Target discovery

Genomics screen in transformed stem cells reveals RNASEH2A, PPAP2C, and ADARB1 as putative anticancer drug targets

Since the sequencing of the human genome, recent efforts in cancer drug target discovery have focused more on the identification of novel functions of known genes and the development of more appropriate tumor models. In the present study, we investigated in vitro transformed human adult mesenchymal stem cells (MSC) to identify novel candidate cancer drug targets by analyzing the transcriptional profile of known enzymes compared with non-transformed MSC. The identified enzymes were compared with published cancer gene expression data sets. Surprisingly, the majority of up-regulated enzymes are already known cancer drug targets or act within known druggable pathways. Only three enzymes (RNASEH2A, ADARB1, and PPAP2C) are potentially novel targets that are up-regulated in transformed MSC and expressed in numerous carcinomas and sarcomas. We confirmed the overexpression of RNASEH2A, PPAP2C, and ADARB1 in transformed MSC, transformed fibroblasts, and cancer cell lines MCF7, SK-LMS1, MG63, and U2OS. In functional assays, we show that small interfering RNA knockdown of RNASEH2A inhibits anchorage-independent growth but does not alter in vitro proliferation of cancer cell lines, normal MSC, or normal fibroblasts. Knockdown of PPAP2C impaired anchorage-dependent in vitro growth of cancer cell lines and impaired the in vitro growth of primary MSC but not differentiated human fibroblasts. We show that the knockdown of PPAP2C decreases cell proliferation by delaying entry into S phase of the cell cycle and is transcriptionally regulated by p53. These in vitro data validate PPAP2C and RNASEH2A as putative cancer targets and endorse this in silico approach for identifying novel candidates.

Target discovery from data mining approaches

Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced.

Removing obstacles in neuroscience drug discovery: the future path for animal models

Despite great advances in basic neuroscience knowledge, the improved understanding of brain functioning has not yet led to the introduction of truly novel pharmacological approaches to the treatment of central nervous system (CNS) disorders. This situation has been partly attributed to the difficulty of predicting efficacy in patients based on results from preclinical studies. To address these issues, this review critically discusses the traditional role of animal models in drug discovery, the difficulties encountered, and the reasons why this approach has led to suboptimal utilization of the information animal models provide. The discussion focuses on how animal models can contribute most effectively to translational medicine and drug discovery and the changes needed to increase the probability of achieving clinical benefit. Emphasis is placed on the need to improve the flow of information from the clinical/human domain to the preclinical domain and the benefits of using truly translational measures in both preclinical and clinical testing. Few would dispute the need to move away from the concept of modeling CNS diseases in their entirety using animals. However, the current emphasis on specific dimensions of psychopathology that can be objectively assessed in both clinical populations and animal models has not yet provided concrete examples of successful preclinical-clinical translation in CNS drug discovery. The purpose of this review is to strongly encourage ever more intensive clinical and preclinical interactions to ensure that basic science knowledge gained from improved animal models with good predictive and construct validity readily becomes available to the pharmaceutical industry and clinical researchers to benefit patients as quickly as possible.

Finding multiple target optimal intervention in disease-related molecular network

Drugs against multiple targets may overcome the many limitations of single targets and achieve a more effective and safer control of the disease. Numerous high-throughput experiments have been performed in this emerging field. However, systematic identification of multiple drug targets and their best intervention requires knowledge of the underlying disease network and calls for innovative computational methods that exploit the network structure and dynamics. Here, we develop a robust computational algorithm for finding multiple target optimal intervention (MTOI) solutions in a disease network. MTOI identifies potential drug targets and suggests optimal combinations of the target intervention that best restore the network to a normal state, which can be customer designed. We applied MTOI to an inflammation-related network. The well-known side effects of the traditional non-steriodal anti-inflammatory drugs and the recently recalled Vioxx were correctly accounted for in our network model. A number of promising MTOI solutions were found to be both effective and safer.

The use of gene array technology and proteomics in the search of new targets of diseases for therapeutics

The advent of functional genomics has been greatly broadening our view and accelerating our way in numerous medical research fields. The complete genomic data acquired from the human genome project and the desperate clinical need of comprehensive analytical tools to study complex diseases, has allowed rapid evolution of genomic and proteomic technologies, speeding the rate and number of discoveries in new biomarkers. By jointly using genomics, proteomics and bioinformatics there is a great potential to make considerable contribution to biomarker identification and to revolutionize both the development of new therapies and drug development process.

Are MAP kinases drug targets? Yes, but difficult ones

Pharmaceutical companies are facing an increasing interest in new target identification and validation. In particular, extensive efforts are being made in the field of protein kinase inhibitors research and development, and the past ten years of effort in this field have altered our perception of the potential of kinases as drug targets. Therefore, in the drug discovery process, the selection of relevant, susceptible protein kinase targets combined with searches for leads and candidates have become a crucial approach. The success of recent launches of protein kinase inhibitors (Gleevec, Imatinib, Sutent, Iressa, Nexavar, Sprycel) gave another push to this field. Numerous other kinase inhibitors are currently undergoing clinical trials or clinical development. Some questions are nevertheless unanswered, mostly related to the great number of known kinases in the human genome, to their similarity with each other, to the existence of functionally redundant kinases for specific pathways, and also because the connection between particular pathways and diseases is not always clear. The review is leading the reader through a panoramic view of protein kinase inhibition with a major focus on MAPK, successful examples and clinical candidates.

Molecular imaging in drug development

Molecular imaging can allow the non-invasive assessment of biological and biochemical processes in living subjects. Such technologies therefore have the potential to enhance our understanding of disease and drug activity during preclinical and clinical drug development, which could aid decisions to select candidates that seem most likely to be successful or to halt the development of drugs that seem likely to ultimately fail. Here, with an emphasis on oncology, we review the applications of molecular imaging in drug development, highlighting successes and identifying key challenges that need to be addressed for successful integration of molecular imaging into the drug development process.

Pharmacoproteomics of a metalloproteinase hydroxamate inhibitor in breast cancer cells: dynamics of membrane type 1 matrix metalloproteinase-mediated membrane protein shedding

Broad-spectrum matrix metalloproteinase (MMP) inhibitors (MMPI) were unsuccessful in cancer clinical trials, partly due to side effects resulting from limited knowledge of the full repertoire of MMP substrates, termed the substrate degradome, and hence the in vivo functions of MMPs. To gain further insight into the degradome of MMP-14 (membrane type 1 MMP) an MMPI, prinomastat (drug code AG3340), was used to reduce proteolytic processing and ectodomain shedding in human MDA-MB-231 breast cancer cells transfected with MMP-14. We report a quantitative proteomic evaluation of the targets and effects of the inhibitor in this cell-based system. Proteins in cell-conditioned medium (the secretome) and membrane fractions with levels that were modulated by the MMPI were identified by isotope-coded affinity tag (ICAT) labeling and tandem mass spectrometry. Comparisons of the expression of MMP-14 with that of a vector control resulted in increased MMP-14/vector ICAT ratios for many proteins in conditioned medium, indicating MMP-14-mediated ectodomain shedding. Following MMPI treatment, the MMPI/vehicle ICAT ratio was reversed, suggesting that MMP-14-mediated shedding of these proteins was blocked by the inhibitor. The reduction in shedding or the release of substrates from pericellular sites in the presence of the MMPI was frequently accompanied by the accumulation of the protein in the plasma membrane, as indicated by high MMPI/vehicle ICAT ratios. Considered together, this is a strong predictor of biologically relevant substrates cleaved in the cellular context that led to the identification of many undescribed MMP-14 substrates, 20 of which we validated biochemically, including DJ-1, galectin-1, Hsp90alpha, pentraxin 3, progranulin, Cyr61, peptidyl-prolyl cis-trans isomerase A, and dickkopf-1. Other proteins with altered levels, such as Kunitz-type protease inhibitor 1 and beta-2-microglobulin, were not substrates in biochemical assays, suggesting an indirect affect of the MMPI, which might be important in drug development as biomarkers or, in preclinical phases, to predict systemic drug actions and adverse side effects. Hence, this approach describes the dynamic pattern of cell membrane ectodomain shedding and its perturbation upon metalloproteinase drug treatment.

Novel markers of subclinical disease for Ewing family tumors from gene expression profiling

PURPOSE

Targeting subclinical disease in the bone marrow is particularly relevant in metastatic Ewing family tumors (EFT) where cure is difficult. Genome-wide expression arrays can uncover novel genes differentially expressed in tumors over normal marrow/blood, which may have potentials as markers of subclinical disease.

EXPERIMENTAL DESIGN

Gene expression array data were obtained on 28 EFT tumors using the Affymetrix U133 gene chip and compared with 10 normal blood samples. Ten genes with high tumor to blood ratios were identified. Quantitative reverse transcription-PCR was done to study (a) the dynamic range of detection of rare tumor cells, (b) the gene expression in normal blood/marrow samples, (c) the gene expression among EFT tumors, and (d) the detection and prognostic impact of marker positivity in histology-negative diagnostic marrows of EFT patients.

RESULTS

Five of 10 genes (i.e., six-transmembrane epithelial antigen of the prostate 1 [STEAP1], cyclin D1 [CCND1], NKX2-2 transcription factor [NKX2-2], plakophilin 1 [PKP1], and transmembrane protein 47 [TMEM47]) were chosen for further analyses based on their steep linear dynamic range in detecting tumor cells seeded in normal mononuclear cells and on their homogeneous expression among EFT tumors. Prognostic effect was evaluated in 35 histology-negative diagnostic marrows. Marker negativity of STEAP1, CCND1, or NKX2-2, as well as three markers in combination, was strongly correlated with patient survival as well as survival without new metastases.

CONCLUSIONS

This gene expression array-based approach identified novel markers that may be informative at diagnosis for risk group assessment. Their clinical utility needs to be tested in large patient cohorts.

In vivo silencing of a molecular target by short interfering RNA electroporation: tumor vascularization correlates to delivery efficiency

Screening for a molecular target for cancer therapy requires multiple steps, of which an important one is evaluation of the knockdown effect of the target molecule on pregrown xenograft tumors. However, methods currently used for local administration of knockdown reagents, such as short interfering RNA (siRNA), are not satisfactory as to simplicity and efficiency. We established an electroporation method involving a constant voltage and "plate and fork" type electrodes and used it for in vivo delivery of siRNA. The delivery efficiency correlated to the electric current. The electric current correlated to the microvascular density and vascular endothelial growth factor (VEGF) expression and exhibited a threshold that guaranteed efficient delivery. Consequently, we showed that the vascularization and VEGF expression in tumors determined the efficiency of delivery of siRNA by electroporation. VEGF was chosen as a model target. VEGF siRNA electroporation suppressed the growth of tumors exhibiting high VEGF expression to less than 10% of the control level, but it had no effect on low VEGF-expressing tumors. Notably, a long interval (20 days) of electroporation was enough to obtain a satisfactory effect. Systemically injected siRNA could also be delivered into tumors by this method. Our data will provide the technical basis for in vivo electroporation, and this simple and efficient siRNA delivery method is applicable to in vivo comprehensive screening for a molecular target.

Proteomic profiling of endothelial cells in human lung cancer

Genomic and proteomic analysis of normal and diseased tissues have yielded an abundance of molecular information for diagnostic and potential therapeutic targets. Changing the target of analysis from poorly accessible cells within tissues to easily accessible vascular endothelium has theoretical advantages in tissue-specific targeting. In this study, we sought to map a large-scale proteome of microvascular endothelium in human non-small cell lung cancer (NSCLC) and normal lung tissues, and identify lung cancer-related endothelial cell (EC)-selective proteins. Endothelial cells were isolated within NSCLC tissues and adjacent-normal lung tissue of lung cancer patients by using CD31-immunomagnetic beads. The complex proteins from the ECs were separated by one-dimensional gel electrophoresis, and the proteins in each gel band were digested by trypsin. Peptides were separated by online reverse-phase liquid-chromatography and analyzed by electrospray ionization (ESI) ion trap tandem mass spectrometry. Approximately 600-1000 proteins were identified in each individual sample. Five patient cases of paired individual data, extracted from the protein identification data sets of both normal- and cancer-derived ECs, were analyzed by subtractive proteomics. An average of 300 proteins was specifically identified from each lung cancer-derived EC isolate, compared to normal lung-derived ECs. With the use of several comparative analyses, we identified among those 300 proteins, 16 common candidate proteins that were detected in at least 3 of 5 cases specific to lung cancer-derived ECs. Proteins selectively identified in cancer-derived ECs, including coatomer protein complex, subunit gamma (COPG), and peroxiredoxin 4 (PRDX4), were validated by Western blot analysis. In an additional experiment in which 16 cancer samples were analyzed by immunohistochemistry, PRDX4, thymopoietin (TMPO), and COPG were confirmed to be abundantly expressed in lung cancer-derived ECs and in cancerous lung cells. Further ongoing analysis of these 16 candidate proteins will determine their potential applicability to NSCLC-specific diagnosis and therapeutics.

Targeting cell signaling pathways for drug discovery: an old lock needs a new key

In this age of targeted therapy, the failure of most current drug-discovery efforts to yield safe, effective, and inexpensive drugs has generated widespread concern. Successful drug development has been stymied by a general focus on target selection rather than clinical safety and efficacy. The very process of validating the targets themselves is inefficient and in many cases leads to drugs having poor efficacy and undesirable side effects. Indeed, some rationally designed drugs (e.g., inhibitors of receptor tyrosine kinases, tumor necrosis factor (TNF), cyclooxygenase-2 (COX-2), vascular endothelial growth factor (VEGF), bcr-abl, and proteasomes) are ineffective against cancers and other inflammatory conditions and produce serious side effects. Since any given cancer carries mutations in an estimated 300 genes, this raises an important question about how effective these targeted therapies can ever be against cancer. Thus, it has become necessary to rethink drug development strategies. This review analyzes the shortcomings of rationally designed target-specific drugs against cancer cell signaling pathways and evaluates the available options for future drug development.

Loss-of-function genetic diseases and the concept of pharmaceutical targets

The biomedical world relies heavily on the definition of pharmaceutical targets as an essential step in the drug design process. It is therefore tempting to apply this model to genetic diseases as well. However, whereas the model applies well to gain-of-function genetic diseases, it is less suited to most loss-of-function genetic diseases. Most common diseases, as well as gain-of-function genetic diseases, are characterized by the activation of specific pathways or the ectopic activity of proteins, which make well identified targets. By contrast, loss-of-function genetic diseases are caused by the impairment of one protein, with potentially distributed consequences. For such diseases, the definition of a pharmaceutical target is less precise, and the identification of pharmaceutically-relevant targets may be difficult. This critical but largely ignored aspect of loss-of-function genetic diseases should be taken into consideration to avoid the commitment of resources to inappropriate strategies in the search for treatments.

Vascular proteomic mapping in vivo

Molecular targeting of drugs and imaging agents remain important yet elusive goals in modern medicine. Technological advancements in genomics and proteomics methods have detected differentially expressed genes and proteins, uncovering many new candidate targets in a wide array of diseases and tissues. However, methods to validate potential targets in vivo tend to be quite laborious so that the validation and testing phase has become rate-limiting in bringing treatments to the clinic. There is a critical need for integrated approaches combining state-of-the-art methodologies in proteomics and in vivo imaging to accelerate validation of newly discovered vascular targets for nanomedicines, drugs, imaging agents, and gene vectors. This paper is a review of vascular targeting and proteomics, and will present recent developments in proteomic imaging. A new in vivo organellar proteomic imaging platform will be discussed, which combines subcellular fractionation, mass spectrometry, bioinformatic database interrogation, monoclonal antibody technology and a battery of imaging modalities to rapidly discover and validate tissue-specific endothelial protein targets in vivo. Technological advancements are permitting large-scale proteomic mapping to be performed. New targets have been discovered that permit organ-specific targeting in vivo. Improvements in imaging are creating standards for validation of targets in vivo. Tumor imaging and radioimmunotherapy have also been improved through these efforts. Although we are moving towards a comprehensive mapping of the protein expression by the endothelium, much more needs to be done.

Dual mitogen-activated protein kinase and epidermal growth factor receptor inhibition in biliary and pancreatic cancer

This study aimed to develop rational combinations of targeted agents against biliary and pancreatic cancers. To this end, we compared the global gene expression profile of biliary cancer cell lines with different degrees of sensibility to the epidermal growth factor receptor tyrosine kinase inhibitors gefitinib and erlotinib using the Affymetrix U133A microarray platform. A set of 32 genes, including genes involved in signal transduction pathways, cell cycle regulation, and angiogenesis, was highly overexpressed in resistant cells. Five of these genes encoded proteins in the Ras/Raf/mitogen-activated protein kinase (MAPK) pathway, a finding that was confirmed by Western blot and immunohistochemistry. Gefitinib failed to inhibit the MAPK pathway in resistant cell lines. Based on these data, we explored the activity of dual treatment with gefitinib in combination with CI-1040, a MAPK inhibitor. This strategy effectively resulted in inhibition of the MAPK signaling pathway and exerted antitumor effects in vitro and in vivo in tumors resistant to each of the agents alone. To further confirm these results, we tested the combined treatment in four tumor xenografts generated from patients with resected pancreatic cancer. Combined treatment was more effective than either single agent alone in this model. This study illustrates the value of global analysis of gene expression to rationally design combinations of mechanistic-based drugs. In addition, the data support the efficacy of combined epidermal growth factor receptor and MAPK inhibitors in biliary and pancreatic cancers, providing the basis to test this combination in the clinic.

Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness

Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets.

The Connectivity Map: a new tool for biomedical research

The ultimate objective of biomedical research is to connect human diseases with the genes that underlie them and drugs that treat them. But this remains a daunting task, and even the most inspired researchers still have to resort to laborious screens of genetic or chemical libraries. What if at least some parts of this screening process could be systematized and centralized? And hits found and hypotheses generated with something resembling an internet search engine? These are the questions the Connectivity Map project set out to answer.

Antibody-based enrichment of peptides on magnetic beads for mass-spectrometry-based quantification of serum biomarkers

A major bottleneck for validation of new clinical diagnostics is the development of highly sensitive and specific assays for quantifying proteins. We previously described a method, stable isotope standards with capture by antipeptide antibodies, wherein a specific tryptic peptide is selected as a stoichiometric representative of the protein from which it is cleaved, is enriched from biological samples using immobilized antibodies, and is quantitated using mass spectrometry against a spiked internal standard to yield a measure of protein concentration. In this study, we optimized a magnetic-bead-based platform amenable to high-throughput peptide capture and demonstrated that antibody capture followed by mass spectrometry can achieve ion signal enhancements on the order of 10(3), with precision (CVs <10%) and accuracy (relative error approximately 20%) sufficient for quantifying biomarkers in the physiologically relevant ng/mL range. These methods are generally applicable to any protein or biological fluid of interest and hold great potential for providing a desperately needed bridging technology between biomarker discovery and clinical application.

DNA-encoded chemical libraries

The discovery and development of novel drugs for the multitude of targets originating from functional genomic research is a challenging task. While antibodies can nowadays be raised against virtually any given target using phage-display methodologies, a similar "selection/amplification" approach for the facile discovery of low-molecular weight compounds capable of specific binding to protein targets of choice has so far been lacking. The development of DNA-encoded chemical libraries, combined with suitable selection and high-throughput sequencing strategies, holds promises to fill this gap. Here, we review the latest developments in the field of DNA-encoded chemical libraries, commenting on the challenges and opportunities for the different experimental strategies in this rapidly evolving research area, which may gain importance for the future drug discovery process.

InSilico-Guided Target Identification of a Scaffold-Focused Library: 1,3,5-Triazepan-2,6-diones as Novel Phospholipase A2 Inhibitors

A collection of 2150 druggable active sites from the Protein Data Bank was screened by high-throughput docking to identify putative targets for five representative molecules of a combinatorial library sharing a 1,3,5-triazepan-2,6-dione scaffold. Five targets were prioritized for experimental evaluation by computing enrichment in individual protein entries among the top 2% scoring targets. Out of the five proposed proteins, secreted phospholipase A2 (sPLA2) was shown to be a true target for a panel of 1,3,5-triazepan-2,6-diones which exhibited micromolar affinities toward two human sPLA2 members.

Small molecule macroarray construction via Ugi four-component reactions

[reaction: see text] We report the construction of small molecule macroarrays via Ugi four-component reactions on planar cellulose supports. Array synthesis was enabled by the development of a high efficiency photocleavable linker system and the strategic use of both water- and microwave-assisted organic reactions.

Using isoform-specific inhibitors to target lipid kinases

In this issue of Cell, Knight et al. (2006) present a detailed pharmacological analysis of the PI3 kinase (PI3-K) family using small molecule inhibitors. They conclude that p110alpha, a PI3-K isoform, has a critical role in insulin signaling. In a related Cancer Cell paper, Fan et al. (2006) show that blocking activation of both p110alpha and the kinase mTOR with a small molecule inhibitor limits the growth of gliomas.

Angiogenesis: from plants to blood vessels

Angiogenesis is a major pathological component of diseases such as cancer and coronary heart disease. Although major advances have been made and encouraging clinical results obtained, safer and more effective approaches are required. The identification of new drugs from plants has a long and successful history, and certain proangiogenic and antiangiogenic plant components have been used in traditional Chinese medicine (TCM) for thousands of years. Similar to Western combination therapy, TCM uses mixtures of plant extracts, termed fufang, to maximize efficacy and minimize adverse effects or toxicity. More evidence-based research and chemical optimization of these compounds could further enhance the effectiveness of these plant-based medicines in angiotherapy.

Visualizing gene determinants of disease in drug discovery

Target discovery, subphenotype detection and the detection of human heterogeneity are major challenges in drug discovery and development on which genetic markers can have an impact. Visualizing gene determinants of traits in case–control study individuals during drug discovery using contrasts of empirically-derived log Bayes factors (LBFs) from whole-genome scan single nucleotide polymorphism (SNP) data is presented to aid this. Examples of the use of eigen analysis, covariate overlays and individualized aggregation to ontologies are included from disease research studies. Displays of individuals, or exposures of biological features of interest, can encompass unlimited numbers of markers in a single multivariate analysis without multiple testing. This filtering approach is aimed at nonspecialists who find themselves asked to undertake work such as that performed by the authors.

Human monogenic disorders — a source of novel drug targets

The decrease in new drug applications and approvals over the past several years results from an underlying crisis in drug target identification and validation. Model organisms are being used to address this problem, in combination with novel approaches such as the International HapMap Project. What has been underappreciated is that discovery of new drug targets can also be revived by traditional Mendelian genetics. A large fraction of the human gene repertoire remains phenotypically uncharacterized, and is likely to encode many unanticipated and novel phenotypes that will be of interest to pharmaceutical and biotechnological drug developers.

Co-expression of anti-NFkappaB RNA aptamers and siRNAs leads to maximal suppression of NFkappaB activity in mammalian cells

The specific down-regulation of gene expression in cells is a powerful method for elucidating a gene's function. A common method for suppressing gene expression is the elimination of mRNA by RNAi or antisense. Alternatively, oligonucleotide-derived aptamers have been used as protein-directed agents for the specific knock-down of both intracellular and extracellular protein activity. Protein-directed methods offer the advantage of more closely mimicking small molecule therapeutics' mechanism of activity. Furthermore, protein-directed methods may synergize with RNA-directed methods since the two methods attack gene expression at different levels. Here we have knocked down a well-characterized intracellular protein's activity, NFκB, by expressing either aptamers or small interfering RNAs (siRNAs). Both methods can diminish NFκB's activity to similar levels (from 29 to 64%). Interestingly, expression of both aptamers and siRNAs simultaneously, suppressed NFκB activity better than either method alone (up to 90%). These results demonstrate that the expression of intracellular aptamers is a viable alternative to siRNA knock-down. Furthermore, for the first time, we show that the use of aptamers and siRNA together can be the most effective way to achieve maximal knock-down of protein activity.

Tumour microenvironment - opinion: validating matrix metalloproteinases as drug targets and anti-targets for cancer therapy

The matrix metalloproteinases (MMPs) mediate homeostasis of the extracellular environment. They have multiple signalling activities that are commonly altered during tumorigenesis and that might serve as intervention points for anticancer drugs. However, there are many criteria to consider in validating MMPs as drug targets and for the development of MMP inhibitors. The inhibition of some MMPs could have pro-tumorigenic effects (making them anti-targets), counterbalancing the benefits of target inhibition. These effects might partially account for the failure of MMP inhibitors in clinical trials. What are the major challenges in MMP target validation and MMP-inhibitor-drug development?

Molecular Imaging in the Development of Cancer Therapeutics

Researchers have made great progress in defining genetic and molecular alterations that contribute to cancer. New therapeutic targets have been identified and targeted therapeutic agents have been developed, but our ability to evaluate potential drugs has not kept pace. Molecular imaging technologies that monitor biological processes and/or measure levels of targeted macromolecules can contribute significantly to preclinical and clinical drug evaluation. This article describes the drug discovery process, economic problems facing drug discovery and development, and successes and failures in this realm. We briefly describe the available molecular imaging tools, with emphasis on positron emission tomography. We discuss biological processes that are altered in tumors and can be measured by molecular imaging; examples include gene expression, signal transduction, tumor cell metabolism, proliferation, apoptosis, hypoxia, and angiogenesis. We conclude with a proposal to integrate molecular imaging into the drug development process.

Screening phage display libraries for organ-specific vascular immunotargeting in vivo

The molecular diversity of the luminal endothelial cell surface arising in vivo from local variations in genetic expression and tissue microenvironment may create opportunities for achieving targeted molecular imaging and therapies. Here, we describe a strategy to identify probes and their cognate antigens for targeting vascular endothelia of specific organs in vivo. We differentially screen phage libraries to select organ-targeting antibodies by using luminal endothelial cell plasma membranes isolated directly from tissue and highly enriched in natively expressed proteins exposed to the bloodstream. To obviate liver uptake of intravenously injected phage, we convert the phage-displayed antibodies into scFv-Fc fusion proteins, which then are able to rapidly target select organ(s) in vivo as visualized directly by gamma-scintigraphic whole-body imaging. Mass spectrometry helps identify the antigen targets. This comprehensive strategy provides new promise for harnessing the power of phage display for mapping vascular endothelia natively in tissue and for achieving vascular targeting of specific tissues in vivo.

Finding new drug targets in the 21st century

The past 30 years have witnessed a steady decline in the number of new drug targets. This review concentrates on the initial process of target identification and argues that current problems have resulted from a decrease in clinical research, an overemphasis on the discovery of new targets through an understanding of the molecular causes of disease and the adoption of cell and animal models that are poor predictors of human disease. To resolve this situation, we argue for increased clinical research and show that an intervention at the physiological level, using drugs to target at the extracellular signalling pathways, will facilitate identification of novel drug targets in the 21st century.

Lysophosphatidic Acid Acyltransferase-β Is a Prognostic Marker and Therapeutic Target in Gynecologic Malignancies

Lysophosphatidic acid, the substrate for lysophosphatidic acid acyltransferase beta (LPAAT-beta), is a well-studied autocrine/paracrine signaling molecule that is secreted by ovarian cancer cells and is found at elevated levels in the blood and ascites fluid of women with ovarian cancer. LPAAT-beta converts lysophosphatidic acid to phosphatidic acid, which functions as a cofactor in Akt/mTOR and Ras/Raf/Erk pathways. We report that elevated expression of LPAAT-beta was associated with reduced survival in ovarian cancer and earlier progression of disease in ovarian and endometrial cancer. Inhibition of LPAAT-beta using small interfering RNA or selective inhibitors, CT32521 and CT32228, two small-molecule noncompetitive antagonists representing two different classes of chemical structures, induces apoptosis in human ovarian and endometrial cancer cell lines in vitro at pharmacologically tenable nanomolar concentrations. Inhibition of LPAAT-beta also enhanced the survival of mice bearing ovarian tumor xenografts. Cytotoxicity was modulated by diacylglycerol effectors including protein kinase C and CalDAG-GEF1. LPAAT-beta was localized to the endoplasmic reticulum and overexpression was associated with redistribution of protein kinase C-alpha. These findings identify LPAAT-beta as a potential prognostic and therapeutic target in ovarian and endometrial cancer.

VeloceGenomics: An Accelerated in Vivo Drug Discovery Approach to Rapidly Predict the Biologic, Drug-Like Activity of Compounds, Proteins, or Genes

PURPOSE

The aim of this study is to test the predictive power of in vivo multiorgan RNA expression profiling in identifying the biologic activity of molecules.

METHODS

Animals were treated with compound A or B. At the end of the treatment period, in vivo multiorgan microarray-based gene expression data were collected. Investigators masked to the identity of the compounds analyzed the transcriptome signatures to define the molecular pathways affected by treatment and to hypothesize the biologic activity and potential therapeutic indications of the blinded compounds.

RESULTS

For compound A, G-protein-coupled receptors and factors associated with cell growth were affected-growth hormone/insulin-like growth factor-1, glucagon/insulin axes, and general somatomedin-like activity. Deblinding showed the compound to be a somatostatin analog, SOM230, confirming the accuracy of the predicted biologic activity. For compound B, components of the inflammatory cascade potentially mediated by lipopolysaccharide, tumor necrosis factor, or proinflammatory cytokines were affected. The gene expression signatures were most consistent with an interleukin-6 family activity. Deblinding revealed that compound B was leukemia inhibitory factor.

CONCLUSIONS

VeloceGenomics is a strategy of coupling in vivo compound testing with genomic technologies. The process enables prediction of the mechanism of action and, coupled with other relevant data, prediction of the suitability of compounds for advancement in the drug development process.

The promise of genomics to identify novel therapeutic targets

The cataloguing of the human genome has provided an unprecedented prospectus for target identification and drug discovery. A current analysis indicates that slightly more than 3000 unique protein encoding loci are potentially amenable to pharmacological intervention (the 'druggable genome', which can be queried at http://function.gnf.org/druggable). However, the assessment of genome sequence data has not resulted in the anticipated acceleration of novel therapeutic developments. The basis for this shortfall lies in the significant attrition rates endemic to preclinical/clinical development, as well as the often underestimated complexity of gene function in higher order biological systems. To address the latter issue, a number of strategies have emerged to facilitate genomics-driven target identification and validation, including cellular profiling of gene function, in silico modelling of gene networks, and systematic analyses of protein complexes. The expectation is that the integration of these and other systems-based technologies may enable the conversion of potential genomic targets into functionally validated molecules, and result in practicable gene-based drug discovery pipelines.

Disease-specific target selection: a critical first step down the right road

Relevance of a drug target for a disease is often inferred with strong belief but fragile evidence. Here, a program for early identification of human disease-specific drug targets using high-throughput genetic associations is described. Large numbers of well-characterized patients (>1000) and matched controls are screened for genetic associations using several thousand (>7000) single nucleotide polymorphisms from more than 1500 genes. The genes were selected because they are members of target classes for which there are precedents for high-throughput chemical screening technology. This review summarizes the methods and intensive data analyses leading to target gene identification for type 2 diabetes mellitus, including the statistical permutation methodology used to correct for many variables.

Determining the functional role of TRPC channels in primary cells

Although the TRPC members of the mammalian transient receptor potential TRP cation channel family were the first to be described in 1995, the depth of knowledge of TRPC channels has fallen behind that of their counterparts in the TRPV and TRPM subfamilies in the intervening years. The complexities and controversies of TRPC channel composition and regulation have hindered their progress as therapeutic targets in the drug discovery environment to date, however embracing these challenges as opportunities may bring TRPC channels to the forefront of the discovery of novel therapies for many diseases. These challenges and opportunities of exploring TRPC channels as therapeutic targets are highlighted and discussed in this review with respect to respiratory diseases.

The target discovery process

In order to minimise attrition rates in drug development projects, a target discovery process is implemented to select and characterise the most suitable candidate kinase targets, before lead identification and lead optimisation are embarked upon. The process consists of 1) target selection, 2) target assessment, and 3) target validation. This rational approach to target discovery, as a prerequisite for lead discovery, ensures that new therapeutic targets fulfil a set of general criteria, as well as indication-specific, descriptive and functional ones. The approach should ultimately maximise the likelihood of achieving target-selective inhibition by small-molecule inhibitors with minimal in vivo side effects and a therapeutic effect based on a sound biological hypothesis.

Genomics and systems biology - how relevant are the developments to veterinary pharmacology, toxicology and therapeutics?

This review discusses some of the recent developments in genomics and its current and future relevance for veterinary pharmacology and toxicology. With the rapid progress made in this field several new approaches in pharmacological and toxicological research have developed and drug discovery and drug development strategies have changed dramatically. In this review, the term genomics is used to encompass the three sub-disciplines transcriptomics, proteomics and metabolomics (or metabonomics) to describe the formation and fate of mRNA, proteins and metabolites, respectively. The current status and methods of the technology and some applications are briefly described. Although the DNA sequencing programmes are receiving considerable attention, the real value of genomics for pharmacology and toxicology is brought by the parallel developments in bio-informatics, bio-statistics and the integration of biology with mathematics and information technology. The ultimate level of integration is now mostly called systems biology, where mRNA, proteins and metabolites are being analysed in parallel, using a complete arsenal of analytical techniques (DNA-array, LC-MS/MS, GC-MS/MS, NMR, etc.). The information thus collected is analysed, integrated, linked to database information and translated to pathways and systems. This approach offers an enormous potential to study disease mechanisms and find new drug targets. Thus far, genomics and systems biology have not been introduced significantly in typical veterinary pharmacological and toxicological research programmes. The high costs and complexity connected to these large projects often form major obstacles for research groups with limited budgets. In other veterinary areas and disciplines, including infectious diseases, animal production and food-safety more examples of application are available. Genomics and bio-informatics provide outstanding opportunities to study pharmacology and toxicology in a more holistic way, taking into account the complexity of biological systems and based on the basic principles of physiology and the concept of homeostasis. Knowledge of biology, in vivo and in vitro models, and comparative pharmacology/toxicology is essential here, creating excellent opportunities for the veterinary trained scientist.

Target identification and validation in drug discovery: the role of proteomics

Proteomics, the study of cellular protein expression, is an evolving technology platform that has the potential to identify novel proteins involved in key biological processes in the cell that may serve as potential drug targets. While proteomics has considerable theoretical promise, individual cells/tissues have the potential to generate many millions of proteins while the current analytical technologies that involve the use of time-consuming two dimensional gel electrophoresis (2DIGE) and various mass spectrometry (MS) techniques are unable to handle complex biological samples without multiple high-resolution purification steps to reduce their complexity. This can significantly limit the speed of data generation and replication and requires the use of bioinformatic algorithms to reconstitute the parent proteome, a process that does not always result in a reproducible outcome. In addition, membrane bound proteins, e.g., receptors and ion channels, that are the targets of many existing drugs, are not amenable to study due, in part, to limitations in current proteomic techniques and also to these being present in low abundance and thus disproportionally represented in proteome profiles. Subproteomes with reduced complexity have been used to generate data related to specific, hypothesis-driven questions regarding target identification, protein-interaction networks and signaling pathways. However progress to date, with the exception of diagnostic proteomics in the field of cancer, has been exceedingly slow with an inability to put such studies in the context of a larger proteome, limiting the value of the information. Additionally the pathway for target validation (which can be more accurately described at the preclinical level as target confidence building) remains unclear. It is important that the ability to measure and interrogate proteomes matches expectations, avoiding a repetition of the disappointment and subsequent skepticism that accompanied what proved to be unrealistic expectations for the rapid contribution of data based on the genome maps, to biomedical research.

How molecular profiling could revolutionize drug discovery

Information from genomic, proteomic and metabolomic measurements has already benefited target discovery and validation, assessment of efficacy and toxicity of compounds, identification of disease subgroups and the prediction of responses of individual patients. Greater benefits can be expected from the application of these technologies on a significantly larger scale; by simultaneously collecting diverse measurements from the same subjects or cell cultures; by exploiting the steadily improving quantitative accuracy of the technologies; and by interpreting the emerging data in the context of underlying biological models of increasing sophistication. The benefits of applying molecular profiling to drug discovery and development will include much lower failure rates at all stages of the drug development pipeline, faster progression from discovery through to clinical trials and more successful therapies for patient subgroups. Upheavals in existing organizational structures in the current 'conveyor belt' models of drug discovery might be required to take full advantage of these methods.

Bioinformatics in drug development and assessment

Bioinformatics is playing an increasingly important role in nearly all aspects of drug discovery, drug assessment, and drug development. This growing importance lies not only in the role that bioinformatics plays in handling large volumes of data, but also in the utility of bioinformatics tools to predict, analyze, or help interpret clinical and preclinical findings. This review focuses on describing and evaluating some of the newer or more important bioinformatics resources (i.e., databases and software) that are of growing importance to understanding or predicting drug metabolism, especially with respect to the absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of both existing drugs and potential drug leads. Detailed descriptions and critical assessments of a number of potentially useful bioinformatics/cheminformatics databases and predictive ADMET software tools are provided. Additionally, several pharmaceutically important applications of both the databases and software are highlighted. Given the rapid growth in this area and the rapid changes that are taking place, a special emphasis is placed on freely available or Web-accessible resources.

In vivo drug discovery in the zebrafish

The zebrafish has become a widely used model organism because of its fecundity, its morphological and physiological similarity to mammals, the existence of many genomic tools and the ease with which large, phenotype-based screens can be performed. Because of these attributes, the zebrafish might also provide opportunities to accelerate the process of drug discovery. By combining the scale and throughput of in vitro screens with the physiological complexity of animal studies, the zebrafish promises to contribute to several aspects of the drug development process, including target identification, disease modelling, lead discovery and toxicology.

Towards quantitative biology: integration of biological information to elucidate disease pathways and to guide drug discovery

Developing a new drug is a tedious and expensive undertaking. The recently developed high-throughput experimental technologies, summarised by the terms genomics, transcriptomics, proteomics and metabolomics provide for the first time ever the means to comprehensively monitor the molecular level of disease processes. The "-omics" technologies facilitate the systematic characterisation of a drug target's physiology, thereby helping to reduce the typically high attrition rates in discovery projects, and improving the overall efficiency of pharmaceutical research processes. Currently, the bottleneck for taking full advantage of the new experimental technologies are the rapidly growing volumes of automatically produced biological data. A lack of scalable database systems and computational tools for target discovery has been recognised as a major hurdle. In this review, an overview will be given on recent progress in computational biology that has an impact on drug discovery applications. The focus will be on novel in silico methods to reconstruct regulatory networks, signalling cascades, and metabolic pathways, with an emphasis on comparative genomics and microarray-based approaches. Promising methods, such as the mathematical simulation of pathway dynamics are discussed in the context of applications in discovery projects. The review concludes by exemplifying concrete data-driven studies in pharmaceutical research that demonstrate the value of integrated computational systems for drug target identification and validation, screening assay development, as well as drug candidate efficacy and toxicity evaluations.