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。     

Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery

The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using in vitro or in vivo experiments. This validation step can further justify the predictions resulting from in silico approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.     

Target assessment for antiparasitic drug discovery

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

Impact of drug discovery on stem cell biology

Despite the rapid technical progress in pharmaceutical industry in the past decade, it is still a great challenge to find new drugs and the situation seems more and more serious. However, the history of pharmaceutical industry clearly indicated that the significance of drug discovery went far beyond providing new drugs. For instance, drugs or candidates could be used as selective probes to reveal novel cellular mechanisms, which is a fundamental tenet of chemical biology. More interestingly, accumulating evidence indicates that drugs and candidates can find important use in stem cell biology. Not only approved drugs but also undeveloped pharmacological agents could serve as efficient agents to regulate stem cell fate. Moreover, the target and activity knowledge accumulated during the drug discovery process will help select the stem cell fate modulators in a rational manner. As the progress in stem cell biology will bring positive influence to drug discovery, it can be expected that the current drug discovery efforts will finally bear great fruits in the future.     

How can natural products serve as a viable source of lead compounds for the development of new/novel anti-malarials?

Malaria continues to be an enormous global health challenge, with millions of new infections and deaths reported annually. This is partly due to the development of resistance by the malaria parasite to the majority of established anti-malarial drugs, a situation that continues to hamper attempts at controlling the disease. This has spurred intensive drug discovery endeavours geared towards identifying novel, highly active anti-malarial drugs, and the identification of quality leads from natural sources would greatly augment these efforts. The current reality is that other than compounds that have their foundation in historic natural products, there are no other compounds in drug discovery as part of lead optimization projects and preclinical development or further that have originated from a natural product start-point in recent years. This paper briefly presents both classical as well as some more modern, but underutilized, approaches that have been applied outside the field of malaria, and which could be considered in enhancing the potential of natural products to provide or inspire the development of anti-malarial lead compounds.     

G‐LoSA: An efficient computational tool for local structure‐centric biological studies and drug design

Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G‐LoSA. G‐LoSA aligns protein local structures in a sequence order independent way and provides a GA‐score, a chemical feature‐based and size‐independent structure similarity score. Our benchmark validation shows the robust performance of G‐LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure‐centric comparative biology studies. In particular, G‐LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G‐LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer‐aided drug design. We hope that G‐LoSA can be a useful computational method for exploring interesting biological problems through large‐scale comparison of protein local structures and facilitating drug discovery research and development. G‐LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/ .     

Large-scale integrated super-computing platform for next generation virtual drug discovery

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.     

Bioinformatics approaches for new drug discovery: a review

Prolonged antibiotic therapy for the bacterial infections has resulted in high levels of antibiotic resistance. Initially, bacteria are susceptible to the antibiotics, but can gradually develop resistance. Treating such drug-resistant bacteria remains difficult or even impossible. Hence, there is a need to develop effective drugs against bacterial pathogens. The drug discovery process is time-consuming, expensive and laborious. The traditionally available drug discovery process initiates with the identification of target as well as the most promising drug molecule, followed by the optimization of this, in-vitro, in-vivo and in pre-clinical studies to decide whether the compound has the potential to be developed as a drug molecule. Drug discovery, drug development and commercialization are complicated processes. To overcome some of these problems, there are many computational tools available for new drug discovery, which could be cost effective and less time-consuming. In-silico approaches can reduce the number of potential compounds from hundreds of thousands to the tens of thousands which could be studied for drug discovery and this results in savings of time, money and human resources. Our review is on the various computational methods employed in new drug discovery processes.     

Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction

In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.     

Drug discovery targeting cell division proteins,microtubules and FtsZ

Eukaryotic cell division or cytokinesis has been a major target for anticancer drug discovery. After the huge success of paclitaxel and docetaxel, microtubule-stabilizing agents (MSAs) appear to have gained a premier status in the discovery of next-generation anticancer agents. However, the drug resistance caused by MDR, point mutations, and overexpression of tubulin subtypes, etc., is a serious issue associated with these agents. Accordingly, the discovery and development of new-generation MSAs that can obviate various drug resistances has a significant meaning. In sharp contrast, prokaryotic cell division has been largely unexploited for the discovery and development of antibacterial drugs. However, recent studies on the mechanism of bacterial cytokinesis revealed that the most abundant and highly conserved cell division protein, FtsZ, would be an excellent new target for the drug discovery of next-generation antibacterial agents that can circumvent drug-resistances to the commonly used drugs for tuberculosis, MRSA and other infections. This review describes an account of our research on these two fronts in drug discovery, targeting eukaryotic as well as prokaryotic cell division.     

Acetyl-coenzyme A carboxylases: versatile targets for drug discovery

Acetyl-coenzyme A carboxylases (ACCs) have crucial roles in fatty acid metabolism in humans and most other living organisms. They are attractive targets for drug discovery against a variety of human diseases, including diabetes, obesity, cancer, and microbial infections. In addition, ACCs from grasses are the targets of herbicides that have been in commercial use for more than 20 years. Significant progresses in both basic research and in drug discovery have been made over the past few years in the studies on these enzymes. At the basic research level, the crystal structures of the biotin carboxylase (BC) and the carboxyltransferase (CT) components of ACC have been determined, and the molecular basis for ACC inhibition by small molecules are beginning to be understood. At the drug discovery level, a large number of nanomolar inhibitors of mammalian ACCs have been reported and the extent of their therapeutic potential is being aggressively explored. This review summarizes these new progresses and also offers some prospects in terms of the future directions for the studies on these important enzymes.     

Linking bioprospecting with sustainable development and conservation: the Panama case

The limited international resources for economic aid and conservation can only mitigate poverty and losses of biodiversity. Hence, developing nations must establish the capacity to resolve their problems. Additionally, policy-makers and donors need to obtain scientific input on issues such as global change and ecosystem services. We propose that for nations rich in biodiversity, ecosystem services derived from bioprospecting, or drug discovery, could contribute to economic development. In the case where unstudied samples are shipped abroad for research, the chances of obtaining royalties are infinitesimally small. Therefore developing nations will only realize benefits from bioprospecting through in-country research on their own biodiversity. Policy-makers and donors have failed to appreciate the value of this approach. In order to provide an example of the inherent links between conservation and sustainable economic development, we initiated a drug discovery effort in Panama that emphasizes local benefit. As much of the drug discovery process as possible is conducted in Panamanian laboratories, providing jobs dependent on intact biodiversity and enhancing local research and training. In short, research, plus the spin-offs from research, provide immediate and long-lasting benefits to Panama. The connection between conservation and development has been highlighted in publicity about the project in Panama’s urban media. This provides a constructive alternative to the perception the among the urban populace that economic development inevitably competes with conservation. In summary, our program uses biodiversity to promote human health as well as to support research capacity, economic development and conservation within Panama. The program provides an example of the widely recognized but little developed concept of bioprospecting research as an ecosystem service.     

Protein-protein interactions as targets for small molecule drug discovery

Protein-protein interactions represent a highly populated class of targets for drug discovery. However, such systems present a number of unique challenges. This review presents an analysis of individual protein-protein interaction systems which have recently yielded success in discovering drug-like inhibitors. The structural characteristics of the protein binding sites and the attributes of the small molecule ligands are focused upon, in an attempt to derive commonly shared principles that may be of general usefulness in future drug discovery efforts within this target class.     

Proteomic methods for drug target discovery

The field of drug target discovery is currently very popular with a great potential for advancing biomedical research and chemical genomics. Innovative strategies have been developed to aid the process of target identification, either by elucidating the primary mechanism-of-action of a drug, by understanding side effects involving unanticipated 'off-target' interactions, or by finding new potential therapeutic value for an established drug. Several promising proteomic methods have been introduced for directly isolating and identifying the protein targets of interest that are bound by active small molecules or for visualizing enzyme activities affected by drug treatment. Significant progress has been made in this rapidly advancing field, speeding the clinical validation of drug candidates and the discovery of the novel targets for lead compounds developed using cell-based phenotypic screens. Using these proteomic methods, further insight into drug activity and toxicity can be ascertained.     

High throughput metabolic stability screen for lead optimization in drug discovery

A high throughput approach for the determination of in vitro metabolic stability and metabolic profiles of drug candidates has been developed. This approach comprises the combination of a Biomek FX liquid handling system with 96-channel pipetting capability and a custom-designed 96-well format on-line incubator with efficient thermal conductivity. This combination facilitates automated reagent preparation, sample incubation, and sample purification for microsome stability studies. The overall process is both fast and accurate and meets the challenges of high throughput screening for drug discovery. A custom designed, user-friendly computer program has been incorporated for large-scale data processing and report generation. Several applications are discussed that implement this strategy for rapid selection of compounds in early drug discovery.     

Zebrafish: A tool for chemical screens

The zebrafish has proven to be an invaluable vertebrate animal model for developmental biology. Recent technological advances have added an arsenal of tools to expand its use into the realm of drug discovery. This includes methodology to generate transgenic reporter lines that allow for the direct visualization of fluorescent markers in live embryos. With the addition of automated imaging and analysis of embryos treated with small molecules, these innovations have expanded its utility into high throughput chemical screens. This review will highlight some of these advances that have propelled zebrafish as a tool for drug discovery. Birth Defects Research (Part C) 90:185–192, 2010. © 2010 Wiley‐Liss, Inc.     

面向药物发现和精准医疗的基因表达谱分析

作为功能基因组学中重要的组成部分,基因表达谱在生物学、医学和药物研发等多个领域发挥着重要作用.特别是随着精准医疗概念的提出,整合多组学数据用于个性化医疗是未来的发展趋势.本文从基因表达谱的基本概念出发,重点介绍面向药物发现的基因表达谱分析方法,即基于关联图谱的方法、基于基因调控网络的方法和基于多组学数据整合的方法.系统整理了各种方法的研究进展,特别是在抗癌药物研发领域的最新进展,为利用基因表达谱数据进行药物研发提供方法借鉴.     

Cryo-EM as a powerful tool for drug discovery

The recent revolution in cryo-EM has produced an explosion of structures at near-atomic or better resolution. This has allowed cryo-EM structures to provide visualization of bound small-molecule ligands in the macromolecules, and these new structures have provided unprecedented insights into the molecular mechanisms of complex biochemical processes. They have also had a profound impact on drug discovery, defining the binding modes and mechanisms of action of well-known drugs as well as driving the design and development of new compounds. This review will summarize and highlight some of these structures. Most excitingly, the latest cryo-EM technology has produced structures at 1.2 Å resolution, further solidifying cryo-EM as a powerful tool for drug discovery. Therefore, cryo-EM will play an ever-increasing role in drug discovery in the coming years.     

A liquid chromatography/mass spectrometry-based generic detection method for biochemical assay and hit discovery of histone methyltransferases

Epigenetic modifications of the genome, such as DNA methylation and posttranslational modifications of histone proteins, contribute to gene regulation. Growing evidence suggests that histone methyltransferases are associated with the development of various human diseases, including cancer, and are promising drug targets. High-quality generic assays will facilitate drug discovery efforts in this area. In this article, we present a liquid chromatography/mass spectrometry (LC/MS)-based S-adenosyl homocysteine (SAH) detection assay for histone methyltransferases (HMTs) and its applications in HMT drug discovery, including analyzing the activity of newly produced enzymes, developing and optimizing assays, performing focused compound library screens and orthogonal assays for hit confirmations, selectivity profiling against a panel of HMTs, and studying mode of action of select hits. This LC/MS-based generic assay has become a critical platform for our methyltransferase drug discovery efforts.