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.     

Peptide ligands in antibacterial drug discovery: use as inhibitors in target validation and target-based screening

There is an urgent need to develop novel classes of antibiotics to counter the inexorable rise of resistant bacterial pathogens. Modern antibacterial drug discovery is focused on the identification and validation of novel protein targets that may have a suitable therapeutic index. In combination with assays for function, the advent of microbial genomics has been invaluable in identifying novel antibacterial drug targets. The major challenge in this field is the implementation of methods that validate protein targets leading to the discovery of new chemical entities. Ligand-directed drug discovery has the distinct advantage of having a concurrent analysis of both the importance of a target in the disease process and its amenability to functional modulation by small molecules. VITA is a process that enables a target-based paradigm by using peptide ligands for direct in vitro and in vivo validation of antibacterial targets and the implementation of high-throughput assays to identify novel inhibitory molecules. This process can establish sufficient levels of confidence indicating that the target is relevant to the disease process and inhibition of the target will lead to effective disease treatment.     

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.     

Using grid techniques for drug target identification

The completion of the sequencing of the human genome has opened an unprecedented opportunity in the discovery of novel drug targets for disease therapy. However, one of the major challenges facing the drug discovery community is the expanding of data and the need of large-scale computational power in a collaborative environment. Grid techniques can present an architectural framework that aims to provide access to heterogeneous resources in a secure, reliable and scalable manner across various administrative boundaries for drug discovery, which has been a promising strategy for solving large-scale problems in modern pharmaceutical R&D. In this review, we discuss the current applications of Grid technology in drug target protein identification process; and an overview of drug target discovery system architecture, focusing in particular on the data manager service system architecture is also proposed.     

Using grid techniques for drug target identification

The completion of the sequencing of the human genome has opened an unprecedented opportunity in the discovery of novel drug targets for disease therapy. However, one of the major challenges facing the drug discovery community is the expanding of data and the need of large-scale computational power in a collaborative environment. Grid techniques can present an architectural framework that aims to provide access to heterogeneous resources in a secure, reliable and scalable manner across various administrative boundaries for drug discovery, which has been a promising strategy for solving large-scale problems in modern pharmaceutical R&D. In this review, we discuss the current applications of Grid technology in drug target protein identification process; and an overview of drug target discovery system architecture, focusing in particular on the data manager service system architecture is also proposed.     

Computational identification and analysis of G protein‐coupled receptor targets

The completion of the human genome sequence has provided a large pool of potential drug targets for disease therapy. G protein–coupled receptors (GPCRs), which are central to signaling networks that regulate basic cellular processes, represent the most important known class of therapeutic targets for multiple disease states. Bioinformatics approaches can be applied to facilitate the identification of novel GPCRs, understanding their physiological and pathological roles, and screening for drug discovery. The present review summarizes current bioinformatics approaches that can be used to identify and analyze GPCR targets. In addition, the limitations of these technologies with the intention of setting reasonable expectations are also discussed together with some potential avenues for GPCR research. Drug Dev. Res. 67:771–780, 2006. © 2007 Wiley‐Liss, Inc.     

Functional Genomics Conference: From Identifying Proteins to Faster Drug Discovery. March 10-11, 1998, Washington DC, USA

The massive effort to sequence the human, mouse, rat, nematode (Caenorhabditis elegans), fruit fly (Drosophila), zebra fish, yeast (Saccharomyces cerevisiae), fungal (Candida albicans and Aspergillus fumigatus) and several bacterial genomes has produced a flood of sequence data. Of the more than 100,000 human genes and thousands from other organisms, many partial sequences and several completed microbial genomes are available in both public and private databases. However, elucidation of function has been achieved for only a very small portion and an even smaller percentage have been validated as drug targets. Many companies interested in identifying new drug targets also see this bounty of opportunity as a major challenge. The raw sequence data say little about the importance of the gene and nothing about its potential as a target for drug discovery. Since 1994, a new term, 'functional genomics', has entered our lexicon. Functional genomics, which in effect is 'high-throughput biology', was originally focused on understanding gene function by studying the genes of simpler organisms, such as the nematode, C. elegans. As the genes from a number of organisms are highly conserved across species, it is believed that studying these basic systems can yield valuable insights for drug companies interested in targeting therapeutics for the higher organisms. More recently, the approach to functional genomics has expanded to include study of gene function in organisms to be targeted for therapeutic intervention. This new approach was the theme of the Functional Genomics Conference: From Identifying Proteins to Faster Drug Discovery held in Washington DC on March 10 and 11, 1998. The organisers (NMHCC) hoped that the breadth of the conference topics would reflect the complexities of the modern drug discovery process and covered technologies from gene chips, bioinformatics, disease models, protein discovery and expression, target validation, high-throughput screening for genes of unknown function, to integration of the drug discovery process. The two day conference placed emphasis on cutting edge technology solutions and the development of high-throughput tools to address the emerging opportunities in genome-based drug discovery.     

Functional Genomics Conference: From Identifying Proteins to Faster Drug Discovery

The massive effort to sequence the human, mouse, rat, nematode (Caenorhabditis elegans), fruit fly (Drosophila), zebra fish, yeast (Saccharomyces cerevisiae), fungal (Candida albicans and Aspergillus fumigatus) and several bacterial genomes has produced a flood of sequence data. Of the more than 100,000 human genes and thousands from other organisms, many partial sequences and several completed microbial genomes are available in both public and private databases. However, elucidation of function has been achieved for only a very small portion and an even smaller percentage have been validated as drug targets. Many companies interested in identifying new drug targets also see this bounty of opportunity as a major challenge. The raw sequence data say little about the importance of the gene and nothing about its potential as a target for drug discovery. Since 1994, a new term, ‘functional genomics’, has entered our lexicon. Functional genomics, which in effect is ‘high-throughput biology’, was originally focused on understanding gene function by studying the genes of simpler organisms, such as the nematode, C. elegans. As the genes from a number of organisms are highly conserved across species, it is believed that studying these basic systems can yield valuable insights for drug companies interested in targeting therapeutics for the higher organisms. More recently, the approach to functional genomics has expanded to include study of gene function in organisms to be targeted for therapeutic intervention. This new approach was the theme of the Functional Genomics Conference: From Identifying Proteins to Faster Drug Discovery held in Washington DC on March 10 and 11, 1998. The organisers (NMHCC) hoped that the breadth of the conference topics would reflect the complexities of the modern drug discovery process and covered technologies from gene chips, bioinformatics, disease models, protein discovery and expression, target validation, high-throughput screening for genes of unknown function, to integration of the drug discovery process. The two day conference placed emphasis on cutting edge technology solutions and the development of high-throughput tools to address the emerging opportunities in genome-based drug discovery.     

Identifying targets for drug discovery using bioinformatics

BACKGROUNd: Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them. OBJECTIVE: The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance the accuracy of the results so as to reduce the cost and time. METHODS: Here we describe recent studies about how to apply bioinformatics techniques in the four phases of drug discovery, how these techniques improve the drug discovery process and some possible difficulties that should be dealt with. Results: We conclude that combining bioinformatics with drug discovery is a very promising method although it faces many problems currently.     

A rational approach to maximize success rate in target discovery

To overcome the problem of high attrition rates in the drug discovery process, an efficient strategy how to identify, select, characterize and validate the most suitable drug targets before embarking on the resource-intense steps of lead discovery and lead optimization is mandatory. We have implemented such an efficient strategy consisting of (i) Target Selection based on gene expression analyses of drugable target genes in clinical samples and relevant in vitro model systems, to identify candidate targets with a specific tissue distribution and presence in human disease; (ii) Target Assessment exploiting the three-dimensional structure of proteins for detailed binding site analysis, to estimate the drugability of the protein for small-molecule inhibitor binding as well as selectivity profiles; and (iii) Target Validation providing evidence for a functional role in in vitro model systems, thus corroborating the biological hypothesis underlying the therapeutic concept. This rational approach has led to the discovery of drug targets for Lead Discovery, maximizing the likelihood for achieving target-selective inhibition by small-molecule inhibitors with minimal in vivo side effects and a therapeutic effect based on a sound biological hypothesis.     

Harnessing public domain data to discover and validate therapeutic targets

Introduction: Discovering, developing and validating new disease treatments is a challenging and time-consuming endeavor. Successful drug discovery hinges on selecting the best drug targets with relevance to human disease and evidence that modulating them will be beneficial for patients. Open data initiatives are increasingly placing such knowledge into the public domain.

Areas covered: In this review, the authors discuss emerging resources such as Open Targets which integrate key information to prioritize target-disease connections. Researchers can use it, along with other resources, to select potential new therapeutic targets to initiate drug discovery projects. They also discuss public resources such as DrugBank and ChEMBL that offer potential tools to interrogate these targets.

Expert opinion: In our opinion, publically available resources are democratizing and connecting information, enabling disease experts to access and prioritize targets of interest in ways that were not possible a few years ago. Moreover, there are several modalities in addition to small molecule perturbation to modulate a target’s activity. Drug discovery scientists can now utilize these new resources to simultaneously evaluate a much larger number of targets than previously possible.     

In silico research in drug discovery

Target and lead discovery constitute the main components of today's early pharmaceutical research. The aim of target discovery is the identification and validation of suitable drug targets for therapeutic intervention, whereas lead discovery identifies novel chemical molecules that act on those targets. With the near completion of the human genome sequencing, bioinformatics has established itself as an essential tool in target discovery and the in silico analysis of gene expression and gene function are now an integral part of it, facilitating the selection of the most relevant targets for a disease under study. In lead discovery, advances in chemoinformatics have led to the design of compound libraries in silico that can be screened virtually. Moreover, computational methods are being developed to predict the drug-likeness of compounds. Thus, drug discovery is already on the road towards electronic R&D.     

Targeting kinases for the treatment of inflammatory diseases

Importance of the field: Inflammatory diseases are one of the major health issues and have become a major focus in the pharmaceutical and biotech industries. To date, drugs prescribed for treatment of these diseases target enzymes that are not specific to the immune system resulting in adverse effects. The main challenge of this research field is, therefore, identifying targets that act specifically on the diseased tissue. Areas covered in this review: This review summarizes drug discovery efforts on kinases that have been identified as key players mediating inflammation and autoimmune disorders. In particular, we discuss recent developments on well-established targets such as mammalian target of rapamycin, JAK3, spleen tyrosine kinase, p38α and lymphocyte specific kinase but provide also a perspective on emerging targets. What the reader will gain: The reader will obtain an overview of drug discovery efforts on kinases in inflammation, recent clinical and preclinical data and developed inhibitor scaffolds. In addition, the reader will be updated on issues in target validation of current drug targets and the potential of selected novel kinase targets in this important disease area. Take home message: Cellular signaling networks that regulate inflammatory response are still poorly understood making rational selection of targets challenging. Recent data suggest that kinase targets that are specific to the immune system and mediate signals immediately downstream of surface receptors are most efficacious in the clinic.     

Identifying targets for drug discovery using bioinformatics

Background: Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them. Objective: The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance the accuracy of the results so as to reduce the cost and time. Methods: Here we describe recent studies about how to apply bioinformatics techniques in the four phases of drug discovery, how these techniques improve the drug discovery process and some possible difficulties that should be dealt with. Results: We conclude that combining bioinformatics with drug discovery is a very promising method although it faces many problems currently.     

Designing drugs on the internet? Free web tools and services supporting medicinal chemistry

The drug discovery process is supported by a multitude of freely available tools on the Internet. This paper summarizes some of the databases and tools that are of particular interest to medicinal chemistry. These include numerous data collections that provide access to valuable chemical data resources, allowing complex queries of compound structures, associated physicochemical properties and biological activities to be performed and, in many cases, providing links to commercial chemical suppliers. Further applications are available for searching protein-ligand complexes and identifying important binding interactions that occur. This is particularly useful for understanding the molecular recognition of ligands in the lead optimization process. The Internet also provides access to databases detailing metabolic pathways and transformations which can provide insight into disease mechanism, identify new targets entities or the potential off-target effects of a drug candidate. Furthermore, sophisticated online cheminformatics tools are available for processing chemical structures, predicting properties, and generating 2D or 3D structure representations--often required prior to more advanced analyses. The Internet provides a wealth of valuable resources that, if fully exploited, can greatly benefit the drug discovery community. In this paper, we provide an overview of some of the more important of these and, in particular, the freely accessible resources that are currently available.     

Drug discovery based on microarray

Gene regulatory networks developed from full genome expression libraries from gene perturbation variant cell lines can be used to quickly and efficiently identify the molecular mechanism of action of drugs or lead compound molecules. We developed an extensive yeast gene expression library consisting of full-genome cDNA array data for over 500 yeast strains each with a single gene disruption. Using this data, combined with dose and time course expression experiments with the oral antifungal agent, we used Boolean network discovery techniques to determine the genes whose expression was most profoundly affected by this drug. Our system identified the gene as the most significantly suppressed target molecule due to exposure to the antifungal agent. This process for network based drug discovery can significantly decrease the time and resources necessary to make rational drug targeting decisions.     

Using bioinformatics techniques for gene identification in drug discovery and development

As more and more evidence has become available, the link between gene and emergent disease has been made including cancer, heart disease and parkinsonism. Analyzing the diseases and designing drugs with respect to the gene and protein level obviously help to find the underlying causes of the diseases, and to improve their rate of cure. The development of modern molecular biology, biochemistry, data collection and analysis techniques provides the scientists with a large amount of gene data. To draw a link between genes and their relation to disease outcomes and drug discovery is a big challenge: how to analyze large datasets and extract useful knowledge? Combining bioinformatics with drug discovery is a promising method to tackle this issue. Most techniques of bioinformatics are used in the first two phases of drug discovery to extract interesting information and find important genes and/or proteins for speeding the process of drug discovery, enhancing the accuracy of analysis and reducing the cost. Gene identification is a very fundamental and important technique among them. In this paper, we have reviewed gene identification algorithms and discussed their usage, relationships and challenges in drug discovery and development.     

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.     

AI-powered therapeutic target discovery

Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.