功能基因组学和组合化学在抗结核新药开发中的应用

结核病耐药性是导致结核病难以治愈的重要原因。为根除结核病，必须开发针对耐药菌和休眠菌的新型抗结核病药物，组合化学和功能基因组学的结合将有助于加快新药开发速度。本文综述了功能基因组学和组合化学在开发抗结核新药中的应用。     

基于细胞功能的超高通量筛选在化学基因组学药物发现中的应用

尽管许多基因组靶标的功能及它们与疾病的关系仍然不清楚，但基因组信息的获得仍极大地丰富了潜在药物靶标的数量。在化学基因组学研究中，基因组靶标超高通量筛选发生在药物开发的初期，在确定靶标之前进行。靶标选择性调控剂可提供药物先导结构和药理学研究工具以鉴定靶标功能，化学基因组策略的有效实施离不开能对大量基因组靶标进行超高通量筛选的分析检测技术的运用。基于细胞功能的分析实验可以实现化学基因组学研究中超高通量筛选的目的，并且因其功能特性使其在鉴定靶标选择性调控剂时明显优于配体结合试验。     

后基因组时代的医药新兴学科

对后基因组时代医药新兴学科进行简略地介绍.这些学科包括：化学生物学、预防药学、疾病基因组学、药理基因组学、蛋白质组学、药理蛋白质组学和环境基因组学.对化学信息学、生物信息学、计算机模拟技术亦有概述.     

生化信息学在药物创新过程中的重要作用

现代药物创新过程中产生大量实验数据,如基因序列信息、化学结构数据、生物活性指纹数据、基于基因或蛋白质等的微阵列(microarray)表达谱数据等.这些数据来源不同,类型不一,数量庞大,如何从中获取对药物研发人员有指导价值的信息是当前生物信息学和化学信息学工作者面临的难题之一.虽然生物学信息和化学信息有各自不同的特点,但它们之间可能存在某些相关性,只有将两类数据综合分析,才有可能发掘更多有价值的信息.生化信息学研究的是生物信息学与化学信息学的整合.本文讨论药物创新过程中如何整合并分析生物学数据和化学数据,为药物创新研究提供全面的信息学支持.     

利用功能基因组学蛋白质组学等筛选抗结核新药

对利用功能基因组学、蛋白质组学、组合库技术发现和鉴定结核分枝杆菌的药物作用靶点,筛选组合化合物库发现药物先导物研究新化合物的作用机制进行了综述.表明综合利用功能基因组学、蛋白质组学、组合库技术有助于加速新机制、新来源抗结核新药的开发.     

利用功能基因组学蛋白质组学等筛选抗结核新药

对利用功能基因组学、蛋白质组学、组合库技术发现和鉴定结核分枝杆菌的药物作用靶点,筛选组合化合物库发现药物先导物研究新化合物的作用机制进行了综述.表明综合利用功能基因组学、蛋白质组学、组合库技术有助于加速新机制、新来源抗结核新药的开发.     

“中药基因组学”与“中药化学组学”

中药对众多疾病的独特疗效已为人们广泛认可，但是由于历史的局限性，其理论基础很难得到现代社会特别是国际社会公认，因此中药现代化势在必行。如何应用现代科学技术，给传统中药理论以现代科学的理论解释是中药现代化的关键。本文基于现代基因组学特别是功能基因组学、现代分析化学及一些生物高技术手段的研究进展及发展趋势，从一个新的视角提出了开展"中药基因组学(TCM Genomics)"和"中药化学组学(TCM Chemics)"研究的策略。     

应对抗生素耐药的新策略

随着抗生素的广泛使用，细菌耐药的问题日益严重，本文着重介绍了基于靶位的新抗生素研发策略。基因组学和分子遗传学的运用为开发新药提供了大量潜在的新靶位。组合化学和组合生物学为筛选和优化先导化合物提供了具有丰富结构多样性的化合物库。同时讨论了β-内酰胺酶抑制剂和外排泵抑制剂的研究现状。阐明了生物学和化学的有机结合是开发新抗生素的最佳途径。     

毒理基因组学在生物标志物研究中的应用

近年来,毒理基因组学的快速发展为生物标志物的研究提供了一个更广阔的空间。生物标志物作为一种可以客观衡量和评价正常生理、病理过程及治疗药物药理学效应的指标,如今已被广泛应用于临床诊断和新药开发等多个领域。就毒理基因组学在发现和筛选生物标志物方面的研究现状和进展进行综述。     

微生物药物产生菌功能基因组学研究进展

微生物药物是一类化学结构和生物活性多样的次级代谢产物,近年来其多个产生菌基因组序列已经被测定完成,在此基础上开展的功能基因组研究方兴未艾,并在抗生素生物合成、形态分化、调控、系统发育与进化以及次级代谢产物挖掘等方面有着新的发现,展现出广阔的研究前景.本文重点阐述了四种重要抗生素产生菌功能基因组学的研究现状,集中于青霉素高产的遗传机制、红霉素产生菌红色糖多孢菌基因组与转录组分析、链霉素产生菌灰色链霉菌中A因子调控网络、阿维菌素产生菌作为次级代谢物异源表达的通用宿主与超高产菌株构建以及新型天然产物的挖掘等研究内容,同时简要介绍了当前我国微生物药物产生菌基因组学的研究概况,并从基础与应用两个角度对其未来发展趋势进行了展望.     

Computational approaches in chemogenomics and chemical biology: current and future impact on drug discovery

Background: Chemical biology and chemogenomics are rapidly evolving disciplines at interfaces between chemistry and the life sciences and are highly interdisciplinary in nature. Chemogenomics has a strong conceptional link to modern drug discovery research, whereas chemical biology focuses more on the use of small molecules as probes for exploring biological functions, rather than drug candidates. However, the boundaries between these areas are fluid, as they should be, given their strong interdisciplinary orientation. Objective: Recently, computational approaches have been introduced for the analysis of research topics that are of considerable relevance for these disciplines including, for example, the systematic study of ligand–target interactions or mapping of pharmacologically relevant chemical space. This contribution introduces key investigations in computational chemical biology and chemogenomics and critically evaluates their current and future potential to impact drug discovery. Conclusions: Computational methods of high relevance for chemogenomics and chemical biology either derive knowledge from large-scale analysis of available drug and target data or interface experimental programs with predictive methods. Approaches for drug target prediction and the systematic analysis of polypharmacology substantially impact research in this area.     

Drug target central

Background: One of the primary pillars of drug discovery is the drug target, its relationship to both the drugs designed against it and the biological processes in which it is involved. Here we review the informatics approaches required to build a complete catalogue of known drug targets. Objective: Using Pfizer's internal target database as a narrative, we review the steps involved in the construction of an integrated, enterprise target-informatics system. We consider how compiling the drug target universe requires integration across several resources such as competitor intelligence and pharmacological activity databases, as well as input from techniques such as text-mining. In particular, we address data standards and the complexities of representing targets in a structured ontology as well as opportunities for future development. Conclusion: Drug target-orientated databases address important areas of drug discovery such as chemogenomics, drug/candidate repurposing and business intelligence. As research in industry and academia drives continued expansion of the druggable genome, it is crucial that such systems be maintained to provide an accurate picture of the landscape. This power of this information stretches beyond drug discovery and into the wider scientific community where small molecule tool compounds can enable the dissection of complex cellular pathways.     

Chemogenomics: structuring the drug discovery process to gene families

In the post-genomic era, if all proteins in a gene family can putatively be identified, how can drug discovery effectively tackle so many novel targets that might lack structural and small-molecule inhibitory data? In response, chemogenomics, a new approach that guides drug discovery based on gene families, has been developed. By integrating all information available within a protein family (sequence, SAR data, protein structure), chemogenomics can efficiently enable cross-SAR exploitation, directed compound selection and early identification of optimum selectivity panel members. This review examines recent developments in chemogenomics technologies and illustrates their predictive capabilities with successful examples from two of the major protein families: protein kinases and G-protein-coupled receptors.     

Cell biology,chemogenomics and chemoproteomics – application to drug discovery

Cell biology has added immensely to the understanding of basic biologic concepts. However, scientists need to use cell biology more in the proteomic–genomic revolution. The authors have developed two novel techniques: transitional structural chemogenomics (TSCg) and transitional structural chemoproteomics (TSCp). TSCg is used to regulate gene expression by using ultrasensitive small-molecule drugs that target nucleic acids. By using chemicals to target transitional changes in the helical conformations of single-stranded (ss) and double-stranded (ds) DNA (e.g., B- to Z-DNA) and RNA (e.g., A- to Z-RNA), gene expression can be regulated (i.e., turning genes ‘on/off’ and variably controlling them). Alternative types of ds- and ssDNA and RNA (e.g., cruciform DNA) and other multistranded nucleic acids (e.g., triplex-DNA) are also targeted by this method. The authors’ second technique, TSCp, targets a protein before, during or after post-translational modifications, which alters the protein’s structure and function. These novel methods represent the next step in the evolution of chemical genomics and chemical proteomics. In addition, a novel multi-stranded (alternative) DNA, RNA and plasmid microarray has been developed that allows for the immobilization of intact, non-denatured dsDNA, alternative (i.e., exotic) and other multiple-stranded nucleic acids. This represents the next generation of nucleic acid microarrays, which will aid in the characterization of nucleic acids, studying the ageing process and improving the drug discovery process. The authors discuss how cell biology can be used to enhance genomics and proteomics. Cell biology will play a greater role during the postgenomic age and will help to enhance the omics/omes and drug discovery. It is the authors’ hope that these novel approaches can be used together with cellular biologic techniques to make major contributions towards understanding and manipulating different genomes.     

Chemogenomic approaches to drug discovery: similar receptors bind similar ligands

Within recent years, a paradigm shift from traditional receptor-specific studies to a cross-receptor view has taken place within pharmaceutical research to increase the efficiency of modern drug discovery. Receptors are no longer viewed as single entities but grouped into sets of related proteins or receptor families that are explored in a systematic manner. This interdisciplinary approach attempting to derive predictive links between the chemical structures of bioactive molecules and the receptors with which these molecules interact is referred to as chemogenomics. Insights from chemogenomics are used for the rational compilation of screening sets and for the rational design and synthesis of directed chemical libraries to accelerate drug discovery.     

Genomics and proteomics tools for compound mode-of-action studies in drug discovery

A broad range of genomics and proteomics technologies are increasingly being integrated into emerging research fields such as pharmacogenomics, pharmacoproteomics, chemogenomics, chemical genetics, and chemical biology. Here we review applications of genomic and proteomic technologies to drug mechanism-of-action studies and how these are beginning to impact the drug discovery process.     

Chemogenomics knowledge-based strategies in drug discovery

In the postgenomic age of drug discovery, targets can no longer be viewed as singular objects having no relationship to one another. All targets are now visible and the systematic exploration of selected target families appears to be a promising way to speed up and further industrialize target-based drug discovery. Chemogenomics refers to such systematic exploration of target families and aims to identify all possible ligands of all target families. Because biology works by applying prior knowledge to an unknown entity, chemogenomics approaches are expected to be especially effective within the previously well-explored target families, for which, in addition to the protein sequence and structure information, considerable knowledge of pharmacologically active structural classes and structure-activity relationships exists. For the new target families, chemical knowledge will have to be generated and beyond biological target validation, the emphasis is on chemistry to provide the molecules with which their novel biology and pharmacology can be studied. Using examples from the previously most successfully explored target families, the GPCR family in particular, we summarize herein our current chemogenomics knowledge-based strategies for drug discovery, which are founded on the high integration of chem and bioinformatics, thereby providing a molecular informatics frame for the exploration of the new target families.     

Exploring the epigenetic drug discovery landscape

Introduction: Epigenetic modification has been implicated in a wide range of diseases and the ability to modulate such systems is a lucrative therapeutic strategy in drug discovery.

Areas covered: This article focuses on the concepts and drug discovery aspects of epigenomics. This is achieved by providing a survey of the following concepts: (i) factors influencing epigenetics, (ii) diseases arising from epigenetics, (iii) epigenetic enzymes as druggable targets along with coverage of existing FDA-approved drugs and pharmacological agents, and (iv) drug repurposing/repositioning as a means for rapid discovery of pharmacological agents targeting epigenetics.

Expert opinion: Despite significant interests in targeting epigenetic modifiers as a therapeutic route, certain classes of target proteins are heavily studied while some are less characterized. Thus, such orphan target proteins are not yet druggable with limited report of active modulators. Current research points towards a great future with novel drugs directed to the many complex multifactorial diseases of humans, which are still often poorly understood and difficult to treat.     

A structural chemogenomics analysis of aminergic GPCRs: lessons for histamine receptor ligand design

Background and Purpose

Chemogenomics focuses on the discovery of new connections between chemical and biological space leading to the discovery of new protein targets and biologically active molecules. G-protein coupled receptors (GPCRs) are a particularly interesting protein family for chemogenomics studies because there is an overwhelming amount of ligand binding affinity data available. The increasing number of aminergic GPCR crystal structures now for the first time allows the integration of chemogenomics studies with high-resolution structural analyses of GPCR-ligand complexes.

Experimental Approach

In this study, we have combined ligand affinity data, receptor mutagenesis studies, and amino acid sequence analyses to high-resolution structural analyses of (hist)aminergic GPCR-ligand interactions. This integrated structural chemogenomics analysis is used to more accurately describe the molecular and structural determinants of ligand affinity and selectivity in different key binding regions of the crystallized aminergic GPCRs, and histamine receptors in particular.

Key Results

Our investigations highlight interesting correlations and differences between ligand similarity and ligand binding site similarity of different aminergic receptors. Apparent discrepancies can be explained by combining detailed analysis of crystallized or predicted protein-ligand binding modes, receptor mutation studies, and ligand structure-selectivity relationships that identify local differences in essential pharmacophore features in the ligand binding sites of different receptors.

Conclusions and Implications

We have performed structural chemogenomics studies that identify links between (hist)aminergic receptor ligands and their binding sites and binding modes. This knowledge can be used to identify structure-selectivity relationships that increase our understanding of ligand binding to (hist)aminergic receptors and hence can be used in future GPCR ligand discovery and design.

Linked Articles

This article is part of a themed issue on Histamine Pharmacology Update. To view the other articles in this issue visit http://dx.doi.org/10.1111/bph.2013.170.issue-1