Preclinical Drug Safety Analysis by Chemogenomic Profiling in the Liver

The economic hurdles of drug development and the emergence of genomic technologies such as chemogenomics are combining to shift the existing paradigms in preclinical drug development. Today, the information gleaned from high content molecular data has begun to augment traditional approaches to the assessment of drug safety. The optimal approach is a hybrid strategy employing chemogenomic data and gene expression-based biomarkers of drug efficacy and toxicity to supplement low content and insensitive methods for risk assessment and mechanistic evaluation of drug candidates. Large reference databases of chemogenomic data are essential to the derivation and validation of accurate and predictive gene expression biomarkers. An example of the development of a predictive biomarker for hepatic bile duct hyperplasia is described herein. As gene expression technologies improve, biomarkers will achieve higher throughput, and become more cost effective and increasingly accurate. This will elevate the value of chemogenomics in drug development, shift attrition to earlier in the process, and reduce the overall cost of drug development. Over the past 2 to 3 years, the transition of chemogenomics from a research tool to a decision-making tool has begun and regulatory agencies are anxiously awaiting implementation of this technology to make faster and more informed evaluations of potential drugs.     

Integrated approaches to therapeutic target gene discovery

The identification of tractable drug targets is the first critical step in the long process of drug development, and the challenge for scientists in the post-genomic era is to couple gene and protein sequence information with biological insight to identify genes with the greatest therapeutic and commercial potential. An extraordinary array of genomics- and proteomics-based techniques is available for this endeavor, and combining multiple, complementary approaches enhances the informative power of such experimentation.     

Approaches to target profiling of natural products

Natural products have long been regarded as excellent sources for drug discovery given their structural diversity and wide variety of biological activities. Accordingly, the identification of the molecular targets of natural products is an important aspect of current drug discovery, as knowledge regarding a compound's molecular targets will greatly aid drug development and design. In this review, we will explore genomic, proteomic, and computational approaches to the elucidation of these mechanisms and the implications of these approaches for the target profiling of natural products. The recent applications of target profiling of natural products will also be reviewed.     

Structural bioinformatic approaches to the discovery of new antimycobacterial drugs

Integrated bioinformatic approaches to drug discovery exploit computational techniques to examine the flow of information from genome to structure to function. Informatics is being be used to accelerate and rationalize the process of antimycobacterial drug discovery and design, with the immediate goals to identify viable drug targets and produce a set of critically evaluated protein target models and corresponding set of probable lead compounds. Bioinformatic approaches are being successfully applied in the selection and prioritization of putative mycobacterial drug target genes; computational modelling and x-ray structure validation of protein targets with drug lead compounds; simulated docking and virtual screening of potential lead compounds; and lead validation and optimization using structure-activity and structure-function relationships. By identifying active sites, characterizing patterns of conserved residues and, where relevant, predicting catalytic residues, bioinformatics provides information to aid the design of selective and efficacious pharmacophores. In this review, we describe selected recent progress in antimycobacterial drug design, illustrating the strengths and limitations of current structural bioinformatic approaches as tools in the fight against tuberculosis.     

Identifying multiple-target ligands via computational chemogenomics approaches

Despite the rapidly growing knowledge of functional and structural information regarding pharmaceutically relevant targets during the past decade, target-based drug discovery has remained a high-cost and low-yield process. Particularly, single-target drugs often turn out to be less effective in treating complicated diseases such as cancers, metabolic disorders and CNS diseases. However, discovering compounds that are effective against multiple desired targets raises an enormous challenge to the current mode of drug innovation. Computational chemogenomics approaches aim at predicting all potential interactions between small molecular ligands and biomolecular targets, thus the derived information can be directly applied to "design in" (i.e. engineer desirable binding spectrum) and "design out" (i.e. eliminate the unwanted interactions) specific biological activities. The present review will focus on introducing the recent methodological development and successful applications of structure-based and ligand-based approaches on predicting the ligand binding profiles, which is the very first and essential step toward rationally designing the multiple-target ligands. Structure-based methods (e.g. binding site mapping and inverse molecular docking) generally require the structures of known targets to navigate the receptor-ligand binding space, while ligand-based approaches (e.g. chemical similarity analysis and pharmacophore search) can only rely on the series of active compounds to derive the structural characteristics for describing certain biological activities.     

Computational Methods to Identify New Antibacterial Targets

The development of resistance to all current antibiotics in the clinic means there is an urgent unmet need for novel antibacterial agents with new modes of action. One of the best ways of finding these is to identify new essential bacterial enzymes to target. The advent of a number of in silico tools has aided classical methods of discovering new antibacterial targets, and these programs are the subject of this review. Many of these tools apply a cheminformatic approach, utilizing the structural information of either ligand or protein, chemogenomic databases, and docking algorithms to identify putative antibacterial targets. Considering the wealth of potential drug targets identified from genomic research, these approaches are perfectly placed to mine this rich resource and complement drug discovery programs.     

Finding the target after screening the phenotype

Although most screening for new drug leads is being directed at known or emerging molecular targets, there has been a renaissance in screening based on changes in cell or organismal phenotypes. Phenotype-based screening is accompanied by the challenge of identifying the molecular target or targets bound by the drug leads and responsible for their pharmacological activity. A variety of technologies and approaches are being explored for target identification after phenotypic screening. Direct approaches employing affinity chromatography, expression cloning and protein microarrays analyze the compound bound to its target. Indirect approaches are based on comparison of the genome-wide activity profile of the compound with databases of the activity profiles of other compounds with known targets or activity profiles following specific genetic changes. This review will use case studies of target identification efforts and highlight the advantages and disadvantages of the various approaches to target identification after phenotypic screening.     

The design of screening libraries targeted at G-protein coupled receptors

The screening of large libraries in order to obtain hits for receptors of interest has been the mainstay of drug research for some time now. It is increasingly being recognised that this is a relatively inefficient way to achieve this end and the screening of libraries either designed or selected to hit particular targets is rapidly becoming the method of choice. The advantages in terms of success rate to achieve viable lead series are magnified by the cost and time savings achieved by screening more carefully selected groups of compounds. A number of approaches have been used for the design and production of such libraries or methods for selection of such focused sets from larger diverse collections. These range from combinatorially produced ligand-mimetic approaches through pharmacophore-based design to those methods based on statistical selection techniques. Most recently, progress in chemogenomic approaches has thrown new light on the relationship between receptor sequence and compounds that interact at particular receptors and this is also having an impact on the design of targeted libraries.     

Analysing microarray data in drug discovery using systems biology

The innovation of present drug design focuses on new targets. However, compound efficacy and safety in human metabolism, including toxicity and pharmacokinetic profiles, but not target selection, are the criteria that determine which drug candidates enter the clinic. Systems biology approaches to disease are developed from the idea that disease-perturbed regulatory networks differ from their normal counterparts. Microarray data analyses reveal global changes in gene or protein expression in response to genetic and environmental changes and, accordingly, are well suited to construct the normal, disease-perturbed and drug-affected networks, which are useful for drug discovery in the pharmaceutical industry. The integration of modelling, microarray data and systems biology approaches will allow for a true breakthrough in in silico absorption, distribution, metabolism, excretion and toxicity assessment in drug design. Therefore, drug discovery through systems biology by means of microarray analyses could significantly reduce the time and cost of new drug development.     

Drug Adverse Reaction Target Database (DART) : proteins related to adverse drug reactions.

An adverse drug reaction (ADR) often results from interaction of a drug or its metabolites with specific protein targets important in normal cellular function. Knowledge about these targets is both important in facilitating the study of the mechanisms of ADRs and in new drug discovery. It is also useful in the development and testing of rational drug design and safety evaluation tools. The Drug Adverse Reaction Database (DART) is intended to provide comprehensive information about adverse effect targets of drugs described in the literature. Moreover, proteins involved in adverse effect targets of chemicals not yet confirmed as ADR targets are also included as potential targets. This database gives physiological function of each target, binding drugs/agonists/antagonists/activators/inhibitors, IC(50) values of the inhibitors, corresponding adverse effects, and type of ADR induced by drug binding to a target. Cross-links to other databases are also introduced to facilitate the access of information about the sequence, 3-dimensional structure, function, and nomenclature of each target along with drug/ligand binding properties, and related literature. The database currently contains entries for 147 ADR targets and 89 potential targets. A total of 187 adverse reaction conditions, 257 drugs, and 1080 ligands known to bind to each of these targets are also currently described. Each entry can be retrieved through multiple search methods including target name, target physiological function, adverse effect, ligand name, and biological pathways. A special page is provided for contribution of new or additional information. This database can be accessed at http://xin.cz3.nus.edu.sg/group/drt/dart.asp.     

基于HDAC的双靶点抗肿瘤药物研究进展

多靶点作用的抗肿瘤药物比目前单靶点药物具有更好的药效,且能够降低耐药性和毒副作用。为了探索多靶点药物在肿瘤化疗中的应用前景,以组蛋白去乙酰化酶(HDAC)抑制剂为基础设计多种作用的双靶点抑制剂已经成为了研究热点,其中部分化合物抑制肿瘤细胞增殖活性比现有的上市药物更好。本文综述了基于HDAC的双靶点抑制剂的研究进展,重点介绍了作用机制、设计策略和生物活性。     

The application of RAGE and GECKO in cell-based target discovery screens

While significant advancements have been made in identifying the genes that comprise the human genome, considerable work remains in gaining an understanding of the functions of these gene products. Improved knowledge of protein function is of particular relevance to the drug discovery process, as the elucidation of new targets that are involved in disease processes will most probably lead to improvements in health care. Reverse genetic approaches that attempt to assign protein function on a gene-by-gene basis are labor intensive and have low throughput. Although forward genetic (function-to-gene) approaches often allow for the more efficient identification of disease-relevant drug targets, most existing methodologies are not capable of sampling the entire genome. Here we review current target discovery strategies and discuss two relatively new technologies, RAGE (random activation of gene expression) and GECKO (genome-wide cellular knockout). These tools provide cellular libraries that can be utilized in genome-wide target discovery screens. Examples are given of how these methodologies may facilitate the identification of new drug targets that are involved in human disease and pathology.     

Rapid structure-activity and selectivity analysis of kinase inhibitors by BioMAP analysis in complex human primary cell-based models

Rapid, quantitative methods for characterizing the biological activities of kinase inhibitors in complex human cell systems could allow the biological consequences of differential target selectivity to be monitored early in development, improving the selection of drug candidates. We have previously shown that Biologically Multiplexed Activity Profiling (BioMAP) permits rapid characterization of drug function based on statistical analysis of protein expression data sets from complex primary human cellbased models of disease biology. Here, using four such model systems containing primary human endothelial cells and peripheral blood mononuclear cells in which multiple signaling pathways relevant to inflammation and immune responses are simultaneously activated, we demonstrate that BioMAP analysis can detect and distinguish a wide range of inhibitors directed against different kinase targets. Using a panel of p38 mitogen-activated protein kinase antagonists as a test set, we show further that related compounds can be distinguished by unique features of the biological responses they induce in complex systems, and can be classified according to their induction of shared (on-target) and secondary activities. Statistical comparisons of quantitative BioMAP profiles and analysis of profile features allow correlation of induced biological effects with chemical structure and mapping of biological responses to chemical series or substituents on a common scaffold. Integration of automated BioMAP analysis for prioritization of hits and for structure-activity relationship studies may improve and accelerate the design and selection of optimal therapeutic candidates.     

Microarray: an approach for current drug targets

Microarrays are a powerful tool has multiple applications both in clinical and cellular and molecular biology arenas. Early assessment of the probable biological importance of drug targets, pharmacogenomics, toxicogenomics and single nucleotide polymorphisms (SNPs). A list of new drug candidates along with proposed targets for intervention is described. Recent advances in the knowledge of microarrays analysis of organisms and the availability of the genomics sequences provide a wide range of novel targets for drug design. This review gives different process of microarray technologies; methods for comparative gene expression study, applications of microarrays in medicine and pharmacogenomics and current drug targets in research, which are relevant to common diseases as they relate to clinical and future perspectives.     

抗生素作用新靶点的发掘策略

抗生素的大量使用和滥用所造成的细菌广泛的耐药性迫使科研人员要加速寻找结构新颖的抗生素。抗生素作用靶点的发掘对药物发现非常重要,本文综述了近几年来国际上运用基因组学、基因芯片等现代生物信息学技术,利用细菌体内的各种酶反应与理化特性,发掘抗生素传统作用新靶点如脂肪酸合成酶、非传统作用新靶点如双信号转导调控系统、群体感应器等的研究进展,这对新抗生素的发现具有一定的指导意义。     

Fragment-based QSAR strategies in drug design

Recently, fragment-based drug design has been established as a crucial strategy for hit identification and lead generation, which has strongly encouraged the development of approaches to specifically recognize and evaluate molecular fragments or structural scaffolds that preferentially interact with particular sites of important biological targets. In this context, fragment-based quantitative structure-activity relationship (FB-QSAR) has emerged as a versatile tool to explore the chemical and biological space of data sets of compounds. FB-QSAR approaches have evolved from a classical use in the generation of standard QSAR models into advanced drug design tools for database mining, pharmacokinetic property prediction and optimization of multiple parameters. This paper provides a brief perspective on the evolution and current status of FB-QSAR, highlighting new opportunities in drug design.     

Intrinsically disordered proteins and novel strategies for drug discovery

INTRODUCTION: There is a natural abundance of intrinsically disordered proteins or intrinsically disordered protein regions (IDPs or IDPRs), that is, biologically active proteins/regions without stable structure. Their wide functional repertoire; the ability to participate in multiple interactions; the capability to fold at binding in a template-dependent manner and their common involvement in the pathogenesis of numerous human diseases suggest that these proteins should be seriously considered as novel drug targets. AREAS COVERED: This article describes the major classes of ordered proteins traditionally used as drug targets and introduces the molecular mechanisms of drugs targeting ordered proteins. Furthermore, it illustrates basic ways of rational drug design for these proteins, and shows why these approaches cannot be directly used for intrinsic disorder-based drug design. Some of the new approaches utilized for finding drugs targeting IDPs/IDPRs are introduced. EXPERT OPINION: There is a continuing progress in the design of small molecules for IDPs/IDPRs and several small molecules are found that specifically inhibit the disorder-based interaction of IDPs with their numerous partners. It is expected that the initial studies will be extended and novel intrinsic disorder-based drug design approaches will be developed. Furthermore, putative new targets will be identified, and a better understanding of the molecular mechanisms underlying modulation of promiscuous IDP binding will be achieved.     

Novel directions in antipsychotic target identification using gene arrays

Schizophrenia is a major health problem that affects 2 million individuals in the United States. Antipsychotics offer considerable symptomatic relief and, although commonly discovered by screening with single biological targets, most interact with multiple receptors and signaling pathways. Considerable evidence from family and twin studies demonstrates genetic components and multiple chromosomal regions associated with schizophrenia. The polygenic nature of schizophrenia and multiple mechanisms for most effective agents indicate the need for broader approaches to target identification. Gene expression profiling of post-mortem human brain tissue simultaneously reveals the expression of many thousands of genes. A comparison of tissue from normals and patients provides a 'disease signature' of aberrantly expressed genes. 'Drug signatures' are the gene expression changes of cultured human or animal neurons treated with psychiatric drugs, and from animals chronically treated with these drugs. A selection of genes from disease and drug signatures can create a set of targets whose changes may better predict disease and its treatment by effective agents. This multi-parameter high throughput screening (MPHTS(SM)) approach evaluates the mRNA expression pattern of cultured cells exposed to candidate compounds. Compounds that normalize genes altered in schizophrenia may better address its underlying causes. Drugs that mimic gene expression changes that are consistently altered by effective antipsychotic agents provide a drug improvement strategy if efficacy is enhanced or side effects are attenuated.