Al-Qadi, Sonia; Schiøtt, Morten; Hansen, Steen Honoré
BACKGROUND: ABC efflux transporters at the blood brain barrier (BBB), namely the P-glycoprotein (P-gp), restrain the development of central nervous system (CNS) drugs. Consequently, early screening of CNS drug candidates is pivotal to identify those affected by efflux activity. Therefore, simple,...... barriers. CONCLUSION: Findings suggest a conserved mechanism of brain efflux activity between insects and vertebrates, confirming that this model holds promise for inexpensive and high-throughput screening relative to in vivo models, for CNS drug discovery......., high-throughput and predictive screening models are required. The grasshopper (locust) has been developed as an invertebrate in situ model for BBB permeability assessment, as it has shown similarities to vertebrate models. METHODS: Transcriptome profiling of ABC efflux transporters in the locust brain......BACKGROUND: ABC efflux transporters at the blood brain barrier (BBB), namely the P-glycoprotein (P-gp), restrain the development of central nervous system (CNS) drugs. Consequently, early screening of CNS drug candidates is pivotal to identify those affected by efflux activity. Therefore, simple...
Pennisi, Marzio; Russo, Giulia; Di Salvatore, Valentina; Candido, Saverio; Libra, Massimo; Pappalardo, Francesco
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.
Full Text Available Mihaly Szabo,1 Sara Svensson Akusjärvi,1 Ankur Saxena,1 Jianping Liu,2 Gayathri Chandrasekar,1 Satish S Kitambi1 1Department of Microbiology Tumor, and Cell Biology, 2Department of Biochemistry and Biophysics, Karolinska Institutet, Solna, Sweden Abstract: The phenotype-based drug discovery (PDD approach is re-emerging as an alternative platform for drug discovery. This review provides an overview of the various model systems and technical advances in imaging and image analyses that strengthen the PDD platform. In PDD screens, compounds of therapeutic value are identified based on the phenotypic perturbations produced irrespective of target(s or mechanism of action. In this article, examples of phenotypic changes that can be detected and quantified with relative ease in a cell-based setup are discussed. In addition, a higher order of PDD screening setup using small animal models is also explored. As PDD screens integrate physiology and multiple signaling mechanisms during the screening process, the identified hits have higher biomedical applicability. Taken together, this review highlights the advantages gained by adopting a PDD approach in drug discovery. Such a PDD platform can complement target-based systems that are currently in practice to accelerate drug discovery. Keywords: phenotype, screening, PDD, discovery, zebrafish, drug
Full Text Available Eric Chatelain, Jean-Robert IosetDrugs for Neglected Diseases Initiative (DNDi, Geneva, SwitzerlandAbstract: New models of drug discovery have been developed to overcome the lack of modern and effective drugs for neglected diseases such as human African trypanosomiasis (HAT; sleeping sickness, leishmaniasis, and Chagas disease, which have no financial viability for the pharmaceutical industry. With the purpose of combining the skills and research capacity in academia, pharmaceutical industry, and contract researchers, public–private partnerships or product development partnerships aim to create focused research consortia that address all aspects of drug discovery and development. These consortia not only emulate the projects within pharmaceutical and biotechnology industries, eg, identification and screening of libraries, medicinal chemistry, pharmacology and pharmacodynamics, formulation development, and manufacturing, but also use and strengthen existing capacity in disease-endemic countries, particularly for the conduct of clinical trials. The Drugs for Neglected Diseases initiative (DNDi has adopted a model closely related to that of a virtual biotechnology company for the identification and optimization of drug leads. The application of this model to the development of drug candidates for the kinetoplastid infections of HAT, Chagas disease, and leishmaniasis has already led to the identification of new candidates issued from DNDi’s own discovery pipeline. This demonstrates that the model DNDi has been implementing is working but its DNDi, neglected diseases sustainability remains to be proven.Keywords: R&D, screening, lead optimization, human African trypanosomiasis, leishmaniasis, Chagas disease, product development partnerships
Chatelain, Eric; Ioset, Jean-Robert
New models of drug discovery have been developed to overcome the lack of modern and effective drugs for neglected diseases such as human African trypanosomiasis (HAT; sleeping sickness), leishmaniasis, and Chagas disease, which have no financial viability for the pharmaceutical industry. With the purpose of combining the skills and research capacity in academia, pharmaceutical industry, and contract researchers, public-private partnerships or product development partnerships aim to create focused research consortia that address all aspects of drug discovery and development. These consortia not only emulate the projects within pharmaceutical and biotechnology industries, eg, identification and screening of libraries, medicinal chemistry, pharmacology and pharmacodynamics, formulation development, and manufacturing, but also use and strengthen existing capacity in disease-endemic countries, particularly for the conduct of clinical trials. The Drugs for Neglected Diseases initiative (DNDi) has adopted a model closely related to that of a virtual biotechnology company for the identification and optimization of drug leads. The application of this model to the development of drug candidates for the kinetoplastid infections of HAT, Chagas disease, and leishmaniasis has already led to the identification of new candidates issued from DNDi's own discovery pipeline. This demonstrates that the model DNDi has been implementing is working but its DNDi, neglected diseases sustainability remains to be proven.
França, Tanos Celmar Costa
In the last decades, homology modeling has become a popular tool to access theoretical three-dimensional (3D) structures of molecular targets. So far several 3D models of proteins have been built by this technique and used in a great diversity of structural biology studies. But are those models consistent enough with experimental structures to make this technique an effective and reliable tool for drug discovery? Here we present, briefly, the fundamentals and current state-of-the-art of the homology modeling techniques used to build 3D structures of molecular targets, which experimental structures are not available in databases, and list some of the more important works, using this technique, available in literature today. In many cases those studies have afforded successful models for the drug design of more selective agonists/antagonists to the molecular targets in focus and guided promising experimental works, proving that, when the appropriate templates are available, useful models can be built using some of the several software available today for this purpose. Limitations of the experimental techniques used to solve 3D structures allied to constant improvements in the homology modeling software will maintain the need for theoretical models, establishing the homology modeling as a fundamental tool for the drug discovery.
Si-sheng OU-YANG; Jun-yan LU; Xiang-qian KONG; Zhong-jie LIANG; Cheng LUO; Hualiang JIANG
Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process.Because of the dramatic increase in the availability of biological macromolecule and small molecule information,the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow,including target identification and validation,lead discovery and optimization and preclinical tests.Over the past decades,computational drug discovery methods such as molecular docking,pharmacophore modeling and mapping,de novo design,molecular similarity calculation and sequence-based virtual screening have been greatly improved.In this review,we present an overview of these important computational methods,platforms and successful applications in this field.
Sacan, Ahmet; Ekins, Sean; Kortagere, Sandhya
Drug discovery in the late twentieth and early twenty-first century has witnessed a myriad of changes that were adopted to predict whether a compound is likely to be successful, or conversely enable identification of molecules with liabilities as early as possible. These changes include integration of in silico strategies for lead design and optimization that perform complementary roles to that of the traditional in vitro and in vivo approaches. The in silico models are facilitated by the availability of large datasets associated with high-throughput screening, bioinformatics algorithms to mine and annotate the data from a target perspective, and chemoinformatics methods to integrate chemistry methods into lead design process. This chapter highlights the applications of some of these methods and their limitations. We hope this serves as an introduction to in silico drug discovery.
Kirkegaard, Henriette Schultz; Valentin, Finn
Academic drug discovery centres (ADDCs) are seen as one of the solutions to fill the innovation gap in early drug discovery, which has proven challenging for previous organisational models. Prior studies of ADDCs have identified the need to analyse them from the angle of their economic...... their performance....
Hussain, Snawar; Barretto, Naina; Uprichard, Susan L
Introduction Hepatitis C virus is a major cause of liver disease worldwide and the leading indication for liver transplantation in the United States. Current treatment options are expensive, not effective in all patients and are associated with serious side effects. While pre-clinical anti-HCV drug screening is still hampered by the lack of readily infectable small animal models, the development of cell culture HCV experimental model systems has driven a promising new wave of HCV antiviral drug discovery. Areas covered This review contains a concise overview of current HCV treatment options and limitations with a subsequent in-depth focus on the available experimental models and novel strategies that have and continue to enable important advances in HCV drug development. Expert opinion With a large cohort of chronically HCV infected patients progressively developing liver disease that puts them at risk for hepatocellular carcinoma and hepatic decompensation, there is an urgent need to develop effective therapeutics that are well-tolerated and effective in all patients and against all HCV genotypes. Significant advances in HCV experimental model development have expedited drug discovery; however, additional progress is needed. Importantly, the current trends and momentum in the field suggests that we will continue to overcome critical experimental challenges to reach this end goal. PMID:22861052
Golovko, Daniel; Kedrin, Dmitriy; Yilmaz, Omer H.; Roper, Jatin
Introduction Despite increased screening rates and advances in targeted therapy, colorectal cancer (CRC) remains the third leading cause of cancer-related mortality. CRC models that recapitulate key features of human disease are essential to the development of novel and effective therapeutics. Classic methods of modeling CRC such as human cell lines and xenograft mice, while useful for many applications, carry significant limitations. Recently developed in vitro and in vivo models overcome some of these deficiencies and thus can be utilized to better model CRC for mechanistic and translational research. Areas Covered The authors review established models of in vitro cell culture and describe advances in organoid culture for studying normal and malignant intestine. They also discuss key features of classic xenograft models and describe other approaches for in vivo CRC research, including patient-derived xenograft, carcinogen-induced, orthotopic transplantation, and transgenic mouse models. We also describe mouse models of metastatic CRC. Expert opinion No single model is optimal for drug discovery in CRC. Genetically engineered models overcome many limitations of xenograft models. Three-dimensional organoids can be efficiently derived from both normal and malignant tissue for large-scale in vitro and in vivo (transplantation) studies, and are thus a significant advance in CRC drug discovery. PMID:26295972
Honório, Kathia M; Moda, Tiago L; Andricopulo, Adriano D
The discovery and development of a new drug are time-consuming, difficult and expensive. This complex process has evolved from classical methods into an integration of modern technologies and innovative strategies addressed to the design of new chemical entities to treat a variety of diseases. The development of new drug candidates is often limited by initial compounds lacking reasonable chemical and biological properties for further lead optimization. Huge libraries of compounds are frequently selected for biological screening using a variety of techniques and standard models to assess potency, affinity and selectivity. In this context, it is very important to study the pharmacokinetic profile of the compounds under investigation. Recent advances have been made in the collection of data and the development of models to assess and predict pharmacokinetic properties (ADME--absorption, distribution, metabolism and excretion) of bioactive compounds in the early stages of drug discovery projects. This paper provides a brief perspective on the evolution of in silico ADME tools, addressing challenges, limitations, and opportunities in medicinal chemistry.
Vyas, V. K.; Ukawala, R. D.; Ghate, M.; Chintha, C.
Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery. PMID:23204616
Avci, Pinar; Sadasivam, Magesh; Gupta, Asheesh; De Melo, Wanessa CMA; Huang, Ying-Ying; Yin, Rui; Rakkiyappan, Chandran; Kumar, Raj; Otufowora, Ayodeji; Nyame, Theodore; Hamblin, Michael R
Introduction Discovery of novel drugs, treatments, and testing of consumer products in the field of dermatology is a multi-billion dollar business. Due to the distressing nature of many dermatological diseases, and the enormous consumer demand for products to reverse the effects of skin photodamage, aging, and hair loss, this is a very active field. Areas covered In this paper, we will cover the use of animal models that have been reported to recapitulate to a greater or lesser extent the features of human dermatological disease. There has been a remarkable increase in the number and variety of transgenic mouse models in recent years, and the basic strategy for constructing them is outlined. Expert opinion Inflammatory and autoimmune skin diseases are all represented by a range of mouse models both transgenic and normal. Skin cancer is mainly studied in mice and fish. Wound healing is studied in a wider range of animal species, and skin infections such as acne and leprosy also have been studied in animal models. Moving to the more consumer-oriented area of dermatology, there are models for studying the harmful effect of sunlight on the skin, and testing of sunscreens, and several different animal models of hair loss or alopecia. PMID:23293893
Sharma, Sulbha K; Dai, Tianhong; Kharkwal, Gitika B; Huang, Ying-Ying; Huang, Liyi; De Arce, Vida J Bil; Tegos, George P; Hamblin, Michael R
, skin abrasions and soft-tissue abscesses. This range of animal models also represents a powerful aid in antimicrobial drug discovery.
Type 2 diabetes is a fast-growing epidemic in industrialized countries, associated with obesity, lack of physical exercise, aging, family history, and ethnic background. Diagnostic criteria are elevated fasting or postprandial blood glucose levels, a consequence of insulin resistance. Early intervention can help patients to revert the progression of the disease together with lifestyle changes or monotherapy. Systemic glucose toxicity can have devastating effects leading to pancreatic beta cell failure, blindness, nephropathy, and neuropathy, progressing to limb ulceration or even amputation. Existing treatments have numerous side effects and demonstrate variability in individual patient responsiveness. However, several emerging areas of discovery research are showing promises with the development of novel classes of antidiabetic drugs.The mouse has proven to be a reliable model for discovering and validating new treatments for type 2 diabetes mellitus. We review here commonly used methods to measure endpoints relevant to glucose metabolism which show good translatability to the diagnostic of type 2 diabetes in humans: baseline fasting glucose and insulin, glucose tolerance test, insulin sensitivity index, and body type composition. Improvements on these clinical values are essential for the progression of a novel potential therapeutic molecule through a preclinical and clinical pipeline.
Chartier, Aymeric; Simonelig, Martine
Oculopharyngeal muscular dystrophy (OPMD) is a late onset disease which affects specific muscles. No pharmacological treatments are currently available for OPMD. In recent years, genetically tractable models of OPMD – Drosophila and Caenorhabditis elegans – have been generated. Although these models have not yet been used for large-scale primary drug screening, they have been very useful in candidate approaches for the identification of potential therapeutic compounds for OPMD. In this brief review, we summarize the data that validated active molecules for OPMD in animal models including Drosophila, C. elegans and mouse.
Kalyaanamoorthy, Subha; Chen, Yi-Ping Phoebe
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.
Full Text Available This article reviews current achievements in the field of chemoinformatics and their impact on modern drug discovery processes. The main data mining approaches used in cheminformatics, such as descriptor computations, structural similarity matrices, and classification algorithms, are outlined. The applications of cheminformatics in drug discovery, such as compound selection, virtual library generation, virtual high throughput screening, HTS data mining, and in silico ADMET are discussed. At the conclusion, future directions of chemoinformatics are suggested.
Balachandran, Premalatha; Govindarajan, Rajgopal
Ayurveda is a major traditional system of Indian medicine that is still being successfully used in many countries. Recapitulation and adaptation of the older science to modern drug discovery processes can bring renewed interest to the pharmaceutical world and offer unique therapeutic solutions for a wide range of human disorders. Eventhough time-tested evidences vouch immense therapeutic benefits for ayurvedic herbs and formulations, several important issues are required to be resolved for successful implementation of ayurvedic principles to present drug discovery methodologies. Additionally, clinical examination in the extent of efficacy, safety and drug interactions of newly developed ayurvedic drugs and formulations are required to be carefully evaluated. Ayurvedic experts suggest a reverse-pharmacology approach focusing on the potential targets for which ayurvedic herbs and herbal products could bring tremendous leads to ayurvedic drug discovery. Although several novel leads and drug molecules have already been discovered from ayurvedic medicinal herbs, further scientific explorations in this arena along with customization of present technologies to ayurvedic drug manufacturing principles would greatly facilitate a standardized ayurvedic drug discovery.
Lavé, Thierry; Caruso, Antonello; Parrott, Neil; Walz, Antje
In this review we present ways in which translational PK/PD modeling can address opportunities to enhance probability of success in drug discovery and early development. This is achieved by impacting efficacy and safety-driven attrition rates, through increased focus on the quantitative understanding and modeling of translational PK/PD. Application of the proposed principles early in the discovery and development phases is anticipated to bolster confidence of successfully evaluating proof of mechanism in humans and ultimately improve Phase II success. The present review is centered on the application of predictive modeling and simulation approaches during drug discovery and early development, and more specifically of mechanism-based PK/PD modeling. Case studies are presented, focused on the relevance of M&S contributions to real-world questions and the impact on decision making.
Schultz Kirkegaard, Henriette; Valentin, Finn
Academic drug discovery centres (ADDCs) are seen as one of the solutions to fill the innovation gap in early drug discovery, which has proven challenging for previous organisational models. Prior studies of ADDCs have identified the need to analyse them from the angle of their economic and organisational sustainability. We take that angle in an in-depth study of four prominent ADDCs. Our findings indicate that there are clear similarities in the way sustainable centres are organised, managed and financed. We also identify factors in the frameworks of academia and research funding affecting their performance.
Sumudu P. Leelananda
Full Text Available The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.
Wohlleben, Wolfgang; Mast, Yvonne; Stegmann, Evi; Ziemert, Nadine
Due to the threat posed by the increase of highly resistant pathogenic bacteria, there is an urgent need for new antibiotics; all the more so since in the last 20 years, the approval for new antibacterial agents had decreased. The field of natural product discovery has undergone a tremendous development over the past few years. This has been the consequence of several new and revolutionizing drug discovery and development techniques, which is initiating a 'New Age of Antibiotic Discovery'. In this review, we concentrate on the most significant discovery approaches during the last and present years and comment on the challenges facing the community in the coming years. © 2016 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.
Flobak, Åsmund; Baudot, Anaïs; Remy, Elisabeth; Thommesen, Liv; Thieffry, Denis; Kuiper, Martin; Lægreid, Astrid
Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.
Puzzo, Daniela; Gulisano, Walter; Palmeri, Agostino; Arancio, Ottavio
Introduction Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by memory loss and personality changes, leading to dementia. Histophatological hallmarks are represented by aggregates of beta-amyloid peptide (Aβ) in senile plaques and deposition of hyperphosphorylated tau protein in neurofibrillary tangles in the brain. Rare forms of early onset familial Alzheimer's disease are due to gene mutations. This has prompted researchers to develop genetically modified animals that could recapitulate the main features of the disease. The use of these models is complemented by non-genetically modified animals. Area covered This review summarizes the characteristics of the most used transgenic (Tg) and non-Tg models of AD. The authors have focused on models mainly used in their laboratories including: APP Tg2576, APP/PS1, 3xAD, single h-Tau, non-Tg mice treated with acute injections of Aβ or tau, and models of physiological aging. Expert opinion Animal models of disease might be very useful for studying the pathophysiology of the disease and for testing new therapeutics in preclinical studies but they do not reproduce the entire clinical features of human AD. When selecting a model, researchers should consider the various factors that might influence the phenotype. They should also consider the timing of testing/treating animals since the age at which each model develops certain aspects of the AD pathology varies. PMID:25927677
Pandey, Udai Bhan
The common fruit fly, Drosophila melanogaster, is a well studied and highly tractable genetic model organism for understanding molecular mechanisms of human diseases. Many basic biological, physiological, and neurological properties are conserved between mammals and D. melanogaster, and nearly 75% of human disease-causing genes are believed to have a functional homolog in the fly. In the discovery process for therapeutics, traditional approaches employ high-throughput screening for small molecules that is based primarily on in vitro cell culture, enzymatic assays, or receptor binding assays. The majority of positive hits identified through these types of in vitro screens, unfortunately, are found to be ineffective and/or toxic in subsequent validation experiments in whole-animal models. New tools and platforms are needed in the discovery arena to overcome these limitations. The incorporation of D. melanogaster into the therapeutic discovery process holds tremendous promise for an enhanced rate of discovery of higher quality leads. D. melanogaster models of human diseases provide several unique features such as powerful genetics, highly conserved disease pathways, and very low comparative costs. The fly can effectively be used for low- to high-throughput drug screens as well as in target discovery. Here, we review the basic biology of the fly and discuss models of human diseases and opportunities for therapeutic discovery for central nervous system disorders, inflammatory disorders, cardiovascular disease, cancer, and diabetes. We also provide information and resources for those interested in pursuing fly models of human disease, as well as those interested in using D. melanogaster in the drug discovery process. PMID:21415126
Jones, H M; Chen, Y; Gibson, C; Heimbach, T; Parrott, N; Peters, S A; Snoeys, J; Upreti, V V; Zheng, M; Hall, S D
The application of physiologically based pharmacokinetic (PBPK) modeling has developed rapidly within the pharmaceutical industry and is becoming an integral part of drug discovery and development. In this study, we provide a cross pharmaceutical industry position on "how PBPK modeling can be applied in industry" focusing on the strategies for application of PBPK at different stages, an associated perspective on the confidence and challenges, as well as guidance on interacting with regulatory agencies and internal best practices.
de Araujo, Fernanda Fortes; Nagarkatti, Rana; Gupta, Charu; Marino, Ana Paula; Debrabant, Alain
Drug discovery initiatives, aimed at Chagas treatment, have been hampered by the lack of standardized drug screening protocols and the absence of simple pre-clinical assays to evaluate treatment efficacy in animal models. In this study, we used a simple Enzyme Linked Aptamer (ELA) assay to detect T. cruzi biomarker in blood and validate murine drug discovery models of Chagas disease. In two mice models, Apt-29 ELA assay demonstrated that biomarker levels were significantly higher in the infected group compared to the control group, and upon Benznidazole treatment, their levels reduced. However, biomarker levels in the infected treated group did not reduce to those seen in the non-infected treated group, with 100% of the mice above the assay cutoff, suggesting that parasitemia was reduced but cure was not achieved. The ELA assay was capable of detecting circulating biomarkers in mice infected with various strains of T. cruzi parasites. Our results showed that the ELA assay could detect residual parasitemia in treated mice by providing an overall picture of the infection in the host. They suggest that the ELA assay can be used in drug discovery applications to assess treatment efficacy in-vivo.
Fernanda Fortes de Araujo
Full Text Available Drug discovery initiatives, aimed at Chagas treatment, have been hampered by the lack of standardized drug screening protocols and the absence of simple pre-clinical assays to evaluate treatment efficacy in animal models. In this study, we used a simple Enzyme Linked Aptamer (ELA assay to detect T. cruzi biomarker in blood and validate murine drug discovery models of Chagas disease. In two mice models, Apt-29 ELA assay demonstrated that biomarker levels were significantly higher in the infected group compared to the control group, and upon Benznidazole treatment, their levels reduced. However, biomarker levels in the infected treated group did not reduce to those seen in the non-infected treated group, with 100% of the mice above the assay cutoff, suggesting that parasitemia was reduced but cure was not achieved. The ELA assay was capable of detecting circulating biomarkers in mice infected with various strains of T. cruzi parasites. Our results showed that the ELA assay could detect residual parasitemia in treated mice by providing an overall picture of the infection in the host. They suggest that the ELA assay can be used in drug discovery applications to assess treatment efficacy in-vivo.
Davies, Shelley L; Moral, Maria Angels; Bozzo, Jordi
Chronicles in Drug Discovery features special interest reports on advances in drug discovery. This month we highlight agents that target and deplete immunosuppressive regulatory T cells, which are produced by tumor cells to hinder innate immunity against, or chemotherapies targeting, tumor-associated antigens. Antiviral treatments for respiratory syncytial virus, a severe and prevalent infection in children, are limited due to their side effect profiles and cost. New strategies currently under clinical development include monoclonal antibodies, siRNAs, vaccines and oral small molecule inhibitors. Recent therapeutic lines for Huntington's disease include gene therapies that target the mutated human huntingtin gene or deliver neuroprotective growth factors and cellular transplantation in apoptotic regions of the brain. Finally, we highlight the antiinflammatory and antinociceptive properties of new compounds targeting the somatostatin receptor subtype sst4, which warrant further study for their potential application as clinical analgesics.
Introduction Animal models have enabled great progress in the discovery and understanding of pharmacological approaches for treating muscle diseases like Duchenne muscular dystrophy. Areas covered With this article, the author provides the reader with a description of the zebrafish animal model, which has been employed to identify and study pharmacological approaches to muscle disease. In particular, the author focuses on how both large-scale chemical screens and targeted drug treatment studies have established zebrafish as an important model for muscle disease drug discovery. Expert opinion There are a number of opportunities arising for the use of zebrafish models for further developing pharmacological approaches to muscle diseases, including studying drug combination therapies and utilizing genome editing to engineer zebrafish muscle disease models. It is the author’s particular belief that the availability of a wide range of zebrafish transgenic strains for labeling immune cell types, combined with live imaging and drug treatment of muscle disease models, should allow for new elegant studies demonstrating how pharmacological approaches might influence inflammation and the immune response in muscle disease. PMID:24931439
Full Text Available The Innovative Medicines Initiative (IMI is a large-scale public–private partnership between the European Commission and the European Federation of Pharmaceutical Industries and Associations (EFPIA. IMI aims to boost the development of new medicines across Europe by implementing new collaborative endeavours between large pharmaceutical companies and other key actors in the health-care ecosystem, i.e., academic institutions, small and medium enterprises, patients, and regulatory authorities. Currently there are more than 40 IMI projects covering the whole value chain of pharmaceutical R&D, but with a strong focus on drug discovery, as an ideal arena where the PPP concept of pre-competitive collaboration can rapidly deliver results. This article review recent achievements of the IMI consortia of relevance to drug discovery, providing proof-of-concept evidence for the efficiency of this new model of collaboration.
Full Text Available The Innovative Medicines Initiative (IMI is a large-scale public–private partnership between the European Commission and the European Federation of Pharmaceutical Industries and Associations (EFPIA. IMI aims to boost the development of new medicines across Europe by implementing new collaborative endeavours between large pharmaceutical companies and other key actors in the health-care ecosystem, i.e., academic institutions, small and medium enterprises, patients, and regulatory authorities. Currently there are more than 40 IMI projects covering the whole value chain of pharmaceutical R&D, but with a strong focus on drug discovery, as an ideal arena where the PPP concept of pre-competitive collaboration can rapidly deliver results. This article review recent achievements of the IMI consortia of relevance to drug discovery, providing proof-of-concept evidence for the efficiency of this new model of collaboration.
Full Text Available Satish Srinivas Kitambi1, Gayathri Chandrasekar21Department of Medical Biochemistry and Biophysics; 2Department of Biosciences, Karolinska Institutet, Stockholm, SwedenAbstract: The identification of normal and cancerous stem cells and the recent advances made in isolation and culture of stem cells have rapidly gained attention in the field of drug discovery and regenerative medicine. The prospect of performing screens aimed at proliferation, directed differentiation, and toxicity and efficacy studies using stem cells offers a reliable platform for the drug discovery process. Advances made in the generation of induced pluripotent stem cells from normal or diseased tissue serves as a platform to perform drug screens aimed at developing cell-based therapies against conditions like Parkinson's disease and diabetes. This review discusses the application of stem cells and cancer stem cells in drug screening and their role in complementing, reducing, and replacing animal testing. In addition to this, target identification and major advances in the field of personalized medicine using induced pluripotent cells are also discussed.Keywords: therapeutics, stem cells, cancer stem cells, screening models, drug development, high throughput screening
Hung, Che-Lun; Chen, Chi-Chun
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.
Bueters, Tjerk; Ploeger, Bart A; Visser, Sandra A G
Translational pharmacokinetic-pharmacodynamic (PKPD) modeling has been fully implemented at AstraZeneca's drug discovery unit for central nervous system and pain indications to facilitate timely progression of the right compound to clinical studies, simultaneously assuring essential preclinical efficacy and safety knowledge. This review illustrates the impact of a translational PKPD paradigm with examples from drug discovery programs. Paradoxically, laboratory animal use decreased owing to better understanding of in vitro-in vivo relationships, optimized in vivo study designs, meta-analyses and hypothesis testing using simulations. From an ethical and effectivity perspective, we advocate that translational PKPD approaches should be implemented more broadly in drug discovery.
The RAS Drug Discovery group aims to develop assays that will reveal aspects of RAS biology upon which cancer cells depend. Successful assay formats are made available for high-throughput screening programs to yield potentially effective drug compounds.
Song, Chenchen; Knöpfel, Thomas
Optogenetics - the use of light and genetics to manipulate and monitor the activities of defined cell populations - has already had a transformative impact on basic neuroscience research. Now, the conceptual and methodological advances associated with optogenetic approaches are providing fresh momentum to neuroscience drug discovery, particularly in areas that are stalled on the concept of 'fixing the brain chemistry'. Optogenetics is beginning to translate and transit into drug discovery in several key domains, including target discovery, high-throughput screening and novel therapeutic approaches to disease states. Here, we discuss the exciting potential of optogenetic technologies to transform neuroscience drug discovery.
In 2006, Shinya Yamanaka first reported that in vitro reprogramming of somatic cells toward pluripotency was achieved by simple induction of specific transcription factors. Induced pluripotent stem cell (iPSC) technology has since revolutionized the ways in which we explore the mechanisms of human diseases and develop therapeutics. Here, I describe the recent advances in human iPSC-based disease modeling and drug discovery and discuss the current challenges. Additionally, I outline potential future applications of human iPSCs in classifying patients based on their response to drugs in clinical trials and elucidating optimal patient-specific therapeutic strategies, which will contribute to reduced attrition rates and the development of precision medicine.
Tonge, Peter J
The development of therapies for the treatment of neurological cancer faces a number of major challenges including the synthesis of small molecule agents that can penetrate the blood-brain barrier (BBB). Given the likelihood that in many cases drug exposure will be lower in the CNS than in systemic circulation, it follows that strategies should be employed that can sustain target engagement at low drug concentration. Time dependent target occupancy is a function of both the drug and target concentration as well as the thermodynamic and kinetic parameters that describe the binding reaction coordinate, and sustained target occupancy can be achieved through structural modifications that increase target (re)binding and/or that decrease the rate of drug dissociation. The discovery and deployment of compounds with optimized kinetic effects requires information on the structure-kinetic relationships that modulate the kinetics of binding, and the molecular factors that control the translation of drug-target kinetics to time-dependent drug activity in the disease state. This Review first introduces the potential benefits of drug-target kinetics, such as the ability to delineate both thermodynamic and kinetic selectivity, and then describes factors, such as target vulnerability, that impact the utility of kinetic selectivity. The Review concludes with a description of a mechanistic PK/PD model that integrates drug-target kinetics into predictions of drug activity.
Cryan, John F; Sweeney, Fabian F
Anxiety disorders are common, serious and a growing health problem worldwide. However, the causative factors, aetiology and underlying mechanisms of anxiety disorders, as for most psychiatric disorders, remain relatively poorly understood. Animal models are an important aid in giving insight into the aetiology, neurobiology and, ultimately, the therapy of human anxiety disorders. The approach, however, is challenged with a number of complexities. In particular, the heterogeneous nature of anxiety disorders in humans coupled with the associated multifaceted and descriptive diagnostic criteria, creates challenges in both animal modelling and in clinical research. In this paper, we describe some of the more widely used approaches for assessing the anxiolytic activity of known and potential therapeutic agents. These include ethological, conflict-based, hyponeophagia, vocalization-based, physiological and cognitive-based paradigms. Developments in the characterization of translational models are also summarized, as are the challenges facing researchers in their drug discovery efforts in developing new anxiolytic drugs, not least the ever-shifting clinical conceptualization of anxiety disorders. In conclusion, to date, although animal models of anxiety have relatively good validity, anxiolytic drugs with novel mechanisms have been slow to emerge. It is clear that a better alignment of the interactions between basic and clinical scientists is needed if this is to change. LINKED ARTICLES This article is part of a themed issue on Translational Neuropharmacology. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2011.164.issue-4 PMID:21545412
Frearson, Julie; Wyatt, Paul
As the pharmaceutical industry continues to re-strategise and focus on low-risk, relatively short term gains for the sake of survival, we need to re-invigorate the early stages of drug discovery and rebalance efforts towards novel modes of action therapeutics and neglected genetic and tropical diseases. Academic drug discovery is one model which offers the promise of new approaches and an alternative organisational culture for drug discovery as it attempts to apply academic innovation and thought processes to the challenge of discovering drugs to address real unmet need. PMID:20922062
Gleeson, M Paul; Hersey, Anne; Hannongbua, Supa
ADME prediction is an extremely challenging area as many of the properties we try to predict are a result of multiple physiological processes. In this review we consider how in-silico predictions of ADME processes can be used to help bias medicinal chemistry into more ideal areas of property space, minimizing the number of compounds needed to be synthesized to obtain the required biochemical/physico-chemical profile. While such models are not sufficiently accurate to act as a replacement for in-vivo or in-vitro methods, in-silico methods nevertheless can help us to understand the underlying physico-chemical dependencies of the different ADME properties, and thus can give us inspiration on how to optimize them. Many global in-silico ADME models (i.e generated on large, diverse datasets) have been reported in the literature. In this paper we selectively review representatives from each distinct class and discuss their relative utility in drug discovery. For each ADME parameter, we limit our discussion to the most recent, most predictive or most insightful examples in the literature to highlight the current state of the art. In each case we briefly summarize the different types of models available for each parameter (i.e simple rules, physico-chemical and 3D based QSAR predictions), their overall accuracy and the underlying SAR. We also discuss the utility of the models as related to lead generation and optimization phases of discovery research.
Full Text Available High-throughput screening (HTS in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb for further development towards new tuberculosis (TB drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15-28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.
Cao, Lei; Tan, Lan; Jiang, Teng; Zhu, Xi-Chen; Yu, Jin-Tai
Although most neurodegenerative diseases have been closely related to aberrant accumulation of aggregation-prone proteins in neurons, understanding their pathogenesis remains incomplete, and there is no treatment to delay the onset or slow the progression of many neurodegenerative diseases. The availability of induced pluripotent stem cells (iPSCs) in recapitulating the phenotypes of several late-onset neurodegenerative diseases marks the new era in in vitro modeling. The iPSC collection represents a unique and well-characterized resource to elucidate disease mechanisms in these diseases and provides a novel human stem cell platform for screening new candidate therapeutics. Modeling human diseases using iPSCs has created novel opportunities for both mechanistic studies as well as for the discovery of new disease therapies. In this review, we introduce iPSC-based disease modeling in neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. In addition, we discuss the implementation of iPSCs in drug discovery associated with some new techniques.
Gawehn, Erik; Hiss, Jan A; Schneider, Gisbert
Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.
Nett, Jeniel E; Andes, David R
During infection, fungi frequently transition to a biofilm lifestyle, proliferating as communities of surface-adherent aggregates of cells. Phenotypically, cells in a biofilm are distinct from free-floating cells. Their high tolerance of antifungals and ability to withstand host defenses are two characteristics that foster resilience. Biofilm infections are particularly difficult to eradicate, and most available antifungals have minimal activity. Therefore, the discovery of novel compounds and innovative strategies to treat fungal biofilms is of great interest. Although many fungi have been observed to form biofilms, the most well-studied is Candida albicans. Animal models have been developed to simulate common Candida device-associated infections, including those involving vascular catheters, dentures, urinary catheters, and subcutaneous implants. Models have also reproduced the most common mucosal biofilm infections: oropharyngeal and vaginal candidiasis. These models incorporate the anatomical site, immune components, and fluid dynamics of clinical niches and have been instrumental in the study of drug resistance and investigation of novel therapies. This chapter describes the significance of fungal biofilm infections, the animal models developed for biofilm study, and how these models have contributed to the development of new strategies for the eradication of fungal biofilm infections.
Full Text Available Integrated computational approaches for Mycobacterium tuberculosis (Mtb are useful to identify new molecules that could lead to future tuberculosis (TB drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively. These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC50 in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10-6 cm/s, kinetic solubility (125 μM at pH 7.4 in PBS and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes. Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.
Sampson, Valerie B; Kamara, Davida F; Kolb, E Anders
Introduction There are > 75 histological types of solid tumors that are classified into two major groups: bone and soft-tissue sarcomas. These diseases are more prevalent in children, and pediatric sarcomas tend to be highly aggressive and rapidly progressive. Sarcomas in adults may follow a more indolent course, but aggressive tumors are also common. Sarcomas that are metastatic at diagnosis, or recurrent following therapy, remain refractory to current treatment options with dismal overall survival rates. A major focus of clinical trials, for patients with sarcoma, is to identify novel and more effective therapeutic strategies targeted to genomic or proteomic aberrations specific to the malignant cells. Critical to the understanding of the potential for targeted therapies are models of disease that are representative of clinical disease and predictive of relevant clinical responses. Areas covered In this article, the authors discuss the use of mouse xenograft models and genetically engineered mice in cancer drug discovery. The authors provide a special focus on models for the two most common bone sarcomas: osteosarcoma (OS) and Ewing's sarcoma (ES). Expert opinion Predicting whether a new anticancer agent will have a positive therapeutic index in patients with OS and ES remains a challenge. The use of mouse sarcoma models for understanding the mechanisms involved in the response of tumors to new treatments is an important step in the process of drug discovery and the development of clinically relevant therapeutic strategies for these diseases. PMID:23844615
Singh, Vimal K; Kalsan, Manisha; Kumar, Neeraj; Saini, Abhishek; Chandra, Ramesh
such as animal models. Many toxic compounds (different chemical compounds, pharmaceutical drugs, other hazardous chemicals, or environmental conditions) which are encountered by humans and newly designed drugs may be evaluated for toxicity and effects by using iPSCs. Thus, the applications of iPSCs in regenerative medicine, disease modeling, and drug discovery are enormous and should be explored in a more comprehensive manner.
Eder, Jörg; Herrling, Paul L
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.
Shirai, Hiroki; Prades, Catherine; Vita, Randi
to the antibody science in every project in antibody drug discovery. Recent experimental technologies allow for the rapid generation of large-scale data on antibody sequences, affinity, potency, structures, and biological functions; this should accelerate drug discovery research. Therefore, a robust bioinformatic......More and more antibody therapeutics are being approved every year, mainly due to their high efficacy and antigen selectivity. However, it is still difficult to identify the antigen, and thereby the function, of an antibody if no other information is available. There are obstacles inherent...... infrastructure for these large data sets has become necessary. In this article, we first identify and discuss the typical obstacles faced during the antibody drug discovery process. We then summarize the current status of three sub-fields of antibody informatics as follows: (i) recent progress in technologies...
Haseltine, W A
Genomics, the systematic study of all the genes of an organism, offers a new and much-needed source of systematic productivity for the pharmaceutical industry. The isolation of the majority of human genes in their most useful form is leading to the creation of new drugs based on human proteins, antibodies, peptides, and genes. Human Genome Sciences, Inc, was the first company to use the systematic, genomics approach to discovering drugs, and we have placed 4 of these in clinical trials. Two are described: repifermin (keratinocyte growth factor-2, KGF-2) for wound healing and treatment of mucositis caused by cancer therapy, and B lymphocyte stimulator (BLyS) for stimulation of the immune system. An anti-BLyS antibody drug is in advanced preclinical development for treatment of autoimmune diseases.
Full Text Available Drug discovery applies multidisciplinary approaches either experimentally, computationally or both ways to identify lead compounds to treat various diseases. While conventional approaches have yielded many US Food and Drug Administration (FDA-approved drugs, researchers continue investigating and designing better approaches to increase the success rate in the discovery process. In this article, we provide an overview of the current strategies and point out where and how the method of maximum entropy has been introduced in this area. The maximum entropy principle has its root in thermodynamics, yet since Jaynes’ pioneering work in the 1950s, the maximum entropy principle has not only been used as a physics law, but also as a reasoning tool that allows us to process information in hand with the least bias. Its applicability in various disciplines has been abundantly demonstrated. We give several examples of applications of maximum entropy in different stages of drug discovery. Finally, we discuss a promising new direction in drug discovery that is likely to hinge on the ways of utilizing maximum entropy.
Desalermos, Athanasios; Muhammed, Maged; Glavis-Bloom, Justin; Mylonakis, Eleftherios
Introduction The number of microorganism strains with resistance to known antimicrobials is increasing. Therefore, there is a high demand for new, non-toxic and efficient antimicrobial agents. Research with the microscopic nematode Caenorhabditis elegans can address this high demand for the discovery of new antimicrobial compounds. In particular, C. elegans can be used as a model host for in vivo drug discovery through high-throughput screens of chemical libraries. Areas covered This review introduces the use of substitute model hosts and especially C. elegans in the study of microbial pathogenesis. The authors also highlight recently published literature on the role of C. elegans in drug discovery and outline its use as a promising host with unique advantages in the discovery of new antimicrobial drugs. Expert opinion C. elegans can be used, as a model host, to research many diseases, including fungal infections and Alzheimer’s disease. In addition, high-throughput techniques, for screening chemical libraries, can also be facilitated. Nevertheless, C. elegans and mammals have significant differences that both limit the use of the nematode in research and the degree by which results can be interpreted. That being said, the use of C. elegans in drug discovery still holds promise and the field continues to grow, with attempts to improve the methodology already underway. PMID:21686092
Animal models for seizures and epilepsy have played a fundamental role in advancing our understanding of basic mechanisms underlying ictogenesis and epileptogenesis and have been instrumental in the discovery and preclinical development of novel antiepileptic drugs (AEDs). However, there is growing concern that the efficacy of drug treatment of epilepsy has not substantially improved with the introduction of new AEDs, which, at least in part, may be due to the fact that the same simple screening models, i.e., the maximal electroshock seizure (MES) and s.c. pentylenetetrazole (PTZ) seizure tests, have been used as gatekeepers in AED discovery for >6 decades. It has been argued that these old models may identify only drugs that share characteristics with existing drugs, and are unlikely to have an effect on refractory epilepsies. Indeed, accumulating evidence with several novel AEDs, including levetiracetan, has shown that the MES and PTZ models do not identify all potential AEDs but instead may fail to discover compounds that have great potential efficacy but work through mechanisms not tested by these models. Awareness of the limitations of acute seizure models comes at a critical crossroad. Clearly, preclinical strategies of AED discovery and development need a conceptual shift that is moving away from using models that identify therapies for the symptomatic treatment of epilepsy to those that may be useful for identifying therapies that are more effective in the refractory population and that may ultimately lead to an effective cure in susceptible individuals by interfering with the processes underlying epilepsy. To realize this goal, the molecular mechanisms of the next generation of therapies must necessarily evolve to include targets that contribute to epileptogenesis and pharmacoresistance in relevant epilepsy models.
Svennebring, Andreas M; Wikberg, Jarl Es
Three dedicated approaches to the calculation of the risk-adjusted net present value (rNPV) in drug discovery projects under different assumptions are suggested. The probability of finding a candidate drug suitable for clinical development and the time to the initiation of the clinical development is assumed to be flexible in contrast to the previously used models. The rNPV of the post-discovery cash flows is calculated as the probability weighted average of the rNPV at each potential time of initiation of clinical development. Practical considerations how to set probability rates, in particular during the initiation and termination of a project is discussed.
Toxicogenomics, drug discovery, and pathologist.The field of toxicogenomics, which currently focuses on the application of large-scale differential gene expression (DGE) data to toxicology, is starting to influence drug discovery and development in the pharmaceutical indu...
Miles, William H.; Smiley, Patricia M.
This experiment describes the isolation and biological testing of eugenol and neutral compounds from commercially available clove oil. By coupling the chemical separation of the components of clove oil (an experiment described in many introductory organic laboratory textbooks) with a simple antibiotic test, the students "discover" the biologically active compound in clove oil. This experiment models one of the primary methods used in the discovery of new pharmaceutical agents.
Hargrave-Thomas, Emily; Yu, Bo; Reynisson, Jóhannes
It was found that the discovery of 5.8% (84/1437) of all drugs on the market involved serendipity. Of these drugs, 31 (2.2%) were discovered following an incident in the laboratory and 53 (3.7%) were discovered in a clinical setting. In addition, 263 (18.3%) of the pharmaceuticals in clinical use today are chemical derivatives of the drugs discovered with the aid of serendipity. Therefore, in total, 24.1% (347/1437) of marketed drugs can be directly traced to serendipitous events confirming the importance of this elusive phenomenon. In the case of anticancer drugs, 35.2% (31/88) can be attributed to a serendipitous event, which is somewhat larger than for all drugs. The therapeutic field that has benefited the most from serendipity are central nervous system active drugs reflecting the difficulty in designing compounds to pass the blood-brain-barrier and the lack of laboratory-based assays for many of the diseases of the mind.
Kocyigit, Yucel; Seker, Huseyin
Identification of drug candidates is an important but also difficult process. Given drug resistance bacteria that we face, this process has become more important to identify protein candidates that demonstrate antibacterial activity. The aim of this study is therefore to develop a bioinformatics approach that is more capable of identifying a small but effective set of proteins that are expected to show antibacterial activity, subsequently to be used as antibiotic drug targets. As this is regarded as an imbalanced data classification problem due to smaller number of antibiotic drugs available, a hybrid classification model was developed and applied to the identification of antibiotic drugs. The model was developed by taking into account of various statistical models leading to the development of six different hybrid models. The best model has reached the accuracy of as high as 50% compared to earlier study with the accuracy of less than 1% as far as the proportion of the candidates identified and actual antibiotics in the candidate list is concerned.
Ru ZHANG; Xin XIE
G-protein-coupled receptors (GPCRs) mediate many important physiological functions and are considered as one of the most successful therapeutic targets for a broad spectrum of diseases.The design and implementation of high-throughput GPCR assays that allow the cost-effective screening of large compound libraries to identify novel drug candidates are critical in early drug discovery.Early functional GPCR assays depend primarily on the measurement of G-protein-mediated 2nd messenger generation.Taking advantage of the continuously deepening understanding of GPCR signal transduction,many G-protein-independent pathways are utilized to detect the activity of GPCRs,and may provide additional information on functional selectivity of candidate compounds.With the combination of automated imaging systems and label-free detection systems,such assays are now suitable for high-throughput screening (HTS).In this review,we summarize the most widely used GPCR assays and recent advances in HTS technologies for GPCR drug discovery.
Kobet, Robert A.; Pan, Xiaoping; Zhang, Baohong; Pak, Stephen C.; Asch, Adam S.; Lee, Myon-Hee
The nematode Caenorhabditis elegans (C. elegans) offers a unique opportunity for biological and basic medical researches due to its genetic tractability and well-defined developmental lineage. It also provides an exceptional model for genetic, molecular, and cellular analysis of human disease-related genes. Recently, C. elegans has been used as an ideal model for the identification and functional analysis of drugs (or small-molecules) in vivo. In this review, we describe conserved oncogenic signaling pathways (Wnt, Notch, and Ras) and their potential roles in the development of cancer stem cells. During C. elegans germline development, these signaling pathways regulate multiple cellular processes such as germline stem cell niche specification, germline stem cell maintenance, and germ cell fate specification. Therefore, the aberrant regulations of these signaling pathways can cause either loss of germline stem cells or overproliferation of a specific cell type, resulting in sterility. This sterility phenotype allows us to identify drugs that can modulate the oncogenic signaling pathways directly or indirectly through a high-throughput screening. Current in vivo or in vitro screening methods are largely focused on the specific core signaling components. However, this phenotype-based screening will identify drugs that possibly target upstream or downstream of core signaling pathways as well as exclude toxic effects. Although phenotype-based drug screening is ideal, the identification of drug targets is a major challenge. We here introduce a new technique, called Drug Affinity Responsive Target Stability (DARTS). This innovative method is able to identify the target of the identified drug. Importantly, signaling pathways and their regulators in C. elegans are highly conserved in most vertebrates, including humans. Therefore, C. elegans will provide a great opportunity to identify therapeutic drugs and their targets, as well as to understand mechanisms underlying the
Jensen, Stine Skov; Aaberg-Jessen, Charlotte; Nørregaard, Annette
the effects of new drugs on tumor cells including tumor stem cells. Implantation of glioblastoma cells into organotypic brain slice cultures has previously been published as a model system, but not using a stem cell favourable environment. Organotypic corticostriatal rat brain slice cultures were prepared......The discovery of tumor stem cells being highly resistant against therapy makes new demands to model systems suitable for evaluation of the effects of new drugs on tumor stem cells. The aim of the present study was therefore to develop an in vivo-like in vitro glioblastoma model for testing...... and cultured in a serum containing medium replaced after three days with a serum-free stem cell medium. Thereafter fluorescent DiI labelled glioblastoma spheroids from the cell line U87 and the tumor stem cell line SJ-1 established in our laboratory were implanted into the brain slices between cortex...
Charles H. Williams
Full Text Available In this article we propose a systematic development method for rational drug design while reviewing paradigms in industry, emerging techniques and technologies in the field. Although the process of drug development today has been accelerated by emergence of computational methodologies, it is a herculean challenge requiring exorbitant resources; and often fails to yield clinically viable results. The current paradigm of target based drug design is often misguided and tends to yield compounds that have poor absorption, distribution, metabolism, and excretion, toxicology (ADMET properties. Therefore, an in vivo organism based approach allowing for a multidisciplinary inquiry into potent and selective molecules is an excellent place to begin rational drug design. We will review how organisms like the zebrafish and Caenorhabditis elegans can not only be starting points, but can be used at various steps of the drug development process from target identification to pre-clinical trial models. This systems biology based approach paired with the power of computational biology; genetics and developmental biology provide a methodological framework to avoid the pitfalls of traditional target based drug design.
Williams, Charles H; Hong, Charles C
In this article we propose a systematic development method for rational drug design while reviewing paradigms in industry, emerging techniques and technologies in the field. Although the process of drug development today has been accelerated by emergence of computational methodologies, it is a herculean challenge requiring exorbitant resources; and often fails to yield clinically viable results. The current paradigm of target based drug design is often misguided and tends to yield compounds that have poor absorption, distribution, metabolism, and excretion, toxicology (ADMET) properties. Therefore, an in vivo organism based approach allowing for a multidisciplinary inquiry into potent and selective molecules is an excellent place to begin rational drug design. We will review how organisms like the zebrafish and Caenorhabditis elegans can not only be starting points, but can be used at various steps of the drug development process from target identification to pre-clinical trial models. This systems biology based approach paired with the power of computational biology; genetics and developmental biology provide a methodological framework to avoid the pitfalls of traditional target based drug design.
Gerwick, William H; Fenner, Amanda M
The marine environment has been a source of more than 20,000 inspirational natural products discovered over the past 50 years. From these efforts, 9 approved drugs and 12 current clinical trial agents have been discovered, either as natural products or as molecules inspired from the natural product structure. To a significant degree, these have come from collections of marine invertebrates largely obtained from shallow-water tropical ecosystems. However, there is a growing recognition that marine invertebrates are oftentimes populated with enormous quantities of "associated" or symbiotic microorganisms and that microorganisms are the true metabolic sources of these most valuable of marine natural products. Also, because of the inherently multidisciplinary nature of this field, a high degree of innovation is characteristic of marine natural product drug discovery efforts.
Lampa, Samuel; Alvarsson, Jonathan; Spjuth, Ola
Predictive modelling in drug discovery is challenging to automate as it often contains multiple analysis steps and might involve cross-validation and parameter tuning that create complex dependencies between tasks. With large-scale data or when using computationally demanding modelling methods, e-infrastructures such as high-performance or cloud computing are required, adding to the existing challenges of fault-tolerant automation. Workflow management systems can aid in many of these challenges, but the currently available systems are lacking in the functionality needed to enable agile and flexible predictive modelling. We here present an approach inspired by elements of the flow-based programming paradigm, implemented as an extension of the Luigi system which we name SciLuigi. We also discuss the experiences from using the approach when modelling a large set of biochemical interactions using a shared computer cluster.Graphical abstract.
Orhan, Ilkay Erdogan
Pharmacognosy deals with the natural drugs obtained from organisms such as most plants, microbes, and animals. Up to date, many important drugs including morphine, atropine, galanthamine, etc. have originated from natural sources which continue to be good model molecules in drug discovery. Traditional medicine is also a part of pharmacognosy and most of the third world countries still depend on the use of herbal medicines. Consequently, pharmacognosy always keeps its popularity in pharmaceutical sciences and plays a critical role in drug discovery.
Brown, Frank K; Kopti, Farida; Chang, Charlie Zhenyu; Johnson, Scott A; Glick, Meir; Waller, Chris L
Merck & Co., Inc., Kenilworth, NJ, USA, is undergoing a transformation in the way that it prosecutes R&D programs. Through the adoption of a "model-driven" culture, enhanced R&D productivity is anticipated, both in the form of decreased attrition at each stage of the process and by providing a rational framework for understanding and learning from the data generated along the way. This new approach focuses on the concept of a "Design Cycle" that makes use of all the data possible, internally and externally, to drive decision-making. These data can take the form of bioactivity, 3D structures, genomics, pathway, PK/PD, safety data, etc. Synthesis of high-quality data into models utilizing both well-established and cutting-edge methods has been shown to yield high confidence predictions to prioritize decision-making and efficiently reposition resources within R&D. The goal is to design an adaptive research operating plan that uses both modeled data and experiments, rather than just testing, to drive project decision-making. To support this emerging culture, an ambitious information management (IT) program has been initiated to implement a harmonized platform to facilitate the construction of cross-domain workflows to enable data-driven decision-making and the construction and validation of predictive models. These goals are achieved through depositing model-ready data, agile persona-driven access to data, a unified cross-domain predictive model lifecycle management platform, and support for flexible scientist-developed workflows that simplify data manipulation and consume model services. The end-to-end nature of the platform, in turn, not only supports but also drives the culture change by enabling scientists to apply predictive sciences throughout their work and over the lifetime of a project. This shift in mindset for both scientists and IT was driven by an early impactful demonstration of the potential benefits of the platform, in which expert-level early discovery
Dudley, Joel T; Schadt, Eric; Sirota, Marina; Butte, Atul J; Ashley, Euan
Despite great strides in revealing and understanding the physiological and molecular bases of cardiovascular disease, efforts to translate this understanding into needed therapeutic interventions continue to lag far behind the initial discoveries. Although pharmaceutical companies continue to increase investments into research and development, the number of drugs gaining federal approval is in decline. Many factors underlie these trends, and a vast number of technological and scientific innovations are being sought through efforts to reinvigorate drug discovery pipelines. Recent advances in molecular profiling technologies and development of sophisticated computational approaches for analyzing these data are providing new, systems-oriented approaches towards drug discovery. Unlike the traditional approach to drug discovery which is typified by a one-drug-one-target mindset, systems-oriented approaches to drug discovery leverage the parallelism and high-dimensionality of the molecular data to construct more comprehensive molecular models that aim to model broader bimolecular systems. These models offer a means to explore complex molecular states (e.g., disease) where thousands to millions of molecular entities comprising multiple molecular data types (e.g., proteomics and gene expression) can be evaluated simultaneously as components of a cohesive biomolecular system. In this paper, we discuss emerging approaches towards systems-oriented drug discovery and contrast these efforts with the traditional, unidimensional approach to drug discovery. We also highlight several applications of these system-oriented approaches across various aspects of drug discovery, including target discovery, drug repositioning and drug toxicity. When available, specific applications to cardiovascular drug discovery are highlighted and discussed.
Herrero, Lara; Nelson, Michelle; Bettadapura, Jayaram; Gahan, Michelle E; Mahalingam, Suresh
Animal models, which mimic human disease, are invaluable tools for understanding the mechanisms of disease pathogenesis and development of treatment strategies. In particular, animal models play important roles in the area of infectious arthritis. Alphaviruses, including Ross River virus (RRV), o'nyong-nyong virus, chikungunya virus (CHIKV), mayaro virus, Semliki Forest virus and sindbis virus, are globally distributed and cause transient illness characterized by fever, rash, myalgia, arthralgia and arthritis in humans. Severe forms of the disease result in chronic incapacitating arthralgia and arthritis. The mechanisms of how these viruses cause musculoskeletal disease are ill defined. In recent years, the use of a mouse model for RRV-induced disease has assisted in unraveling the pathobiology of infection and in discovering novel drugs to ameliorate disease. RRV as an infection model has the potential to provide key insights into such disease processes, particularly as many viruses, other than alphaviruses, are known to cause infectious arthritides. The emergence and outbreak of CHIKV in many parts of the world has necessitated the need to develop animal models of CHIKV disease. The development of non-human primate models of CHIKV disease has given insights into viral tropism and disease pathogenesis and facilitated the development of new treatment strategies. This review highlights the application of animal models of alphaviral diseases in the fundamental understanding of the mechanisms that contribute to disease and for defining the role that the immune response may have on disease pathogenesis, with the view of providing the foundation for new treatments.
Bosia, Marta; Pigoni, Alessandro; Cavallaro, Roberto
Schizophrenia is a major psychiatric disorder that afflicts about 1% of the world's population, falling into the top 10 medical disorders causing disability. Existing therapeutic strategies have had limited success; they have poor effects on core cognitive impairment and long-term disability. They are also burdened by relevant side effects. Although new antipsychotic medications have been launched in the past decades, there has been a general lack of significant innovation over the past 60 years. This lack of significant progress in the pharmacotherapy of schizophrenia is a reflection of the complexity and heterogeneity of its etiopathogenetic mechanisms. In this article, the authors briefly review genetic models of schizophrenia, focusing on examples of how new therapeutic strategies have been developed from them. They report on the evidence of epigenetic alterations in schizophrenia and their relevance to pharmacological studies. Further, they describe the implications of epigenetic mechanisms in the etiopathogenesis of the disease and the effects of current antipsychotic drugs on epigenetic processes. Finally, they provide their perspective of using epigenetic drugs for treating schizophrenia. Current genetic and epigenetic studies are finally shedding light on the biomolecular mechanisms linked to the core pathogenetic alterations in schizophrenia, rather than just their symptoms. These advancements in the understanding of the physiopathology of schizophrenia provide exciting new perspectives for treatments. Indeed, the possibility of looking directly at the biomolecular level allows us to bypass the age-old issues of animal studies pertaining to their questionable validity as behavioral models.
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
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.
In this review article I explore the suitability of human epidermal neural crest stem cells (hEPI-NCSC) for translational medicine. hEPI-NCSC are multipotent somatic stem cells that are derived from the embryonic neural crest. hEPI-NCSC are located in the bulge of hair follicles where they persist postnatally and into adulthood. Because of their location in the hairy skin and their migratory behavior, hEPI-NCSC can be easily isolated as a highly pure population of stem cells without the need for purification. Furthermore they can be expanded ex vivo into millions of stem cells, they do not form tumors in vivo, and they can undergo directed differentiation into crest and noncrest-derived cell types of clinical relevance. Taken together, these characteristics make hEPI-NCSC attractive candidates for cell-based therapies, drug discovery, and disease modeling. © 2014 Wiley Periodicals, Inc.
Ilkay Erdogan Orhan
Full Text Available Pharmacognosy deals with the natural drugs obtained fromorganisms such as most plants, microbes, and animals. Up todate, many important drugs including morphine, atropine,galanthamine, etc. have originated from natural sources whichcontinue to be good model molecules in drug discovery.Traditional medicine is also a part of pharmacognosy and mostof the third world countries still depend on the use of herbalmedicines. Consequently, pharmacognosy always keeps itspopularity in pharmaceutical sciences and plays a critical role indrug discovery.
Jacobson, Kenneth A
G protein-coupled receptors (GPCRs) remain a major domain of pharmaceutical discovery. The identification of GPCR lead compounds and their optimization are now structure-based, thanks to advances in X-ray crystallography, molecular modeling, protein engineering and biophysical techniques. In silico screening provides useful hit molecules. New pharmacological approaches to tuning the pleotropic action of GPCRs include: allosteric modulators, biased ligands, GPCR heterodimer-targeted compounds, manipulation of polypharmacology, receptor antibodies and tailoring of drug molecules to fit GPCR pharmacogenomics. Measurements of kinetics and drug efficacy are factors influencing clinical success. With the exception of inhibitors of GPCR kinases, targeting of intracellular GPCR signaling or receptor cycling for therapeutic purposes remains a futuristic concept. New assay approaches are more efficient and multidimensional: cell-based, label-free, fluorescence-based assays, and biosensors. Tailoring GPCR drugs to a patient's genetic background is now being considered. Chemoinformatic tools can predict ADME-tox properties. New imaging technology visualizes drug action in vivo. Thus, there is reason to be optimistic that new technology for GPCR ligand discovery will help reverse the current narrowing of the pharmaceutical pipeline.
Williams, Charles H.; Hong, Charles C.
In this article we propose a systematic development method for rational drug design while reviewing paradigms in industry, emerging techniques and technologies in the field. Although the process of drug development today has been accelerated by emergence of computational methodologies, it is a herculean challenge requiring exorbitant resources; and often fails to yield clinically viable results. The current paradigm of target based drug design is often misguided and tends to yield compounds t...
Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies. PMID:19674801
Yao, Lixia; Evans, James A; Rzhetsky, Andrey
Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved in pharmaceutical development,explore a range of novel, high-value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery.These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use.Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry links for scientific and human benefit. Attention to these opportunities could promise punctuated advance and will complement the well-established computational work on which drug discovery currently relies.
Agarwal, Pankaj; Searls, David B
The drug discovery enterprise provides strong drivers for data integration. While attention in this arena has tended to focus on integration of primary data from omics and other large platform technologies contributing to drug discovery and development, the scientific literature remains a major source of information valuable to pharmaceutical enterprises, and therefore tools for mining such data and integrating it with other sources are of vital interest and economic impact. This review provides a brief overview of approaches to literature mining as they relate to drug discovery, and offers an illustrative case study of a 'lightweight' approach we have implemented within an industrial context.
Williams, Antony J; Harland, Lee; Groth, Paul; Pettifer, Stephen; Chichester, Christine; Willighagen, Egon L; Evelo, Chris T; Blomberg, Niklas; Ecker, Gerhard; Goble, Carole; Mons, Barend
Open PHACTS is a public-private partnership between academia, publishers, small and medium sized enterprises and pharmaceutical companies. The goal of the project is to deliver and sustain an 'open pharmacological space' using and enhancing state-of-the-art semantic web standards and technologies. It is focused on practical and robust applications to solve specific questions in drug discovery research. OPS is intended to facilitate improvements in drug discovery in academia and industry and to support open innovation and in-house non-public drug discovery research. This paper lays out the challenges and how the Open PHACTS project is hoping to address these challenges technically and socially.
Jubb, Adrian M; Koeppen, Hartmut; Reis-Filho, Jorge S
The rapid pace of drug discovery and drug development in oncology, immunology and ophthalmology brings new challenges; the efficient and effective development of new targeted drugs will require more detailed molecular classifications of histologically homogeneous diseases that show heterogeneous clinical outcomes. To this end, single companion diagnostics for specific drugs will be replaced by multiplex diagnostics for entire therapeutic areas, preserving tissue and enabling rapid molecular taxonomy. The field will move away from the development of new molecular entities as single agents, to which resistance is common. Instead, a detailed understanding of the pathological mechanisms of resistance, in patients and in preclinical models, will be key to the validation of scientifically rational and clinically effective drug combinations. To remain at the heart of disease diagnosis and appropriate management, pathologists must evolve into translational biologists and biomarker scientists. Herein, we provide examples of where this metamorphosis has already taken place, in lung cancer and melanoma, where the transformation has yet to begin, in the use of immunotherapies for ophthalmology and oncology, and where there is fertile soil for a revolution in treatment, in efforts to classify glioblastoma and personalize treatment. The challenges of disease heterogeneity, the regulatory environment and adequate tissue are ever present, but these too are being overcome in dedicated academic centres. In summary, the tools necessary to overcome the 'whens' and 'ifs' of the molecular revolution are in the hands of pathologists today; it is a matter of standardization, training and leadership to bring these into routine practice and translate science into patient benefit. This Annual Review Issue of the Journal of Pathology highlights the central role for pathology in modern drug discovery and development.
... Matters NIH Research Matters January 13, 2014 Arthritis Genetics Analysis Aids Drug Discovery An international research team ... may play a role in triggering the disease. Genetic factors are also thought to play a role. ...
Jain, Kewal K
The potential applications of nanotechnology in life sciences, particularly nanobiotechnology, include those for drug discovery. This chapter shows how several of the nanotechnologies including nanoparticles and various nanodevices such as nanobiosensors and nanobiochips are being used to improve drug discovery. Nanoscale assays using nanoliter volumes contribute to cost saving. Some nanosubstances such as fullerenes are drug candidates. There are some safety concerns about the in vivo use of nanoparticles that are being investigated. However, future prospects for applications in healthcare of drugs discovered through nanotechnology and their role in the development of personalized medicine appear to be excellent.
Folkersen, Lasse; Biswas, Shameek; Frederiksen, Klaus Stensgaard
Recent groundbreaking work in genetics has identified thousands of small-effect genetic variants throughout the genome that are associated with almost all major diseases. These genome-wide association studies (GWAS) are often proposed as a source of future medical breakthroughs. However......, with several notable exceptions, the journey from a small-effect genetic variant to a functional drug has proven arduous, and few examples of actual contributions to drug discovery exist. Here, we discuss novel approaches of overcoming this hurdle by using instead public genetics resources as a pragmatic guide...... alongside existing drug discovery methods. Our aim is to evaluate human genetic confidence as a rationale for drug target selection....
Mohammed Kawser Hossain
Full Text Available Diabetes mellitus (DM is a widespread metabolic disease with a progressive incidence of morbidity and mortality worldwide. Despite extensive research, treatment options for diabetic patients remains limited. Although significant challenges remain, induced pluripotent stem cells (iPSCs have the capacity to differentiate into any cell type, including insulin-secreting pancreatic β cells, highlighting its potential as a treatment option for DM. Several iPSC lines have recently been derived from both diabetic and healthy donors. Using different reprogramming techniques, iPSCs were differentiated into insulin-secreting pancreatic βcells. Furthermore, diabetes patient-derived iPSCs (DiPSCs are increasingly being used as a platform to perform cell-based drug screening in order to develop DiPSC-based cell therapies against DM. Toxicity and teratogenicity assays based on iPSC-derived cells can also provide additional information on safety before advancing drugs to clinical trials. In this review, we summarize recent advances in the development of techniques for differentiation of iPSCs or DiPSCs into insulin-secreting pancreatic β cells, their applications in drug screening, and their role in complementing and replacing animal testing in clinical use. Advances in iPSC technologies will provide new knowledge needed to develop patient-specific iPSC-based diabetic therapies.
Zheng, Heping; Hou, Jing; Zimmerman, Matthew D; Wlodawer, Alexander; Minor, Wladek
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
Chen, Haijun; Wu, Jianlei; Gao, Yu; Chen, Haiying; Zhou, Jia
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.
Gilardoni, Francois; Arvanites, Anthony C
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.
Bard, Jonathan; Wall, Michael D; Lazari, Ovadia; Arjomand, Jamshid; Munoz-Sanjuan, Ignacio
Huntington disease is a monogenic, autosomal dominant, progressive neurodegenerative disorder caused by a trinucleotide CAG repeat expansion in exon 1 of the huntingtin (HTT) gene; age of onset of clinical symptoms inversely correlates with expanded CAG repeat length. HD leads to extensive degeneration of the basal ganglia, hypothalamic nuclei, and selected cortical areas, and a wide range of molecular mechanisms have been implicated in disease pathology in animal or cellular models expressing mutated HTT (mHTT) proteins, either full-length or amino-terminal fragments. However, HD cellular models that recapitulate the slow progression of the disease have not been available due to the toxicity of overexpressed exogenous mHTT or to limitations with using primary cells for long-term studies. Most investigations of the effects of mHTT relied on cytotoxicity or aggregation end points in heterologous systems or in primary embryonic neuroglial cultures derived from HD mouse models. More innovative approaches are currently under active investigation, including screening using electrophysiological endpoints, as well as the recent use of primary blood mononuclear cells and of human embryonic stem cells derived from a variety of HD research participants. Here we describe how these cellular systems are being used to investigate HD biology as well as to identify mechanisms with therapeutic potential.
Vimal kishor Singh
Full Text Available Recent progresses in the field of Induced Pluripotent Stem Cells (iPSCs have opened up many gateways for the research in therapeutics. iPSCs are the cells which are reprogrammed from somatic cells using different transcription factors. IPSCs possess unique properties of self renewal and differentiation to many types of cell lineage. Hence could replace the use of embryonic stem cells, and may overcome the various ethical issues regarding the use of embryos in research and clinics. Overwhelming responses prompted worldwide by a large number of researchers about the use of iPSCs evoked a large number of peple to establish more authentic methods for iPSC generation. This would require understanding the underlying mechanism in a detailed manner. There have been a large number of reports showing potential role of different molecules as putative regulators of iPSC generating methods. The molecular mechanisms that play role in reprogramming to generate iPSCs from different types of somatic cell sources involves a plethora of molecules including miRNAs, DNA modifying agents (viz. DNA methyl transferases, NANOG, etc. While promising a number of important roles in various clinical/research studies, iPSCs could also be of great use in studying molecular mechanism of many diseases. There are various diseases that have been modelled by uing iPSCs for better understanding of their etiology which maybe further utilized for developing putative treatments for these diseases. In addition, iPSCs are used for the production of patient-specific cells which can be transplanted to the site of injury or the site of tissue degeneration due to various disease conditions. The use of iPSCs may eliminate the chances of immune rejection as patient specific cells may be used for transplantation in various engraftment processes. Moreover, iPSC technology has been employed in various diseases for disease modelling and gene therapy. The technique offers benefits over other
Zhou, Wei; Wang, Yonghua; Lu, Aiping; Zhang, Ge
Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity), target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level.
Zhou, Wei; Wang, Yonghua; Lu, Aiping; Zhang, Ge
Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity), target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level. PMID:26901192
Full Text Available Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity, target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level.
Yokley, Brian H; Hartman, Matthew; Slusher, Barbara S
There was a greater than 50% decline in central nervous system (CNS) drug discovery and development programs by major pharmaceutical companies from 2009 to 2014. This decline was paralleled by a rise in the number of university led drug discovery centers, many in the CNS area, and a growth in the number of public-private drug discovery partnerships. Diverse operating models have emerged as the academic drug discovery centers adapt to this changing ecosystem.
Mears, Emily Rose; Modabber, Farrokh; Don, Robert; Johnson, George E
The current in vivo models for the utility and discovery of new potential anti-leishmanial drugs targeting Cutaneous Leishmaniasis (CL) differ vastly in their immunological responses to the disease and clinical presentation of symptoms. Animal models that show similarities to the human form of CL after infection with Leishmania should be more representative as to the effect of the parasite within a human. Thus, these models are used to evaluate the efficacy of new anti-leishmanial compounds before human clinical trials. Current animal models aim to investigate (i) host-parasite interactions, (ii) pathogenesis, (iii) biochemical changes/pathways, (iv) in vivo maintenance of parasites, and (v) clinical evaluation of drug candidates. This review focuses on the trends of infection observed between Leishmania parasites, the predictability of different strains, and the determination of parasite load. These factors were used to investigate the overall effectiveness of the current animal models. The main aim was to assess the efficacy and limitations of the various CL models and their potential for drug discovery and evaluation. In conclusion, we found that the following models are the most suitable for the assessment of anti-leishmanial drugs: L. major-C57BL/6 mice (or-vervet monkey, or-rhesus monkeys), L. tropica-CsS-16 mice, L. amazonensis-CBA mice, L. braziliensis-golden hamster (or-rhesus monkey). We also provide in-depth guidance for which models are not suitable for these investigations.
Emily Rose Mears
Full Text Available The current in vivo models for the utility and discovery of new potential anti-leishmanial drugs targeting Cutaneous Leishmaniasis (CL differ vastly in their immunological responses to the disease and clinical presentation of symptoms. Animal models that show similarities to the human form of CL after infection with Leishmania should be more representative as to the effect of the parasite within a human. Thus, these models are used to evaluate the efficacy of new anti-leishmanial compounds before human clinical trials. Current animal models aim to investigate (i host-parasite interactions, (ii pathogenesis, (iii biochemical changes/pathways, (iv in vivo maintenance of parasites, and (v clinical evaluation of drug candidates. This review focuses on the trends of infection observed between Leishmania parasites, the predictability of different strains, and the determination of parasite load. These factors were used to investigate the overall effectiveness of the current animal models. The main aim was to assess the efficacy and limitations of the various CL models and their potential for drug discovery and evaluation. In conclusion, we found that the following models are the most suitable for the assessment of anti-leishmanial drugs: L. major-C57BL/6 mice (or-vervet monkey, or-rhesus monkeys, L. tropica-CsS-16 mice, L. amazonensis-CBA mice, L. braziliensis-golden hamster (or-rhesus monkey. We also provide in-depth guidance for which models are not suitable for these investigations.
Heath, James R.; Ribas, Antoni; Mischel, Paul S.
The genetic, functional, or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development.1-3 In cancers, heterogeneity may be essential for tumor stability,4 but its precise role in tumor biology is poorly resolved. This challenges the design of accurate disease models for use in drug development, and can confound the interpretation of biomarker levels, and of patient responses to specific therapies. The complex nature of heterogeneous tissues has motivated the development of tools for single cell genomic, transcriptomic, and multiplex proteomic analysis. We review these tools, assess their advantages and limitations, and explore their potential applications in drug discovery and development. PMID:26669673
Bates, Susan E; Amiri-Kordestani, Laleh; Giaccone, Giuseppe
A British humorist said, "There is much to be said for failure. It is much more interesting than success." This CCR Focus section is aimed at identifying lessons to be learned from difficulties encountered in recent years during development of anticancer agents. Clearly, we have not found a silver bullet tyrosine kinase inhibitor against solid tumors comparable with imatinib in chronic myelogenous leukemia. Although vemurafenib for B-Raf-mutated melanoma and crizotinib for non-small cell lung cancers with echinoderm microtubule-associated protein-like 4 (EML4)-anaplastic lymphoma kinase (ALK) rearrangements were developed rapidly and offer hope for individualized targeted therapies, the development of agents targeting a number of other pathways has been slower and less successful. These agents include drugs for blocking the insulin-like growth factor I/insulin receptor pathways, mitotic kinase inhibitors, and Hsp90 antagonists. Several potentially useful, if not groundbreaking, agents have had setbacks in clinical development, including trastuzumab emtansine, gemtuzumab ozogamicin, and satraplatin. From experience, we have learned the following: (i) not every altered protein or pathway is a valid anticancer target; (ii) drugs must effectively engage the target; (iii) the biology of the systems we use must be very well understood; and (iv) clinical trials must be designed to assess whether the drug reached and impaired the target. It is also important that we improve the drug development enterprise to enhance enrollment, streamline clinical trials, reduce financial risk, and encourage the development of agents for niche indications. Such enormous challenges are offset by potentially tremendous gains in our understanding and treatment of cancer.
Chung, Thomas D Y
There has been increased concern that the current "blockbuster" model of drug discovery and development practiced by "Big Pharma" are unsustainable in terms of cost (> $1 billion/approved drug) and time to market (10 - 15 years). The recent mergers and acquisitions (M&A), shuttering of internal research programs, closure of "redundant" sites of operations, senior management turnover and continued workforce reductions among the top 10 major pharmaceutical companies reflect draconian responses to reduce costs. However, the resultant exodus of intellectual capital, loss in motivation and momentum, and exit from early stage discovery programs by pharmaceutical companies has contributed to an "innovation deficit". Disease advocacy groups, investment communities and the government are calling for new innovative business models to address this deficit. In particular they are looking towards academia and clinical trials centers to catalyze new innovations in translational research. Indeed over the last decade many academic institutions have launched drug discovery centers largely comprising high-throughput screening (HTS) to accelerate "translational" research. A major impetus for this "open innovation" effort has been the National Institutes of Health (NIH) "Roadmap" and Molecular Libraries Initiative/Program (MLI/MLP), which is in its last year, and will be transitioned into the National Center for the Advancement of Translational Sciences (NCATS). With the end of Roadmap funding, general reduction in Federal government funding and its recent sequestration, academic drug discovery centers are being challenged to become selfsustaining, adding financial value, while remaining aligned with the missions of their respective academic non-profit institutions. We describe herein, a brief history of our bi-coastal Conrad Prebys Center for Chemical Genomics (Prebys Center) at the Sanford|Burnham Medical Research Institute (SBMRI), the key components of its infrastructure, core
Full Text Available Boesenbergia rotunda is a herb from the Boesenbergia genera under the Zingiberaceae family. B. rotunda is widely found in Asian countries where it is commonly used as a food ingredient and in ethnomedicinal preparations. The popularity of its ethnomedicinal usage has drawn the attention of scientists worldwide to further investigate its medicinal properties. Advancement in drug design and discovery research has led to the development of synthetic drugs from B. rotunda metabolites via bioinformatics and medicinal chemistry studies. Furthermore, with the advent of genomics, transcriptomics, proteomics, and metabolomics, new insights on the biosynthetic pathways of B. rotunda metabolites can be elucidated, enabling researchers to predict the potential bioactive compounds responsible for the medicinal properties of the plant. The vast biological activities exhibited by the compounds obtained from B. rotunda warrant further investigation through studies such as drug discovery, polypharmacology, and drug delivery using nanotechnology.
O’Reilly, Linda P.; Cliff J Luke; Perlmutter, David H.; Silverman, Gary A.; Pak, Stephen C.
C. elegans has proven to be a useful model organism for investigating molecular and cellular aspects of numerous human diseases. More recently, investigators have explored the use of this organism as a tool for drug discovery. Although earlier drug screens were labor-intensive and low in throughput, recent advances in high-throughput liquid workflows, imaging platforms and data analysis software have made C. elegans a viable option for automated high-throughput drug screens. This review will ...
The increased use of drugs (and the concurrent increased risks of drug-induced illness) require definition of relevant research areas and strategy. For established marketed drugs, research needs depend on the magnitudes of risk of an illness from a drug and the base-line risk. With the drug risk high and the base-line risk low, the problem surfaces in premarketing studies or through the epidemic that develops after marketing. If the drug adds slightly to a high base-line risk, the effect is undetectable. When both risks are low, adverse effects can be discovered by chance, but systematic case-referent studies can speed discovery. If both risks are high, clinical trials and nonexperimental studies may be used. With both risks intermediate, systematic evaluations, especially case-referent studies are needed. Newly marketed drugs should be routinely evaluated through compulsory registration and follow-up study of the earliest users.
Litterman, Nadia K.; Rhee, Michele; Swinney, David C.; Ekins, Sean
Rare disease research has reached a tipping point, with the confluence of scientific and technologic developments that if appropriately harnessed, could lead to key breakthroughs and treatments for this set of devastating disorders. Industry-wide trends have revealed that the traditional drug discovery research and development (R&D) model is no longer viable, and drug companies are evolving their approach. Rather than only pursue blockbuster therapeutics for heterogeneous, common diseases, drug companies have increasingly begun to shift their focus to rare diseases. In academia, advances in genetics analyses and disease mechanisms have allowed scientific understanding to mature, but the lack of funding and translational capability severely limits the rare disease research that leads to clinical trials. Simultaneously, there is a movement towards increased research collaboration, more data sharing, and heightened engagement and active involvement by patients, advocates, and foundations. The growth in networks and social networking tools presents an opportunity to help reach other patients but also find researchers and build collaborations. The growth of collaborative software that can enable researchers to share their data could also enable rare disease patients and foundations to manage their portfolio of funded projects for developing new therapeutics and suggest drug repurposing opportunities. Still there are many thousands of diseases without treatments and with only fragmented research efforts. We will describe some recent progress in several rare diseases used as examples and propose how collaborations could be facilitated. We propose that the development of a center of excellence that integrates and shares informatics resources for rare diseases sponsored by all of the stakeholders would help foster these initiatives. PMID:25685324
Litterman, Nadia K; Rhee, Michele; Swinney, David C; Ekins, Sean
Rare disease research has reached a tipping point, with the confluence of scientific and technologic developments that if appropriately harnessed, could lead to key breakthroughs and treatments for this set of devastating disorders. Industry-wide trends have revealed that the traditional drug discovery research and development (R&D) model is no longer viable, and drug companies are evolving their approach. Rather than only pursue blockbuster therapeutics for heterogeneous, common diseases, drug companies have increasingly begun to shift their focus to rare diseases. In academia, advances in genetics analyses and disease mechanisms have allowed scientific understanding to mature, but the lack of funding and translational capability severely limits the rare disease research that leads to clinical trials. Simultaneously, there is a movement towards increased research collaboration, more data sharing, and heightened engagement and active involvement by patients, advocates, and foundations. The growth in networks and social networking tools presents an opportunity to help reach other patients but also find researchers and build collaborations. The growth of collaborative software that can enable researchers to share their data could also enable rare disease patients and foundations to manage their portfolio of funded projects for developing new therapeutics and suggest drug repurposing opportunities. Still there are many thousands of diseases without treatments and with only fragmented research efforts. We will describe some recent progress in several rare diseases used as examples and propose how collaborations could be facilitated. We propose that the development of a center of excellence that integrates and shares informatics resources for rare diseases sponsored by all of the stakeholders would help foster these initiatives.
Liu, Fa; Mayer, John P
The discovery of novel therapeutics to combat human disease has traditionally been among the most important goals of research chemists. After a century of innovation, state-of-the-art chemical protein synthesis is now capable of efficiently assembling proteins of up to several hundred residues in length from individual amino acids. By virtue of its unique ability to incorporate non-native structural elements, chemical protein synthesis has been seminal in the recent development of several novel drug discovery technologies. In this chapter, we review the key advances in peptide and protein chemistry which have enabled our current synthetic capabilities. We also discuss the synthesis of D-proteins and their applications in mirror image phage-display and racemic protein crystallography, the synthesis of enzymes for structure-based drug discovery, and the direct synthesis of homogenous protein pharmaceuticals.
Kerns, Edward H; Di, Li; Carter, Guy T
The solubility of a compound depends on its structure and solution conditions. Structure determines the lipophilicity, hydrogen bonding, molecular volume, crystal energy and ionizability, which determine solubility. Solution conditions are affected by pH, co-solvents, additives, ionic strength, time and temperature. Many drug discovery experiments are conducted under "kinetic" solubility conditions. In drug discovery, solubility has a major impact on bioassays, formulation for in vivo dosing, and intestinal absorption. A good goal for the solubility of drug discovery compounds is >60 ug/mL. Equilibrium solubility assays can be conducted in moderate throughput, by incubating excess solid with buffer and agitating for several days, prior to filtration and HPLC quantitation. Kinetic solubility assays are performed in high throughput with shorter incubation times and high throughput analyses using plate readers. The most frequently used of these are the nephelometric assay and direct UV assay, which begin by adding a small volume of DMSO stock solution of each test compound to buffer. In nephelometry, this solution is serially diluted across a microtitre plate and undissolved particles are detected via light scattering. In direct UV, undissolved particles are separated by filtration, after which the dissolved material is quantitated using UV absorption. Equilibrium solubility is useful for preformulation. Kinetic solubility is useful for rapid compound assessment, guiding optimization via structure modification, and diagnosing bioassays. It is often useful to customize solubility experiments using conditions that answer specific research questions of drug discovery teams, such as compound selection and vehicle development for pharmacology and PK studies.
Full Text Available Abstract There is currently a crisis in drug discovery for neuropsychiatric disorders, with a profound, yet unexpected drought in new drug development across the spectrum. In this commentary, the sources of this dilemma and potential avenues to redress the issue are explored. These include a critical review of diagnostic issues and of selection of participants for clinical trials, and the mechanisms for identifying new drugs and new drug targets. Historically, the vast majority of agents have been discovered serendipitously or have been modifications of existing agents. Serendipitous discoveries, based on astute clinical observation or data mining, remain a valid option, as is illustrated by the suggestion in the paper by Wahlqvist and colleagues that treatment with sulfonylurea and metformin reduces the risk of affective disorder. However, the identification of agents targeting disorder-related biomarkers is currently proving particularly fruitful. There is considerable hope for genetics as a purist, pathophysiologically valid pathway to drug discovery; however, it is unclear whether the science is ready to meet this promise. Fruitful paradigms will require a break from the orthodoxy, and creativity and risk may well be the fingerprints of success. See related article http://www.biomedcentral.com/1741-7015/10/150
There is currently a crisis in drug discovery for neuropsychiatric disorders, with a profound, yet unexpected drought in new drug development across the spectrum. In this commentary, the sources of this dilemma and potential avenues to redress the issue are explored. These include a critical review of diagnostic issues and of selection of participants for clinical trials, and the mechanisms for identifying new drugs and new drug targets. Historically, the vast majority of agents have been discovered serendipitously or have been modifications of existing agents. Serendipitous discoveries, based on astute clinical observation or data mining, remain a valid option, as is illustrated by the suggestion in the paper by Wahlqvist and colleagues that treatment with sulfonylurea and metformin reduces the risk of affective disorder. However, the identification of agents targeting disorder-related biomarkers is currently proving particularly fruitful. There is considerable hope for genetics as a purist, pathophysiologically valid pathway to drug discovery; however, it is unclear whether the science is ready to meet this promise. Fruitful paradigms will require a break from the orthodoxy, and creativity and risk may well be the fingerprints of success.See related article http://www.biomedcentral.com/1741-7015/10/150.
Kolb, V M
Selected works are discussed which clearly demonstrate that mimicking various aspects of the process by which natural products evolved is becoming a powerful tool in contemporary drug discovery. Natural products are an established and rich source of drugs. The term "natural product" is often used synonymously with "secondary metabolite." Knowledge of genetics and molecular evolution helps us understand how biosynthesis of many classes of secondary metabolites evolved. One proposed hypothesis is termed "inventive evolution." It invokes duplication of genes, and mutation of the gene copies, among other genetic events. The modified duplicate genes, per se or in conjunction with other genetic events, may give rise to new enzymes, which, in turn, may generate new products, some of which may be selected for. Steps of the inventive evolution can be mimicked in several ways for purpose of drug discovery. For example, libraries of chemical compounds of any imaginable structure may be produced by combinatorial synthesis. Out of these libraries new active compounds can be selected. In another example, genetic system can be manipulated to produce modified natural products ("unnatural natural products"), from which new drugs can be selected. In some instances, similar natural products turn up in species that are not direct descendants of each other. This is presumably due to a horizontal gene transfer. The mechanism of this inter-species gene transfer can be mimicked in therapeutic gene delivery. Mimicking specifics or principles of chemical evolution including experimental and test-tube evolution also provides leads for new drug discovery.
Kufahl, Peter R.; Watterson, Lucas R.
Introduction Globally, alcohol abuse and dependence are significant contributors to chronic disease and injury and are responsible for nearly 4% of all deaths annually. Acamprosate (Campral), one of only three pharmacological treatments approved for the treatment of alcohol dependence, has shown mixed efficacy in clinical trials in maintaining abstinence of detoxified alcoholics since studies began in the 1980’s. Yielding inconsistent results, these studies have prompted skepticism. Areas Covered Herein, the authors review the preclinical studies which have assessed the efficacy of acamprosate in various animal models of alcohol dependence and discuss the disparate findings from the major clinical trials. Moreover, the authors discuss the major limitations of these preclinical and clinical studies and offer explanations for the often contradictory findings. The article also looks at the importance of the calcium moiety that accompanies the salt form of acamprosate and its relevance to its activity. Expert opinion The recent discovery that large doses of calcium largely duplicate the effects of acamprosate in animal models has introduced a serious challenge to the widely-held functional association between this drug and the glutamate neurotransmission system. Future research on acamprosate or newer pharmacotherapeutics should consider assessing plasma and/or brain levels of calcium as a correlate or mediating factor in anti-relapse efficacy. Furthermore, preclinical research on acamprosate has thus far lacked animal models of chemical dependence on alcohol, and the testing of rodents with histories of alcohol intoxication and withdrawal is suggested. PMID:25258174
Donnelly, David J
The process of discovering and developing a new pharmaceutical is a long, difficult, and risky process that requires numerous resources. Molecular imaging techniques such as PET have recently become a useful tool for making decisions along a drug candidate's development timeline. PET is a translational, noninvasive imaging technique that provides quantitative information about a potential drug candidate and its target at the molecular level. Using this technique provides decisional information to ensure that the right drug candidate is being chosen, for the right target, at the right dose within the right patient population. This review will focus on small molecule PET tracers and how they are used within the drug discovery process. PET provides key information about a drug candidate's pharmacokinetic and pharmacodynamic properties in both preclinical and clinical studies. PET is being used in all phases of the drug discovery and development process, and the goal of these studies are to accelerate the process in which drugs are developed. Copyright © 2017. Published by Elsevier Inc.
Wójcikowski, Maciej; Zielenkiewicz, Piotr; Siedlecki, Pawel
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).
Bhar, Shanta; Ramana, Mucheli M V
With reference to challenges in developing varied and exceedingly complex scaffolds expeditiously through atom economy, domino reactions have assumed a significant role in several transformative endeavors towards established pharmaceuticals and new chemical entities across diverse therapeutic classes such as HIV integrase inhibitors, DPP4 [dipeptidyl peptidase IV] inhibitors, GSK- 3 (Glycogen Synthase Kinase 3) inhibitors, neoplastic drugs and microtubule antagonists. The very large chemical space of Domino Reactions can be leveraged for the design strategy of drugs and drug- like candidates with leading examples like Indinavir (Crixivan), Trandolapril (Mavik), Biyouyanagin A, endo pyrrolizidinone diastereomer [GSK] and several others. Domino reactions therefore constitute an integral part of both creative and functional aspects of drug design and discovery, contributing both enhanced efficiency as well as synthetic versatility to pharmaceutical drug design.
Heath, James R; Ribas, Antoni; Mischel, Paul S
The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed.
Trosset, Jean-Yves; Carbonell, Pablo
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.
Anna Caroline C Aguiar
Full Text Available Malaria remains a major world health problem following the emergence and spread of Plasmodium falciparum that is resistant to the majority of antimalarial drugs. This problem has since been aggravated by a decreased sensitivity of Plasmodium vivax to chloroquine. This review discusses strategies for evaluating the antimalarial activity of new compounds in vitro and in animal models ranging from conventional tests to the latest high-throughput screening technologies. Antimalarial discovery approaches include the following: the discovery of antimalarials from natural sources, chemical modifications of existing antimalarials, the development of hybrid compounds, testing of commercially available drugs that have been approved for human use for other diseases and molecular modelling using virtual screening technology and docking. Using these approaches, thousands of new drugs with known molecular specificity and active against P. falciparum have been selected. The inhibition of haemozoin formation in vitro, an indirect test that does not require P. falciparum cultures, has been described and this test is believed to improve antimalarial drug discovery. Clinical trials conducted with new funds from international agencies and the participation of several industries committed to the eradication of malaria should accelerate the discovery of drugs that are as effective as artemisinin derivatives, thus providing new hope for the control of malaria.
Chen, G; Jayawickreme, C; Way, J; Armour, S; Queen, K; Watson, C; Ignar, D; Chen, W J; Kenakin, T
This paper discusses the use of constitutively active G-protein-coupled receptor systems for drug discovery. Specifically, the ternary complex model is used to define the two major theoretical advantages of constitutive receptor screening-namely, the ability to detect antagonists as well as agonists directly and the fact that constitutive systems are more sensitive to agonists. In experimental studies, transient transfection of Chinese hamster ovary cyclic AMP response element (CRE) luciferase reporter cells with cDNA for human parathyroid hormone receptor, glucagon receptor, and glucagon-like peptide (GLP-1) receptor showed cDNA concentration-dependent constitutive activity with parathyroid hormone (PTH-1) and glucagon. In contrast, no constitutive activity was observed for GLP-1 receptor, yet responses to GLP-1 indicated that receptor expression had taken place. In another functional system, Xenopus laevi melanophores transfected with cDNA for human calcitonin receptor showed constitutive activity. Nine ligands for the calcitonin receptor either increased or decreased constitutive activity in this assay. The sensitivity of the system to human calcitonin increased with increasing constitutive activity. These data indicate that, for those receptors which naturally produce constitutive activity, screening in this mode could be advantageous over other methods.
Santos, Sofia Alexandre
Tese de doutoramento, Farmácia (Química Farmacêutica e Terapêutica), Universidade de Lisboa, Faculdade de Farmácia, 2016 Malaria remains a major burden to global public health, causing nearly 600,000 deaths annually. Efforts to control malaria are hampered by parasite drug resistance, insecticide resistance in mosquitoes, and the lack of an effective vaccine. However antimalarial drugs are a mainstay in efforts to control and eventually eradicate this disease, thus the discovery of new ant...
Gómez-Outes, Antonio; Suárez-Gea, Ma Luisa; Calvo-Rojas, Gonzalo; Lecumberri, Ramón; Rocha, Eduardo; Pozo-Hernández, Carmen; Terleira-Fernández, Ana Isabel; Vargas-Castrillón, Emilio
The history of the traditional anticoagulants is marked by both perseverance and serendipity. The anticoagulant effect of heparin was discovered by McLean in 1915, while he was searching for a procoagulant in dog liver. Link identified dicumarol from spoiled sweet clover hay in 1939 as the causal agent of the sweet clover disease, a hemorrhagic disorder in cattle. Hirudin extracts from the medicinal leech were first used for parenteral anticoagulation in the clinic in 1909, but their use was limited due to adverse effects and difficulties in achieving highly purified extracts. Heparins and coumarins (i.e.: warfarin, phenprocoumon, acenocoumarol) have been the mainstay of anticoagulant therapy for more than 60 years. Over the past decades, the drug discovery paradigm has shifted toward rational design following a target-based approach, in which specific proteins, or "targets", are chosen on current understandings of pathophysiology, small molecules that inhibit the target's activity may be identified by high-throughput screening and, in selected cases, these new molecules can be developed further as drugs. Despite the application of rational design, serendipity has still played a significant role in some of the new discoveries. This review will focus on the discovery of the main anticoagulant drugs in current clinical use, like unfractionated heparin, low-molecular-weight heparins, fondaparinux, coumarins (i.e.: warfarin, acenocoumarol, phenprocoumon), parenteral direct thrombin inhibitors (DTIs) (i.e.: argatroban, recombinant hirudins, bivalirudin), oral DTIs (i.e.: dabigatran) and oral direct factor Xa inhibitors (i.e.: rivaroxaban, apixaban).
Maiese, Kenneth; Chong, Zhao Zhong; Shang, Yan Chen; Wang, Shaohui
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
Low productivity, rising R&D costs, dissipating proprietary products and dwindling pipelines are driving the pharmaceutical industry to unprecedented challenges and scrutiny. In this article I reflect on the current status of the pharmaceutical industry and reasons for continued low productivity. An emerging 'symbiotic model of innovation', that addresses underlying issues in drug failure and attempts to narrow gaps in current drug discovery processes, is discussed to boost productivity. The model emphasizes partnerships in innovation to deliver quality products in a cost-effective system. I also discuss diverse options to build a balanced research portfolio with higher potential for persistent delivery of drug molecules.
Full Text Available Jean-Yves Trosset,1 Pablo Carbonell2,3 1Bioinformation Research Laboratory, Sup’Biotech, Villejuif, France; 2Faculty of Life Sciences, SYNBIOCHEM Centre, Manchester Institute of Biotechnology, University of Manchester, Manchester, UK; 3Department of Experimental and Health Sciences (DCEXS, Research Programme on Biomedical Informatics (GRIB, Hospital del Mar Medical Research Institute (IMIM, Universitat Pompeu Fabra (UPF, Barcelona, Spain Abstract: Synthetic biology (SB is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell–cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity. Keywords: metabolic engineering, plant synthetic biology, natural products, synthetic quorum sensing, drug resistance
Fagnan, David E; Gromatzky, Austin A; Stein, Roger M; Fernandez, Jose-Maria; Lo, Andrew W
Recently proposed 'megafund' financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures among disease targets) the amount of capital needed to de-risk such portfolios is much lower in this field. Numerical simulations suggest that an orphan disease megafund of only US$575 million can yield double-digit expected rates of return with only 10-20 projects in the portfolio. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.
LaPlante, Steven R; Edwards, Paul J; Fader, Lee D; Jakalian, Araz; Hucke, Oliver
An often overlooked source of chirality is atropisomerism, which results from slow rotation along a bond axis due to steric hindrance and/or electronic factors. If undetected or not managed properly, this time-dependent chirality has the potential to lead to serious consequences, because atropisomers can be present as distinct enantiomers or diastereoisomers with their attendant different properties. Herein we introduce a strategy to reveal and classify compounds that have atropisomeric chirality. Energy barriers to axial rotation were calculated using quantum mechanics, from which predicted high barriers could be experimentally validated. A calculated rotational energy barrier of 20 kcal mol(-1) was established as a suitable threshold to distinguish between atropisomers and non-atropisomers with a prediction accuracy of 86%. This methodology was applied to subsets of drug databases in the course of which atropisomeric drugs were identified. In addition, some drugs were exposed that were not yet known to have this chiral attribute. The most valuable utility of this tool will be to predict atropisomerism along the drug discovery pathway. When used in concert with our compound classification scheme, decisions can be made during early discovery stages such as "hit-to-lead" and "lead optimization," to foresee and validate the presence of atropisomers and to exercise options of removing, further stabilizing, or rendering the chiral axis of interest more freely rotatable via SAR design, thereby decreasing this potential liability within a compound series. The strategy can also improve drug development plans, such as determining whether a drug or series should be developed as a racemic mixture or as an isolated single compound. Moreover, the work described herein can be extended to other chemical fields that require the assessment of potential chiral axes.
Prasad, Sahdeo; Gupta, Subash C; Aggarwal, Bharat B
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.
The progress made in genome research raises the question whether the new knowledge bases that have emerged may also lead to better antidepressants. The past has seen many remarkable improvements over traditional drugs, but not a real breakthrough. More recently hypothesis-driven research in depression has focussed upon stress-hormone regulation as a possible target, but validation of new drugs is not yet in sight. In parallel, we see an upsurge of systematic unbiased research in a biotechnology-driven drug discovery effort. This research can only lead to results if clinical research adapts to these new demands by phenotyping depressed patients not only according to psychopathological characteristics but also by utilising functional (e.g. neuroendocrine, neuropsychological, neurophysiological, neuroimaging and clinical drug response) data that are to be correlated with data from genotyping. To achieve the goal of genotype/phenotype-based differential therapy, large-scale efforts with regards to both patient samples and genotyping capacities are needed. In the long term, increasingly detailed patient information, if translated into specific pharmacological treatments, will lead to customized drugs and thus to a partial fragmentation of the antidepressant market. Concurrently, the improved genotyping/phenotyping efforts will also lead to more widely applicable drugs that promise to avoid side effects and refractoriness and also to hasten the time to onset of action. Once these goals are achieved notorious undertreatment of depression may come to an end.
Ming Wai Hung
Full Text Available The zebrafish (Danio rerio has recently become a common model in the fields of genetics, environmental science, toxicology, and especially drug screening. Zebrafish has emerged as a biomedically relevant model for in vivo high content drug screening and the simultaneous determination of multiple efficacy parameters, including behaviour, selectivity, and toxicity in the content of the whole organism. A zebrafish behavioural assay has been demonstrated as a novel, rapid, and high-throughput approach to the discovery of neuroactive, psychoactive, and memory-modulating compounds. Recent studies found a functional similarity of drug metabolism systems in zebrafish and mammals, providing a clue with why some compounds are active in zebrafish in vivo but not in vitro, as well as providing grounds for the rationales supporting the use of a zebrafish screen to identify prodrugs. Here, we discuss the advantages of the zebrafish model for evaluating drug metabolism and the mode of pharmacological action with the emerging omics approaches. Why this model is suitable for identifying lead compounds from natural products for therapy of disorders with multifactorial etiopathogenesis and imbalance of angiogenesis, such as Parkinson's disease, epilepsy, cardiotoxicity, cerebral hemorrhage, dyslipidemia, and hyperlipidemia, is addressed.
Prathipati, Philip; Mizuguchi, Kenji
Ligand- and structure-based drug design approaches complement phenotypic and target screens, respectively, and are the two major frameworks for guiding early-stage drug discovery efforts. Since the beginning of this century, the advent of the genomic era has presented researchers with a myriad of high throughput biological data (parts lists and their interaction networks) to address efficacy and toxicity, augmenting the traditional ligand- and structure-based approaches. This data rich era has also presented us with challenges related to integrating and analyzing these multi-platform and multi-dimensional datasets and translating them into viable hypotheses. Hence in the present paper, we review these existing approaches to drug discovery research and argue the case for a new systems biology based approach. We present the basic principles and the foundational arguments/underlying assumptions of the systems biology based approaches to drug design. Also discussed are systems biology data types (key entities, their attributes and their relationships with each other, and data models/representations), software and tools used for both retrospective and prospective analysis, and the hypotheses that can be inferred. In addition, we summarize some of the existing resources for a systems biology based drug discovery paradigm (open TG-GATEs, DrugMatrix, CMap and LINCs) in terms of their strengths and limitations.
Ying SUN; Hong ZHOU; Bao-xue YANG
In polycystic kidney disease (PKD), a most common human genetic diseases, fluid-filled cysts displace normal renal tubules and cause end-stage renal failure. PKD is a serious and costly disorder. There is no available therapy that prevents or slows down the cystogenesis and cyst expansion in PKD. Numerous efforts have been made to find drug targets and the candidate drugs to treat PKD. Recent studies have defined the mechanisms underlying PKD and new therapies directed toward them. In this review article, we summarize the pathogenesis of PKD, possible drug targets, available PKD models for screening and evaluating new drugs as well as candidate drugs that are being developed.
Tari, Leslie W
Access to detailed three-dimensional structural information on protein drug targets can streamline many aspects of drug discovery, from target selection and target product profile determination, to the discovery of novel molecular scaffolds that form the basis of potential drugs, to lead optimization. The information content of X-ray crystal structures, as well as the utility of structural methods in supporting the different phases of the drug discovery process, are described in this chapter.
Pineda, Sandy S; Undheim, Eivind A B; Rupasinghe, Darshani B; Ikonomopoulou, Maria P; King, Glenn F
Over a period of more than 300 million years, spiders have evolved complex venoms containing an extraordinary array of toxins for prey capture and defense against predators. The major components of most spider venoms are small disulfide-bridged peptides that are highly stable and resistant to proteolytic degradation. Moreover, many of these peptides have high specificity and potency toward molecular targets of therapeutic importance. This unique combination of bioactivity and stability has made spider-venom peptides valuable both as pharmacological tools and as leads for drug development. This review describes recent advances in spider-venom-based drug discovery pipelines. We discuss spider-venom-derived peptides that are currently under investigation for treatment of a diverse range of pathologies including pain, stroke and cancer.
Thota, Sreekanth; Yerra, Rajeshwar
Malaria, a deadly infectious parasitic disease, is a major issue of public health in the world today and already produces serious economic constraints in the endemic countries. Most of the malarial infections and deaths are due to Plasmodium falciparum and Plasmodium vivax species. The recent emergence of resistance necessitates the search for new antimalarial drugs, which overcome the resistance and act through new mechanisms. Although much effort has been directed towards the discovery of novel antimalarial drugs. 4-anilino quinolone triazines as potent antimalarial agents, their in silico modelling and bioevaluation as Plasmodium falciparum transketolase and β-hematin inhibitors has been reported. This review is primarily focused on the drug discovery of the recent advances in the development of antimalarial agents and their mechanism of action.
Farrow, Stuart N; Solari, Roberto; Willson, Timothy M
Drug discovery scientists, faced with the myriad challenges involved in developing novel therapeutics as medicines, have tended to overlook the question of the most beneficial time to administer the drug. Recent developments in our understanding of circadian biology and the availability of tools to characterise the molecular clock indicate that time and duration of dosing may have profound consequences for the efficacy and safety of new and existing therapeutic agents. Progress in the field also suggests that many key physiological mechanisms are remarkably dependent on the circadian clock. It has also become clear that a number of diseases with important unmet medical need display marked circadian variation in their symptoms and severity. These discoveries now reveal opportunities for new therapeutic strategies to be developed that act by modulation of biological rhythms. These novel therapeutic approaches are likely to be facilitated by the continuing development of chemical probes and synthetic ligands targeted to an increasing number of the key proteins that regulate the molecular clock.
Ortega, Santiago Schiaffino; Cara, Luisa Carlota López; Salvador, María Kimatrai
The process of bringing new and innovative drugs, from conception and synthesis through to approval on the market can take the pharmaceutical industry 8-15 years and cost approximately $1.8 billion. Two key technologies are improving the hit-to-drug timeline: high-throughput screening (HTS) and rational drug design. In the latter case, starting from some known ligand-based or target-based information, a lead structure will be rationally designed to be tested in vitro or in vivo. Computational methods are part of many drug discovery programs, including the assessment of ADME (absorption-distribution-metabolism-excretion) and toxicity (ADMET) properties of compounds at the early stages of discovery/development with impressive results. The aim of this paper is to review, in a simple way, some of the most popular strategies used by modelers and some successful applications on computational chemistry to raise awareness of its importance and potential for an actual multidisciplinary drug discovery process.
We have conducted genome-wide association studies (GWAS) for rheumatoid arthritis (RA). We previously found that myelin basic protein (MBP) is associated with RA. One of the MBP isoforms (Golli-MBP) is expressed not only in nerve cells, but also in hematopoietic cells, and may negatively regulate T-cell receptor signaling. We expanded the GWAS level by collaborating with laboratories in Japan and then throughout the world. Meta-analysis of GWAS data resulted in the identification of -100 genomic loci associated with RA development. The -100 genomic loci contain -400 candidate genes, and it is not easy to find out which genes actually play important roles in RA. By incorporating available public databases, we succeeded in narrowing down the susceptibility genes from 377 to 98. We also showed that regulatory T cells are associated with RA based on the combination of the histone methylation database and our mega-GWAS results. Protein-protein interaction and drug discovery databases gave us information that some of the drugs have already been developed as therapeutic medicines for RA, and some of them were used for diseases other than RA. These drugs may be used for RA in the near future (drug repurposing). The combination of biological databases and GWAS results may be a novel method to identify new therapeutic targets.
Siegel, Marshall M
Electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) mass spectrometric methods useful for early discovery drug screening are reviewed. All methods described involve studies of non-covalent complexes between biopolymer receptors and small molecule ligands formed in the condensed phase. The complexes can be sprayed intact directly into the gas phase by ESI-MS using gentle experimental conditions. Gas phase screening applications are illustrated for drug ligand candidates non-covalently interacting with peptides, proteins, RNA, and DNA. In the condensed phase, the complexes can be also isolated, denatured and analyzed by ESI-MS to identify the small molecule ligands. Condensed phase drug screening examples are illustrated for the ESI-MS ancillary techniques of affinity chromatography, ultrafiltration, ultracentrifugation, gel permeation chromatography (GPC), reverse phase-high performance liquid chromatography (RP-HPLC) and capillary electrophoretic methods. Solid phase drug screening using MALDI-MS is illustrated for small molecule ligands bound to MALDI affinity probe tips and to beads. Since ESI and MALDI principally produce molecular ions, high throughput screening is achieved by analyzing mass indexed mixtures.
Patel, Asha Parbhu; Deacon, Andrew; Getti, Giulia
Green fluorescent protein (GFP)-parasite transfectants have been widely used as a tool for studying disease pathogenesis in several protozoan models and their application in drug screening assays has increased rapidly. In the past decade, the expression of GFP has been established in several Leishmania species, mostly for in vitro studies. The current work reports generation of four transgenic parasites constitutively expressing GFP (Leishmania mexicana, Leishmania aethiopica, Leishmania tropica and Leishmania major) and their validation as a representative model of infection. This is the first report where stable expression of GFP has been achieved in L. aethiopica and L. tropica. Integration of GFP was accomplished through homologous recombination of the expression construct, pRib1.2αNEOαGFP downstream of the 18S rRNA promoter in all species. A homogeneous and high level expression of GFP was detected in both the promastigote and the intracellular amastigote stages. All transgenic species showed the same growth pattern, ability to infect mammalian host cells and sensitivity to reference drugs as their wild type counterparts. All four transgenic Leishmania are confirmed as models for in vitro and possibly in vivo infections and represent an ideal tool for medium throughput testing of compound libraries.
Santos, Zenildo; Avci, Pinar; Hamblin, Michael R
Introduction Hair loss or alopecia affects the majority of the population at some time in their life, and increasingly, sufferers are demanding treatment. Three main types of alopecia (androgenic [AGA], areata [AA] and chemotherapy-induced [CIA]) are very different, and have their own laboratory models and separate drug-discovery efforts. Areas covered In this article, the authors review the biology of hair, hair follicle (HF) cycling, stem cells and signaling pathways. AGA, due to dihydrotesterone, is treated by 5-α reductase inhibitors, androgen receptor blockers and ATP-sensitive potassium channel-openers. AA, which involves attack by CD8+NK group 2D-positive (NKG2D+) T cells, is treated with immunosuppressives, biologics and JAK inhibitors. Meanwhile, CIA is treated by apoptosis inhibitors, cytokines and topical immunotherapy. Expert opinion The desire to treat alopecia with an easy topical preparation is expected to grow with time, particularly with an increasing aging population. The discovery of epidermal stem cells in the HF has given new life to the search for a cure for baldness. Drug discovery efforts are being increasingly centered on these stem cells, boosting the hair cycle and reversing miniaturization of HF. Better understanding of the molecular mechanisms underlying the immune attack in AA will yield new drugs. New discoveries in HF neogenesis and low-level light therapy will undoubtedly have a role to play. PMID:25662177
Zheng, Chunli; Guo, Zihu; Huang, Chao; Wu, Ziyin; Li, Yan; Chen, Xuetong; Fu, Yingxue; Ru, Jinlong; Ali Shar, Piar; Wang, Yuan; Wang, Yonghua
A system-level identification of drug-target direct interactions is vital to drug repositioning and discovery. However, the biological means on a large scale remains challenging and expensive even nowadays. The available computational models mainly focus on predicting indirect interactions or direct interactions on a small scale. To address these problems, in this work, a novel algorithm termed weighted ensemble similarity (WES) has been developed to identify drug direct targets based on a large-scale of 98,327 drug-target relationships. WES includes: (1) identifying the key ligand structural features that are highly-related to the pharmacological properties in a framework of ensemble; (2) determining a drug’s affiliation of a target by evaluation of the overall similarity (ensemble) rather than a single ligand judgment; and (3) integrating the standardized ensemble similarities (Z score) by Bayesian network and multi-variate kernel approach to make predictions. All these lead WES to predict drug direct targets with external and experimental test accuracies of 70% and 71%, respectively. This shows that the WES method provides a potential in silico model for drug repositioning and discovery. PMID:26155766
Manly, Charles J.
Drug discovery today requires the focused use of laboratory automation and other resources in combinatorial chemistry and high-throughput screening (HTS). The ultimate value of both combinatorial chemistry and HTS technologies and the lasting impact they will have on the drug discovery process is a chapter that remains to be written. Central to their success and impact is how well they are integrated with each other and with the rest of the drug discovery processes-informatics is key to this ...
Mullane, Kevin; Winquist, Raymond J; Williams, Michael
The translational sciences represent the core element in enabling and utilizing the output from the biomedical sciences and to improving drug discovery metrics by reducing the attrition rate as compounds move from preclinical research to clinical proof of concept. Key to understanding the basis of disease causality and to developing therapeutics is an ability to accurately diagnose the disease and to identify and develop safe and effective therapeutics for its treatment. The former requires validated biomarkers and the latter, qualified targets. Progress has been hampered by semantic issues, specifically those that define the end product, and by scientific issues that include data reliability, an overt reductionistic cultural focus and a lack of hierarchically integrated data gathering and systematic analysis. A necessary framework for these activities is represented by the discipline of pharmacology, efforts and training in which require recognition and revitalization. Copyright © 2013 Elsevier Inc. All rights reserved.
Ciura, Krzesimir; Dziomba, Szymon; Nowakowska, Joanna; Markuszewski, Michał J
The review is mainly focused on application of thin layer chromatography (TLC) as simple, rapid and inexpensive method for lipophilicity assessment. Among separation techniques, TLC is still one of the most popular for lipophilicity measurement. The principles and methodology of Quantitative Structure Retention Relationship (QSRR) employed to lipophilicity prediction from retention data are presented. Moreover, applications of TLC retention constants in Quantitative Structure Activity Relationship (QSAR) studies were critically overviewed. The paper concerns also bioautography as a TLC method complementary to QSAR studies. In the article, the advantages and limitations of well established and less common planar chromatography modes applied for drug discovery process were discussed. Copyright © 2017 Elsevier B.V. All rights reserved.
Singeç, Ilyas; Simeonov, Anton
Pluripotent stem cell research has made extraordinary progress over the last decade. The robustness of nuclear reprogramming of somatic cells into induced pluripotent stem cells (iPSCs) has created entirely novel opportunities for drug discovery and personalized regenerative medicine. Patient- and disease-specific iPSCs can be expanded indefinitely and differentiated into relevant cell types of different organ systems. As the utilization of iPSCs is becoming a key enabling technology across various scientific disciplines, there are still important challenges that need to be addressed. Here we review the current state and reflect on the issues that the stem cell and translational communities are facing in bringing iPSCs closer to clinical application.
Dalrymple, Michael; Taylor, Debbie; Kettleborough, Catherine; Bryans, Justin; Solari, Roberto
The movement of ideas and innovation from academia into the world of business has a long and fruitful history. Ironically, it might be argued that the recent pressure put on universities and basic research organisations to protect and exploit their intellectual property has, in many ways, created a less conducive environment to successful commercialisation than existed 30 years ago. This movement has been concurrent with the drift of the Pharmaceutical industry towards a more risk-averse R&D strategy in which it has increasingly concentrated its resources on a reductionist drug discovery process and later stage clinical development. In effect, these two strategies have created a discontinuity between academic scientific output and industry at a time when academia as a source of innovation is perhaps more important to industry than ever.
Tsvetanova, Billyana; Peng, Lansha; Liang, Xiquan; Li, Ke; Hammond, Linda; Peterson, Todd C; Katzen, Federico
Recombinant DNA technologies have had a fundamental impact on drug discovery. The continuous emergence of unique gene assembly techniques resulted in the generation of a variety of therapeutic reagents such as vaccines, cancer treatment molecules and regenerative medicine precursors. With the advent of synthetic biology there is a growing need for precise and concerted assembly of multiple DNA fragments of various sizes, including chromosomes. In this article, we summarize the highlights of the recombinant DNA technology since its inception in the early 1970s, emphasizing on the most recent advances, and underscoring their principles, advantages and shortcomings. Current and prior cloning trends are discussed in the context of sequence requirements and scars left behind. Our opinion is that despite the remarkable progress that has enabled the generation and manipulation of very large DNA sequences, a better understanding of the cell's natural circuits is needed in order to fully exploit the current state-of-the-art gene assembly technologies.
Decher, Niels; Netter, Michael F; Streit, Anne K
Virtually all organisms use RNA editing as a powerful post-transcriptional mechanism to recode genomic information and to increase functional protein diversity. The enzymatic editing of pre-mRNA by ADARs and CDARs is known to change the functional properties of neuronal receptors and ion channels regulating cellular excitability. However, RNA editing is also an important mechanism for genes expressed outside the brain. The fact that RNA editing breaks the 'one gene encodes one protein' hypothesis is daunting for scientists and a probable drawback for drug development, as scientists might search for drugs targeting the 'wrong' protein. This possible difficulty for drug discovery and development became more evident from recent publications, describing that RNA editing events have profound impact on the pharmacology of some common drug targets. These recent studies highlight that RNA editing can cause massive discrepancies between the in vitro and in vivo pharmacology. Here, we review the putative impact of RNA editing on drug discovery, as RNA editing has to be considered before using high-throughput screens, rational drug design or choosing the right model organism for target validation.
Rondla, Rohini; Padma Rao, Lavanya Souda; Ramatenki, Vishwanath; Vadija, Rajender; Mukkera, Thirupathi; Potlapally, Sarita Rajender; Vuruputuri, Uma
The cyclin-dependent kinase 4 (CDK4) enzyme is a key regulator in cell cycle G1 phase progression. It is often overexpressed in variety of cancer cells, which makes it an attractive therapeutic target for cancer treatment. A number of chemical scaffolds have been reported as CDK4 inhibitors in the literature, and in particular azolium scaffolds as potential inhibitors. Here, a ligand based pharmacophore modeling and an atom based 3D-QSAR analyses for a series of azolium based CDK4 inhibitors are presented. A five point pharmacophore hypothesis, i.e. APRRR with one H-bond acceptor (A), one positive cationic feature (P) and three ring aromatic sites (R) is developed, which yielded an atom based 3D-QSAR model that shows an excellent correlation coefficient value- R2 = 0.93, fisher ratio- F = 207, along with good predictive ability- Q2 = 0.79, and Pearson R value = 0.89. The visual inspection of the 3D-QSAR model, with the most active and the least active ligands, demonstrates the favorable and unfavorable structural regions for the activity towards CDK4. The roles of positively charged nitrogen, the steric effect, ligand flexibility, and the substituents on the activity are in good agreement with the previously reported experimental results. The generated 3D QSAR model is further applied as query for a 3D database screening, which identifies 23 lead drug candidates with good predicted activities and diverse scaffolds. The ADME analysis reveals that, the pharmacokinetic parameters of all the identified new leads are within the acceptable range.
Persico, Marco; Di Dato, Antonio; Orteca, Nausicaa; Fattorusso, Caterina; Novellino, Ettore; Andreoli, Mirko; Ferlini, Cristiano
The majority of functionally important biological processes are regulated by allosteric communication within individual proteins and across protein complexes. The proteins controlling these communication networks respond to changes in the cellular environment by switching between different conformational states. Targeting the interface residues mediating these processes through the rational identification of molecules modulating or mimicking their effects holds great therapeutic potential. Protein-protein interactions (PPIs) have shown to have a high degree of plasticity since they occur through small regions, called hot spots, which are included in binding surfaces or in binding clefts of the proteins and are characterized by a high degree of complementarity. This prompted several researchers to compare the protein structure to human grammar proposing terms like "protein language". The decoding of this language represent a new paradigm not only to clarify the dynamics of many biological processes but also to improve the opportunities in drug discovery. In this review, we try to give an overview on intra-molecular and inter-molecular protein communication mechanisms describing the protein interaction domains (PIDs) and short linear motifs (SLiMs), which delineate the authentic syntactic and semantic units in a protein. Moreover, we illustrate some novel approaches performed on natural compounds and on synthetic derivatives aimed at developing new classes of potential drugs able to interfere with intra-molecular and inter-molecular protein communication.
Singh, Sheo B; Pelaez, Fernando
Drugs developed from microbial natural products are in the fundaments of modern pharmaceutical companies. Despite decades of research, all evidences suggest that there must remain many interesting natural molecules with potential therapeutic application yet to be discovered. Any efforts to successfully exploit the chemical diversity of microbial secondary metabolites need to rely heavily on a good understanding of microbial diversity, being the working hypothesis that maximizing biological diversity is the key strategy to maximizing chemical diversity. This chapter presents an overview of diverse topics related with this basic principle, always in relation with the discovery of novel secondary metabolites. The types of microorganisms more frequently used for natural products discovery are briefly reviewed, as well as the differences between terrestrial and marine habitats as sources of bioactive secondary metabolite producers. The concepts about microbial diversity as applied to prokaryotes have evolved in the last years, but recent data suggest the existence of true biogeographic patterns of bacterial diversity, which are also discussed. Special attention is dedicated to the existing strategies to exploit the microbial diversity that is not easy to tackle by conventional approaches. This refers explicitly to the current attempts to isolate and cultivate the previously uncultured bacteria, including the application of high throughput techniques. Likewise, the advances of microbial molecular biology has allowed the development of metagenomic approaches, i.e., the expression of biosynthetic pathways directly obtained from environmental DNA and cloned in a suitable host, as another way of accessing microbial genetic resources. Also, approaches relying on the genomics of metabolite producers are reviewed.
Elmer, Greg I; Kafkafi, Neri
The discovery of truly efficacious treatments that lead to full recovery is a daunting task in psychiatric illness. A systems-based orientation to in vivo pharmacology has been suggested as a way to transform psychiatric drug discovery and development. A critical catalyst in the success of recent systems biology efforts has been the incorporation of data mining strategies. Our approach to the drug discovery problem has been to utilize the whole animal to provide a systems response that is sub...
Cele, Favourite N; Ramesh, Muthusamy; Soliman, Mahmoud Es
A novel virtual screening approach is implemented herein, which is a further improvement of our previously published "target-bound pharmacophore modeling approach". The generated pharmacophore library is based only on highly contributing amino acid residues, instead of arbitrary pharmacophores, which are most commonly used in the conventional approaches in literature. Highly contributing amino acid residues were distinguished based on free binding energy contributions obtained from calculation from molecular dynamic (MD) simulations. To the best of our knowledge; this is the first attempt in the literature using such an approach; previous approaches have relied on the docking score to generate energy-based pharmacophore models. However, docking scores are reportedly unreliable. Thus, we present a model for a per-residue energy decomposition, constructed from MD simulation ensembles generating a more trustworthy pharmacophore model, which can be applied in drug discovery workflow. This work is aimed at introducing a more rational approach to the field of drug design, rather than comparing the validity of this approach against those previously reported. We recommend additional computational and experimental work to further validate this approach. This approach was used to screen for potential reverse transcriptase inhibitors using the pharmacophoric features of compound GSK952. The complex was subjected to docking, thereafter, MD simulation confirmed the stability of the system. Experimentally determined inhibitors with known HIV-reverse transcriptase inhibitory activity were used to validate the protocol. Two potential hits (ZINC46849657 and ZINC54359621) showed a significant potential with regard to free binding energy. Reported results obtained from this work confirm that this new approach is favorable in the future of the drug design industry.
Sailer, Martin H M; Sarvepalli, Durga; Brégère, Catherine; Fisch, Urs; Guentchev, Marin; Weller, Michael; Guzman, Raphael; Bettler, Bernhard; Ghosh, Arkasubhra; Hutter, Gregor
Epithelial to mesenchymal transition (EMT) describes the process of epithelium transdifferentiating into mesenchyme. EMT is a fundamental process during embryonic development that also commonly occurs in glioblastoma, the most frequent malignant brain tumor. EMT has also been observed in multiple carcinomas outside the brain including breast cancer, lung cancer, colon cancer, gastric cancer. EMT is centrally linked to malignancy by promoting migration, invasion and metastasis formation. The mechanisms of EMT induction are not fully understood. Here we describe an in vitro system for standardized isolation of cortical neural stem cells (NSCs) and subsequent EMT-induction. This system provides the flexibility to use either single cells or explant culture. In this system, rat or mouse embryonic forebrain NSCs are cultured in a defined medium, devoid of serum and enzymes. The NSCs expressed Olig2 and Sox10, two transcription factors observed in oligodendrocyte precursor cells (OPCs). Using this system, interactions between FGF-, BMP- and TGFβ-signaling involving Zeb1, Zeb2, and Twist2 were observed where TGFβ-activation significantly enhanced cell migration, suggesting a synergistic BMP-/TGFβ-interaction. The results point to a network of FGF-, BMP- and TGFβ-signaling to be involved in EMT induction and maintenance. This model system is relevant to investigate EMT in vitro. It is cost-efficient and shows high reproducibility. It also allows for the comparison of different compounds with respect to their migration responses (quantitative distance measurement), and high-throughput screening of compounds to inhibit or enhance EMT (qualitative measurement). The model is therefore well suited to test drug libraries for substances affecting EMT.
Hendrie, Colin; Pickles, Alasdair; Stanford, S Clare; Robinson, Emma
Current antidepressants are crude compared with the ideal and patents on most have expired. There are therefore strong clinical and commercial pressures for new drugs to replace them. The prospects for this are, however, now markedly reduced as several major pharmaceutical companies have abandoned work in this area whilst many others have sharply decreased their research investment. These changes and the lack of progress over such a long period are indicative of a catastrophic systems failure which, it is argued, has been caused in large part by a logical flaw at the animal modelling stage. This tautology has served to lock the current antidepressant drug discovery process into an iterative loop capable only of producing further variations of that which has gone before. Drugs produced by this approach have proved to be only poorly effective in the context of the clinically depressed population as a whole. Hence, the inevitable failure of the current antidepressant drug discovery process has left little behind that can be salvaged. Therefore, it is suggested that this be urgently reformulated on more rational grounds using more appropriate species in new animal models based upon a thorough understanding of the behavioural expressions of depression in the clinic.
Full Text Available BACKGROUND: Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the absence of a critical mass of preexisting work that volunteers can improve through a series of granular contributions. Historically, open source software collaborations have almost never succeeded without such "kernels". METHODOLOGY/PRINCIPAL FINDINGS: HERE, WE USE A COMPUTATIONAL PIPELINE FOR: (i comparative structure modeling of target proteins, (ii predicting the localization of ligand binding sites on their surfaces, and (iii assessing the similarity of the predicted ligands to known drugs. Our kernel currently contains 143 and 297 protein targets from ten pathogen genomes that are predicted to bind a known drug or a molecule similar to a known drug, respectively. The kernel provides a source of potential drug targets and drug candidates around which an online open source community can nucleate. Using NMR spectroscopy, we have experimentally tested our predictions for two of these targets, confirming one and invalidating the other. CONCLUSIONS/SIGNIFICANCE: The TDI kernel, which is being offered under the Creative Commons attribution share-alike license for free and unrestricted use, can be accessed on the World Wide Web at http://www.tropicaldisease.org. We hope that the kernel will facilitate collaborative efforts towards the discovery of new drugs against parasites that cause tropical diseases.
Frigeri, Antonio; Nicchia, Grazia Paola; Svelto, Maria
The intracellular hydric balance is an essential process of mammalian cells. The water movement across cell membranes is driven by osmotic and hydrostatic forces and the speed of this process is dependent on the presence of specific aquaporin water channels. Since the molecular identification of the first water channel, AQP1, by Peter Agre's group, 13 homologous members have been found in mammals with varying degree of homology. The fundamental importance of these proteins in all living cells is suggested by their genetic conservation in eukaryotic organisms through plants to mammals. A number of recent studies have revealed the importance of mammalian AQPs in both physiology and pathophysiology and have suggested that pharmacological modulation of aquaporins expression and activity may provide new tools for the treatment of variety of human disorders, such as brain edema, glaucoma, tumour growth, congestive heart failure and obesity in which water and small solute transport may be involved. This review will highlight the physiological role and the pathological involvement of AQPs in mammals and the potential use of some recent therapeutic approaches, such as RNAi and immunotherapy, for AQP-related diseases. Furthermore, strategies that can be developed for the discovery of selective AQP-drugs will be introduced and discussed.
Khalid, Nauman; Kobayashi, Isao; Nakajima, Mitsutoshi
Microelectromechanical systems (MEMS) and micro total analysis systems (μTAS) revolutionized the biochemical and electronic industries, and this miniaturization process became a key driver for many markets. Now, it is a driving force for innovations in life sciences, diagnostics, analytical sciences, and chemistry, which are called 'lab-on-a-chip, (LOC)' devices. The use of these devices allows the development of fast, portable, and easy-to-use systems with a high level of functional integration for applications such as point-of-care diagnostics, forensics, the analysis of biomolecules, environmental or food analysis, and drug development. In this review, we report on the latest developments in fabrication methods and production methodologies to tailor LOC devices. A brief overview of scale-up strategies is also presented together with their potential applications in drug delivery and discovery. The impact of LOC devices on drug development and discovery has been extensively reviewed in the past. The current research focuses on fast and accurate detection of genomics, cell mutations and analysis, drug delivery, and discovery. The current research also differentiates the LOC devices into new terminology of microengineering, like organ-on-a-chip, stem cells-on-a-chip, human-on-a-chip, and body-on-a-chip. Key challenges will be the transfer of fabricated LOC devices from lab-scale to industrial large-scale production. Moreover, extensive toxicological studies are needed to justify the use of microfabricated drug delivery vehicles in biological systems. It will also be challenging to transfer the in vitro findings to suitable and promising in vivo models. For further resources related to this article, please visit the WIREs website.
Haston Kelly M
Full Text Available Abstract There are many reasons to be interested in stem cells, one of the most prominent being their potential use in finding better drugs to treat human disease. This article focuses on how this may be implemented. Recent advances in the production of reprogrammed adult cells and their regulated differentiation to disease-relevant cells are presented, and diseases that have been modeled using these methods are discussed. Remaining difficulties are highlighted, as are new therapeutic insights that have emerged.
Maurizio Del Poeta
Full Text Available This Special Issue is designed to highlight the latest research and development on new antifungal compounds with mechanisms of action different from the ones of polyenes, azoles, and echinocandins. The papers presented here highlight new pathways and targets that could be exploited for the future development of new antifungal agents to be used alone or in combination with existing antifungals. A computational model for better predicting antifungal drug resistance is also presented.
Elmer, Greg I; Kafkafi, Neri
The discovery of truly efficacious treatments that lead to full recovery is a daunting task in psychiatric illness. A systems-based orientation to in vivo pharmacology has been suggested as a way to transform psychiatric drug discovery and development. A critical catalyst in the success of recent systems biology efforts has been the incorporation of data mining strategies. Our approach to the drug discovery problem has been to utilize the whole animal to provide a systems response that is subsequently mined for predictive attributes with known psychopharmacological value. Our in vivo data mining approach, termed Pattern Array, establishes a framework for screening novel chemical entities based upon a response that represents the net pharmacological effect on the system of interest, namely the central nervous system (CNS). Large scale screening of small molecules by non-conventional approaches such as this at a systems level may improve the identification of novel chemical entities with psychiatric utility. This type of approach will compliment the more labor-intensive models based upon construct validity. It will take the collective effort of many disciplines and numerous strategies in close association with clinical colleagues to address quality of life issues and breakthrough treatment barriers in psychiatric illness.
Dawson, Neil; Morris, Brian J; Pratt, Judith A
While our knowledge of the pathophysiology of schizophrenia has increased dramatically, this has not translated into the development of new and improved drugs to treat this disorder. Human brain imaging and electrophysiological studies have provided dramatic new insight into the mechanisms of brain dysfunction in the disease, with a swathe of recent studies highlighting the differences in functional brain network and neural system connectivity present in the disorder. Only recently has the value of applying these approaches in preclinical rodent models relevant to the disorder started to be recognised. Here we highlight recent findings of altered functional brain connectivity in preclinical rodent models and consider their relevance to those alterations seen in the brains of schizophrenia patients. Furthermore, we highlight the potential translational value of using the paradigm of functional brain connectivity phenotypes in the context of preclinical schizophrenia drug discovery, as a means both to understand the mechanisms of brain dysfunction in the disorder and to reduce the current high attrition rate in schizophrenia drug discovery.
Full Text Available We introduce a methodology to efficiently exploit natural-language expressed biomedical knowledge for repurposing existing drugs towards diseases for which they were not initially intended. Leveraging on developments in Computational Linguistics and Graph Theory, a methodology is defined to build a graph representation of knowledge, which is automatically analysed to discover hidden relations between any drug and any disease: these relations are specific paths among the biomedical entities of the graph, representing possible Modes of Action for any given pharmacological compound. We propose a measure for the likeliness of these paths based on a stochastic process on the graph. This measure depends on the abundance of indirect paths between a peptide and a disease, rather than solely on the strength of the shortest path connecting them. We provide real-world examples, showing how the method successfully retrieves known pathophysiological Mode of Action and finds new ones by meaningfully selecting and aggregating contributions from known bio-molecular interactions. Applications of this methodology are presented, and prove the efficacy of the method for selecting drugs as treatment options for rare diseases.
Gramatica, Ruggero; Di Matteo, T.; Giorgetti, Stefano; Barbiani, Massimo; Bevec, Dorian; Aste, Tomaso
We introduce a methodology to efficiently exploit natural-language expressed biomedical knowledge for repurposing existing drugs towards diseases for which they were not initially intended. Leveraging on developments in Computational Linguistics and Graph Theory, a methodology is defined to build a graph representation of knowledge, which is automatically analysed to discover hidden relations between any drug and any disease: these relations are specific paths among the biomedical entities of the graph, representing possible Modes of Action for any given pharmacological compound. We propose a measure for the likeliness of these paths based on a stochastic process on the graph. This measure depends on the abundance of indirect paths between a peptide and a disease, rather than solely on the strength of the shortest path connecting them. We provide real-world examples, showing how the method successfully retrieves known pathophysiological Mode of Action and finds new ones by meaningfully selecting and aggregating contributions from known bio-molecular interactions. Applications of this methodology are presented, and prove the efficacy of the method for selecting drugs as treatment options for rare diseases. PMID:24416311
Gramatica, Ruggero; Di Matteo, T; Giorgetti, Stefano; Barbiani, Massimo; Bevec, Dorian; Aste, Tomaso
We introduce a methodology to efficiently exploit natural-language expressed biomedical knowledge for repurposing existing drugs towards diseases for which they were not initially intended. Leveraging on developments in Computational Linguistics and Graph Theory, a methodology is defined to build a graph representation of knowledge, which is automatically analysed to discover hidden relations between any drug and any disease: these relations are specific paths among the biomedical entities of the graph, representing possible Modes of Action for any given pharmacological compound. We propose a measure for the likeliness of these paths based on a stochastic process on the graph. This measure depends on the abundance of indirect paths between a peptide and a disease, rather than solely on the strength of the shortest path connecting them. We provide real-world examples, showing how the method successfully retrieves known pathophysiological Mode of Action and finds new ones by meaningfully selecting and aggregating contributions from known bio-molecular interactions. Applications of this methodology are presented, and prove the efficacy of the method for selecting drugs as treatment options for rare diseases.
Kinch, Michael S; Flath, Richard
The way in which new medicines are discovered has irreversibly changed and the future sustainability of the enterprise is characterized by an unprecedented period of uncertainty. Herein, we convey that these changes provide unprecedented opportunities for many different players within the private and public sectors to work together and develop new models that ensure the sustainability of activities that have had an extraordinary impact; in terms of promoting public health and driving economic value. Specific examples of experiments are provided to demonstrate some of the new thinking that will be needed to ensure continuation of new drug discovery.
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.
Porsolt, Roger D; Moser, Paul C; Castagné, Vincent
Schizophrenia is characterized by three major symptom classes: positive symptoms, negative symptoms, and cognitive deficits. Classical antipsychotics (phenothiazines, thioxanthenes, and butyrophenones) are effective against positive symptoms but induce major side effects, in particular, extrapyramidal symptoms (EPS). The discovery of clozapine, which does not induce EPS and is thought effective against all three classes of symptom, has driven research for novel antipsychotics with a wider activity spectrum and lower EPS liability. To increase predictiveness, current efforts aim to develop translational models where direct parallels can be drawn between the processes studied in animals and in humans. The present article reviews existing procedures in animals for their ability to predict compound efficacy and EPS liability in relation to their translational validity. Rodent models of positive symptoms include procedures related to dysfunction in central dopamine and glutamatergic (N-methyl-D-aspartate) and serotonin (5-hydroxytryptamine) neurotransmission. Procedures for evaluating negative symptoms include rodent models of anhedonia, affective flattening, and diminished social interaction. Cognitive deficits can be assessed in rodent models of attention (prepulse inhibition) and of learning/memory (object and social recognition, Morris water maze and operant-delayed alternation). The relevance of the conditioned avoidance response is also discussed. A final section reviews procedures for assessing EPS liability, in particular, parkinsonism (catalepsy in rodents), acute dystonia (purposeless chewing in rodents, dystonia in monkeys), akathisia (defecation in rodents), and tardive dyskinesia (long-term antipsychotic treatment in rodents and monkeys). It is concluded that, with notable exceptions (attention, learning/memory, EPS liability), current predictive models for antipsychotics fall short of clear translational validity.
The advances of genomics, sequencing, and high throughput technologies have led to the creation of large volumes of diverse datasets for drug discovery. Analyzing these datasets to better understand disease and discover new drugs is becoming more common. Recent open data initiatives in basic and clinical research have dramatically increased the types of data available to the public. The past few years have witnessed successful use of big data in many sectors across the whole drug discovery pipeline. In this review, we will highlight the state of the art in leveraging big data to identify new targets, drug indications, and drug response biomarkers in this era of precision medicine. PMID:26659699
White, R E
The application of rapid methods currently used for screening discovery drug candidates for metabolism and pharmacokinetic characteristics is discussed. General considerations are given for screening in this context, including the criteria for good screens, the use of counterscreens, the proper sequencing of screens, ambiguity in the interpretation of results, strategies for false positives and negatives, and the special difficulties encountered in drug metabolism and pharmacokinetic screening. Detailed descriptions of the present status of screening are provided for absorption potential, blood-brain barrier penetration, inhibition and induction of cytochrome P450, pharmacokinetics, biotransformation, and computer modeling. Although none of the systems currently employed for drug metabolism and pharmacokinetic screening can be considered truly high-throughput, several of them are rapid enough to be a practical part of the screening paradigm for modern, fast-moving discovery programs.
Full Text Available A mathematical model which predicts the intraerythrocytic stages of Plasmodium falciparum infection was developed using data from malaria-infected mice. Variables selected accounted for levels of healthy red blood cells, merozoite (Plasmodium asexual phase infected red blood cells, gametocyte (Plasmodium sexual phase infected red blood cells and a phenomenological variable which accounts for the mean activity of the immune system of the host. The model built was able to reproduce the behavior of three different scenarios of malaria. It predicts the later dynamics of malaria-infected humans well after the first peak of parasitemia, the qualitative response of malaria-infected monkeys to vaccination and the changes observed in malaria-infected mice when they are treated with antimalarial drugs. The mathematical model was used to identify new targets to be focused on drug design. Optimization methodologies were applied to identify five targets for minimizing the parasite load; four of the targets thus identified have never before been taken into account in drug design. The potential targets include: 1 increasing the death rate of the gametocytes, 2 decreasing the invasion rate of the red blood cells by the merozoites, 3 increasing the transformation of merozoites into gametocytes, 4 decreasing the activation of the immune system by the gametocytes, and finally 5 a combination of the previous target with decreasing the recycling rate of the red blood cells. The first target is already used in current therapies, whereas the remainders are proposals for potential new targets. Furthermore, the combined target (the simultaneous decrease of the activation of IS by gRBC and the decrease of the influence of IS on the recycling of hRBC is interesting, since this combination does not affect the parasite directly. Thus, it is not expected to generate selective pressure on the parasites, which means that it would not produce resistance in Plasmodium.
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
Following the first CNS Anticancer Drug Discovery and Development Conference, the speakers from the first 4 sessions and organizers of the conference created this White Paper hoping to stimulate more and better CNS anticancer drug discovery and development. The first part of the White Paper reviews, comments, and, in some cases, expands on the 4 session areas critical to new drug development: pharmacological challenges, recent drug approaches, drug targets and discovery, and clinical paths. Following this concise review of the science and clinical aspects of new CNS anticancer drug discovery and development, we discuss, under the rubric "Accelerating Drug Discovery and Development for Brain Tumors," further reasons why the pharmaceutical industry and academia have failed to develop new anticancer drugs for CNS malignancies and what it will take to change the current status quo and develop the drugs so desperately needed by our patients with malignant CNS tumors. While this White Paper is not a formal roadmap to that end, it should be an educational guide to clinicians and scientists to help move a stagnant field forward. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org.
The 2015 Nobel Prize in Physiology or Medicine has been awarded to William C. Campbell and Satoshi Omura, and Youyou Tu for the discovery of avermectins and artemisinin, respectively, therapies that revolutionized the treatment of devastating parasite diseases. With the recent technological advances, a New Golden Age of natural products drug discovery is dawning. PMID:26638061
Lei, Tailong; Chen, Fu; Liu, Hui; Sun, Huiyong; Kang, Yu; Li, Dan; Li, Youyong; Hou, Tingjun
As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chemical industries have a strong urge to develop precise and convenient computational tools to evaluate the respiratory toxicity of compounds as early as possible. Most of the reported theoretical models were developed based on the respiratory toxicity data sets with one single symptom, such as respiratory sensitization, and therefore these models may not afford reliable predictions for toxic compounds with other respiratory symptoms, such as pneumonia or rhinitis. Here, based on a diverse data set of mouse intraperitoneal respiratory toxicity characterized by multiple symptoms, a number of quantitative and qualitative predictions models with high reliability were developed by machine learning approaches. First, a four-tier dimension reduction strategy was employed to find an optimal set of 20 molecular descriptors for model building. Then, six machine learning approaches were used to develop the prediction models, including relevance vector machine (RVM), support vector machine (SVM), regularized random forest (RRF), extreme gradient boosting (XGBoost), naïve Bayes (NB), and linear discriminant analysis (LDA). Among all of the models, the SVM regression model shows the most accurate quantitative predictions for the test set (q(2)ext = 0.707), and the XGBoost classification model achieves the most accurate qualitative predictions for the test set (MCC of 0.644, AUC of 0.893, and global accuracy of 82.62%). The application domains were analyzed, and all of the tested compounds fall within the application domain coverage. We also examined the structural features of the compounds and important fragments with large prediction errors. In conclusion, the SVM regression model and the XGBoost classification model can be employed as accurate prediction tools
Wild, David J
Increasingly, effective drug discovery involves the searching and data mining of large volumes of information from many sources covering the domains of chemistry, biology and pharmacology amongst others. This has led to a proliferation of databases and data sources relevant to drug discovery. This paper provides a review of the publicly-available large-scale databases relevant to drug discovery, describes the kinds of data mining approaches that can be applied to them and discusses recent work in integrative data mining that looks for associations that pan multiple sources, including the use of Semantic Web techniques. The future of mining large data sets for drug discovery requires intelligent, semantic aggregation of information from all of the data sources described in this review, along with the application of advanced methods such as intelligent agents and inference engines in client applications.
Rotella, David P
Small molecules remain the backbone for modern drug discovery. They are conceived and synthesized by medicinal chemists, many of whom were originally trained as organic chemists. Support from government and industry to provide training and personnel for continued development of this critical skill set has been declining for many years. This Viewpoint highlights the value of organic chemistry and organic medicinal chemists in the complex journey of drug discovery as a reminder that basic science support must be restored.
Stitziel, Nathan O; Kathiresan, Sekar
Identifying appropriate molecular targets is a critical step in drug development. Despite many advantages, the traditional tools of observational epidemiology and cellular or animal models of disease can be misleading in identifying causal pathways likely to lead to successful therapeutics. Here, we review some favorable aspects of human genetics studies that have the potential to accelerate drug target discovery. These include using genetic studies to identify pathways relevant to human disease, leveraging human genetics to discern causal relationships between biomarkers and disease, and studying genetic variation in humans to predict the potential efficacy and safety of inhibitory compounds aimed at molecular targets. We present some examples taken from studies of plasma lipids and coronary artery disease to highlight how human genetics can accelerate therapeutics development. Copyright © 2017 Elsevier Inc. All rights reserved.
Phase II attrition remains the most important challenge for drug discovery. Tackling the problem requires improved understanding of the complexity of disease biology. Systems biology approaches to this problem can, in principle, deliver this. This article reviews the reports of the application of mechanistic systems models to drug discovery questions and discusses the added value. Although we are on the journey to the virtual human, the length, path and rate of learning from this remain an open question. Success will be dependent on the will to invest and make the most of the insight generated along the way.
Full Text Available From this current research, Syk (spleen tyrosine kinase protein and gene information is analyzed by different genomics, proteomics tools & databases. One crystal ligand 4DFL was collected from protein data bank (pdb. From different literature review 131 syk protein inhibitors were collected. Molecular modeling of these 131 molecules was done through Accelrys discovery studio (ADS. Choose appropriate force-field & minimization (Smart, Stephent Descent, and Conjugate Gradient according to selected molecules. Then collected crystal ligand is purified by protein purification method and used appropriate conformation (BEST, FAST, and CAESAR. Docking methods were analyzed with protein, crystal ligand and similar inhibitors to know the best protein-ligand interaction. Pharmacophore research is done through HIPHOP and HYPOGEN method. Protein with final compound docking method is done after completion of virtual screening method. Pharmacophore research with final molecule was done. Quantitative structure activity relationship (qsar method is analyzed to know the correlation between the above selective structures. From virtual screening method, best and final compound is analyzed. So, final molecule can be a drug molecule for SYK protein abnormality diseases. However, the scope for fine tuning and optimizing this potent class of syK inhibitors could lead to the generation of new therapeutic agents.
Hatakeyama, Hideyuki; Goto, Yu-Ichi
Mitochondria contain multiple copies of their own genome (mitochondrial DNA; mtDNA). Once mitochondria are damaged by mutant mtDNA, mitochondrial dysfunction is strongly induced, followed by symptomatic appearance of mitochondrial diseases. Major genetic causes of mitochondrial diseases are defects in mtDNA, and the others are defects of mitochondria-associating genes that are encoded in nuclear DNA (nDNA). Numerous pathogenic mutations responsible for various types of mitochondrial diseases have been identified in mtDNA; however, it remains uncertain why mitochondrial diseases present a wide variety of clinical spectrum even among patients carrying the same mtDNA mutations (e.g., variations in age of onset, in affected tissues and organs, or in disease progression and phenotypic severity). Disease-relevant induced pluripotent stem cells (iPSCs) derived from mitochondrial disease patients have therefore opened new avenues for understanding the definitive genotype-phenotype relationship of affected tissues and organs in various types of mitochondrial diseases triggered by mtDNA mutations. In this concise review, we briefly summarize several recent approaches using patient-derived iPSCs and their derivatives carrying various mtDNA mutations for applications in human mitochondrial disease modeling, drug discovery, and future regenerative therapeutics.
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.
Full Text Available Opportunistic fungi are a major cause of morbidity and mortality world-wide, particularly in immunocompromised individuals. Developing new treatments to combat invasive fungal disease is challenging given that fungal and mammalian host cells are eukaryotic, with similar organization and physiology. Even therapies targeting unique fungal cell features have limitations and drug resistance is emerging. New approaches to the development of antifungal drugs are therefore needed urgently. Cryptococcus neoformans, the commonest cause of fungal meningitis worldwide, is an accepted model for studying fungal pathogenicity and driving drug discovery. We recently characterized a phospholipase C (Plc1-dependent pathway in C. neoformans comprising of sequentially-acting inositol polyphosphate kinases (IPK, which are involved in synthesizing inositol polyphosphates (IP. We also showed that the pathway is essential for fungal cellular function and pathogenicity. The IP products of the pathway are structurally diverse, each consisting of an inositol ring, with phosphate (P and pyrophosphate (PP groups covalently attached at different positions. This review focuses on (1 the characterization of the Plc1/IPK pathway in C. neoformans; (2 the identification of PP-IP5 (IP7 as the most crucial IP species for fungal fitness and virulence in a mouse model of fungal infection; and (3 why IPK enzymes represent suitable candidates for drug development.
Gao, Guangxun; Chen, Liang; Huang, Chuanshu
Discovery of novel cancer chemotherapeutics focuses on screening and identifying compounds that can target 'cancer-specific' biological processes while causing minimal toxicity to non-tumor cells. Alternatively, model organisms with highly conserved cancer-related cellular processes relative to human cells may offer new opportunities for anticancer drug discovery when combined with chemical screening. Some organisms used for chemotherapeutic discovery include yeast, Drosophila, and zebrafish which are similar in important ways relevant to cancer study but offer distinct advantages as well. Here, we describe these model attributes and the rationale for using them in cancer drug screening research.
Tracy, Timothy S
The Michaelis-Menten model is commonly used to estimate a drug's potential in vivo hepatic clearance based on in vitro data obtained during drug discovery and development. This paradigm assumes that the drug obeys 'typical' enzyme kinetics and thus can be described by this model. However, it is increasingly being recognised that a number of drugs metabolised not only by the cytochrome P450 enzymes but also by other enzymes and transporters can exhibit atypical kinetic profiles, and thus are not accurately modeled with the Michaelis-Menten model. Application of an incorrect model can then lead to mis-estimation of in vitro intrinsic clearance and thus affect the prediction of in vivo clearance. This review discusses several types of atypical kinetic profiles that may be observed, including examples of homotropic cooperativity (i.e. sigmoidal kinetics, biphasic kinetics and substrate inhibition kinetics) as well as heterotropic cooperativity (i.e. activation). Application of the incorrect kinetic model may profoundly affect estimations of intrinsic clearance. For example, incorrectly applying the Michaelis-Menten model to a kinetic profile exhibiting substrate inhibition kinetics will result in an underestimation of Km (Michaelis-Menten constant) and V(max) (maximal velocity), whereas application of the Michaelis-Menten model to sigmoidal kinetic data typically results in an overestimation of Km and V(max) at the lower substrate concentrations that are typically therapeutically relevant. One must also be careful of potential artefactual causes of atypical kinetic profiles, such as enzyme activation by solvents, buffer dependent kinetic profiles, or altered kinetic parameter estimates due to nonspecific binding of the substrate to proteins. Despite a plethora of data on the effects of atypical kinetic profiles in vitro, only modest effects have been noted in vivo (with the exception of substrate dependent inhibition). Thus, the clinical relevance of these phenomena
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.
Three-dimensional (3D) in vitro systems that can mimic organ and tissue structure and function in vivo, will be of great benefit for a variety of biological applications from basic biology to toxicity testing and drug discovery. There have been several attempts to generate 3D tissue models but most of these models require costly equipment, and the most serious disadvantage in them is that they are too far from the mature human organs in vivo. Because of these problems, research and development in drug discovery, toxicity testing and biotech industries are highly expensive, and involve sacrifice of countless animals and it takes several years to bring a single drug/product to the market or to find the toxicity or otherwise of chemical entities. Our group has been actively working on several alternative models by merging biomaterials science, nanotechnology and biological principles to generate 3D in vitro living organs, to be called "Human Organs-on-Chip", to mimic natural organ/tissues, in order to reduce animal testing and clinical trials. We have fabricated a novel type of mechanically and biologically bio-mimicking collagen-based hydrogel that would provide for interconnected mini-wells in which 3D cell/organ culture of human samples in a manner similar to human organs with extracellular matrix (ECM) molecules would be possible. These products mimic the physical, chemical, and biological properties of natural organs and tissues at different scales. This paper will review the outcome of our several experiments so far in this direction and the future perspectives.
von Korff, Modest; Rufener, Christian; Stritt, Manuel; Freyss, Joel; Bär, Roman; Sander, Thomas
Grid computing offers an opportunity to gain massive computing power at low costs. We give a short introduction into the drug discovery process and exemplify the use of grid computing for image processing, docking and 3D pharmacophore descriptor calculations. The principle of a grid and its architecture are briefly explained. More emphasis is laid on the issues related to a company-wide grid installation and embedding the grid into the research process. The future of grid computing in drug discovery is discussed in the expert opinion section. Most needed, besides reliable algorithms to predict compound properties, is embedding the grid seamlessly into the discovery process. User friendly access to powerful algorithms without any restrictions, that is, by a limited number of licenses, has to be the goal of grid computing in drug discovery.
Background Drug discovery is a complex and unpredictable endeavor with a high failure rate. Current trends in the pharmaceutical industry have exasperated these challenges and are contributing to the dramatic decline in productivity observed over the last decade. The industrialization of science by forcing the drug discovery process to adhere to assembly-line protocols is imposing unnecessary restrictions, such as short project time-lines. Recent advances in nuclear magnetic resonance are responding to these self-imposed limitations and are providing opportunities to increase the success rate of drug discovery. Objective/Method A review of recent advancements in NMR technology that have the potential of significantly impacting and benefiting the drug discovery process will be presented. These include fast NMR data collection protocols and high-throughput protein structure determination, rapid protein-ligand co-structure determination, lead discovery using fragment-based NMR affinity screens, NMR metabolomics to monitor in vivo efficacy and toxicity for lead compounds, and the identification of new therapeutic targets through the functional annotation of proteins by FAST-NMR. Conclusion NMR is a critical component of the drug discovery process, where the versatility of the technique enables it to continually expand and evolve its role. NMR is expected to maintain this growth over the next decade with advancements in automation, speed of structure calculation, in-cell imaging techniques, and the expansion of NMR amenable targets. PMID:20333269
Ardal, Christine; Alstadsæter, Annette; Røttingen, John-Arne
Innovation through an open source model has proven to be successful for software development. This success has led many to speculate if open source can be applied to other industries with similar success. We attempt to provide an understanding of open source software development characteristics for researchers, business leaders and government officials who may be interested in utilizing open source innovation in other contexts and with an emphasis on drug discovery. A systematic review was performed by searching relevant, multidisciplinary databases to extract empirical research regarding the common characteristics and barriers of initiating and maintaining an open source software development project. Common characteristics to open source software development pertinent to open source drug discovery were extracted. The characteristics were then grouped into the areas of participant attraction, management of volunteers, control mechanisms, legal framework and physical constraints. Lastly, their applicability to drug discovery was examined. We believe that the open source model is viable for drug discovery, although it is unlikely that it will exactly follow the form used in software development. Hybrids will likely develop that suit the unique characteristics of drug discovery. We suggest potential motivations for organizations to join an open source drug discovery project. We also examine specific differences between software and medicines, specifically how the need for laboratories and physical goods will impact the model as well as the effect of patents.
Full Text Available A data journal in the biomedical field is an innovative and interesting task with potential benefits to the scientific society, the pharmaceutical and biotechnology industrials, as well as the medical institutions and authorities. It can serve as a source to stimulate, using computational modeling and bioinformatics, the bridging of biomedicine and basic biology researches and connecting of pharmaceutical and biotechnology industrials with academy. In the era of various high throughput technologies and big data, it could become a powerful driving force to combine expertise to understand the complexity of life and to catalyze new innovative therapeutics and diagnosis. The developments of various high-throughput and/or high-content drug-screening techniques are aiming not only to probe a large number of chemical compounds, but also to obtain deep insights into the molecule/molecule interaction associated with the diversity of chemical space, the structure-activity relationship, as well as the pharmacological mechanism. Moreover, the cell-based drug-screening approaches can also shed new light on the dynamics and regulation of proteins and genes associated with various physiological and pathological states. To reflect on these developments, the BMDJ Editorial Board announed a Call for Papers for a special issue on datasets in the field of drug screening and discovery: http://biomed-data.eu/calls-for-papers/high-throughput-drug-screening
Pharmaceutical discovery and development is expensive and highly risky, even for multinational corporations. As a developing country with limited financial resources, China has been seeking the most cost-effective means to reach the same level of innovation and productivity as Western countries in the pharmaceutical industry sector. After more than 50 years of building up talent and experience, the time for China to become a powerhouse in pharmaceutical innovation is finally approaching. Returnee scientists to China are one of the reasons for the wave of new discovery and commercialization occurring within the country. The consolidation of local Chinese pharmaceutical companies and foreign investment is also providing an agreeable environment for the evolution of a new generation of biotechnology. The opportunity for pharmaceutical innovation is also being expedited by the entry of multinational companies into the Chinese pharmaceutical market, and by the outsourcing of research from these companies to China.
Yasgar, Adam; Simeonov, Anton
Introduction Much has been presented and debated on the topic of drug abuse and its multidimensional nature, including the role of society and its customs and laws, economical factors, and the magnitude and nature of the burden. Given the complex nature of the receptors and pathways implicated in regulation of the cognitive and behavioral processes associated with addiction, a large number of molecular targets have been interrogated during recent years to discover starting points for development of small molecule interventions. Areas covered This review describes recent developments in the field of early drug discovery for drug abuse interventions, with a special emphasis on advances published during the 2012-2014 period. Expert Opinion Technologically, the processes/platforms utilized in drug abuse drug discovery are nearly identical to those used in the other disease areas. A key complicating factor in drug abuse research is the enormous biological complexity surrounding the brain processes involved and the associated difficulty in finding “good” targets and achieving exquisite selectivity of treatment agents. While tremendous progress has been made during recent years to use the power of high-throughput technologies to discover proof-of-principle molecules for many new targets, next-generation models will be especially important in this field; examples include seeking advantageous drug-drug combinations, use of automated whole-animal behavioral screening systems, advancing our understanding of the role of epigenetics in drug addiction, and the employment of organoid-level 3D test platforms (also referred to as tissue-chip or organs-on-chip). PMID:25251069
Yan, Ming; Baran, Phil S.
A synthetic strategy has been developed that provides easy access to structurally diverse analogues of naturally occurring antibiotics, providing a fresh means of attack in the war against drug-resistant bacteria. See Article p.338
Colman, Peter M
The influences of Lawrence Bragg and Max Perutz are evident in the contemporary emphasis on 'structural enablement' in drug discovery. On this occasion of the centenary of Bragg's equation, his role in supporting the earliest structural studies of biological materials at the Cavendish Laboratory is remembered. The 1962 Nobel Prizes for the structures of DNA and proteins marked the golden anniversary of the von Laue and Bragg discoveries.
Chris M. Ireland
Full Text Available The high-throughput screening and drug discovery paradigm has necessitated a change in preparation of natural product samples for screening programs. In an attempt to improve the quality of marine natural products samples for screening, several fractionation strategies were investigated. The final method used HP20SS as a solid support to effectively desalt extracts and fractionate the organic components. Additionally, methods to integrate an automated LCMS fractionation approach to shorten discovery time lines have been implemented.
Wu, Wan-Ying; Hou, Jin-Jun; Long, Hua-Li; Yang, Wen-Zhi; Liang, Jian; Guo, De-An
Over the past 30 years, China has significantly improved the drug development environment by establishing a series of policies for the regulation of new drug approval. The regulatory system for new drug evaluation and registration in China was gradually developed in accordance with international standards. The approval and registration of TCM in China became as strict as those of chemical drugs and biological products. In this review, TCM-based new drug discovery and development are introduced according to the TCM classification of nine categories.
Chen Shudong; Zhang Liang; Ma Fanyuan; Shen Jianhua
This paper presents DDGrid, a novel Grid computing system for drug discovery and design. By utilizing the idle resources donated by the clusters that scatter over the Internet, DDGrid can implement efficient data-intensive biologic applications. The high-level resource management framework with a Grid-P2P hybrid architecture is described. With P2P technologies, some problems which are inevitable in the master-slave model can be avoided, such as single point of failure or performance bottleneck. Then an agent-based resource scheduling algorithm is presented. With this scheduling algorithm, the idle computational resources are dynamically scheduled according to the real-time working load on each execution node. Thus DDGrid can hold an excellent load balance state. Furthermore, the framework is introduced into the practical protein molecules docking applications. Solid experimental results show the load balance and robustness of the proposed system, which can greatly speed up the process of protein molecules docking.
Grabowski, Marek; Chruszcz, Maksymilian; Zimmerman, Matthew D; Kirillova, Olga; Minor, Wladek
While three dimensional structures have long been used to search for new drug targets, only a fraction of new drugs coming to the market has been developed with the use of a structure-based drug discovery approach. However, the recent years have brought not only an avalanche of new macromolecular structures, but also significant advances in the protein structure determination methodology only now making their way into structure-based drug discovery. In this paper, we review recent developments resulting from the Structural Genomics (SG) programs, focusing on the methods and results most likely to improve our understanding of the molecular foundation of human diseases. SG programs have been around for almost a decade, and in that time, have contributed a significant part of the structural coverage of both the genomes of pathogens causing infectious diseases and structurally uncharacterized biological processes in general. Perhaps most importantly, SG programs have developed new methodology at all steps of the structure determination process, not only to determine new structures highly efficiently, but also to screen protein/ligand interactions. We describe the methodologies, experience and technologies developed by SG, which range from improvements to cloning protocols to improved procedures for crystallographic structure solution that may be applied in "traditional" structural biology laboratories particularly those performing drug discovery. We also discuss the conditions that must be met to convert the present high-throughput structure determination pipeline into a high-output structure-based drug discovery system.
Grabowski, M.; Chruszcz, M; Zimmerman, M; Kirillova, O; Minor, W
While three dimensional structures have long been used to search for new drug targets, only a fraction of new drugs coming to the market has been developed with the use of a structure-based drug discovery approach. However, the recent years have brought not only an avalanche of new macromolecular structures, but also significant advances in the protein structure determination methodology only now making their way into structure-based drug discovery. In this paper, we review recent developments resulting from the Structural Genomics (SG) programs, focusing on the methods and results most likely to improve our understanding of the molecular foundation of human diseases. SG programs have been around for almost a decade, and in that time, have contributed a significant part of the structural coverage of both the genomes of pathogens causing infectious diseases and structurally uncharacterized biological processes in general. Perhaps most importantly, SG programs have developed new methodology at all steps of the structure determination process, not only to determine new structures highly efficiently, but also to screen protein/ligand interactions. We describe the methodologies, experience and technologies developed by SG, which range from improvements to cloning protocols to improved procedures for crystallographic structure solution that may be applied in 'traditional' structural biology laboratories particularly those performing drug discovery. We also discuss the conditions that must be met to convert the present high-throughput structure determination pipeline into a high-output structure-based drug discovery system.
Renaud, Jean-Paul; Chung, Chun-Wa; Danielson, U Helena; Egner, Ursula; Hennig, Michael; Hubbard, Roderick E; Nar, Herbert
Over the past 25 years, biophysical technologies such as X-ray crystallography, nuclear magnetic resonance spectroscopy, surface plasmon resonance spectroscopy and isothermal titration calorimetry have become key components of drug discovery platforms in many pharmaceutical companies and academic laboratories. There have been great improvements in the speed, sensitivity and range of possible measurements, providing high-resolution mechanistic, kinetic, thermodynamic and structural information on compound-target interactions. This Review provides a framework to understand this evolution by describing the key biophysical methods, the information they can provide and the ways in which they can be applied at different stages of the drug discovery process. We also discuss the challenges for current technologies and future opportunities to use biophysical methods to solve drug discovery problems.
Jones, Lyn H; Bunnage, Mark E
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.
Bowling, John J; Kochanowska, Anna J; Kasanah, Noer; Hamann, Mark T
With ~ 40 years of research completed after the development of self-contained underwater breathing apparatus, drug discovery opportunities in the sea are still too numerous to count. Since the FDA approval of the direct-from-the-sea calcium channel blocker ziconotide, marine natural products have been validated as a source for new medicines. However, the demand for natural products is extremely high due to the development of high-throughput assays and this bottleneck has created the need for an intense focus on increasing the rate of isolating and elucidating the structures of new bioactive secondary metabolites. In addition to highlighting the drug discovery potential of the marine environment, this review discusses several of the pressing needs to increase the rate of drug discovery in marine natural products, and describes some of the work and new technologies that are contributing in this regard. PMID:23484601
Lucreţia Udrescu; Laura Sbârcea; Alexandru Topîrceanu; Alexandru Iovanovici; Ludovic Kurunczi; Paul Bogdan; Mihai Udrescu
Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection...
Janero, David R
Research universities continue to produce new scientists capable of generating knowledge with the potential to inform disease etiology and treatment. Mounting interest of doctoral-level experimental science students in therapeutics-related research careers is discordant with the widespread lack of direct drug-discovery and development experience, let alone commercialization success, among university faculty and administrators. Likewise, the archetypical publication- and grant-fueled, principal investigator (PI)-focused academic system ("PI-stan") risks commoditization of science students pursuing their doctorates as a labor source, rendering them ill-prepared for career options related to therapeutics innovation by marginalizing their development of "beyond-the-bench" professional skills foundational to modern drug-discovery campaigns and career fluency. To militate against professionalization deficits in doctoral drug-discovery researchers, the author--a scientist-administrator-consultant with decades of discovery research and development (R&D), business, and educator experience in commercial and university settings--posits a critical need for pluridimensionality in graduate education and mentorship that extends well beyond thesis-related scientific domains/laboratory techniques to instill transferable operational-intelligence, project/people-management, and communication competencies. Specific initiatives are advocated to help enhance the doctoral science student's market competitiveness, adaptability, and navigation of the significant research, commercial, and occupational challenges associated with contemporary preclinical drug-discovery R&D.
Itoh, Yukihiro; Suzuki, Takayoshi
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.
Arshad, Zeeshaan; Smith, James; Roberts, Mackenna; Lee, Wen Hwa; Davies, Ben; Bure, Kim; Hollander, Georg A; Dopson, Sue; Bountra, Chas; Brindley, David
The cost to develop a new drug from target discovery to market is a staggering $1.8 billion, largely due to the very high attrition rate of drug candidates and the lengthy transition times during development. Open access is an emerging model of open innovation that places no restriction on the use of information and has the potential to accelerate the development of new drugs. To date, no quantitative assessment has yet taken place to determine the effects and viability of open access on the process of drug translation. This need is addressed within this study. The literature and intellectual property landscapes of the drug candidate JQ1, which was made available on an open access basis when discovered, and conventionally developed equivalents that were not are compared using the Web of Science and Thomson Innovation software, respectively. Results demonstrate that openly sharing the JQ1 molecule led to a greater uptake by a wider and more multi-disciplinary research community. A comparative analysis of the patent landscapes for each candidate also found that the broader scientific diaspora of the publically released JQ1 data enhanced innovation, evidenced by a greater number of downstream patents filed in relation to JQ1. The authors' findings counter the notion that open access drug discovery would leak commercial intellectual property. On the contrary, JQ1 serves as a test case to evidence that open access drug discovery can be an economic model that potentially improves efficiency and cost of drug discovery and its subsequent commercialization.
Aswani, Anil Jayanti
Biological networks are comprised of thousands of interacting components, and these networks have complicated patterns of feedback and feed-forward motifs. It is practically impossible to use intuition to determine whether simultaneously modifying multiple pharmaceutical targets has a good therapeutic response. Even when a drug is discovered which is safe in humans and highly-effective against its target, the medical effect on the disease may be underwhelming. This provides a strong impetu...
Bo Liu; Jin-Ku Bao; Jin-Ming Yang; Yan Cheng
Autophagy,an evolutionarily conserved lysosomal degradation process,has drawn an increasing amount of attention in recent years for its role in a variety of human diseases,such as cancer.Notably,autophagy plays an important role in regulating several survival and death signaling pathways that determine cell fate in cancer.To date,substantial evidence has demonstrated that some key autophagic mediators,such as autophagy-related genes (ATGs),PI3K,mTOR,p53,and Beclin-1,may play crucial roles in modulating autophagic activity in cancer initiation and progression.Because autophagy-modulating agents such as rapamycin and chloroquine have already been used clinically to treat cancer,it is conceivable that targeting autophagic pathways may provide a new opportunity for discovery and development of more novel cancer therapeutics.With a deeper understanding of the regulatory mechanisms governing autophagy,we will have a better opportunity to facilitate the exploitation of autophagy as a target for therapeutic intervention in cancer.This review discusses the current status of targeting autophagic pathways as a potential cancer therapy.
Cain, Ricky; Narramore, Sarah; McPhillie, Martin; Simmons, Katie; Fishwick, Colin W G
In recent years bacterial resistance has been observed against many of our current antibiotics, for instance most worryingly against the cephalosporins which are typically the last line of defence against many bacterial infections. Additionally the failure of high throughput screening in the discovery of new antibacterial drug leads has led to a decline in the number of antibacterial agents reaching the market. Alternative methods of drug discovery including structure based drug design are needed to meet the threats caused by the emergence of resistance. In this review we explore the latest advancements in the identification of new antibacterial agents through the use of a number of structure based drug design programs. Copyright © 2014 Elsevier Inc. All rights reserved.
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.
Van Eldik, Linda J; Koppal, Tanuja; Watterson, D Martin
The drug discovery and the drug development processes represent a continuum of recursive activities that range from initial drug target identification to final Food and Drug Administration approval and marketing of a new therapeutic. Drug discovery, as its name implies, is more exploratory and less focused in many cases, whereas drug development has a clinically defined endpoint and a specific disease goal. Academia has historically made major contributions to this process at the early discovery phases. However, current trends in the organization of the pharmaceutical industry suggest an expanded role for academia in the near future. Megamergers among major pharmaceutical corporations indicate their movement toward a focus on end-stage clinical trials, manufacturing, and marketing. There has been a parallel increase in outsourcing of intermediate steps to specialty small pharmaceutical, biotechnology, and contract service companies. The new paradigm suggests that academia will play an increasingly important role at the proof-of-principle stage of basic and clinical drug discovery research, in training the future skilled work force, and in close partnerships with small pharmaceutical and biotechnology companies. However, academic drug discovery research faces a set of barriers to progress, the relative importance of which varies with the home institution and the details of the research area. These barriers fall into four general categories: (1) the historical administrative structure and environment of academia; (2) the structure and emphasis of peer review panels that control research funding by government and private agencies; (3) the organization and operation of the academic infrastructure; and (4) the structure and availability of specialized resources and information management. Selected examples of barriers to drug discovery and drug development research and training in academia are presented, as are some specific recommendations designed to minimize or
McCammon, J. Andrew
This lecture will provide a general introduction to some of the ways that modern computational physics is contributing to the discovery of new pharmaceuticals, with special emphasis on drugs for infectious diseases. The basic sciences and computing technologies involved have advanced to the point that physics-based simulations of drug targets are now yielding truly valuable suggestions for new compounds. Supported in part by NSF, NIH, HHMI, CTBP, NBCR, and SDSC.
Leticia Ortí; Carbajo, Rodrigo J.; Ursula Pieper; Narayanan Eswar; Maurer, Stephen M.; Rai, Arti K.; Ginger Taylor; Todd, Matthew H; Antonio Pineda-Lucena; Andrej Sali; Marti-Renom, Marc A.
BACKGROUND: Conventional patent-based drug development incentives work badly for the developing world, where commercial markets are usually small to non-existent. For this reason, the past decade has seen extensive experimentation with alternative R&D institutions ranging from private-public partnerships to development prizes. Despite extensive discussion, however, one of the most promising avenues-open source drug discovery-has remained elusive. We argue that the stumbling block has been the...
Jensen, Janne; Hyllner, Johan; Björquist, Petter
Development of new drugs is costly and takes huge resources into consideration. The big pharmaceutical companies are currently facing increasing developmental costs and a lower success-rate of bringing new compounds to the market. Therefore, it is now of outmost importance that the drug-hunting companies minimize late attritions due to sub-optimal pharmacokinetic properties or unexpected toxicity when entering the clinical programs. To achieve this, a strong need to test new candidate drugs in assays of high human relevance in vitro as early as possible has been identified. The traditionally used cell systems are however remarkably limited in this sense, and new improved technologies are of greatest importance. The human embryonic stem cells (hESC) is one of the most powerful cell types known. They have not only the possibility to divide indefinitely; these cells can also differentiate into all mature cell types of the human body. This makes them potentially very valuable for pharmaceutical development, spanning from use as tools in early target studies, DMPK or safety assessment, as screening models to find new chemical entities modulating adult stem cell fate, or as the direct use in cell therapies. This review illustrates the use of hESC in the drug discovery process, today, as well as in a future perspective. This will specifically be exemplified with the most important cell type for pharmaceutical development-the hepatocyte. We discuss how hESC-derived hepatocyte-like cells could improve this process, and how these cells should be cultured if optimized functionality and usefulness should be achieved. J. Cell. Physiol. 219: 513-519, 2009. (c) 2009 Wiley-Liss, Inc.
Shon, John; Ohkawa, Hitomi; Hammer, Juergen
Large pharmaceutical companies annually invest tens to hundreds of millions of US dollars in research informatics to support their early drug discovery processes. Traditionally, most of these investments are designed to increase the efficiency of drug discovery. The introduction of do-it-yourself scientific workflow platforms has enabled research informatics organizations to shift their efforts toward scientific innovation, ultimately resulting in a possible increase in return on their investments. Unlike the handling of most scientific data and application integration approaches, researchers apply scientific workflows to in silico experimentation and exploration, leading to scientific discoveries that lie beyond automation and integration. This review highlights some key requirements for scientific workflow environments in the pharmaceutical industry that are necessary for increasing research productivity. Examples of the application of scientific workflows in research and a summary of recent platform advances are also provided.
Introduction There is an immediate need for functional and molecular studies to decipher differences between disease and “normal” settings to identify large quantities of validated targets with the highest therapeutic utilities. Furthermore, drug mechanism of action and biomarkers to predict drug efficacy and safety need to be identified for effective design of clinical trials, decreasing attrition rates, regulatory agency approval process and drug repositioning. By expanding the power of genetics and pharmacogenetics studies, next generation nucleic acid sequencing technologies have started to play an important role in all stages of drug discovery. Areas covered This article reviews the first and second generation sequencing technologies (SGSTs) and challenges they pose to biomedicine. The article then focuses on the emerging third generation sequencing technologies (TGSTs), their technological foundations and potential contributions to drug discovery. Expert Opinion Despite the scientific and commercial success of SGSTs, the goal of rapid, comprehensive and unbiased sequencing of nucleic acids has not been achieved. TGSTs promise to increase sequencing throughput and read lengths, decrease costs, run times and error rates, eliminate biases inherent in SGSTs, and offer capabilities beyond nucleic acid sequencing. Such changes will have positive impact in all sequencing applications to drug discovery. PMID:22468954
Grandjean, Nicolas; Charpiot, Brigitte; Pena, Carlos Andres; Peitsch, Manuel C
Patents are a major source of information in drug discovery and, when properly processed and analyzed, can yield a wealth of information on competitors activities, R&D trends, emerging fields, collaborations, among others. This review discusses the current state-of-the-art in textual data analysis and exploration methods as applied to patent analysis.:
Andrei, Sebastian A; Sijbesma, Eline; Hann, Michael; Davis, Jeremy; O'Mahony, Gavin; Perry, Matthew W D; Karawajczyk, Anna; Eickhoff, Jan; Brunsveld, Luc; Doveston, Richard G; Milroy, Lech-Gustav; Ottmann, Christian
PPIs are involved in every disease and specific modulation of these PPIs with small molecules would significantly improve our prospects of developing therapeutic agents. Both industry and academia have engaged in the identification and use of PPI inhibitors. However in comparison, the opposite strategy of employing small-molecule stabilizers of PPIs is underrepresented in drug discovery. Areas covered: PPI stabilization has not been exploited in a systematic manner. Rather, this concept validated by a number of therapeutically used natural products like rapamycin and paclitaxel has been shown retrospectively to be the basis of the activity of synthetic molecules originating from drug discovery projects among them lenalidomide and tafamidis. Here, the authors cover the growing number of synthetic small-molecule PPI stabilizers to advocate for a stronger consideration of this as a drug discovery approach. Expert opinion: Both the natural products and the growing number of synthetic molecules show that PPI stabilization is a viable strategy for drug discovery. There is certainly a significant challenge to adapt compound libraries, screening techniques and downstream methodologies to identify, characterize and optimize PPI stabilizers, but the examples of molecules reviewed here in our opinion justify these efforts.
Wells, Timothy N C; Willis, Paul; Burrows, Jeremy N; Hooft van Huijsduijnen, Rob
There is a growing consensus that drug discovery thrives in an open environment. Here, we describe how the malaria community has embraced four levels of open data - open science, open innovation, open access and open source - to catalyse the development of new medicines, and consider principles that could enable open data approaches to be applied to other disease areas.
Okada, Yukinori; Wu, Di; Trynka, Gosia; Raj, Towfique; Terao, Chikashi; Ikari, Katsunori; Kochi, Yuta; Ohmura, Koichiro; Suzuki, Akari; Yoshida, Shinji; Graham, Robert R.; Manoharan, Arun; Ortmann, Ward; Bhangale, Tushar; Denny, Joshua C.; Carroll, Robert J.; Eyler, Anne E.; Greenberg, Jeffrey D.; Kremer, Joel M.; Pappas, Dimitrios A.; Jiang, Lei; Yin, Jian; Ye, Lingying; Su, Ding-Feng; Yang, Jian; Xie, Gang; Keystone, Ed; Westra, Harm-Jan; Esko, Tonu; Metspalu, Andres; Zhou, Xuezhong; Gupta, Namrata; Mirel, Daniel; Stahl, Eli A.; Diogo, Dorothee; Cui, Jing; Liao, Katherine; Guo, Michael H.; Myouzen, Keiko; Kawaguchi, Takahisa; Coenen, Marieke J. H.; van Riel, Piet L. C. M.; van de laar, Mart A. F. J.; Guchelaar, Henk-Jan; Huizinga, Tom W. J.; Dieude, Philippe; Mariette, Xavier; Bridges, S. Louis; Zhernakova, Alexandra; Toes, Rene E. M.; Tak, Paul P.; Miceli-Richard, Corinne; Bang, So-Young; Lee, Hye-Soon; Martin, Javier; Gonzalez-Gay, Miguel A.; Rodriguez-Rodriguez, Luis; Rantapaa-Dahlqvist, Solbritt; Arlestig, Lisbeth; Choi, Hyon K.; Kamatani, Yoichiro; Galan, Pilar; Lathrop, Mark; Eyre, Steve; Bowes, John; Barton, Anne; de Vries, Niek; Moreland, Larry W.; Criswell, Lindsey A.; Karlson, Elizabeth W.; Taniguchi, Atsuo; Yamada, Ryo; Kubo, Michiaki; Liu, Jun S.; Bae, Sang-Cheol; Worthington, Jane; Padyukov, Leonid; Klareskog, Lars; Gregersen, Peter K.; Raychaudhuri, Soumya; Stranger, Barbara E.; De Jager, Philip L.; Franke, Lude; Visscher, Peter M.; Brown, Matthew A.; Yamanaka, Hisashi; Mimori, Tsuneyo; Takahashi, Atsushi; Xu, Huji; Behrens, Timothy W.; Siminovitch, Katherine A.; Momohara, Shigeki; Matsuda, Fumihiko; Yamamoto, Kazuhiko; Plenge, Robert M.
A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)(1). Here we perform
Okada, Y.; Wu, D.; Trynka, G.; Raj, T.; Terao, C.; Ikari, K.; Kochi, Y.; Ohmura, K.; Suzuki, A.; Yoshida, S.; Graham, R.R.; Manoharan, A.; Ortmann, W.; Bhangale, T.; Denny, J.C.; Carroll, R.J.; Eyler, A.E.; Greenberg, J.D.; Kremer, J.M.; Pappas, D.A.; Jiang, L.; Yin, L.; Ye, L.; Su, D.F.; Yang, J.; Xie, G.; Keystone, E.; Westra, H.J.; Esko, T.; Metspalu, A.; Zhou, X.; Gupta, N.; Mirel, D.; Stahl, Eli A.; Diogo, D.; Cui, J.; Liao, K.; Guo, M.H.; Myouzen, K.; Kawaguchi, T.; Coenen, M.J.; Riel, van P.L.; Laar, van de M.A.; Guchelaar, H.J.; Huizinga, T.W.; Dieudé, P.; Mariette, X.; Louis Bridges Jr, S.; Zhernakova, A.; Toes, R.E.; Tak, P.P.; Miceli-Richard, C.; Bang, S.Y.; Lee, H.S.; Martin, J.; Gonzales-Gay, M.A.; Rodriguez-Rodriguez, L.; Rantapää-Dhlqvist, S.; Arlestig, L.; Choi, H.K.; Kamatani, Y.; Galan, P.; Lathrop, M.; Eyre, S.; Bowes, J.; Barton, A.; Vries, de N.; Moreland, L.W.; Criswell, L.A.; Karlson, E.W.; Taniguchi, A.; Yamada, R; Kubo, M.; Bae, S.C.; Worthington, J.; Padyukov, L.; Klareskog, L.; Gregersen, Peter K.; Raychaudhuri, S.; Stranger, B.E.; Jager, de P.L.; Franke, L.; Visscher, P.M.; Brown, M.A.; Yamanaka, H.; Mimori, T.; Takahashi, A.; Xu, H.; Behrens, T.W.; Siminovitch, K.A.; Momohara, S.; Matsuda, F.; Yamamoto, K.; Plenge, Robert M.
A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here we performed
When I was asked to help organize an American Association for the Advancement of Science symposium about how mathematical models have con tributed to biology, I agreed immediately. The subject is of immense importance and wide-spread interest. However, too often it is discussed in biologically sterile environments by "mutual admiration society" groups of "theoreticians", many of whom have never seen, and most of whom have never done, an original scientific experiment with the biolog ical materials they attempt to describe in abstract (and often prejudiced) terms. The opportunity to address the topic during an annual meeting of the AAAS was irresistable. In order to try to maintain the integrity ;,f the original intent of the symposium, it was entitled, "Contributions of Mathematical Models to Biological Discovery". This symposium was organized by Daniel Solomon and myself, held during the 141st annual meeting of the AAAS in New York during January, 1975, sponsored by sections G and N (Biological and Medic...
Bustamante, Juan M.; Tarleton, Rick L.
Introduction Chagas disease is the highest impact human infectious disease in Latin America, and the leading worldwide cause of myocarditis. Despite the availability of several compounds that have demonstrated efficacy in limiting the effects of T. cruzi, these compounds are rarely used due to their variable efficacy, substantial side effects and the lack of methodologies for confirming their effectiveness. Furthermore, the development of more efficacious compounds is challenged by limitations of systems for assessing drug efficacy in vitro and in vivo. Areas covered Herein, the authors review the development of Chagas disease drug discovery methodology, focusing on recent developments in high throughput screening, in vivo testing methods and assessments of efficacy in humans. Particularly, this review documents the significant progress that has taken place over the last 5 years that have paved the way for both target-focused and high-throughput screens of compound libraries. Expert opinion The tools for in vitro and in vivo screening of anti-T. cruzi compounds have improved dramatically in the last few years and there are now a number of excellent in vivo testing models available; this somewhat alleviates the bottleneck issue of quickly and definitively demonstrating in vivo efficacy in a relevant host animal system. These advances emphasize the potential for additional progress resulting in new treatments for Chagas disease in the coming years. That being said, national and international agencies must improve the coordination of research and development efforts in addition to cultivating the funding sources for the development of these new treatments. PMID:21712965
Lee, Wen Hwa
There is a scarcity of novel treatments to address many unmet medical needs. Industry and academia are finally coming to terms with the fact that the prevalent models and incentives for innovation in early stage drug discovery are failing to promote progress quickly enough. Here we will examine how an open model of precompetitive public–private research partnership is enabling efficient derisking and acceleration in the early stages of drug discovery, whilst also widening the range of communities participating in the process, such as patient and disease foundations. PMID:26042736
Vandamme, Drieke; Minke, Benedikt A; Fitzmaurice, William; Kholodenko, Boris N; Kolch, Walter
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.
Li, Linfeng; Zhou, Qiong; Voss, Ty C; Quick, Kevin L; LaBarbera, Daniel V
3D organotypic culture models such as organoids and multicellular tumor spheroids (MCTS) are becoming more widely used for drug discovery and toxicology screening. As a result, 3D culture technologies adapted for high-throughput screening formats are prevalent. While a multitude of assays have been reported and validated for high-throughput imaging (HTI) and high-content screening (HCS) for novel drug discovery and toxicology, limited HTI/HCS with large compound libraries have been reported. Nonetheless, 3D HTI instrumentation technology is advancing and this technology is now on the verge of allowing for 3D HCS of thousands of samples. This review focuses on the state-of-the-art high-throughput imaging systems, including hardware and software, and recent literature examples of 3D organotypic culture models employing this technology for drug discovery and toxicology screening.
Subramaniam, Swaminathan; Dugar, Sundeep
Global pharmaceutical companies face an increasingly harsh environment for their primary business of selling medicines. They have to contend with a spiraling decline in the productivity of their R&D programs that is guaranteed to severely diminish their growth prospects. Outsourcing of drug discovery activities to low-cost locations is a growing response to this crisis. However, the upsides to outsourcing are capped by the failure of global pharmaceutical companies to take advantage of the full range of possibilities that this model provides. Companies that radically rethink and transform the way they conduct R&D, such as seeking the benefits of low-cost locations in India and China will be the ones that thrive in this environment. In this article we present our views on how the outsourcing model in drug discovery should go beyond increasing the efficiency of existing drug discovery processes to a fundamental rethink and re-engineering of these processes.
Alonso-Padilla, Julio; Rodríguez, Ana
The discovery of new therapeutic options against Trypanosoma cruzi, the causative agent of Chagas disease, stands as a fundamental need. Currently, there are only two drugs available to treat this neglected disease, which represents a major public health problem in Latin America. Both available therapies, benznidazole and nifurtimox, have significant toxic side effects and their efficacy against the life-threatening symptomatic chronic stage of the disease is variable. Thus, there is an urgent need for new, improved anti-T. cruzi drugs. With the objective to reliably accelerate the drug discovery process against Chagas disease, several advances have been made in the last few years. Availability of engineered reporter gene expressing parasites triggered the development of phenotypic in vitro assays suitable for high throughput screening (HTS) as well as the establishment of new in vivo protocols that allow faster experimental outcomes. Recently, automated high content microscopy approaches have also been used to identify new parasitic inhibitors. These in vitro and in vivo early drug discovery approaches, which hopefully will contribute to bring better anti-T. cruzi drug entities in the near future, are reviewed here.
Full Text Available The discovery of new therapeutic options against Trypanosoma cruzi, the causative agent of Chagas disease, stands as a fundamental need. Currently, there are only two drugs available to treat this neglected disease, which represents a major public health problem in Latin America. Both available therapies, benznidazole and nifurtimox, have significant toxic side effects and their efficacy against the life-threatening symptomatic chronic stage of the disease is variable. Thus, there is an urgent need for new, improved anti-T. cruzi drugs. With the objective to reliably accelerate the drug discovery process against Chagas disease, several advances have been made in the last few years. Availability of engineered reporter gene expressing parasites triggered the development of phenotypic in vitro assays suitable for high throughput screening (HTS as well as the establishment of new in vivo protocols that allow faster experimental outcomes. Recently, automated high content microscopy approaches have also been used to identify new parasitic inhibitors. These in vitro and in vivo early drug discovery approaches, which hopefully will contribute to bring better anti-T. cruzi drug entities in the near future, are reviewed here.
Full Text Available Thiazolidinedione (TZD is a powerful insulin sensitizer in the treatment of type 2 diabetes. It acts as a ligand to the nuclear receptor PPARγ (peroxisome proliferator-activated receptor-gamma and induces transcription of PPARγ-responsive genes. TZD controls lipid synthesis and storage in adipose tissue, liver and many other tissues through PPARγ. Derivatives of TZD, such as rosiglitazone (Avandia and pioglitazone (Actos, are more powerful than metformin or berberine in insulin sensitization. Although they have common side effects such as weight gain and edema, these did not influence their clinical application in general. However, recent findings of risk for congestive heart failure and bladder cancer have significantly impaired their future in many countries. European countries have prohibited those drugs, and US will terminate application of rosiglitazone in clinics and hospitals. The multiple country actions may mark the end of TZD era. As a result, there is a strong demand for identification of TZD substitute in the treatment of type 2 diabetes. In this regard, literature about PPARγ ligands and potential TZD substitute are reviewed in this article. Histone deacetylase (HDAC inhibitor is emphasized as a new class of insulin sensitizer here. Regulators of SIRT1, CREB, NO, p38, ERK and Cdk5 are discussed in the activation of PPARγ.
Noble, Wendy; Pooler, Amy M.; Hanger, Diane P.
Introduction Tauopathies, including Alzheimer’s disease (AD) and some frontotemporal dementias, are neurodegenerative diseases characterised by pathological lesions comprised of tau protein. There is currently a significant and urgent unmet need for disease-modifying therapies for these conditions and recently attention has turned to tau as a potential target for intervention. Areas covered Increasing evidence has highlighted pathways associated with tau-mediated neurodegeneration as important targets for drug development. Here, the authors review recently published papers in this area and summarise the genetic and pharmacological approaches that have shown efficacy in reducing tau-associated neurodegeneration. These include the use of agents to prevent abnormal tau processing and increase tau clearance, therapies targeting the immune system, and the manipulation of tau pre-mRNA to modify tau isoform expression. Expert opinion Several small molecule tau-based treatments are currently being assessed in clinical trials, the outcomes of which are eagerly awaited. Current evidence suggests that therapies targeting tau are likely, at least in part, to form the basis of an effective and safe treatment for Alzheimer’s disease and related neurodegenerative disorders in which tau deposition is evident. PMID:22003359
Awasthi, Divya; Freundlich, Joel S
Bacteria are capable of performing a number of biotransformations that may activate or deactivate xenobiotics. Recent efforts have utilized metabolomics techniques to study the fate of small-molecule antibacterials within the targeted organism. Examples involving Mycobacterium tuberculosis are reviewed and analyzed with regard to the insights they provide as to both activation and deactivation of the antibacterial. The studies, in particular, shed light on biosynthetic transformations performed by M. tuberculosis while suggesting avenues for the evolution of chemical tools, highlighting potential areas for drug discovery, and mechanisms of approved drugs. A two-pronged approach investigating the metabolism of antibacterials within both the host and bacterium is outlined and will be of value to both the chemical biology and drug discovery fields. Copyright © 2017 Elsevier Ltd. All rights reserved.
Cromie, Karen D; Van Heeke, Gino; Boutton, Carlo
Nanobodies are therapeutic proteins derived from the variable domain (VHH) of naturally occurring heavy-chain antibodies. These VHH domains are the smallest functional fragments derived from a naturally occurring immunoglobulin. Nanobodies can be easily produced in prokaryotic or eukaryotic host organisms and their unique biophysical characteristics render these molecules ideal candidates for drug development. They are also emerging as an interesting new class of potential therapeutics for targets such as GPCRs, which have historically been challenging for small molecule drug discovery and even more difficult for biologics discovery. The ability to easily combine Nanobodies with different binding sites and different modes of action can be used to generate highly selective and highly potent drug candidates with very attractive pharmacological profiles. In addition, Nanobodies have been used as crystallization chaperones to enable or facilitate the structural determination of an active GPCR conformation.
Weaver, Ian N; Weaver, Donald F
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.
Myung, Kyung; Klittich, Carla J R
Twelve drugs from four chemical classes are currently available for treatment of systemic fungal infections in humans. By contrast, more than 100 structurally distinct compounds from over 30 chemical classes have been developed as agricultural fungicides, and these fungicides target many modes of action not represented among human antifungal drugs. In this article we introduce the diverse aspects of agricultural fungicides and compare them with human antifungal drugs. We propose that the information gained from the development of agricultural fungicides can be applied to the discovery of new mechanisms of action and new antifungal agents for the management of human fungal infections.
Garg, Vibhav; Arora, Suchir; Gupta, Chitra
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.
Macalino, Stephani Joy Y; Gosu, Vijayakumar; Hong, Sunhye; Choi, Sun
Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.
Piel, Markus; Vernaleken, Ingo; Rösch, Frank
Molecular imaging methods such as positron emission tomography (PET) are increasingly involved in the development of new drugs. Using radioactive tracers as imaging probes, PET allows the determination of the pharmacokinetic and pharmacodynamic properties of a drug candidate, via recording target engagement, the pattern of distribution, and metabolism. Because of the noninvasive nature and quantitative end point obtainable by molecular imaging, it seems inherently suited for the examination of a pharmaceutical's behavior in the brain. Molecular imaging, most especially PET, can therefore be a valuable tool in CNS drug research. In this Perspective, we present the basic principles of PET, the importance of appropriate tracer selection, the impact of improved radiopharmaceutical chemistry in radiotracer development, and the different roles that PET can fulfill in CNS drug research.
Chen, Yang; Li, Li; Zhang, Guo-Qiang; Xu, Rong
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 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.
Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao
Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.
Mdluli, Khisimuzi; Kaneko, Takushi; Upton, Anna
The recent accelerated approval for use in extensively drug-resistant and multidrug-resistant-tuberculosis (MDR-TB) of two first-in-class TB drugs, bedaquiline and delamanid, has reinvigorated the TB drug discovery and development field. However, although several promising clinical development programs are ongoing to evaluate new TB drugs and regimens, the number of novel series represented is few. The global early-development pipeline is also woefully thin. To have a chance of achieving the goal of better, shorter, safer TB drug regimens with utility against drug-sensitive and drug-resistant disease, a robust and diverse global TB drug discovery pipeline is key, including innovative approaches that make use of recently acquired knowledge on the biology of TB. Fortunately, drug discovery for TB has resurged in recent years, generating compounds with varying potential for progression into developable leads. In parallel, advances have been made in understanding TB pathogenesis. It is now possible to apply the lessons learned from recent TB hit generation efforts and newly validated TB drug targets to generate the next wave of TB drug leads. Use of currently underexploited sources of chemical matter and lead-optimization strategies may also improve the efficiency of future TB drug discovery. Novel TB drug regimens with shorter treatment durations must target all subpopulations of Mycobacterium tuberculosis existing in an infection, including those responsible for the protracted TB treatment duration. This review summarizes the current TB drug development pipeline and proposes strategies for generating improved hits and leads in the discovery phase that could help achieve this goal.
Full Text Available Due to extensive bioprospecting efforts of the past and technology factors, there have been questions about drug discovery prospect from untapped species. We analyzed recent trends of approved drugs derived from previously untapped species, which show no sign of untapped drug-productive species being near extinction and suggest high probability of deriving new drugs from new species in existing drug-productive species families and clusters. Case histories of recently approved drugs reveal useful strategies for deriving new drugs from the scaffolds and pharmacophores of the natural product leads of these untapped species. New technologies such as cryptic gene-cluster exploration may generate novel natural products with highly anticipated potential impact on drug discovery.
Bruno J. Neves
Full Text Available Schistosomiasis is a neglected parasitic tropical disease that claims around 200,000 human lives every year. Praziquantel (PZQ, the only drug recommended by the World Health Organization for the treatment and control of human schistosomiasis, is now facing the threat of drug resistance, indicating the urgent need for new effective compounds to treat this disease. Therefore, globally, there is renewed interest in natural products (NPs as a starting point for drug discovery and development for schistosomiasis. Recent advances in genomics, proteomics, bioinformatics, and cheminformatics have brought about unprecedented opportunities for the rapid and more cost-effective discovery of new bioactive compounds against neglected tropical diseases. This review highlights the main contributions that NP drug discovery and development have made in the treatment of schistosomiasis and it discusses how integration with virtual screening (VS strategies may contribute to accelerating the development of new schistosomidal leads, especially through the identification of unexplored, biologically active chemical scaffolds and structural optimization of NPs with previously established activity.
Pasquato, Antonella; Burri, Dominique J; Kunz, Stefan
Arenaviruses are a large group of emerging viruses including several causative agents of severe hemorrhagic fevers with high mortality in man. Considering the number of people affected and the currently limited therapeutic options, novel efficacious therapeutics against arenaviruses are urgently needed. Over the past decade, significant advances in knowledge about the basic virology of arenaviruses have been accompanied by the development of novel therapeutics targeting different steps of the arenaviral life cycle. High-throughput, small-molecule screens identified potent and broadly active inhibitors of arenavirus entry that were instrumental for the dissection of unique features of arenavirus fusion. Novel inhibitors of arenavirus replication have been successfully tested in animal models and hold promise for application in humans. Late in the arenavirus life cycle, the proteolytic processing of the arenavirus envelope glycoprotein precursor and cellular factors critically involved virion assembly and budding provide further promising 'druggable' targets for novel therapeutics to combat human arenavirus infection.
Tsui, Vickie; Ortwine, Daniel F.; Blaney, Jeffrey M.
Computational chemistry/informatics scientists and software engineers in Genentech Small Molecule Drug Discovery collaborate with experimental scientists in a therapeutic project-centric environment. Our mission is to enable and improve pre-clinical drug discovery design and decisions. Our goal is to deliver timely data, analysis, and modeling to our therapeutic project teams using best-in-class software tools. We describe our strategy, the organization of our group, and our approaches to reach this goal. We conclude with a summary of the interdisciplinary skills required for computational scientists and recommendations for their training.
Tsui, Vickie; Ortwine, Daniel F; Blaney, Jeffrey M
Computational chemistry/informatics scientists and software engineers in Genentech Small Molecule Drug Discovery collaborate with experimental scientists in a therapeutic project-centric environment. Our mission is to enable and improve pre-clinical drug discovery design and decisions. Our goal is to deliver timely data, analysis, and modeling to our therapeutic project teams using best-in-class software tools. We describe our strategy, the organization of our group, and our approaches to reach this goal. We conclude with a summary of the interdisciplinary skills required for computational scientists and recommendations for their training.
O’Hayre, Morgan; Salanga, Catherina L.; Handel, Tracy M.; Hamel, Damon J.
Importance of the field Chemokine receptors are G protein-coupled receptors (GPCRs) most noted for their role in cell migration. However, inappropriate utilization or regulation of these receptors is implicated in many inflammatory diseases, cancer and HIV, making them important drug targets. Areas covered in this review Allostery, oligomerization, and ligand bias are presented as they pertain to chemokine receptors and their associated pathologies. Specific examples of each are described from the recent literature and their implications are discussed in terms of drug discovery efforts targeting chemokine receptors. What the reader will gain Insight into the expanding view of the multitude of pharmacological variables that need to be considered or that may be exploited in chemokine receptor drug discovery. Take home message Since 2007, two drugs targeting chemokine receptors have been approved by the FDA, Maraviroc for preventing HIV infection and Mozobil™ for hematopoietic stem cell mobilization. While these successes permit optimism for chemokine receptors as drug targets, only recently has the complexity of this system begun to be appreciated. The concepts of allosteric inhibitors, biased ligands and functional selectivity raise the possibility that drugs with precisely-defined properties can be developed. Other complexities such as receptor oligomerization and tissue-specific functional states of receptors also offer opportunities for increased target and response specificity, although it will be more challenging to translate these ideas into approved therapeutics compared to traditional approaches. PMID:21132095
Lightstone, F C; Bennion, B J
We proposed to determine the underpinnings of a high-throughput computational infrastructure that would support future efforts in therapeutics against biothreat pathogens. Existing modeling capabilities focus on pathogen detection, but extending such capabilities to high-throughput molecular docking would lead to a proactive method to guide the development of therapeutics. This project will focus on determining the feasibility of extending current databases to accommodate molecular docking. We will also examine the feasibility of massive parallelization of docking algorithms and the utility of docking libraries. Transferring this new technique to a high-performance computing (HPC) platform at LLNL would result in a unique capability not available elsewhere in government or industry. We have accomplished the proposed work defined in this LDRD FS study. (1) We successfully defined the feasibility of using three different small-molecule databases for high-throughput docking, the NCI diversity set, ZINC and the ACD. (2) We analyzed the accuracy and parallelization capabilities of six separate docking programs: DOCK, AutoDock, FlexX, Glide, and eHiTS. Each program is completely amenable to parallel execution. The fastest code was eHiTS, and Glide was the most accurate. (3) Customizing large libraries was cumbersome without the proper software, making the databases a bit difficult to tailor. The ZINC database has some prefiltered versions. (4) Scripts were created for quality and job control functions. Further development is needed for analysis and visualization needs. The successful conclusion of this project enables LLNL to have a high-throughput computational docking capability where we have evaluated the codes to specific docking problems and utilized LLNL's HPC for significant gains in performance. We have established a CRADA with an industrial partner (funded by the National Institutes of Health) that will fully utilize this technology for biodefense
Kido, Yasuto; Kosaka, Alan; Zhang, Xuexiang; Morrissey, Kari M.; Sali, Andrej; Huang, Yong; Giacomini, Kathleen M.
The human multidrug and toxin extrusion (MATE) transporter 1 contributes to the tissue distribution and excretion of many drugs. Inhibition of MATE1 may result in potential drug-drug interactions (DDIs) and alterations in drug exposure and accumulation in various tissues. The primary goals of this project were to identify MATE1 inhibitors with clinical importance or in vitro utility and to elucidate the physicochemical properties that differ between MATE1 and OCT2 inhibitors. Using a fluorescence assay of ASP+ uptake in cells stably expressing MATE1, over 900 prescription drugs were screened and 84 potential MATE1 inhibitors were found. We identified several MATE1 selective inhibitors including four FDA-approved medications that may be clinically relevant MATE1 inhibitors and could cause a clinical DDI. In parallel, a QSAR model identified distinct molecular properties of MATE1 versus OCT2 inhibitors and was used to screen the DrugBank in silico library for new hits in a larger chemical space. PMID:23241029
Rossi, Tino; Braggio, Simone
The constant decline in drug discovery productivity despite the continuous growth in R&D investments has been on the table for many years and is driving changes in the current business model. We have focused our attention on what appears to be by far the major cause of attrition, the intrinsic quality of drug candidates; with the assumption that candidate quality can be designed and assessed at a rather early stage in drug discovery we have developed tools such as CNS chemical space mapping through PLS analysis, Drug Efficiency (DRUG(eff)) and the mechanistic PK/PD hypothesis. We also introduced best practices that were found extremely valuable which will be discussed in this article. Copyright © 2011 Elsevier Ltd. All rights reserved.
Cromm, Philipp M; Crews, Craig M
Traditional pharmaceutical drug discovery is almost exclusively focused on directly controlling protein activity to cure diseases. Modulators of protein activity, especially inhibitors, are developed and applied at high concentration to achieve maximal effects. Thereby, reduced bioavailability and off-target effects can hamper compound efficacy. Nucleic acid-based strategies that control protein function by affecting expression have emerged as an alternative. However, metabolic stability and broad bioavailability represent development hurdles that remain to be overcome for these approaches. More recently, utilizing the cell's own protein destruction machinery for selective degradation of essential drivers of human disorders has opened up a new and exciting area of drug discovery. Small-molecule-induced proteolysis of selected substrates offers the potential of reaching beyond the limitations of the current pharmaceutical paradigm to expand the druggable target space. Copyright © 2017 Elsevier Ltd. All rights reserved.
Blair, Wade; Perros, Manos
The 5th Antiviral Drug Discovery and Development Summit provided an up-to-date snapshot of the ongoing developments in the area. The topics covered ranged from updates on recently launched drugs (Kaletra), Fuzeon) and new investigational inhibitors (T-1249, Reverset, UK-427857, L-870810, PA-457, remofovir, VX-950), to the discovery of new antiviral targets and advances in technologies that may provide the substrate for the next generation of therapeutics. It is apparent from the range of presentations that much of today's efforts are focused on developing new classes of HIV inhibitors (gp41, integrase), while there is also considerable progress in hepatitis C, where a number of inhibitors have or should reach proof-of-concept studies in the coming months. Here we provide the highlights of this meeting, with particular emphasis on the new developments in HIV and hepatitis C virus.
Dias, Daniel A.; Urban, Sylvia; Roessner, Ute
Historically, natural products have been used since ancient times and in folklore for the treatment of many diseases and illnesses. Classical natural product chemistry methodologies enabled a vast array of bioactive secondary metabolites from terrestrial and marine sources to be discovered. Many of these natural products have gone on to become current drug candidates. This brief review aims to highlight historically significant bioactive marine and terrestrial natural products, their use in folklore and dereplication techniques to rapidly facilitate their discovery. Furthermore a discussion of how natural product chemistry has resulted in the identification of many drug candidates; the application of advanced hyphenated spectroscopic techniques to aid in their discovery, the future of natural product chemistry and finally adopting metabolomic profiling and dereplication approaches for the comprehensive study of natural product extracts will be discussed. PMID:24957513
Hansen, Kasper Bø; Bräuner-Osborne, Hans
Fluorescent dyes sensitive to changes in intracellular calcium have become increasingly popular in G protein-coupled receptor (GPCR) drug discovery for several reasons. First of all, the assays using the dyes are easy to perform and are of low cost compared to other assays. Second, most non-Galph...... making them obtainable even for academic groups. Here, we present a protocol for measuring changes in intracellular calcium levels in living mammalian cells based on the fluorescent calcium binding dye, fluo-4....
Holdgate, Geoffrey; Geschwindner, Stefan; Breeze, Alex; Davies, Gareth; Colclough, Nicola; Temesi, David; Ward, Lara
Biophysical methods have become established in many areas of drug discovery. Application of these methods was once restricted to a relatively small number of scientists using specialized, low throughput technologies and methods. Now, automated high-throughput instruments are to be found in a growing number of laboratories. Many biophysical methods are capable of measuring the equilibrium binding constants between pairs of molecules crucial for molecular recognition processes, encompassing protein-protein, protein-small molecule, and protein-nucleic acid interactions, and several can be used to measure the kinetic or thermodynamic components controlling these biological processes. For a full characterization of a binding process, determinations of stoichiometry, binding mode, and any conformational changes associated with such interactions are also required. The suite of biophysical methods that are now available represents a powerful toolbox of techniques which can effectively deliver this full characterization.The aim of this chapter is to provide the reader with an overview of the drug discovery process and how biophysical methods, such as surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), nuclear magnetic resonance, mass spectrometry (MS), and thermal unfolding methods can answer specific questions in order to influence project progression and outcomes. The selection of these examples is based upon the experiences of the authors at AstraZeneca, and relevant approaches are highlighted where they have utility in a particular drug discovery scenario.
Barbault, Florent; Maurel, François
Biological macromolecules, such as proteins or nucleic acids, are (still) molecules and thus they follow the same chemical rules that any simple molecule follows, even if their size generally renders accurate studies unhelpful. However, in the context of drug discovery, a detailed analysis of ligand association is required for understanding or predicting their interactions and hybrid quantum mechanics/molecular mechanics (QM/MM) computations are relevant tools to help elucidate this process. In this review, the authors explore the use of QM/MM for drug discovery. After a brief description of the molecular mechanics (MM) technique, the authors describe the subtractive and additive techniques for QM/MM computations. The authors then present several application cases in topics involved in drug discovery. QM/MM have been widely employed during the last decades to study chemical processes such as enzyme-inhibitor interactions. However, despite the enthusiasm around this area, plain MM simulations may be more meaningful than QM/MM. To obtain reliable results, the authors suggest fixing several keystone parameters according to the underlying chemistry of each studied system.
Full Text Available Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.
Matsumoto, Mitsuyuki; Walton, Noah M; Yamada, Hiroshi; Kondo, Yuji; Marek, Gerard J; Tajinda, Katsunori
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.
Shu, Chih-Wen; Liu, Pei-Feng; Huang, Chun-Ming
Autophagy is an evolutionally conserved process in cells for cleaning abnormal proteins and organelles in a lysosome dependent manner. Growing studies have shown that defects or induced autophagy contributes to many diseases including aging, neurodegeneration, pathogen infection, and cancer. However, the precise involvement of autophagy in health and disease remains controversial because the theories are built on limited assays and chemical modulators, indicating that the role of autophagy in diseases may require further verification. Many food and drug administration (FDA) approved drugs modulate autophagy signaling, suggesting that modulation of autophagy with pharmacological agonists or antagonists provides a potential therapy for autophagy-related diseases. This suggestion raises an attractive issue on drug discovery for exploring chemical modulators of autophagy. High throughput screening (HTS) is becoming a powerful tool for drug discovery that may accelerate screening specific autophagy modulators to clarify the role of autophagy in diseases. Herein, this review lays out current autophagy assays to specifically measure autophagy components such as LC3 (mammalian homologue of yeast Atg8) and Atg4. These assays are feasible or successful for HTS with certain chemical libraries, which might be informative for this intensively growing field as research tools and hopefully developing new drugs for autophagy-related diseases.
Rustum S. Boyce
Full Text Available In the past decade there has been a significant growth in the sales of pharmaceutical drugs worldwide, but more importantly there has been a dramatic growth in the sales of single enantiomer drugs. The pharmaceutical industry has a rising demand for chiral intermediates and research reagents because of the continuing imperative to improve drug efficacy. This in turn impacts on researchers involved in preclinical discovery work. Besides traditional chiral pool and resolution of racemates as sources of chiral building blocks, many new synthetic methods including a great variety of catalytic reactions have been developed which facilitate the production of complex chiral drug candidates for clinical trials. The most ambitious technique is to synthesise homochiral compounds from non-chiral starting materials using chiral metal catalysts and related chemistry. Examples of the synthesis of chiral building blocks from achiral materials utilizing asymmetric hydrogenation and asymmetric epoxidation are presented.
Sollano, J A; Kirsch, J M; Bala, M V; Chambers, M G; Harpole, L H
Although it is commonly believed that the innovation of new medicines is of paramount importance for improving the health and quality of life of patients, there is also a keen recognition regarding upward-spiraling costs of innovation, drug discovery, and drug development against a backdrop of dwindling successes in research and development (R&D) efforts. We propose a new model of valuation of pharmacotherapies that attempts to secure an adequate return on investment in innovation by ensuring optimal pricing and reimbursement.
Adam Michael Stewart; Robert eGerlai; Kalueff, Allan V.
The high prevalence of brain disorders and the lack of their efficient treatments necessitate improved in-vivo pre-clinical models and tests. The zebrafish (Danio rerio), a vertebrate species with high genetic and physiological homology to humans, is an excellent organism for innovative central nervous system (CNS) drug discovery and small molecule screening. Here, we outline new strategies for developing higher-throughput zebrafish screens to test neuroactive drugs and predict their pharmaco...
Swaminathan, Soumya; Sundaramurthi, Jagadish Chandrabose; Palaniappan, Alangudi Natarajan; Narayanan, Sujatha
Emergence of drug-resistant tuberculosis (DR-TB) is a big challenge in TB control. The delay in diagnosis of DR-TB leads to its increased transmission, and therefore prevalence. Recent developments in genomics have enabled whole genome sequencing (WGS) of Mycobacterium tuberculosis (M. tuberculosis) from 3-day-old liquid culture and directly from uncultured sputa, while new bioinformatics tools facilitate to determine DR mutations rapidly from the resulting sequences. The present drug discovery and development pipeline is filled with candidate drugs which have shown efficacy against DR-TB. Furthermore, some of the FDA-approved drugs are being evaluated for repurposing, and this approach appears promising as several drugs are reported to enhance efficacy of the standard TB drugs, reduce drug tolerance, or modulate the host immune response to control the growth of intracellular M. tuberculosis. Recent developments in genomics and bioinformatics along with new drug discovery collectively have the potential to result in synergistic impact leading to the development of a rapid protocol to determine the drug resistance profile of the infecting strain so as to provide personalized medicine. Hence, in this review, we discuss recent developments in WGS, bioinformatics and drug discovery to perceive how they would transform the management of tuberculosis in a timely manner.
Quan, Yuan; Xiong, Le; Chen, Jing; Zhang, Hong-Yu
Mycobacterium tuberculosis (Mtb), the pathogen of tuberculosis (TB), is one of the most infectious bacteria in the world. The traditional strategy to combat TB involves targeting the pathogen directly; however, the rapid evolution of drug resistance lessens the efficiency of this anti-TB method. Therefore, in recent years, some researchers have turned to an alternative anti-TB strategy, which hinders Mtb infection through targeting host genes. In this work, using a theoretical genetic analysis, we identified 170 Mtb infection-associated genes from human genetic variations related to Mtb infection. Then, the agents targeting these genes were identified to have high potential as anti-TB drugs. In particular, the agents that can target multiple Mtb infection-associated genes are more druggable than the single-target counterparts. These potential anti-TB agents were further screened by gene expression data derived from connectivity map. As a result, some agents were revealed to have high interest for experimental evaluation. This study not only has important implications for anti-TB drug discovery, but also provides inspirations for streamlining the pipeline of modern drug discovery.
Frey, Jeremy G; Bird, Colin L
Reviews of the development of drug discovery through the 20(th) century recognised the importance of chemistry and increasingly bioinformatics, but had relatively little to say about the importance of computing and networked computing in particular. However, the design and discovery of new drugs is arguably the most significant single application of bioinformatics and cheminformatics to have benefitted from the increases in the range and power of the computational techniques since the emergence of the World Wide Web, commonly now referred to as simply 'the Web'. Web services have enabled researchers to access shared resources and to deploy standardized calculations in their search for new drugs. This article first considers the fundamental principles of Web services and workflows, and then explores the facilities and resources that have evolved to meet the specific needs of chem- and bio-informatics. This strategy leads to a more detailed examination of the basic components that characterise molecules and the essential predictive techniques, followed by a discussion of the emerging networked services that transcend the basic provisions, and the growing trend towards embracing modern techniques, in particular the Semantic Web. In the opinion of the authors, the issues that require community action are: increasing the amount of chemical data available for open access; validating the data as provided; and developing more efficient links between the worlds of cheminformatics and bioinformatics. The goal is to create ever better drug design services.
Willumsen, Niels J; Bech, Morten; Olesen, Søren-Peter; Jensen, Bo Skaaning; Korsgaard, Mads P G; Christophersen, Palle
Proper function of ion channels is crucial for all living cells. Ion channel dysfunction may lead to a number of diseases, so-called channelopathies, and a number of common diseases, including epilepsy, arrhythmia, and type II diabetes, are primarily treated by drugs that modulate ion channels. A cornerstone in current drug discovery is high throughput screening assays which allow examination of the activity of specific ion channels though only to a limited extent. Conventional patch clamp remains the sole technique with sufficiently high time resolution and sensitivity required for precise and direct characterization of ion channel properties. However, patch clamp is a slow, labor-intensive, and thus expensive, technique. New techniques combining the reliability and high information content of patch clamping with the virtues of high throughput philosophy are emerging and predicted to make a number of ion channel targets accessible for drug screening. Specifically, genuine HTS parallel processing techniques based on arrays of planar silicon chips are being developed, but also lower throughput sequential techniques may be of value in compound screening, lead optimization, and safety screening. The introduction of new powerful HTS electrophysiological techniques is predicted to cause a revolution in ion channel drug discovery.
Full Text Available 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.
Hubbard, Roderick E., E-mail: email@example.com [Vernalis R& D Ltd and University of York (United Kingdom)
Fragment-based methods are successfully generating novel and selective drug-like inhibitors of protein targets, with a number of groups reporting compounds entering clinical trials. This paper summarizes the key features of the approach as one of the tools in structure-guided drug discovery. There has been considerable interest recently in what is known as 'fragment-based lead discovery'. The novel feature of the approach is to begin with small low-affinity compounds. The main advantage is that a larger potential chemical diversity can be sampled with fewer compounds, which is particularly important for new target classes. The approach relies on careful design of the fragment library, a method that can detect binding of the fragment to the protein target, determination of the structure of the fragment bound to the target, and the conventional use of structural information to guide compound optimization. In this article the methods are reviewed, and experiences in fragment-based discovery of lead series of compounds against kinases such as PDK1 and ATPases such as Hsp90 are discussed. The examples illustrate some of the key benefits and issues of the approach and also provide anecdotal examples of the patterns seen in selectivity and the binding mode of fragments across different protein targets.
Doweyko, Arthur M; Doweyko, Lidia M
Humankind has been in the business of discovering drugs for thousands of years. At present, small-molecule drug design is based on specific macromolecular receptors as targets for inhibition or modulation. To this end, a number of clever approaches have evolved over time: computer-aided techniques including structure-activity relationships and synthesis, high-throughput screening, quantitative structure-activity relationships, hypotheses derived from ligand- and/or structure-based information and focused library approaches. In recent years, several alternative strategies have appeared in the form of the emerging paradigms of polypharmacology, systems biology and personalized medicine. These innovations point to key challenges and breakthroughs likely to affect the future of small-molecule drug discovery.
Darvesh, Altaf S.; Carroll, Richard T.; Geldenhuys, Werner J.; Gudelsky, Gary A.; Klein, Jochen; Meshul, Charles K.; Van der Schyf, Cornelis J.
Introduction Microdialysis is an important in vivo sampling technique, useful in the assay of extracellular tissue fluid. The technique has both pre-clinical and clinical applications but is most widely used in neuroscience. The in vivo microdialysis technique allows measurement of neurotransmitters such as acetycholine (ACh), the biogenic amines including dopamine (DA), norepinephrine (NE) and serotonin (5-HT), amino acids such as glutamate (Glu) and gamma aminobutyric acid (GABA), as well as the metabolites of the aforementioned neurotransmitters, and neuropeptides in neuronal extracellular fluid in discrete brain regions of laboratory animals such as rodents and non-human primates. Areas covered In this review we present a brief overview of the principles and procedures related to in vivo microdialysis and detail the use of this technique in the pre-clinical measurement of drugs designed to be used in the treatment of chemical addiction, neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and as well as psychiatric disorders such as attention-deficit/hyperactivity disorder (ADHD) and schizophrenia. This review offers insight into the tremendous utility and versatility of this technique in pursuing neuropharmacological investigations as well its significant potential in rational drug discovery. Expert opinion In vivo microdialysis is an extremely versatile technique, routinely used in the neuropharmacological investigation of drugs used for the treatment of neurological disorders. This technique has been a boon in the elucidation of the neurochemical profile and mechanism of action of several classes of drugs especially their effects on neurotransmitter systems. The exploitation and development of this technique for drug discovery in the near future will enable investigational new drug candidates to be rapidly moved into the clinical trial stages and to market thus providing new successful therapies for neurological diseases
Taglang, Guillaume; Jackson, David B
Oncology is undergoing a data-driven metamorphosis. Armed with new and ever more efficient molecular and information technologies, we have entered an era where data is helping us spearhead the fight against cancer. This technology driven data explosion, often referred to as "big data", is not only expediting biomedical discovery, but it is also rapidly transforming the practice of oncology into an information science. This evolution is critical, as results to-date have revealed the immense complexity and genetic heterogeneity of patients and their tumors, a sobering reminder of the challenge facing every patient and their oncologist. This can only be addressed through development of clinico-molecular data analytics that provide a deeper understanding of the mechanisms controlling the biological and clinical response to available therapeutic options. Beyond the exciting implications for improved patient care, such advancements in predictive and evidence-based analytics stand to profoundly affect the processes of cancer drug discovery and associated clinical trials.
Walker, Stephen M; Davies, Barry J
In addressing the challenges facing pharmaceutical R&D one question is frequently asked: how can continuous improvement (CI), delivered through a Lean Sigma approach, be applied in a research environment to deliver overall benefit? We show that taking a value chain approach to improvement projects in a discovery research organization, initially focusing on the drug discovery project delivery level (i.e. middle layer of the value chain), provides the foundation for an effective CI programme. The adaptation of Lean Sigma principles and methodology, combined with the tenacity and creativity of scientists, enabled the delivery of significant improvements in challenging areas, including target selection, project decision making and the compound design-make-test-analyse (DMTA) cycle. Copyright © 2011 Elsevier Ltd. All rights reserved.
Brian, William; Tremaine, Larry M; Arefayene, Million; de Kanter, Ruben; Evers, Raymond; Guo, Yingying; Kalabus, James; Lin, Wen; Loi, Cho-Ming; Xiao, Guangqing
Genetic variants of drug metabolism enzymes and transporters can result in high pharmacokinetic and pharmacodynamic variability, unwanted characteristics of efficacious and safe drugs. Ideally, the contributions of these enzymes and transporters to drug disposition can be predicted from in vitro experiments and in silico modeling in discovery or early development, and then be utilized during clinical development. Recently, regulatory agencies have provided guidance on the preclinical investigation of pharmacogenetics, for application to clinical drug development. This white paper summarizes the results of an industry survey conducted by the Industry Pharmacogenomics Working Group on current practice and challenges with using in vitro systems and in silico models to understand pharmacogenetic causes of variability in drug disposition.
Full Text Available For a series of 35 piperazino-phthalimide and piperazino-isoindolinone based urotensin-II receptor (UT antagonists, a thoroughly validated 3D pharmacophore model has been developed, consisting of four chemical features: one hydrogen bond acceptor lipid (HBA_L, one hydrophobe (HY, and two ring aromatic (RA. Multiple validation techniques like CatScramble, test set prediction, and mapping analysis of advanced known antagonists have been employed to check the predictive power and robustness of the developed model. The results demonstrate that the best model, Hypo 1, shows a correlation (r of 0.902, a root mean square deviation (RMSD of 0.886, and the cost difference of 39.69 bits. The model obtained is highly predictive with good correlation values for both internal (r2=0.707 as well as external (r2=0.614 test set compounds. Moreover, the pharmacophore model has been used as a 3D query for virtual screening which served to detect prospective new lead compounds which can be further optimized as UT antagonists with potential for treatment of cardiovascular diseases.
Bertucci, Carlo; Pistolozzi, Marco; De Simone, Angela
Chirality plays a fundamental role in determining the pharmacodynamic and pharmacokinetic properties of drugs, and contributes significantly to our understanding of the mechanisms that lie behind biorecognition phenomena. Circular dichroism spectroscopy is the technique of choice for determining the stereochemistry of chiral drugs and proteins, and for monitoring and characterizing molecular recognition phenomena in solution. The role of chirality in our understanding of recognition phenomena at the molecular level is discussed here via several selected systems of interest in the drug discovery and development area. The examples were selected in order to underline the utility of circular dichroism in emerging studies of protein-protein interactions in biological context. In particular, the following aspects are discussed here: the relationship between stereochemistry and pharmacological activity--stereochemical characterization of new leads and drugs; stereoselective binding of leads and drugs to target proteins--the binding of drugs to serum albumins; conformational transitions of peptides and proteins of physiological relevance, and the stereochemical characterization of therapeutic peptides.
Okada, Yukinori; Wu, Di; Trynka, Gosia; Raj, Towfique; Terao, Chikashi; Ikari, Katsunori; Kochi, Yuta; Ohmura, Koichiro; Suzuki, Akari; Yoshida, Shinji; Graham, Robert R.; Manoharan, Arun; Ortmann, Ward; Bhangale, Tushar; Denny, Joshua C.; Carroll, Robert J.; Eyler, Anne E.; Greenberg, Jeffrey D.; Kremer, Joel M.; Pappas, Dimitrios A.; Jiang, Lei; Yin, Jian; Ye, Lingying; Su, Ding-Feng; Yang, Jian; Xie, Gang; Keystone, Ed; Westra, Harm-Jan; Esko, Tõnu; Metspalu, Andres; Zhou, Xuezhong; Gupta, Namrata; Mirel, Daniel; Stahl, Eli A.; Diogo, Dorothée; Cui, Jing; Liao, Katherine; Guo, Michael H.; Myouzen, Keiko; Kawaguchi, Takahisa; Coenen, Marieke J.H.; van Riel, Piet L.C.M.; van de Laar, Mart A.F.J.; Guchelaar, Henk-Jan; Huizinga, Tom W.J.; Dieudé, Philippe; Mariette, Xavier; Bridges, S. Louis; Zhernakova, Alexandra; Toes, Rene E.M.; Tak, Paul P.; Miceli-Richard, Corinne; Bang, So-Young; Lee, Hye-Soon; Martin, Javier; Gonzalez-Gay, Miguel A.; Rodriguez-Rodriguez, Luis; Rantapää-Dahlqvist, Solbritt; Ärlestig, Lisbeth; Choi, Hyon K.; Kamatani, Yoichiro; Galan, Pilar; Lathrop, Mark; Eyre, Steve; Bowes, John; Barton, Anne; de Vries, Niek; Moreland, Larry W.; Criswell, Lindsey A.; Karlson, Elizabeth W.; Taniguchi, Atsuo; Yamada, Ryo; Kubo, Michiaki; Liu, Jun S.; Bae, Sang-Cheol; Worthington, Jane; Padyukov, Leonid; Klareskog, Lars; Gregersen, Peter K.; Raychaudhuri, Soumya; Stranger, Barbara E.; De Jager, Philip L.; Franke, Lude; Visscher, Peter M.; Brown, Matthew A.; Yamanaka, Hisashi; Mimori, Tsuneyo; Takahashi, Atsushi; Xu, Huji; Behrens, Timothy W.; Siminovitch, Katherine A.; Momohara, Shigeki; Matsuda, Fumihiko; Yamamoto, Kazuhiko; Plenge, Robert M.
A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological datasets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here, we performed a genome-wide association study (GWAS) meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single nucleotide polymorphisms (SNPs). We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 1012–4. We devised an in-silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci (cis-eQTL)6, and pathway analyses7–9 – as well as novel methods based on genetic overlap with human primary immunodeficiency (PID), hematological cancer somatic mutations and knock-out mouse phenotypes – to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery. PMID:24390342
He Qing-Yu; Chiu Jen-Fu
Proteomics is a research field aiming to characterize molecular and cellular dynamics in protein expression and function on a global level. The introduction of proteomics has been greatly broadening our view and accelerating our path in various medical researches. The most significant advantage of proteomics is its ability to examine a whole proteome or sub-proteome in a single experiment so that the protein alterations corresponding to a pathological or biochemical condition at a given time can be considered in an integrated way. Proteomic technology has been extensively used to tackle a wide variety of medical subjects including biomarker discovery and drug development. By complement with other new technique advance in genomics and bioinformatics,proteomics has a great potential to make considerable contribution to biomarker identification and revolutionize drug development process. A brief overview of the proteomic technologies will be provided and the application of proteomics in biomarker discovery and drug development will be discussed using our current research projects as examples.
Lushington, Gerald H; Dong, Yinghua; Theertham, Bhargav
The magnitude of the challenges in preclinical drug discovery is evident in the large amount of capital invested in such efforts in pursuit of a small static number of eventually successful marketable therapeutics. An explosion in the availability of potentially drug-like compounds and chemical biology data on these molecules can provide us with the means to improve the eventual success rates for compounds being considered at the preclinical level, but only if the community is able to access available information in an efficient and meaningful way. Thus, chemical database resources are critical to any serious drug discovery effort. This paper explores the basic principles underlying the development and implementation of chemical databases, and examines key issues of how molecular information may be encoded within these databases so as to enhance the likelihood that users will be able to extract meaningful information from data queries. In addition to a broad survey of conventional data representation and query strategies, key enabling technologies such as new context-sensitive chemical similarity measures and chemical cartridges are examined, with recommendations on how such resources may be integrated into a practical database environment.
Nguta, Joseph Mwanzia; Appiah-Opong, Regina; Nyarko, Alexander K; Yeboah-Manu, Dorothy; Addo, Phyllis G A
Currently, one third of the world's population is latently infected with Mycobacterium tuberculosis (MTB), while 8.9-9.9 million new and relapse cases of tuberculosis (TB) are reported yearly. The renewed research interests in natural products in the hope of discovering new and novel antitubercular leads have been driven partly by the increased incidence of multidrug-resistant strains of MTB and the adverse effects associated with the first- and second-line antitubercular drugs. Natural products have been, and will continue to be a rich source of new drugs against many diseases. The depth and breadth of therapeutic agents that have their origins in the secondary metabolites produced by living organisms cannot be compared with any other source of therapeutic agents. Discovery of new chemical molecules against active and latent TB from natural products requires an interdisciplinary approach, which is a major challenge facing scientists in this field. In order to overcome this challenge, cutting edge techniques in mycobacteriology and innovative natural product chemistry tools need to be developed and used in tandem. The present review provides a cross-linkage to the most recent literature in both fields and their potential to impact the early phase of drug discovery against TB if seamlessly combined.
Maiese, Kenneth; Chong, Zhao Zhong; Shang, Yan Chen; Hou, Jinling
Globally, developed nations spend a significant amount of their resources on health care initiatives that poorly translate into increased population life expectancy. As an example, the United States devotes 16% of its gross domestic product to health care, the highest level in the world, but falls behind other nations that enjoy greater individual life expectancy. These observations point to the need for pioneering avenues of drug discovery to increase life span with controlled costs. In particular, innovative drug development for metabolic disorders such as diabetes mellitus becomes increasingly critical given that the number of diabetic people will increase exponentially over the next 20 years. This article discusses the elucidation and targeting of novel cellular pathways that are intimately tied to oxidative stress in diabetes mellitus for new treatment strategies. Pathways that involve wingless, β-nicotinamide adenine dinucleotide (NAD(+)) precursors, and cytokines govern complex biological pathways that determine both cell survival and longevity during diabetes mellitus and its complications. Furthermore, the role of these entities as biomarkers for disease can further enhance their utility irrespective of their treatment potential. Greater understanding of the intricacies of these unique cellular mechanisms will shape future drug discovery for diabetes mellitus to provide focused clinical care with limited or absent long-term complications.
Smith, Thomas J.
Green tea is made from unfermented dried leaves from Camellia sinensis and has been consumed by humans for thousands of years. For nearly as long, it has been used as a folk remedy for a wide array of diseases. More recently, a large number of in-vitro and in-vivo scientific studies have supported this ancient contention that the polyphenols from green tea can provide a number of health benefits. Since these compounds are clearly safe for human consumption and ubiquitous in the food supply, they are highly attractive as lead compounds for drug discovery programs. However, as drugs, they are far from optimum. They are relatively unstable, poorly absorbed, and readily undergo a number of metabolic transformations by intestinal microbiota and human enzymes. Further, since these compounds target a wide array of biological systems, in-vivo testing is rather difficult since effects on alternative pathways need to be carefully eliminated. The purpose of this review is to discuss some of the challenges and benefits of pursuing this family of compounds for drug discovery. PMID:21731575
Schreyer, Adrian; Blundell, Tom
Harnessing data from the growing number of protein-ligand complexes in the Protein Data Bank is an important task in drug discovery. In order to benefit from the abundance of three-dimensional structures, structural data must be integrated with sequence as well as chemical data and the protein-small molecule interactions characterized structurally at the inter-atomic level. In this study, we present CREDO, a new publicly available database of protein-ligand interactions, which represents contacts as structural interaction fingerprints, implements novel features and is completely scriptable through its application programming interface. Features of CREDO include implementation of molecular shape descriptors with ultrafast shape recognition, fragmentation of ligands in the Protein Data Bank, sequence-to-structure mapping and the identification of approved drugs. Selected analyses of these key features are presented to highlight a range of potential applications of CREDO. The CREDO dataset has been released into the public domain together with the application programming interface under a Creative Commons license at http://www-cryst.bioc.cam.ac.uk/credo. We believe that the free availability and numerous features of CREDO database will be useful not only for commercial but also for academia-driven drug discovery programmes.
Schreyer, Adrian M; Blundell, Tom L
CREDO is a unique relational database storing all pairwise atomic interactions of inter- as well as intra-molecular contacts between small molecules and macromolecules found in experimentally determined structures from the Protein Data Bank. These interactions are integrated with further chemical and biological data. The database implements useful data structures and algorithms such as cheminformatics routines to create a comprehensive analysis platform for drug discovery. The database can be accessed through a web-based interface, downloads of data sets and web services at http://www-cryst.bioc.cam.ac.uk/credo. Database URL: http://www-cryst.bioc.cam.ac.uk/credo.
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).
Full Text Available RNA editing is vital to provide the RNA and protein complexity to regulate the gene expression. Correct RNA editing maintains the cell function and organism development. Imbalance of the RNA editing machinery may lead to diseases and cancers. Recently, RNA editing has been recognized as a target for drug discovery although few studies targeting RNA editing for disease and cancer therapy were reported in the field of natural products. Therefore, RNA editing may be a potential target for therapeutic natural products. In this review, we provide a literature overview of the biological functions of RNA editing on gene expression, diseases, cancers, and drugs. The bioinformatics resources of RNA editing were also summarized.
Zdrazil, Barbara; Chichester, Christine; Zander Balderud, Linda; Engkvist, Ola; Gaulton, Anna; Overington, John P
Transport proteins represent an eminent class of drug targets and ADMET (absorption, distribution, metabolism, excretion, toxicity) associated genes. There exists a large number of distinct activity assays for transport proteins, depending on not only the measurement needed (e.g. transport activity, strength of ligand–protein interaction), but also due to heterogeneous assay setups used by different research groups. Efforts to systematically organize this (divergent) bioassay data have large potential impact in Public-Private partnership and conventional commercial drug discovery. In this short review, we highlight some of the frequently used high-throughput assays for transport proteins, and we discuss emerging assay ontologies and their application to this field. Focusing on human P-glycoprotein (Multidrug resistance protein 1; gene name: ABCB1, MDR1), we exemplify how annotation of bioassay data per target class could improve and add to existing ontologies, and we propose to include an additional layer of metadata supporting data fusion across different bioassays.
Gao, Lixia; Teng, Yong
Electrochemistry has emerged as a powerful analytical technique for chemical analysis of living cells, biologically active molecules and metabolites. Electrochemical biosensor, microfluidics and mass spectrometry are the most frequently used methods for electrochemical detection and monitory, which comprise a collection of extremely useful measurement tools for various fields of biology and medicine. Most recently, electrochemistry has been shown to be coupled with nanotechnology and genetic engineering to generate new enabling technologies, providing rapid, selective, and sensitive detection and diagnosis platforms. The primary focus of this review is to highlight the utility of electrochemical strategies and their conjunction with other approaches for drug metabolism and discovery. Current challenges and possible future developments and applications of electrochemistry in drug studies are also discussed.
Willumsen, Niels J; Bech, Morten; Olesen, Søren-Peter
characterization of ion channel properties. However, patch clamp is a slow, labor-intensive, and thus expensive, technique. New techniques combining the reliability and high information content of patch clamping with the virtues of high throughput philosophy are emerging and predicted to make a number of ion....... The introduction of new powerful HTS electrophysiological techniques is predicted to cause a revolution in ion channel drug discovery.......Proper function of ion channels is crucial for all living cells. Ion channel dysfunction may lead to a number of diseases, so-called channelopathies, and a number of common diseases, including epilepsy, arrhythmia, and type II diabetes, are primarily treated by drugs that modulate ion channels...
Carlino, Luca; Rastelli, Giulio
Protein kinases play crucial roles in several cell transformation processes and are validated drug targets for many human diseases, including cancer. Nevertheless, most tumors have eluded the effects of inhibition of a single kinase by activating resistance mechanisms and/or alternative pathways and escape mechanisms. In recent years, multitarget approaches directed toward inhibition of kinases and targets of different families have received increasing attention. In particular, co-targeting kinases and bromodomain epigenetic reader proteins has rapidly emerged as a promising approach to cancer drug development. In this manuscript, we will review the recent discoveries that led to the identification and optimization of dual kinase/bromodomain inhibitors. We will analyze and compare the structural features required for dual inhibition and comment on the potential of this approach in anticancer drug discovery. Moreover, we will introduce computational approaches useful for the identification of dual kinase/bromodomain inhibitors and generate ad hoc pharmacophore and docking models.
Perryman, Alexander L.; Horta Andrade, Carolina
The Zika virus outbreak in the Americas has caused global concern. To help accelerate this fight against Zika, we launched the OpenZika project. OpenZika is an IBM World Community Grid Project that uses distributed computing on millions of computers and Android devices to run docking experiments, in order to dock tens of millions of drug-like compounds against crystal structures and homology models of Zika proteins (and other related flavivirus targets). This will enable the identification of new candidates that can then be tested in vitro, to advance the discovery and development of new antiviral drugs against the Zika virus. The docking data is being made openly accessible so that all members of the global research community can use it to further advance drug discovery studies against Zika and other related flaviviruses. PMID:27764115
Matter, Alex; Keller, Thomas H
Non-profit organizations (NPO) play an increasingly important role in drug discovery and development for diseases that are neglected by the pharmaceutical industry because of low or absent commercial incentives. Governments and major private foundations such as the Wellcome Trust and the Bill & Melinda Gates Foundation increasingly step in to provide strategic direction, communication platforms and major resources, motivated by the fact that major healthcare problems remain unsolved. Drug discovery in the field of neglected diseases is fraught with complexities since, in many cases, important tools are lacking including readily available diagnostics, molecular epidemiology, appropriate model systems, representative strain collections, biomarkers, up-to-date trial methodologies and regulatory strategies. On top of this, the high hurdles addressing novel drug targets must be cleared.
Full Text Available Huihua Sun,1,* Zihe Liu,1,* Huimin Zhao,1,2 Ee Lui Ang1 1Metabolic Engineering Research Laboratory, Institute of Chemical and Engineering Sciences, Agency for Science, Technology and Research, Singapore; 2Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA *These authors contributed equally to this work Abstract: Because of extraordinary structural diversity and broad biological activities, natural products have played a significant role in drug discovery. These therapeutically important secondary metabolites are assembled and modified by dedicated biosynthetic pathways in their host living organisms. Traditionally, chemists have attempted to synthesize natural product analogs that are important sources of new drugs. However, the extraordinary structural complexity of natural products sometimes makes it challenging for traditional chemical synthesis, which usually involves multiple steps, harsh conditions, toxic organic solvents, and byproduct wastes. In contrast, combinatorial biosynthesis exploits substrate promiscuity and employs engineered enzymes and pathways to produce novel “unnatural” natural products, substantially expanding the structural diversity of natural products with potential pharmaceutical value. Thus, combinatorial biosynthesis provides an environmentally friendly way to produce natural product analogs. Efficient expression of the combinatorial biosynthetic pathway in genetically tractable heterologous hosts can increase the titer of the compound, eventually resulting in less expensive drugs. In this review, we will discuss three major strategies for combinatorial biosynthesis: 1 precursor-directed biosynthesis; 2 enzyme-level modification, which includes swapping of the entire domains, modules and subunits, site-specific mutagenesis, and directed evolution; 3 pathway-level recombination. Recent examples of combinatorial biosynthesis employing these
Leung, Elaine L; Cao, Zhi-Wei; Jiang, Zhi-Hong; Zhou, Hua; Liu, Liang
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.
Iorio, Francesco; Bosotti, Roberta; Scacheri, Emanuela; Belcastro, Vincenzo; Mithbaokar, Pratibha; Ferriero, Rosa; Murino, Loredana; Tagliaferri, Roberto; Brunetti-Pierri, Nicola; Isacchi, Antonella; di Bernardo, Diego
A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmac...
Wright, Peter M; Seiple, Ian B; Myers, Andrew G
The discovery and implementation of antibiotics in the early twentieth century transformed human health and wellbeing. Chemical synthesis enabled the development of the first antibacterial substances, organoarsenicals and sulfa drugs, but these were soon outshone by a host of more powerful and vastly more complex antibiotics from nature: penicillin, streptomycin, tetracycline, and erythromycin, among others. These primary defences are now significantly less effective as an unavoidable consequence of rapid evolution of resistance within pathogenic bacteria, made worse by widespread misuse of antibiotics. For decades medicinal chemists replenished the arsenal of antibiotics by semisynthetic and to a lesser degree fully synthetic routes, but economic factors have led to a subsidence of this effort, which places society on the precipice of a disaster. We believe that the strategic application of modern chemical synthesis to antibacterial drug discovery must play a critical role if a crisis of global proportions is to be averted. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Wuitschik, Georg; Carreira, Erick M; Wagner, Björn; Fischer, Holger; Parrilla, Isabelle; Schuler, Franz; Rogers-Evans, Mark; Müller, Klaus
An oxetane can trigger profound changes in aqueous solubility, lipophilicity, metabolic stability, and conformational preference when replacing commonly employed functionalities such as gem-dimethyl or carbonyl groups. The magnitude of these changes depends on the structural context. Thus, by substitution of a gem-dimethyl group with an oxetane, aqueous solubility may increase by a factor of 4 to more than 4000 while reducing the rate of metabolic degradation in most cases. The incorporation of an oxetane into an aliphatic chain can cause conformational changes favoring synclinal rather than antiplanar arrangements of the chain. Additionally spirocyclic oxetanes (e.g., 2-oxa-6-aza-spiro[3.3]heptane) bear remarkable analogies to commonly used fragments in drug discovery, such as morpholine, and are even able to supplant the latter in its solubilizing ability. A rich chemistry of oxetan-3-one and derived Michael acceptors provide venues for the preparation of a broad variety of novel oxetanes not previously documented, thus providing the foundation for their broad use in chemistry and drug discovery.
Pastor, Manuel; Benedetti, Paolo; Carotti, Angelo; Carrieri, Antonio; Díaz, Carlos; Herráiz, Cristina; Höltje, Hans-Dieter; Loza, M. Isabel; Oprea, Tudor; Padín, Fernando; Pubill, Francesc; Sanz, Ferran; Stoll, Friederike; the LINK3D Consortium
The work describes the development of novel software supporting synchronous distant collaboration between scientists involved in drug discovery and development projects. The program allows to visualize and share data as well as to interact in real time using standard intranets and Internet resources. Direct visualization of 2D and 3D molecular structures is supported and original tools for facilitating remote discussion have been integrated. The software is multiplatform (MS-Windows, SGI-IRIX, Linux), allowing for a seamless integration of heterogeneous working environments. The project aims to support collaboration both within and between academic and industrial institutions. Since confidentiality is very important in some scenarios, special attention has been paid to security aspects. The article presents the research carried out to gather the requirements of collaborative software in the field of drug discovery and development and describes the features of the first fully functional prototype obtained. Real-world testing activities carried out on this prototype in order to guarantee its adequacy in diverse environments are also described and discussed.
Full Text Available In the past decade, it was observed that the relationship between the emerging New Molecular Entities and the quantum of R&D investment has not been favorable. There might be numerous reasons but few studies stress the introduction of target based drug discovery approach as one of the factors. Although a number of drugs have been developed with an emphasis on a single protein target, yet identification of valid target is complex. The approach focuses on an in vitro single target, which overlooks the complexity of cell and makes process of validation drug targets uncertain. Thus, it is imperative to search for alternatives rather than looking at success stories of target-based drug discovery. It would be beneficial if the drugs were developed to target multiple components. New approaches like reverse engineering and translational research need to take into account both system and target-based approach. This review evaluates the strengths and limitations of known drug discovery approaches and proposes alternative approaches for increasing efficiency against treatment.
Luo, Fang; Gu, Jiangyong; Chen, Lirong; Xu, Xiaojie
Cancer is a complex disease, known medically as malignant neoplasm. Natural products (NPs) play a very important role in anticancer drug discovery and a large number of NPs have been proven to have potential anticancer effects. Compared with newly synthesized chemical compounds, NPs show a favorable profile in terms of their absorption and metabolism in the body with low toxicity. Searching for multi-target natural drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 104 cancer-associated target proteins from the Protein Data Bank. Based on the Universal Natural Products Database, all of the NPs were docked to 104 cancer-associated target proteins. Then we explored the potential of NPs and several herbs in anticancer drug discovery by using a network-based multi-target computational approach. The NPs with the most potential for anticancer drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between NPs and cancer target proteins to find the pathological networks, potential drug candidates and new indications.
Tom L. Blundell
Full Text Available The development of structure-guided drug discovery is a story of knowledge exchange where new ideas originate from all parts of the research ecosystem. Dorothy Crowfoot Hodgkin obtained insulin from Boots Pure Drug Company in the 1930s and insulin crystallization was optimized in the company Novo in the 1950s, allowing the structure to be determined at Oxford University. The structure of renin was developed in academia, on this occasion in London, in response to a need to develop antihypertensives in pharma. The idea of a dimeric aspartic protease came from an international academic team and was discovered in HIV; it eventually led to new HIV antivirals being developed in industry. Structure-guided fragment-based discovery was developed in large pharma and biotechs, but has been exploited in academia for the development of new inhibitors targeting protein–protein interactions and also antimicrobials to combat mycobacterial infections such as tuberculosis. These observations provide a strong argument against the so-called `linear model', where ideas flow only in one direction from academic institutions to industry. Structure-guided drug discovery is a story of applications of protein crystallography and knowledge exhange between academia and industry that has led to new drug approvals for cancer and other common medical conditions by the Food and Drug Administration in the USA, as well as hope for the treatment of rare genetic diseases and infectious diseases that are a particular challenge in the developing world.
Tan, M H Eileen; Li, Jun; Xu, H Eric; Melcher, Karsten; Yong, Eu-leong
Androgens and androgen receptors (AR) play a pivotal role in expression of the male phenotype. Several diseases, such as androgen insensitivity syndrome (AIS) and prostate cancer, are associated with alterations in AR functions. Indeed, androgen blockade by drugs that prevent the production of androgens and/or block the action of the AR inhibits prostate cancer growth. However, resistance to these drugs often occurs after 2-3 years as the patients develop castration-resistant prostate cancer (CRPC). In CRPC, a functional AR remains a key regulator. Early studies focused on the functional domains of the AR and its crucial role in the pathology. The elucidation of the structures of the AR DNA binding domain (DBD) and ligand binding domain (LBD) provides a new framework for understanding the functions of this receptor and leads to the development of rational drug design for the treatment of prostate cancer. An overview of androgen receptor structure and activity, its actions in prostate cancer, and how structural information and high-throughput screening have been or can be used for drug discovery are provided herein.
Raffa, Robert B.; Raffa, Kenneth F.
Introduction There is a pervasive and growing concern about the small number of new pharmaceutical agents. There are many proposed explanations for this trend that do not involve the drug-discovery process per se, but the discovery process itself has also come under scrutiny. If the current paradigms are indeed not working, where are novel ideas to come from? Perhaps it is time to look to novel sources. Areas covered The receptor-signaling and 2nd-messenger transduction processes present in insects are quite similar to those in mammals (involving G proteins, ion channels, etc.). However, a review of these systems reveals an unprecedented degree of high potency and receptor selectivity to an extent greater than that modeled in most current drug-discovery approaches. Expert opinion A better understanding of insect receptor pharmacology could stimulate novel theoretical and practical ideas in mammalian pharmacology (drug discovery) and, conversely, the application of pharmacology and medicinal chemistry principles could stimulate novel advances in entomology (safer and more targeted control of pest species). PMID:21984882
Full Text Available BACKGROUND: Open source drug discovery offers potential for developing new and inexpensive drugs to combat diseases that disproportionally affect the poor. The concept borrows two principle aspects from open source computing (i.e., collaboration and open access and applies them to pharmaceutical innovation. By opening a project to external contributors, its research capacity may increase significantly. To date there are only a handful of open source R&D projects focusing on neglected diseases. We wanted to learn from these first movers, their successes and failures, in order to generate a better understanding of how a much-discussed theoretical concept works in practice and may be implemented. METHODOLOGY/PRINCIPAL FINDINGS: A descriptive case study was performed, evaluating two specific R&D projects focused on neglected diseases. CSIR Team India Consortium's Open Source Drug Discovery project (CSIR OSDD and The Synaptic Leap's Schistosomiasis project (TSLS. Data were gathered from four sources: interviews of participating members (n = 14, a survey of potential members (n = 61, an analysis of the websites and a literature review. Both cases have made significant achievements; however, they have done so in very different ways. CSIR OSDD encourages international collaboration, but its process facilitates contributions from mostly Indian researchers and students. Its processes are formal with each task being reviewed by a mentor (almost always offline before a result is made public. TSLS, on the other hand, has attracted contributors internationally, albeit significantly fewer than CSIR OSDD. Both have obtained funding used to pay for access to facilities, physical resources and, at times, labor costs. TSLS releases its results into the public domain, whereas CSIR OSDD asserts ownership over its results. CONCLUSIONS/SIGNIFICANCE: Technically TSLS is an open source project, whereas CSIR OSDD is a crowdsourced project. However, both have enabled high
Årdal, Christine; Røttingen, John-Arne
Open source drug discovery offers potential for developing new and inexpensive drugs to combat diseases that disproportionally affect the poor. The concept borrows two principle aspects from open source computing (i.e., collaboration and open access) and applies them to pharmaceutical innovation. By opening a project to external contributors, its research capacity may increase significantly. To date there are only a handful of open source R&D projects focusing on neglected diseases. We wanted to learn from these first movers, their successes and failures, in order to generate a better understanding of how a much-discussed theoretical concept works in practice and may be implemented. A descriptive case study was performed, evaluating two specific R&D projects focused on neglected diseases. CSIR Team India Consortium's Open Source Drug Discovery project (CSIR OSDD) and The Synaptic Leap's Schistosomiasis project (TSLS). Data were gathered from four sources: interviews of participating members (n = 14), a survey of potential members (n = 61), an analysis of the websites and a literature review. Both cases have made significant achievements; however, they have done so in very different ways. CSIR OSDD encourages international collaboration, but its process facilitates contributions from mostly Indian researchers and students. Its processes are formal with each task being reviewed by a mentor (almost always offline) before a result is made public. TSLS, on the other hand, has attracted contributors internationally, albeit significantly fewer than CSIR OSDD. Both have obtained funding used to pay for access to facilities, physical resources and, at times, labor costs. TSLS releases its results into the public domain, whereas CSIR OSDD asserts ownership over its results. Technically TSLS is an open source project, whereas CSIR OSDD is a crowdsourced project. However, both have enabled high quality research at low cost. The critical success factors appear to be clearly
Mohammed M. Eissa
Full Text Available Medical domain has become one of the most important areas of research in order to richness huge amounts of medical information about the symptoms of diseases and how to distinguish between them to diagnose it correctly. Knowledge discovery models play vital role in refinement and mining of medical indicators to help medical experts to settle treatment decisions. This paper introduces four hybrid Rough – Granular Computing knowledge discovery models based on Rough Sets Theory, Artificial Neural Networks, Genetic Algorithm and Rough Mereology Theory. A comparative analysis of various knowledge discovery models that use different knowledge discovery techniques for data pre-processing, reduction, and data mining supports medical experts to extract the main medical indicators, to reduce the misdiagnosis rates and to improve decision-making for medical diagnosis and treatment. The proposed models utilized two medical datasets: Coronary Heart Disease dataset and Hepatitis C Virus dataset. The main purpose of this paper was to explore and evaluate the proposed models based on Granular Computing methodology for knowledge extraction according to different evaluation criteria for classification of medical datasets. Another purpose is to make enhancement in the frame of KDD processes for supervised learning using Granular Computing methodology.
Kang, Lifeng; Chung, Bong Geun; Langer, Robert; Khademhosseini, Ali
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.
Full Text Available The discovery of new uses of existing drugs offers the possibility to reduce time and risk because the approved drugs have passed several phases along the drug development pipeline. In this study, we present a computational method for novel drug use prediction based on the idea that similar drugs are indicated for similar diseases. When computing drug pairwise similarities, we considered both chemical structure and drug target similarities. In validation, our new drug use predictions were found to be significantly enriched in both the biomedical literature and clinical trials. These results indicate that our method is able to successfully integrate both biomedical scientific data and literature for drug discovery.
One of the foremost challenges of drug discovery in any therapeutic area is that of solidifying the correlation between in vitro activity and clinical efficacy. Between these is the confirmation that affecting a particular target in vivo will lead to a therapeutic benefit. In antibacterial drug discovery, there is a key advantage from the start, since the targets are bacteria-therefore, it is simple to ascertain in vitro whether a drug has the desired effect, i.e., bacterial cell inhibition or killing, and to understand the mechanism by which that occurs. The downstream criteria, whether a compound reaches the infection site and achieves appropriately high levels to affect bacterial viability, can be evaluated in animal models of infection. In this way animal models of infection can be a highly valuable and predictive bridge between in vitro drug discovery and early clinical evaluation.The Gram-positive pathogen Staphylococcus aureus causes a wide variety of infections in humans (Archer, Clin Infect Dis 26:1179-1181, 1998) and has been said to be able to infect every tissue type. Fortunately, over the years a great deal of effort has been expended toward developing infection models in rodents using this organism, with good success. This chapter will describe the advantages, methods, and outcome measurements of the rodent models most used in drug discovery for S. aureus. Mouse models will be the focus of this chapter, as they are the most economical and thus most commonly used, but a rat infection model is included as well.
Winchester, Catherine L; Pratt, Judith A; Morris, Brian J
Despite intensive research over many years, the treatment of schizophrenia remains a major health issue. Current and emerging treatments for schizophrenia are based upon the classical dopamine and glutamate hypotheses of disease. Existing first and second generation antipsychotic drugs based upon the dopamine hypothesis are limited by their inability to treat all symptom domains and their undesirable side effect profiles. Third generation drugs based upon the glutamate hypothesis of disease are currently under evaluation but are more likely to be used as add on treatments. Hence there is a large unmet clinical need. A major challenge in neuropsychiatric disease research is the relatively limited knowledge of disease mechanisms. However, as our understanding of the genetic causes of the disease evolves, novel strategies for the development of improved therapeutic agents will become apparent. In this review we consider the current status of knowledge of the genetic basis of schizophrenia, including methods for identifying genetic variants associated with the disorder and how they impact on gene function. Although the genetic architecture of schizophrenia is complex, some targets amenable to pharmacological intervention can be discerned. We conclude that many challenges lie ahead but the stratification of patients according to biobehavioural constructs that cross existing disease classifications but with common genetic and neurobiological bases, offer opportunities for new approaches to effective drug discovery.
Wager, Travis T; Hou, Xinjun; Verhoest, Patrick R; Villalobos, Anabella
Significant progress has been made in prospectively designing molecules using the central nervous system multiparameter optimization (CNS MPO) desirability tool, as evidenced by the analysis reported herein of a second wave of drug candidates that originated after the development and implementation of this tool. This simple-to-use design algorithm has expanded design space for CNS candidates and has further demonstrated the advantages of utilizing a flexible, multiparameter approach in drug discovery rather than individual parameters and hard cutoffs of physicochemical properties. The CNS MPO tool has helped to increase the percentage of compounds nominated for clinical development that exhibit alignment of ADME attributes, cross the blood-brain barrier, and reside in lower-risk safety space (low ClogP and high TPSA). The use of this tool has played a role in reducing the number of compounds submitted to exploratory toxicity studies and increasing the survival of our drug candidates through regulatory toxicology into First in Human studies. Overall, the CNS MPO algorithm has helped to improve the prioritization of design ideas and the quality of the compounds nominated for clinical development.
The Human Genome Project was completed in 2003. A catalog of common genetic variants in humans was built at the International HapMap Project. These variants, known as single nucleotide polymorphisms (SNPs), occur in human DNA and distributed among populations in different parts of the world. By using the Linkage Disequilibrium and mapping blocks are able to define quantitative characters of inherited diseases. Currently 50 K-5.0 M microarray are available commercially, which based on the results of following the ENCODE & 1000 genome projects. Therefore the genome wide association study (GWAS) has become a key tool for discovering variants that contribute to human diseases and provide maximum coverage of the genome, in contrast to the traditional approach in which only a few candidates genes was targeted. The available public GWAS databases provided valuable biological insights and new discovery for many common diseases, due to the availability of low cost microarray. The GWAS has the potential to provide a solution for the lack of new drug targets and reducing drug failure due to adverse drug reactions either. These are critical issues for pharmaceutical companies. Here, the Japan PGx Data Science Consortium (JPDSC), which was established on February 20, 2009 by six leading pharmaceutical companies in Japan, was introduced. We believe that the efforts of stakeholders including the regulatory authorities, health providers, and pharmaceutical companies to understand the potential and ethical risk of using genetic information including GWAS will bring benefits to patients in the future.
Buller, Fabian; Mannocci, Luca; Scheuermann, Jörg; Neri, Dario
DNA-encoded chemical libraries represent a novel avenue for the facile discovery of small molecule ligands against target proteins of biological or pharmaceutical importance. Library members consist of small molecules covalently attached to unique DNA fragments that serve as amplifiable identification barcodes. This encoding allows the in vitro selection of ligands at subpicomolar concentrations from large library populations by affinity capture on a target protein of interest, in analogy to established technologies for the selection of binding polypeptides (e.g., antibodies). Different library formats have been explored by various groups, allowing the construction of chemical libraries comprising up to millions of DNA-encoded compounds. Libraries before and after selection have been characterized by PCR amplification of the DNA codes and subsequent relative quantification of library members using high-throughput sequencing. The most enriched compounds have then been further analyzed in biological assays, in the presence or in the absence of linked DNA. This article reviews experimental strategies used for the construction of DNA-encoded chemical libraries, revealing how selection, decoding, and hit validation technologies have been used for drug discovery programs.
Xin LI; Huai-long XU; Yong-xi LIU; Na AN; Si ZHAO; Jin-ku BAO
Autophagy,an evolutionarily conserved catabolic process involving the engulfment and degradation of non-essential or abnormal cellular organelles and proteins,is crucial for homeostatic maintenance in living cells.This highly regulated,multi-step process has been implicated in diverse diseases including cancer.Autophagy can function as either a promoter or a suppressor of cancer,which makes it a promising and challenging therapeutic target.Herein,we overview the regulatory mechanisms and dual roles of autophagy in cancer.We also describe some of the representative agents that exert their anticancer effects by regulating autophagy.Additionally,some emerging strategies aimed at modulating autophagy are discussed as having the potential for future anticancer drug discovery.In summary,these findings will provide valuable information to better utilize autophagy in the future development of anticancer therapeutics that meet clinical requirements.
Long, Daniel D; Aggen, James B; Christensen, Burton G; Judice, J Kevin; Hegde, Sharath S; Kaniga, Koné; Krause, Kevin M; Linsell, Martin S; Moran, Edmund J; Pace, John L
The design, synthesis and antibacterial activity of novel glycopeptide/beta-lactam heterodimers is reported. Employing a multivalent approach to drug discovery, vancomycin and cephalosporin synthons, A and B respectively, were chemically linked to yield heterodimer antibiotics. These novel compounds were designed to inhibit Gram-positive bacterial cell wall biosynthesis by simultaneously targeting the principal cellular targets of both glycopeptides and beta-lactams. The antibiotics 8a-f displayed remarkable potency against a wide range of Gram-positive organisms including methicillin-resistant Staphylococcus aureus (MRSA). Compound 8e demonstrated excellent bactericidal activity against MRSA (ATCC 33591) and initial evidence supports a multivalent mechanism of action for this important new class of antibiotic.
Dressler, Oliver J; Maceiczyk, Richard M; Chang, Soo-Ik; deMello, Andrew J
Over the past two decades, the application of microengineered systems in the chemical and biological sciences has transformed the way in which high-throughput experimentation is performed. The ability to fabricate complex microfluidic architectures has allowed scientists to create new experimental formats for processing ultra-small analytical volumes in short periods and with high efficiency. The development of such microfluidic systems has been driven by a range of fundamental features that accompany miniaturization. These include the ability to handle small sample volumes, ultra-low fabrication costs, reduced analysis times, enhanced operational flexibility, facile automation, and the ability to integrate functional components within complex analytical schemes. Herein we discuss the impact of microfluidics in the area of high-throughput screening and drug discovery and highlight some of the most pertinent studies in the recent literature.
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.
Cruz-Monteagudo, Maykel; Medina-Franco, José L; Pérez-Castillo, Yunierkis; Nicolotti, Orazio; Cordeiro, M Natália D S; Borges, Fernanda
The impact activity cliffs have on drug discovery is double-edged. For instance, whereas medicinal chemists can take advantage of regions in chemical space rich in activity cliffs, QSAR practitioners need to escape from such regions. The influence of activity cliffs in medicinal chemistry applications is extensively documented. However, the 'dark side' of activity cliffs (i.e. their detrimental effect on the development of predictive machine learning algorithms) has been understudied. Similarly, limited amounts of work have been devoted to propose potential solutions to the drawbacks of activity cliffs in similarity-based approaches. In this review, the duality of activity cliffs in medicinal chemistry and computational approaches is addressed, with emphasis on the rationale and potential solutions for handling the 'ugly face' of activity cliffs. Copyright © 2014 Elsevier Ltd. All rights reserved.
Geschwindner, Stefan; Ulander, Johan; Johansson, Patrik
The use of ligand binding thermodynamics has been proposed as a potential success factor to accelerate drug discovery. However, despite the intuitive appeal of optimizing binding enthalpy, a number of factors complicate routine use of thermodynamic data. On a macroscopic level, a range of experimental parameters including temperature and buffer choice significantly influence the observed thermodynamic signatures. On a microscopic level, solute effects, structural flexibility, and cooperativity lead to nonlinear changes in enthalpy. This multifactorial character hides essential enthalpy contributions of intermolecular contacts, making them experimentally nonobservable. In this perspective, we present three case studies, reflect on some key factors affecting thermodynamic signatures, and investigate their relation to the hydrophobic effect, enthalpy-entropy compensation, lipophilic ligand efficiency, and promiscuity. The studies highlight that enthalpy and entropy cannot be used as direct end points but can together with calculations increase our understanding of ligand binding and identify interesting outliers that do not behave as expected.
Full Text Available The advent of native mass spectrometry (MS in 1990 led to the development of new mass spectrometry instrumentation and methodologies for the analysis of noncovalent protein–ligand complexes. Native MS has matured to become a fast, simple, highly sensitive and automatable technique with well-established utility for fragment-based drug discovery (FBDD. Native MS has the capability to directly detect weak ligand binding to proteins, to determine stoichiometry, relative or absolute binding affinities and specificities. Native MS can be used to delineate ligand-binding sites, to elucidate mechanisms of cooperativity and to study the thermodynamics of binding. This review highlights key attributes of native MS for FBDD campaigns.
Terricabras, Emma; Benjamim, Claudia; Godessart, Nuria
The blockade of leukocyte migration has been demonstrated to be a valid option for the treatment of several autoimmune diseases. Chemokines play an active role in regulating cell infiltration into inflammatory sites and disrupting chemokine-receptor interactions has emerged as an alternative therapeutic approach. Pharmaceutical companies have developed an intense activity in the drug discovery of chemokine receptor antagonists in the last 10 years. Potent and selective compounds have been obtained and some of them are currently being evaluated in the clinic. The success of these trials will demonstrate whether the blockade of a single receptor is of therapeutic benefit. Alternative approaches, such as pan-receptor antagonists or inhibitors of the signalling pathways evoked by chemokines, are also being explored. In the meantime, new relationships between chemokines and receptors will be revealed, increasing our knowledge of such a fascinating field.
Full Text Available Treatment of tuberculosis, like other infectious diseases, is increasingly hindered by the emergence of drug resistance. Drug discovery efforts would be facilitated by facile screening tools that incorporate the complexities of human disease. Mycobacterium marinum-infected zebrafish larvae recapitulate key aspects of tuberculosis pathogenesis and drug treatment. Here, we develop a model for rapid in vivo drug screening using fluorescence-based methods for serial quantitative assessment of drug efficacy and toxicity. We provide proof-of-concept that both traditional bacterial-targeting antitubercular drugs and newly identified host-targeting drugs would be discovered through the use of this model. We demonstrate the model’s utility for the identification of synergistic combinations of antibacterial drugs and demonstrate synergy between bacterial- and host-targeting compounds. Thus, the platform can be used to identify new antibacterial agents and entirely new classes of drugs that thwart infection by targeting host pathways. The methods developed here should be widely applicable to small-molecule screens for other infectious and noninfectious diseases.
Li, Jiao; Lu, Zhiyong
The discovery of new uses of existing drugs offers the possibility to reduce time and risk because the approved drugs have passed several phases along the drug development pipeline. In this study, we present a computational method for novel drug use prediction based on the idea that similar drugs are indicated for similar diseases. When computing drug pairwise similarities, we considered both chemical structure and drug target similarities. In validation, our new drug use predictions were fou...
Bellis, Louisa J; Akhtar, Ruth; Al-Lazikani, Bissan; Atkinson, Francis; Bento, A Patricia; Chambers, Jon; Davies, Mark; Gaulton, Anna; Hersey, Anne; Ikeda, Kazuyoshi; Krüger, Felix A; Light, Yvonne; McGlinchey, Shaun; Santos, Rita; Stauch, Benjamin; Overington, John P
The challenge of translating the huge amount of genomic and biochemical data into new drugs is a costly and challenging task. Historically, there has been comparatively little focus on linking the biochemical and chemical worlds. To address this need, we have developed ChEMBL, an online resource of small-molecule SAR (structure-activity relationship) data, which can be used to support chemical biology, lead discovery and target selection in drug discovery. The database contains the abstracted structures, properties and biological activities for over 700000 distinct compounds and in excess of more than 3 million bioactivity records abstracted from over 40000 publications. Additional public domain resources can be readily integrated into the same data model (e.g. PubChem BioAssay data). The compounds in ChEMBL are largely extracted from the primary medicinal chemistry literature, and are therefore usually 'drug-like' or 'lead-like' small molecules with full experimental context. The data cover a significant fraction of the discovery of modern drugs, and are useful in a wide range of drug design and discovery tasks. In addition to the compound data, ChEMBL also contains information for over 8000 protein, cell line and whole-organism 'targets', with over 4000 of those being proteins linked to their underlying genes. The database is searchable both chemically, using an interactive compound sketch tool, protein sequences, family hierarchies, SMILES strings, compound research codes and key words, and biologically, using a variety of gene identifiers, protein sequence similarity and protein families. The information retrieved can then be readily filtered and downloaded into various formats. ChEMBL can be accessed online at https://www.ebi.ac.uk/chembldb.
Moretti, Loris; Sartori, Luca
In the field of Computer-Aided Drug Discovery and Development (CADDD) the proper software infrastructure is essential for everyday investigations. The creation of such an environment should be carefully planned and implemented with certain features in order to be productive and efficient. Here we describe a solution to integrate standard computational services into a functional unit that empowers modelling applications for drug discovery. This system allows users with various level of expertise to run in silico experiments automatically and without the burden of file formatting for different software, managing the actual computation, keeping track of the activities and graphical rendering of the structural outcomes. To showcase the potential of this approach, performances of five different docking programs on an Hiv-1 protease test set are presented.
Helgren, Travis R; Hagen, Timothy J
Drug design and discovery remains a popular topic of study to many students interested in visible, real-world applications of the chemical sciences. It is important that laboratory experiments detailing the early stages of drug discovery incorporate both compound design and an exploration of ligand/receptor interactions. Molecular modeling is widely employed in research endeavors seeking to predict the activity of potential compounds prior to synthesis and can therefore be used to illustrate these concepts. The following activity therefore details the use of AutoDock to predict the binding affinity and docked pose of a series of CDK2 inhibitors. Students can then compare their docking output to experimentally determined inhibitory activities and crystal structures. Finally, the AutoDock workflow detailed in this activity can be used in research settings, provided the receptor crystal structure is known.
Sander, Thomas; Freyss, Joel; von Korff, Modest; Reich, Jacqueline Renée; Rufener, Christian
We present OSIRIS, an entirely in-house developed drug discovery informatics system. Its components cover all information handling aspects from compound synthesis via biological testing to preclinical development. Its design principles are platform and vendor independence, a consistent look and feel, and complete coverage of the drug discovery process by custom tailored applications. These include electronic laboratory notebook applications for biology and chemistry, tools for high-throughput and secondary screening evaluation, chemistry-aware data visualization, physicochemical property prediction, 3D-pharmacophore comparisons, interactive modeling, computing grid based ligand-protein docking, and more. Most applications are developed in Java and are built on top of a Java library layer that provides reusable cheminformatics functionality and GUI components such as chemical editors, structure canonicalization, substructure search, combinatorial enumeration, enhanced stereo perception, force field minimization, and conformation generation.
Doyle, Orla M; Mehta, Mitul A; Brammer, Michael J
Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging can provide insight into drug penetration and distribution, target engagement, pharmacodynamics, mechanistic action and potential indicators of clinical efficacy. In this review, we focus on machine learning approaches for neuroimaging which enable us to make predictions at the individual level based on the distributed effects across the whole brain. Crucially, these approaches can be trained on data from one study and applied to an independent study and, unlike group-level statistics, can be readily use to assess the generalisability to unseen data. In this review, we present examples and suggestions for how machine learning could help answer fundamental questions spanning the drug discovery pipeline: (1) Who should I recruit for this study? (2) What should I measure and when should I measure it? (3) How does the pharmacological agent behave using an experimental medicine model?, and (4) How does a compound differ from and/or resemble existing compounds? Specifically, we present studies from the literature and we suggest areas for the focus of future development. Further refinement and tailoring of machine learning techniques may help realise their tremendous potential for drug discovery and drug validation.
Gozalbes, Rafael; Carbajo, Rodrigo J; Pineda-Lucena, Antonio
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.
Anzellotti, Stefano; Kliemann, Dorit; Jacoby, Nir; Saxe, Rebecca
Cognitive tasks recruit multiple brain regions. Understanding how these regions influence each other (the network structure) is an important step to characterize the neural basis of cognitive processes. Often, limited evidence is available to restrict the range of hypotheses a priori, and techniques that sift efficiently through a large number of possible network structures are needed (network discovery). This article introduces a novel modelling technique for network discovery (Dynamic Network Modelling or DNM) that builds on ideas from Granger Causality and Dynamic Causal Modelling introducing three key changes: (1) efficient network discovery is implemented with statistical tests on the consistency of model parameters across participants, (2) the tests take into account the magnitude and sign of each influence, and (3) variance explained in independent data is used as an absolute (rather than relative) measure of the quality of the network model. In this article, we outline the functioning of DNM, we validate DNM in simulated data for which the ground truth is known, and we report an example of its application to the investigation of influences between regions during emotion recognition, revealing top-down influences from brain regions encoding abstract representations of emotions (medial prefrontal cortex and superior temporal sulcus) onto regions engaged in the perceptual analysis of facial expressions (occipital face area and fusiform face area) when participants are asked to switch between reporting the emotional valence and the age of a face. Copyright © 2017 Elsevier Ltd. All rights reserved.
Quinn, Robert A; Nothias, Louis-Felix; Vining, Oliver; Meehan, Michael; Esquenazi, Eduardo; Dorrestein, Pieter C
Molecular networking is a tandem mass spectrometry (MS/MS) data organizational approach that has been recently introduced in the drug discovery, metabolomics, and medical fields. The chemistry of molecules dictates how they will be fragmented by MS/MS in the gas phase and, therefore, two related molecules are likely to display similar fragment ion spectra. Molecular networking organizes the MS/MS data as a relational spectral network thereby mapping the chemistry that was detected in an MS/MS-based metabolomics experiment. Although the wider utility of molecular networking is just beginning to be recognized, in this review we highlight the principles behind molecular networking and its use for the discovery of therapeutic leads, monitoring drug metabolism, clinical diagnostics, and emerging applications in precision medicine. Copyright © 2016. Published by Elsevier Ltd.
Vo-Dinh, Tuan; Scaffidi, Jonathan; Gregas, Molly; Zhang, Yan; Seewaldt, Victoria
Background 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, etc) selective to target 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. Objective This article provides an overview of the development and application of fiber-optic nanosensors for drug discovery. Conclusions The nanosensors provide minimally invasive tools to probe sub-cellular 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 anti-cancer drugs). PMID:23496274
Materi, Wayne; Wishart, David S
Computational systems biology is an emerging field in biological simulation that attempts to model or simulate intra- and intercellular events using data gathered from genomic, proteomic or metabolomic experiments. The need to model complex temporal and spatiotemporal processes at many different scales has led to the emergence of numerous techniques, including systems of differential equations, Petri nets, cellular automata simulators, agent-based models and pi calculus. This review provides a brief summary and an assessment of most of these approaches. It also provides examples of how these methods are being used to facilitate drug discovery and development.
Liu, Fangkun; Huang, Jing; Ning, Bo; Liu, Zhixiong; Chen, Shen; Zhao, Wei
Patient-derived cell lines and animal models have proven invaluable for the understanding of human intestinal diseases and for drug development although both inherently comprise disadvantages and caveats. Many genetically determined intestinal diseases occur in specific tissue microenvironments that are not adequately modeled by monolayer cell culture. Likewise, animal models incompletely recapitulate the complex pathologies of intestinal diseases of humans and fall short in predicting the effects of candidate drugs. Patient-derived stem cell organoids are new and effective models for the development of novel targeted therapies. With the use of intestinal organoids from patients with inherited diseases, the potency and toxicity of drug candidates can be evaluated better. Moreover, owing to the novel clustered regularly interspaced short palindromic repeats/CRISPR-associated protein-9 genome-editing technologies, researchers can use organoids to precisely modulate human genetic status and identify pathogenesis-related genes of intestinal diseases. Therefore, here we discuss how patient-derived organoids should be grown and how advanced genome-editing tools may be applied to research on modeling of cancer and infectious diseases. We also highlight practical applications of organoids ranging from basic studies to drug screening and precision medicine. PMID:27713700
Sun, Yishan; Dolmetsch, Ricardo E
Compared with other medical fields, psychiatry is particularly challenging for rational drug discovery. The therapeutic endpoints are abstract measures of cognitive and behavioral performance, for which we have a very limited understanding of the underlying biological mechanisms. Existing preclinical disease models are also limited in their translational fidelity. Recently, there have been active discussions on the use of human induced pluripotent stem cells (iPSCs) as a catalyzing research tool in psychiatry, but very few review articles in the field have given specific considerations to their use at the interface between psychiatric research and drug discovery. Here, we discuss recent perspectives emerging from this interface. For physicians and researchers on the clinical side, we explain how iPSC-based experimental approaches are placed at the crossroads with psychiatric genetics and how representative studies in the field are addressing biological mechanisms underlying psychiatric disorders. For researchers who directly work with iPSCs and aspire to develop new research techniques, we direct their attention to the utility of this approach for unmet needs in drug discovery workflows.
In order to evaluate to what extent will genomics and in silico related technologies improve overall drug discovery process, we analyzed three studies comparing cost, time and attrition rate at each step of the drug discovery process, between standard pharmaceutical and genomics based approaches.
Pearson, Lesley-Anne; Foley, David William
The complexities of modern drug discovery-an interdisciplinary process that often takes years and costs billions-can be extremely challenging to explain to a public audience. We present details of a 30 minute demonstrative lecture that uses well-known experiments to illustrate key concepts in drug discovery including synthesis, assay and metabolism.
Alvim-Gaston, Maria; Grese, Timothy; Mahoui, Abdelaziz; Palkowitz, Alan D; Pineiro-Nunez, Marta; Watson, Ian
The continued development of computational and synthetic methods has enabled the enumeration or preparation of a nearly endless universe of chemical structures. Nevertheless, the ability of this chemical universe to deliver small molecules that can both modulate biological targets and have drug-like physicochemical properties continues to be a topic of interest to the pharmaceutical industry and academic researchers alike. The chemical space described by public, commercial, in-house and virtual compound collections has been interrogated by multiple approaches including biochemical, cellular and virtual screening, diversity analysis, and in-silico profiling. However, current drugs and known chemical probes derived from these efforts are contained within a remarkably small volume of the predicted chemical space. Access to more diverse classes of chemical scaffolds that maintain the properties relevant for drug discovery is certainly needed to meet the increasing demands for pharmaceutical innovation. The Lilly Open Innovation Drug Discovery platform (OIDD) was designed to tackle barriers to innovation through the identification of novel molecules active in relevant disease biology models. In this article we will discuss several computational approaches towards describing novel, biologically active, drug-like chemical space and illustrate how the OIDD program may facilitate access to previously untapped molecules that may aid in the search for innovative pharmaceuticals.
Emoto, Chie; Murayama, Norie; Rostami-Hodjegan, Amin; Yamazaki, Hiroshi
The attrition rate in drug development is being reduced by continuous advances in science and technology introduced by various academic institutions and pharmaceutical companies. This has been certainly noticeable in reducing the frequency with which unfavorable absorption, distribution, metabolism, and elimination (ADME) characteristics of any candidate drug causes failure in clinical development. Nonetheless, it is important that the objectives in reducing attrition during later stages of development are matched by information generated in the earliest stage of discovery. In this review, we summarize the methodologies employed during the early stages of drug discovery and discuss new findings in the areas of (1) drug metabolism enzymes, (2) the contribution of cytochrome P450 enzymes (P450, CYP) to hepatic metabolism, (3) prediction of hepatic intrinsic clearance, (4) reaction phenotyping, and (5) the metabolic differences between highly homologous enzymes such as CYP3A4 and CYP3A5. The total contribution of P450 and UDP-glucuronosyltransferases to drug metabolism is reported to be more than 80%; therefore, glucuronidation is increasingly recognized as an important clearance pathway in addition to that of P450 enzymes. When estimating the contribution of P450, interpreting the results of inhibition studies using a single P450 inhibitor can lead to false conclusions. For instance, 1-aminobenzotriazole and SKF-525A have a varying range of IC(50) values for inhibition of drug exidation-reaction by different CYP450 enzymes. There are disparities between methodologies at early stage drug discovery and late stage development. For example, although the drug depletion approach for the prediction of hepatic intrinsic clearance may not be desirable at late stages of development, it is suitable at the early drug discovery stage since kinetic characterization and measurement of specific drug metabolites are not required. Data from protein binding assays in plasma and
Joolingen, van Wouter R.; Jong, de Ton
This article describes a theory of scientific discovery learning which is an extension of Klahr and Dunbar''s model of Scientific Discovery as Dual Search (SDDS) model. We present a model capable of describing and understanding scientific discovery learning in complex domains in terms of the SDDS fr
Full Text Available Value creation and value capture are central to technology entrepreneurship. The ways in which a particular firm creates and captures value are the foundation of that firm's business model, which is an explanation of how the business delivers value to a set of customers at attractive profits. Despite the deep conceptual link between business models and technology entrepreneurship, little is known about the processes by which technology entrepreneurs produce successful business models. This article makes three contributions to partially address this knowledge gap. First, it argues that business model discovery by technology entrepreneurs can be, and often should be, disciplined by both intention and structure. Second, it provides a tool for disciplined business model discovery that includes an actionable process and a worksheet for describing a business model in a form that is both concise and explicit. Third, it shares preliminary results and lessons learned from six technology entrepreneurs applying a disciplined process to strengthen or reinvent the business models of their own nascent technology businesses.
Dunlop, John; Brandon, Nicholas J
Current therapeutics for schizophrenia, the typical and atypical antipsychotic class of drugs, derive their therapeutic benefit predominantly by antagonism of the dopamine D2 receptor subtype and have robust clinical benefit on positive symptoms of the disease with limited to no impact on negative symptoms and cognitive impairment. Driven by these therapeutic limitations of current treatments and the recognition that transmitter systems beyond the dopaminergic system in particular glutamatergic transmission contribute to the etiology of schizophrenia significant recent efforts have focused on the discovery and development of novel treatments for schizophrenia with mechanisms of action that are distinct from current drugs. Specifically, compounds selectively targeting the metabotropic glutamate receptor 2/3 subtype, phosphodiesterase subtype 10, glycine transporter subtype 1 and the alpha7 nicotinic acetylcholine receptor have been the subject of intense drug discovery and development efforts. Here we review recent clinical experience with the most advanced drug candidates targeting each of these novel mechanisms and discuss whether these new agents are living up to expectations.
Full Text Available A disruptive approach to therapeutic discovery and development is required in order to significantly improve the success rate of drug discovery for central nervous system (CNS disorders. In this review, we first assess the key factors contributing to the frequent clinical failures for novel drugs. Second, we discuss cancer translational research paradigms that addressed key issues in drug discovery and development and have resulted in delivering drugs with significantly improved outcomes for patients. Finally, we discuss two emerging technologies that could improve the success rate of CNS therapies: human induced pluripotent stem cell (hiPSC-based studies and multiscale biology models. Coincident with advances in cellular technologies that enable the generation of hiPSCs directly from patient blood or skin cells, together with methods to differentiate these hiPSC lines into specific neural cell types relevant to neurological disease, it is also now possible to combine data from large-scale forward genetics and post-mortem global epigenetic and expression studies in order to generate novel predictive models. The application of systems biology approaches to account for the multiscale nature of different data types, from genetic to molecular and cellular to clinical, can lead to new insights into human diseases that are emergent properties of biological networks, not the result of changes to single genes. Such studies have demonstrated the heterogeneity in etiological pathways and the need for studies on model systems that are patient-derived and thereby recapitulate neurological disease pathways with higher fidelity. In the context of two common and presumably representative neurological diseases, the neurodegenerative disease Alzheimer’s Disease (AD, and the psychiatric disorder schizophrenia (SZ, we propose the need for, and exemplify the impact of, a multiscale biology approach that can integrate panomic, clinical, imaging, and literature
Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis
Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .
Full Text Available This opinion piece presents an approach to standardisation of an important aspect of Chagas disease drug discovery and development: selecting Trypanosoma cruzi strains for in vitro screening. We discuss the rationale for strain selection representing T. cruzi diversity and provide recommendations on the preferred parasite stage for drug discovery, T. cruzi discrete typing units to include in the panel of strains and the number of strains/clones for primary screens and lead compounds. We also consider experimental approaches for in vitro drug assays. The Figure illustrates the current Chagas disease drug-discovery and development landscape.
Deshpande, Sudhir S.; Mineyev, I.; Owicki, John C.
A fluorescence polarization assay was developed as an alternative to the radiolabeled SPA assays currently used to monitor the activity of tyrosine kinases in drug discovery. The assay can be used with enzymes having substrate specificity similar to that of the insulin receptor, the EGF receptor and the Src kinase receptor enzymes. The assay is easy to configure in 96, 384 and 1536-well microplates in assay volumes ranging from (mu) L with minimal efforts. The reconstituted reagents are stable for up to 24 hr at ambient temperatures, thereby minimizing the need for replenishing the stock solutions during the course of a high-throughput screen. Because of the stability and equilibrium kinetics, the assay allows the user the luxury of scheduling the reading of plates any time up to 24 hr after the completion of the assay without substantial deterioration in the assay signal. The antibody and the tracer solutions can also be premixed and added as a preformed complex in a single step. The performance of the assay with the insulin receptor kinase is described. In addition, given the diversity of the substrates used in measuring the activity of different tyrosine kinases, LJL's on-going efforts to provide different antibodies of wide ranging specificity and sensitivity are described.
Following the success of small-molecule high-throughput screening (HTS) in drug discovery, other large-scale screening techniques are currently revolutionizing the biological sciences. Powerful new statistical tools have been developed to analyze the vast amounts of data in DNA chip studies, but have not yet found their way into compound screening. In HTS, characterization of single-point hit lists is often done only in retrospect after the results of confirmation experiments are available. However, for prioritization, for optimal use of resources, for quality control, and for comparison of screens it would be extremely valuable to predict the rates of false positives and false negatives directly from the primary screening results. Making full use of the available information about compounds and controls contained in HTS results and replicated pilot runs, the Z score and from it the p value can be estimated for each measurement. Based on this consideration, we have applied the concept of p-value distribution analysis (PVDA), which was originally developed for gene expression studies, to HTS data. PVDA allowed prediction of all relevant error rates as well as the rate of true inactives, and excellent agreement with confirmation experiments was found.
MacRae, Calum A.
Importance of the field Cardiac arrhythmias remain a major challenge for modern drug discovery. Clinical events are paroxysmal, often rare and may be asymptomatic until a highly morbid complication. Target selection is often based on limited information and though highly specific agents are identified in screening, the final efficacy is often compromised by unanticipated systemic responses, a narrow therapeutic index and substantial toxicities. Areas covered in this review Our understanding of complexity of arrhythmogenesis has grown dramatically over the last two decades, and the range of potential disease mechanisms now includes pathways previously thought only tangentially involved in arrhythmia. This review surveys the literature on arrhythmia mechanisms from 1965 to the present day, outlines the complex biology underlying potentially each and every rhythm disturbance, and highlights the problems for rational target identification. The rationale for in vivo screening is described and the utility of the zebrafish for this approach and for complementary work in functional genomics is discussed. Current limitations of the model in this setting and the need for careful validation in new disease areas are also described. What the reader will gain An overview of the complex mechanisms underlying most clinical arrhythmias, and insight into the limits of ion channel conductances as drug targets. An introduction to the zebrafish as a model organism, in particular for cardiovascular biology. Potential approaches to overcoming the hurdles to drug discovery in the face of complex biology including in vivo screening of zebrafish genetic disease models. Take home message In vivo screening in faithful disease models allows the effects of drugs on integrative physiology and disease biology to be captured during the screening process, in a manner agnostic to potential drug target or targets. This systematic strategy bypasses current gaps in our understanding of disease biology
Duffy, Sandra; Sykes, Melissa L; Jones, Amy J; Shelper, Todd B; Simpson, Moana; Lang, Rebecca; Poulsen, Sally-Ann; Sleebs, Brad E; Avery, Vicky M
Open-access drug discovery provides a substantial resource for diseases primarily affecting the poor and disadvantaged. The open-access Pathogen Box collection is comprised of compounds with demonstrated biological activity against specific pathogenic organisms. The supply of this resource by the Medicines for Malaria Venture has the potential to provide new chemical starting points for a number of tropical and neglected diseases, through repurposing of these compounds for use in drug discovery campaigns for these additional pathogens. We tested the Pathogen Box against kinetoplastid parasites and malaria life cycle stages in vitro Consequently, chemical starting points for malaria, human African trypanosomiasis, Chagas disease, and leishmaniasis drug discovery efforts have been identified. Inclusive of this in vitro biological evaluation, outcomes from extensive literature reviews and database searches are provided. This information encompasses commercial availability, literature reference citations, other aliases and ChEMBL number with associated biological activity, where available. The release of this new data for the Pathogen Box collection into the public domain will aid the open-source model of drug discovery. Importantly, this will provide novel chemical starting points for drug discovery and target identification in tropical disease research. Copyright © 2017 Duffy et al.
Zhang, Aihua; Sun, Hui; Wang, Xijun
Herbal medicine (HM) has made a major contribution to the drug discovery process with regard to identifying products compounds. Currently, more attention has been focused on drug discovery from natural compounds of HM. Despite the rapid advancement of modern analytical techniques, drug discovery is still a difficult and lengthy process. Fortunately, mass spectrometry (MS) can provide us with useful structural information for drug discovery, has been recognized as a sensitive, rapid, and high-throughput technology for advancing drug discovery from HM in the post-genomic era. It is essential to develop an efficient, high-quality, high-throughput screening method integrated with an MS platform for early screening of candidate drug molecules from natural products. We have developed a new chinmedomics strategy reliant on MS that is capable of capturing the candidate molecules, facilitating their identification of novel chemical structures in the early phase; chinmedomics-guided natural product discovery based on MS may provide an effective tool that addresses challenges in early screening of effective constituents of herbs against disease. This critical review covers the use of MS with related techniques and methodologies for natural product discovery, biomarker identification, and determination of mechanisms of action. It also highlights high-throughput chinmedomics screening methods suitable for lead compound discovery illustrated by recent successes.
Ahmad Rezaei Kolahchi
Full Text Available Development of predictive multi-organ models before implementing costly clinical trials is central for screening the toxicity, efficacy, and side effects of new therapeutic agents. Despite significant efforts that have been recently made to develop biomimetic in vitro tissue models, the clinical application of such platforms is still far from reality. Recent advances in physiologically-based pharmacokinetic and pharmacodynamic (PBPK-PD modeling, micro- and nanotechnology, and in silico modeling have enabled single- and multi-organ platforms for investigation of new chemical agents and tissue-tissue interactions. This review provides an overview of the principles of designing microfluidic-based organ-on-chip models for drug testing and highlights current state-of-the-art in developing predictive multi-organ models for studying the cross-talk of interconnected organs. We further discuss the challenges associated with establishing a predictive body-on-chip (BOC model such as the scaling, cell types, the common medium, and principles of the study design for characterizing the interaction of drugs with multiple targets.
Geromichalos, George D; Alifieris, Constantinos E; Geromichalou, Elena G; Trafalis, Dimitrios T
Conventional drug design embraces the "one gene, one drug, one disease" philosophy. Nowadays, new generation of anticancer drugs, able to inhibit more than one pathway, is believed to play a major role in contemporary anticancer drug research. In this way, polypharmacology, focusing on multi-target drugs, has emerged as a new paradigm in drug discovery. A number of recent successful drugs have in part or in whole emerged from a structure-based research approach. Many advances including crystallography and informatics are behind these successes. In this part II we will review the role and methodology of ligand-, structure- and fragment-based computer-aided drug design computer aided drug desing (CADD), virtual high throughput screening (vHTS), de novo drug design, fragment-based design and structure-based molecular docking, homology modeling, combinatorial chemistry and library design, pharmacophore model chemistry and informatics in modern drug discovery.
Djuric, Stevan W.; Hutchins, Charles W.; Talaty, Nari N.
This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of “dangerous” reagents. Also featured are advances in the “computer-assisted drug design” area and the expanding application of novel mass spectrometry-based techniques to a wide range of drug discovery activities. PMID:27781094
Guo, Yingying; Ding, Yan; Xu, Feifei; Liu, Baoyue; Kou, Zinong; Xiao, Wei; Zhu, Jingbo
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.
Geromichalos, George D; Alifieris, Constantinos E; Geromichalou, Elena G; Trafalis, Dimitrios T
Conventional drug design embraces the "one gene, one drug, one disease" philosophy. Nowadays, new generation of anti- cancer drugs, able to inhibit more than one pathway, is believed to play a major role in contemporary anticancer drug research. In this way, polypharmacology, focusing on multi-target drugs, has emerged as a new paradigm in drug discovery. A number of recent successful drugs have in part or in whole emerged from a structure-based research approach. Many advances including crystallography and informatics are behind these successes. Increasing insight into the genetics and molecular biology of cancer has resulted in the identification of an increasing number of potential molecular targets, for anticancer drug discovery and development. These targets can be approached through exploitation of emerging structural biology, "rational" drug design, screening of chemical libraries, or a combination of these methods. The result is the rapid discovery of new anticancer drugs. In this article we discuss the application of molecular modeling, molecular docking and virtual high-throughput screening to multi-targeted anticancer drug discovery. Efforts have been made to employ in silico methods for facilitating the search and design of selective multi-target agents. These computer aided molecular design methods have shown promising potential in facilitating drug discovery directed at selective multiple targets and is expected to contribute to intelligent lead anticancer drugs.
Liu, Yang; Zhai, Hua-Qiang; Xiang, Jia-Mei; Wang, Jing-Juan; Zhao, Bao-Sheng; Wang, Gang; Dong, Hong-Huan; Ouyang, Guo-Qing
With the kernel of efficacy, "Xiaohe Silian" was a pattern and method for new drug discovery which was constituted with "metabolism-efficacy, toxicity-efficacy, quality-efficacy and structure-efficacy". Its connotation was in keeping with traditional Chinese medicine (TCM) clinical pharmacy. This paper systematically summarized the research method of new drug discovery practice process for TCM. To avoid western drug like in TCM new drug discovery, we carried out combination analysis with TCM clinical pharmacy. The correlation analysis between basic elements of "Xiaohe Silian(n) and TCM clinical pharmacy was studied to guarantee this method could integrate closely with TCM clinic from all angles. Hence, this method aimed to provide a new method for TCM new drug discovery on the basis of TCM clinical pharmacy with insisting on holistic view of multicomponent study, kinetic view of metabolic process when the curative effect occurred and molecular material view of quality control and structure-activity exposition.
Haring, Alexander P; Sontheimer, Harald; Johnson, Blake N
Translational challenges associated with reductionist modeling approaches, as well as ethical concerns and economic implications of small animal testing, drive the need for developing microphysiological neural systems for modeling human neurological diseases, disorders, and injuries. Here, we provide a comprehensive review of microphysiological brain and neural systems-on-a-chip (NSCs) for modeling higher order trajectories in the human nervous system. Societal, economic, and national security impacts of neurological diseases, disorders, and injuries are highlighted to identify critical NSC application spaces. Hierarchical design and manufacturing of NSCs are discussed with distinction for surface- and bulk-based systems. Three broad NSC classes are identified and reviewed: microfluidic NSCs, compartmentalized NSCs, and hydrogel NSCs. Emerging areas and future directions are highlighted, including the application of 3D printing to design and manufacturing of next-generation NSCs, the use of stem cells for constructing patient-specific NSCs, and the application of human NSCs to 'personalized neurology'. Technical hurdles and remaining challenges are discussed. This review identifies the state-of-the-art design methodologies, manufacturing approaches, and performance capabilities of NSCs. This work suggests NSCs appear poised to revolutionize the modeling of human neurological diseases, disorders, and injuries.
Glicksman, Marcie A.
Introduction Amyotrophic Lateral Sclerosis, also referred to as Lou Gehrig’s disease is characterized by the progressive loss of cells in the brain and spinal cord that leads to debilitation and death in 3–5 years. Only one therapeutic drug, Riluzole, has been approved for ALS and that drug improves survival by 2–3 months. The need for new therapeutics, either that can postpone or slow the progression of the motor deficits and prolong survival, is still a strong unmet medical need. Areas Covered Although there are a number of drugs currently in clinical trials for ALS, this review provides an overview of the most promising biological targets and preclinical strategies that are currently being developed and deployed. The list of targets for ALS was compiled from a variety of websites including: individual companies that have ALS programs, and the author’s experience. Expert Opinion Progress is being made in the identification of possible new therapeutics for ALS with recent efforts in: understanding the genetic causes of the disease, susceptibility factors, and the development of additional preclinical animal models. However, many challenges remain in the identification of new ALS therapeutics including: the use of relevant biomarkers, the need for earlier diagnosis of the disease, and additional animal models. Multiple strategies need to be tested, in the clinic, in order to determine what will be effective in patients. PMID:22646982
Ekins, Sean; Clark, Alex M; Williams, Antony J
The Open Drug Discovery Teams (ODDT) project provides a mobile app primarily intended as a research topic aggregator of predominantly open science data collected from various sources on the internet. It exists to facilitate interdisciplinary teamwork and to relieve the user from data overload, delivering access to information that is highly relevant and focused on their topic areas of interest. Research topics include areas of chemistry and adjacent molecule-oriented biomedical sciences, with an emphasis on those which are most amenable to open research at present. These include rare and neglected diseases, and precompetitive and public-good initiatives such as green chemistry. The ODDT project uses a free mobile app as user entry point. The app has a magazine-like interface, and server-side infrastructure for hosting chemistry-related data as well as value added services. The project is open to participation from anyone and provides the ability for users to make annotations and assertions, thereby contributing to the collective value of the data to the engaged community. Much of the content is derived from public sources, but the platform is also amenable to commercial data input. The technology could also be readily used in-house by organizations as a research aggregator that could integrate internal and external science and discussion. The infrastructure for the app is currently based upon the Twitter API as a useful proof of concept for a real time source of publicly generated content. This could be extended further by accessing other APIs providing news and data feeds of relevance to a particular area of interest. As the project evolves, social networking features will be developed for organizing participants into teams, with various forms of communication and content management possible.
Basavaraj S; Guru V. Betageri
Drug discovery and development has become longer and costlier process. The fear of failure and stringent regulatory review process is driving pharmaceutical companies towards “me too” drugs and improved generics (505(b) (2)) fillings. The discontinuance of molecules at late stage clinical trials is common these years. The molecules are withdrawn at various stages of discovery and development process for reasons such as poor ADME properties, lack of efficacy and safety reasons. Hence this revi...
The experimental models for drug screening are very important points in drug discovery. Although the drug screening techniques have been developed, such as high throughput screening (HTS), the screening assay methods (models) still limited drug discovery. In present paper, advanced animal models, cell assays and molecular methodology used for drug discovery were reviewed. The characteristics and requires of the assay methods used for drug discovery in HTS were also discussed.
Full Text Available BACKGROUND: We consider the problem of optimizing a liposomal drug formulation: a complex chemical system with many components (e.g., elements of a lipid library that interact nonlinearly and synergistically in ways that cannot be predicted from first principles. METHODOLOGY/PRINCIPAL FINDINGS: The optimization criterion in our experiments was the percent encapsulation of a target drug, Amphotericin B, detected experimentally via spectrophotometric assay. Optimization of such a complex system requires strategies that efficiently discover solutions in extremely large volumes of potential experimental space. We have designed and implemented a new strategy of evolutionary design of experiments (Evo-DoE, that efficiently explores high-dimensional spaces by coupling the power of computer and statistical modeling with experimentally measured responses in an iterative loop. CONCLUSIONS: We demonstrate how iterative looping of modeling and experimentation can quickly produce new discoveries with significantly better experimental response, and how such looping can discover the chemical landscape underlying complex chemical systems.
Industrial pharmaceutical companies (Pharma) share a common goal with academic scientists (Academia) in that they wish to create an environment in which patients are treated for diseases with ever more effective therapies. As disease biology has proven to be ever more complex and money and new drugs are becoming more elusive, Pharma and Academia are reaching toward each other with ever greater collaborative intent. There are a growing number of collaboration models that allow scientists to work together and profit from the creation of new drugs. Here I give a personal view of how we came to where we are, present an overview of a number of these models and look to the future in terms of running successful discovery alliances.
Full Text Available Current strategies for drug discovery have reached a bottleneck where the paradigm is generally “one gene, one drug, one disease.” However, using holistic and systemic views, network pharmacology may be the next paradigm in drug discovery. Based on network pharmacology, a combinational drug with two or more compounds could offer beneficial synergistic effects for complex diseases. Interestingly, traditional chinese medicine (TCM has been practicing holistic views for over 3,000 years, and its distinguished feature is using herbal formulas to treat diseases based on the unique pattern classification. Though TCM herbal formulas are acknowledged as a great source for drug discovery, no drug discovery strategies compatible with the multidimensional complexities of TCM herbal formulas have been developed. In this paper, we highlighted some novel paradigms in TCM-based network pharmacology and new drug discovery. A multiple compound drug can be discovered by merging herbal formula-based pharmacological networks with TCM pattern-based disease molecular networks. Herbal formulas would be a source for multiple compound drug candidates, and the TCM pattern in the disease would be an indication for a new drug.
Zhi, Shi; Zhao, Bo; Tong, Wenzhu; Gao, Jing; Yu, Dian; Ji, Heng; Han, Jiawei
When integrating information from multiple sources, it is common to encounter conflicting answers to the same question. Truth discovery is to infer the most accurate and complete integrated answers from conflicting sources. In some cases, there exist questions for which the true answers are excluded from the candidate answers provided by all sources. Without any prior knowledge, these questions, named no-truth questions, are difficult to be distinguished from the questions that have true answers, named has-truth questions. In particular, these no-truth questions degrade the precision of the answer integration system. We address such a challenge by introducing source quality, which is made up of three fine-grained measures: silent rate, false spoken rate and true spoken rate. By incorporating these three measures, we propose a probabilistic graphical model, which simultaneously infers truth as well as source quality without any a priori training involving ground truth answers. Moreover, since inferring this graphical model requires parameter tuning of the prior of truth, we propose an initialization scheme based upon a quantity named truth existence score, which synthesizes two indicators, namely, participation rate and consistency rate. Compared with existing methods, our method can effectively filter out no-truth questions, which results in more accurate source quality estimation. Consequently, our method provides more accurate and complete answers to both has-truth and no-truth questions. Experiments on three real-world datasets illustrate the notable advantage of our method over existing state-of-the-art truth discovery methods.
Full Text Available The concept of drug discovery through stem cell biology is based on technological developments whose genesis is now coincident. The first is automated cell microscopy with concurrent advances in image acquisition and analysis, known as high content screening (HCS. The second is patient-derived stem cells for modelling the cell biology of brain diseases. HCS has developed from the requirements of the pharmaceutical industry for high throughput assays to screen thousands of chemical compounds in the search for new drugs. HCS combines new fluorescent probes with automated microscopy and computational power to quantify the effects of compounds on cell functions. Stem cell biology has advanced greatly since the discovery of genetic reprogramming of somatic cells into induced pluripotent stem cells (iPSCs. There is now a rush of papers describing their generation from patients with various diseases of the nervous system. Although the majority of these have been genetic diseases, iPSCs have been generated from patients with complex diseases (schizophrenia and sporadic Parkinson’s disease. Some genetic diseases are also modelled in embryonic stem cells generated from blastocysts rejected during in vitro fertilisation. Neural stem cells have been isolated from post-mortem brain of Alzheimer’s patients and neural stem cells generated from biopsies of the olfactory organ of patients is another approach. These olfactory neurosphere-derived cells demonstrate robust disease-specific phenotypes in patients with schizophrenia and Parkinson’s disease. High content screening is already in use to find small molecules for the generation and differentiation of embryonic stem cells and induced pluripotent stem cells. The challenges for using stem cells for drug discovery are to develop robust stem cell culture methods that meet the rigorous requirements for repeatable, consistent quantities of defined cell types at the industrial scale necessary for high
Jones, Alana Wayne
This collection of short essays deal with the history of drug discovery and covers a wide range of pharmaceutical substances, including prescription medication as well as illicit recreational drugs of abuse. Consideration was also given to the plethora of drugs encountered in routine forensic casework, especially in traffic crimes, such as driving under the influence of drugs (DUID) and in post-mortem toxicology when drug poisoning deaths are investigated. The essays were written over a numbe...
Ekins, Sean; Boulanger, Bruno; Swaan, Peter W.; Hupcey, Maggie A. Z.
With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.
Greef, J. van der; Adourian, A.; Muntendam, P.; McBurney, R.N.
Too few drug discovery projects generate a marketed drug product, often because preclinical studies fail to predict the clinical experience with a drug candidate. Improving the success of preclinical-to-clinical translation is of paramount importance in optimizing the pharmaceutical value chain. Her
Greef, J. van der; Adourian, A.; Muntendam, P.; McBurney, R.N.
Too few drug discovery projects generate a marketed drug product, often because preclinical studies fail to predict the clinical experience with a drug candidate. Improving the success of preclinical-to-clinical translation is of paramount importance in optimizing the pharmaceutical value chain. Her
It is suggested in this article that patient engagement should occur further upstream during the drug discovery stage. 'Lead patients', namely those patients who are proactive with respect to their health, possess knowledge of their disease and resulting symptoms. They are also well informed about the conventional as well as non-conventional treatments for disease management; and so can provide a nuanced perspective to drug design. Understanding how patients view the management of their diseases and how they view the use of conventional versus non-conventional interventions is of imperative importance to researchers. Indeed, this can provide insight into how conventional treatments might be designed from the outset to encourage compliance and positive health outcomes. Consequently, a continuum of lead patient engagement is employed that focuses on drug discovery processes ranging from participative, informative to collaborative engagement. This article looks at a variety of open innovation models that are currently employed across this engagement spectrum. It is no longer sufficient for industry stakeholders to consider conventional therapies as the only mechanisms being sought after by patients. Without patient engagement, the industry risks being re-prioritized in terms of its role in the patient journey towards not only recovery of health, but also sustained health and wellness before disease onset.
Ru ZHANG; Li-hong ZHANG; Xin XIE
The revolutionary induced pluripotent stem cell (iPSC) technology provides a new path for cell replacement therapies and drug screening.Patient-specific iPSCs and subsequent differentiated cells manifesting disease phenotypes will finally position human disease pathology at the core of drug discovery.Cells used to test the toxic effects of drugs can also be generated from normal iPSCs and provide a much more accurate and cost-effective system than many animal models.Here,we highlight the recent progress in iPSC-based cell therapy,disease modeling and drug evaluations.In addition,we discuss the use of small molecule drugs to improve the generation of iPSCs and understand the reprogramming mechanism.It is foreseeable that the interplay between iPSC technology and small molecule compounds will push forward the applications of iPSC-based therapy and screening systems in the real world and eventually revolutionize the methods used to treat diseases.
Campbell, Robert M; Tummino, Peter J
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.
Asmild, Margit; Oswald, Nicholas; Krzywkowski, Karen M
Effective screening of large compound libraries in ion channel drug discovery requires the development of new electrophysiological techniques with substantially increased throughputs compared to the conventional patch clamp technique. Sophion Bioscience is aiming to meet this challenge...
Weller, Harold N; Nirschl, David S; Petrillo, Edward W; Poss, Michael A; Andres, Charles J; Cavallaro, Cullen L; Echols, Martin M; Grant-Young, Katherine A; Houston, John G; Miller, Arthur V; Swann, R Thomas
The application of parallel synthesis to lead optimization programs in drug discovery has been an ongoing challenge since the first reports of library synthesis. A number of approaches to the application of parallel array synthesis to lead optimization have been attempted over the years, ranging from widespread deployment by (and support of) individual medicinal chemists to centralization as a service by an expert core team. This manuscript describes our experience with the latter approach, which was undertaken as part of a larger initiative to optimize drug discovery. In particular, we highlight how concepts taken from the manufacturing sector can be applied to drug discovery and parallel synthesis to improve the timeliness and thus the impact of arrays on drug discovery.
Roy, Anuradha; McDonald, Peter R; Sittampalam, Sitta; Chaguturu, Rathnam
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.
Full Text Available The pharmaceutical industry faces unsustainable program failure despite significant increases in investment. Dwindling discovery pipelines, rapidly expanding R&D budgets and increasing regulatory control, predict significant gaps in the future drug markets. The cumulative duration of discovery from concept to commercialisation is unacceptably lengthy, and adds to the deepening crisis. Existing animal models predicting clinical translations are simplistic, highly reductionist and, therefore, not fit for purpose. The catastrophic consequences of ever-increasing attrition rates are most likely to be felt in the developing world, where resistance acquisition by killer diseases like malaria, tuberculosis and HIV have paced far ahead of new drug discovery. The coming of age of Omics-based applications makes available a formidable technological resource to further expand our knowledge of the complexities of human disease. The standardisation, analysis and comprehensive collation of the “data-heavy” outputs of these sciences are indeed challenging. A renewed focus on increasing reproducibility by understanding inherent biological, methodological, technical and analytical variables is crucial if reliable and useful inferences with potential for translation are to be achieved. The individual Omics sciences—genomics, transcriptomics, proteomics and metabolomics—have the singular advantage of being complimentary for cross validation, and together could potentially enable a much-needed systems biology perspective of the perturbations underlying disease processes. If current adverse trends are to be reversed, it is imperative that a shift in the R&D focus from speed to quality is achieved. In this review, we discuss the potential implications of recent Omics-based advances for the drug development process.
Prabhu, Vidya; Xu, Han
Site specific genome editing has been gradually employed in drug discovery and development process over the past few decades. Recent development of CRISPR technology has significantly accelerated the incorporation of genome editing in the bench side to bedside process. In this review, we summarize examples of applications of genome editing in the drug discovery and development process. We also discuss current hurdles and solutions of genome editing.
Gullo, Vincent P; Hughes, Dallas E
In recent years, large pharmaceutical companies have significantly reduced or eliminated the search for new therapeutic agents from natural sources. In spite of the many successes from natural product drug discovery, these companies have chosen to focus on compound libraries as the source of new lead compounds. Smaller biotechnology companies are continuing the search for novel natural products by developing and employing new and innovative approaches. This paper will describe some of these recent approaches to natural product drug discovery.:
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, excret...
Buscher, Brigitte; Laakso, Sirpa; Mascher, Hermann; Pusecker, Klaus; Doig, Mira; Dillen, Lieve; Wagner-Redeker, Winfried; Pfeifer, Thomas; Delrat, Pascal; Timmerman, Philip
Plasma protein binding (PPB) is an important parameter for a drug's efficacy and safety that needs to be investigated during each drug-development program. Even though regulatory guidance exists to study the extent of PPB before initiating clinical studies, there are no detailed instructions on how to perform and validate such studies. To explore how PPB studies involving bioanalysis are currently executed in the industry, the European Bioanalysis Forum (EBF) has conducted three surveys among their member companies: PPB studies in drug discovery (Part I); in vitro PPB studies in drug development (Part II); and in vivo PPB studies in drug development. This paper reflects the outcome of the three surveys, which, together with the team discussions, formed the basis of the EBF recommendation. The EBF recommends a tiered approach to the design of PPB studies and the bioanalysis of PPB samples: 'PPB screening' experiments in (early) drug discovery versus qualified/validated procedures in drug development.
Papassotiropoulos, Andreas; de Quervain, Dominique J F
Our knowledge about the molecular and neural mechanisms of emotional and cognitive processes has increased exponentially in the past decades. Unfortunately, there has been no translation of this knowledge into the development of novel and improved pharmacological treatments for psychiatric disorders. We comment on some of the reasons for failed drug discovery in psychiatry, particularly on the use of ill-suited disease models and on the use of diagnostic constructs unrelated to the underlying biological mechanisms. Furthermore, we argue that the use of human genetic findings together with biologically informed phenotypes and advanced data-mining methodology will catalyze the identification of promising drug targets and, finally, will lead to improved therapeutic outcomes.
In the past decade Clostridium difficile has become a bacterial pathogen of global significance. Epidemic strains have spread throughout hospitals, while community acquired infections and other sources ensure a constant inoculation of spores into hospitals. In response to the increasing medical burden, a new C. difficile antibiotic, fidaxomicin, was approved in 2011 for the treatment of C. difficile-associated diarrhea. Rudimentary fecal transplants are also being trialed as effective treatments. Despite these advances, therapies that are more effective against C. difficile spores and less damaging to the resident gastrointestinal microbiome and that reduce recurrent disease are still desperately needed. However, bringing a new treatment for C. difficile infection to market involves particular challenges. This review covers the current drug discovery pipeline, including both small molecule and biologic therapies, and highlights the challenges associated with in vitro and in vivo models of C. difficile infection for drug screening and lead optimization. PMID:25760275
Open source drug discovery is increasingly being sought as a solution for managing product development complexities. Three drivers encouraging the use of the open source strategy include: upstream knowledge-based complexities associated with complementary assets, technological complexities given the scale of research and interdependencies between disciplines and downstream commercialization complexities. While literature currently discusses the need for open source strategies and their outcomes, we have reached a critical stage for a framework to cohesively understand how the drivers affect the open source models chosen as well as the governance strategies to ensure a successful outcome both in terms of knowledge access and product development. In this paper, an initial framework is designed with a focus on the type of participant as impacting the motivation to participate in an open source initiative, the objective of any open source strategy as impacting the structural model adopted and the structure of knowledge produced as impacting its management. It is anticipated that this framework should then provide an opportunity to develop governance rules for open source drug discovery initiatives.
Sarah L Kinnings
Full Text Available The rise of multi-drug resistant (MDR and extensively drug resistant (XDR tuberculosis around the world, including in industrialized nations, poses a great threat to human health and defines a need to develop new, effective and inexpensive anti-tubercular agents. Previously we developed a chemical systems biology approach to identify off-targets of major pharmaceuticals on a proteome-wide scale. In this paper we further demonstrate the value of this approach through the discovery that existing commercially available drugs, prescribed for the treatment of Parkinson's disease, have the potential to treat MDR and XDR tuberculosis. These drugs, entacapone and tolcapone, are predicted to bind to the enzyme InhA and directly inhibit substrate binding. The prediction is validated by in vitro and InhA kinetic assays using tablets of Comtan, whose active component is entacapone. The minimal inhibition concentration (MIC(99 of entacapone for Mycobacterium tuberculosis (M.tuberculosis is approximately 260.0 microM, well below the toxicity concentration determined by an in vitro cytotoxicity model using a human neuroblastoma cell line. Moreover, kinetic assays indicate that Comtan inhibits InhA activity by 47.0% at an entacapone concentration of approximately 80 microM. Thus the active component in Comtan represents a promising lead compound for developing a new class of anti-tubercular therapeutics with excellent safety profiles. More generally, the protocol described in this paper can be included in a drug discovery pipeline in an effort to discover novel drug leads with desired safety profiles, and therefore accelerate the development of new drugs.
Kitchen, Douglas B.
Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20 years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.
Kitchen, Douglas B.
Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20 years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.
Hoyer, Patrik O
Methods for automated discovery of causal relationships from non-interventional data have received much attention recently. A widely used and well understood model family is given by linear acyclic causal models (recursive structural equation models). For Gaussian data both constraint-based methods (Spirtes et al., 1993; Pearl, 2000) (which output a single equivalence class) and Bayesian score-based methods (Geiger and Heckerman, 1994) (which assign relative scores to the equivalence classes) are available. On the contrary, all current methods able to utilize non-Gaussianity in the data (Shimizu et al., 2006; Hoyer et al., 2008) always return only a single graph or a single equivalence class, and so are fundamentally unable to express the degree of certainty attached to that output. In this paper we develop a Bayesian score-based approach able to take advantage of non-Gaussianity when estimating linear acyclic causal models, and we empirically demonstrate that, at least on very modest size networks, its accur...
Scannell, Jack W; Bosley, Jim
A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a) to provide quantitatively and historically plausible explanations of the contrast; and (b) identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc.) within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc.) whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (e.g., 10 fold, even 100 fold) changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated screening and
Jack W Scannell
Full Text Available A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a to provide quantitatively and historically plausible explanations of the contrast; and (b identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc. within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc. whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development, changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man can offset large (e.g., 10 fold, even 100 fold changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated
Cumming, John G; Finlay, M Raymond V; Giordanetto, Fabrizio; Hemmerling, Martin; Lister, Troy; Sanganee, Hitesh; Waring, Michael J
The productivity challenge facing the pharmaceutical industry is well documented. Strategies to improve productivity have mainly focused on enhancing efficiency, such as the application of Lean Six Sigma process improvement methods and the introduction of modeling and simulation in place of 'wet' experiments. While these strategies have their benefits, the real challenge is to improve effectiveness by reducing clinical failure rates. We advocate redesigning the screening cascade to identify and optimize novel compounds with improved efficacy against disease, not just with improved potency against the target. There should be greater use of disease-relevant phenotypic screens in conjunction with target-based assays to drive medicinal chemistry optimization. An opportunistic approach to polypharmacology is recommended. There should also be more emphasis on optimization of the molecular mechanism of action incorporating understanding of binding kinetics, consideration of covalent drug strategies and targeting allosteric modulators.
Full Text Available The International Cooperative Biodiversity Groups (ICBG Program based at the University of Illinois at Chicago (UIC is a program aimed to address the interdependent issues of inventory and conservation of biodiversity, drug discovery and sustained economic growth in both developing and developed countries. It is an interdisciplinary program involving the extensive synergies and collaborative efforts of botanists, chemists and biologists in the countries of Vietnam, Laos and the USA. The UIC-ICBG drug discovery efforts over the past 18 years have resulted in the collection of a cumulative total of more than 5500 plant samples (representing more than 2000 species, that were evaluated for their potential biological effects against cancer, HIV, bird flu, tuberculosis and malaria. The bioassay-guided fractionation and separation of the bioactive plant leads resulted in the isolation of approximately 300 compounds of varying degrees of structural complexity and/or biological activity. The present paper summarizes the significant drug discovery achievements made by the UIC-ICBG team of multidisciplinary collaborators in the project over the period of 1998–2012 and the projects carried on in the subsequent years by involving the researchers in Hong Kong.
Trägårdh, Magnus; Chappell, Michael J; Ahnmark, Andrea; Lindén, Daniel; Evans, Neil D; Gennemark, Peter
Input estimation is employed in cases where it is desirable to recover the form of an input function which cannot be directly observed and for which there is no model for the generating process. In pharmacokinetic and pharmacodynamic modelling, input estimation in linear systems (deconvolution) is well established, while the nonlinear case is largely unexplored. In this paper, a rigorous definition of the input-estimation problem is given, and the choices involved in terms of modelling assumptions and estimation algorithms are discussed. In particular, the paper covers Maximum a Posteriori estimates using techniques from optimal control theory, and full Bayesian estimation using Markov Chain Monte Carlo (MCMC) approaches. These techniques are implemented using the optimisation software CasADi, and applied to two example problems: one where the oral absorption rate and bioavailability of the drug eflornithine are estimated using pharmacokinetic data from rats, and one where energy intake is estimated from body-mass measurements of mice exposed to monoclonal antibodies targeting the fibroblast growth factor receptor (FGFR) 1c. The results from the analysis are used to highlight the strengths and weaknesses of the methods used when applied to sparsely sampled data. The presented methods for optimal control are fast and robust, and can be recommended for use in drug discovery. The MCMC-based methods can have long running times and require more expertise from the user. The rigorous definition together with the illustrative examples and suggestions for software serve as a highly promising starting point for application of input-estimation methods to problems in drug discovery.
Lee, Jonathan A; Chu, Shaoyou; Willard, Francis S; Cox, Karen L; Sells Galvin, Rachelle J; Peery, Robert B; Oliver, Sarah E; Oler, Jennifer; Meredith, Tamika D; Heidler, Steven A; Gough, Wendy H; Husain, Saba; Palkowitz, Alan D; Moxham, Christopher M
Phenotypic lead generation strategies seek to identify compounds that modulate complex, physiologically relevant systems, an approach that is complementary to traditional, target-directed strategies. Unlike gene-specific assays, phenotypic assays interrogate multiple molecular targets and signaling pathways in a target "agnostic" fashion, which may reveal novel functions for well-studied proteins and discover new pathways of therapeutic value. Significantly, existing compound libraries may not have sufficient chemical diversity to fully leverage a phenotypic strategy. To address this issue, Eli Lilly and Company launched the Phenotypic Drug Discovery Initiative (PD(2)), a model of open innovation whereby external research groups can submit compounds for testing in a panel of Lilly phenotypic assays. This communication describes the statistical validation, operations, and initial screening results from the first PD(2) assay panel. Analysis of PD(2) submissions indicates that chemical diversity from open source collaborations complements internal sources. Screening results for the first 4691 compounds submitted to PD(2) have confirmed hit rates from 1.6% to 10%, with the majority of active compounds exhibiting acceptable potency and selectivity. Phenotypic lead generation strategies, in conjunction with novel chemical diversity obtained via open-source initiatives such as PD(2), may provide a means to identify compounds that modulate biology by novel mechanisms and expand the innovation potential of drug discovery.
Ke, Zhipeng; Zhang, Xinzhuang; Cao, Zeyu; Ding, Yue; Li, Na; Cao, Liang; Wang, Tuanjie; Zhang, Chenfeng; Ding, Gang; Wang, Zhenzhong; Xu, Xiaojie; Xiao, Wei
Neurodegenerative diseases, referring to as the progressive loss of structure and function of neurons, constitute one of the major challenges of modern medicine. Traditional Chinese herbs have been used as a major preventive and therapeutic strategy against disease for thousands years. The numerous species of medicinal herbs and Traditional Chinese Medicine (TCM) compound formulas in nervous system disease therapy make it a large chemical resource library for drug discovery. In this work, we collected 7362 kinds of herbs and 58,147 Traditional Chinese medicinal compounds (Tcmcs). The predicted active compounds in herbs have good oral bioavailability and central nervous system (CNS) permeability. The molecular docking and network analysis were employed to analyze the effects of herbs on neurodegenerative diseases. In order to evaluate the predicted efficacy of herbs, automated text mining was utilized to exhaustively search in PubMed by some related keywords. After that, receiver operator characteristic (ROC) curves was used to estimate the accuracy of predictions. Our study suggested that most herbs were distributed in family of Asteraceae, Fabaceae, Lamiaceae and Apocynaceae. The predictive model yielded good sensitivity and specificity with the AUC values above 0.800. At last, 504 kinds of herbs were obtained by using the optimal cutoff values in ROC curves. These 504 herbs would be the most potential herb resources for neurodegenerative diseases treatment. This study would give us an opportunity to use these herbs as a chemical resource library for drug discovery of anti-neurodegenerative disease.
Warmuth, Markus; Kim, Sungjoon; Gu, Xiang-ju; Xia, Gang; Adrián, Francisco
Due to their ability to function as dominant oncogenes, protein kinases have become favored targets in the quest for 'molecularly-targeted' cancer chemotherapeutics. The discovery of a large number of cancer-associated mutations in the kinome, and the progress in developing specific small-molecule kinase inhibitors has increased the need for accurate, reproducible, and efficient kinase activity-dependent cellular assay systems. Ba/F3, a murine interleukin-3 dependent pro-B cell line is increasingly popular as a model system for assessing both the potency and downstream signaling of kinase oncogenes, and the ability of small-molecule kinase inhibitors to block kinase activity. Facilitated by their growth properties, Ba/F3 cells have recently been adapted to high-throughput assay formats for compound profiling. Further, several published approaches show promise in predicting resistance to small-molecule kinase inhibitors elicited by point mutations interfering with inhibitor binding. Ba/F3 cells are an increasingly popular tool in kinase drug discovery. The ability to test the transforming capacity of newly identified kinase mutations, and to profile drug candidates and compound libraries in high-throughput fashion, combined with the use of Ba/F3 cells to predict clinical resistance will greatly facilitate developments in this field.
@@ The role of NMR in the pharmaceutical industry has changed dramatically over the last decade. Once thought of as an analytical technique used primarily to support synthetic chemistry, NMR now has an important role in the investigation of biochemical changes involved in clinical diseases and drug toxicity. It is also used extensively to elucidate the structures of drug metabolites. Data obtained using LC NMR MS and 19F NMR will be used to illustrate the utility of hyphenated methods in identifying xenobiotic metabolites as part of a drug development program. The application of NMR to the study of potential drug toxicity will also be described using the cationic, amphiphilic drugs chloroquine and amiodarone. These drugs are known to induce phospholipidosis characterized by lysosomal lamellar bodies and drug accumulation. Using a metabonomic approach, NMR spectroscopy of urine allowed the identification of a combination of urinary biomarkers of phospholipidosis.
Leek, Hanna; Andersson, Shalini
The provision of pure enantiomers is of increasing importance not only for the pharmaceutical industry but also for agro-chemistry and biotechnology. In drug discovery and development, the enantiomers of a chiral drug depict unique chemical and pharmacological behaviors in a chiral environment, such as the human body, in which the stereochemistry of the chiral drugs determines their pharmacokinetic, pharmacodynamic and toxicological properties. We present a number of challenging case studies of up-to-kilogram separations of racemic or enriched isomer mixtures using preparative liquid chromatography and super critical fluid chromatography to generate individual enantiomers that have enabled the development of new candidate drugs within AstraZeneca. The combination of chromatography and racemization as well as strategies on when to apply preparative chiral chromatography of enantiomers in a multi-step synthesis of a drug compound can further facilitate accelerated drug discovery and the early clinical evaluation of the drug candidates.
Full Text Available The provision of pure enantiomers is of increasing importance not only for the pharmaceutical industry but also for agro-chemistry and biotechnology. In drug discovery and development, the enantiomers of a chiral drug depict unique chemical and pharmacological behaviors in a chiral environment, such as the human body, in which the stereochemistry of the chiral drugs determines their pharmacokinetic, pharmacodynamic and toxicological properties. We present a number of challenging case studies of up-to-kilogram separations of racemic or enriched isomer mixtures using preparative liquid chromatography and super critical fluid chromatography to generate individual enantiomers that have enabled the development of new candidate drugs within AstraZeneca. The combination of chromatography and racemization as well as strategies on when to apply preparative chiral chromatography of enantiomers in a multi-step synthesis of a drug compound can further facilitate accelerated drug discovery and the early clinical evaluation of the drug candidates.
Chen, Huajun; Xie, Guotong
To optimize drug development processes, pharmaceutical companies require principled approaches to integrate disparate data on a unified infrastructure, such as the web. The semantic web, developed on the web technology, provides a common, open framework capable of harmonizing diversified resources to enable networked and collaborative drug discovery. We survey the state of art of utilizing web ontologies and other semantic web technologies to interlink both data and people to support integrated drug discovery across domains and multiple disciplines. Particularly, the survey covers three major application categories including: i) semantic integration and open data linking; ii) semantic web service and scientific collaboration and iii) semantic data mining and integrative network analysis. The reader will gain: i) basic knowledge of the semantic web technologies; ii) an overview of the web ontology landscape for drug discovery and iii) a basic understanding of the values and benefits of utilizing the web ontologies in drug discovery. i) The semantic web enables a network effect for linking open data for integrated drug discovery; ii) The semantic web service technology can support instant ad hoc collaboration to improve pipeline productivity and iii) The semantic web encourages publishing data in a semantic way such as resource description framework attributes and thus helps move away from a reliance on pure textual content analysis toward more efficient semantic data mining.
Williams S. Ettouati, Pharm.D.
Full Text Available Objective: To describe the design and implementation of an elective course in drug discovery, development, and commercialization for pharmacy, medical, biomedical graduate, business, and law students. Case Study: This course included didactic lectures, student group discussions, a longitudinal assignment, and a question and answer panel session. A 9-item instrument using a 5-point response scale was used for course evaluation. The longitudinal assignment was the creation and presentation of a product lifecycle strategic plan (PLSP. Respondents rated ‘agree’ and ‘strongly agree’ in the course providing useful information on drug discovery (39% and 53%, drug development (39% and 60%, and drug commercialization (33% and 60%. The majority of student-reported overall understanding of the drug discovery and drug development process was rated ‘very good’ (49% and 46%, while the drug commercialization process was rated ‘good’ (46%. Conclusions: An elective course on drug discovery, development, and commercialization included enrollment of students with diverse educational training. The course provided useful information and improved overall student understanding.
Ribas, J.; Sadeghi, H.; Manbachi, A.; Leijten, Jeroen Christianus Hermanus; Brinegar, K.; Zhang, Y.S.; Ferreira, L.; Khademhosseini, A.
Cardiovascular diseases are prevalent worldwide and are the most frequent causes of death in the United States. Although spending in drug discovery/development has increased, the amount of drug approvals has seen a progressive decline. Particularly, adverse side effects to the heart and general
Ribas, J.; Sadeghi, H.; Manbachi, A.; Leijten, Jeroen Christianus Hermanus; Brinegar, K.; Zhang, Y.S.; Ferreira, L.; Khademhosseini, A.
Cardiovascular diseases are prevalent worldwide and are the most frequent causes of death in the United States. Although spending in drug discovery/development has increased, the amount of drug approvals has seen a progressive decline. Particularly, adverse side effects to the heart and general vasc
Steeds, Hannah; Carhart-Harris, Robin L.
Schizophrenia is a complex mental health disorder with positive, negative and cognitive symptom domains. Approximately one third of patients are resistant to currently available medication. New therapeutic targets and a better understanding of the basic biological processes that drive pathogenesis are needed in order to develop therapies that will improve quality of life for these patients. Several drugs that act on neurotransmitter systems in the brain have been suggested to model aspects of schizophrenia in animals and in man. In this paper, we selectively review findings from dopaminergic, glutamatergic, serotonergic, cannabinoid, GABA, cholinergic and kappa opioid pharmacological drug models to evaluate their similarity to schizophrenia. Understanding the interactions between these different neurotransmitter systems and their relationship with symptoms will be an important step towards building a coherent hypothesis for the pathogenesis of schizophrenia. PMID:25653831
Warner, Katherine Deigan; Ferré-D'Amaré, Adrian R
Riboswitches are structured mRNA elements that regulate gene expression in response to metabolite or second-messenger binding and are promising targets for drug discovery. Fragment-based drug discovery methods have identified weakly binding small molecule "fragments" that bind a thiamine pyrophosphate (TPP) riboswitch. However, these fragments require substantial chemical elaboration into more potent, drug-like molecules. Structure determination of the fragments bound to the riboswitch is the necessary next step. In this chapter, we describe the methods for co-crystallization and structure determination of fragment-bound TPP riboswitch structures. We focus on considerations for screening crystallization conditions across multiple crystal forms and provide guidance for building the fragment into the refined crystallographic model. These methods are broadly applicable for crystallographic analyses of any small molecules that bind structured RNAs.
Pinheiro, Alessandra C; Mendonça Nogueira, Thais C; de Souza, Marcus V N
Heterocyclic compounds are a class of substances, which play a critical role in modern drug discovery being incorporated in the structure of a large variety of drugs used in many different types of diseases. Quinoxaline is an important heterocyclic nucleus with a wide spectrum of biological activities, and recently much attention has been found on anticancer drug discovery based on this class. Owing to the importance of this system, the aim of this review is to provide an update on the synthesis and anticancer activity of quinoxaline derivatives covering articles published between 2010 and 2015.
Liu, Ke; Liu, Yanli; Lau, Johnathan L; Min, Jinrong
Chromatin structure is dynamically modulated by various chromatin modifications, such as histone/DNA methylation and demethylation. We have reviewed histone methyltransferases and methyllysine binders in terms of small molecule screening and drug discovery in the first part of this review series. In this part, we will summarize recent progress in chemical probe and drug discovery of histone demethylases and DNA methyltransferases. Histone demethylation and DNA methylation have attracted a lot of attention regarding their biology and disease implications. Correspondingly, many small molecule compounds have been designed to modulate the activity of histone demethylases and DNA methyltransferases, and some of them have been developed into therapeutic drugs or put into clinical trials.
Harati, Sahar; Cooper, Lee A D; Moran, Josue D; Giuste, Felipe O; Du, Yuhong; Ivanov, Andrei A; Johns, Margaret A; Khuri, Fadlo R; Fu, Haian; Moreno, Carlos S
Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal.
Khalil, Hilal S; Mitev, Vanio; Vlaykova, Tatyana; Cavicchi, Laura; Zhelev, Nikolai
Seliciclib (R-Roscovitine) was identified as an inhibitor of CDKs and has undergone drug development and clinical testing as an anticancer agent. In this review, the authors describe the discovery of Seliciclib and give a brief summary of the biology of the CDKs Seliciclib inhibits. An overview of the published in vitro and in vivo work supporting the development as an anti-cancer agent, from in vitro experiments to animal model studies ending with a summary of the clinical trial results and trials underway is presented. In addition some potential non-oncology applications are explored and the potential mode of action of Seliciclib in these areas is described. Finally the authors argue that optimisation of the therapeutic effects of kinase inhibitors such as Seliciclib could be enhanced using a systems biology approach involving mathematical modelling of the molecular pathways regulating cell growth and division.
Ye, Libin; Maji, Suvrajit; Sanghera, Narinder; Gopalasingam, Piraveen; Gorbunov, Evgeniy; Tarasov, Sergey; Epstein, Oleg; Klein-Seetharaman, Judith
Recently, major progress has been made in uncovering the mechanisms of how insulin engages its receptor and modulates downstream signal transduction. Here, we present in detail the current structural knowledge surrounding the individual components of the complex, binding sites, and dynamics during the activation process. A novel kinase triggering mechanism, the 'bow-arrow model', is proposed based on current knowledge and computational simulations of this system, in which insulin, after its initial interaction with binding site 1, engages with site 2 between the fibronectin type III (FnIII)-1 and -2 domains, which changes the conformation of FnIII-3 and eventually translates into structural changes across the membrane. This model provides a new perspective on the process of insulin binding to its receptor and, thus, could lead to future novel drug discovery efforts. Copyright © 2017 Elsevier Ltd. All rights reserved.
Nettleton, David O; Einolf, Heidi J
Evaluation of the potential of a drug candidate to inhibit or inactivate cytochrome P450 (CYP) enzymes remains an important part of pharmaceutical drug Discovery and Development programs. CYP enzymes are considered to be one of the most important enzyme families involved in the metabolic clearance of the vast majority of prescribed drugs. Clinical drug-drug interactions (DDI) involving inhibition or time-dependent inactivation of these enzymes can result in dangerous side effects resulting from reduced clearance/increased exposure of the drug being affected (the 'victim' drug). In this regard, pharmaceutical companies have become quite vigilant in mitigating CYP inhibition/inactivation liabilities of drug candidates early in Discovery including continued risk assessment throughout Development. In this review, common strategies and decision making processes for the assessment of DDI risk in the different stages of pharmaceutical development are discussed. In addition, in vitro study designs, analysis, and interpretation of CYP inhibition and inactivation data are described in stage appropriate context. The in vitro tools and knowledge available now enable the Discovery Chemist to place the potential CYP DDI liability of a drug candidate into perspective and to aid in the optimization of chemical drug design to further mitigate this risk.
Greef, J. van der; McBurney, R.N.
The pharmaceutical industry is currently beleaguered by close scrutiny from the financial community, regulators and the general public. Productivity, in terms of new drug approvals, has generally been falling for almost a decade and the safety of a number of highly successful drugs has recently been
Greef, J. van der; McBurney, R.N.
The pharmaceutical industry is currently beleaguered by close scrutiny from the financial community, regulators and the general public. Productivity, in terms of new drug approvals, has generally been falling for almost a decade and the safety of a number of highly successful drugs has recently been
Wang, Xia; Chen, Haipeng; Yang, Feng; Gong, Jiayu; Li, Shiliang; Pei, Jianfeng; Liu, Xiaofeng; Jiang, Hualiang; Lai, Luhua; Li, Honglin
The progress in computer-aided drug design (CADD) approaches over the past decades accelerated the early-stage pharmaceutical research. Many powerful standalone tools for CADD have been developed in academia. As programs are developed by various research groups, a consistent user-friendly online graphical working environment, combining computational techniques such as pharmacophore mapping, similarity calculation, scoring, and target identification is needed. We presented a versatile, user-friendly, and efficient online tool for computer-aided drug design based on pharmacophore and 3D molecular similarity searching. The web interface enables binding sites detection, virtual screening hits identification, and drug targets prediction in an interactive manner through a seamless interface to all adapted packages (e.g., Cavity, PocketV.2, PharmMapper, SHAFTS). Several commercially available compound databases for hit identification and a well-annotated pharmacophore database for drug targets prediction were integrated in iDrug as well. The web interface provides tools for real-time molecular building/editing, converting, displaying, and analyzing. All the customized configurations of the functional modules can be accessed through featured session files provided, which can be saved to the local disk and uploaded to resume or update the history work. iDrug is easy to use, and provides a novel, fast and reliable tool for conducting drug design experiments. By using iDrug, various molecular design processing tasks can be submitted and visualized simply in one browser without installing locally any standalone modeling softwares. iDrug is accessible free of charge at http://lilab.ecust.edu.cn/idrug.
Walker, Michael J A
James Black has many claims to pharmacological fame as the creator of two new classes of drugs (beta-blockers and H2 antihistamines) and as a tireless innovator in drug discovery strategies and analytical procedures. The latter attributes in particular assisted Black in the invention of the prototypes for the two major classes of drugs for which he is best known, propranolol and cimetidine. The clinical impact of these drugs on both morbidity and mortality has been profound. In addition, the application of his analytical approach to drug discovery and pharmacology led others in the field to create many other new classes of drugs. Shortly before he died in 2010, Black wrote a retrospective review of his research career that provides insight into his innovative thinking and career success. This overview affords readers a very personal picture of the man, his ideas and his contributions. Copyright © 2011 Elsevier Ltd. All rights reserved.
Lee, Jae W; Komar, Chad A; Bengsch, Fee; Graham, Kathleen; Beatty, Gregory L
Pancreatic ductal adenocarcinoma (PDAC) ranks fourth among cancer-related deaths in the United States. For patients with unresectable disease, treatment options are limited and lack curative potential. Preclinical mouse models of PDAC that recapitulate the biology of human pancreatic cancer offer an opportunity for the rational development of novel treatment approaches that may improve patient outcomes. With the recent success of immunotherapy for subsets of patients with solid malignancies, interest is mounting in the possible use of immunotherapy for the treatment of PDAC. Considered in this unit is the value of genetic mouse models for characterizing the immunobiology of PDAC and for investigating novel immunotherapeutics. Several variants of these models are described, all of which may be used in drug development and for providing information on unique aspects of disease biology and therapeutic responsiveness. © 2016 by John Wiley & Sons, Inc.
Bhattacharya, Sukanta S; Yadav, Jagjit S
Cytochrome P450 enzymes are a structurally conserved but functionally diverse group of heme-containing mixed function oxidases found across both prokaryotic and eukaryotic forms of the microbial world. Microbial P450s are known to perform diverse functions ranging from the synthesis of cell wall components to xenobiotic/drug metabolism to biodegradation of environmental chemicals. Conventionally, many microbial systems have been reported to mimic mammalian P450-like activation of drugs and were proposed as the in-vitro models of mammalian drug metabolism. Recent reports suggest that native or engineered forms of specific microbial P450s from these and other microbial systems could be employed for desired specific biotransformation reactions toward natural and synthetic (drug) compounds underscoring their emerging potential in drug improvement and discovery. On the other hand, microorganisms particularly fungi and actinomycetes have been shown to possess catabolic P450s with unusual potential to degrade toxic environmental chemicals including persistent organic pollutants (POPs). Wood-rotting basidiomycete fungi in particular have revealed the presence of exceptionally large P450 repertoire (P450ome) in their genomes, majority of which are however orphan (with no known function). Our pre- and post-genomic studies have led to functional characterization of several fungal P450s inducible in response to exposure to several environmental toxicants and their potential in bioremediation of these chemicals. This review is an attempt to summarize the post-genomic unveiling of this versatile enzyme superfamily in microbial systems and investigation of their potential to synthesize new drugs and degrade persistent pollutants, among other biotechnological applications.
Gilbert, Ian H
Target-based approaches for human African trypanosomiasis (HAT) and related parasites can be a valuable route for drug discovery for these diseases. However, care needs to be taken in selection of both the actual drug target and the chemical matter that is developed. In this article, potential criteria to aid target selection are described. Then the physiochemical properties of typical oral drugs are discussed and compared to those of known anti-parasitics.
Bai, Jane P. F.; Alekseyenko, Alexander V.; Statnikov, Alexander; Wang, I-Ming; Wong, Peggy H.
Gene expression is useful for identifying the molecular signature of a disease and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. Gene expression offers utility to guide drug discovery by illustrating engagement of the desired cellular pathways/networks, as well as avoidance of acting on the toxicological pathways. Successful employment of gene-expression signatures in the later stages of drug development depends on their linkage to ...
San Lucas, F Anthony; Fowler, Jerry; Chang, Kyle; Kopetz, Scott; Vilar, Eduardo; Scheet, Paul
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.
Gledhill, Robert; Kent, Sarah; Hudson, Brian; Richards, W Graham; Essex, Jonathan W; Frey, Jeremy G
The Schools Malaria Project (http://emalaria.soton.ac.uk/) brings together school students with university researchers in the hunt for a new antimalaria drug. The design challenge being offered to students is to use a distributed drug search and selection system to design potential antimalaria drugs. The system is accessed via a Web interface. This e-science project displays the results of the trials in an accessible manner, giving students an opportunity for discussion and debate both with peers and with the university contacts. The project has been implemented by using distributed computing techniques, spreading computer load over a network of machines that cross institutional boundaries, forming a grid. This provides access to greater computing power and allows a much more complex and detailed formulation of the drug design problem to be tackled for research, teaching, and learning.
Manly, Charles J.
Drug discovery today includes considerable focus of laboratory automation and other resources on both combinatorial chemistry and high-throughput screening, and computational chemistry has been a part of pharmaceutical research for many years. The real benefit of these technologies is beyond the exploitation of each individually. Only recently have significant efforts focused on effectively integrating these and other discovery disciplines to realize their larger potential. This technical not...
Nathan, Pradeep J; Phan, K Luan; Harmer, Catherine J; Mehta, Mitul A; Bullmore, Edward T
Functional imaging methods such as fMRI have been widely used to gain greater understanding of brain circuitry abnormalities in CNS disorders and their underlying neurochemical basis. Findings suggest that: (1) drugs with known clinical efficacy have consistent effects on disease relevant brain circuitry, (2) brain activation changes at baseline or early drug effects on brain activity can predict long-term efficacy; and (3) fMRI together with pharmacological challenges could serve as experimental models of disease phenotypes and be used for screening novel drugs. Together, these observations suggest that drug related modulation of disease relevant brain circuitry may serve as a promising biomarker/method for use in drug discovery to demonstrate target engagement, differential efficacy, dose-response relationships, and prediction of clinically relevant changes.
This work postulates the thesis that the development of the contemporary psychopharmacology, which began with the chemical changes imposed to molecules with antihistaminergic properties, modelled the current ethiopatogenic theories of mental diseases. The development of chlorpromazine and imipramine was coincident with the beginning of the research about neurotransmission. This coincidence contributed for the construction of the dopaminergic theory of schizophrenia and in the monoaminergic theory of depression. Limitations of the effectivity of current drugs, as observed in the trials CATIE and STAR-D may justify a change of perspective in the search for new molecular targets for the treatment of both diseases. Historical data are provided to illustrate the above mentioned thesis, in the perspective of two epistemological concepts: the context of discovery proposed by Hans Reichenbach and the epistemological obstacle, proposed by Gaston Bachelard.
Full Text Available Drug discovery and development has become longer and costlier process. The fear of failure and stringent regulatory review process is driving pharmaceutical companies towards “me too” drugs and improved generics (505(b (2 fillings. The discontinuance of molecules at late stage clinical trials is common these years. The molecules are withdrawn at various stages of discovery and development process for reasons such as poor ADME properties, lack of efficacy and safety reasons. Hence this review focuses on possible applications of formulation and drug delivery to salvage molecules and improve the drugability. The formulation and drug delivery technologies are suitable for addressing various issues contributing to attrition are discussed in detail.
Mitchell, Wayne; Matsumoto, Shunji
Traditional drug discovery starts by experimentally screening chemical libraries to find hit compounds that bind to protein targets, modulating their activity. Subsequent rounds of iterative chemical derivitization and rescreening are conducted to enhance the potency, selectivity, and pharmacological properties of hit compounds. Although computational docking of ligands to targets has been used to augment the empirical discovery process, its historical effectiveness has been limited because of the poor correlation of ligand dock scores and experimentally determined binding constants. Recent progress in super-computing, coupled to theoretical insights, allows the calculation of the Gibbs free energy, and therefore accurate binding constants, for usually large ligand-receptor systems. This advance extends the potential of virtual drug discovery. A specific embodiment of the technology, integrating de novo, abstract fragment based drug design, sophisticated molecular simulation, and the ability to calculate thermodynamic binding constants with unprecedented accuracy, are discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.
Harvey, Alan L; Edrada-Ebel, RuAngelie; Quinn, Ronald J
Natural products have been a rich source of compounds for drug discovery. However, their use has diminished in the past two decades, in part because of technical barriers to screening natural products in high-throughput assays against molecular targets. Here, we review strategies for natural product screening that harness the recent technical advances that have reduced these barriers. We also assess the use of genomic and metabolomic approaches to augment traditional methods of studying natural products, and highlight recent examples of natural products in antimicrobial drug discovery and as inhibitors of protein-protein interactions. The growing appreciation of functional assays and phenotypic screens may further contribute to a revival of interest in natural products for drug discovery.
Chang, Junghwa; Kwon, Ho Jeong
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.
Arvidsson, Per I; Sandberg, Kristian; Forsberg-Nilsson, Karin
The Science for Life Laboratory Drug Discovery and Development (SciLifeLab DDD) platform reaches out to Swedish academia with an industry-standard infrastructure for academic drug discovery, supported by earmarked funds from the Swedish government. In this review, we describe the build-up and operation of the platform, and reflect on our first two years of operation, with the ambition to share learnings and best practice with academic drug discovery centers globally. We also discuss how the Swedish Teacher Exemption Law, an internationally unique aspect of the innovation system, has shaped the operation. Furthermore, we address how this investment in infrastructure and expertise can be utilized to facilitate international collaboration between academia and industry in the best interest of those ultimately benefiting the most from translational pharmaceutical research - the patients.
Gordon, Laurie J.; Wayne, Gareth J.; Almqvist, Helena; Axelsson, Hanna; Seashore-Ludlow, Brinton; Treyer, Andrea; Lundbäck, Thomas; West, Andy; Hann, Michael M.; Artursson, Per
Inadequate target exposure is a major cause of high attrition in drug discovery. Here, we show that a label-free method for quantifying the intracellular bioavailability (Fic) of drug molecules predicts drug access to intracellular targets and hence, pharmacological effect. We determined Fic in multiple cellular assays and cell types representing different targets from a number of therapeutic areas, including cancer, inflammation, and dementia. Both cytosolic targets and targets localized in subcellular compartments were investigated. Fic gives insights on membrane-permeable compounds in terms of cellular potency and intracellular target engagement, compared with biochemical potency measurements alone. Knowledge of the amount of drug that is locally available to bind intracellular targets provides a powerful tool for compound selection in early drug discovery. PMID:28701380
Hagedorn, Peter H; Hansen, Bo R; Koch, Troels; Lindow, Morten
All drugs perturb the expression of many genes in the cells that are exposed to them. These gene expression changes can be divided into effects resulting from engaging the intended target and effects resulting from engaging unintended targets. For antisense oligonucleotides, developments in bioinformatics algorithms, and the quality of sequence databases, allow oligonucleotide sequences to be analyzed computationally, in terms of the predictability of their interactions with intended and unintended RNA targets. Applying these tools enables selection of sequence-specific oligonucleotides where no- or only few unintended RNA targets are expected. To evaluate oligonucleotide sequence-specificity experimentally, we recommend a transcriptomics protocol where two or more oligonucleotides targeting the same RNA molecule, but with entirely different sequences, are evaluated together. This helps to clarify which changes in cellular RNA levels result from downstream processes of engaging the intended target, and which are likely to be related to engaging unintended targets. As required for all classes of drugs, the toxic potential of oligonucleotides must be evaluated in cell- and animal models before clinical testing. Since potential adverse effects related to unintended targeting are sequence-dependent and therefore species-specific, in vitro toxicology assays in human cells are especially relevant in oligonucleotide drug discovery. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.
Borsook, David; Hargreaves, Richard; Becerra, Lino
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
Sakakibara, Noriko; Yoshioka, Ryuzo; Matsumoto, Kazuo
In 1970s, the material patent system was introduced in Japan. Since then, many Japanese pharmaceutical companies have endeavored to create original in-house products. From 1980s, many of the innovative products were small molecular drugs and were developed using powerful medicinal-chemical technologies. Among them were antibiotics and effective remedies for the digestive organs and circulatory organs. During this period, Japanese companies were able to launch some blockbuster drugs. At the same time, the pharmaceutical market, which had grown rapidly for two decades, was beginning to level off. From the late 1990s, drug development was slowing down due to the lack of expertise in biotechnology such as genetic engineering. In response to the circumstances, the research and development on biotechnology-based drugs such as antibody drugs have become more dynamic and popular at companies than small molecule drugs. In this paper, the writers reviewed in detail the transitions in drug discovery and development between 1980 and 2010.
Tan, Tsung; Watts, Stephanie W.; Davis, Robert Patrick
Successful drug delivery using implantable pumps may be found in over 12,500 published articles. Their versatility in delivering continuous infusion, intermittent or complex infusion protocols acutely or chronically has made them ubiquitous in drug discovery and basic research. The recent availability of iPRECIO®, a programmable, refillable, and implantable infusion pump has made it possible to carry out quantitative pharmacology (PKPD) in single animals. When combined with specialized cathet...
Frijters, Raoul; van Vugt, Marianne; Smeets, Ruben; van Schaik, René; de Vlieg, Jacob; Alkema, Wynand
The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs.
@@ NS5A is one of the non-structural gene products encoded by Hepatitis C virus (HCV) and related viruses that are essential for viral replication. The amino acid sequence of NS5A is conserved between different HCV genotypes and the primary amino acid sequence of NS5A is unique to HCV and closely related viruses. Importantly, NS5A is unrelated to any human protein. This indicates that drugs designed to block the actions of NS5A could inhibit the replication of HCV without showing toxic side effects in human host cells, thus making NS5A inhibitors ideal anti-viral drugs. However, there are presently no functional assays for this essential viral protein. Therefore, conventional high throughput screening (HTS) approaches can not be used to discover antiviral drugs against NS5A.
Radoshitzky, Sheli R.; Kuhn, Jens H.; de Kok-Mercado, Fabian; Jahrling, Peter B.; Bavari, Sina
Introduction Seven arenaviruses cause viral hemorrhagic fever in humans: the Old World arenaviruses Lassa and Lujo, and the New World Clade B arenaviruses Machupo (MACV), Junín (JUNV), Guanarito (GTOV), Sabiá (SABV), and Chapare (CHPV). All of these viruses are Risk Group 4 biosafety pathogens. MACV causes human disease outbreak with high case-fatality rates. To date, at least 1,200 cases with ≈200 fatalities have been recorded 1, 2. Areas covered This review summarizes available systems and technologies for the identification of antivirals against MACV, animal models for in vivo evaluation of novel inhibitors, present treatment of arenaviral diseases, overview of efficacious small molecules and other therapeutics reported to date, and strategies to identify novel inhibitors for anti-arenaviral therapy. Expert opinion New high-throughput approaches to quantitate infection rates of areaviruses, as well as viruses modified to carry reporter genes, will accelerate compound screens and drug discovery efforts. RNAi, gene expression profiling and proteomics studies will identify host targets for therapeutic intervention. New discoveries in the cell entry mechanism of MACV and other arenaviruses as well as extensive structural studies of arenaviral L and NP could facilitate the rational design of antivirals effective against all pathogenic New World arenaviruses. PMID:22607481
Comparison of triple quadrupole, hybrid linear ion trap triple quadrupole, time-of-flight and LTQ-Orbitrap mass spectrometers in drug discovery phase metabolite screening and identification in vitro--amitriptyline and verapamil as model compounds.
Rousu, Timo; Herttuainen, Jukka; Tolonen, Ari
Liquid chromatography in combination with mass spectrometry (LC/MS) is a superior analytical technique for metabolite profiling and identification studies performed in drug discovery and development laboratories. In the early phase of drug discovery the analytical approach should be both time- and cost-effective, thus providing as much data as possible with only one visit to the laboratory, without the need for further experiments. Recent developments in mass spectrometers have created a situation where many different mass spectrometers are available for the task, each with their specific strengths and drawbacks. We compared the metabolite screening properties of four main types of mass spectrometers used in analytical laboratories, considering both the ability to detect the metabolites and provide structural information, as well as the issues related to time consumption in laboratory and thereafter in data processing. Human liver microsomal incubations with amitriptyline and verapamil were used as test samples, and early-phase 'one lab visit only' approaches were used with all instruments. In total, 28 amitriptyline and 69 verapamil metabolites were found and tentatively identified. Time-of-flight mass spectrometry (TOFMS) was the only approach detecting all of them, shown to be the most suitable instrument for elucidating as comprehensive metabolite profile as possible leading also to lowest overall time consumption together with the LTQ-Orbitrap approach. The latter however suffered from lower detection sensitivity and false negatives, and due to slow data acquisition rate required slower chromatography. Approaches with triple quadrupole mass spectrometry (QqQ) and hybrid linear ion trap triple quadrupole mass spectrometry (Q-Trap) provided the highest amount of fragment ion data for structural elucidation, but, in addition to being unable to produce very high-important accurate mass data, they suffered from many false negatives, and especially with the Qq
The introduction of antibiotics into clinical practice revolutionized the treatment and management of infectious diseases. Before the introduction of antibiotics, these diseases were the leading cause of morbidity and mortality in human populations. This review presents a brief history of discovery of the main antimicrobial classes (arsphenamines, β-lactams, sulphonamides, polypeptides, aminoglycosides, tetracyclines, amphenicols, lipopeptides, macrolides, oxazolidinones, glycopeptides, streptogramins, ansamycins, quinolones, and lincosamides) that have changed the landscape of contemporary medicine. Given within a historical timeline context, the review discusses how the introduction of certain antimicrobial classes affected the morbidity and mortality rates due to bacterial infectious diseases in human populations. Problems of resistance to antibiotics of different classes are also extensively discussed. Copyright © 2016 Elsevier Inc. All rights reserved.
Full Text Available The present review describes research on novel natural antitumor alkaloids isolated from marine invertebrates. The structure, origin, and confirmed cytotoxic activity of more than 130 novel alkaloids belonging to several structural families (indoles, pyrroles, pyrazines, quinolines, and pyridoacridines, together with some of their synthetic analogs, are illustrated. Recent discoveries concerning the current state of the potential and/or development of some of them as new drugs, as well as the current knowledge regarding their modes of action, are also summarized. A special emphasis is given to the role of marine invertebrate alkaloids as an important source of leads for anticancer drug discovery.
@@ Since being developed approximately 20 years ago, high throughput screening (HTS) has become one of the key techniques used in drug discovery. However, three main problems are recognized with the use of HTS; namely, with the compound library, drug targets, and assay methods. Until now, the compound library has evolved based on the techniques of combinatorial chemistry and modern phytochemistry. Several functional proteins have emerged following the advance of genomics and proteomics. However,although many functional proteins have been discovered recently, they are not, as sometimes claimed, real drug targets;at best, they might be potential drug targets. The ideal targets selected for drug screening should qualify as drug targets. The selection of targets for drug screening is a crucial procedure in drug screening.
Brown, Dean G; Lister, Troy; May-Dracka, Tricia L
Natural products have been a rich source of antibacterial drugs for many decades, but investments in this area have declined over the past two decades. The purpose of this review article is to provide a recent survey of new natural product classes and the mechanisms by which they work.
Kim, Wooseong; Hendricks, Gabriel Lambert; Lee, Kiho; Mylonakis, Eleftherios
The emergence of antibiotic-resistant and -tolerant bacteria is a major threat to human health. Although efforts for drug discovery are ongoing, conventional bacteria-centered screening strategies have thus far failed to yield new classes of effective antibiotics. Therefore, new paradigms for discovering novel antibiotics are of critical importance. Caenorhabditis elegans, a model organism used for in vivo, offers a promising solution for identification of anti-infective compounds. Areas covered: This review examines the advantages of C. elegans-based high-throughput screening over conventional, bacteria-centered in vitro screens. It discusses major anti-infective compounds identified from large-scale C. elegans-based screens and presents the first clinically-approved drugs, then known bioactive compounds, and finally novel small molecules. Expert opinion: There are clear advantages of using a C. elegans-infection based screening method. A C. elegans-based screen produces an enriched pool of non-toxic, efficacious, potential anti-infectives, covering: conventional antimicrobial agents, immunomodulators, and anti-virulence agents. Although C. elegans-based screens do not denote the mode of action of hit compounds, this can be elucidated in secondary studies by comparing the results to target-based screens, or conducting subsequent target-based screens, including the genetic knock-down of host or bacterial genes.
Full Text Available In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on, as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets.
Biggin, Philip C; Aldeghi, Matteo; Bodkin, Michael J; Heifetz, Alexander
Most of the previous content of this book has focused on obtaining the structures of membrane proteins. In this chapter we explore how those structures can be further used in two key ways. The first is their use in structure based drug design (SBDD) and the second is how they can be used to extend our understanding of their functional activity via the use of molecular dynamics. Both aspects now heavily rely on computations. This area is vast, and alas, too large to consider in depth in a single book chapter. Thus where appropriate we have referred the reader to recent reviews for deeper assessment of the field. We discuss progress via the use of examples from two main drug target areas; G-protein coupled receptors (GPCRs) and ion channels. We end with a discussion of some of the main challenges in the area.
Bauer, Armin; Brönstrup, Mark
Covering: up to March 2013. In addition to their prominent role in basic biological and chemical research, natural products are a rich source of commercial products for the pharmaceutical and other industries. Industrial natural product chemistry is of fundamental importance for successful product development, as the vast majority (ca. 80%) of commercial drugs derived from natural products require synthetic efforts, either to enable economical access to bulk material, and/or to optimize drug properties through structural modifications. This review aims to illustrate issues on the pathway from lead to product, and how they have been successfully addressed by modern natural product chemistry. It is focused on natural products of current relevance that are, or are intended to be, used as pharmaceuticals.
agonists or antagonists) and then virtually screen the USDA Phytochemical, Chinese Herbal Medicine, and the FDA Marketed Drug Databases for new estrogens...the basis for potent ER agonists and antagonists that are in the registered pharmaceuticals and herbal medicine databases. The 29 analogs obtained...Kundu, Biology Department, “Formulation of a targeted nanoparticle system for the treatment of breast cancer” 7- Monday November 25, 2013, Dr. Partha
In the period 1970-2006, a total of 24 unique natural products were discovered that led to an approved drug. We analyze these successful leads in terms of drug-like properties, and show that they can be divided into two equal subsets. The first falls in the 'Lipinski universe' and complies with the Rule of Five. The second is a 'parallel universe' that violates the rules. Nevertheless, the latter compounds remain largely compliant in terms of logP and H-bond donors, highlighting the importance of these two metrics in predicting bioavailability. Natural products are often cited as an exception to Lipinski's rules. We believe this is because nature has learned to maintain low hydrophobicity and intermolecular H-bond donating potential when it needs to make biologically active compounds with high molecular weight and large numbers of rotatable bonds. In addition, natural products are more likely than purely synthetic compounds to resemble biosynthetic intermediates or endogenous metabolites, and hence take advantage of active transport mechanisms. Interestingly, the natural product leads in the Lipinski and parallel universe had an identical success rate (50%) in delivering an oral drug.
Smith, Paul F; Darlington, Cynthia L
Progress has been made in understanding the neural basis of subjective tinnitus (ST); however, this has not, as yet, translated into many new drug treatments. One reason for this is that realistic behavioral models of ST in animals have been developed only recently, and are still not widely used. Nonetheless, some significant pharmacological advances have been made. At present, there is evidence to support the efficacy of transtympanic gentamicin administration in the treatment of tinnitus associated with Meniere's disease; there is also some evidence to support the efficacy of intratympanic steroid and lidocaine application in the management of ST. Although benzodiazepines and anti-epileptic drugs appear to be effective in many cases of this condition, there is concern about their adverse side effect profile. Based on well-controlled clinical trials, vasodilators such as misoprostol, and histamine receptor ligands should be further investigated. Finally, given the evidence that ST is a form of sensory epilepsy, new antiepileptic drugs should be tested for potential efficacy as they are developed; such drugs may include novel N-methyl-D-aspartate receptor antagonists, as well as cannabinoids.
Saraswat, Komal; Rizvi, Syed Ibrahim
Scientific achievements in the last few decades, leading to effective therapeutic interventions, have dramatically improved human life expectancy. Consequently, aging has become a significant problem and represents the major risk factor for most human pathologies including diabetes, cardiovascular diseases, neurological disorders, and cancer. Scientific discoveries over the past two decades have been instrumental in dissecting molecular mechanism(s) which play important roles in determining longevity. The same understanding has also led to the acknowledgement of the plurality of 'causes' which act either alone or in combination to create the condition which can be defined as 'aging'. Areas covered: Over the years, several concepts have been put forward for the development of a viable anti-aging regimen. In this review, the authors extensively review anti aging interventions based on caloric restriction, activation of telomerase, autophagy inducers, senolytic therapeutics, plasma membrane redox system (PMRS) activators, epigenetic modulators, and stem cell therapies. Expert opinion: Based upon our current understanding, one of the most promising approaches for a successful anti-aging strategy includes the activation of adenosine monophosphate dependent protein kinase (AMPK). Another strategy may involve activation of PMRS. Future research efforts are likely to focus on nutrient and energy sensing molecular pathways which include mTOR, IGF-1, AMPK and the sirtuins.
Scanlon, Thomas C; Dostal, Sarah M; Griswold, Karl E
We describe an ultra-high-throughput screening platform enabling discovery and/or engineering of natural product antibiotics. The methodology involves creation of hydrogel-in-oil emulsions in which recombinant microorganisms are co-emulsified with bacterial pathogens; antibiotic activity is assayed by use of a fluorescent viability dye. We have successfully utilized both bulk emulsification and microfluidic technology for the generation of hydrogel microdroplets that are size-compatible with conventional flow cytometry. Hydrogel droplets are ∼25 pL in volume, and can be synthesized and sorted at rates exceeding 3,000 drops/s. Using this technique, we have achieved screening throughputs exceeding 5 million clones/day. Proof-of-concept experiments demonstrate efficient selection of antibiotic-secreting yeast from a vast excess of negative controls. In addition, we have successfully used this technique to screen a metagenomic library for secreted antibiotics that kill the human pathogen Staphylococcus aureus. Our results establish the practical utility of the screening platform, and we anticipate that the accessible nature of our methods will enable others seeking to identify and engineer the next generation of antibacterial biomolecules. © 2013 Wiley Periodicals, Inc.
Williams, Glyn; Ferenczy, György G; Ulander, Johan; Keserű, György M
Small is beautiful - reducing the size and complexity of chemical starting points for drug design allows better sampling of chemical space, reveals the most energetically important interactions within protein-binding sites and can lead to improvements in the physicochemical properties of the final drug. The impact of fragment-based drug discovery (FBDD) on recent drug discovery projects and our improved knowledge of the structural and thermodynamic details of ligand binding has prompted us to explore the relationships between ligand-binding thermodynamics and FBDD. Information on binding thermodynamics can give insights into the contributions to protein-ligand interactions and could therefore be used to prioritise compounds with a high degree of specificity in forming key interactions.
von Korff, Modest; Sander, Thomas
A new method is introduced to calculate the complexity of organic molecules in drug discovery. The complexity is calculated by taking the number of unique connected subgraphs u as basis c = f(a, b, p, u). With a and b are the number of atoms and bonds, respectively and p is the ratio of covered bonds by redundant fragments. A set of five datasets with 50 molecules each was analyzed. The datasets were compiled from bioactive natural products, approved drugs, highly bioactive molecules, commercially available compounds for high throughput screening and artificial generated molecules. Comparing the median of c for the five datasets showed a significant increase in the following order: commercially available compounds drugs drug discovery.
Morgado, Pedro; Manna, Dipak; Singh, Upinder
In recent years, substantial progress has been made in understanding the molecular and cell biology of the human parasite Entamoeba histolytica, an important pathogen with significant global impact. This review outlines some recent advances in the Entamoeba field in the last five years, focusing on areas that have not recently been discussed in detail: (i) molecular mechanisms regulating parasite gene expression, (ii) new efforts at drug discovery using high-throughput drug screens, and (iii) the effect of gut microbiota on amoebiasis.
Full Text Available The productivity decline in drug discovery and development is mainly caused by two factors; higher regulatory hurdles and low-hanging fruits being all picked. In addition, the recent target-based approach is thought to be increasing the price of innovation. Although target-based approach had many successes, a postreductionism method, which is systems biology, is on the rise. In this review, we discuss the foundations of two distinct approaches in finding a new drug.
Djuric, Stevan W.; Hutchins, Charles W; Talaty, Nari N.
This review covers recent advances in the implementation of enabling chemistry technologies into the drug discovery process. Areas covered include parallel synthesis chemistry, high-throughput experimentation, automated synthesis and purification methods, flow chemistry methodology including photochemistry, electrochemistry, and the handling of “dangerous” reagents. Also featured are advances in the “computer-assisted drug design” area and the expanding application of novel mass spectrometry-...
Wu, Baojian; Dong, Dong
Glutathione transferases (GSTs) are important detoxifying enzymes that catalyze the conjugation of electrophilic substrates to glutathione. In recent years, GSTs have been of great interest in pharmacology and drug development because of their involvement in many important biological processes such as steroid and prostaglandin biosynthesis, tyrosine catabolism, and cell apoptosis. This review describes crystal structures for cytosolic GSTs and correlates active-site features with enzyme functions (e.g., steroid synthesis, tyrosine degradation, and dehydroascorbate reduction) and substrate selectivity. Use of these crystal structures for the design of specific inhibitors for several GST enzymes is also discussed.
Saeidnia, Soodabeh; Gohari, Ahmad R; Manayi, Azadeh
Pharmacognosy is a science, which study natural products as a source of new drug leads and effective drug development. Rational and economic search for novel lead structures could maximize the speed of drug discovery by using powerful high technology methods. Reverse pharmacognosy, a complementary to pharmacognosy, couples the high throughput screening (HTS), virtual screening and databases along with the knowledge of traditional medicines. These strategies lead to identification of numerous in vitro active and selective hits enhancing the speed of drug discovery from natural sources. Besides, reverse pharmacology is a target base drug discovery approach; in the first step, a hypothesis is made that the alteration of specific protein activity will produce beneficial curative effects. Both, reverse pharmacognosy and reverse pharmacology take advantages of high technology methods to accomplish their particular purposes. Moreover, reverse pharmacognosy effectively utilize traditional medicines and natural products as promising sources to provide new drug leads as well as promote the rational use of them by using valuable information like protein structure databases and chemical libraries which prepare pharmacological profile of traditional medicine, plant extract or natural compounds.
Villellas, Cristina; Lu, Ping
ABSTRACT Drug-resistant mycobacterial infections are a serious global health challenge, leading to high mortality and socioeconomic burdens in developing countries worldwide. New innovative approaches, from identification of new targets to discovery of novel chemical scaffolds, are urgently needed. Recently, energy metabolism in mycobacteria, in particular the oxidative phosphorylation pathway, has emerged as an object of intense microbiological investigation and as a novel target pathway in drug discovery. New classes of antibacterials interfering with elements of the oxidative phosphorylation pathway are highly active in combating dormant or latent mycobacterial infections, with a promise of shortening tuberculosis chemotherapy. The regulatory approval of the ATP synthase inhibitor bedaquiline and the discovery of Q203, a candidate drug targeting the cytochrome bc1 complex, have highlighted the central importance of this new target pathway. In this review, we discuss key features and potential applications of inhibiting energy metabolism in our quest for discovering potent novel and sterilizing drug combinations for combating tuberculosis. We believe that the combination of drugs targeting elements of the oxidative phosphorylation pathway can lead to a completely new regimen for drug-susceptible and multidrug-resistant tuberculosis.
Qian Cutrone, Jingfang Jenny; Huang, Xiaohua Stella; Kozlowski, Edward S; Bao, Ye; Wang, Yingzi; Poronsky, Christopher S; Drexler, Dieter M; Tymiak, Adrienne A
Synthetic macrocyclic peptides with natural and unnatural amino acids have gained considerable attention from a number of pharmaceutical/biopharmaceutical companies in recent years as a promising approach to drug discovery, particularly for targets involving protein-protein or protein-peptide interactions. Analytical scientists charged with characterizing these leads face multiple challenges including dealing with a class of complex molecules with the potential for multiple isomers and variable charge states and no established standards for acceptable analytical characterization of materials used in drug discovery. In addition, due to the lack of intermediate purification during solid phase peptide synthesis, the final products usually contain a complex profile of impurities. In this paper, practical analytical strategies and methodologies were developed to address these challenges, including a tiered approach to assessing the purity of macrocyclic peptides at different stages of drug discovery. Our results also showed that successful progression and characterization of a new drug discovery modality benefited from active analytical engagement, focusing on fit-for-purpose analyses and leveraging a broad palette of analytical technologies and resources.
Full Text Available How can diversity-oriented strategies for chemical synthesis provide chemical tools to help shape our understanding of complex cancer pathways and progress anti-cancer drug discovery efforts? This review (surveying the literature from 2003 to the present considers the applications of diversity-oriented synthesis (DOS, biology-oriented synthesis (BIOS and associated strategies to cancer biology and drug discovery, summarising the syntheses of novel and often highly complex scaffolds from pluripotent or synthetically versatile building blocks. We highlight the role of diversity-oriented synthetic strategies in producing new chemical tools to interrogate cancer biology pathways through the assembly of relevant libraries and their application to phenotypic and biochemical screens. The use of diversity-oriented strategies to explore structure-activity relationships in more advanced drug discovery projects is discussed. We show how considering appropriate and variable focus in library design has provided a spectrum of DOS approaches relevant at all stages in anti-cancer drug discovery.
Staniek, Agata; Woerdenbag, Herman J.; Kayser, Oliver
Endophytes, microorganisms that colonize internal tissues of all plant species, create a huge biodiversity with yet unknown novel natural products, presumed to push forward the frontiers of drug discovery. Next to the clinically acknowledged antineoplastic agent, paclitaxel, endophyte research has y
The discovery of antibiotics has enabled serious infections to be treated. However, bacteria resistant to several families of antibiotics and the emergence of new highly drug-resistant bacteria constitute a public health issue in France and across the world. Actions to prevent their transmission are being put in place. Copyright © 2015 Elsevier Masson SAS. All rights reserved.
Full Text Available The Zika virus (ZIKV outbreak in the Americas has caused global concern that we may be on the brink of a healthcare crisis. The lack of research on ZIKV in the over 60 years that we have known about it has left us with little in the way of starting points for drug discovery. Our response can build on previous efforts with virus outbreaks and lean heavily on work done on other flaviviruses such as dengue virus. We provide some suggestions of what might be possible and propose an open drug discovery effort that mobilizes global science efforts and provides leadership, which thus far has been lacking. We also provide a listing of potential resources and molecules that could be prioritized for testing as in vitro assays for ZIKV are developed. We propose also that in order to incentivize drug discovery, a neglected disease priority review voucher should be available to those who successfully develop an FDA approved treatment. Learning from the response to the ZIKV, the approaches to drug discovery used and the success and failures will be critical for future infectious disease outbreaks.
Staniek, Agata; Woerdenbag, Herman J.; Kayser, Oliver
Endophytes, microorganisms that colonize internal tissues of all plant species, create a huge biodiversity with yet unknown novel natural products, presumed to push forward the frontiers of drug discovery. Next to the clinically acknowledged antineoplastic agent, paclitaxel, endophyte research has y
Campbell, Kristyn; Booth, Stephanie A
Neurodegenerative diseases occur when neuronal cells in the brain or spinal cord progressively lose function and eventually die. Pathological analysis of these tissues reveals changes that include the loss of synapses, tangles of misfolded protein and immune cell activation, even during very early stages of disease well before debilitating clinical signs are apparent. This suggests that if neurodegeneration is treated early enough, drugs designed to delay the progress of these diseases by either repairing the early damage and loss of neurons, or protecting neuron functionality from further insult, may be efficacious. MicroRNAs (miRNAs) are small non-coding RNAs that can post-transcriptionally regulate gene expression. They are particularly numerous within neurons where many are expressed with high specificity, which suggests that they have important roles in the healthy brain. Indeed, miRNAs are essential for the post-mitotic survival of neurons, implying a crucial role in survival and neuroprotection. This has focused attention on exploring the use of miRNA-based drugs as a means to correct cellular abnormalities and maintain neuronal function in neurodegenerative diseases. These efforts are spurred on by the rapid progress to clinical trials for a number of miRNA-based therapies for other diseases such as cardiovascular diseases, fibrosis and cancer.
Smithers, Cameron C; Overduin, Michael
Rho GTPases regulate cellular morphology and dynamics, and some are key drivers of cancer progression. This superfamily offers attractive potential targets for therapeutic intervention, with RhoA, Rac1 and Cdc42 being prime examples. The challenges in developing agents that act on these signaling enzymes include the lack of obvious druggable pockets and their membrane-bound activities. However, progress in targeting the similar Ras protein is illuminating new strategies for specifically inhibiting oncogenic GTPases. The structures of multiple signaling and regulatory states of Rho proteins have been determined, and the post-translational modifications including acylation and phosphorylation points have been mapped and their functional effects examined. The development of inhibitors to probe the significance of overexpression and mutational hyperactivation of these GTPases underscores their importance in cancer progression. The ability to integrate in silico, in vitro, and in vivo investigations of drug-like molecules indicates the growing tractability of GTPase systems for lead optimization. Although no Rho-targeted drug molecules have yet been clinically approved, this family is clearly showing increasing promise for the development of precision medicine and combination cancer therapies.
Lin, Yuan; Zheng, Yi
Introduction Rho GTPases are master regulators of actomyosin structure and dynamics and play pivotal roles in a variety of cellular processes including cell morphology, gene transcription, cell cycle progression and cell adhesion. Because aberrant Rho GTPase signaling activities are widely associated with human cancer, key components of Rho GTPase signaling pathways have attracted increasing interest as potential therapeutic targets. Similar to Ras, Rho GTPases themselves were, until recently, deemed “undruggable” because of structure-function considerations. Several approaches to interfere with Rho GTPase signaling have been explored and show promise as new ways for tackling cancer cells. Areas covered This review focuses on the recent progress in targeting the signaling activities of three prototypical Rho GTPases, i.e. RhoA, Rac1, and Cdc42. The authors describe the involvement of these Rho GTPases, their key regulators and effectors in cancer. Furthermore, the authors discuss the current approaches for rationally targeting aberrant Rho GTPases along their signaling cascades, upstream and downstream of Rho GTPases and posttranslational modifications at a molecular level. Expert opinion To date, while no clinically effective drugs targeting Rho GTPase signaling for cancer treatment are available, tool compounds and lead drugs that pharmacologically inhibit Rho GTPase pathways have shown promise. Small molecule inhibitors targeting Rho GTPase signaling may add new treatment options for future precision cancer therapy, particularly in combination with other anti-cancer agents. PMID:26087073
It is expected that the incidence of various adverse effects of anticancer agents maybe decreased owing to the reduced drug distribution in normal tissue. Anticancer agent incorporating nanoparticles including micelles and liposomes can evade non-specific capture by the reticuloendothelial system because the outer shell of the nanoparticles is covered with polyethylene glycol. Consequently, the micellar and liposomal carrier can be delivered selectively to a tumor by utilizing the enhanced permeability and retention effect. Presently, several anticancer agent-incorporating nano-carrier systems are under preclinical and clinical evaluation. Several drug delivery system formulations have been approved worldwide. Regarding a pipeline of clinical development of anticancer agent incorporating micelle carrier system, several clinical trials are now underway not only in Japan but also in other countries. A Phase 3 trial of NK105, a paclitaxel incorporating micelle is now underway. In this paper, preclinical and clinical studies of NK105, NC-6004, cisplatin incorporating micelle, NC-6300, epirubicin incorporating micelle and the concept of cancer stromal targeting therapy using nanoparticles and monoclonal antibodies against cancer related stromal components are reviewed.
Ito, Takumi; Ando, Hideki; Handa, Hiroshi
Half a century ago, the sedative thalidomide caused a serious drug disaster because of its teratogenicity and was withdrawn from the market. However, thalidomide, which has returned to the market, is now used for the treatment of leprosy and multiple myeloma (MM) under strict control. The mechanism of thalidomide action had been a long-standing question. We developed a new affinity bead technology and identified cereblon (CRBN) as a thalidomide-binding protein. We found that CRBN functions as a substrate receptor of an E3 cullin-Ring ligase complex 4 (CRL4) and is a primary target of thalidomide teratogenicity. Recently, new thalidomide derivatives, called immunomodulatory drugs (IMiDs), have been developed by Celgene. Among them, lenalidomide (Len) and pomalidomide (Pom) were shown to exert strong therapeutic effects against MM. It was found that Len and Pom both bind CRBN-CRL4 and recruit neomorphic substrates (Ikaros and Aiolos). More recently it was reported that casein kinase 1a (Ck1a) was identified as a substrate for CRBN-CRL4 in the presence of Len, but not Pom. Ck1a breakdown explains why Len is specifically effective for myelodysplastic syndrome with 5q deletion. It is now proposed that binding of IMiDs to CRBN appears to alter the substrate specificity of CRBN-CRL4. In this review, we introduce recent findings on IMiDs.
Eribol, P; Uguz, A K; Ulgen, K O
Microfluidics has been the focus of interest for the last two decades for all the advantages such as low chemical consumption, reduced analysis time, high throughput, better control of mass and heat transfer, downsizing a bench-top laboratory to a chip, i.e., lab-on-a-chip, and many others it has offered. Microfluidic technology quickly found applications in the pharmaceutical industry, which demands working with leading edge scientific and technological breakthroughs, as drug screening and commercialization are very long and expensive processes and require many tests due to unpredictable results. This review paper is on drug candidate screening methods with microfluidic technology and focuses specifically on fabrication techniques and materials for the microchip, types of flow such as continuous or discrete and their advantages, determination of kinetic parameters and their comparison with conventional systems, assessment of toxicities and cytotoxicities, concentration generations for high throughput, and the computational methods that were employed. An important conclusion of this review is that even though microfluidic technology has been in this field for around 20 years there is still room for research and development, as this cutting edge technology requires ingenuity to design and find solutions for each individual case. Recent extensions of these microsystems are microengineered organs-on-chips and organ arrays.
Kim, Jong Kyoung; Choi, Seungjin
Methods for discriminative motif discovery in DNA sequences identify transcription factor binding sites (TFBSs), searching only for patterns that differentiate two sets (positive and negative sets) of sequences. On one hand, discriminative methods increase the sensitivity and specificity of motif discovery, compared to generative models. On the other hand, generative models can easily exploit unlabeled sequences to better detect functional motifs when labeled training samples are limited. In this paper, we develop a hybrid generative/discriminative model which enables us to make use of unlabeled sequences in the framework of discriminative motif discovery, leading to semisupervised discriminative motif discovery. Numerical experiments on yeast ChIP-chip data for discovering DNA motifs demonstrate that the best performance is obtained between the purely-generative and the purely-discriminative and the semisupervised learning improves the performance when labeled sequences are limited.
Chen, Yang; Xu, Rong
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 ( pdisease using CSNs, and demonstrated that the top-ranked genes are highly relevant to PD pathologenesis. We pin-pointed a top-ranked drug 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/. firstname.lastname@example.org.
Teichert, Russell W; Schmidt, Eric W; Olivera, Baldomero M
Constellation pharmacology is a cell-based high-content phenotypic-screening platform that utilizes subtype-selective pharmacological agents to elucidate the cell-specific combinations (constellations) of key signaling proteins that define specific cell types. Heterogeneous populations of native cells, in which the different individual cell types have been identified and characterized, are the foundation for this screening platform. Constellation pharmacology is useful for screening small molecules or for deconvoluting complex mixtures of biologically active natural products. This platform has been used to purify natural products and discover their molecular mechanisms. In the ongoing development of constellation pharmacology, there is a positive feedback loop between the pharmacological characterization of cell types and screening for new drug candidates. As constellation pharmacology is used to discover compounds with novel targeting-selectivity profiles, those new compounds then further help to elucidate the constellations of specific cell types, thereby increasing the content of this high-content platform.
Yang, Xiao; Ma, Cong
In vitro transcription assays have been developed and widely used for many years to study the molecular mechanisms involved in transcription. This process requires multi-subunit DNA-dependent RNA polymerase (RNAP) and a series of transcription factors that act to modulate the activity of RNAP during gene expression. Sequencing gel electrophoresis of radiolabeled transcripts is used to provide detailed mechanistic information on how transcription proceeds and what parameters can affect it. In this paper we describe the protocol to study how the essential elongation factor NusA regulates transcriptional pausing, as well as a method to identify an antibacterial agent targeting transcription initiation through inhibition of RNAP holoenzyme formation. These methods can be used a as platform for the development of additional approaches to explore the mechanism of action of the transcription factors which still remain unclear, as well as new antibacterial agents targeting transcription which is an underutilized drug target in antibiotic research and development.
Duch, Włodzisław; Swaminathan, Karthikeyan; Meller, Jarosław
Pattern recognition, machine learning and artificial intelligence approaches play an increasingly important role in rational drug design, screening and identification of candidate molecules and studies on quantitative structure-activity relationships (QSAR). In this review, we present an overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI). An emphasis is put on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand. We also discuss representative applications of AI methods to docking, screening and QSAR studies. The growing trend to integrate computational and experimental efforts in that regard and some future developments are discussed. In addition, we comment on a broader role of machine learning and artificial intelligence approaches in biomedical research.
Elmore, Charles S; Bragg, Ryan A
As Medicinal Chemists are responsible for the synthesis and optimization of compounds, they often provide intermediates for use by isotope chemistry. Nevertheless, there is generally an incomplete understanding of the critical factors involved in the labeling of compounds. The remit of an Isotope Chemistry group varies from company to company, but often includes the synthesis of compounds labeled with radioisotopes, especially H-3 and C-14 and occasionally I-125, and stable isotopes, especially H-2, C-13, and N-15. Often the remit will also include the synthesis of drug metabolites. The methods used to prepare radiolabeled compounds by Isotope Chemists have been reviewed relatively recently. However, the organization and utilization of Isotope Chemistry has not been discussed recently and will be reviewed herein.
Meek, Peter J; Liu, ZhiWei; Tian, LiFeng; Wang, Ching Y; Welsh, William J; Zauhar, Randy J
Identifying potential lead molecules is becoming a more automated process. We review Shape Signatures, a tool that is effective and easy to use compared with most computer aided drug design techniques. Laboratory researchers can apply this in silico technique cost-effectively without the need for specialized computer backgrounds. Identifying a potential lead molecule requires database screening, and this becomes rate-limiting once the database becomes too large. The use of Shape Signatures eliminates this concern and offers molecule screening rates that are in advance of any currently available method. Shape Signatures provides a conduit for researchers to conduct rapid identification of potential active molecules, and studies with this tool can be initiated with only one bioactive lead or receptor site.
Mukherjee, Arnab; Sasikala, Wilbee D
The ability of small molecules to perturb the natural structure and dynamics of nucleic acids is intriguing and has potential applications in cancer therapeutics. Intercalation is a special binding mode where the planar aromatic moiety of a small molecule is inserted between a pair of base pairs, causing structural changes in the DNA and leading to its functional arrest. Enormous progress has been made to understand the nature of the intercalation process since its idealistic conception five decades ago. However, the biological functions were detected even earlier. In this review, we focus mainly on the acridine and anthracycline types of drugs and provide a brief overview of the development in the field through various experimental methods that led to our present understanding of the subject. Subsequently, we discuss the molecular mechanism of the intercalation process, free-energy landscapes, and kinetics that was revealed recently through detailed and rigorous computational studies.
Malergue, Fabrice; van Agthoven, Andreas; Scifo, Caroline; Egan, Dave; Strous, Ger J
Drug discovery often requires the screening of compound libraries on tissue cultured cells. Some major targets in drug discovery belong to signal transduction pathways, and PerFix EXPOSE* allows easy flow cytometry phospho assays. We thus investigated the possibility to further simplify and automate
Paul A Novick
Full Text Available In the face of drastically rising drug discovery costs, strategies promising to reduce development timelines and expenditures are being pursued. Computer-aided virtual screening and repurposing approved drugs are two such strategies that have shown recent success. Herein, we report the creation of a highly-curated in silico database of chemical structures representing approved drugs, chemical isolates from traditional medicinal herbs, and regulated chemicals, termed the SWEETLEAD database. The motivation for SWEETLEAD stems from the observance of conflicting information in publicly available chemical databases and the lack of a highly curated database of chemical structures for the globally approved drugs. A consensus building scheme surveying information from several publicly accessible databases was employed to identify the correct structure for each chemical. Resulting structures are filtered for the active pharmaceutical ingredient, standardized, and differing formulations of the same drug were combined in the final database. The publically available release of SWEETLEAD (https://simtk.org/home/sweetlead provides an important tool to enable the successful completion of computer-aided repurposing and drug discovery campaigns.
Full Text Available The accumulation of abnormal prion protein (PrPSc converted from the normal cellular isoform of PrP (PrPC is assumed to induce pathogenesis in prion diseases. Therefore, drug discovery studies for these diseases have focused on the protein conversion process. We used a structure-based drug discovery algorithm (termed Nagasaki University Docking Engine: NUDE that ran on an intensive supercomputer with a graphic-processing unit to identify several compounds with anti-prion effects. Among the candidates showing a high-binding score, the compounds exhibited direct interaction with recombinant PrP in vitro, and drastically reduced PrPSc and protein-aggresomes in the prion-infected cells. The fragment molecular orbital calculation showed that the van der Waals interaction played a key role in PrPC binding as the intermolecular interaction mode. Furthermore, PrPSc accumulation and microgliosis were significantly reduced in the brains of treated mice, suggesting that the drug candidates provided protection from prion disease, although further in vivo tests are needed to confirm these findings. This NUDE-based structure-based drug discovery for normal protein structures is likely useful for the development of drugs to treat other conformational disorders, such as Alzheimer's disease.
Lee, Jonathan A; Berg, Ellen L
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.
Islam, Md Asiful; Alam, Fahmida; Khalil, Md Ibrahim; Sasongko, Teguh Haryo; Gan, Siew Hua
Globally, thrombosis-associated disorders are one of the main contributors to fatalities. Besides genetic influences, there are some acquired and environmental risk factors dominating thrombotic diseases. Although standard regimens have been used for a long time, many side effects still occur which can be life threatening. Therefore, natural products are good alternatives. Although the quest for antithrombotic natural products came to light only since the end of last century, in the last two decades, a considerable number of natural products showing antithrombotic activities (antiplatelet, anticoagulant and fibrinolytic) with no or minimal side effects have been reported. In this review, several natural products used as antithrombotic agents including medicinal plants, vegetables, fruits, spices and edible mushrooms which have been discovered in the last 15 years and their target sites (thrombogenic components, factors and thrombotic pathways) are described. In addition, the side effects, limitations and interactions of standard regimens with natural products are also discussed. The active compounds could serve as potential sources for future research on antithrombotic drug development. As a future direction, more advanced researches (in quest of the target cofactor or component involved in antithrombotic pathways) are warranted for the development of potential natural antithrombotic medications (alone or combined with standard regimens) to ensure maximum safety and efficacy.
Ma, Haiching; Horiuchi, Kurumi Y
HTS with microtiter plates has been the major tool used in the pharmaceutical industry to explore chemical diversity space and to identify active compounds and pharmacophores for specific biological targets. However, HTS faces a daunting challenge regarding the fast-growing numbers of drug targets arising from genomic and proteomic research, and large chemical libraries generated from high-throughput synthesis. There is an urgent need to find new ways to profile the activity of large numbers of chemicals against hundreds of biological targets in a fast, low-cost fashion. Chemical microarray can rise to this challenge because it has the capability of identifying and evaluating small molecules as potential therapeutic reagents. During the past few years, chemical microarray technology, with different surface chemistries and activation strategies, has generated many successes in the evaluation of chemical-protein interactions, enzyme activity inhibition, target identification, signal pathway elucidation and cell-based functional analysis. The success of chemical microarray technology will provide unprecedented possibilities and capabilities for parallel functional analysis of tremendous amounts of chemical compounds.
Zhan, Peng; Pannecouque, Christophe; De Clercq, Erik; Liu, Xinyong
The early effectiveness of combinatorial antiretroviral therapy (cART) in the treatment of HIV infection has been compromised to some extent by rapid development of multidrug-resistant HIV strains, poor bioavailability, and cumulative toxicities, and so there is a need for alternative strategies of antiretroviral drug discovery and additional therapeutic agents with novel action modes or targets. From this perspective, we first review current strategies of antiretroviral drug discovery and optimization, with the aid of selected examples from the recent literature. We highlight the development of phosphate ester-based prodrugs as a means to improve the aqueous solubility of HIV inhibitors, and the introduction of the substrate envelope hypothesis as a new approach for overcoming HIV drug resistance. Finally, we discuss future directions for research, including opportunities for exploitation of novel antiretroviral targets, and the strategy of activation of latent HIV reservoirs as a means to eradicate the virus.
Knutsen, Lars J S
This review provides an account of why more companies involved in drug discovery fail than succeed at releasing the creative energy of gifted scientists, whose invention of new drugs they rely upon to remain at the forefront of the biopharma industry. Initiatives aimed at improving output of new chemical entities often have the opposite effect from that intended and scientists become demotivated. Those with drive, vision and enthusiasm may move to smaller companies to rediscover the spirit of discovery. Some executives fail to understand the psyche of researchers; an applied understanding of the intrinsic motivation of scientists would improve research performance. Entities that focus on smaller autonomous units and sound ethical values will discover the most innovative and successful new drugs. Copyright © 2011 Elsevier Ltd. All rights reserved.
Davies, Mark; Nowotka, Michał; Papadatos, George; Dedman, Nathan; Gaulton, Anna; Atkinson, Francis; Bellis, Louisa; Overington, John P
ChEMBL is now a well-established resource in the fields of drug discovery and medicinal chemistry research. The ChEMBL database curates and stores standardized bioactivity, molecule, target and drug data extracted from multiple sources, including the primary medicinal chemistry literature. Programmatic access to ChEMBL data has been improved by a recent update to the ChEMBL web services (version 2.0.x, https://www.ebi.ac.uk/chembl/api/data/docs), which exposes significantly more data from the underlying database and introduces new functionality. To complement the data-focused services, a utility service (version 1.0.x, https://www.ebi.ac.uk/chembl/api/utils/docs), which provides RESTful access to commonly used cheminformatics methods, has also been concurrently developed. The ChEMBL web services can be used together or independently to build applications and data processing workflows relevant to drug discovery and chemical biology.
Dos Santos Fernandes, Guilherme Felipe; Jornada, Daniela Hartmann; de Souza, Paula Carolina; Chin, Chung Man; Pavan, Fernando Rogerio; Dos Santos, Jean Leandro
Tuberculosis (TB) is an infectious disease caused by bacterium of the Mycobacterium genus, mainly by Mycobacterium tuberculosis (MTB). The World Health Organization aims to substantially reduce the number of cases in the coming years; however, the increased number of multidrug-resistant (MDR) and extremely drug-resistant (XDR) forms of the bacterium and the lack of treatment for latent tuberculosis are challenges to be overcome. In this review, we have identified the most potent compounds described in the literature during recent years with MIC values < 7 µM, low toxicity and a high selective index. In addition, emerging targets in MTB are presented to provide new perspectives for the discovery of new antitubercular drugs. This review aims to summarize the current advances in and promote insights into antitubercular drug discovery.
Ballell, Lluís; Bates, Robert H; Young, Rob J; Alvarez-Gomez, Daniel; Alvarez-Ruiz, Emilio; Barroso, Vanessa; Blanco, Delia; Crespo, Benigno; Escribano, Jaime; González, Rubén; Lozano, Sonia; Huss, Sophie; Santos-Villarejo, Angel; Martín-Plaza, José Julio; Mendoza, Alfonso; Rebollo-Lopez, María José; Remuiñan-Blanco, Modesto; Lavandera, José Luis; Pérez-Herran, Esther; Gamo-Benito, Francisco Javier; García-Bustos, José Francisco; Barros, David; Castro, Julia P; Cammack, Nicholas
With the aim of fuelling open-source, translational, early-stage drug discovery activities, the results of the recently completed antimycobacterial phenotypic screening campaign against Mycobacterium bovis BCG with hit confirmation in M. tuberculosis H37Rv were made publicly accessible. A set of 177 potent non-cytotoxic H37Rv hits was identified and will be made available to maximize the potential impact of the compounds toward a chemical genetics/proteomics exercise, while at the same time providing a plethora of potential starting points for new synthetic lead-generation activities. Two additional drug-discovery-relevant datasets are included: a) a drug-like property analysis reflecting the latest lead-like guidelines and b) an early lead-generation package of the most promising hits within the clusters identified. PMID:23307663
@@ The group, headed by Prof.JIANG Hualiang with the CAS Shanghai Institute of Materia Medica, has been centering on the basic research of pharmaceutical science, including identifying new targets, studying new drug action mechanisms and discovering new drug candidates.On the basis of new methodology development, an effective multi-disciplinary research platform for drug research and discovery has been established through the integration of different disciplines of computational chemistry, organic synthesis, molecular and cellular biology.A bunch of creative results have been achieved in these areas.
Luciana Arantes Soares
Full Text Available Millions of people and animals suffer from superficial infections caused by a group of highly specialized filamentous fungi, the dermatophytes, which only infect keratinized structures. With the appearance of AIDS, the incidence of dermatophytosis has increased. Current drug therapy used for these infections is often toxic, long-term, and expensive and has limited effectiveness; therefore, the discovery of new anti dermatophytic compounds is a necessity. Natural products have been the most productive source for new drug development. This paper provides a brief review of the current literature regarding the presence of dermatophytes in immunocompromised patients, drug resistance to conventional treatments and new anti dermatophytic treatments.
Soares, Luciana Arantes; de Cássia Orlandi Sardi, Janaína; Gullo, Fernanda Patrícia; de Souza Pitangui, Nayla; Scorzoni, Liliana; Leite, Fernanda Sangalli; Giannini, Maria José Soares Mendes; Almeida, Ana Marisa Fusco
Millions of people and animals suffer from superficial infections caused by a group of highly specialized filamentous fungi, the dermatophytes, which only infect keratinized structures. With the appearance of AIDS, the incidence of dermatophytosis has increased. Current drug therapy used for these infections is often toxic, long-term, and expensive and has limited effectiveness; therefore, the discovery of new anti dermatophytic compounds is a necessity. Natural products have been the most productive source for new drug development. This paper provides a brief review of the current literature regarding the presence of dermatophytes in immunocompromised patients, drug resistance to conventional treatments and new anti dermatophytic treatments.
Mignani, Serge; Huber, Scot; Tomás, Helena; Rodrigues, João; Majoral, Jean-Pierre
In the pharmaceutical industry the long-term challenge of drug innovation is the key phrase throughout R&D that refers to increasing the output of original drug candidate molecules. To increase R&D productivity, implementation of new and strategic R&D orientations to develop new approaches or systems to identify hits and leads efficiently has taken place and enabled all scientists working in the drug discovery domain to develop innovative medicines for the 21st century. Copyright © 2015 Elsevier Ltd. All rights reserved.
Burns, Andrew R.; Luciani, Genna M.; Musso, Gabriel; Bagg, Rachel; Yeo, May; Zhang, Yuqian; Rajendran, Luckshika; Glavin, John; Hunter, Robert; Redman, Elizabeth; Stasiuk, Susan; Schertzberg, Michael; Angus McQuibban, G.; Caffrey, Conor R.; Cutler, Sean R.; Tyers, Mike; Giaever, Guri; Nislow, Corey; Fraser, Andy G.; MacRae, Calum A.; Gilleard, John; Roy, Peter J.
Parasitic nematodes infect one quarter of the world's population and impact all humans through widespread infection of crops and livestock. Resistance to current anthelmintics has prompted the search for new drugs. Traditional screens that rely on parasitic worms are costly and labour intensive and target-based approaches have failed to yield novel anthelmintics. Here, we present our screen of 67,012 compounds to identify those that kill the non-parasitic nematode Caenorhabditis elegans. We then rescreen our hits in two parasitic nematode species and two vertebrate models (HEK293 cells and zebrafish), and identify 30 structurally distinct anthelmintic lead molecules. Genetic screens of 19 million C. elegans mutants reveal those nematicides for which the generation of resistance is and is not likely. We identify the target of one lead with nematode specificity and nanomolar potency as complex II of the electron transport chain. This work establishes C. elegans as an effective and cost-efficient model system for anthelmintic discovery. PMID:26108372
Riese, David J.
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. PMID:21532939
White, David T; Eroglu, Arife Unal; Wang, Guohua; Zhang, Liyun; Sengupta, Sumitra; Ding, Ding; Rajpurohit, Surendra K; Walker, Steven L; Ji, Hongkai; Qian, Jiang; Mumm, Jeff S
The zebrafish has emerged as an important model for whole-organism small-molecule screening. However, most zebrafish-based chemical screens have achieved only mid-throughput rates. Here we describe a versatile whole-organism drug discovery platform that can achieve true high-throughput screening (HTS) capacities. This system combines our automated reporter quantification in vivo (ARQiv) system with customized robotics, and is termed 'ARQiv-HTS'. We detail the process of establishing and implementing ARQiv-HTS: (i) assay design and optimization, (ii) calculation of sample size and hit criteria, (iii) large-scale egg production, (iv) automated compound titration, (v) dispensing of embryos into microtiter plates, and (vi) reporter quantification. We also outline what we see as best practice strategies for leveraging the power of ARQiv-HTS for zebrafish-based drug discovery, and address technical challenges of applying zebrafish to large-scale chemical screens. Finally, we provide a detailed protocol for a recently completed inaugural ARQiv-HTS effort, which involved the identification of compounds that elevate insulin reporter activity. Compounds that increased the number of insulin-producing pancreatic beta cells represent potential new therapeutics for diabetic patients. For this effort, individual screening sessions took 1 week to conclude, and sessions were performed iteratively approximately every other day to increase throughput. At the conclusion of the screen, more than a half million drug-treated larvae had been evaluated. Beyond this initial example, however, the ARQiv-HTS platform is adaptable to almost any reporter-based assay designed to evaluate the effects of chemical compounds in living small-animal models. ARQiv-HTS thus enables large-scale whole-organism drug discovery for a variety of model species and from numerous disease-oriented perspectives.
class that occurs naturally in a variety of plants , not used, widely sold as an ingredient in nutritional supplements 7. Adrenalone (99-45-6...were first docked onto the HER2 homology model to study their binding modes with the help of MOE docking tools. Iressa did not bind to the hinge...These were Thr95, Gln96, Met98, Asp160, Lys50, Glu67, Thr159 (Figure 1). Figure 1. Binding modes of emodin onto HER2 protein homology model and a
Parenti, Marco Daniele; Rastelli, Giulio
Nowadays, the improvement of R&D productivity is the primary commitment in pharmaceutical research, both in big pharma and smaller biotech companies. To reduce costs, to speed up the discovery process and to increase the chance of success, advanced methods of rational drug design are very helpful, as demonstrated by several successful applications. Among these, computational methods able to predict the binding affinity of small molecules to specific biological targets are of special interest because they can accelerate the discovery of new hit compounds. Here we provide an overview of the most widely used methods in the field of binding affinity prediction, as well as of our own work in developing BEAR, an innovative methodology specifically devised to overtake some limitations in existing approaches. The BEAR method was successfully validated against different biological targets, and proved its efficacy in retrieving active compounds from virtual screening campaigns. The results obtained so far indicate that BEAR may become a leading tool in the drug discovery pipeline. We primarily discuss advantages and drawbacks of each technique and show relevant examples and applications in drug discovery.
De Clercq, Erik
Antiviral drug development has often followed a curious meandrous route, guided by serendipity rather than rationality. This will be illustrated by ten examples. The polyanionic compounds (i) polyethylene alanine (PEA) and (ii) suramin were designed as an antiviral agent (PEA) or known as an antitrypanosomal agent (suramin), before they emerged as, respectively, a depilatory agent, or reverse transcriptase inhibitor. The 2',3'-dideoxynucleosides (ddNs analogues) (iii) have been (and are still) used in the "Sanger" DNA sequencing technique, although they are now commercialized as nucleoside reverse transcriptase inhibitors (NRTIs) in the treatment of HIV infections. (E)-5-(2-Bromovinyl)-2'-deoxyuridine (iv) was discovered as a selective anti-herpes simplex virus compound and is now primarily used for the treatment of varicella-zoster virus infections. The prototype of the acyclic nucleoside phosphonates (ANPs), (S)-9-(3-hydroxy-2-phosphonylmethoxypropyl)adenine [(S)-HPMPA], (v) was never commercialized, although it gave rise to several marketed products (cidofovir, adefovir, and tenofovir). 1-[2-(Hydroxyethoxy)methyl]-6-(phenylthio)thymine (vi) and TIBO (tetrahydroimidazo[4,5,1-jk][1,4-benzodiazepin-2(1H)]-one and -thione) (vii) paved the way to a number of compounds (i.e., nevirapine, delavirdine, etravirine, and rilpivirine), which are now collectively called non-NRTIs. The bicyclam AMD3100 (viii) was originally described as an anti-HIV agent before it became later marketed as a stem cell mobilizer. The S-adenosylhomocysteine hydrolase inhibitors (ix), while active against a broad range of (-)RNA viruses and poxviruses may be particularly effective against Ebola virus, and for (x) the O-ANP derivatives, the potential application range encompasses virtually all DNA viruses.