochemistry and molecular biology. The availability of HTVS programs coupled with decreasing costs and advances in computer hardware have made computational approaches to drug discovery possible at institutional and non-profit budgets. This paper focuses on HTVS programs with graphical user interfaces (GUIs) that use either DOCK or AutoDock for the prediction of DockoMatic, PyRx, DockingServer, and MOLA since their utility has been proven by the research community, they are free or affordable, and the programs operate on a range of computer platforms.

The Poster Visits Nottingham Castle in England | Poster

Cancer.gov

By Nancy Parrish, Staff Writer Last September, Nadya Tarasova, Ph.D., head, Synthetic Biologics and Drug Discovery Facility, Cancer and Inflammation Program, traveled to Nottingham, England, where she was an invited speaker and chaired a session on JAK/STAT signaling in cancer at the second special meeting of the Federation of European Biochemical Societies (FEBS).

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 model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

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.

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.

Case study: impact of technology investment on lead discovery at Bristol-Myers Squibb, 1998-2006.

PubMed

Houston, John G; Banks, Martyn N; Binnie, Alastair; Brenner, Stephen; O'Connell, Jonathan; Petrillo, Edward W

2008-01-01

We review strategic approaches taken over an eight-year period at BMS to implement new high-throughput approaches to lead discovery. Investments in compound management infrastructure and chemistry library production capability allowed significant growth in the size, diversity and quality of the BMS compound collection. Screening platforms were upgraded with robust automated technology to support miniaturized assay formats, while workflows and information handling technologies were streamlined for improved performance. These technology changes drove the need for a supporting organization in which critical engineering, informatics and scientific skills were more strongly represented. Taken together, these investments led to significant improvements in speed and productivity as well a greater impact of screening campaigns on the initiation of new drug discovery programs.

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.

"Seeing is believing": perspectives of applying imaging technology in discovery toxicology.

PubMed

Xu, Jinghai James; Dunn, Margaret Condon; Smith, Arthur Russell

2009-11-01

Efficiency and accuracy in addressing drug safety issues proactively are critical in minimizing late-stage drug attritions. Discovery toxicology has become a specialty subdivision of toxicology seeking to effectively provide early predictions and safety assessment in the drug discovery process. Among the many technologies utilized to select safer compounds for further development, in vitro imaging technology is one of the best characterized and validated to provide translatable biomarkers towards clinically-relevant outcomes of drug safety. By carefully applying imaging technologies in genetic, hepatic, and cardiac toxicology, and integrating them with the rest of the drug discovery processes, it was possible to demonstrate significant impact of imaging technology on drug research and development and substantial returns on investment.

QPatch: the missing link between HTS and ion channel drug discovery.

PubMed

Mathes, Chris; Friis, Søren; Finley, Michael; Liu, Yi

2009-01-01

The conventional patch clamp has long been considered the best approach for studying ion channel function and pharmacology. However, its low throughput has been a major hurdle to overcome for ion channel drug discovery. The recent emergence of higher throughput, automated patch clamp technology begins to break this bottleneck by providing medicinal chemists with high-quality, information-rich data in a more timely fashion. As such, these technologies have the potential to bridge a critical missing link between high-throughput primary screening and meaningful ion channel drug discovery programs. One of these technologies, the QPatch automated patch clamp system developed by Sophion Bioscience, records whole-cell ion channel currents from 16 or 48 individual cells in a parallel fashion. Here, we review the general applicability of the QPatch to studying a wide variety of ion channel types (voltage-/ligand-gated cationic/anionic channels) in various expression systems. The success rate of gigaseals, formation of the whole-cell configuration and usable cells ranged from 40-80%, depending on a number of factors including the cell line used, ion channel expressed, assay development or optimization time and expression level in these studies. We present detailed analyses of the QPatch features and results in case studies in which secondary screening assays were successfully developed for a voltage-gated calcium channel and a ligand-gated TRP channel. The increase in throughput compared to conventional patch clamp with the same cells was approximately 10-fold. We conclude that the QPatch, combining high data quality and speed with user friendliness and suitability for a wide array of ion channels, resides on the cutting edge of automated patch clamp technology and plays a pivotal role in expediting ion channel drug discovery.

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.

The discovery of a class of novel HIV-1 maturation inhibitors and their potential in the therapy of HIV.

PubMed

Yu, Donglei; Wild, Carl T; Martin, David E; Morris-Natschke, Susan L; Chen, Chin-Ho; Allaway, Graham P; Lee, Kuo-Hsiung

2005-06-01

Although HIV infection is now primarily treated with reverse transcriptase and protease inhibitors, HIV therapy must look toward new drugs with novel mechanism(s) of action to both improve efficacy and address the growing problem of drug resistance. Using natural products as a source of biologically active compounds, our drug discovery program has successfully optimised the natural product betulinic acid to the first-in-class maturation inhibitor 3-O-(3',3'-dimethylsuccinyl)-betulinic acid (DSB). DSB's unique viral target has been identified as a late step in Gag processing. Specifically, it inhibits the cleavage of the capsid precursor, CA-SP1, resulting in a block to the processing of mature capsid protein leading to a defect in viral core condensation. DSB represents a unique class of anti-HIV compounds that inhibit virus maturation and provide additional opportunities for anti-HIV therapy. In this review, the discovery of DSB and its mode of action are summarised. Anti-AIDS Agents part 64. For part 63 in the series, see YU D, LEE KH: Recent progress and prospects on plant-derived anti-HIV agents and analogs. In: Medicinal Chemistry of Bioactive Natural Products. XT Liang, WS Fang (Eds), Wiley, New York, USA (2005) (In Press).

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

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.

Low hanging fruit in infectious disease drug development.

PubMed

Kraus, Carl N

2008-10-01

Cost estimates for developing new molecular entities (NME) are reaching non-sustainable levels and coupled with increasing regulatory requirements and oversight have led many pharmaceutical sponsors to divest their anti-microbial development portfolios [Projan SJ: Why is big Pharma getting out of anti-bacterial drug discovery?Curr Opin Microbiol 2003, 6:427-430] [Spellberg B, Powers JH, Brass EP, Miller LG, Edwards JE, Jr: Trends in antimicrobial drug development: implications for the future.Clin Infect Dis 2004, 38:1279-1286]. Operational issues such as study planning and execution are significant contributors to the overall cost of drug development that can benefit from the leveraging of pre-randomization data in an evidence-based approach to protocol development, site selection and patient recruitment. For non-NME products there is even greater benefit from available data resources since these data may permit smaller and shorter study programs. There are now many available open source intelligence (OSINT) resources that are being integrated into drug development programs, permitting an evidence-based or 'operational epidemiology' approach to study planning and execution.

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.

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.

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

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.

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.

The drug-induced degradation of oncoproteins: an unexpected Achilles' heel of cancer cells?

PubMed

Ablain, Julien; Nasr, Rihab; Bazarbachi, Ali; de Thé, Hugues

2011-07-01

Many targeted therapies against cancer are aimed at inhibiting the enzymatic activity of kinases. Thus far, this approach has undoubtedly yielded significant clinical improvements, but has only rarely achieved cures. Other drugs, which selectively elicit proteasome-dependent degradation of oncoproteins, induce the loss of cancer cell self-renewal and promote cell differentiation and/or apoptosis. In acute promyelocytic leukemia, the cooperative degradation of PML/RARA by arsenic and retinoic acid cures most patients. In this condition and others, drug-induced proteolysis of oncoproteins is feasible and underlies improved clinical outcome. Several transcription factors, nuclear receptors, or fusion proteins driving cancer growth could be candidates for proteolysis-based drug-discovery programs.

Pharmacogenomics to Revive Drug Development in Cardiovascular Disease.

PubMed

Dubé, Marie-Pierre; de Denus, Simon; Tardif, Jean-Claude

2016-02-01

Investment in cardiovascular drug development is on the decline as large cardiovascular outcomes trials require considerable investments in time, efforts and financial resources. Pharmacogenomics has the potential to help revive the cardiovascular drug development pipeline by providing new and better drug targets at an earlier stage and by enabling more efficient outcomes trials. This article will review some of the recent developments highlighting the value of pharmacogenomics for drug development. We discuss how genetic biomarkers can enable the conduct of more efficient clinical outcomes trials by enriching patient populations for good responders to the medication. In addition, we assess past drug development programs which support the added value of selecting drug targets that have established genetic evidence supporting the targeted mechanism of disease. Finally, we discuss how pharmacogenomics can provide valuable evidence linking a drug target to clinically relevant outcomes, enabling novel drug discovery and drug repositioning opportunities.

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…

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.

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.

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.

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.

Open-source approaches for the repurposing of existing or failed candidate drugs: learning from and applying the lessons across diseases

PubMed Central

Allarakhia, Minna

2013-01-01

Repurposing has the objective of targeting existing drugs and failed, abandoned, or yet-to-be-pursued clinical candidates to new disease areas. The open-source model permits for the sharing of data, resources, compounds, clinical molecules, small libraries, and screening platforms to cost-effectively advance old drugs and/or candidates into clinical re-development. Clearly, at the core of drug-repurposing activities is collaboration, in many cases progressing beyond the open sharing of resources, technology, and intellectual property, to the sharing of facilities and joint program development to foster drug-repurposing human-capacity development. A variety of initiatives under way for drug repurposing, including those targeting rare and neglected diseases, are discussed in this review and provide insight into the stakeholders engaged in drug-repurposing discovery, the models of collaboration used, the intellectual property-management policies crafted, and human capacity developed. In the case of neglected tropical diseases, it is suggested that the development of human capital be a central aspect of drug-repurposing programs. Open-source models can support human-capital development through collaborative data generation, open compound access, open and collaborative screening, preclinical and possibly clinical studies. Given the urgency of drug development for neglected tropical diseases, the review suggests elements from current repurposing programs be extended to the neglected tropical diseases arena. PMID:23966771

Open-source approaches for the repurposing of existing or failed candidate drugs: learning from and applying the lessons across diseases.

PubMed

Allarakhia, Minna

2013-01-01

Repurposing has the objective of targeting existing drugs and failed, abandoned, or yet-to-be-pursued clinical candidates to new disease areas. The open-source model permits for the sharing of data, resources, compounds, clinical molecules, small libraries, and screening platforms to cost-effectively advance old drugs and/or candidates into clinical re-development. Clearly, at the core of drug-repurposing activities is collaboration, in many cases progressing beyond the open sharing of resources, technology, and intellectual property, to the sharing of facilities and joint program development to foster drug-repurposing human-capacity development. A variety of initiatives under way for drug repurposing, including those targeting rare and neglected diseases, are discussed in this review and provide insight into the stakeholders engaged in drug-repurposing discovery, the models of collaboration used, the intellectual property-management policies crafted, and human capacity developed. In the case of neglected tropical diseases, it is suggested that the development of human capital be a central aspect of drug-repurposing programs. Open-source models can support human-capital development through collaborative data generation, open compound access, open and collaborative screening, preclinical and possibly clinical studies. Given the urgency of drug development for neglected tropical diseases, the review suggests elements from current repurposing programs be extended to the neglected tropical diseases arena.

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

PubMed Central

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

2016-01-01

A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemistry programs. The data for all of these assays are presented and analyzed to show how outstanding leads for many indications can be selected. These results reveal the immense potential for translating the dispersed expertise in biological assays involving human pathogens into drug discovery starting points, by providing open access to new families of molecules, and emphasize how a small additional investment made to help acquire and distribute compounds, and sharing the data, can catalyze drug discovery for dozens of different indications. Another lesson is that when multiple screens from different groups are run on the same library, results can be integrated quickly to select the most valuable starting points for subsequent medicinal chemistry efforts. PMID:27467575

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

PubMed

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

2016-07-01

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

Undergraduate Program: New Orleans

NASA Astrophysics Data System (ADS)

Betsock, Lori

2008-03-01

Undergraduate chemical science students—join us in New Orleans on April 6-7, 2008 for an educational program designed specifically for you. Attend symposia on chemistry in sports and health and learn how it impacts your life everyday; meet with graduate school recruiters. Focus on your professional future in chemistry by learning more about careers in public health and how to communicate and work effectively with cross-functional teams. Hear eminent scientist Richard B. Silverman (John Evans Professor of Chemistry, Northwestern University and author of The Organic Chemistry of Drug Design and Drug Action 2004) speak about "Drug Discovery: Ingenuity or Serendipity?" All events will take place at the Hilton Riverside Hotel in New Orleans, except the Undergraduate Research Poster Sessions and Sci-Mix, both of which will be held in Hall A of the Ernest N. Morial Convention Center.

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.

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.

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

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.

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

Discovery and development of anticancer agents from marine sponges: perspectives based on a chemistry-experimental therapeutics collaborative program.

PubMed

Valeriote, Frederick A; Tenney, Karen; Media, Joseph; Pietraszkiewicz, Halina; Edelstein, Matthew; Johnson, Tyler A; Amagata, Taro; Crews, Phillip

2012-01-01

A collaborative program was initiated in 1990 between the natural product chemistry laboratory of Dr. Phillip Crews at the University of California Santa Cruz and the experimental therapeutics laboratory of Dr. Fred Valeriote at the Henry Ford Hospital in Detroit. The program focused on the discovery and development of anticancer drugs from sponge extracts. A novel in vitro disk diffusion, solid tumor selective assay was used to examine 2,036 extracts from 683 individual sponges. The bioassay-directed fractionation discovery component led to the identification of active pure compounds from many of these sponges. In most cases, pure compound was prepared in sufficient quantities to both chemically identify the active compound(s) as well as pursue one or more of the biological development components. The latter included IC50, clonogenic survival-concentration exposure, maximum tolerated dose, pharmacokinetics and therapeutic assessment studies. Solid tumor selective compounds included fascaplysin and 10-bromofascaplysin (Fascaplysinopsis), neoamphimedine, 5-methoxyneoamphimedine and alpkinidine (Xestospongia), makaluvamine C and makaluvamine H (Zyzzya), psymberin (Psammocinia and Ircinia), and ethylplakortide Z and ethyldidehydroplakortide Z (Plakortis). These compounds or analogs thereof continue to have therapeutic potential.

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.

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.

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.

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.

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

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.

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

Quantum mechanics implementation in drug-design workflows: does it really help?

PubMed

Arodola, Olayide A; Soliman, Mahmoud Es

2017-01-01

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug-protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry?

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.

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.

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

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.

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.

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

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.

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

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.

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.

Genotyping for Severe Drug Hypersensitivity

PubMed Central

Karlin, Eric; Phillips, Elizabeth

2014-01-01

Over the past decade, there have been significant advances in our understanding of the immunopathogenesis and pharmacogenomics of severe immunologically-mediated adverse drug reactions. Such T-cell-mediated adverse drug reactions such as Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN), drug-induced liver disease (DILI) and other drug hypersensitivity syndromes have more recently been shown to be mediated through interactions with various class I and II HLA alleles. Key examples have included the associations of HLA-B*15:02 and carbamazepine induced SJS/TEN in Southeast Asian populations and HLA-B*57:01 and abacavir hypersensitivity. HLA-B*57:01 screening to prevent abacavir hypersensitivity exemplifies a successful translational roadmap from pharmacogenomic discovery through to widespread clinical implementation. Ultimately, our increased understanding of the interaction between drugs and the MHC could be used to inform drug design and drive pre-clinical toxicity programs to improve drug safety. PMID:24429903

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

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

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.

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.

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

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.

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.

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.

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.

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.

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

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.

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

PubMed

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

2018-08-01

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

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

PubMed

Hao, Ming; Bryant, Stephen H; Wang, Yanli

2018-02-06

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

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.

How to translate a bioassay into a screening assay for natural products: general considerations and implementation of antimicrobial screens.

PubMed

Fallarero, Adyary; Hanski, Leena; Vuorela, Pia

2014-09-01

Natural product sources have been a valuable provider of molecular diversity in many drug discovery programs and several therapeutically important drugs have been isolated from these. However, the screening of such materials can be very complicated due to the fact that they contain a complex mixture of secondary metabolites, but also the purified natural compounds exert a challenge for bioactivity screening. Success in identifying new therapeutics using in vitro bioassays is largely dependent upon the proper design, validation, and implementation of the screening assay. In this review, we discuss some aspects which are of significant concern when screening natural products in a microtiter plate-based format, being partly applicable to other assay formats as well, such as validation parameters, layouts for assay protocols, and common interferences caused by natural products samples, as well as various troubleshooting strategies. Examples from the field of natural product drug discovery of antibacterial compounds are discussed, and contributions from the realm of academic screenings are highlighted. Georg Thieme Verlag KG Stuttgart · New York.

"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.

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.

[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.

Drug Repositioning for Effective Prostate Cancer Treatment.

PubMed

Turanli, Beste; Grøtli, Morten; Boren, Jan; Nielsen, Jens; Uhlen, Mathias; Arga, Kazim Y; Mardinoglu, Adil

2018-01-01

Drug repositioning has gained attention from both academia and pharmaceutical companies as an auxiliary process to conventional drug discovery. Chemotherapeutic agents have notorious adverse effects that drastically reduce the life quality of cancer patients so drug repositioning is a promising strategy to identify non-cancer drugs which have anti-cancer activity as well as tolerable adverse effects for human health. There are various strategies for discovery and validation of repurposed drugs. In this review, 25 repurposed drug candidates are presented as result of different strategies, 15 of which are already under clinical investigation for treatment of prostate cancer (PCa). To date, zoledronic acid is the only repurposed, clinically used, and approved non-cancer drug for PCa. Anti-cancer activities of existing drugs presented in this review cover diverse and also known mechanisms such as inhibition of mTOR and VEGFR2 signaling, inhibition of PI3K/Akt signaling, COX and selective COX-2 inhibition, NF-κB inhibition, Wnt/β-Catenin pathway inhibition, DNMT1 inhibition, and GSK-3β inhibition. In addition to monotherapy option, combination therapy with current anti-cancer drugs may also increase drug efficacy and reduce adverse effects. Thus, drug repositioning may become a key approach for drug discovery in terms of time- and cost-efficiency comparing to conventional drug discovery and development process.

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

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

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

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

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.

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

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.

Discovery and resupply of pharmacologically active plant-derived natural products: A review.

PubMed

Atanasov, Atanas G; Waltenberger, Birgit; Pferschy-Wenzig, Eva-Maria; Linder, Thomas; Wawrosch, Christoph; Uhrin, Pavel; Temml, Veronika; Wang, Limei; Schwaiger, Stefan; Heiss, Elke H; Rollinger, Judith M; Schuster, Daniela; Breuss, Johannes M; Bochkov, Valery; Mihovilovic, Marko D; Kopp, Brigitte; Bauer, Rudolf; Dirsch, Verena M; Stuppner, Hermann

2015-12-01

Medicinal plants have historically proven their value as a source of molecules with therapeutic potential, and nowadays still represent an important pool for the identification of novel drug leads. In the past decades, pharmaceutical industry focused mainly on libraries of synthetic compounds as drug discovery source. They are comparably easy to produce and resupply, and demonstrate good compatibility with established high throughput screening (HTS) platforms. However, at the same time there has been a declining trend in the number of new drugs reaching the market, raising renewed scientific interest in drug discovery from natural sources, despite of its known challenges. In this survey, a brief outline of historical development is provided together with a comprehensive overview of used approaches and recent developments relevant to plant-derived natural product drug discovery. Associated challenges and major strengths of natural product-based drug discovery are critically discussed. A snapshot of the advanced plant-derived natural products that are currently in actively recruiting clinical trials is also presented. Importantly, the transition of a natural compound from a "screening hit" through a "drug lead" to a "marketed drug" is associated with increasingly challenging demands for compound amount, which often cannot be met by re-isolation from the respective plant sources. In this regard, existing alternatives for resupply are also discussed, including different biotechnology approaches and total organic synthesis. While the intrinsic complexity of natural product-based drug discovery necessitates highly integrated interdisciplinary approaches, the reviewed scientific developments, recent technological advances, and research trends clearly indicate that natural products will be among the most important sources of new drugs also in the future. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

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.

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'.

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.

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.

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

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.

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.

Strategies for target identification of antimicrobial natural products.

PubMed

Farha, Maya A; Brown, Eric D

2016-05-04

Covering: 2000 to 2015Despite a pervasive decline in natural product research at many pharmaceutical companies over the last two decades, natural products have undeniably been a prolific and unsurpassed source for new lead antibacterial compounds. Due to their inherent complexity, natural extracts face several hurdles in high-throughout discovery programs, including target identification. Target identification and validation is a crucial process for advancing hits through the discovery pipeline, but has remained a major bottleneck. In the case of natural products, extremely low yields and limited compound supply further impede the process. Here, we review the wealth of target identification strategies that have been proposed and implemented for the characterization of novel antibacterials. Traditionally, these have included genomic and biochemical-based approaches, which, in recent years, have been improved with modern-day technology and better honed for natural product discovery. Further, we discuss the more recent innovative approaches for uncovering the target of new antibacterial natural products, which have resulted from modern advances in chemical biology tools. Finally, we present unique screening platforms implemented to streamline the process of target identification. The different innovative methods to respond to the challenge of characterizing the mode of action for antibacterial natural products have cumulatively built useful frameworks that may advocate a renovated interest in natural product drug discovery programs.

National Heart, Lung, and Blood Institute and the translation of cardiovascular discoveries into therapeutic approaches.

PubMed

Galis, Zorina S; Black, Jodi B; Skarlatos, Sonia I

2013-04-26

The molecular causes of ≈4000 medical conditions have been described, yet only 5% have associated therapies. For decades, the average time for drug development through approval has taken 10 to 20 years. In recent years, the serious challenges that confront the private sector have made it difficult to capitalize on new opportunities presented by advances in genomics and cellular therapies. Current trends are disturbing. Pharmaceutical companies are reducing their investments in research, and biotechnology companies are struggling to obtain venture funds. To support early-stage translation of the discoveries in basic science, the National Institutes of Health and the National Heart, Lung, and Blood Institute have developed new approaches to facilitating the translation of basic discoveries into clinical applications and will continue to develop a variety of programs that create teams of academic investigators and industry partners. The goal of these programs is to maximize the public benefit of investment of taxpayer dollars in biomedical research and to lessen the risk required for industry partners to make substantial investments. This article highlights several examples of National Heart, Lung, and Blood Institute-initiated translational programs and National Institutes of Health translational resources designed to catalyze and enable the earliest stages of the biomedical product development process. The translation of latest discoveries into therapeutic approaches depends on continued federal funding to enhance the early stages of the product development process and to stimulate and catalyze partnerships between academia, industry, and other sources of capital.

Changing paradigm from one target one ligand towards multi target directed ligand design for key drug targets of Alzheimer disease: An important role of Insilco methods in multi target directed ligands design.

PubMed

Kumar, Akhil; Tiwari, Ashish; Sharma, Ashok

2018-03-15

Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis is not able to provide complete solution of AD due to multifactorial nature of disease and one target one drug seems to fail to provide better treatment against AD. Moreover, current available treatments are limited and most of the upcoming treatments under clinical trials are based on modulating single target. So the current AD drug discovery research shifting towards new approach for better solution that simultaneously modulate more than one targets in the neurodegenerative cascade. This can be achieved by network pharmacology, multi-modal therapies, multifaceted, and/or the more recently proposed term "multi-targeted designed drugs. Drug discovery project is tedious, costly and long term project. Moreover, multi target AD drug discovery added extra challenges such as good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off target side effect and crossing of the blood brain barrier. These hurdles may be addressed by insilico methods for efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here we are summarizing some of the most prominent and computationally explored single target against AD and further we discussed successful example of dual or multiple inhibitors for same targets. Moreover we focused on ligand and structure based computational approach to design MTDL against AD. However is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy are useful in future MTDLs drug discovery alone or in combination with fragment based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug discovery and play an important role in optimizing multi-target drug discovery. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Discovery and resupply of pharmacologically active plant-derived natural products: A review

PubMed Central

Linder, Thomas; Wawrosch, Christoph; Uhrin, Pavel; Temml, Veronika; Wang, Limei; Schwaiger, Stefan; Heiss, Elke H.; Rollinger, Judith M.; Schuster, Daniela; Breuss, Johannes M.; Bochkov, Valery; Mihovilovic, Marko D.; Kopp, Brigitte; Bauer, Rudolf; Dirsch, Verena M.; Stuppner, Hermann

2016-01-01

Medicinal plants have historically proven their value as a source of molecules with therapeutic potential, and nowadays still represent an important pool for the identification of novel drug leads. In the past decades, pharmaceutical industry focused mainly on libraries of synthetic compounds as drug discovery source. They are comparably easy to produce and resupply, and demonstrate good compatibility with established high throughput screening (HTS) platforms. However, at the same time there has been a declining trend in the number of new drugs reaching the market, raising renewed scientific interest in drug discovery from natural sources, despite of its known challenges. In this survey, a brief outline of historical development is provided together with a comprehensive overview of used approaches and recent developments relevant to plant-derived natural product drug discovery. Associated challenges and major strengths of natural product-based drug discovery are critically discussed. A snapshot of the advanced plant-derived natural products that are currently in actively recruiting clinical trials is also presented. Importantly, the transition of a natural compound from a “screening hit” through a “drug lead” to a “marketed drug” is associated with increasingly challenging demands for compound amount, which often cannot be met by re-isolation from the respective plant sources. In this regard, existing alternatives for resupply are also discussed, including different biotechnology approaches and total organic synthesis. While the intrinsic complexity of natural product-based drug discovery necessitates highly integrated interdisciplinary approaches, the reviewed scientific developments, recent technological advances, and research trends clearly indicate that natural products will be among the most important sources of new drugs also in the future. PMID:26281720

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.

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.

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

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.

From laptop to benchtop to bedside: Structure-based Drug Design on Protein Targets

PubMed Central

Chen, Lu; Morrow, John K.; Tran, Hoang T.; Phatak, Sharangdhar S.; Du-Cuny, Lei; Zhang, Shuxing

2013-01-01

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting protein-ligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. PMID:22316152

[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.

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.

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.

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.

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.

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.

Integrated quantitative and qualitative workflow for in vivo bioanalytical support in drug discovery using hybrid Q-TOF-MS.

PubMed

Ranasinghe, Asoka; Ramanathan, Ragu; Jemal, Mohammed; D'Arienzo, Celia J; Humphreys, W Griffith; Olah, Timothy V

2012-03-01

UHPLC coupled with orthogonal acceleration hybrid quadrupole-TOF (Q-TOF)-MS is an emerging technique offering new strategies for the efficient screening of new chemical entities and related molecules at the early discovery stage within the pharmaceutical industry. In the first part of this article, we examine the main instrumental parameters that are critical for the integration of UHPLC-Q-TOF technology to existing bioanalytical workflows, in order to provide simultaneous quantitative and qualitative bioanalysis of samples generated following in vivo studies. Three modern Q-TOF mass spectrometers, including Bruker maXis™, Agilent 6540 and Sciex TripleTOF™ 5600, all interfaced with UHPLC systems, are evaluated in the second part of the article. The scope of this work is to demonstrate the potential of Q-TOF for the analysis of typical small molecules, therapeutic peptides (molecular weight <6000 Da), and enzymatically (i.e., trypsin, chymotrypsin and pepsin) cleaved peptides from larger proteins. This work focuses mainly on full-scan TOF data obtained under ESI conditions, the major mode of TOF operation in discovery bioanalytical research, where the compounds are selected based on their pharmacokinetic/pharmacodynamic behaviors using animal models prior to selecting a few desirable candidates for further development. Finally, important emerging TOF technologies that could potentially benefit bioanalytical research in the semi-quantification of metabolites without synthesized standards are discussed. Particularly, the utility of captive spray ionization coupled with TripleTOF 5600 was evaluated for improving sensitivity and providing normalized MS response for drugs and their metabolites. The workflow proposed compromises neither the efficiency, nor the quality of pharmacokinetic data in support of early drug discovery programs.

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.

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.

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

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

Quantitative Systems Pharmacology: A Case for Disease Models

PubMed Central

Ramanujan, S; Schmidt, BJ; Ghobrial, OG; Lu, J; Heatherington, AC

2016-01-01

Quantitative systems pharmacology (QSP) has emerged as an innovative approach in model‐informed drug discovery and development, supporting program decisions from exploratory research through late‐stage clinical trials. In this commentary, we discuss the unique value of disease‐scale “platform” QSP models that are amenable to reuse and repurposing to support diverse clinical decisions in ways distinct from other pharmacometrics strategies. PMID:27709613

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

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.

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.

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.

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.

Computational prediction of chemical reactions: current status and outlook.

PubMed

Engkvist, Ola; Norrby, Per-Ola; Selmi, Nidhal; Lam, Yu-Hong; Peng, Zhengwei; Sherer, Edward C; Amberg, Willi; Erhard, Thomas; Smyth, Lynette A

2018-06-01

Over the past few decades, various computational methods have become increasingly important for discovering and developing novel drugs. Computational prediction of chemical reactions is a key part of an efficient drug discovery process. In this review, we discuss important parts of this field, with a focus on utilizing reaction data to build predictive models, the existing programs for synthesis prediction, and usage of quantum mechanics and molecular mechanics (QM/MM) to explore chemical reactions. We also outline potential future developments with an emphasis on pre-competitive collaboration opportunities. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

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.

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.

The path to producing pharmaceuticals from natural products uncovered by academia-from the perspective of a science coordinator.

PubMed

Fujie, Akihiko

2017-01-01

To actualize the invention of all-Japanese medicines, the Department of Innovative Drug Discovery and Development (iD3) in the Japan Agency for Medical Research and Development (AMED) serves as the headquarters for the Drug Discovery Support Network. iD3 assists with creating research strategies for the seeds of medicines discovered by academia and provides technological support, intellectual property management, and aid for applying the seeds through industry-led efforts. In this review, from the perspective of a science coordinator, I will describe the current activities of the drug discovery support network and iD3 as well as the challenges and future developments of pharmaceutical research and development using the natural product drug discovery method.

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.

Prediction on the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase based on gene expression programming.

PubMed

Li, Yuqin; You, Guirong; Jia, Baoxiu; Si, Hongzong; Yao, Xiaojun

2014-01-01

Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R (2)) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs.

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.

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.

Discovery of Novel Drugs to Improve Bone Health in Neurofibromatosis Type 1: The Wnt/Beta-Catenin Pathway in Fracture Repair and Pseudarthrosis

DTIC Science & Technology

2015-08-01

AWARD NUMBER: W81XWH-13-1-0113 TITLE: Discovery of Novel Drugs To Improve Bone Health in Neurofibromatosis Type 1: The Wnt/Beta-Catenin...Discovery of Novel Drugs To Improve Bone Health in Neurofibromatosis Type 1: The Wnt/Beta-Catenin Pathway in Fracture Repair and Pseudarthrosis 5a...AVAILABILITY STATEMENT Approved for Public Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Patients with Neurofibromatosis (NF1

Discovery of Novel Drugs To Improve Bone Health in Neurofibromatosis Type 1: The Wnt/Beta-Catenin Pathway in Fracture Repair and Pseudarthrosis

DTIC Science & Technology

2014-06-01

Bone Health in Neurofibromatosis Type 1: The Wnt/Beta-Catenin Pathway in Fracture Repair and Pseudarthrosis PRINCIPAL INVESTIGATOR...Award Number: W81XWH-13-1-0113 TITLE: Discovery of Novel Drugs To Improve Bone Health in Neurofibromatosis Type 1: The Wnt/Beta-Catenin...31 May 2014 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Discovery of Novel Drugs To Improve Bone Health in Neurofibromatosis Type 1: The Wnt/Beta

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.

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.

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.

In silico and in vivo anti-malarial studies of 18β glycyrrhetinic acid from Glycyrrhiza glabra.

PubMed

Kalani, Komal; Agarwal, Jyoti; Alam, Sarfaraz; Khan, Feroz; Pal, Anirban; Srivastava, Santosh Kumar

2013-01-01

Malaria is one of the most prevailing fatal diseases causing between 1.2 and 2.7 million deaths all over the world each year. Further, development of resistance against the frontline anti-malarial drugs has created an alarming situation, which requires intensive drug discovery to develop new, more effective, affordable and accessible anti-malarial agents possessing novel modes of action. Over the past few years triterpenoids from higher plants have shown a wide range of anti-malarial activities. As a part of our drug discovery program for anti-malarial agents from Indian medicinal plants, roots of Glycyrrhizaglabra were chemically investigated, which resulted in the isolation and characterization of 18β-glycyrrhetinic acid (GA) as a major constituent. The in vitro studies against P. falciparum showed significant (IC50 1.69 µg/ml) anti-malarial potential for GA. Similarly, the molecular docking studies showed adequate docking (LibDock) score of 71.18 for GA and 131.15 for standard anti-malarial drug chloroquine. Further, in silico pharmacokinetic and drug-likeness studies showed that GA possesses drug-like properties. Finally, in vivo evaluation showed a dose dependent anti-malarial activity ranging from 68-100% at doses of 62.5-250 mg/kg on day 8. To the best of our knowledge this is the first ever report on the anti-malarial potential of GA. Further work on optimization of the anti-malarial lead is under progress.

In Silico and In Vivo Anti-Malarial Studies of 18β Glycyrrhetinic Acid from Glycyrrhiza glabra

PubMed Central

Kalani, Komal; Agarwal, Jyoti; Alam, Sarfaraz; Khan, Feroz; Pal, Anirban; Srivastava, Santosh Kumar

2013-01-01

Malaria is one of the most prevailing fatal diseases causing between 1.2 and 2.7 million deaths all over the world each year. Further, development of resistance against the frontline anti-malarial drugs has created an alarming situation, which requires intensive drug discovery to develop new, more effective, affordable and accessible anti-malarial agents possessing novel modes of action. Over the past few years triterpenoids from higher plants have shown a wide range of anti-malarial activities. As a part of our drug discovery program for anti-malarial agents from Indian medicinal plants, roots of Glycyrrhiza glabra were chemically investigated, which resulted in the isolation and characterization of 18β-glycyrrhetinic acid (GA) as a major constituent. The in vitro studies against P. falciparum showed significant (IC50 1.69µg/ml) anti-malarial potential for GA. Similarly, the molecular docking studies showed adequate docking (LibDock) score of 71.18 for GA and 131.15 for standard anti-malarial drug chloroquine. Further, in silico pharmacokinetic and drug-likeness studies showed that GA possesses drug-like properties. Finally, in vivo evaluation showed a dose dependent anti-malarial activity ranging from 68–100% at doses of 62.5–250mg/kg on day 8. To the best of our knowledge this is the first ever report on the anti-malarial potential of GA. Further work on optimization of the anti-malarial lead is under progress. PMID:24086367

Discovery of innovative therapeutics: today's realities and tomorrow's vision. 2. Pharma's challenges and their commitment to innovation.

PubMed

Abou-Gharbia, Magid; Childers, Wayne E

2014-07-10

The pharmaceutical industry is facing enormous challenges, including reduced efficiency, stagnant success rate, patent expirations for key drugs, fierce price competition from generics, high regulatory hurdles, and the industry's perceived tarnished image. Pharma has responded by embarking on a range of initiatives. Other sectors, including NIH, have also responded. Academic drug discovery groups have appeared to support the transition of innovative academic discoveries and ideas into attractive drug discovery opportunities. Part 1 of this two-part series discussed the criticisms that have been leveled at the pharmaceutical industry over the past 3 decades and summarized the supporting data for and against these criticisms. This second installment will focus on the current challenges facing the pharmaceutical industry and Pharma's responses, focusing on the industry's changing perspective and new business models for coping with the loss of talent and declining clinical pipelines as well as presenting some examples of recent drug discovery successes.

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.

Recent development in software and automation tools for high-throughput discovery bioanalysis.

PubMed

Shou, Wilson Z; Zhang, Jun

2012-05-01

Bioanalysis with LC-MS/MS has been established as the method of choice for quantitative determination of drug candidates in biological matrices in drug discovery and development. The LC-MS/MS bioanalytical support for drug discovery, especially for early discovery, often requires high-throughput (HT) analysis of large numbers of samples (hundreds to thousands per day) generated from many structurally diverse compounds (tens to hundreds per day) with a very quick turnaround time, in order to provide important activity and liability data to move discovery projects forward. Another important consideration for discovery bioanalysis is its fit-for-purpose quality requirement depending on the particular experiments being conducted at this stage, and it is usually not as stringent as those required in bioanalysis supporting drug development. These aforementioned attributes of HT discovery bioanalysis made it an ideal candidate for using software and automation tools to eliminate manual steps, remove bottlenecks, improve efficiency and reduce turnaround time while maintaining adequate quality. In this article we will review various recent developments that facilitate automation of individual bioanalytical procedures, such as sample preparation, MS/MS method development, sample analysis and data review, as well as fully integrated software tools that manage the entire bioanalytical workflow in HT discovery bioanalysis. In addition, software tools supporting the emerging high-resolution accurate MS bioanalytical approach are also discussed.

Using dried blood spot sampling to improve data quality and reduce animal use in mouse pharmacokinetic studies.

PubMed

Wickremsinhe, Enaksha R; Perkins, Everett J

2015-03-01

Traditional pharmacokinetic analysis in nonclinical studies is based on the concentration of a test compound in plasma and requires approximately 100 to 200 μL blood collected per time point. However, the total blood volume of mice limits the number of samples that can be collected from an individual animal-often to a single collection per mouse-thus necessitating dosing multiple mice to generate a pharmacokinetic profile in a sparse-sampling design. Compared with traditional methods, dried blood spot (DBS) analysis requires smaller volumes of blood (15 to 20 μL), thus supporting serial blood sampling and the generation of a complete pharmacokinetic profile from a single mouse. Here we compare plasma-derived data with DBS-derived data, explain how to adopt DBS sampling to support discovery mouse studies, and describe how to generate pharmacokinetic and pharmacodynamic data from a single mouse. Executing novel study designs that use DBS enhances the ability to identify and streamline better drug candidates during drug discovery. Implementing DBS sampling can reduce the number of mice needed in a drug discovery program. In addition, the simplicity of DBS sampling and the smaller numbers of mice needed translate to decreased study costs. Overall, DBS sampling is consistent with 3Rs principles by achieving reductions in the number of animals used, decreased restraint-associated stress, improved data quality, direct comparison of interanimal variability, and the generation of multiple endpoints from a single study.

Using Dried Blood Spot Sampling to Improve Data Quality and Reduce Animal Use in Mouse Pharmacokinetic Studies

PubMed Central

Wickremsinhe, Enaksha R; Perkins, Everett J

2015-01-01

Traditional pharmacokinetic analysis in nonclinical studies is based on the concentration of a test compound in plasma and requires approximately 100 to 200 µL blood collected per time point. However, the total blood volume of mice limits the number of samples that can be collected from an individual animal—often to a single collection per mouse—thus necessitating dosing multiple mice to generate a pharmacokinetic profile in a sparse-sampling design. Compared with traditional methods, dried blood spot (DBS) analysis requires smaller volumes of blood (15 to 20 µL), thus supporting serial blood sampling and the generation of a complete pharmacokinetic profile from a single mouse. Here we compare plasma-derived data with DBS-derived data, explain how to adopt DBS sampling to support discovery mouse studies, and describe how to generate pharmacokinetic and pharmacodynamic data from a single mouse. Executing novel study designs that use DBS enhances the ability to identify and streamline better drug candidates during drug discovery. Implementing DBS sampling can reduce the number of mice needed in a drug discovery program. In addition, the simplicity of DBS sampling and the smaller numbers of mice needed translate to decreased study costs. Overall, DBS sampling is consistent with 3Rs principles by achieving reductions in the number of animals used, decreased restraint-associated stress, improved data quality, direct comparison of interanimal variability, and the generation of multiple endpoints from a single study. PMID:25836959

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

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

Production and crystallization of recombinant JAK proteins.

PubMed

Lucet, Isabelle S; Bamert, Rebecca

2013-01-01

JAK kinases are critical mediators in development, differentiation, and homeostasis and accordingly, have become well-validated targets for drug discovery efforts. In recent years, the integration of X-ray crystallography in kinase-focused drug discovery programs has provided a powerful rationale for chemical modification by allowing a unique glimpse of a bound inhibitor to its target. Such structural information has not only led to an improved understanding of the key drivers of potency and specificity of several JAK-specific compounds but has greatly facilitated and accelerated the design of compounds with improved pharmacokinetic properties.JAK kinases are traditionally difficult candidates to express in significant quantities, generally requiring eukaryotic expression systems, protein engineering, mutations to yield soluble, homogeneous samples suitable for crystallization studies. Here we review the key methods utilized to express, purify, and crystallize the JAK kinases and provide a detail description of the methods that we have developed to express, purify, and crystallize recombinant JAK1 and JAK2 proteins in the presence of small molecule inhibitors.

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.

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

PubMed

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

2016-01-01

Human immunodeficiency virus (HIV) remains a global health problem. While combined antiretroviral therapy has been successful in controlling the virus in patients, HIV can develop resistance to drugs used for treatment, rendering available drugs less effective and limiting treatment options. Initiatives to find novel drugs for HIV treatment are ongoing, although traditional drug design approaches often focus on known binding sites for inhibition of established drug targets like reverse transcriptase and integrase. These approaches tend towards generating more inhibitors in the same drug classes already used in the clinic. Lack of diversity in antiretroviral drug classes can result in limited treatment options, as cross-resistance can emerge to a whole drug class in patients treated with only one drug from that class. A fresh approach in the search for new HIV-1 drugs is fragment-based drug discovery (FBDD), a validated strategy for drug discovery based on using smaller libraries of low molecular weight molecules (<300 Da) screened using primarily biophysical assays. FBDD is aimed at not only finding novel drug scaffolds, but also probing the target protein to find new, often allosteric, inhibitory binding sites. Several fragment-based strategies have been successful in identifying novel inhibitory sites or scaffolds for two proven drug targets for HIV-1, reverse transcriptase and integrase. While any FBDD-generated HIV-1 drugs have yet to enter the clinic, recent FBDD initiatives against these two well-characterised HIV-1 targets have reinvigorated antiretroviral drug discovery and the search for novel classes of HIV-1 drugs.

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.

Recent Advances and Perspectives in Cancer Drug Design.

PubMed

Magalhaes, Luma G; Ferreira, Leonardo L G; Andricopulo, Adriano D

2018-01-01

Cancer is one of the leading causes of death worldwide. With the increase in life expectancy, the number of cancer cases has reached unprecedented levels. In this scenario, the pharmaceutical industry has made significant investments in this therapeutic area. Despite these efforts, cancer drug research remains a remarkably challenging field, and therapeutic innovations have not yet achieved expected clinical results. However, the physiopathology of the disease is now better understood, and the discovery of novel molecular targets has refreshed the expectations of developing improved treatments. Several noteworthy advances have been made, among which the development of targeted therapies is the most significant. Monoclonal antibodies and antibody-small molecule conjugates have emerged as a worthwhile approach to improve drug selectivity and reduce adverse effects, which are the main challenges in cancer drug discovery. This review will examine the current panorama of drug research and development (R&D) with emphasis on some of the major advances brought to clinical trials and to the market in the past five years. Breakthrough discoveries will be highlighted along with the medicinal chemistry strategies used throughout the discovery process. In addition, this review will provide perspectives and updates on the discovery of novel molecular targets as well as drugs with innovative mechanisms of action.

NATURAL PRODUCTS: A CONTINUING SOURCE OF NOVEL DRUG LEADS

PubMed Central

Cragg, Gordon M.; Newman, David J.

2013-01-01

1. Background Nature has been a source of medicinal products for millennia, with many useful drugs developed from plant sources. Following discovery of the penicillins, drug discovery from microbial sources occurred and diving techniques in the 1970s opened the seas. Combinatorial chemistry (late 1980s), shifted the focus of drug discovery efforts from Nature to the laboratory bench. 2. Scope of Review This review traces natural products drug discovery, outlining important drugs from natural sources that revolutionized treatment of serious diseases. It is clear Nature will continue to be a major source of new structural leads, and effective drug development depends on multidisciplinary collaborations. 3. Major Conclusions The explosion of genetic information led not only to novel screens, but the genetic techniques permitted the implementation of combinatorial biosynthetic technology and genome mining. The knowledge gained has allowed unknown molecules to be identified. These novel bioactive structures can be optimized by using combinatorial chemistry generating new drug candidates for many diseases. 4 General Significance: The advent of genetic techniques that permitted the isolation / expression of biosynthetic cassettes from microbes may well be the new frontier for natural products lead discovery. It is now apparent that biodiversity may be much greater in those organisms. The numbers of potential species involved in the microbial world are many orders of magnitude greater than those of plants and multi-celled animals. Coupling these numbers to the number of currently unexpressed biosynthetic clusters now identified (>10 per species) the potential of microbial diversity remains essentially untapped. PMID:23428572

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.

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.

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

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.

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.

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.

Generation of cell lines for drug discovery through random activation of gene expression: application to the human histamine H3 receptor.

PubMed

Song, J; Doucette, C; Hanniford, D; Hunady, K; Wang, N; Sherf, B; Harrington, J J; Brunden, K R; Stricker-Krongrad, A

2005-06-01

Target-based high-throughput screening (HTS) plays an integral role in drug discovery. The implementation of HTS assays generally requires high expression levels of the target protein, and this is typically accomplished using recombinant cDNA methodologies. However, the isolated gene sequences to many drug targets have intellectual property claims that restrict the ability to implement drug discovery programs. The present study describes the pharmacological characterization of the human histamine H3 receptor that was expressed using random activation of gene expression (RAGE), a technology that over-expresses proteins by up-regulating endogenous genes rather than introducing cDNA expression vectors into the cell. Saturation binding analysis using [125I]iodoproxyfan and RAGE-H3 membranes revealed a single class of binding sites with a K(D) value of 0.77 nM and a B(max) equal to 756 fmol/mg of protein. Competition binding studies showed that the rank order of potency for H3 agonists was N(alpha)-methylhistamine approximately (R)-alpha- methylhistamine > histamine and that the rank order of potency for H3 antagonists was clobenpropit > iodophenpropit > thioperamide. The same rank order of potency for H3 agonists and antagonists was observed in the functional assays as in the binding assays. The Fluorometic Imaging Plate Reader assays in RAGE-H3 cells gave high Z' values for agonist and antagonist screening, respectively. These results reveal that the human H3 receptor expressed with the RAGE technology is pharmacologically comparable to that expressed through recombinant methods. Moreover, the level of expression of the H3 receptor in the RAGE-H3 cells is suitable for HTS and secondary assays.

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

Pyrrolizidine Alkaloids: Chemistry, Pharmacology, Toxicology and Food Safety.

PubMed

Moreira, Rute; Pereira, David M; Valentão, Patrícia; Andrade, Paula B

2018-06-05

Pyrrolizidine alkaloids (PA) are widely distributed in plants throughout the world, frequently in species relevant for human consumption. Apart from the toxicity that these molecules can cause in humans and livestock, PA are also known for their wide range of pharmacological properties, which can be exploited in drug discovery programs. In this work we review the current body of knowledge regarding the chemistry, toxicology, pharmacology and food safety of PA.

Sixth BHD Symposium and First International Upstate Kidney Cancer Symposium: latest scientific and clinical discoveries.

PubMed

Bratslavsky, Gennady; Woodford, Mark R; Daneshvar, Michael; Mollapour, Mehdi

2016-03-29

The Sixth BHD Symposium and First International Upstate Kidney Cancer Symposium concluded in September 2015, in Syracuse, NY, USA. The program highlighted recent findings in a variety of areas, including drug development, therapeutics and surgical management of patients with BHD and multi-focal renal tumors, as well as multidisciplinary approaches for patients with localized, locally advanced and metastatic renal cell carcinoma.

Modeling & Informatics at Vertex Pharmaceuticals Incorporated: our philosophy for sustained impact

NASA Astrophysics Data System (ADS)

McGaughey, Georgia; Patrick Walters, W.

2017-03-01

Molecular modelers and informaticians have the unique opportunity to integrate cross-functional data using a myriad of tools, methods and visuals to generate information. Using their drug discovery expertise, information is transformed to knowledge that impacts drug discovery. These insights are often times formulated locally and then applied more broadly, which influence the discovery of new medicines. This is particularly true in an organization where the members are exposed to projects throughout an organization, such as in the case of the global Modeling & Informatics group at Vertex Pharmaceuticals. From its inception, Vertex has been a leader in the development and use of computational methods for drug discovery. In this paper, we describe the Modeling & Informatics group at Vertex and the underlying philosophy, which has driven this team to sustain impact on the discovery of first-in-class transformative medicines.

Biomarker Qualification: Toward a Multiple Stakeholder Framework for Biomarker Development, Regulatory Acceptance, and Utilization.

PubMed

Amur, S; LaVange, L; Zineh, I; Buckman-Garner, S; Woodcock, J

2015-07-01

The discovery, development, and use of biomarkers for a variety of drug development purposes are areas of tremendous interest and need. Biomarkers can become accepted for use through submission of biomarker data during the drug approval process. Another emerging pathway for acceptance of biomarkers is via the biomarker qualification program developed by the Center for Drug Evaluation and Research (CDER, US Food and Drug Administration). Evidentiary standards are needed to develop and evaluate various types of biomarkers for their intended use and multiple stakeholders, including academia, industry, government, and consortia must work together to help develop this evidence. The article describes various types of biomarkers that can be useful in drug development and evidentiary considerations that are important for qualification. A path forward for coordinating efforts to identify and explore needed biomarkers is proposed for consideration. © 2015 American Society for Clinical Pharmacology and Therapeutics.

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.

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.

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.

[Drug innovation and reverse thinking].

PubMed

Guo, Zong-ru

2016-03-01

Drug innovation involves an individual molecular operation, and every new molecular entity features a hard-duplicated track of R&D. The transformation from an active compound to a new medicine carries out almost in a chaotic system devoid of regularity and periodic alteration. Since new millennium the dominant position in drug innovation has been occupied by the first-in-class drugs, yet the number of launched follow-on drugs has been distinctly decreased. The innovation of first-in-class drugs is characterized by a high risk throughout the whole process. To achieve initiative and uniqueness of drug discovery, the strategy and method of the inverse thinking might be a feasible way, because the inertial and conformity thinkings in drug discovery normally lead to ensemble with similar drug category. However, the study from the flipside or opposite of things(e.g. targets or effects) brand new routes might be opened. This article is to describe the strategy of reverse thinking in drug discovery by some examples including opioid receptor antagonist eluxadoline, HSP90 activator, h ERG channel agonist, covalent drugs, and ultra-small drugs.

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

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.

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

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.

News | Frederick National Laboratory for Cancer Research

Cancer.gov

Consortium aims to accelerate drug discovery process(Physics Today) Why big pharma and biotech are betting big on AI(NBC News) Scientists launch SF-based effort to dramatically cut cancer drug discovery time(SF Chronicle

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

Strategy of Daiichi Sankyo discovery research in oncology.

PubMed

Akahane, Kouichi; Hirokawa, Kazunori

2014-02-01

We would like to introduce Daiichi Sankyo's approach to developing cancer targeted medicines with special reference to the drug discovery strategy, global discovery activities and external research collaboration leading to generation of innovative drugs for cancer patients. We are developing 14 clinical projects for cancer treatment and three of them have been previously approved. These are mostly targeted for growth and survival signals of cancer cells. To overcome the drug resistance mechanism derived from the heterogeneous nature of cancer, we are developing selective inhibitors in three major clusters of signal pathways which may allow future rational combinations of oncology products. In addition to the main research facility in Japan, research sites in the EU and the USA provide us with different technical expertise and diversified ideas of drug discovery. To access novel drug targets, we are facilitating research collaboration with leading academia and successful cancer research scientists. In conclusion, we intend to focus more on developing innovative personalized medicines for better treatment of cancer.

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

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.

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.

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.

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.

Optimizing the discovery organization for innovation.

PubMed

Sams-Dodd, Frank

2005-08-01

Strategic management is the process of adapting organizational structure and management principles to fit the strategic goal of the business unit. The pharmaceutical industry has generally been expert at optimizing its organizations for drug development, but has rarely implemented different structures for the early discovery process, where the objective is innovation and the transformation of innovation into drug projects. Here, a set of strategic management methods is proposed, covering team composition, organizational structure, management principles and portfolio management, which are designed to increase the level of innovation in the early drug discovery process.

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.

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

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.

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.

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, is potentially transformative in enabling hard to drug targets to be attacked, and in facilitating the development of superior compounds, in various dimensions, for a wide range of targets. More effective integration of FEP+ calculations into the drug discovery process will ensure that the results are deployed in an optimal fashion for yielding the best possible compounds entering the clinic; this is where the greatest payoff is in the exploitation of computer driven design capabilities. A key conclusion from the work described is the surprisingly robust and accurate results that are attainable within the conventional classical simulation, fixed charge paradigm. No doubt there are individual cases that would benefit from a more sophisticated energy model or dynamical treatment, and properties other than protein-ligand binding energies may be more sensitive to these approximations. We conclude that an inflection point in the ability of MD simulations to impact drug discovery has now been attained, due to the confluence of hardware and software development along with the formulation of "good enough" theoretical methods and models.

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.

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.

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.

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).

Chemoprevention of Melanoma

PubMed Central

Madhunapantula, SubbaRao V.; Robertson, Gavin P.

2013-01-01

Despite advances in drug discovery programs and molecular approaches for identifying the drug targets, incidence and mortality rates due to melanoma continues to rise at an alarming rate. Existing preventive strategies generally involve mole screening followed by surgical removal of the benign nevi and abnormal moles. However, due to lack of effective programs for screening and disease recurrence after surgical resection there is a need for better chemopreventive agents. Although sunscreens have been used extensively for protecting from UV-induced skin cancer, results of correlative population based studies are controversial, requiring further authentication to conclusively confirm the chemoprotective efficacy of sunscreens. Certain studies suggest increased skin-cancer rates in sunscreen users. Therefore, effective chemopreventive agents for preventing melanoma are urgently required. This book-chapter, reviews the current understanding regarding melanoma chemoprevention and the various strategies used to accomplish this objective. PMID:22959032

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.

Application of industrial scale genomics to discovery of therapeutic targets in heart failure.

PubMed

Mehraban, F; Tomlinson, J E

2001-12-01

In recent years intense activity in both academic and industrial sectors has provided a wealth of information on the human genome with an associated impressive increase in the number of novel gene sequences deposited in sequence data repositories and patent applications. This genomic industrial revolution has transformed the way in which drug target discovery is now approached. In this article we discuss how various differential gene expression (DGE) technologies are being utilized for cardiovascular disease (CVD) drug target discovery. Other approaches such as sequencing cDNA from cardiovascular derived tissues and cells coupled with bioinformatic sequence analysis are used with the aim of identifying novel gene sequences that may be exploited towards target discovery. Additional leverage from gene sequence information is obtained through identification of polymorphisms that may confer disease susceptibility and/or affect drug responsiveness. Pharmacogenomic studies are described wherein gene expression-based techniques are used to evaluate drug response and/or efficacy. Industrial-scale genomics supports and addresses not only novel target gene discovery but also the burgeoning issues in pharmaceutical and clinical cardiovascular medicine relative to polymorphic gene responses.

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

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.

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.

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.

76 FR 4924 - Center for Scientific Review; Notice of Closed Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-27

... Emphasis Panel; RFA Panel: Drug Discovery for the Nervous System. Date: February 17-18, 2011. Time: 8 a.m... Scientific Review Special Emphasis Panel; RFA Panel: Drug Discovery for the Nervous System. Date: February 18...

Targeting the human genome-microbiome axis for drug discovery: inspirations from global systems biology and traditional Chinese medicine.

PubMed

Zhao, Liping; Nicholson, Jeremy K; Lu, Aiping; Wang, Zhengtao; Tang, Huiru; Holmes, Elaine; Shen, Jian; Zhang, Xu; Li, Jia V; Lindon, John C

2012-07-06

Most chronic diseases impairing current human public health involve not only the human genome but also gene-environment interactions, and in the latter case the gut microbiome is an important factor. This makes the classical single drug-receptor target drug discovery paradigm much less applicable. There is widespread and increasing international interest in understanding the properties of traditional Chinese medicines (TCMs) for their potential utilization as a source of new drugs for Western markets as emerging evidence indicates that most TCM drugs are actually targeting both the host and its symbiotic microbes. In this review, we explore the challenges of and opportunities for harmonizing Eastern-Western drug discovery paradigms by focusing on emergent functions at the whole body level of humans as superorganisms. This could lead to new drug candidate compounds for chronic diseases targeting receptors outside the currently accepted "druggable genome" and shed light on current high interest issues in Western medicine such as drug-drug and drug-diet-gut microbial interactions that will be crucial in the development and delivery of future therapeutic regimes optimized for the individual patient.

Pharmacokinetic/Pharmacodynamic-Driven Drug Development

PubMed Central

Gallo, James M.

2010-01-01

The drug discovery and development enterprise, traditionally an industrial juggernaut, has spanned into the academic arena that is partially motivated by the National Institutes of Health Roadmap highlighting translational science and medicine. Since drug discovery and development represents a pipeline of basic to clinical investigations it meshes well with the prime “bench to the bedside” directive of translational medicine. The renewed interest in drug discovery and develpoment in academia provides an opportunity to rethink the hiearchary of studies with the hope to improve the staid approaches that have been critizied for lacking innovation. One area that has received limited attention concerns the use of pharmacokinetic [PK] and pharmacodynamic [PD] studies in the drug development process. Using anticancer drug development as a focus, this review will address past and current deficencies in how PK/PD studies are conducted and offer new strategies that might bridge the gap between preclinical and clinical trials. PMID:20687184

[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.

Using transcriptomics to guide lead optimization in drug discovery projects: Lessons learned from the QSTAR project.

PubMed

Verbist, Bie; Klambauer, Günter; Vervoort, Liesbet; Talloen, Willem; Shkedy, Ziv; Thas, Olivier; Bender, Andreas; Göhlmann, Hinrich W H; Hochreiter, Sepp

2015-05-01

The pharmaceutical industry is faced with steadily declining R&D efficiency which results in fewer drugs reaching the market despite increased investment. A major cause for this low efficiency is the failure of drug candidates in late-stage development owing to safety issues or previously undiscovered side-effects. We analyzed to what extent gene expression data can help to de-risk drug development in early phases by detecting the biological effects of compounds across disease areas, targets and scaffolds. For eight drug discovery projects within a global pharmaceutical company, gene expression data were informative and able to support go/no-go decisions. Our studies show that gene expression profiling can detect adverse effects of compounds, and is a valuable tool in early-stage drug discovery decision making. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

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.

Benefits and challenges of a QSP approach through case study: Evaluation of a hypothetical GLP-1/GIP dual agonist therapy.

PubMed

Rieger, Theodore R; Musante, Cynthia J

2016-10-30

Quantitative Systems Pharmacology (QSP) is an emerging science with increasing application to pharmaceutical research and development paradigms. Through case study we provide an overview of the benefits and challenges of applying QSP approaches to inform program decisions in the early stages of drug discovery and development. Specifically, we describe the use of a type 2 diabetes systems model to inform a No-Go decision prior to lead development for a potential GLP-1/GIP dual agonist program, enabling prioritization of exploratory programs with higher probability of clinical success. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

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

PubMed

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

2015-06-01

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

76 FR 30371 - Center for Scientific Review; Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-25

... Panel; RFA Panel: Drug Discovery for the Nervous System. Date: June 16-17, 2011. Time: 8 a.m. to 10 a.m... Special Emphasis Panel; RFA Panel: Drug Discovery for the Nervous System. Date: June 17, 2011. Time: 10 a...

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

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

The role of academic institutions in the development of drugs for rare and neglected diseases.

PubMed

Coles, L D; Cloyd, J C

2012-08-01

There are approximately 7,000 rare disorders, many of which are life-threatening. Diagnosis is often problematic, and therapies are few. Before the passage of the Orphan Drug Act in 1983, neither the pharmaceutical industry nor universities devoted much effort to research on rare diseases. Important changes have occurred within and outside universities that position them to play a significant role in developing orphan drugs. Several models are being employed to promote drug-related research, including disease-focused, discovery-focused, development-focused, and industry-partnership-focused approaches. However, significant barriers challenge universities' ability to fully contribute to orphan drug development. Academic institutions, along with industry, government, and not-for-profit organizations, must address these issues in order to advance the field. New initiatives designed to increase university-based orphan drug research include creating mechanisms to ensure program continuity, building research and regulatory support infrastructure, facilitating commercialization, expanding government support, and developing mutually beneficial partnerships among academe, industry, and government.

Target validation: linking target and chemical properties to desired product profile.

PubMed

Wyatt, Paul G; Gilbert, Ian H; Read, Kevin D; Fairlamb, Alan H

2011-01-01

The discovery of drugs is a lengthy, high-risk and expensive business taking at least 12 years and is estimated to cost upwards of US$800 million for each drug to be successfully approved for clinical use. Much of this cost is driven by the late phase clinical trials and therefore the ability to terminate early those projects destined to fail is paramount to prevent unwanted costs and wasted effort. Although neglected diseases drug discovery is driven more by unmet medical need rather than financial considerations, the need to minimise wasted money and resources is even more vital in this under-funded area. To ensure any drug discovery project is addressing the requirements of the patients and health care providers and delivering a benefit over existing therapies, the ideal attributes of a novel drug needs to be pre-defined by a set of criteria called a target product profile. Using a target product profile the drug discovery process, clinical study design, and compound characteristics can be defined all the way back through to the suitability or druggability of the intended biochemical target. Assessment and prioritisation of the most promising targets for entry into screening programmes is crucial for maximising chances of success.

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.

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

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.

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.

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.

A systematic study of chemogenomics of carbohydrates.

PubMed

Gu, Jiangyong; Luo, Fang; Chen, Lirong; Yuan, Gu; Xu, Xiaojie

2014-03-04

Chemogenomics focuses on the interactions between biologically active molecules and protein targets for drug discovery. Carbohydrates are the most abundant compounds in natural products. Compared with other drugs, the carbohydrate drugs show weaker side effects. Searching for multi-target carbohydrate drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 60 344 carbohydrates from the Universal Natural Products Database (UNPD) and explored the chemical space of carbohydrates by principal component analysis. We found that there is a large quantity of potential lead compounds among carbohydrates. Then we explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach. All carbohydrates were docked to 2389 target proteins. The most potential carbohydrates for drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between carbohydrates and target proteins to find the pathological networks, potential drug candidates and new indications.

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.

New strategy for drug discovery by large-scale association analysis of molecular networks of different species.

PubMed

Zhang, Bo; Fu, Yingxue; Huang, Chao; Zheng, Chunli; Wu, Ziyin; Zhang, Wenjuan; Yang, Xiaoyan; Gong, Fukai; Li, Yuerong; Chen, Xiaoyu; Gao, Shuo; Chen, Xuetong; Li, Yan; Lu, Aiping; Wang, Yonghua

2016-02-25

The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented. The algorithm integrates molecular network omics data from humans and 267 plants and microbes, establishing the biological relationships between them and extracting evolutionarily convergent chemicals. This technique allows the researcher to assess targeted drugs for specific human diseases based on specific plant or microbe pathways. In a perspective validation, connections between the plant Halliwell-Asada (HA) cycle and the human Nrf2-ARE pathway were verified and the manner by which the HA cycle molecules act on the human Nrf2-ARE pathway as antioxidants was determined. This shows the potential applicability of this approach in drug discovery. The current method integrates disparate evolutionary species into chemico-biologically coherent circuits, suggesting a new cross-species omics analysis strategy for rational drug development.

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.

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.

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.

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.

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.

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.

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.

A low-cost, high-quality new drug discovery process using patient-derived induced pluripotent stem cells.

PubMed

Giri, Shibashish; Bader, Augustinus

2015-01-01

Knockout, knock-in and conditional mutant gene-targeted mice are routinely used for disease modeling in the drug discovery process, but the human response is often difficult to predict from these models. It is believed that patient-derived induced pluripotent stem cells (iPSCs) could replace millions of animals currently sacrificed in preclinical testing and provide a route to new safer pharmaceutical products. In this review, we discuss the use of IPSCs in the drug discovery process. We highlight how they can be used to assess the toxicity and clinical efficacy of drug candidates before the latter are moved into costly and lengthy preclinical and clinical trials. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

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.

Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein-Ligand Docking Method.

PubMed

Shin, Woong-Hee; Kihara, Daisuke

2018-01-01

Virtual screening is a computational technique for predicting a potent binding compound for a receptor protein from a ligand library. It has been a widely used in the drug discovery field to reduce the efforts of medicinal chemists to find hit compounds by experiments.Here, we introduce our novel structure-based virtual screening program, PL-PatchSurfer, which uses molecular surface representation with the three-dimensional Zernike descriptors, which is an effective mathematical representation for identifying physicochemical complementarities between local surfaces of a target protein and a ligand. The advantage of the surface-patch description is its tolerance on a receptor and compound structure variation. PL-PatchSurfer2 achieves higher accuracy on apo form and computationally modeled receptor structures than conventional structure-based virtual screening programs. Thus, PL-PatchSurfer2 opens up an opportunity for targets that do not have their crystal structures. The program is provided as a stand-alone program at http://kiharalab.org/plps2 . We also provide files for two ligand libraries, ChEMBL and ZINC Drug-like.

Animal Models of Seizures and Epilepsy: Past, Present, and Future Role for the Discovery of Antiseizure Drugs.

PubMed

Löscher, Wolfgang

2017-07-01

The identification of potential therapeutic agents for the treatment of epilepsy requires the use of seizure models. Except for some early treatments, including bromides and phenobarbital, the antiseizure activity of all clinically used drugs was, for the most part, defined by acute seizure models in rodents using the maximal electroshock and subcutaneous pentylenetetrazole seizure tests and the electrically kindled rat. Unfortunately, the clinical evidence to date would suggest that none of these models, albeit useful, are likely to identify those therapeutics that will effectively manage patients with drug resistant seizures. Over the last 30 years, a number of animal models have been developed that display varying degrees of pharmacoresistance, such as the phenytoin- or lamotrigine-resistant kindled rat, the 6-Hz mouse model of partial seizures, the intrahippocampal kainate model in mice, or rats in which spontaneous recurrent seizures develops after inducing status epilepticus by chemical or electrical stimulation. As such, these models can be used to study mechanisms of drug resistance and may provide a unique opportunity for identifying a truly novel antiseizure drug (ASD), but thus far clinical evidence for this hope is lacking. Although animal models of drug resistant seizures are now included in ASD discovery approaches such as the ETSP (epilepsy therapy screening program), it is important to note that no single model has been validated for use to identify potential compounds for as yet drug resistant seizures, but rather a battery of such models should be employed, thus enhancing the sensitivity to discover novel, highly effective ASDs. The present review describes the previous and current approaches used in the search for new ASDs and offers some insight into future directions incorporating new and emerging animal models of therapy resistance.

Multidrug-resistant and extensively drug-resistant tuberculosis: implications for the HIV epidemic and antiretroviral therapy rollout in South Africa.

PubMed

Andrews, Jason R; Shah, N Sarita; Gandhi, Neel; Moll, Tony; Friedland, Gerald

2007-12-01

Drug-resistant tuberculosis (TB) is emerging as a major clinical and public health challenge in areas of sub-Saharan Africa where there is a high prevalence of human immunodeficiency virus (HIV) infection. TB drug-resistance surveillance in this region has been limited by laboratory capacity and the public health infrastructure; however, with the maturation of the HIV epidemic, the burden of drug-resistant TB is increasing rapidly. The recent discovery of large numbers of cases of multidrug-resistant (MDR) TB and extensively drug-resistant (XDR) TB in South Africa likely represents an unrecognized and evolving epidemic rather than sporadic, localized outbreaks. The combination of a large population of HIV-infected susceptible hosts with poor TB treatment success rates, a lack of airborne infection control, limited drug-resistance testing, and an overburdened MDR-TB treatment program provides ideal conditions for an MDR-TB and XDR-TB epidemic of unparalleled magnitude. In the present article, we review the history of drug-resistant TB in South Africa, describe its interaction with the HIV epidemic and the resultant consequences, and suggest measures necessary for controlling MDR-TB and XDR-TB in this context. A successful response to the emergence of MDR-TB and XDR-TB will necessitate increased resources for and collaboration between TB and HIV programs.

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

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.

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.

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.

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

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.

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

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.

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.case.edu/public/data/DMN Contact: rxx@case.edu PMID:26072493

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.

Rho Chi lecture. Pharmaceutical sciences in the next millennium.

PubMed

Triggle, D J

1999-02-01

Even a cursory survey of this article suggests that the pharmaceutical sciences are being rapidly transformed under the influence of both the new technologies and sciences and the economic imperatives. Of particular importance are scientific and technological advances that may greatly accelerate the critical process of discovery. The possibility of a drug discovery process built around the principles of directed diversity, self-reproduction, evolution, and self-targeting suggests a new paradigm of lead discovery, one based quite directly on the paradigms of molecular biology. Coupled with the principles of nanotechnology, we may contemplate miniature molecular machines containing directed drug factories, circulating the body and capable of self-targeting against defective cells and pathways -- the ultimate "drug delivery machine." However, science and technology are not the only factors that will transform the pharmaceutical sciences in the next century. The necessary reductions in the costs of drug discovery brought about by the rapidly increasing costs of the current drug discovery paradigms means that efforts to decrease the discovery phase and to make drug development part of drug discovery will become increasingly important. This is likely to involve increasing numbers of "alliances," as well as the creation of pharmaceutical research cells -- highly mobile and entrepreneurial groups within or outside of a pharmaceutical company that are formed to carry out specific discovery processes. Some of these will be in the biotechnology industry, but an increasing number will be in universities. The linear process from basic science to applied technology that has been the Western model since Vannevar Bush's Science: The Endless Frontier has probably never been particularly linear and, in any event, is likely to be rapidly supplanted by models where science, scientific development, and technology are more intimately linked. The pharmaceutical sciences have always been an example of use-directed basic research, but the relationships between the pharmaceutical industry, small and large, and the universities seems likely to become increasingly developed in the next century. This may serve as a significant catalyst for the continued transformation of universities into the "knowledge factories" of the 21st century. Regardless, we may expect to see major changes in the research organizational structure in the pharmaceutical sciences even as pharmaceutical companies enjoy record prosperity. And this is in anticipation of tough times to come.

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.

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.

A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the 'rule-of-two' model.

PubMed

Chen, Minjun; Tung, Chun-Wei; Shi, Qiang; Guo, Lei; Shi, Leming; Fang, Hong; Borlak, Jürgen; Tong, Weida

2014-07-01

Drug-induced liver injury (DILI) is a major cause of drug failures in both the preclinical and clinical phase. Consequently, improving prediction of DILI at an early stage of drug discovery will reduce the potential failures in the subsequent drug development program. In this regard, high-content screening (HCS) assays are considered as a promising strategy for the study of DILI; however, the predictive performance of HCS assays is frequently insufficient. In the present study, a new testing strategy was developed to improve DILI prediction by employing in vitro assays that was combined with the RO2 model (i.e., 'rule-of-two' defined by daily dose ≥100 mg/day & logP ≥3). The RO2 model was derived from the observation that high daily doses and lipophilicity of an oral medication were associated with significant DILI risk in humans. In the developed testing strategy, the RO2 model was used for the rational selection of candidates for HCS assays, and only the negatives predicted by the RO2 model were further investigated by HCS. Subsequently, the effects of drug treatment on cell loss, nuclear size, DNA damage/fragmentation, apoptosis, lysosomal mass, mitochondrial membrane potential, and steatosis were studied in cultures of primary rat hepatocytes. Using a set of 70 drugs with clear evidence of clinically relevant DILI, the testing strategy improved the accuracies by 10 % and reduced the number of drugs requiring experimental assessment by approximately 20 %, as compared to the HCS assay alone. Moreover, the testing strategy was further validated by including published data (Cosgrove et al. in Toxicol Appl Pharmacol 237:317-330, 2009) on drug-cytokine-induced hepatotoxicity, which improved the accuracies by 7 %. Taken collectively, the proposed testing strategy can significantly improve the prediction of in vitro assays for detecting DILI liability in an early drug discovery phase.

A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

PubMed

Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

2014-08-01

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

Lessons from black pepper: piperine and derivatives thereof.

PubMed

Chavarria, D; Silva, T; Magalhães e Silva, D; Remião, F; Borges, F

2016-01-01

Piperine is a simple and pungent alkaloid found in the seeds of black pepper (Piper nigrum). Following its isolation and full characterization, the biological properties of piperine have been extensively studied, and piperine-like derivatives have shown an interesting range of pharmacological activities. In this context, significant advances have been made in the discovery of new chemical entities based on the piperine scaffold endowed with therapeutic potential. The aim of this review is to provide a thorough inquiry on the therapeutic potential of piperine and related derivatives. It provides an overview of recent developments in patented processes and applications thereof between 2000 and 2015. Cumulative evidence shows that piperine is currently paving its way to become a privileged scaffold for the development of bioactive compounds with therapeutic application in multiple human diseases. In particular, piperine derivatives were shown to modulate the activity of several targets related to neurological disorders, including epilepsy, Parkinson's disease, depression and pain related disorders. Moreover, the efflux pump inhibitory ability of piperine and its analogues tackles important drug resistance mechanisms and may improve the clinical efficacy of antibiotic and anticancer drugs. Although the use of piperine as a scaffold for bioactive compounds is still in its early stages, the continuous exploration of this structure may lead to remarkable advances in drug discovery programs.

Exploiting Pharmacological Similarity to Identify Safety Concerns - Listen to What the Data Tells You.

PubMed

Muthas, Daniel; Boyer, Scott

2013-01-01

Whilst most new drugs are designed to act on a single target or a small number of targets, many do show broad pharmacological activity. In some cases this can be beneficial and necessary for efficacy and in others it can be detrimental, leading to increased safety liability. To probe off-target pharmacology most drug discovery programs include screening against a broad panel of targets that represent known troublesome pharmacology. Hits against any one of these targets can then be subjected to a risk assessment for potential safety problems in preclinical or clinical studies. In addition, the secondary pharmacology profile can also be thought of as an alternative description of the compound and as such can be used as a method for assessing 'similarity'. Consequently, inspection of the in vivo findings of pharmacological neighbors can give important insights into potential safety liabilities that are neither identified by pure chemical similarity searches nor by risk assessment on individual targets. Here we show that the pharmacological profile contains additional information as compared to chemical similarity, and also demonstrate how this can be used in the hazard assessment done during drug discovery and development. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Applying Dataflow Architecture and Visualization Tools to In Vitro Pharmacology Data Automation.

PubMed

Pechter, David; Xu, Serena; Kurtz, Marc; Williams, Steven; Sonatore, Lisa; Villafania, Artjohn; Agrawal, Sony

2016-12-01

The pace and complexity of modern drug discovery places ever-increasing demands on scientists for data analysis and interpretation. Data flow programming and modern visualization tools address these demands directly. Three different requirements-one for allosteric modulator analysis, one for a specialized clotting analysis, and one for enzyme global progress curve analysis-are reviewed, and their execution in a combined data flow/visualization environment is outlined. © 2016 Society for Laboratory Automation and Screening.

Expression, purification and characterization of inactive and active forms of ERK2 from insect expression system.

PubMed

Yan, Kelly; Merritt, Hanne; Crawford, Kenneth; Pardee, Gwynn; Cheng, Jan Marie; Widger, Stephania; Hekmat-Nejad, Mohammad; Zaror, Isabel; Sim, Janet

2015-06-01

Extracellular signal-regulated kinase 2 (ERK2) is a serine/threonine protein kinase involved in many cellular programs, such as cell proliferation, differentiation, motility and programed cell-death. It is therefore considered an important target in the treatment of cancer. In an effort to support biochemical screening and small molecule drug discovery, we established a robust system to generate both inactive and active forms of ERK2 using insect expression system. We report here, for the first time, that inactive ERK2 can be expressed and purified with 100% homogeneity in the unphosphorylated form using insect system. This resulted in a significant 20-fold yield improvement compared to that previously reported using bacterial expression system. We also report a newly developed system to generate active ERK2 in insect cells through in vivo co-expression with a constitutively active MEK1 (S218D S222D). Isolated active ERK2 was confirmed to be doubly phosphorylated at the correct sites, T185 and Y187, in the activation loop of ERK2. Both ERK2 forms, inactive and active, were well characterized by biochemical activity assay for their kinase function. Inactive and active ERK2 were the two key reagents that enabled successful high through-put biochemical assay screen and structural drug discovery studies. Copyright © 2015 Elsevier Inc. All rights reserved.

Docking-based classification models for exploratory toxicology ...

EPA Pesticide Factsheets

Background: Exploratory toxicology is a new emerging research area whose ultimate mission is that of protecting human health and environment from risks posed by chemicals. In this regard, the ethical and practical limitation of animal testing has encouraged the promotion of computational methods for the fast screening of huge collections of chemicals available on the market. Results: We derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals having high quality experimental data, kindly provided by the U.S. Environmental Protection Agency (EPA). The predictive power of our docking-based models was supported by values of AUC, EF1% (EFmax = 7.1), -LR (at SE = 0.75) and +LR (at SE = 0.25) ranging from 0.63 to 0.72, from 2.5 to 6.2, from 0.35 to 0.67 and from 2.05 to 9.84, respectively. In addition, external predictions were successfully made on some representative known estrogenic chemicals. Conclusion: We show how structure-based methods, widely applied to drug discovery programs, can be adapted to meet the conditions of the regulatory context. Importantly, these methods enable one to employ the physicochemical information contained in the X-ray solved biological target and to screen structurally-unrelated chemicals. Shows how structure-based methods, widely applied to drug discovery programs, can be adapted to meet the conditions of the regulatory context. Evaluation of 24 reliable dockin

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

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

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.

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.

Marine Microorganism-Invertebrate Assemblages: Perspectives to Solve the “Supply Problem” in the Initial Steps of Drug Discovery

PubMed Central

Leal, Miguel Costa; Sheridan, Christopher; Osinga, Ronald; Dionísio, Gisela; Rocha, Rui Jorge Miranda; Silva, Bruna; Rosa, Rui; Calado, Ricardo

2014-01-01

The chemical diversity associated with marine natural products (MNP) is unanimously acknowledged as the “blue gold” in the urgent quest for new drugs. Consequently, a significant increase in the discovery of MNP published in the literature has been observed in the past decades, particularly from marine invertebrates. However, it remains unclear whether target metabolites originate from the marine invertebrates themselves or from their microbial symbionts. This issue underlines critical challenges associated with the lack of biomass required to supply the early stages of the drug discovery pipeline. The present review discusses potential solutions for such challenges, with particular emphasis on innovative approaches to culture invertebrate holobionts (microorganism-invertebrate assemblages) through in toto aquaculture, together with methods for the discovery and initial production of bioactive compounds from these microbial symbionts. PMID:24983638

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.

Pharmacokinetic de-risking tools for selection of monoclonal antibody lead candidates

PubMed Central

Dostalek, Miroslav; Prueksaritanont, Thomayant; Kelley, Robert F.

2017-01-01

ABSTRACT Pharmacokinetic studies play an important role in all stages of drug discovery and development. Recent advancements in the tools for discovery and optimization of therapeutic proteins have created an abundance of candidates that may fulfill target product profile criteria. Implementing a set of in silico, small scale in vitro and in vivo tools can help to identify a clinical lead molecule with promising properties at the early stages of drug discovery, thus reducing the labor and cost in advancing multiple candidates toward clinical development. In this review, we describe tools that should be considered during drug discovery, and discuss approaches that could be included in the pharmacokinetic screening part of the lead candidate generation process to de-risk unexpected pharmacokinetic behaviors of Fc-based therapeutic proteins, with an emphasis on monoclonal antibodies. PMID:28463063

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.

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

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.

Brazil: An emerging partner in drug R&D.

PubMed

Rodrigues, Debora G

2009-08-01

With the need for innovation in drug discovery and development and changes to patent laws that are enabling greater IP protection, many pharmaceutical companies are pursuing international cooperation agreements with foreign companies as part of a global development strategy to enhance product pipelines. Brazil, the largest pharmaceutical market in Latin America, has improved its infrastructure, scientific and technological capabilities and has created a sustainable strategy to promote drug discovery research activities. Positive economic growth, a stable political structure, expanding patient populations an increasing governmental, private and foreign investments are creating a new landscape for drug R&D in the country. As Brazilian-based pharmaceutical companies become further established, new opportunities for partnerships and collaborative alliances are becoming available for the drug discovery process, as well as for co-manufacturing and co-marketing efforts. This feature review provides an overview of the Brazilian pharmaceutical market and discusses current opportunities, emerging trends and challenges for this expanding market.

The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development.

PubMed

Denny, Joshua C; Van Driest, Sara L; Wei, Wei-Qi; Roden, Dan M

2018-03-01

Drug development continues to be costly and slow, with medications failing due to lack of efficacy or presence of toxicity. The promise of pharmacogenomic discovery includes tailoring therapeutics based on an individual's genetic makeup, rational drug development, and repurposing medications. Rapid growth of large research cohorts, linked to electronic health record (EHR) data, fuels discovery of new genetic variants predicting drug action, supports Mendelian randomization experiments to show drug efficacy, and suggests new indications for existing medications. New biomedical informatics and machine-learning approaches advance the ability to interpret clinical information, enabling identification of complex phenotypes and subpopulations of patients. We review the recent history of use of "big data" from EHR-based cohorts and biobanks supporting these activities. Future studies using EHR data, other information sources, and new methods will promote a foundation for discovery to more rapidly advance precision medicine. © 2017 American Society for Clinical Pharmacology and Therapeutics.

Therapeutic Potential of Foldamers: From Chemical Biology Tools To Drug Candidates?

PubMed

Gopalakrishnan, Ranganath; Frolov, Andrey I; Knerr, Laurent; Drury, William J; Valeur, Eric

2016-11-10

Over the past decade, foldamers have progressively emerged as useful architectures to mimic secondary structures of proteins. Peptidic foldamers, consisting of various amino acid based backbones, have been the most studied from a therapeutic perspective, while polyaromatic foldamers have barely evolved from their nascency and remain perplexing for medicinal chemists due to their poor drug-like nature. Despite these limitations, this compound class may still offer opportunities to study challenging targets or provide chemical biology tools. The potential of foldamer drug candidates reaching the clinic is still a stretch. Nevertheless, advances in the field have demonstrated their potential for the discovery of next generation therapeutics. In this perspective, the current knowledge of foldamers is reviewed in a drug discovery context. Recent advances in the early phases of drug discovery including hit finding, target validation, and optimization and molecular modeling are discussed. In addition, challenges and focus areas are debated and gaps highlighted.

Mesoionic Pyrido[1,2-a]pyrimidinone Insecticides: From Discovery to Triflumezopyrim and Dicloromezotiaz.

PubMed

Zhang, Wenming

2017-09-19

One of the greatest global challenges is to feed the ever-increasing world population. The agrochemical tools growers currently utilize are also under continuous pressure, due to a number of factors that contribute to the loss of existing products. Mesoionic pyrido[1,2-a]pyrimidinones are an unusual yet very intriguing class of compounds. Known for several decades, this class of compounds had not been systemically studied until we started our insecticide discovery program. This Account provides an overview of the efforts on mesoionic pyrido[1,2-a]pyridinone insecticide discovery, beginning from the initial high throughput screen (HTS) discovery to ultimate identification of triflumezopyrim (4, DuPont Pyraxalt) and dicloromezotiaz (5) for commercialization as novel insecticides. Mesoionic pyrido[1,2-a]pyrimidinones with a n-propyl group at the 1-position, such as compound 1, were initially isolated as undesired byproducts from reactions for a fungicide discovery program at DuPont Crop Protection. Such compounds showed interesting insecticidal activity in a follow-up screen and against an expanded insect species list. The area became an insecticide hit for exploration and then a lead area for optimization. At the lead optimization stage, variations at three regions of compound 1, i.e., side-chain (n-propyl group), substituents on the 3-phenyl group, and substitutions on the pyrido- moiety, were explored with many analogues prepared and evaluated. Breakthrough discoveries included replacing the n-propyl group with a 2,2,2-trifluoroethyl group to generate compound 2, and then with a 2-chlorothiazol-5-ylmethyl group to form compound 3. 3 possesses potent insecticidal activity not only against a group of hopper species, including corn planthopper (Peregrinus maidis (Ashmead), CPH) and potato leafhopper (Empoasca fabae (Harris), PLH), as well as two key rice hopper species, namely, brown planthopper (Nilaparvata lugens (Stål), BPH) and rice green leafhopper (Nephotettix virescens (Distant), GLH), but also against representative lepidoptera species Diamondback moth (Plutella xylostella (Linnaeus), DBM) and fall armyworm (Spodoptera frugiperda (J.E. Smith), FAW). Further optimization based on 3 led to discovery of triflumezopyrim (4), with a 5-pyrimidinylmethyl group, as a potent hopper insecticide for rice usage. Optimization of the substituents on the pyrido- moiety of 3 resulted in discovery of dicloromezotiaz (5) as a lepidoptera insecticide. In this Account, we present the discovery and optimization of mesoionic pyrido[1,2-a]pyrimidinone insecticides toward the identification of triflumezopyrim (4) and dicloromezotiaz (5). We hope that knowledge and lessons derived from this discovery program will provide valuable information for future agrochemical and drug discovery. Our successful discovery and commercialization development of two novel insecticides based on meosoionic pyrido[1,2-a]pyridiminones may also stimulate interests of scientists from other disciplines to adopt this uncommon yet intriguing heterocycle ring system in pharmaceutical and other material science discovery research.

New marine natural products from sponges (Porifera) of the order Dictyoceratida (2001 to 2012); a promising source for drug discovery, exploration and future prospects.

PubMed

Mehbub, Mohammad F; Perkins, Michael V; Zhang, Wei; Franco, Christopher M M

2016-01-01

The discovery of new drugs can no longer rely primarily on terrestrial resources, as they have been heavily exploited for over a century. During the last few decades marine sources, particularly sponges, have proven to be a most promising source of new natural products for drug discovery. This review considers the order Dictyoceratida in the Phylum Porifera from which the largest number of new marine natural products have been reported over the period 2001-2012. This paper examines all the sponges from the order Dictyoceratida that were reported as new compounds during the time period in a comprehensive manner. The distinctive physical characteristics and the geographical distribution of the different families are presented. The wide structural diversity of the compounds produced and the variety of biological activities they exhibited is highlighted. As a representative of sponges, insights into this order and avenues for future effective natural product discovery are presented. The research institutions associated with the various studies are also highlighted with the aim of facilitating collaborative relationships, as well as to acknowledge the major international contributors to the discovery of novel sponge metabolites. The order Dictyoceratida is a valuable source of novel chemical structures which will continue to contribute to a new era of drug discovery. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

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.

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.

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.

Dynamic Structure-Based Pharmacophore Model Development: A New and Effective Addition in the Histone Deacetylase 8 (HDAC8) Inhibitor Discovery

PubMed Central

Thangapandian, Sundarapandian; John, Shalini; Lee, Yuno; Kim, Songmi; Lee, Keun Woo

2011-01-01

Histone deacetylase 8 (HDAC8) is an enzyme involved in deacetylating the amino groups of terminal lysine residues, thereby repressing the transcription of various genes including tumor suppressor gene. The over expression of HDAC8 was observed in many cancers and thus inhibition of this enzyme has emerged as an efficient cancer therapeutic strategy. In an effort to facilitate the future discovery of HDAC8 inhibitors, we developed two pharmacophore models containing six and five pharmacophoric features, respectively, using the representative structures from two molecular dynamic (MD) simulations performed in Gromacs 4.0.5 package. Various analyses of trajectories obtained from MD simulations have displayed the changes upon inhibitor binding. Thus utilization of the dynamically-responded protein structures in pharmacophore development has the added advantage of considering the conformational flexibility of protein. The MD trajectories were clustered based on single-linkage method and representative structures were taken to be used in the pharmacophore model development. Active site complimenting structure-based pharmacophore models were developed using Discovery Studio 2.5 program and validated using a dataset of known HDAC8 inhibitors. Virtual screening of chemical database coupled with drug-like filter has identified drug-like hit compounds that match the pharmacophore models. Molecular docking of these hits reduced the false positives and identified two potential compounds to be used in future HDAC8 inhibitor design. PMID:22272142

Discovery and Development of Natural Product-derived Chemotherapeutic Agents Based on a Medicinal Chemistry Approach⊥†

PubMed Central

Lee, Kuo-Hsiung

2010-01-01

Medicinal plants have long been an excellent source of pharmaceutical agents. Accordingly, the long term objectives of the author's research program are to discover and design new chemotherapeutic agents based on plant-derived compound leads by using a medicinal chemistry approach, which is a combination of chemistry and biology. Different examples of promising bioactive natural products and their synthetic analogs, including sesquiterpene lactones, quassinoids, naphthoquinones, phenylquinolones, dithiophenediones, neo-tanshinlactone, tylophorine, suksdorfin, DCK, and DCP, will be presented with respect to their discovery and preclinical development as potential clinical trial candidates. Research approaches include bioactivity- or mechanism of action-directed isolation and characterization of active compounds, rational drug design-based modification and analog synthesis, as well as structure-activity relationship and mechanism of action studies. Current clinical trials agents discovered by the Natural Products Research Laboratories, University of North Carolina, include bevirimat (dimethyl succinyl betulinic acid), which is now in Phase IIb trials for treating AIDS. Bevirimat is also the first in a new class of HIV drug candidates called “maturation inhibitors”. In addition, an etoposide analog, GL-331, progressed to anticancer Phase II clinical trials, and the curcumin analog JC-9 is in Phase II clinical trials for treating acne and in development for trials against prostate cancer. The discovery and development of these clinical trials candidates will also be discussed. PMID:20187635

In silico toxicology for the pharmaceutical sciences

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

Valerio, Luis G., E-mail: Luis.Valerio@fda.hhs.go

2009-12-15

The applied use of in silico technologies (a.k.a. computational toxicology, in silico toxicology, computer-assisted tox, e-tox, i-drug discovery, predictive ADME, etc.) for predicting preclinical toxicological endpoints, clinical adverse effects, and metabolism of pharmaceutical substances has become of high interest to the scientific community and the public. The increased accessibility of these technologies for scientists and recent regulations permitting their use for chemical risk assessment supports this notion. The scientific community is interested in the appropriate use of such technologies as a tool to enhance product development and safety of pharmaceuticals and other xenobiotics, while ensuring the reliability and accuracy ofmore » in silico approaches for the toxicological and pharmacological sciences. For pharmaceutical substances, this means active and impurity chemicals in the drug product may be screened using specialized software and databases designed to cover these substances through a chemical structure-based screening process and algorithm specific to a given software program. A major goal for use of these software programs is to enable industry scientists not only to enhance the discovery process but also to ensure the judicious use of in silico tools to support risk assessments of drug-induced toxicities and in safety evaluations. However, a great amount of applied research is still needed, and there are many limitations with these approaches which are described in this review. Currently, there is a wide range of endpoints available from predictive quantitative structure-activity relationship models driven by many different computational software programs and data sources, and this is only expected to grow. For example, there are models based on non-proprietary and/or proprietary information specific to assessing potential rodent carcinogenicity, in silico screens for ICH genetic toxicity assays, reproductive and developmental toxicity, theoretical prediction of human drug metabolism, mechanisms of action for pharmaceuticals, and newer models for predicting human adverse effects. How accurate are these approaches is both a statistical issue and challenge in toxicology. In this review, fundamental concepts and the current capabilities and limitations of this technology will be critically addressed.« less

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

PubMed

Ferreira, Leonardo G; Andricopulo, Adriano D

2017-01-01

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

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.

Why is neuroimmunopharmacology crucial for the future of addiction research?

PubMed

Hutchinson, Mark R; Watkins, Linda R

2014-01-01

A major development in drug addiction research in recent years has been the discovery that immune signaling within the central nervous system contributes significantly to mesolimbic dopamine reward signaling induced by drugs of abuse, and hence is involved in the presentation of reward behaviors. Additionally, in the case of opioids, these hypotheses have advanced through to the discovery of the novel site of opioid action at the innate immune pattern recognition receptor Toll-like receptor 4 as the necessary triggering event that engages this reward facilitating central immune signaling. Thus, the hypothesis of major proinflammatory contributions to drug abuse was born. This review will examine these key discoveries, but also address several key lingering questions of how central immune signaling is able to contribute in this fashion to the pharmacodynamics of drugs of abuse. It is hoped that by combining the collective wisdom of neuroscience, immunology and pharmacology, into Neuroimmunopharmacology, we may more fully understanding the neuronal and immune complexities of how drugs of abuse, such as opioids, create their rewarding and addiction states. Such discoveries will point us in the direction such that one day soon we might successfully intervene to successfully treat drug addiction. This article is part of a Special Issue entitled 'NIDA 40th Anniversary Issue'. Copyright © 2013 Elsevier Ltd. All rights reserved.

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.

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.

How state and federal policies as well as advances in genome science contribute to the high cost of cancer drugs.

PubMed

Ramsey, Scott D

2015-04-01

During a time when cancer drug prices are increasing at an unprecedented rate, a debate has emerged as to whether these drugs continue to provide good value. In this article I argue that this debate is irrelevant because under today's highly distorted market, prices will not be set with value considerations in mind. As an alternative, I suggest considering the "value" of three policy changes—Medicare's "average sales price plus 6 percent" payment program, laws that require insurance coverage of all new cancer drugs, and the Affordable Care Act—that are fueling manufacturers' willingness to set higher prices. More important than these issues, however, is the revolution that is occurring in molecular biology and its impact on scientists' ability to detect changes in the cancer genome. The lowered cost of discovery is driving more competitors into the market, which under distorted pricing paradoxically encourages drug makers to charge ever higher prices for their products. Project HOPE—The People-to-People Health Foundation, Inc.

ChemoPy: freely available python package for computational biology and chemoinformatics.

PubMed

Cao, Dong-Sheng; Xu, Qing-Song; Hu, Qian-Nan; Liang, Yi-Zeng

2013-04-15

Molecular representation for small molecules has been routinely used in QSAR/SAR, virtual screening, database search, ranking, drug ADME/T prediction and other drug discovery processes. To facilitate extensive studies of drug molecules, we developed a freely available, open-source python package called chemoinformatics in python (ChemoPy) for calculating the commonly used structural and physicochemical features. It computes 16 drug feature groups composed of 19 descriptors that include 1135 descriptor values. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pairs fingerprints, topological torsion fingerprints and Morgan/circular fingerprints. By applying a semi-empirical quantum chemistry program MOPAC, ChemoPy can also compute a large number of 3D molecular descriptors conveniently. The python package, ChemoPy, is freely available via http://code.google.com/p/pychem/downloads/list, and it runs on Linux and MS-Windows. Supplementary data are available at Bioinformatics online.

Lead optimization attrition analysis (LOAA): a novel and general methodology for medicinal chemistry.

PubMed

Munson, Mark; Lieberman, Harvey; Tserlin, Elina; Rocnik, Jennifer; Ge, Jie; Fitzgerald, Maria; Patel, Vinod; Garcia-Echeverria, Carlos

2015-08-01

Herein, we report a novel and general method, lead optimization attrition analysis (LOAA), to benchmark two distinct small-molecule lead series using a relatively unbiased, simple technique and commercially available software. We illustrate this approach with data collected during lead optimization of two independent oncology programs as a case study. Easily generated graphics and attrition curves enabled us to calibrate progress and support go/no go decisions on each program. We believe that this data-driven technique could be used broadly by medicinal chemists and management to guide strategic decisions during drug discovery. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Next Step: 25 Discoveries That Could Change Our Lives.

ERIC Educational Resources Information Center

Science85, 1985

1985-01-01

Describes (in separate articles) 25 developments in science, technology, and medicine that have potential impact on the near future. They include discoveries related to space butterflies, drugs, twenty-first century software, experimental mathematics, brain drugs, egg development, ultrasmall microchips, the biology of birth, cancer-causing genes,…

A Methods-Based Biotechnology Course for Undergraduates

ERIC Educational Resources Information Center

Chakrabarti, Debopam

2009-01-01

This new course in biotechnology for upper division undergraduates provides a comprehensive overview of the process of drug discovery that is relevant to biopharmaceutical industry. The laboratory exercises train students in both cell-free and cell-based assays. Oral presentations by the students delve into recent progress in drug discovery.…

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.

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.

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.

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.

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

PubMed

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

2017-11-01

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

Getting the Most out of PubChem for Virtual Screening

PubMed Central

Kim, Sunghwan

2016-01-01

Introduction With the emergence of the “big data” era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge. Areas covered This article provides an overview of how PubChem’s data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery. Expert opinion PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area. PMID:27454129

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

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.

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

Biodiversity as a source of anticancer drugs.

PubMed

Tan, G; Gyllenhaal, C; Soejarto, D D

2006-03-01

Natural Products have been the most significant source of drugs and drug leads in history. Their dominant role in cancer chemotherapeutics is clear with about 74% of anticancer compounds being either natural products, or natural product-derived. The biodiversity of the world provides a resource of unlimited structural diversity for bioprospecting by international drug discovery programs such as the ICBGs and NCDDGs, the latter focusing exclusively on anticancer compounds. However, many sources of natural products remain largely untapped. Technology is gradually overcoming the traditional difficulties encountered in natural products research by improving access to biodiverse resources, and ensuring the compatibility of samples with high throughput procedures. However, the acquisition of predictive biodiversity remains challenging. Plant and organism species may be selected on the basis of potentially useful phytochemical composition by consulting ethnopharmacological, chemosystematic, and ecological information. On the conservation/political front, the Convention on Biological Diversity (CBD) is allaying the anxiety surrounding the notion of biopiracy, which has defeated many attempts to discover and develop new natural products for human benefit. As it becomes increasingly evident and important, the CBD fosters cooperation and adaptation to new regulations and collaborative research agreements with source countries. Even as the past inadequacies of combinatorial chemistry are being analyzed, the intrinsic value of natural products as a source of drug leads is being increasingly appreciated. Their rich structural and stereochemical characteristics make them valuable as templates for exploring novel molecular diversity with the aim of synthesizing lead generation libraries with greater biological relevance. This will ensure an ample supply of starting materials for screening against the multitude of potentially "druggable" targets uncovered by genomics technologies. Far from being mutually exclusive, biodiversity and genomics should be the driving force of drug discovery in the 21st century.

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.

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.

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

PubMed

Langer, Robert

2013-09-01

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

Application of a novel microtitre plate-based assay for the discovery of new inhibitors of DNA gyrase and DNA topoisomerase VI.

PubMed

Taylor, James A; Mitchenall, Lesley A; Rejzek, Martin; Field, Robert A; Maxwell, Anthony

2013-01-01

DNA topoisomerases are highly exploited targets for antimicrobial drugs. The spread of antibiotic resistance represents a significant threat to public health and necessitates the discovery of inhibitors that target topoisomerases in novel ways. However, the traditional assays for topoisomerase activity are not suitable for the high-throughput approaches necessary for drug discovery. In this study we validate a novel assay for screening topoisomerase inhibitors. A library of 960 compounds was screened against Escherichia coli DNA gyrase and archaeal Methanosarcina mazei DNA topoisomerase VI. Several novel inhibitors were identified for both enzymes, and subsequently characterised in vitro and in vivo. Inhibitors from the M. mazei topoisomerase VI screen were tested for their ability to inhibit Arabidopsis topoisomerase VI in planta. The data from this work present new options for antibiotic drug discovery and provide insight into the mechanism of topoisomerase VI.

Application of a Novel Microtitre Plate-Based Assay for the Discovery of New Inhibitors of DNA Gyrase and DNA Topoisomerase VI

PubMed Central

Taylor, James A.; Mitchenall, Lesley A.; Rejzek, Martin; Field, Robert A.; Maxwell, Anthony

2013-01-01

DNA topoisomerases are highly exploited targets for antimicrobial drugs. The spread of antibiotic resistance represents a significant threat to public health and necessitates the discovery of inhibitors that target topoisomerases in novel ways. However, the traditional assays for topoisomerase activity are not suitable for the high-throughput approaches necessary for drug discovery. In this study we validate a novel assay for screening topoisomerase inhibitors. A library of 960 compounds was screened against Escherichia coli DNA gyrase and archaeal Methanosarcina mazei DNA topoisomerase VI. Several novel inhibitors were identified for both enzymes, and subsequently characterised in vitro and in vivo. Inhibitors from the M. mazei topoisomerase VI screen were tested for their ability to inhibit Arabidopsis topoisomerase VI in planta. The data from this work present new options for antibiotic drug discovery and provide insight into the mechanism of topoisomerase VI. PMID:23469129

Ketamine and phencyclidine: the good, the bad and the unexpected

PubMed Central

Lodge, D; Mercier, M S

2015-01-01

The history of ketamine and phencyclidine from their development as potential clinical anaesthetics through drugs of abuse and animal models of schizophrenia to potential rapidly acting antidepressants is reviewed. The discovery in 1983 of the NMDA receptor antagonist property of ketamine and phencyclidine was a key step to understanding their pharmacology, including their psychotomimetic effects in man. This review describes the historical context and the course of that discovery and its expansion into other hallucinatory drugs. The relevance of these findings to modern hypotheses of schizophrenia and the implications for drug discovery are reviewed. The findings of the rapidly acting antidepressant effects of ketamine in man are discussed in relation to other glutamatergic mechanisms. PMID:26075331

Neoadjuvant trials in ER+ breast cancer: A tool for acceleration of drug development and discovery

PubMed Central

Guerrero-Zotano, Angel L.; Arteaga, Carlos L.

2017-01-01

Neoadjuvant therapy trials offer an excellent strategy for drug development and discovery in breast cancer, particularly in triple negative and HER2-overexpressing subtypes, where pathologic complete response is a good surrogate of long term patient benefit. For estrogen receptor (ER)-positive breast cancers, however, use of this strategy has been challenging because of the lack of validated surrogates of long term efficacy and the overall good prognosis of the majority of patients with this cancer subtype. We review below the clinical benefits of neodjuvant endocrine therapy for ER+/HER2-negative breast cancer, its use and limitations for drug development, prioritization of adjuvant and metastatic trials, and biomarker discovery. PMID:28495849

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.

Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds.

PubMed

Feng, Yan; Mitchison, Timothy J; Bender, Andreas; Young, Daniel W; Tallarico, John A

2009-07-01

Multi-parameter phenotypic profiling of small molecules provides important insights into their mechanisms of action, as well as a systems level understanding of biological pathways and their responses to small molecule treatments. It therefore deserves more attention at an early step in the drug discovery pipeline. Here, we summarize the technologies that are currently in use for phenotypic profiling--including mRNA-, protein- and imaging-based multi-parameter profiling--in the drug discovery context. We think that an earlier integration of phenotypic profiling technologies, combined with effective experimental and in silico target identification approaches, can improve success rates of lead selection and optimization in the drug discovery process.

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.

Quantitative Systems Pharmacology: A Case for Disease Models.

PubMed

Musante, C J; Ramanujan, S; Schmidt, B J; Ghobrial, O G; Lu, J; Heatherington, A C

2017-01-01

Quantitative systems pharmacology (QSP) has emerged as an innovative approach in model-informed drug discovery and development, supporting program decisions from exploratory research through late-stage clinical trials. In this commentary, we discuss the unique value of disease-scale "platform" QSP models that are amenable to reuse and repurposing to support diverse clinical decisions in ways distinct from other pharmacometrics strategies. © 2016 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of The American Society for Clinical Pharmacology and Therapeutics.

Translational aspects of blood-brain barrier transport and central nervous system effects of drugs: from discovery to patients.

PubMed

de Lange, E C M; Hammarlund-Udenaes, M

2015-04-01

The development of CNS drugs is associated with high failure rates. It is postulated that too much focus has been put on BBB permeability and too little on understanding BBB transport, which is the main limiting factor in drug delivery to the brain. An integrated approach to collecting, understanding, and handling pharmacokinetic-pharmacodynamic information from early discovery stages to the clinic is therefore recommended in order to improve translation to human drug treatment. © 2015 American Society for Clinical Pharmacology and Therapeutics.

A fortran program for Monte Carlo simulation of oil-field discovery sequences

USGS Publications Warehouse

Bohling, Geoffrey C.; Davis, J.C.

1993-01-01

We have developed a program for performing Monte Carlo simulation of oil-field discovery histories. A synthetic parent population of fields is generated as a finite sample from a distribution of specified form. The discovery sequence then is simulated by sampling without replacement from this parent population in accordance with a probabilistic discovery process model. The program computes a chi-squared deviation between synthetic and actual discovery sequences as a function of the parameters of the discovery process model, the number of fields in the parent population, and the distributional parameters of the parent population. The program employs the three-parameter log gamma model for the distribution of field sizes and employs a two-parameter discovery process model, allowing the simulation of a wide range of scenarios. ?? 1993.

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.

Lost but making progress—Where will new analgesic drugs come from?

PubMed Central

Borsook, David; Hargreaves, Richard; Bountra, Chas; Porreca, Frank

2015-01-01

There is a critical need for effective new pharmacotherapies for pain. The paucity of new drugs successfully reaching the clinic calls for a reassessment of current analgesic drug discovery approaches. Many points early in the discovery process present significant hurdles, making it critical to exploit advances in pain neurobiology to increase the probability of success. In this review, we highlight approaches that are being pursued vigorously by the pain community for drug discovery, including innovative preclinical pain models, insights from genetics, mechanistic phenotyping of pain patients, development of biomarkers, and emerging insights into chronic pain as a disorder of both the periphery and the brain. Collaborative efforts between pharmaceutical, academic, and public entities to advance research in these areas promise to de-risk potential targets, stimulate investment, and speed evaluation and development of better pain therapies. PMID:25122640

Discovery of potent HIV-1 non-nucleoside reverse transcriptase inhibitors from arylthioacetanilide structural motif.

PubMed

Li, Wenxin; Li, Xiao; De Clercq, Erik; Zhan, Peng; Liu, Xinyong

2015-09-18

The poor pharmacokinetics, side effects and particularly the rapid emergence of drug resistance compromise the efficiency of the clinically used anti-HIV drugs. Therefore, the discovery of novel and effective NNRTIs is still an extremely primary mission. Arylthioacetanilide family is one of the highly active HIV-1 NNRTIs against wide-type (WT) HIV-1 and a wide range of drug-resistant mutant strains. Especially, VRX-480773 and RDEA806 have been chosen as candidates for further clinical studies. In this article, we review the discovery and development of the arylthioacetanilides, and, especially, pay much attention to the structural modifications, SARs conclusions and molecular modeling. Moreover, several medicinal chemistry strategies to overcome drug resistance involved in the optimization process of arylthioacetanilides are highlighted, providing valuable clues for further investigations. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Drug discovery management, small is still beautiful: Why a number of companies get it wrong.

PubMed

Knutsen, Lars J S

2011-06-01

This review provides an account of why more companies involved in drug discovery fail than succeed at releasing the creative energy of gifted scientists, whose invention of new drugs they rely upon to remain at the forefront of the biopharma industry. Initiatives aimed at improving output of new chemical entities often have the opposite effect from that intended and scientists become demotivated. Those with drive, vision and enthusiasm may move to smaller companies to rediscover the spirit of discovery. Some executives fail to understand the psyche of researchers; an applied understanding of the intrinsic motivation of scientists would improve research performance. Entities that focus on smaller autonomous units and sound ethical values will discover the most innovative and successful new drugs. Copyright © 2011 Elsevier Ltd. All rights reserved.

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

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

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.

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.

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.

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.

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.

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

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.

75 FR 57965 - Center for Scientific Review; Notice of Closed Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-23

... Emphasis Panel; RFA Panel: Drug Discovery for the Nervous System. Date: October 14-15, 2010. Time: 8 a.m...: Center for Scientific Review Special Emphasis Panel; RFA Panel: Drug Discovery for the Nervous System... Review Special Emphasis Panel; Small Business: Visual Systems. Date: October 28, 2010. Time: 8 a.m. to 6...

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.

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.

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.

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

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

PubMed

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

2015-01-01

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

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.

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.

Importance of microbial natural products and the need to revitalize their discovery.

PubMed

Demain, Arnold L

2014-02-01

Microbes are the leading producers of useful natural products. Natural products from microbes and plants make excellent drugs. Significant portions of the microbial genomes are devoted to production of these useful secondary metabolites. A single microbe can make a number of secondary metabolites, as high as 50 compounds. The most useful products include antibiotics, anticancer agents, immunosuppressants, but products for many other applications, e.g., antivirals, anthelmintics, enzyme inhibitors, nutraceuticals, polymers, surfactants, bioherbicides, and vaccines have been commercialized. Unfortunately, due to the decrease in natural product discovery efforts, drug discovery has decreased in the past 20 years. The reasons include excessive costs for clinical trials, too short a window before the products become generics, difficulty in discovery of antibiotics against resistant organisms, and short treatment times by patients for products such as antibiotics. Despite these difficulties, technology to discover new drugs has advanced, e.g., combinatorial chemistry of natural product scaffolds, discoveries in biodiversity, genome mining, and systems biology. Of great help would be government extension of the time before products become generic.

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.

Metabolism of 4-Aminopiperidine Drugs by Cytochrome P450s: Molecular and Quantum Mechanical Insights into Drug Design

PubMed Central

2011-01-01

4-Aminopiperidines are a variety of therapeutic agents that are extensively metabolized by cytochrome P450s with CYP3A4 as a major isoform catalyzing their N-dealkylation reaction. However, its catalytic mechanism has not been fully elucidated in a molecular interaction level. Here, we applied theoretical approaches including the molecular mechanics-based docking to study the binding patterns and quantum mechanics-based reactivity calculations. They were supported by the experimental human liver microsomal clearance and P450 isoform phenotyping data. Our results herein suggested that the molecular interactions between substrates and CYP3A4 active site residues are essential for the N-dealkylation of 4-aminopiperidines. We also found that the serine 119 residue of CYP3A4 may serve as a key hydrogen-bonding partner to interact with the 4-amino groups of the studied drugs. The reactivity of the side chain α-carbon hydrogens drives the direction of catalysis as well. As a result, structure-based drug design approaches look promising to guide drug discovery programs into the optimized drug metabolism space. PMID:21841964