Applications of NMR in drug discovery

NMR, already some 50 years old, has long been an invaluable analytical method in industry for verification of chemical synthesis and compound characterisation. The range of molecular information accessible through NMR, however, offers a far larger horizon of applications. Of these, ligand screening by NMR has emerged as a very promising new method in drug discovery. Its unmatched screening sensitivity, combined with the abundance of available information on the structure and nature of molecular binding, justifies the growing interest in this dynamically expanding NMR application.     

REDOR NMR for drug discovery

Rotational-echo double-resonance (REDOR) NMR is a powerful and versatile solid-state NMR measurement that has been recruited to elucidate drug modes of action and to drive the design of new therapeutics. REDOR has been implemented to examine composition, structure, and dynamics in diverse macromolecular and whole-cell systems, including taxol-bound microtubules, enzyme–cofactor–inhibitor ternary complexes, and antibiotic–whole-cell complexes. The REDOR approach involves the integrated design of specific isotopic labeling strategies and the selection of appropriate REDOR experiments. By way of example, this digest illustrates the versatility of the REDOR approach, with an emphasis on the practical considerations of experimental design and data interpretation.     

Virtual screening strategies in drug discovery

The identification of novel therapeutic targets and characterization of their 3D structures is increasing at a dramatic rate. Computational screening methods continue to be developed and improved as credible and complementary alternatives to high-throughput biochemical compound screening (HTS). While the majority of drug candidates currently being developed have been found using HTS methods, high-throughput docking and pharmacophore-based searching algorithms are gaining acceptance and becoming a major source of lead molecules in drug discovery. Refinements and optimization of high-throughput docking methods have lead to improvements in reproducing experimental data and in hit rates obtained, validating their use in hit identification. In parallel with virtual screening methods, concomitant developments in cheminformatics including identification, design and manipulation of drug-like small molecule libraries have been achieved. Herein, currently used in silico screening techniques and their utility on a comparative and target dependent basis is discussed.     

Fragment-based drug discovery using NMR spectroscopy

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

Miniaturized screening technologies for drug discovery

Adaptive Profiling (APL) and other biochip companies aim to harness the power of microsystems technology together with advances in chemistry and molecular biology, to become service and technology providers to organizations involved in pharmaceutical research and development. By supplying a unique range of decision-making tools that aid an earlier identification of qualified drug candidates for clinical development, the company should gain a significant share of the 10 billion US dollar biological screening, bioavailability and toxicity assessment market.     

虚拟筛选与新药发现

虚拟筛选是创新药物研究的新方法和新技术,近年来引起了研究机构和制药公司的高度重视,并且已经成为一种与高通量筛选互补的实用化工具,加入到了创新药物研究的工作流程(pipeline)中。本文介绍国际上虚拟筛选及其在创新药物发现中应用的研究进展,特别介绍了我国这方面研究的状况。     

Hypothesis testing in high-throughput screening for drug discovery

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.     

Biomolecular NMR: a chaperone to drug discovery

Biomolecular NMR now contributes routinely to every step in the development of new chemical entities ahead of clinical trials. The versatility of NMR--from detection of ligand binding over a wide range of affinities and a wide range of drug targets with its wealth of molecular information, to metabolomic profiling, both ex vivo and in vivo--has paved the way for broadly distributed applications in academia and the pharmaceutical industry. Proteomics and initial target selection both benefit from NMR: screenings by NMR identify lead compounds capable of inhibiting protein-protein interactions, still one of the most difficult development tasks in drug discovery. NMR hardware improvements have given access to the microgram domain of phytochemistry, which should lead to the discovery of novel bioactive natural compounds. Steering medicinal chemists through the lead optimisation process by providing detailed information about protein-ligand interactions has led to impressive success in the development of novel drugs. The study of biofluid composition--metabonomics--provides information about pharmacokinetics and helps toxicological safety assessment in animal model systems. In vivo, magnetic resonance spectroscopy interrogates metabolite distributions in living cells and tissues with increasing precision, which significantly impacts the development of anticancer or neurological disorder therapeutics. An overview of different steps in recent drug discovery is presented to illuminate the links with the most recent advances in NMR methodology.     

Label-free target identification in drug discovery via phenotypic screening

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High-throughput screening of biocatalytic activity: applications in drug discovery

Enzymes catalyze a diverse set of reactions that propel life's processes and hence serve as valuable therapeutic targets. High-throughput screening methods have become essential for sifting through large chemical libraries in search of drug candidates, and several sensitive and reliable analytical techniques have been specifically adapted to high-throughput measurements of biocatalytic activity. High-throughput biocatalytic assay platforms thus enable rapid screening against enzymatic targets, and have vast potential to impact various stages of the drug discovery process, including lead identification and optimization, and ADME/Tox assessment. These advances are paving the way for the adoption of high-throughput biocatalytic assays as an indispensable tool for the pharmaceutical industry.     

Structure-based virtual screening of chemical libraries for drug discovery

One of the main goals in drug discovery is to identify new chemical entities that have a high likelihood of binding to the target protein to elicit the desired biological response. To this end, virtual screening is being increasingly used as a complement to high-throughput screening to improve the speed and efficiency of the drug discovery and development process. The availability of inexpensive high-performance computing platforms in recent years has transformed this field into one that is highly diverse and rapidly evolving, where large chemical databases have been successfully screened to identify hits for a wide range of targets such as Bcl-2 family proteins, G protein-coupled receptors, kinases, metalloproteins, nuclear hormone receptors, proteases and many more.     

Trends in ion channel drug discovery: advances in screening technologies

Ion channels mediate and regulate crucial electrical functions throughout the body. They are therapeutic drug targets for a variety of disorders and, in some cases, the direct cause of unwanted side-effects. Advances in medical genetics have increased our knowledge of ion channel structure–function relationships and identified disease-causing mutations in ion channel genes. The recognized importance of these proteins in health and disease has led to an active search for ion channel targets in the multi-billion-dollar worldwide drug discovery market. Trends in ion channel screening technologies have focused on increasing throughput and enhancing information content of assays through electrophysiological approaches. The ability to study ion channels by voltage clamp and their time-, voltage- and state-dependent drug interactions with enhanced throughput will ultimately play a key role in the development of novel, safe ion channel-targeted drugs.     

A solid-phase glycosyltransferase assay for high-throughput screening in drug discovery research

Glycosyltransferases mediate changes in glycosylation patterns which, in turn, may affect the function of glycoproteins and/or glycolipids and, further downstream, processes of development, differentiation, transformation and cell-cell recognition. Such enzymes, therefore, represent valid targets for drug discovery. We have developed a solid-phase glycosyltransferase assay for use in a robotic high-throughput format. Carbohydrate acceptors coupled covalently to polyacrylamide are coated onto 96-well plastic plates. The glycosyltransferase reaction is performed with recombinant enzymes and radiolabeled sugar-nucleotide donor at 37°C, followed by washing, addition of scintillation counting fluid, and measurement of radioactivity using a 96-well -counter. Glycopolymer construction and coating of the plastic plates, enzyme and substrate concentrations, and linearity with time were optimized using recombinant Core 2 1-6-N-acetylglucosaminyltransferase (Core 2 GlcNAc-T). This enzyme catalyzes a rate-limiting reaction for expression of polylactosamine and the selectin ligand sialyl-Lewis^x in -glycans. A glycopolymer acceptor for 1-6-N-acetylglucosaminyltransferase V was also designed and shown to be effective in the solid-phase assay. In a high-throughput screen of a microbial extract library, the coefficient of variance for positive controls was 9.4%, and high concordance for hit validation was observed between the Core 2 GlcNAc-T solid-phase assay and a standard solution-phase assay. The solid-phase assay format, which can be adapted for a variety of glycosyltransferase enzymes, allowed a 5–6 fold increase in throughput compared to the corresponding solution-phase assay.     

BEAR, a novel virtual screening methodology for drug discovery

BEAR (binding estimation after refinement) is a new virtual screening technology based on the conformational refinement of docking poses through molecular dynamics and prediction of binding free energies using accurate scoring functions. Here, the authors report the results of an extensive benchmark of the BEAR performance in identifying a smaller subset of known inhibitors seeded in a large (1.5 million) database of compounds. BEAR performance proved strikingly better if compared with standard docking screening methods. The validations performed so far showed that BEAR is a reliable tool for drug discovery. It is fast, modular, and automated, and it can be applied to virtual screenings against any biological target with known structure and any database of compounds.     

药物筛选新方法--高通量筛选

介绍了高通量筛选的概念、原理及其各个组成部分,着重阐明了高通量筛选的筛选模式,检测方法和应用实例,并简单介绍了国内在这方面的研究进展。     

Affinity selection-mass spectrometry screening techniques for small molecule drug discovery

Affinity selection-mass spectrometry (AS-MS) techniques assess the binding of candidate molecules to immobilized or soluble receptors, and these methods are gaining acceptance in high throughput screening laboratories as valuable complements to traditional drug discovery technologies. A diversity of receptor types have been evaluated by AS-MS, including those that are difficult to screen using traditional biochemical approaches. AS-MS techniques that couple liquid chromatography-MS with size-based separation methods, such as ultrafiltration, gel permeation, or size-exclusion chromatography, are particularly amenable to the demands of MS-based screening and have demonstrated the greatest success across a broad range of drug targets. MS measurements of receptor function have many of the same advantages as AS-MS screening and are increasingly used for drug discovery as well.     

Rational screening of oligonucleotide combinatorial libraries for drug discovery.

Combinatorial strategies offer the potential to generate and screen extremely large numbers of compounds and to identify individual molecules with a desired binding specificity or pharmacological activity. We describe a combinatorial strategy for oligonucleotides in which the library is generated and screened without using enzymes. Freedom from enzymes enables the use of oligonucleotide analogues. This dramatically extends the scope of both the compounds and the targets that may be screened. We demonstrate the utility of the method by screening 2'-O-Methyl and phosphorothioate oligonucleotide analogue libraries. Compounds have been identified that bind to the activated H-ras mRNA and that have potent antiviral activity against the human herpes simplex virus.     

Pharmacologically active metabolites,combination screening and target identification-driven drug repositioning in antituberculosis drug discovery

There has been renewed interest in alternative strategies to address bottlenecks in antibiotic development. These include the repurposing of approved drugs for use as novel anti-infective agents, or their exploitation as leads in drug repositioning. Such approaches are especially attractive for tuberculosis (TB), a disease which remains a leading cause of morbidity and mortality globally and, increasingly, is associated with the emergence of drug-resistance. In this review article, we introduce a refinement of traditional drug repositioning and repurposing strategies involving the development of drugs that are based on the active metabolite(s) of parental compounds with demonstrated efficacy. In addition, we describe an approach to repositioning the natural product antibiotic, fusidic acid, for use against Mycobacterium tuberculosis. Finally, we consider the potential to exploit the chemical matter arising from these activities in combination screens and permeation assays which are designed to confirm mechanism of action (MoA), elucidate potential synergies in polypharmacy, and to develop rules for drug permeability in an organism that poses a special challenge to new drug development.     

High-throughput microsomal stability assay for screening new chemical entities in drug discovery

In this work, the authors present a novel, robotic, automated protocol for assessing a metabolic stability protocol assembled on a Hamilton platform and a new strategy for pooling samples (cassette analysis). To increase the high throughput of the liquid chromatography (LC) step, fast chromatography and automated liquid chromatography tandem mass spectrometry (LC/MS/MS) analytical methods were also developed, and a rapid data analysis system was generated that converts peak areas obtained by LC/MS/MS in intrinsic clearance values. All of the steps of the microsomal stability assay were carefully studied and optimized. Standard errors and confidence intervals of the measured clearances were also automatically generated in the process to allow an immediate evaluation of the significance of observed values. Methods based on pooling analysis of 2 and 4 different analytes were compared with a standard method without pooling. A simple statistical treatment was used to show their equivalence. The different protocols developed were analyzed in terms of the best compromise between accuracy and high-throughput capabilities.