Emerging principles in protease-based drug discovery

PubMed Central

Drag, Marcin; Salvesen, Guy S.

2010-01-01

Proteases have an important role in many signalling pathways, and represent potential drug targets for diseases ranging from cardiovascular disorders to cancer, as well as for combating many parasites and viruses. Although inhibitors of well-established protease targets such as angiotensin-converting enzyme and HIV protease have shown substantial therapeutic success, developing drugs for new protease targets has proved challenging in recent years. This in part could be due to issues such as the difficulty of achieving selectivity when targeting protease active sites. This Perspective discusses the general principles in protease-based drug discovery, highlighting the lessons learned and the emerging strategies, such as targeting allosteric sites, which could help harness the therapeutic potential of new protease targets. PMID:20811381

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.

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.

Experiences in fragment-based drug discovery.

PubMed

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

2012-05-01

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

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.

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

PubMed

Penrod, Nadia M; Moore, Jason H

2014-02-05

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

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

PubMed Central

2014-01-01

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

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.

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.

Fragment-Based Phenotypic Lead Discovery: Cell-Based Assay to Target Leishmaniasis.

PubMed

Ayotte, Yann; Bilodeau, François; Descoteaux, Albert; LaPlante, Steven R

2018-05-02

A rapid and practical approach for the discovery of new chemical matter for targeting pathogens and diseases is described. Fragment-based phenotypic lead discovery (FPLD) combines aspects of traditional fragment-based lead discovery (FBLD), which involves the screening of small-molecule fragment libraries to target specific proteins, with phenotypic lead discovery (PLD), which typically involves the screening of drug-like compounds in cell-based assays. To enable FPLD, a diverse library of fragments was first designed, assembled, and curated. This library of soluble, low-molecular-weight compounds was then pooled to expedite screening. Axenic cultures of Leishmania promastigotes were screened, and single hits were then tested for leishmanicidal activity against intracellular amastigote forms in infected murine bone-marrow-derived macrophages without evidence of toxicity toward mammalian cells. These studies demonstrate that FPLD can be a rapid and effective means to discover hits that can serve as leads for further medicinal chemistry purposes or as tool compounds for identifying known or novel targets. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Aging Biology and Novel Targets for Drug Discovery

PubMed Central

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

2012-01-01

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

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.

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.

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

PubMed Central

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

2013-01-01

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

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.

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.

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

PubMed

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

2017-08-18

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

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

PubMed Central

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

2013-01-01

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

Molecular targets for flavivirus drug discovery

PubMed Central

Sampath, Aruna; Padmanabhan, R.

2009-01-01

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

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.

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.

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.

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.

Using in Vitro Evolution and Whole Genome Analysis To Discover Next Generation Targets for Antimalarial Drug Discovery

PubMed Central

2018-01-01

Although many new anti-infectives have been discovered and developed solely using phenotypic cellular screening and assay optimization, most researchers recognize that structure-guided drug design is more practical and less costly. In addition, a greater chemical space can be interrogated with structure-guided drug design. The practicality of structure-guided drug design has launched a search for the targets of compounds discovered in phenotypic screens. One method that has been used extensively in malaria parasites for target discovery and chemical validation is in vitro evolution and whole genome analysis (IVIEWGA). Here, small molecules from phenotypic screens with demonstrated antiparasitic activity are used in genome-based target discovery methods. In this Review, we discuss the newest, most promising druggable targets discovered or further validated by evolution-based methods, as well as some exceptions. PMID:29451780

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

PubMed

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

2017-08-25

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

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.

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

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'.

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.

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.

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

Analysis of A Drug Target-based Classification System using Molecular Descriptors.

PubMed

Lu, Jing; Zhang, Pin; Bi, Yi; Luo, Xiaomin

2016-01-01

Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes.

Structure based drug discovery for designing leads for the non-toxic metabolic targets in multi drug resistant Mycobacterium tuberculosis.

PubMed

Kaur, Divneet; Mathew, Shalu; Nair, Chinchu G S; Begum, Azitha; Jainanarayan, Ashwin K; Sharma, Mukta; Brahmachari, Samir K

2017-12-21

The problem of drug resistance and bacterial persistence in tuberculosis is a cause of global alarm. Although, the UN's Sustainable Development Goals for 2030 has targeted a Tb free world, the treatment gap exists and only a few new drug candidates are in the pipeline. In spite of large information from medicinal chemistry to 'omics' data, there has been a little effort from pharmaceutical companies to generate pipelines for the development of novel drug candidates against the multi drug resistant Mycobacterium tuberculosis. In the present study, we describe an integrated methodology; utilizing systems level information to optimize ligand selection to lower the failure rates at the pre-clinical and clinical levels. In the present study, metabolic targets (Rv2763c, Rv3247c, Rv1094, Rv3607c, Rv3048c, Rv2965c, Rv2361c, Rv0865, Rv0321, Rv0098, Rv0390, Rv3588c, Rv2244, Rv2465c and Rv2607) in M. tuberculosis, identified using our previous Systems Biology and data-intensive genome level analysis, have been used to design potential lead molecules, which are likely to be non-toxic. Various in silico drug discovery tools have been utilized to generate small molecular leads for each of the 15 targets with available crystal structures. The present study resulted in identification of 20 novel lead molecules including 4 FDA approved drugs (droxidropa, tetroxoprim, domperidone and nemonapride) which can be further taken for drug repurposing. This comprehensive integrated methodology, with both experimental and in silico approaches, has the potential to not only tackle the MDR form of Mtb but also the most important persister population of the bacterium, with a potential to reduce the failures in the Tb drug discovery. We propose an integrated approach of systems and structural biology for identifying targets that address the high attrition rate issue in lead identification and drug development We expect that this system level analysis will be applicable for identification of drug

Network-Based Approaches in Drug Discovery and Early Development

PubMed Central

Harrold, JM; Ramanathan, M; Mager, DE

2015-01-01

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

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

PubMed

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

2017-12-01

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

Preparation and Immunoaffinity Depletion of Fresh Frozen Tissue Homogenates for Mass Spectrometry-Based Proteomics in the Context of Drug Target/Biomarker Discovery.

PubMed

Prieto, DaRue A; Chan, King C; Johann, Donald J; Ye, Xiaoying; Whitely, Gordon; Blonder, Josip

2017-01-01

The discovery of novel drug targets and biomarkers via mass spectrometry (MS)-based proteomic analysis of clinical specimens has proven to be challenging. The wide dynamic range of protein concentration in clinical specimens and the high background/noise originating from highly abundant proteins in tissue homogenates and serum/plasma encompass two major analytical obstacles. Immunoaffinity depletion of highly abundant blood-derived proteins from serum/plasma is a well-established approach adopted by numerous researchers; however, the utilization of this technique for immunodepletion of tissue homogenates obtained from fresh frozen clinical specimens is lacking. We first developed immunoaffinity depletion of highly abundant blood-derived proteins from tissue homogenates, using renal cell carcinoma as a model disease, and followed this study by applying it to different tissue types. Tissue homogenate immunoaffinity depletion of highly abundant proteins may be equally important as is the recognized need for depletion of serum/plasma, enabling more sensitive MS-based discovery of novel drug targets, and/or clinical biomarkers from complex clinical samples. Provided is a detailed protocol designed to guide the researcher through the preparation and immunoaffinity depletion of fresh frozen tissue homogenates for two-dimensional liquid chromatography, tandem mass spectrometry (2D-LC-MS/MS)-based molecular profiling of tissue specimens in the context of drug target and/or biomarker discovery.

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

PubMed Central

Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun

2012-01-01

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709

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

PubMed

Rosell, Mireia; Fernández-Recio, Juan

2018-04-01

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

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

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.

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.

"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.

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

PubMed Central

2017-01-01

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

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.

Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

PubMed Central

Lionta, Evanthia; Spyrou, George; Vassilatis, Demetrios K.; Cournia, Zoe

2014-01-01

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process. PMID:25262799

Computational approaches for drug discovery.

PubMed

Hung, Che-Lun; Chen, Chi-Chun

2014-09-01

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

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 drug discovery using rational design.

PubMed

Jhoti, H

2007-01-01

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

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

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

The rise of fragment-based drug discovery.

PubMed

Murray, Christopher W; Rees, David C

2009-06-01

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

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

PubMed

Tanaka, Daisuke

2010-03-01

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

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.

Drug discovery in tuberculosis. New drug targets and antimycobacterial agents.

PubMed

Campaniço, André; Moreira, Rui; Lopes, Francisca

2018-04-25

Tuberculosis (TB) remains a major health problem worldwide. The infectious agent, Mycobacterium tuberculosis, has a unique ability to survive within the host, alternating between active and latent disease states, and escaping the immune system defences. The extended duration of anti-TB regimens and the increasing prevalence of multidrug- (MDR) and extensively drug-resistant (XDR) M. tuberculosis strains have created an urgent need for new antibiotics active against drug-resistant organisms and that can shorten standard therapy. However, despite success in identifying active compounds through phenotypic screens, the conversion of hits into novel chemical series and ultimately into clinical candidates is hampered by the poor efficacy in eliminating M. tuberculosis within different host compartments, including macrophages, as well as a lack of knowledge about the specific target(s) inhibited and/or upregulated. The current status of anti-TB lead generation has much improved over the last decade, as exemplified by the recent approval of bedaquiline and delamanid to treat MDR-TB and XDR-TB. This review provides a critical analysis on the strategies used to progress hit compounds into viable lead candidates, and how emerging targets may play a role in TB drug discovery in the near future. Four new relevant targets are addressed: the enoyl-acyl carrier protein reductase, InhA; the transmembrane transport protein large, MmpL3; the decaprenylphospho-beta-d-ribofuranose 2-oxidase, DprE1; and the ubiquinol-cytochrome C reductase, QcrB. Validated hit compounds for each target are presented and explored, and the medicinal chemistry strategies to expand SAR around novel chemotypes analyzed. In addition, very recent emerging targets are also discussed. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

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.

Fruitful research: drug target discovery for neurodegenerative diseases in Drosophila.

PubMed

Konsolaki, Mary

2013-12-01

Although vertebrate model systems have obvious advantages in the study of human disease, invertebrate organisms have contributed enormously to this field as well. The conservation of genome structure and physiology among organisms poses unexpected peculiarities, and the redundancy in certain gene families or the presence of polymorphisms that can slightly alter gene expression can, in certain instances, bring invertebrate systems, such as Drosophila, closer to humans than mice and vice versa. This necessitates the analysis of disease pathways in multiple model organisms. The author highlights findings from Drosophila models of neurodegenerative diseases that have occurred in the past few years. She also highlights and discusses various molecular, genetic and genomic tools used in flies, as well as methods for generating disease models. Finally, the author describes Drosophila models of Alzheimer's, Parkinson's tri-nucleotide repeat diseases, and Fragile X syndrome and summarizes insights in disease mechanisms that have been discovered directly in fly models. Full genome genetic screens in Drosophila can lead to the rapid identification of drug target candidates that can be subsequently validated in a vertebrate system. In addition, the Drosophila models of neurodegeneration may often show disease phenotypes that are absent in equivalent mouse models. The author believes that the extensive contribution of Drosophila to both new disease drug target discovery, in addition to target validation, makes them indispensible to drug discovery and development.

Bioinformatics in translational drug discovery.

PubMed

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

2017-08-31

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

[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.

Discovery: an interactive resource for the rational selection and comparison of putative drug target proteins in malaria

PubMed Central

Joubert, Fourie; Harrison, Claudia M; Koegelenberg, Riaan J; Odendaal, Christiaan J; de Beer, Tjaart AP

2009-01-01

Background Up to half a billion human clinical cases of malaria are reported each year, resulting in about 2.7 million deaths, most of which occur in sub-Saharan Africa. Due to the over-and misuse of anti-malarials, widespread resistance to all the known drugs is increasing at an alarming rate. Rational methods to select new drug target proteins and lead compounds are urgently needed. The Discovery system provides data mining functionality on extensive annotations of five malaria species together with the human and mosquito hosts, enabling the selection of new targets based on multiple protein and ligand properties. Methods A web-based system was developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein features used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions and host-pathogen interactions among others. Searching by chemical structure is also available. Results An in silico system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from Plasmodium falciparum. Conclusion The Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties. PMID:19642978

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.

Reactivity-based drug discovery using vitamin B(6)-derived pharmacophores.

PubMed

Wondrak, Georg T

2008-05-01

Endogenous reactive intermediates including photoexcited states of tissue chromophores, reactive oxygen species (ROS), reactive carbonyl species (RCS), transition metal ions, and Schiff bases have been implicated in the initiation and progression of diverse human pathologies including tumorigenesis, atherosclerosis, diabetes, and neurodegenerative disease. In contrast to structure-based approaches that target macromolecules by selective ligands, reactivity-based drug discovery uses chemical reagents as therapeutics that target reactive chemical species involved in human pathology. Reactivity-based design of prototype agents that effectively antagonize, modulate, and potentially even reverse the chemistry underlying tissue damage from oxidative and carbonyl stress therefore holds great promise in delivering significant therapeutic benefit. Apart from its established role as an essential cofactor for numerous enzymes, a large body of evidence suggests that B(6)-vitamers contain reactive pharmacophores that mediate therapeutically useful non-vitamin drug actions as potent antioxidants, metal chelators, carbonyl scavengers, Schiff base forming agents, and photosensitizers. Based on the fascinating chemical versatility of B(6)-derived pharmacophores, B(6)-vitamers are therefore promising lead compounds for reactivity-based drug design.

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

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.

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.

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

[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.

Postgenomic strategies in antibacterial drug discovery.

PubMed

Brötz-Oesterhelt, Heike; Sass, Peter

2010-10-01

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

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.

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.

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 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

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.

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.

Discovery-2: an interactive resource for the rational selection and comparison of putative drug target proteins in malaria

PubMed Central

2013-01-01

Background Drug resistance to anti-malarial compounds remains a serious problem, with resistance to newer pharmaceuticals developing at an alarming rate. The development of new anti-malarials remains a priority, and the rational selection of putative targets is a key element of this process. Discovery-2 is an update of the original Discovery in silico resource for the rational selection of putative drug target proteins, enabling researchers to obtain information for a protein which may be useful for the selection of putative drug targets, and to perform advanced filtering of proteins encoded by the malaria genome based on a series of molecular properties. Methods An updated in silico resource has been developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein properties used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions. Newly added features include drugability measures from ChEMBL, automated literature relations and links to clinical trial information. Searching by chemical structure is also available. Results The updated functionality of the Discovery-2 resource is presented, together with a detailed case study of the Plasmodium falciparum S-adenosyl-L-homocysteine hydrolase (PfSAHH) protein. A short example of a chemical search with pyrimethamine is also illustrated. Conclusion The updated Discovery-2 resource allows researchers to obtain detailed properties of proteins from the malaria genome, which may be of interest in the target selection process, and to perform advanced filtering and selection of proteins based on a relevant range of molecular characteristics. PMID:23537208

A Framework of Knowledge Integration and Discovery for Supporting Pharmacogenomics Target Predication of Adverse Drug Events: A Case Study of Drug-Induced Long QT Syndrome.

PubMed

Jiang, Guoqian; Wang, Chen; Zhu, Qian; Chute, Christopher G

2013-01-01

Knowledge-driven text mining is becoming an important research area for identifying pharmacogenomics target genes. However, few of such studies have been focused on the pharmacogenomics targets of adverse drug events (ADEs). The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework. We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach. We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.

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.

The enemy within: Targeting host-parasite interaction for antileishmanial drug discovery.

PubMed

Lamotte, Suzanne; Späth, Gerald F; Rachidi, Najma; Prina, Eric

2017-06-01

The state of antileishmanial chemotherapy is strongly compromised by the emergence of drug-resistant Leishmania. The evolution of drug-resistant phenotypes has been linked to the parasites' intrinsic genome instability, with frequent gene and chromosome amplifications causing fitness gains that are directly selected by environmental factors, including the presence of antileishmanial drugs. Thus, even though the unique eukaryotic biology of Leishmania and its dependence on parasite-specific virulence factors provide valid opportunities for chemotherapeutical intervention, all strategies that target the parasite in a direct fashion are likely prone to select for resistance. Here, we review the current state of antileishmanial chemotherapy and discuss the limitations of ongoing drug discovery efforts. We finally propose new strategies that target Leishmania viability indirectly via mechanisms of host-parasite interaction, including parasite-released ectokinases and host epigenetic regulation, which modulate host cell signaling and transcriptional regulation, respectively, to establish permissive conditions for intracellular Leishmania survival.

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

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

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

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.

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

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.

The enemy within: Targeting host–parasite interaction for antileishmanial drug discovery

PubMed Central

Späth, Gerald F.; Rachidi, Najma; Prina, Eric

2017-01-01

The state of antileishmanial chemotherapy is strongly compromised by the emergence of drug-resistant Leishmania. The evolution of drug-resistant phenotypes has been linked to the parasites’ intrinsic genome instability, with frequent gene and chromosome amplifications causing fitness gains that are directly selected by environmental factors, including the presence of antileishmanial drugs. Thus, even though the unique eukaryotic biology of Leishmania and its dependence on parasite-specific virulence factors provide valid opportunities for chemotherapeutical intervention, all strategies that target the parasite in a direct fashion are likely prone to select for resistance. Here, we review the current state of antileishmanial chemotherapy and discuss the limitations of ongoing drug discovery efforts. We finally propose new strategies that target Leishmania viability indirectly via mechanisms of host–parasite interaction, including parasite-released ectokinases and host epigenetic regulation, which modulate host cell signaling and transcriptional regulation, respectively, to establish permissive conditions for intracellular Leishmania survival. PMID:28594938

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.

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

PubMed

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

2018-02-02

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

2013 Philip S. Portoghese Medicinal Chemistry Lectureship: Drug Discovery Targeting Allosteric Sites†

PubMed Central

2015-01-01

The identification of sites on receptors topographically distinct from the orthosteric sites, so-called allosteric sites, has heralded novel approaches and modes of pharmacology for target modulation. Over the past 20 years, our understanding of allosteric modulation has grown significantly, and numerous advantages, as well as caveats (e.g., flat structure–activity relationships, species differences, “molecular switches”), have been identified. For multiple receptors and proteins, numerous examples have been described where unprecedented levels of selectivity are achieved along with improved physiochemical properties. While not a panacea, these novel approaches represent exciting opportunities for tool compound development to probe the pharmacology and therapeutic potential of discrete molecular targets, as well as new medicines. In this Perspective, in commemoration of the 2013 Philip S. Portoghese Medicinal Chemistry Lectureship (LindsleyC. W.Adventures in allosteric drug discovery. Presented at the 246th National Meeting of the American Chemical Society, Indianapolis, IN, September 10, 2013; The 2013 Portoghese Lectureship), several vignettes of drug discovery campaigns targeting novel allosteric mechanisms will be recounted, along with lessons learned and guidelines that have emerged for successful lead optimization. PMID:25180768

Drug discovery based on genetic and metabolic findings in schizophrenia.

PubMed

Dwyer, Donard S; Weeks, Kathrine; Aamodt, Eric J

2008-11-01

Recent progress in the genetics of schizophrenia provides the rationale for re-evaluating causative factors and therapeutic strategies for this disease. Here, we review the major candidate susceptibility genes and relate the aberrant function of these genes to defective regulation of energy metabolism in the schizophrenic brain. Disturbances in energy metabolism potentially lead to neurodevelopmental deficits, impaired function of the mature nervous system and failure to maintain neurites/dendrites and synaptic connections. Current antipsychotic drugs do not specifically address these underlying deficits; therefore, a new generation of more effective medications is urgently needed. Novel targets for future drug discovery are identified in this review. The coordinated application of structure-based drug design, systems biology and research on model organisms may greatly facilitate the search for next-generation antipsychotic drugs.

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.

Application of multi-target phytotherapeutic concept in malaria drug discovery: a systems biology approach in biomarker identification.

PubMed

Tarkang, Protus Arrey; Appiah-Opong, Regina; Ofori, Michael F; Ayong, Lawrence S; Nyarko, Alexander K

2016-01-01

There is an urgent need for new anti-malaria drugs with broad therapeutic potential and novel mode of action, for effective treatment and to overcome emerging drug resistance. Plant-derived anti-malarials remain a significant source of bioactive molecules in this regard. The multicomponent formulation forms the basis of phytotherapy. Mechanistic reasons for the poly-pharmacological effects of plants constitute increased bioavailability, interference with cellular transport processes, activation of pro-drugs/deactivation of active compounds to inactive metabolites and action of synergistic partners at different points of the same signaling cascade. These effects are known as the multi-target concept. However, due to the intrinsic complexity of natural products-based drug discovery, there is need to rethink the approaches toward understanding their therapeutic effect. This review discusses the multi-target phytotherapeutic concept and its application in biomarker identification using the modified reverse pharmacology - systems biology approach. Considerations include the generation of a product library, high throughput screening (HTS) techniques for efficacy and interaction assessment, High Performance Liquid Chromatography (HPLC)-based anti-malarial profiling and animal pharmacology. This approach is an integrated interdisciplinary implementation of tailored technology platforms coupled to miniaturized biological assays, to track and characterize the multi-target bioactive components of botanicals as well as identify potential biomarkers. While preserving biodiversity, this will serve as a primary step towards the development of standardized phytomedicines, as well as facilitate lead discovery for chemical prioritization and downstream clinical development.

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.

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

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.

The p38α mitogen-activated protein kinase as a central nervous system drug discovery target

PubMed Central

Borders, Aaron S; de Almeida, Lucia; Van Eldik, Linda J; Watterson, D Martin

2008-01-01

Protein kinases are critical modulators of a variety of cellular signal transduction pathways, and abnormal phosphorylation events can be a cause or contributor to disease progression in a variety of disorders. This has led to the emergence of protein kinases as an important new class of drug targets for small molecule therapeutics. A serine/threonine protein kinase, p38α mitogen-activated protein kinase (MAPK), is an established therapeutic target for peripheral inflammatory disorders because of its critical role in regulation of proinflammatory cytokine production. There is increasing evidence that p38α MAPK is also an important regulator of proinflammatory cytokine levels in the central nervous system, raising the possibility that the kinase may be a drug discovery target for central nervous system disorders where cytokine overproduction contributes to disease progression. Development of bioavailable, central nervous system-penetrant p38α MAPK inhibitors provides the required foundation for drug discovery campaigns targeting p38α MAPK in neurodegenerative disorders. PMID:19090985

The p38alpha mitogen-activated protein kinase as a central nervous system drug discovery target.

PubMed

Borders, Aaron S; de Almeida, Lucia; Van Eldik, Linda J; Watterson, D Martin

2008-12-03

Protein kinases are critical modulators of a variety of cellular signal transduction pathways, and abnormal phosphorylation events can be a cause or contributor to disease progression in a variety of disorders. This has led to the emergence of protein kinases as an important new class of drug targets for small molecule therapeutics. A serine/threonine protein kinase, p38alpha mitogen-activated protein kinase (MAPK), is an established therapeutic target for peripheral inflammatory disorders because of its critical role in regulation of proinflammatory cytokine production. There is increasing evidence that p38alpha MAPK is also an important regulator of proinflammatory cytokine levels in the central nervous system, raising the possibility that the kinase may be a drug discovery target for central nervous system disorders where cytokine overproduction contributes to disease progression. Development of bioavailable, central nervous system-penetrant p38alpha MAPK inhibitors provides the required foundation for drug discovery campaigns targeting p38alpha MAPK in neurodegenerative disorders.

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.

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

PubMed

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

2015-01-01

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

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.

Systems genetics for drug target discovery

PubMed Central

Penrod, Nadia M.; Cowper-Sal_lari, Richard; Moore, Jason H.

2011-01-01

The collection and analysis of genomic data has the potential to reveal novel druggable targets by providing insight into the genetic basis of disease. However, the number of drugs, targeting new molecular entities, approved by the US Food and Drug Administration (FDA) has not increased in the years since the collection of genomic data has become commonplace. The paucity of translatable results can be partly attributed to conventional analysis methods that test one gene at a time in an effort to identify disease-associated factors as candidate drug targets. By disengaging genetic factors from their position within the genetic regulatory system, much of the information stored within the genomic data set is lost. Here we discuss how genomic data is used to identify disease-associated genes or genomic regions, how disease-associated regions are validated as functional targets, and the role network analysis can play in bridging the gap between data generation and effective drug target identification. PMID:21862141

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.

Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

PubMed Central

Emig, Dorothea; Ivliev, Alexander; Pustovalova, Olga; Lancashire, Lee; Bureeva, Svetlana; Nikolsky, Yuri; Bessarabova, Marina

2013-01-01

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example. PMID:23593264

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

PubMed

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

2015-05-13

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

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.

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

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

PubMed

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

2012-01-01

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

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.

Novel drug discovery for Chagas disease.

PubMed

Moraes, Carolina B; Franco, Caio H

2016-01-01

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

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.

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.

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.

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.

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

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.

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.

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.

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

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

PubMed

Radwan, Mostafa; Serya, Rabah

2017-08-01

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

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.

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.

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.

Chronicles in drug discovery.

PubMed

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

2006-06-01

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

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

PubMed

Lipinski, Christopher A

2016-06-01

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

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.

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.

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.

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

PubMed Central

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

2016-01-01

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

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

PubMed Central

Sharma, Charu; Sadek, Bassem; Goyal, Sameer N.; Sinha, Satyesh; Ojha, Shreesh

2015-01-01

The cannabinoid molecules are derived from Cannabis sativa plant which acts on the cannabinoid receptors types 1 and 2 (CB1 and CB2) which have been explored as potential therapeutic targets for drug discovery and development. Currently, there are numerous cannabinoid based synthetic drugs used in clinical practice like the popular ones such as nabilone, dronabinol, and Δ9-tetrahydrocannabinol mediates its action through CB1/CB2 receptors. However, these synthetic based Cannabis derived compounds are known to exert adverse psychiatric effect and have also been exploited for drug abuse. This encourages us to find out an alternative and safe drug with the least psychiatric adverse effects. In recent years, many phytocannabinoids have been isolated from plants other than Cannabis. Several studies have shown that these phytocannabinoids show affinity, potency, selectivity, and efficacy towards cannabinoid receptors and inhibit endocannabinoid metabolizing enzymes, thus reducing hyperactivity of endocannabinoid systems. Also, these naturally derived molecules possess the least adverse effects opposed to the synthetically derived cannabinoids. Therefore, the plant based cannabinoid molecules proved to be promising and emerging therapeutic alternative. The present review provides an overview of therapeutic potential of ligands and plants modulating cannabinoid receptors that may be of interest to pharmaceutical industry in search of new and safer drug discovery and development for future therapeutics. PMID:26664449

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.

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.

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

PubMed

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

2011-12-01

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

Chloride channels as drug targets

PubMed Central

Verkman, Alan S.; Galietta, Luis J. V.

2013-01-01

Chloride channels represent a relatively under-explored target class for drug discovery as elucidation of their identity and physiological roles has lagged behind that of many other drug targets. Chloride channels are involved in a wide range of biological functions, including epithelial fluid secretion, cell-volume regulation, neuroexcitation, smooth-muscle contraction and acidification of intracellular organelles. Mutations in several chloride channels cause human diseases, including cystic fibrosis, macular degeneration, myotonia, kidney stones, renal salt wasting and hyperekplexia. Chloride-channel modulators have potential applications in the treatment of some of these disorders, as well as in secretory diarrhoeas, polycystic kidney disease, osteoporosis and hypertension. Modulators of GABAA (γ-aminobutyric acid A) receptor chloride channels are in clinical use and several small-molecule chloride-channel modulators are in preclinical development and clinical trials. Here, we discuss the broad opportunities that remain in chloride-channel-based drug discovery. PMID:19153558

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.

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.

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

PubMed

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

2016-07-01

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

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.

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.

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

NMR-based platform for fragment-based lead discovery used in screening BRD4-targeted compounds

PubMed Central

Yu, Jun-lan; Chen, Tian-tian; Zhou, Chen; Lian, Fu-lin; Tang, Xu-long; Wen, Yi; Shen, Jing-kang; Xu, Ye-chun; Xiong, Bing; Zhang, Nai-xia

2016-01-01

Aim: Fragment-based lead discovery (FBLD) is a complementary approach in drug research and development. In this study, we established an NMR-based FBLD platform that was used to screen novel scaffolds targeting human bromodomain of BRD4, and investigated the binding interactions between hit compounds and the target protein. Methods: 1D NMR techniques were primarily used to generate the fragment library and to screen compounds. The inhibitory activity of hits on the first bromodomain of BRD4 [BRD4(I)] was examined using fluorescence anisotropy binding assay. 2D NMR and X-ray crystallography were applied to characterize the binding interactions between hit compounds and the target protein. Results: An NMR-based fragment library containing 539 compounds was established, which were clustered into 56 groups (8–10 compounds in each group). Eight hits with new scaffolds were found to inhibit BRD4(I). Four out of the 8 hits (compounds 1, 2, 8 and 9) had IC50 values of 100–260 μmol/L, demonstrating their potential for further BRD4-targeted hit-to-lead optimization. Analysis of the binding interactions revealed that compounds 1 and 2 shared a common quinazolin core structure and bound to BRD4(I) in a non-acetylated lysine mimetic mode. Conclusion: An NMR-based platform for FBLD was established and used in discovery of BRD4-targeted compounds. Four potential hit-to-lead optimization candidates have been found, two of them bound to BRD4(I) in a non-acetylated lysine mimetic mode, being selective BRD4(I) inhibitors. PMID:27238211

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

PubMed Central

Dias, David M.; Ciulli, Alessio

2014-01-01

Nuclear magnetic resonance (NMR) spectroscopy is a pivotal method for structure-based and fragment-based lead discovery because it is one of the most robust techniques to provide information on protein structure, dynamics and interaction at an atomic level in solution. Nowadays, in most ligand screening cascades, NMR-based methods are applied to identify and structurally validate small molecule binding. These can be high-throughput and are often used synergistically with other biophysical assays. Here, we describe current state-of-the-art in the portfolio of available NMR-based experiments that are used to aid early-stage lead discovery. We then focus on multi-protein complexes as targets and how NMR spectroscopy allows studying of interactions within the high molecular weight assemblies that make up a vast fraction of the yet untargeted proteome. Finally, we give our perspective on how currently available methods could build an improved strategy for drug discovery against such challenging targets. PMID:25175337

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

PubMed

Dias, David M; Ciulli, Alessio

2014-01-01

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

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.

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

PubMed

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

2012-01-26

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

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.

DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.

PubMed

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-06-15

Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. http://datamining-iip.fudan.edu.cn/service/DrugE-Rank zhusf@fudan.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Yeast as a potential vehicle for neglected tropical disease drug discovery.

PubMed

Denny, P W; Steel, P G

2015-01-01

High-throughput screening (HTS) efforts for neglected tropical disease (NTD) drug discovery have recently received increased attention because several initiatives have begun to attempt to reduce the deficit in new and clinically acceptable therapies for this spectrum of infectious diseases. HTS primarily uses two basic approaches, cell-based and in vitro target-directed screening. Both of these approaches have problems; for example, cell-based screening does not reveal the target or targets that are hit, whereas in vitro methodologies lack a cellular context. Furthermore, both can be technically challenging, expensive, and difficult to miniaturize for ultra-HTS [(u)HTS]. The application of yeast-based systems may overcome some of these problems and offer a cost-effective platform for target-directed screening within a eukaryotic cell context. Here, we review the advantages and limitations of the technologies that may be used in yeast cell-based, target-directed screening protocols, and we discuss how these are beginning to be used in NTD drug discovery. © 2014 Society for Laboratory Automation and Screening.

Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

PubMed

Alaimo, Salvatore; Giugno, Rosalba; Pulvirenti, Alfredo

2016-01-01

The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

Stem cell signaling as a target for novel drug discovery: recent progress in the WNT and Hedgehog pathways.

PubMed

An, Songzhu Michael; Ding, Qiang Peter; Li, Ling-song

2013-06-01

One of the most exciting fields in biomedical research over the past few years is stem cell biology, and therapeutic application of stem cells to replace the diseased or damaged tissues is also an active area in development. Although stem cell therapy has a number of technical challenges and regulatory hurdles to overcome, the use of stem cells as tools in drug discovery supported by mature technologies and established regulatory paths is expected to generate more immediate returns. In particular, the targeting of stem cell signaling pathways is opening up a new avenue for drug discovery. Aberrations in these pathways result in various diseases, including cancer, fibrosis and degenerative diseases. A number of drug targets in stem cell signaling pathways have been identified. Among them, WNT and Hedgehog are two most important signaling pathways, which are the focus of this review. A hedgehog pathway inhibitor, vismodegib (Erivedge), has recently been approved by the US FDA for the treatment of skin cancer, while several drug candidates for the WNT pathway are entering clinical trials. We have discovered that the stem cell signaling pathways respond to traditional Chinese medicines. Substances isolated from herbal medicine may act specifically on components of stem cell signaling pathways with high affinities. As many of these events can be explained through molecular interactions, these phenomena suggest that discovery of stem cell-targeting drugs from natural products may prove to be highly successful.

Zebrafish as tools for drug discovery.

PubMed

MacRae, Calum A; Peterson, Randall T

2015-10-01

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

Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

PubMed

Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

2015-07-15

Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

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

Drug Target Discovery Methods In Targeting Neurotropic Parasitic Amoebae.

PubMed

Baig, Abdul Mannan; Waliani, Nuzair; Karim, Saiqa

2018-02-21

Neurotropic parasitic amoebal infections have imposed an enormous challenge to chemotherapy in patients who fall victims to the infections caused by them. Conventional antibiotics that are given to treat these infections have a low patient compliance because of the serious adverse effects that are associated with their use. Additionally, the growing incidence of the development of drug resistance by the neurotropic parasites like Naegleria fowleri, Balamuthia mandrillaris, and Acanthamoeba spp has made the drug therapy more challenging. Recent studies have reported some cellular targets in the neurotropic parasitic Acanthamoeba that are used as receptors by human neurotransmitters like acetylcholine. This Viewpoint attempts to highlight the novel methodologies that use drug assays and structural modeling to uncover cellular targets of diverse groups of drugs and the safety issues of the drugs proposed for their use in brain infections caused by the neurotropic parasitic amoebae.

Proteogenomic studies on cancer drug resistance: towards biomarker discovery and target identification.

PubMed

Fu, Shuyue; Liu, Xiang; Luo, Maochao; Xie, Ke; Nice, Edouard C; Zhang, Haiyuan; Huang, Canhua

2017-04-01

Chemoresistance is a major obstacle for current cancer treatment. Proteogenomics is a powerful multi-omics research field that uses customized protein sequence databases generated by genomic and transcriptomic information to identify novel genes (e.g. noncoding, mutation and fusion genes) from mass spectrometry-based proteomic data. By identifying aberrations that are differentially expressed between tumor and normal pairs, this approach can also be applied to validate protein variants in cancer, which may reveal the response to drug treatment. Areas covered: In this review, we will present recent advances in proteogenomic investigations of cancer drug resistance with an emphasis on integrative proteogenomic pipelines and the biomarker discovery which contributes to achieving the goal of using precision/personalized medicine for cancer treatment. Expert commentary: The discovery and comprehensive understanding of potential biomarkers help identify the cohort of patients who may benefit from particular treatments, and will assist real-time clinical decision-making to maximize therapeutic efficacy and minimize adverse effects. With the development of MS-based proteomics and NGS-based sequencing, a growing number of proteogenomic tools are being developed specifically to investigate cancer drug resistance.

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).

Cell-based assays in GPCR drug discovery.

PubMed

Siehler, Sandra

2008-04-01

G protein-coupled receptors (GPCRs) transmit extracellular signals into the intracellular space, and play key roles in the physiological regulation of virtually every cell and tissue. Characteristic for the GPCR superfamily of cell surface receptors are their seven transmembrane-spanning alpha-helices, an extracellular N terminus and intracellular C-terminal tail. Besides transmission of extracellular signals, their activity is modulated by cellular signals in an auto- or transregulatory fashion. The molecular complexity of GPCRs and their regulated signaling networks triggered the interest in academic research groups to explore them further, and their drugability and role in pathophysiology triggers pharmaceutical research towards small molecular weight ligands and therapeutic antibodies. About 30% of marketed drugs target GPCRs, which underlines the importance of this target class. This review describes current and emerging cellular assays for the ligand discovery of GPCRs.

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

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.

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.

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.

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.

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.

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 ".

Drug target inference through pathway analysis of genomics data

PubMed Central

Ma, Haisu; Zhao, Hongyu

2013-01-01

Statistical modeling coupled with bioinformatics is commonly used for drug discovery. Although there exist many approaches for single target based drug design and target inference, recent years have seen a paradigm shift to system-level pharmacological research. Pathway analysis of genomics data represents one promising direction for computational inference of drug targets. This article aims at providing a comprehensive review on the evolving issues is this field, covering methodological developments, their pros and cons, as well as future research directions. PMID:23369829

Scientometrics of drug discovery efforts: pain-related molecular targets.

PubMed

Kissin, Igor

2015-01-01

The aim of this study was to make a scientometric assessment of drug discovery efforts centered on pain-related molecular targets. The following scientometric indices were used: the popularity index, representing the share of articles (or patents) on a specific topic among all articles (or patents) on pain over the same 5-year period; the index of change, representing the change in the number of articles (or patents) on a topic from one 5-year period to the next; the index of expectations, representing the ratio of the number of all types of articles on a topic in the top 20 journals relative to the number of articles in all (>5,000) biomedical journals covered by PubMed over a 5-year period; the total number of articles representing Phase I-III trials of investigational drugs over a 5-year period; and the trial balance index, a ratio of Phase I-II publications to Phase III publications. Articles (PubMed database) and patents (US Patent and Trademark Office database) on 17 topics related to pain mechanisms were assessed during six 5-year periods from 1984 to 2013. During the most recent 5-year period (2009-2013), seven of 17 topics have demonstrated high research activity (purinergic receptors, serotonin, transient receptor potential channels, cytokines, gamma aminobutyric acid, glutamate, and protein kinases). However, even with these seven topics, the index of expectations decreased or did not change compared with the 2004-2008 period. In addition, publications representing Phase I-III trials of investigational drugs (2009-2013) did not indicate great enthusiasm on the part of the pharmaceutical industry regarding drugs specifically designed for treatment of pain. A promising development related to the new tool of molecular targeting, ie, monoclonal antibodies, for pain treatment has not yet resulted in real success. This approach has not yet demonstrated clinical effectiveness (at least with nerve growth factor) much beyond conventional analgesics, when its

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.

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

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

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

PubMed

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

2009-08-01

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

The future of crystallography in drug discovery

PubMed Central

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

2014-01-01

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

Fragment-based approaches to the discovery of kinase inhibitors.

PubMed

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

2014-01-01

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

Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens.

PubMed

Sosa, Ezequiel J; Burguener, Germán; Lanzarotti, Esteban; Defelipe, Lucas; Radusky, Leandro; Pardo, Agustín M; Marti, Marcelo; Turjanski, Adrián G; Fernández Do Porto, Darío

2018-01-04

Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with both, gene information and experimental results. In this sense, there is a lack of an online resource that allows genome wide-based data consolidation from diverse sources together with thorough bioinformatic analysis that allows easy filtering and scoring for fast target selection for drug discovery. Here, we present Target-Pathogen database (http://target.sbg.qb.fcen.uba.ar/patho), designed and developed as an online resource that allows the integration and weighting of protein information such as: function, metabolic role, off-targeting, structural properties including druggability, essentiality and omic experiments, to facilitate the identification and prioritization of candidate drug targets in pathogens. We include in the database 10 genomes of some of the most relevant microorganisms for human health (Mycobacterium tuberculosis, Mycobacterium leprae, Klebsiella pneumoniae, Plasmodium vivax, Toxoplasma gondii, Leishmania major, Wolbachia bancrofti, Trypanosoma brucei, Shigella dysenteriae and Schistosoma Smanosoni) and show its applicability. New genomes can be uploaded upon request. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens

PubMed Central

Sosa, Ezequiel J; Burguener, Germán; Lanzarotti, Esteban; Radusky, Leandro; Pardo, Agustín M; Marti, Marcelo

2018-01-01

Abstract Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with both, gene information and experimental results. In this sense, there is a lack of an online resource that allows genome wide-based data consolidation from diverse sources together with thorough bioinformatic analysis that allows easy filtering and scoring for fast target selection for drug discovery. Here, we present Target-Pathogen database (http://target.sbg.qb.fcen.uba.ar/patho), designed and developed as an online resource that allows the integration and weighting of protein information such as: function, metabolic role, off-targeting, structural properties including druggability, essentiality and omic experiments, to facilitate the identification and prioritization of candidate drug targets in pathogens. We include in the database 10 genomes of some of the most relevant microorganisms for human health (Mycobacterium tuberculosis, Mycobacterium leprae, Klebsiella pneumoniae, Plasmodium vivax, Toxoplasma gondii, Leishmania major, Wolbachia bancrofti, Trypanosoma brucei, Shigella dysenteriae and Schistosoma Smanosoni) and show its applicability. New genomes can be uploaded upon request. PMID:29106651

Optogenetic Approaches to Drug Discovery in Neuroscience and Beyond.

PubMed

Zhang, Hongkang; Cohen, Adam E

2017-07-01

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

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.

Multi-target drugs: the trend of drug research and development.

PubMed

Lu, Jin-Jian; Pan, Wei; Hu, Yuan-Jia; Wang, Yi-Tao

2012-01-01

Summarizing the status of drugs in the market and examining the trend of drug research and development is important in drug discovery. In this study, we compared the drug targets and the market sales of the new molecular entities approved by the U.S. Food and Drug Administration from January 2000 to December 2009. Two networks, namely, the target-target and drug-drug networks, have been set up using the network analysis tools. The multi-target drugs have much more potential, as shown by the network visualization and the market trends. We discussed the possible reasons and proposed the rational strategies for drug research and development in the future.

Regulators of G-protein signaling and their Gα substrates: promises and challenges in their use as drug discovery targets.

PubMed

Kimple, Adam J; Bosch, Dustin E; Giguère, Patrick M; Siderovski, David P

2011-09-01

Because G-protein coupled receptors (GPCRs) continue to represent excellent targets for the discovery and development of small-molecule therapeutics, it is posited that additional protein components of the signal transduction pathways emanating from activated GPCRs themselves are attractive as drug discovery targets. This review considers the drug discovery potential of two such components: members of the "regulators of G-protein signaling" (RGS protein) superfamily, as well as their substrates, the heterotrimeric G-protein α subunits. Highlighted are recent advances, stemming from mouse knockout studies and the use of "RGS-insensitivity" and fast-hydrolysis mutations to Gα, in our understanding of how RGS proteins selectively act in (patho)physiologic conditions controlled by GPCR signaling and how they act on the nucleotide cycling of heterotrimeric G-proteins in shaping the kinetics and sensitivity of GPCR signaling. Progress is documented regarding recent activities along the path to devising screening assays and chemical probes for the RGS protein target, not only in pursuits of inhibitors of RGS domain-mediated acceleration of Gα GTP hydrolysis but also to embrace the potential of finding allosteric activators of this RGS protein action. The review concludes in considering the Gα subunit itself as a drug target, as brought to focus by recent reports of activating mutations to GNAQ and GNA11 in ocular (uveal) melanoma. We consider the likelihood of several strategies for antagonizing the function of these oncogene alleles and their gene products, including the use of RGS proteins with Gα(q) selectivity.

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

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.

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

Drug Target Mining and Analysis of the Chinese Tree Shrew for Pharmacological Testing

PubMed Central

Liu, Jie; Lee, Wen-hui; Zhang, Yun

2014-01-01

The discovery of new drugs requires the development of improved animal models for drug testing. The Chinese tree shrew is considered to be a realistic candidate model. To assess the potential of the Chinese tree shrew for pharmacological testing, we performed drug target prediction and analysis on genomic and transcriptomic scales. Using our pipeline, 3,482 proteins were predicted to be drug targets. Of these predicted targets, 446 and 1,049 proteins with the highest rank and total scores, respectively, included homologs of targets for cancer chemotherapy, depression, age-related decline and cardiovascular disease. Based on comparative analyses, more than half of drug target proteins identified from the tree shrew genome were shown to be higher similarity to human targets than in the mouse. Target validation also demonstrated that the constitutive expression of the proteinase-activated receptors of tree shrew platelets is similar to that of human platelets but differs from that of mouse platelets. We developed an effective pipeline and search strategy for drug target prediction and the evaluation of model-based target identification for drug testing. This work provides useful information for future studies of the Chinese tree shrew as a source of novel targets for drug discovery research. PMID:25105297

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

Update on apelin peptides as putative targets for cardiovascular drug discovery.

PubMed

Charles, Christopher J

2011-06-01

The physiological importance of GPCR/ligand pathways is highlighted by the fact that numerous pathologies are attributed to their signaling dysfunction. Over 50% of the pharmaceutical drugs currently used to treat human disease are based on compounds that interact with GPCRs. Apelin/APJ constitutes a novel endogenous peptide/GPCR system proposed to be involved in a wide range of physiological functions. Early evidence suggests that apelin/APJ may hold promise as a target for development of novel therapeutic agents which may counteract a number of pathologies including cardiovascular disease. Despite advances in treatment of cardiovascular disease, incidence, prevalence, morbidity and economic costs remain high necessitating the development of new treatment paradigms. This review summarizes apelin/APJ structure, distribution and regulation; presents evidence for a role of apelin in pressure/volume homeostasis and in the pathophysiology of cardiovascular disease; summarizes data on beneficial effects of apelin in preclinical, animal models of cardiovascular disease and measurement of plasma levels of apelin across the full spectrum of cardiovascular disease in humans; and notes the first studies describing bioactivity of apelin peptides in human healthy volunteers and patients with heart failure. More clarity is needed on the precise physiological/pathophysiological role of the apelin/APJ system in human health and disease. Nonetheless, preclinical studies and initial studies in humans show that APJ antagonism may represent a novel therapeutic target for patients with cardiovascular disease. Development of appropriately validated assays for apelin will clarify circulating levels of the peptide in health and disease. Development of suitable agonists/antagonists will pave the way for much needed future studies essential for advancing this promising field of drug discovery.

In silico pharmacology for drug discovery: applications to targets and beyond

PubMed Central

Ekins, S; Mestres, J; Testa, B

2007-01-01

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets. PMID:17549046

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

Deep-Learning-Based Drug-Target Interaction Prediction.

PubMed

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

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.

Protein Complex Production from the Drug Discovery Standpoint.

PubMed

Moarefi, Ismail

2016-01-01

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

ACFIS: a web server for fragment-based drug discovery

PubMed Central

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

2016-01-01

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

Potential Targets for Antifungal Drug Discovery Based on Growth and Virulence in Candida albicans

PubMed Central

Li, Xiuyun; Hou, Yinglong; Yue, Longtao; Liu, Shuyuan; Du, Juan

2015-01-01

Fungal infections, especially infections caused by Candida albicans, remain a challenging problem in clinical settings. Despite the development of more-effective antifungal drugs, their application is limited for various reasons. Thus, alternative treatments with drugs aimed at novel targets in C. albicans are needed. Knowledge of growth and virulence in fungal cells is essential not only to understand their pathogenic mechanisms but also to identify potential antifungal targets. This article reviews the current knowledge of the mechanisms of growth and virulence in C. albicans and examines potential targets for the development of new antifungal drugs. PMID:26195510

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.

Shifting from the single- to the multitarget paradigm in drug discovery

PubMed Central

Medina-Franco, José L.; Giulianotti, Marc A.; Welmaker, Gregory S.; Houghten, Richard A.

2013-01-01

Increasing evidence that several drug compounds exert their effects through interactions with multiple targets is boosting the development of research fields that challenge the data reductionism approach. In this article, we review and discuss the concepts of drug repurposing, polypharmacology, chemogenomics, phenotypic screening and highthroughput in vivo testing of mixture-based libraries in an integrated manner. These research fields offer alternatives to the current paradigm of drug discovery, from a one target–one drug model to a multiple-target approach. Furthermore, the goals of lead identification are being expanded accordingly to identify not only ‘key’ compounds that fit with a single-target ‘lock’, but also ‘master key’ compounds that favorably interact with multiple targets (i.e. operate a set of desired locks to gain access to the expected clinical effects). PMID:23340113

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.

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

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.

GalenOWL: Ontology-based drug recommendations discovery

PubMed Central

2012-01-01

Background Identification of drug-drug and drug-diseases interactions can pose a difficult problem to cope with, as the increasingly large number of available drugs coupled with the ongoing research activities in the pharmaceutical domain, make the task of discovering relevant information difficult. Although international standards, such as the ICD-10 classification and the UNII registration, have been developed in order to enable efficient knowledge sharing, medical staff needs to be constantly updated in order to effectively discover drug interactions before prescription. The use of Semantic Web technologies has been proposed in earlier works, in order to tackle this problem. Results This work presents a semantic-enabled online service, named GalenOWL, capable of offering real time drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standards such as the aforementioned ICD-10 and UNII, provide the backbone of the common representation of medical data, while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. A comparison of the developed ontology-based system with a similar system developed using a traditional business logic rule engine is performed, giving insights on the advantages and drawbacks of both implementations. Conclusions The use of Semantic Web technologies has been found to be a good match for developing drug recommendation systems. Ontologies can effectively encapsulate medical knowledge and rule-based reasoning can capture and encode the drug interactions knowledge. PMID:23256945

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

Dual-acting of Hybrid Compounds - A New Dawn in the Discovery of Multi-target Drugs: Lead Generation Approaches.

PubMed

Abdolmaleki, Azizeh; Ghasemi, Jahan B

2017-01-01

Finding high quality beginning compounds is a critical job at the start of the lead generation stage for multi-target drug discovery (MTDD). Designing hybrid compounds as selective multitarget chemical entity is a challenge, opportunity, and new idea to better act against specific multiple targets. One hybrid molecule is formed by two (or more) pharmacophore group's participation. So, these new compounds often exhibit two or more activities going about as multi-target drugs (mtdrugs) and may have superior safety or efficacy. Application of integrating a range of information and sophisticated new in silico, bioinformatics, structural biology, pharmacogenomics methods may be useful to discover/design, and synthesis of the new hybrid molecules. In this regard, many rational and screening approaches have followed by medicinal chemists for the lead generation in MTDD. Here, we review some popular lead generation approaches that have been used for designing multiple ligands (DMLs). This paper focuses on dual- acting chemical entities that incorporate a part of two drugs or bioactive compounds to compose hybrid molecules. Also, it presents some of key concepts and limitations/strengths of lead generation methods by comparing combination framework method with screening approaches. Besides, a number of examples to represent applications of hybrid molecules in the drug discovery are included. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Scientometrics of drug discovery efforts: pain-related molecular targets

PubMed Central

Kissin, Igor

2015-01-01

The aim of this study was to make a scientometric assessment of drug discovery efforts centered on pain-related molecular targets. The following scientometric indices were used: the popularity index, representing the share of articles (or patents) on a specific topic among all articles (or patents) on pain over the same 5-year period; the index of change, representing the change in the number of articles (or patents) on a topic from one 5-year period to the next; the index of expectations, representing the ratio of the number of all types of articles on a topic in the top 20 journals relative to the number of articles in all (>5,000) biomedical journals covered by PubMed over a 5-year period; the total number of articles representing Phase I–III trials of investigational drugs over a 5-year period; and the trial balance index, a ratio of Phase I–II publications to Phase III publications. Articles (PubMed database) and patents (US Patent and Trademark Office database) on 17 topics related to pain mechanisms were assessed during six 5-year periods from 1984 to 2013. During the most recent 5-year period (2009–2013), seven of 17 topics have demonstrated high research activity (purinergic receptors, serotonin, transient receptor potential channels, cytokines, gamma aminobutyric acid, glutamate, and protein kinases). However, even with these seven topics, the index of expectations decreased or did not change compared with the 2004–2008 period. In addition, publications representing Phase I–III trials of investigational drugs (2009–2013) did not indicate great enthusiasm on the part of the pharmaceutical industry regarding drugs specifically designed for treatment of pain. A promising development related to the new tool of molecular targeting, ie, monoclonal antibodies, for pain treatment has not yet resulted in real success. This approach has not yet demonstrated clinical effectiveness (at least with nerve growth factor) much beyond conventional analgesics

DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

PubMed Central

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-01-01

Motivation: Identifying drug–target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug–target interactions of new candidate drugs or targets. Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. Availability: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307615

Epigenetic Drug Repositioning for Alzheimer's Disease Based on Epigenetic Targets in Human Interactome.

PubMed

Chatterjee, Paulami; Roy, Debjani; Rathi, Nitin

2018-01-01

Epigenetics has emerged as an important field in drug discovery. Alzheimer's disease (AD), the leading neurodegenerative disorder throughout the world, is shown to have an epigenetic basis. Currently, there are very few effective epigenetic drugs available for AD. In this work, for the first time we have proposed 14 AD repositioning epigenetic drugs and identified their targets from extensive human interactome. Interacting partners of the AD epigenetic proteins were identified from the extensive human interactome to construct Epigenetic Protein-Protein Interaction Network (EP-PPIN). Epigenetic Drug-Target Network (EP-DTN) was constructed with the drugs associated with the proteins of EP-PPIN. Regulation of non-coding RNAs associated with the target proteins of these drugs was also studied. AD related target proteins, epigenetic targets, enriched pathways, and functional categories of the proposed repositioning drugs were also studied. The proposed 14 AD epigenetic repositioning drugs have overlapping targets and miRs with known AD epigenetic targets and miRs. Furthermore, several shared functional categories and enriched pathways were obtained for these drugs with FDA approved epigenetic drugs and known AD drugs. The findings of our work might provide insight into future AD epigenetic-therapeutics.

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

PubMed

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

2014-01-01

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

Predicting Drug-Target Interactions Based on Small Positive Samples.

PubMed

Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

2018-01-01

A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance

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

PubMed

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

2016-07-08

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

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.

Blueprint for antimicrobial hit discovery targeting metabolic networks.

PubMed

Shen, Y; Liu, J; Estiu, G; Isin, B; Ahn, Y-Y; Lee, D-S; Barabási, A-L; Kapatral, V; Wiest, O; Oltvai, Z N

2010-01-19

Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy.

Modulation of Epigenetic Targets for Anticancer Therapy: Clinicopathological Relevance, Structural Data and Drug Discovery Perspectives

PubMed Central

Andreol, Federico; Barbosa, Arménio Jorge Moura; Daniele Parenti, Marco; Rio, Alberto Del

2013-01-01

Research on cancer epigenetics has flourished in the last decade. Nevertheless growing evidence point on the importance to understand the mechanisms by which epigenetic changes regulate the genesis and progression of cancer growth. Several epigenetic targets have been discovered and are currently under validation for new anticancer therapies. Drug discovery approaches aiming to target these epigenetic enzymes with small-molecules inhibitors have produced the first pre-clinical and clinical outcomes and many other compounds are now entering the pipeline as new candidate epidrugs. The most studied targets can be ascribed to histone deacetylases and DNA methyltransferases, although several other classes of enzymes are able to operate post-translational modifications to histone tails are also likely to represent new frontiers for therapeutic interventions. By acknowledging that the field of cancer epigenetics is evolving with an impressive rate of new findings, with this review we aim to provide a current overview of pre-clinical applications of small-molecules for cancer pathologies, combining them with the current knowledge of epigenetic targets in terms of available structural data and drug design perspectives. PMID:23016851

Lessons from hot spot analysis for fragment-based drug discovery

PubMed Central

Hall, David R.; Vajda, Sandor

2015-01-01

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

Importance of target-mediated drug disposition for small molecules.

PubMed

Smith, Dennis A; van Waterschoot, Robert A B; Parrott, Neil J; Olivares-Morales, Andrés; Lavé, Thierry; Rowland, Malcolm

2018-06-18

Target concentration is typically not considered in drug discovery. However, if targets are expressed at relatively high concentrations and compounds have high affinity, such that most of the drug is bound to its target, in vitro screens can give unreliable information on compound affinity. In vivo, a similar situation will generate pharmacokinetic (PK) profiles that deviate greatly from those normally expected, owing to target binding affecting drug distribution and clearance. Such target-mediated drug disposition (TMDD) effects on small molecules have received little attention and might only become apparent during clinical trials, with the potential for data misinterpretation. TMDD also confounds human microdosing approaches by providing therapeutically unrepresentative PK profiles. Being aware of these phenomena will improve the likelihood of successful drug discovery and development. Copyright © 2018. Published by Elsevier Ltd.

Drug-Target Interactions: Prediction Methods and Applications.

PubMed

Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

2018-01-01

Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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

PubMed

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

2015-02-01

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

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.

The Expanding Diversity of Mycobacterium tuberculosis Drug Targets.

PubMed

Wellington, Samantha; Hung, Deborah T

2018-05-11

After decades of relative inactivity, a large increase in efforts to discover antitubercular therapeutics has brought insights into the biology of Mycobacterium tuberculosis (Mtb) and promising new drugs such as bedaquiline, which inhibits ATP synthase, and the nitroimidazoles delamanid and pretomanid, which inhibit both mycolic acid synthesis and energy production. Despite these advances, the drug discovery pipeline remains underpopulated. The field desperately needs compounds with novel mechanisms of action capable of inhibiting multi- and extensively drug -resistant Mtb (M/XDR-TB) and, potentially, nonreplicating Mtb with the hope of shortening the duration of required therapy. New knowledge about Mtb, along with new methods and technologies, has driven exploration into novel target areas, such as energy production and central metabolism, that diverge from the classical targets in macromolecular synthesis. Here, we review new small molecule drug candidates that act on these novel targets to highlight the methods and perspectives advancing the field. These new targets bring with them the aspiration of shortening treatment duration as well as a pipeline of effective regimens against XDR-TB, positioning Mtb drug discovery to become a model for anti-infective discovery.

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.

DrugBank: a knowledgebase for drugs, drug actions and drug targets

PubMed Central

Wishart, David S.; Knox, Craig; Guo, An Chi; Cheng, Dean; Shrivastava, Savita; Tzur, Dan; Gautam, Bijaya; Hassanali, Murtaza

2008-01-01

DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With ∼4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more ‘experimental’ drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food–drug interactions, drug–drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca PMID:18048412

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.

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.

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

PubMed

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

2010-05-01

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

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

Blueprint for antimicrobial hit discovery targeting metabolic networks

PubMed Central

Shen, Y.; Liu, J.; Estiu, G.; Isin, B.; Ahn, Y-Y.; Lee, D-S.; Barabási, A-L.; Kapatral, V.; Wiest, O.; Oltvai, Z. N.

2010-01-01

Advances in genome analysis, network biology, and computational chemistry have the potential to revolutionize drug discovery by combining system-level identification of drug targets with the atomistic modeling of small molecules capable of modulating their activity. To demonstrate the effectiveness of such a discovery pipeline, we deduced common antibiotic targets in Escherichia coli and Staphylococcus aureus by identifying shared tissue-specific or uniformly essential metabolic reactions in their metabolic networks. We then predicted through virtual screening dozens of potential inhibitors for several enzymes of these reactions and showed experimentally that a subset of these inhibited both enzyme activities in vitro and bacterial cell viability. This blueprint is applicable for any sequenced organism with high-quality metabolic reconstruction and suggests a general strategy for strain-specific antiinfective therapy. PMID:20080587

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.

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.

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.

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.

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.

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.

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.

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.

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.

Genome-Scale Screening of Drug-Target Associations Relevant to Ki Using a Chemogenomics Approach

PubMed Central

Cao, Dong-Sheng; Liang, Yi-Zeng; Deng, Zhe; Hu, Qian-Nan; He, Min; Xu, Qing-Song; Zhou, Guang-Hua; Zhang, Liu-Xia; Deng, Zi-xin; Liu, Shao

2013-01-01

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki. PMID:23577055

The impact of genetics on future drug discovery in schizophrenia.

PubMed

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

2017-07-01

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

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

PubMed

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

NASA Astrophysics Data System (ADS)

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

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.

Focus on flaviviruses: current and future drug targets.

PubMed

Geiss, Brian J; Stahla, Hillary; Hannah, Amanda M; Gari, Amanda M; Keenan, Susan M

2009-05-01

Infection by mosquito-borne flaviviruses (family Flaviviridae) is increasing in prevalence worldwide. The vast global, social and economic impact due to the morbidity and mortality associated with the diseases caused by these viruses necessitates therapeutic intervention. There is currently no effective clinical treatment for any flaviviral infection. Therefore, there is a great need for the identification of novel inhibitors to target the virus life cycle. In this article, we discuss structural and nonstructural viral proteins that are the focus of current target validation and drug discovery efforts. Both inhibition of essential enzymatic activities and disruption of necessary protein–protein interactions are considered. In addition, we address promising new targets for future research. As our molecular and biochemical understanding of the flavivirus life cycle increases, the number of targets for antiviral therapeutic discovery grows and the possibility for novel drug discovery continues to strengthen.

The Flavivirus Protease As a Target for Drug Discovery

PubMed Central

Brecher, Matthew; Zhang, Jing; Li, Hongmin

2014-01-01

Many flaviviruses are significant human pathogens causing considerable disease burdens, including encephalitis and hemorrhagic fever, in the regions in which they are endemic. A paucity of treatments for flaviviral infections has driven interest in drug development targeting proteins essential to flavivirus replication, such as the viral protease. During viral replication, the flavivirus genome is translated as a single polyprotein precursor, which must be cleaved into individual proteins by a complex of the viral protease, NS3, and its cofactor, NS2B. Because this cleavage is an obligate step of the viral life-cycle, the flavivirus protease is an attractive target for antiviral drug development. In this review, we will survey recent drug development studies targeting the NS3 active site, as well as studies targeting an NS2B/NS3 interaction site determined from flavivirus protease crystal structures. PMID:24242363

The flavivirus protease as a target for drug discovery.

PubMed

Brecher, Matthew; Zhang, Jing; Li, Hongmin

2013-12-01

Many flaviviruses are significant human pathogens causing considerable disease burdens, including encephalitis and hemorrhagic fever, in the regions in which they are endemic. A paucity of treatments for flaviviral infections has driven interest in drug development targeting proteins essential to flavivirus replication, such as the viral protease. During viral replication, the flavivirus genome is translated as a single polyprotein precursor, which must be cleaved into individual proteins by a complex of the viral protease, NS3, and its cofactor, NS2B. Because this cleavage is an obligate step of the viral life-cycle, the flavivirus protease is an attractive target for antiviral drug development. In this review, we will survey recent drug development studies targeting the NS3 active site, as well as studies targeting an NS2B/NS3 interaction site determined from flavivirus protease crystal structures.

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

PubMed

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

2016-06-15

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

Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery

PubMed Central

Lounnas, Valère; Ritschel, Tina; Kelder, Jan; McGuire, Ross; Bywater, Robert P.; Foloppe, Nicolas

2013-01-01

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented. PMID:24688704

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets.

PubMed

Wasko, Michael J; Pellegrene, Kendy A; Madura, Jeffry D; Surratt, Christopher K

2015-01-01

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for "growing" the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies.

A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets

PubMed Central

Wasko, Michael J.; Pellegrene, Kendy A.; Madura, Jeffry D.; Surratt, Christopher K.

2015-01-01

Hundreds of millions of U.S. dollars are invested in the research and development of a single drug. Lead compound development is an area ripe for new design strategies. Therapeutic lead candidates have been traditionally found using high-throughput in vitro pharmacological screening, a costly method for assaying thousands of compounds. This approach has recently been augmented by virtual screening (VS), which employs computer models of the target protein to narrow the search for possible leads. A variant of VS is fragment-based drug design (FBDD), an emerging in silico lead discovery method that introduces low-molecular weight fragments, rather than intact compounds, into the binding pocket of the receptor model. These fragments serve as starting points for “growing” the lead candidate. Current efforts in virtual FBDD within central nervous system (CNS) targets are reviewed, as is a recent rule-based optimization strategy in which new molecules are generated within a 3D receptor-binding pocket using the fragment as a scaffold. This process not only places special emphasis on creating synthesizable molecules but also exposes computational questions worth addressing. Fragment-based methods provide a viable, relatively low-cost alternative for therapeutic lead discovery and optimization that can be applied to CNS targets to augment current design strategies. PMID:26441817

Oncology drug discovery: planning a turnaround.

PubMed

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

2014-04-01

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

Discovery of a drug targeting microenvironmental support for lymphoma cells by screening using patient-derived xenograft cells

PubMed Central

Sugimoto, Keiki; Hayakawa, Fumihiko; Shimada, Satoko; Morishita, Takanobu; Shimada, Kazuyuki; Katakai, Tomoya; Tomita, Akihiro; Kiyoi, Hitoshi; Naoe, Tomoki

2015-01-01

Cell lines have been used for drug discovery as useful models of cancers; however, they do not recapitulate cancers faithfully, especially in the points of rapid growth rate and microenvironment independency. Consequently, the majority of conventional anti-cancer drugs are less sensitive to slow growing cells and do not target microenvironmental support, although most primary cancer cells grow slower than cell lines and depend on microenvironmental support. Here, we developed a novel high throughput drug screening system using patient-derived xenograft (PDX) cells of lymphoma that maintained primary cancer cell phenotype more than cell lines. The library containing 2613 known pharmacologically active substance and off-patent drugs were screened by this system. We could find many compounds showing higher cytotoxicity than conventional anti-tumor drugs. Especially, pyruvinium pamoate showed the highest activity and its strong anti-tumor effect was confirmed also in vivo. We extensively investigated its mechanism of action and found that it inhibited glutathione supply from stromal cells to lymphoma cells, implying the importance of the stromal protection from oxidative stress for lymphoma cell survival and a new therapeutic strategy for lymphoma. Our system introduces a primary cancer cell phenotype into cell-based phenotype screening and sheds new light on anti-cancer drug development. PMID:26278963

Assessment of berberine as a multi-target antimicrobial: a multi-omics study for drug discovery and repositioning.

PubMed

Karaosmanoglu, Kubra; Sayar, Nihat Alpagu; Kurnaz, Isil Aksan; Akbulut, Berna Sariyar

2014-01-01

Postgenomics drug development is undergoing major transformation in the age of multi-omics studies and drug repositioning. Rather than applications solely in personalized medicine, omics science thus additionally offers a better understanding of a broader range of drug targets and drug repositioning. Berberine is an isoquinoline alkaloid found in many medicinal plants. We report here a whole genome microarray study in tandem with proteomics techniques for mining the plethora of targets that are putatively involved in the antimicrobial activity of berberine against Escherichia coli. We found DNA replication/repair and transcription to be triggered by berberine, indicating that nucleic acids, in general, are among its targets. Our combined transcriptomics and proteomics multi-omics findings underscore that, in the presence of berberine, cell wall or cell membrane transport and motility-related functions are also specifically regulated. We further report a general decline in metabolism, as seen by repression of genes in carbohydrate and amino acid metabolism, energy production, and conversion. An involvement of multidrug efflux pumps, as well as reduced membrane permeability for developing resistance against berberine in E. coli was noted. Collectively, these findings offer original and significant leads for omics-guided drug discovery and future repositioning approaches in the postgenomics era, using berberine as a multi-omics case study.

The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective.

PubMed

Li, Ying Hong; Wang, Pan Pan; Li, Xiao Xu; Yu, Chun Yan; Yang, Hong; Zhou, Jin; Xue, Wei Wei; Tan, Jun; Zhu, Feng

2016-01-01

The human kinome is one of the most productive classes of drug target, and there is emerging necessity for treating complex diseases by means of polypharmacology (multi-target drugs and combination products). However, the advantages of the multi-target drugs and the combination products are still under debate. A comparative analysis between FDA approved multi-target drugs and combination products, targeting the human kinome, was conducted by mapping targets onto the phylogenetic tree of the human kinome. The approach of network medicine illustrating the drug-target interactions was applied to identify popular targets of multi-target drugs and combination products. As identified, the multi-target drugs tended to inhibit target pairs in the human kinome, especially the receptor tyrosine kinase family, while the combination products were able to against targets of distant homology relationship. This finding asked for choosing the combination products as a better solution for designing drugs aiming at targets of distant homology relationship. Moreover, sub-networks of drug-target interactions in specific disease were generated, and mechanisms shared by multi-target drugs and combination products were identified. In conclusion, this study performed an analysis between approved multi-target drugs and combination products against the human kinome, which could assist the discovery of next generation polypharmacology.

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.

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

PubMed

Smith, Dennis; Schmid, Esther; Jones, Barry

2002-01-01

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

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.

Drug discovery in the next millennium.

PubMed

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

2000-01-01

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

TaxKB: a knowledge base for new taxane-related drug discovery.

PubMed

Murugan, Kasi; Shanmugasamy, Sangeetha; Al-Sohaibani, Saleh; Vignesh, Naga; Palanikannan, Kandavel; Vimala, Antonydhason; Kumar, Gopal Ramesh

2015-01-01

Taxanes are naturally occurring compounds which belong to a powerful group of chemotherapeutic drugs with anticancer properties. Their current use, clinical efficacy, and unique mechanism of action indicate their potentiality for cancer drug discovery and development thereby promising to reduce the high economy associated with cancer worldwide. Extensive research has been carried out on taxanes with the aim to combat issues of drug resistance, side effects, limited natural supply, and also to increase the therapeutic index of these molecules. These efforts have led to the isolation of many naturally occurring compounds belonging to this family (more than 350 different kinds), and the synthesis of semisynthetic analogs of the naturally existing molecules (>500), and has also led to the characterization of many (>1000) of them. A web-based database system on clinically exploitable taxanes, providing a link between the structure and the pharmacological property of these molecules could help to reduce the druggability gap for these molecules. Taxane knowledge base (TaxKB, http://bioinfo.au-kbc.org.in/taxane/Taxkb/), is an online multi-tier relational database that currently holds data on 42 parameters of 250 natural and 503 semisynthetic analogs of taxanes. This database provides researchers with much-needed information necessary for drug development. TaxKB enables the user to search data on the structure, drug-likeness, and physicochemical properties of both natural and synthetic taxanes with a "General Search" option in addition to a "Parameter Specific Search." It displays 2D structure and allows the user to download the 3D structure (a PDB file) of taxanes that can be viewed with any molecular visualization tool. The ultimate aim of TaxKB is to provide information on Absorption, Distribution, Metabolism, and Excretion/Toxicity (ADME/T) as well as data on bioavailability and target interaction properties of candidate anticancer taxanes, ahead of expensive clinical

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

PubMed

Wang, QuanQiu; Xu, Rong

2015-01-01

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

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.

Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it.

PubMed

Kell, Douglas B

2013-12-01

Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from 'function-first' to 'target-first' methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing 'wet' experiments, such that 'what if?' experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that

Context-sensitive network-based disease genetics prediction and its implications in drug discovery.

PubMed

Chen, Yang; Xu, Rong

2017-04-01

Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery. We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach ( pdrug target gene for PD, and found its association with neurodegeneration supported by literature. In summary, CSNs lead to significantly improve the disease genetics prediction comparing with SBNs and provide leads for potential drug targets. nlp.case.edu/public/data/. rxx@case.edu. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Context-sensitive network-based disease genetics prediction and its implications in drug discovery

PubMed Central

Chen, Yang; Xu, Rong

2017-01-01

Abstract Motivation: Disease phenotype networks play an important role in computational approaches to identifying new disease-gene associations. Current disease phenotype networks often model disease relationships based on pairwise similarities, therefore ignore the specific context on how two diseases are connected. In this study, we propose a new strategy to model disease associations using context-sensitive networks (CSNs). We developed a CSN-based phenome-driven approach for disease genetics prediction, and investigated the translational potential of the predicted genes in drug discovery. Results: We constructed CSNs by directly connecting diseases with associated phenotypes. Here, we constructed two CSNs using different data sources; the two networks contain 26 790 and 13 822 nodes respectively. We integrated the CSNs with a genetic functional relationship network and predicted disease genes using a network-based ranking algorithm. For comparison, we built Similarity-Based disease Networks (SBN) using the same disease phenotype data. In a de novo cross validation for 3324 diseases, the CSN-based approach significantly increased the average rank from top 12.6 to top 8.8% for all tested genes comparing with the SBN-based approach (pdrug target gene for PD, and found its association with neurodegeneration supported by literature. In summary, CSNs lead to significantly improve the disease genetics prediction comparing with SBNs and provide leads for potential drug targets. Availability and Implementation: nlp.case.edu/public/data/ Contact: rxx@case.edu PMID:28062449

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

PubMed

Lötsch, Jörn; Ultsch, Alfred

2016-12-01

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

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.

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).

Voltage gated sodium channels as drug discovery targets

PubMed Central

Bagal, Sharan K; Marron, Brian E; Owen, Robert M; Storer, R Ian; Swain, Nigel A

2015-01-01

Voltage-gated sodium (NaV) channels are a family of transmembrane ion channel proteins. They function by forming a gated, water-filled pore to help establish and control cell membrane potential via control of the flow of ions between the intracellular and the extracellular environments. Blockade of NaVs has been successfully accomplished in the clinic to enable control of pathological firing patterns that occur in a diverse range of conditions such as chronic pain, epilepsy, and cardiac arrhythmias. First generation sodium channel modulator drugs, despite low inherent subtype selectivity, preferentially act on over-excited cells which reduces undesirable side effects in the clinic. However, the limited therapeutic indices observed with the first generation demanded a new generation of sodium channel inhibitors. The structure, function and the state of the art in sodium channel modulator drug discovery are discussed in this chapter. PMID:26646477

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.

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.

Targeting efflux pumps to overcome antifungal drug resistance

PubMed Central

Holmes, Ann R; Cardno, Tony S; Strouse, J Jacob; Ivnitski-Steele, Irena; Keniya, Mikhail V; Lackovic, Kurt; Monk, Brian C; Sklar, Larry A; Cannon, Richard D

2016-01-01

Resistance to antifungal drugs is an increasingly significant clinical problem. The most common antifungal resistance encountered is efflux pump-mediated resistance of Candida species to azole drugs. One approach to overcome this resistance is to inhibit the pumps and chemosensitize resistant strains to azole drugs. Drug discovery targeting fungal efflux pumps could thus result in the development of azole-enhancing combination therapy. Heterologous expression of fungal efflux pumps in Saccharomyces cerevisiae provides a versatile system for screening for pump inhibitors. Fungal efflux pumps transport a range of xenobiotics including fluorescent compounds. This enables the use of fluorescence-based detection, as well as growth inhibition assays, in screens to discover compounds targeting efflux-mediated antifungal drug resistance. A variety of medium- and high-throughput screens have been used to identify a number of chemical entities that inhibit fungal efflux pumps. PMID:27463566

Exploring drug-target interaction networks of illicit drugs.

PubMed

Atreya, Ravi V; Sun, Jingchun; Zhao, Zhongming

2013-01-01

Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care. In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies. This study presents the first systematic review of the network

Exploring drug-target interaction networks of illicit drugs

PubMed Central

2013-01-01

Background Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care. Results In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies. Conclusions This study presents the first systematic

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

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.

Drugs and Targets in Fibrosis

PubMed Central

Li, Xiaoyi; Zhu, Lixin; Wang, Beibei; Yuan, Meifei; Zhu, Ruixin

2017-01-01

Fibrosis contributes to the development of many diseases and many target molecules are involved in fibrosis. Currently, the majority of fibrosis treatment strategies are limited to specific diseases or organs. However, accumulating evidence demonstrates great similarities among fibroproliferative diseases, and more and more drugs are proved to be effective anti-fibrotic therapies across different diseases and organs. Here we comprehensively review the current knowledge on the pathological mechanisms of fibrosis, and divide factors mediating fibrosis progression into extracellular and intracellular groups. Furthermore, we systematically summarize both single and multiple component drugs that target fibrosis. Future directions of fibrosis drug discovery are also proposed. PMID:29218009

Targeting Cullin–RING E3 ubiquitin ligases for drug discovery: structure, assembly and small-molecule modulation

PubMed Central

Bulatov, Emil; Ciulli, Alessio

2015-01-01

In the last decade, the ubiquitin–proteasome system has emerged as a valid target for the development of novel therapeutics. E3 ubiquitin ligases are particularly attractive targets because they confer substrate specificity on the ubiquitin system. CRLs [Cullin–RING (really interesting new gene) E3 ubiquitin ligases] draw particular attention, being the largest family of E3s. The CRLs assemble into functional multisubunit complexes using a repertoire of substrate receptors, adaptors, Cullin scaffolds and RING-box proteins. Drug discovery targeting CRLs is growing in importance due to mounting evidence pointing to significant roles of these enzymes in diverse biological processes and human diseases, including cancer, where CRLs and their substrates often function as tumour suppressors or oncogenes. In the present review, we provide an account of the assembly and structure of CRL complexes, and outline the current state of the field in terms of available knowledge of small-molecule inhibitors and modulators of CRL activity. A comprehensive overview of the reported crystal structures of CRL subunits, components and full-size complexes, alone or with bound small molecules and substrate peptides, is included. This information is providing increasing opportunities to aid the rational structure-based design of chemical probes and potential small-molecule therapeutics targeting CRLs. PMID:25886174

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

PubMed

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

2016-01-28

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

Application of Fragment Based Drug Discovery to Membrane Proteins: Biophysical Identification of Ligands of the Integral Membrane Enzyme DsbB

PubMed Central

Früh, Virginie; Zhou, Yunpeng; Chen, Dan; Loch, Caroline; Eiso, AB; Grinkova, Yelena N.; Verheij, Herman; Sligar, Stephen G; Bushweller, John H.; Siegal, Gregg

2014-01-01

Summary Membrane proteins are important pharmaceutical targets, but they pose significant challenges for fragment based drug discovery approaches. Here we present the first successful use of biophysical methods to screen for fragment ligands to an integral membrane protein. The E. coli inner membrane protein DsbB was solubilized in detergent micelles and lipid bilayer nanodiscs. The solubilized protein was immobilized with retention of functionality and used to screen 1,071 drug fragments for binding using Target Immobilized NMR Screening. Biochemical and biophysical validation of the 8 most potent hits revealed an IC50 range of 7 to 200 μM. The ability to insert a broad array of membrane proteins into nanodiscs, combined with the efficiency of TINS, demonstrates the feasibility of finding fragments targeting membrane proteins. PMID:20797617

Polysaccharide-based micro/nanocarriers for oral colon-targeted drug delivery.

PubMed

Zhang, Lin; Sang, Yuan; Feng, Jing; Li, Zhaoming; Zhao, Aili

2016-08-01

Oral colon-targeted drug delivery has attracted many researchers because of its distinct advantages of increasing the bioavailability of the drug at the target site and reducing the side effects. Polysaccharides that are precisely activated by the physiological environment of the colon hold greater promise for colon targeting. Considerable research efforts have been directed towards developing polysaccharide-based micro/nanocarriers. Types of polysaccharides for colon targeting and in vitro/in vivo assessments of polysaccharide-based carriers for oral colon-targeted drug delivery are summarised. Polysaccharide-based microspheres have gained increased importance not just for the delivery of the drugs for the treatment of local diseases associated with the colon (colon cancer, inflammatory bowel disease (IBD), amoebiasis and irritable bowel syndrome (IBS)), but also for it's potential for the delivery of anti-rheumatoid arthritis and anti-chronic stable angina drugs. Besides, Polysaccharide-based micro/nanocarriers such as microbeads, microcapsules, microparticles, nanoparticles, nanogels and nanospheres are also introduced in this review.

Extracellular proteases as targets for drug development

PubMed Central

Cudic, Mare

2015-01-01

Proteases constitute one of the primary targets in drug discovery. In the present review, we focus on extracellular proteases (ECPs) because of their differential expression in many pathophysiological processes, including cancer, cardiovascular conditions, and inflammatory, pulmonary, and periodontal diseases. Many new ECP inhibitors are currently under clinical investigation and a significant increase in new therapies based on protease inhibition can be expected in the coming years. In addition to directly blocking the activity of a targeted protease, one can take advantage of differential expression in disease states to selectively deliver therapeutic or imaging agents. Recent studies in targeted drug development for the metalloproteases (matrix metalloproteinases, adamalysins, pappalysins, neprilysin, angiotensin-converting enzyme, metallocarboxypeptidases, and glutamate carboxypeptidase II), serine proteases (elastase, coagulation factors, tissue/urokinase plasminogen activator system, kallikreins, tryptase, dipeptidyl peptidase IV), cysteine proteases (cathepsin B), and renin system are discussed herein. PMID:19689354

Mechanistic models enable the rational use of in vitro drug-target binding kinetics for better drug effects in patients.

PubMed

de Witte, Wilhelmus E A; Wong, Yin Cheong; Nederpelt, Indira; Heitman, Laura H; Danhof, Meindert; van der Graaf, Piet H; Gilissen, Ron A H J; de Lange, Elizabeth C M

2016-01-01

Drug-target binding kinetics are major determinants of the time course of drug action for several drugs, as clearly described for the irreversible binders omeprazole and aspirin. This supports the increasing interest to incorporate newly developed high-throughput assays for drug-target binding kinetics in drug discovery. A meaningful application of in vitro drug-target binding kinetics in drug discovery requires insight into the relation between in vivo drug effect and in vitro measured drug-target binding kinetics. In this review, the authors discuss both the relation between in vitro and in vivo measured binding kinetics and the relation between in vivo binding kinetics, target occupancy and effect profiles. More scientific evidence is required for the rational selection and development of drug-candidates on the basis of in vitro estimates of drug-target binding kinetics. To elucidate the value of in vitro binding kinetics measurements, it is necessary to obtain information on system-specific properties which influence the kinetics of target occupancy and drug effect. Mathematical integration of this information enables the identification of drug-specific properties which lead to optimal target occupancy and drug effect in patients.

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

PubMed

Tan, Hepan; Ge, Xiaoxia; Xie, Lei

2013-08-01

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

Antimycobacterial drug discovery using Mycobacteria-infected amoebae identifies anti-infectives and new molecular targets.

PubMed

Trofimov, Valentin; Kicka, Sébastien; Mucaria, Sabrina; Hanna, Nabil; Ramon-Olayo, Fernando; Del Peral, Laura Vela-Gonzalez; Lelièvre, Joël; Ballell, Lluís; Scapozza, Leonardo; Besra, Gurdyal S; Cox, Jonathan A G; Soldati, Thierry

2018-03-02

Tuberculosis remains a serious threat to human health world-wide, and improved efficiency of medical treatment requires a better understanding of the pathogenesis and the discovery of new drugs. In the present study, we performed a whole-cell based screen in order to complete the characterization of 168 compounds from the GlaxoSmithKline TB-set. We have established and utilized novel previously unexplored host-model systems to characterize the GSK compounds, i.e. the amoeboid organisms D. discoideum and A. castellanii, as well as a microglial phagocytic cell line, BV2. We infected these host cells with Mycobacterium marinum to monitor and characterize the anti-infective activity of the compounds with quantitative fluorescence measurements and high-content microscopy. In summary, 88.1% of the compounds were confirmed as antibiotics against M. marinum, 11.3% and 4.8% displayed strong anti-infective activity in, respectively, the mammalian and protozoan infection models. Additionally, in the two systems, 13-14% of the compounds displayed pro-infective activity. Our studies underline the relevance of using evolutionarily distant pathogen and host models in order to reveal conserved mechanisms of virulence and defence, respectively, which are potential "universal" targets for intervention. Subsequent mechanism of action studies based on generation of over-expresser M. bovis BCG strains, generation of spontaneous resistant mutants and whole genome sequencing revealed four new molecular targets, including FbpA, MurC, MmpL3 and GlpK.

Towards the discovery of drug-like RNA ligands?

PubMed

Foloppe, Nicolas; Matassova, Natalia; Aboul-Ela, Fareed

2006-11-01

Targeting RNA with small molecule drugs is an area of great potential for therapeutic treatment of infections and possibly genetic and autoimmune diseases. However, a mature set of precedents and established methodology is lacking. The physicochemical properties of RNA raise specific issues and obstacles to development, and contribute to explain the distinct characteristics of natural RNA ligands, including antibiotics. Yet, RNA-targeting strategies are being implemented to reinvigorate antibacterial discovery by using the ribosomal X-ray structures to modify known antibiotics. To exploit further these structures, we suggest the use of existing protein kinase-directed libraries of drug-like compounds to target the A-site of the bacterial ribosome, on the basis of a specific structural hypothesis.

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

Novel drug discovery approaches for treating arenavirus infections.

PubMed

Pasquato, Antonella; Kunz, Stefan

2016-01-01

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

Structural Genomics and Drug Discovery for Infectious Diseases

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

Anderson, W.F.

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

Drug targets for resistant malaria: Historic to future perspectives.

PubMed

Kumar, Sahil; Bhardwaj, T R; Prasad, D N; Singh, Rajesh K

2018-05-11

New antimalarial targets are the prime need for the discovery of potent drug candidates. In order to fulfill this objective, antimalarial drug researches are focusing on promising targets in order to develop new drug candidates. Basic metabolism and biochemical process in the malaria parasite, i.e. Plasmodium falciparum can play an indispensable role in the identification of these targets. But, the emergence of resistance to antimalarial drugs is an escalating comprehensive problem with the progress of antimalarial drug development. The development of resistance has highlighted the need for the search of novel antimalarial molecules. The pharmaceutical industries are committed to new drug development due to the global recognition of this life threatening resistance to the currently available antimalarial therapy. The recent developments in the understanding of parasite biology are exhilarating this resistance issue which is further being ignited by malaria genome project. With this background of information, this review was aimed to highlights and provides useful information on various present and promising treatment approaches for resistant malaria, new progresses, pursued by some innovative targets that have been explored till date. This review also discusses modern and futuristic multiple approaches to antimalarial drug discovery and development with pictorial presentations highlighting the various targets, that could be exploited for generating promising new drugs in the future for drug resistant malaria. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Predicting Drug-Target Interactions With Multi-Information Fusion.

PubMed

Peng, Lihong; Liao, Bo; Zhu, Wen; Li, Zejun; Li, Keqin

2017-03-01

Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.

Assessment of deoxyhypusine hydroxylase as a putative, novel drug target.

PubMed

Kerscher, B; Nzukou, E; Kaiser, A

2010-02-01

Antimalarial drug resistance has nowadays reached each drug class on the market for longer than 10 years. The focus on validated, classical targets has severe drawbacks. If resistance is arising or already present in the field, a target-based High-Throughput-Screening (HTS) with the respective target involves the risk of identifying compounds to which field populations are also resistant. Thus, it appears that a rewarding albeit demanding challenge for target-based drug discovery is to identify novel drug targets. In the search for new targets for antimalarials, we have investigated the biosynthesis of hypusine, present in eukaryotic initiation factor 5A (eIF5A). Deoxyhypusine hydroxylase (DOHH), which has recently been cloned and expressed from P. falciparum, completes the modification of eIF5A through hydroxylation. Here, we assess the present druggable data on Plasmodium DOHH and its human counterpart. Plasmodium DOHH arose from a cyanobacterial phycobilin lyase by loss of function. It has a low FASTA score of 27 to its human counterpart. The HEAT-like repeats present in the parasite DOHH differ in number and amino acid identity from its human ortholog and might be of considerable interest for inhibitor design.

Computational modeling-based discovery of novel classes of anti-inflammatory drugs that target lanthionine synthetase C-like protein 2.

PubMed

Lu, Pinyi; Hontecillas, Raquel; Horne, William T; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R; Lewis, Stephanie N; Bassaganya-Riera, Josep

2012-01-01

Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates.

Computational Modeling-Based Discovery of Novel Classes of Anti-Inflammatory Drugs That Target Lanthionine Synthetase C-Like Protein 2

PubMed Central

Lu, Pinyi; Hontecillas, Raquel; Horne, William T.; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R.; Lewis, Stephanie N.; Bassaganya-Riera, Josep

2012-01-01

Background Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. Methodology/Principal Findings The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. Conclusions/Significance LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates. PMID:22509338

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

PubMed Central

2016-01-01

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages. PMID:27074285

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs.

PubMed

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

2016-06-08

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

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

PubMed

Geerts, Hugo; Kennis, Ludo

2014-01-01

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

Editorial: Current status and perspective on drug targets in tubercle bacilli and drug design of antituberculous agents based on structure-activity relationship.

PubMed

Tomioka, Haruaki

2014-01-01

promoting the elucidation of the molecular structures of drug targets in MTB, and are consequently markedly useful for the design of new, promising antituberculous drugs using QSAR techniques. In this issue, we review the following areas. Firstly, Dr. Li M. Fu reviews the perspective that combines machine learning and genomics for drug discovery in tuberculosis, in relation to the problem that the exhaustive search for useful drug targets over the entire MTB genome would not be as productive as expected in practice [1]. Secondly, the review article by Drs. R. S. Chauhan. S. K. Chanumolu, C. Rout, and R. Shrivastava focuses on analysis of the current state of MTB genomic resources, host-pathogen interaction studies in the context of mycobacterial persistence, and drug target discovery based on the utilization of computational tools and metabolic network analyses [2]. Thirdly, Drs. Daria Bottai, Agnese Serafini, Alessandro Cascioferro, Roland Brosch, and Riccardo Manganelli review the current knowledge on MTB T7SS/ESX secretion systems and their impact on MTB physiology and virulence, and the possible approaches to develop T7SS/ESX inhibitors [3]. Fourthly, Drs. E. Jeffrey North, Mary Jackson, and Richard E. Lee review and analyze new and emerging inhibitors of the mycolic acid biosynthetic pathway, including mycobacterial enzymes for fatty acid synthesis, mycolic acid-modifying enzymes, fatty acid-activating and -condensing enzymes, transporters, and transferases, that have been discovered in the post-genomic era of tuberculosis drug discovery [4]. Fifthly, Drs. Katarina Mikusova, Vadim Makarov, and Joao Neres review the mycobacterial enzyme DprE1, which catalyzes a unique epimerization reaction in the biosynthesis of decaprenylphosphoryl arabinose, a single donor of the arabinosyl residue for the build-up of arabinans, one of the mycobacterial cell wall components, as an important drug target especially for the development of benzothiazinones [5]. Sixthly, I review the

Current perspectives in fragment-based lead discovery (FBLD)

PubMed Central

Lamoree, Bas; Hubbard, Roderick E.

2017-01-01

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

Antibacterial Drug Discovery: Some Assembly Required.

PubMed

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

2018-05-11

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

Successes in drug discovery and design.

PubMed

2004-04-01

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

Drug-target interaction prediction from PSSM based evolutionary information.

PubMed

Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

2016-01-01

The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

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.

Native Mass Spectrometry in Fragment-Based Drug Discovery.

PubMed

Pedro, Liliana; Quinn, Ronald J

2016-07-28

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

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

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