Target discovery

In silico Tools for Target Identification and Drug Molecular Docking in Leishmania

Neglected tropical diseases represent a significant health burden in large parts of the world. Drug discovery is currently a key bottleneck in the pipeline of these diseases. In this chapter, the in silico approaches used for the processes involved in drug discovery, identification and validation of druggable Leishmania targets, and design and optimisation of new anti-leishmanial drugs are discussed. We also provide a general view of the different computational tools that can be employed in pursuit of this aim, along with the most interesting cases found in the literature.

Substrate mediated enzyme prodrug therapy

Substrate mediated enzyme prodrug therapy (SMEPT) is a biomedical platform developed to perform a localized synthesis of drugs mediated by implantable biomaterials. This approach combines the benefits and at the same time offers to overcome the drawbacks for traditional pill-based drug administration and site-specific, implant mediated drug delivery. Specifically, SMEPT offers the flexibility of delivering multiple drugs - individually as monotherapy, in sequence, or as a combination therapy, all of which is also accomplished in a site-specific manner. This technology is also unique for site-specific synthesis of drugs with short half-life, such as nitric oxide. This review presents historical development of SMEPT from early reports to the most recent examples, and also outlines potential avenues for subsequent development of this platform.

Drug Discovery by Molecular Imaging and Monitoring Therapy Response in Lymphoma

Molecular imaging allows a noninvasive assessment of biochemical and biological processes in living subjects. Treatment strategies for malignant lymphoma depend on histology and tumor stage. For the last two decades, molecular imaging has been the mainstay diagnostic test for the staging of malignant lymphoma and the assessment of response to treatment. This technology enhances our understanding of disease and drug activity during preclinical and clinical drug development. Here, we review molecular imaging applications in drug development, with an emphasis on oncology. Monitoring and assessing the efficacy of anti-cancer therapies in preclinical or clinical models are essential and the multimodal molecular imaging approach may represent a new stage for pharmacologic development in cancer. Monitoring the progress of lymphoma therapy with imaging modalities will help patients. Identifying and addressing key challenges is essential for successful integration of molecular imaging into the drug development process. In this review, we highlight the general usefulness of molecular imaging in drug development and radionuclide-based reporter genes. Further, we discuss the different molecular imaging modalities for lymphoma therapy and their preclinical and clinical applications.

Prediction of Novel Drugs for Hepatocellular Carcinoma Based on Multi-Source Random Walk

Computational approaches for predicting drug-disease associations by integrating gene expression and biological network provide great insights to the complex relationships among drugs, targets, disease genes, and diseases at a system level. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high rate of morbidity and mortality. We provide an integrative framework to predict novel d rugs for HCC based on multi-source random walk (PD-MRW). Firstly, based on gene expression and protein interaction network, we construct a gene-gene weighted i nteraction network (GWIN). Then, based on multi-source random walk in GWIN, we build a drug-drug similarity network. Finally, based on the known drugs for HCC, we score all drugs in the drug-drug similarity network. The robustness of our predictions, their overlap with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched KEGG pathway demonstrate our approach can effectively identify new drug indications. Specifically, regorafenib (Rank = 9 in top-20 list) is proven to be effective in Phase I and II clinical trials of HCC, and the Phase III trial is ongoing. And, it has 11 overlapping pathways with HCC with lower p-values. Focusing on a particular disease, we believe our approach is more accurate and possesses better scalability.

Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target's homologue set containing 102 potential target proteins is predicted in the paper.

miRNAs target databases: developmental methods and target identification techniques with functional annotations

PURPOSE

microRNA (miRNA) regulates diverse biological mechanisms and metabolisms in plants and animals. Thus, the discoveries of miRNA has revolutionized the life sciences and medical research.The miRNA represses and cleaves the targeted mRNA by binding perfect or near perfect or imperfect complementary base pairs by RNA-induced silencing complex (RISC) formation during biogenesis process. One miRNA interacts with one or more mRNA genes and vice versa, hence takes part in causing various diseases. In this paper, the different microRNA target databases and their functional annotations developed by various researchers have been reviewed. The concurrent research review aims at comprehending the significance of miRNA and presenting the existing status of annotated miRNA target resources built by researchers henceforth discovering the knowledge for diagnosis and prognosis.

METHODS AND RESULTS

This review discusses the applications and developmental methodologies for constructing target database as well as the utility of user interface design. An integrated architecture is drawn and a graphically comparative study of present status of miRNA targets in diverse diseases and various biological processes is performed. These databases comprise of information such as miRNA target-associated disease, transcription factor binding sites (TFBSs) in miRNA genomic locations, polymorphism in miRNA target, A-to-I edited target, Gene Ontology (GO), genome annotations, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, target expression analysis, TF-miRNA and miRNA-mRNA interaction networks, drugs-targets interactions, etc.

CONCLUSION

miRNA target databases contain diverse experimentally and computationally predicted target through various algorithms. The comparison of various miRNA target database has been performed on various parameters. The computationally predicted target databases suffer from false positive information as there is no common theory for prediction of miRNA targets. The review conclusion emphasizes the need of more intelligent computational improvement for the miRNA target identification, their functional annotations and datasbase development.

A novel framework for the identification of drug target proteins: Combining stacked auto-encoders with a biased support vector machine

The identification of drug target proteins (IDTP) plays a critical role in biometrics. The aim of this study was to retrieve potential drug target proteins (DTPs) from a collected protein dataset, which represents an overwhelming task of great significance. Previously reported methodologies for this task generally employ protein-protein interactive networks but neglect informative biochemical attributes. We formulated a novel framework utilizing biochemical attributes to address this problem. In the framework, a biased support vector machine (BSVM) was combined with the deep embedded representation extracted using a deep learning model, stacked auto-encoders (SAEs). In cases of non-drug target proteins (NDTPs) contaminated by DTPs, the framework is beneficial due to the efficient representation of the SAE and relief of the imbalance effect by the BSVM. The experimental results demonstrated the effectiveness of our framework, and the generalization capability was confirmed via comparisons to other models. This study is the first to exploit a deep learning model for IDTP. In summary, nearly 23% of the NDTPs were predicted as likely DTPs, which are awaiting further verification based on biomedical experiments.

Emerging Approaches to Tuberculosis Drug Development: At Home in the Metabolome

Once considered a crowning achievement of modern drug development, tuberculosis (TB) chemotherapy has proven increasingly unable to keep pace with the spread of the pandemic and rise of drug resistance. Efforts to revive the TB drug development pipeline have, in the meantime, faltered. Closer analysis reveals key experimental deficiencies that have hindered our ability to 'reverse engineer' knowledge of antibiotic mechanisms into rational drug development. Here, we discuss the emerging potential of metabolomics; the systems level study of small molecule metabolites, to help overcome these gaps and serve as a unique biochemical bridge between the phenotypic properties of chemical compounds and biological targets.

Current Trends in Multidrug Optimization: An Alley of Future Successful Treatment of Complex Disorders

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

Current Trends in Multidrug Optimization

The identification of effective and long-lasting cancer therapies still remains elusive, partially due to patient and tumor heterogeneity, acquired drug resistance, and single-drug dose-limiting toxicities. The use of drug combinations may help to overcome some limitations of current cancer therapies by challenging the robustness and redundancy of biological processes. However, effective drug combination optimization requires the careful consideration of numerous parameters. The complexity of this optimization problem is clearly nontrivial and likely requires the assistance of advanced heuristic optimization techniques. In the current review, we discuss the application of optimization techniques for the identification of optimal drug combinations. More specifically, we focus on the application of phenotype-based screening approaches in the field of cancer therapy. These methods are divided into three categories: (1) modeling methods, (2) model-free approaches based on biological search algorithms, and (3) merged approaches, particularly phenotypically driven network biology methods and computation network models relying on phenotypic data. In addition to a brief description of each approach, we include a critical discussion of the advantages and disadvantages of each method, with a strong focus on the limitations and considerations needed to successfully apply such methods in biological research.

The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective

The human kinome is one of the most productive classes of drug target, and there is emerging necessity for treating complex diseases by means of polypharmacology (multi-target drugs and combination products). However, the advantages of the multi-target drugs and the combination products are still under debate. A comparative analysis between FDA approved multi-target drugs and combination products, targeting the human kinome, was conducted by mapping targets onto the phylogenetic tree of the human kinome. The approach of network medicine illustrating the drug-target interactions was applied to identify popular targets of multi-target drugs and combination products. As identified, the multi-target drugs tended to inhibit target pairs in the human kinome, especially the receptor tyrosine kinase family, while the combination products were able to against targets of distant homology relationship. This finding asked for choosing the combination products as a better solution for designing drugs aiming at targets of distant homology relationship. Moreover, sub-networks of drug-target interactions in specific disease were generated, and mechanisms shared by multi-target drugs and combination products were identified. In conclusion, this study performed an analysis between approved multi-target drugs and combination products against the human kinome, which could assist the discovery of next generation polypharmacology.

Network motifs modulate druggability of cellular targets

Druggability refers to the capacity of a cellular target to be modulated by a small-molecule drug. To date, druggability is mainly studied by focusing on direct binding interactions between a drug and its target. However, druggability is impacted by cellular networks connected to a drug target. Here, we use computational approaches to reveal basic principles of network motifs that modulate druggability. Through quantitative analysis, we find that inhibiting self-positive feedback loop is a more robust and effective treatment strategy than inhibiting other regulations, and adding direct regulations to a drug-target generally reduces its druggability. The findings are explained through analytical solution of the motifs. Furthermore, we find that a consensus topology of highly druggable motifs consists of a negative feedback loop without any positive feedback loops, and consensus motifs with low druggability have multiple positive direct regulations and positive feedback loops. Based on the discovered principles, we predict potential genetic targets in Escherichia coli that have either high or low druggability based on their network context. Our work establishes the foundation toward identifying and predicting druggable targets based on their network topology.

Non-clinical studies required for new drug development - Part I: early in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies

This review presents a historical overview of drug discovery and the non-clinical stages of the drug development process, from initial target identification and validation, through in silico assays and high throughput screening (HTS), identification of leader molecules and their optimization, the selection of a candidate substance for clinical development, and the use of animal models during the early studies of proof-of-concept (or principle). This report also discusses the relevance of validated and predictive animal models selection, as well as the correct use of animal tests concerning the experimental design, execution and interpretation, which affect the reproducibility, quality and reliability of non-clinical studies necessary to translate to and support clinical studies. Collectively, improving these aspects will certainly contribute to the robustness of both scientific publications and the translation of new substances to clinical development.

Comparison of FDA Approved Kinase Targets to Clinical Trial Ones: Insights from Their System Profiles and Drug-Target Interaction Networks

Kinase is one of the most productive classes of established targets, but the majority of approved drugs against kinase were developed only for cancer. Intensive efforts were therefore exerted for releasing its therapeutic potential by discovering new therapeutic area. Kinases in clinical trial could provide great opportunities for treating various diseases. However, no systematic comparison between system profiles of established targets and those of clinical trial ones was conducted. The reveal of probable difference or shift of trend would help to identify key factors defining druggability of established targets. In this study, a comparative analysis of system profiles of both types of targets was conducted. Consequently, the systems profiles of the majority of clinical trial kinases were identified to be very similar to those of established ones, but percentages of established targets obeying the system profiles appeared to be slightly but consistently higher than those of clinical trial targets. Moreover, a shift of trend in the system profiles from the clinical trial to the established targets was identified, and popular kinase targets were discovered. In sum, this comparative study may help to facilitate the identification of the druggability of established drug targets by their system profiles and drug-target interaction networks.

Development of a vestibular schwannoma xenograft zebrafish model for in vivo antitumor drug screening

OBJECTIVES/HYPOTHESIS

The development of a simple, reliable, and cost-effective animal model greatly facilitates disease treatment. We aimed to establish a rapid, simple, and reproducible live zebrafish vestibular schwannoma xenograft model for antitumor drug screening.

METHODS

We optimized each of the following conditions for tumor cell xenografts in zebrafish larvae: larval stage, incubation temperature, and injected cell number. We used NF2-/-mouse Schwann (SC4) cells and generated mCherry fluorescent protein-expressing cells prior to injection into zebrafish larvae. SC4 cells were counted using a fluorescence microscope, suspended in 10% fetal bovine serum, and injected into the center of the yolk sac using a microinjection system. The injected embryos were transferred to E3 medium (for zebrafish embryos), and subsequent tumor formation was observed by fluorescence microscopy over a 5-day period. To validate our model, xenografted embryos were transferred into 6-well plates (5 embryos per well) and treated with everolimus, a known antitumor drug.

RESULTS

mCherry fluorescent protein-expressing SC4 cells were successfully grafted into the yolk sacs of zebrafish embryos without any immunosuppressant treatment. At 2 days postinjection, the xenografted cells had grown into tumor masses. The optimal speed of tumor formation depended on the larval stage (30 hpf), incubation temperature (31°C), and injected cell number (200 cells). In preliminary tests, everolimus treatment yielded a > 20% reduction in the number of SC4 cells in the yolk.

CONCLUSION

Our in vivo model has the potential to greatly facilitate vestibular schwannoma treatment because of its speed, simplicity, reproducibility, and amenability to live imaging.

LEVEL OF EVIDENCE

NA Laryngoscope, 126:E409-E415, 2016.

A high-content screening platform with fluorescent chemical probes for the discovery of first-in-class therapeutics

Phenotypic screening has emerged as a promising approach to discover novel first-in-class therapeutic agents. Rapid advances in phenotypic screening systems facilitate a high-throughput unbiased evaluation of compound libraries. However, limited sets of phenotypic changes are utilized in high-content screening, which require extensive genetic engineering. Therefore, it is critical to develop new chemical probes that can reflect phenotypic changes in any type of cells, especially primary cells, tissues, and organisms. Herein, we introduce our continuous efforts in the development of fluorescent bioprobes and their application to phenotypic screening. In addition, we emphasize the importance of the phenotype-based approach in conjunction with target identification at an early stage of research to accelerate the discovery of therapeutics with new modes of action.

Liquid-based three-dimensional tumor models for cancer research and drug discovery

Tumors are three-dimensional tissues where close contacts between cancer cells, intercellular interactions between cancer and stromal cells, adhesion of cancer cells to the extracellular matrix, and signaling of soluble factors modulate functions of cancer cells and their response to therapeutics. Three-dimensional cultures of cancer cells overcome limitations of traditionally used monolayer cultures and recreate essential characteristics of tumors such as spatial gradients of oxygen, growth factors, and metabolites and presence of necrotic, hypoxic, quiescent, and proliferative cells. As such, three-dimensional tumor models provide a valuable tool for cancer research and oncology drug discovery. Here, we describe different tumor models and primarily focus on a model known as tumor spheroid. We summarize different technologies of spheroid formation, and discuss the use of spheroids to address the influence of stromal fibroblasts and immune cells on cancer cells in tumor microenvironment, study cancer stem cells, and facilitate compound screening in the drug discovery process. We review major techniques for quantification of cellular responses to drugs and discuss challenges ahead to enable broad utility of tumor spheroids in research laboratories, integrate spheroid models into drug development and discovery pipeline, and use primary tumor cells for drug screening studies to realize personalized cancer treatment.

Optimization of drug combinations using Feedback System Control

We describe a protocol for the discovery of synergistic drug combinations for the treatment of disease. Synergistic drug combinations lead to the use of drugs at lower doses, which reduces side effects and can potentially lead to reduced drug resistance, while being clinically more effective than the individual drugs. To cope with the extremely large search space for these combinations, we developed an efficient combinatorial drug screening method called the Feedback System Control (FSC) technique. Starting with a broad selection of drugs, the method follows an iterative approach of experimental testing in a relevant bioassay and analysis of the results by FSC. First, the protocol uses a cell viability assay to generate broad dose-response curves to assess the efficacy of individual compounds. These curves are then used to guide the dosage input of each drug to be tested in combination. Data from applied drug combinations are input into the differential evolution (DE) algorithm, which predicts new combinations to be tested in vitro. This process identifies optimal drug-dose combinations, while saving orders of magnitude in experimental effort. The complete optimization process is estimated to take ∼4 weeks. FSC does not require insight into the disease mechanism, and it has therefore been applied to find combination therapies for many different pathologies, including cancer and infectious diseases, and it has also been used in organ transplantation.

Fishing for Nature's Hits: Establishment of the Zebrafish as a Model for Screening Antidiabetic Natural Products

Diabetes mellitus affects millions of people worldwide and significantly impacts their quality of life. Moreover, life threatening diseases, such as myocardial infarction, blindness, and renal disorders, increase the morbidity rate associated with diabetes. Various natural products from medicinal plants have shown potential as antidiabetes agents in cell-based screening systems. However, many of these potential "hits" fail in mammalian tests, due to issues such as poor pharmacokinetics and/or toxic side effects. To address this problem, the zebrafish (Danio rerio) model has been developed as a "bridge" to provide an experimentally convenient animal-based screening system to identify drug candidates that are active in vivo. In this review, we discuss the application of zebrafish to drug screening technologies for diabetes research. Specifically, the discovery of natural product-based antidiabetes compounds using zebrafish will be described. For example, it has recently been demonstrated that antidiabetic natural compounds can be identified in zebrafish using activity guided fractionation of crude plant extracts. Moreover, the development of fluorescent-tagged glucose bioprobes has allowed the screening of natural product-based modulators of glucose homeostasis in zebrafish. We hope that the discussion of these advances will illustrate the value and simplicity of establishing zebrafish-based assays for antidiabetic compounds in natural products-based laboratories.

Polypharmacology Shakes Hands with Complex Aetiopathology

Chronic diseases are due to deviations of fundamental physiological systems, with different pathologies being characterised by similar malfunctioning biological networks. The ensuing compensatory mechanisms may weaken the body's dynamic ability to respond to further insults and reduce the efficacy of conventional single target treatments. The multitarget, systemic, and prohomeostatic actions emerging for plant cannabinoids exemplify what might be needed for future medicines. Indeed, two combined cannabis extracts were approved as a single medicine (Sativex(®)), while pure cannabidiol, a multitarget cannabinoid, is emerging as a treatment for paediatric drug-resistant epilepsy. Using emerging cannabinoid medicines as an example, we revisit the concept of polypharmacology and describe a new empirical model, the 'therapeutic handshake', to predict efficacy/safety of compound combinations of either natural or synthetic origin.

Potential therapeutic targets and the role of technology in developing novel antileishmanial drugs

Leishmaniasis is the most prevalent pathogenic disease in many countries around the world, but there are few drugs available to treat it. Most antileishmanial drugs available are highly toxic, have resistance issues or require hospitalization for their use; therefore, they are not suitable for use in most of the affected countries. Over the past decade, the completion of the genomes of many human pathogens, including that of Leishmania spp., has opened new doors for target identification and validation. Here, we focus on the potential drug targets that can be used for the treatment of leishmaniasis and bring to light how recent technological advances, such as structure-based drug design, structural genomics, and molecular dynamics (MD), can be used to our advantage to develop potent and affordable antileishmanial drugs.

In silico identification of targets for a novel scaffold, 2-thiazolylimino-5-benzylidin-thiazolidin-4-one

Thiazolidinone derivatives have been found to exhibit a wide range of pharmacological activities. 2-Thiazolylimino-5-benzylidene-thiazolidin-4-one derivatives show antibacterial activity in in vitro tests which are comparable to marketed drugs. However, the target for this scaffold remains yet to be identified. In our work, we identified seven putative targets for this scaffold using web servers such as DRAR-CPI, PharmMapper, and TarFisDock and databases such as BindingDB and ChEMBL. Each of these servers used different algorithms and scoring functions for protein target identification. Further, these targets are substantiated by molecular docking analysis. Based on the docking studies, scaffold 2-thiazolylimino-5-benzylidene-thiazolidin-4-one is observed to exhibit affinity against diverse targets, particularly, towards COX-2, acetylcholinesterase, aldose reductase, and thyroid hormone receptor alpha. This study describes an initial probability that these proteins may be targeted by this scaffold.

Network output controllability-based method for drug target identification

Biomolecules do not perform their functions alone, but interactively with one another to form so called biomolecular networks. It is well known that a complex disease stems from the malfunctions of corresponding biomolecular networks. Therefore, one of important tasks is to identify drug targets from biomolecular networks. In this study, the drug target identification is formulated as a problem of finding steering nodes in biomolecular networks while the concept of network output controllability is applied to the problem of drug target identification. By applying control signals to these steering nodes, the biomolecular networks are expected to be transited from one state to another. A graph-theoretic algorithm has been proposed to find a minimum set of steering nodes in biomolecular networks which can be a potential set of drug targets. Application results of the method to real biomolecular networks show that identified potential drug targets are in agreement with existing research results. This indicates that the method can generate testable predictions and provide insights into experimental design of drug discovery.

Utility of Human Stem Cells for Drug Discovery

The pharmaceutical industry continues to struggle to deliver novel and innovative medicines to the market. One of the major challenges in deriving new therapeutics is to more accurately predict the safety and efficacy of the candidate molecule. The current paradigm of drug discovery has several limitations but perhaps the most conspicuous deficiency is the lack of human-based experimental models. The advent of human embryonic stem cells followed by the discovery of induced pluripotent stem (iPS) cells offers unprecedented opportunities for integrating human cellular assays in drug discovery and development. Human iPS cell lines of many diseases have been obtained and iPSC-derived disease affected cells have been utilised for proof-of-concept drug screens to assess efficacy or potential toxicology. The incorporation of iPSC technology thus provides an invaluable opportunity to reduce drug attrition during the process of drug development.

Delineation of the role of glycosylation in the cytotoxic properties of quercetin using novel assays in living vertebrates

Quercetin is a plant-derived flavonoid and its cytotoxic properties have been widely reported. However, in nature, quercetin predominantly occurs as various glycosides. Thus far the cytotoxic activity of these glycosides has not been investigated to the same extent as quercetin, especially in animal models. In this study, the cytotoxic properties of quercetin (1), hyperoside (quercetin 3-O-galactoside, 2), isoquercitrin (quercetin 3-O-glucoside, 3), quercitrin (quercetin 3-O-rhamnoside, 4), and spiraeoside (quercetin 4'-O-glucoside, 5) were directly compared in vitro using assays of cancer cell viability. To further characterize the influence of glycosylation in vivo, a novel zebrafish-based assay was developed that allows the rapid and experimentally convenient visualization of glycoside cleavage in the digestive tract. This assay was correlated with a novel human tumor xenograft assay in the same animal model. The results showed that 3 is as effective as 1 at inhibiting cancer cell proliferation in vivo. Moreover, it was observed that 3 can be effectively deglycosylated in the digestive tract. Collectively, these results indicate that 3 is a very promising drug candidate for cancer therapy, because glycosylation confers advantageous pharmacological changes compared with the aglycone, 1. Importantly, the development of a novel and convenient fluorescence-based assay for monitoring deglycosylation in living vertebrates provides a valuable platform for determining the metabolic fate of naturally occurring glycosides.

An in silico target identification using Boolean network attractors: Avoiding pathological phenotypes

Target identification aims at identifying biomolecules whose function should be therapeutically altered to cure the considered pathology. An algorithm for in silico target identification using Boolean network attractors is proposed. It assumes that attractors correspond to phenotypes produced by the modeled biological network. It identifies target combinations which allow disturbed networks to avoid attractors associated with pathological phenotypes. The algorithm is tested on a Boolean model of the mammalian cell cycle and its applications are illustrated on a Boolean model of Fanconi anemia. Results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice. Nevertheless, it is expected that the algorithm is of interest for target identification.

Specific modulation of protein activity by using a bioorthogonal reaction

Unnatural amino acids with bioorthogonal reactive groups have the potential to provide a rapid and specific mechanism for covalently inhibiting a protein of interest. Here, we use mutagenesis to insert an unnatural amino acid containing an azide group (Z) into the target protein at positions such that a "click" reaction with an alkyne modulator (X) will alter the function of the protein. This bioorthogonally reactive pair can engender specificity of X for the Z-containing protein, even if the target is otherwise identical to another protein, allowing for rapid target validation in living cells. We demonstrate our method using inhibition of the Escherichia coli enzyme aminoacyl transferase by both active-site occlusion and allosteric mechanisms. We have termed this a "clickable magic bullet" strategy, and it should be generally applicable to studying the effects of protein inhibition, within the limits of unnatural amino acid mutagenesis.

In vivo proteomic imaging analysis of caveolae reveals pumping system to penetrate solid tumors

Technologies are needed to map and image biological barriers in vivo that limit solid tumor delivery and, ultimately, the effectiveness of imaging and therapeutic agents. Here we integrate proteomic and imaging analyses of caveolae at the blood-tumor interface to discover an active transendothelial portal to infiltrate tumors. A post-translationally modified form of annexin A1 (AnnA1) is selectively concentrated in human and rodent tumor caveolae. To follow trafficking, we generated a specific AnnA1 antibody that targets caveolae in the tumor endothelium. Intravital microscopy of caveolae-immunotargeted fluorophores even at low intravenous doses showed rapid and robust pumping across the endothelium to enter mammary, prostate and lung tumors. Within 1 h, the fluorescence signal concentrated throughout tumors to exceed the peak levels in blood. This transvascular pumping required the expression of caveolin 1 and annexin A1. Tumor uptake with other antibodies were >100-fold less. This proteomic imaging strategy reveals a unique target, antibody and caveolae pumping system for solid tumor penetration.

Discovery and characterization of olokizumab: a humanized antibody targeting interleukin-6 and neutralizing gp130-signaling

Interleukin-6 (IL-6) is a critical regulator of the immune system and has been widely implicated in autoimmune disease. Here, we describe the discovery and characterization of olokizumab, a humanized antibody to IL-6. Data from structural biology, cell biology and primate pharmacology demonstrate the therapeutic potential of targeting IL-6 at “Site 3”, blocking the interaction with the signaling co-receptor gp130.

Exploiting pluripotent stem cell technology for drug discovery, screening, safety, and toxicology assessments

In order for the pharmaceutical industry to maintain a constant flow of novel drugs and therapeutics into the clinic, compounds must be thoroughly validated for safety and efficacy in multiple biological and biochemical systems. Pluripotent stem cells, because of their ability to develop into any cell type in the body and recapitulate human disease, may be an important cellular system to add to the drug development repertoire. This review will discuss some of the benefits of using pluripotent stem cells for drug discovery and safety studies as well as some of the recent applications of stem cells in drug screening studies. We will also address some of the hurdles that need to be overcome in order to make stem cell-based approaches an efficient and effective tool in the quest to produce clinically successful drug compounds.

Discovery and characterization of olokizumab: a humanized antibody targeting interleukin-6 and neutralizing gp130-signaling

Interleukin-6 (IL-6) is a critical regulator of the immune system and has been widely implicated in autoimmune disease. Here, we describe the discovery and characterization of olokizumab, a humanized antibody to IL-6. Data from structural biology, cell biology and primate pharmacology demonstrate the therapeutic potential of targeting IL-6 at "Site 3", blocking the interaction with the signaling co-receptor gp130.

iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking

Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.

How academic labs can approach the drug discovery process as a way to synergize with big pharma

While the pharmaceutical industry is facing highly challenging times, the academic drug discovery sector has the potential to contribute meaningfully to the discovery of novel drug targets and to the development of new mode-of-action therapeutics against a range of diseases, including rare and neglected diseases.

MicroSPECT and MicroPET Imaging of Small Animals for Drug Development

The process of drug discovery and development requires substantial resources and time. The drug industry has tried to reduce costs by conducting appropriate animal studies together with molecular biological and genetic analyses. Basic science research has been limited to in vitro studies of cellular processes and ex vivo tissue examination using suitable animal models of disease. However, in the past two decades new technologies have been developed that permit the imaging of live animals using radiotracer emission, Xrays, magnetic resonance signals, fluorescence, and bioluminescence. The main objective of this review is to provide an overview of small animal molecular imaging, with a focus on nuclear imaging (single photon emission computed tomography and positron emission tomography). These technologies permit visualization of toxicodynamics as well as toxicity to specific organs by directly monitoring drug accumulation and assessing physiological and/or molecular alterations. Nuclear imaging technology has great potential for improving the efficiency of the drug development process.

High-throughput identification of off-targets for the mechanistic study of severe adverse drug reactions induced by analgesics

Drugs may induce adverse drug reactions (ADRs) when they unexpectedly bind to proteins other than their therapeutic targets. Identification of these undesired protein binding partners, called off-targets, can facilitate toxicity assessment in the early stages of drug development. In this study, a computational framework was introduced for the exploration of idiosyncratic mechanisms underlying analgesic-induced severe adverse drug reactions (SADRs). The putative analgesic-target interactions were predicted by performing reverse docking of analgesics or their active metabolites against human/mammal protein structures in a high-throughput manner. Subsequently, bioinformatics analyses were undertaken to identify ADR-associated proteins (ADRAPs) and pathways. Using the pathways and ADRAPs that this analysis identified, the mechanisms of SADRs such as cardiac disorders were explored. For instance, 53 putative ADRAPs and 24 pathways were linked with cardiac disorders, of which 10 ADRAPs were confirmed by previous experiments. Moreover, it was inferred that pathways such as base excision repair, glycolysis/glyconeogenesis, ErbB signaling, calcium signaling, and phosphatidyl inositol signaling likely play pivotal roles in drug-induced cardiac disorders. In conclusion, our framework offers an opportunity to globally understand SADRs at the molecular level, which has been difficult to realize through experiments. It also provides some valuable clues for drug repurposing.

Development of a highly visual, simple, and rapid test for the discovery of novel insulin mimetics in living vertebrates

Diabetes mellitus is a global epidemic with major impacts on human health and society. Drug discovery for diabetes can be facilitated by the development of a rapid, vertebrate-based screen for identifying new insulin mimetic compounds. Our study describes the first development of a zebrafish-based system based on direct monitoring of glucose flux and validated for identifying novel anti-diabetic drugs. Our system utilizes a fluorescent-tagged glucose probe in an experimentally convenient 96-well plate format. To validate our new system, we identified compounds that can induce glucose uptake via activity-guided fractionation of the inner shell from the Japanese Chestnut (Castanea crenata). The best performing compound, UP3.2, was identified as fraxidin and validated as a novel insulin mimetic using a mammalian adipocyte system. Additional screening using sets of saponin- and triazine-based compounds was undertaken to further validate this assay, which led to the discovery of triazine PP-II-A03 as a novel insulin mimetic. Moreover, we demonstrate that our zebrafish-based system allows concomitant toxicological analysis of anti-diabetic drug candidates. Thus, we have developed a rapid and inexpensive vertebrate model that can enhance diabetes drug discovery by preselecting hits from chemical library screens, before testing in relatively expensive rodent assays.

Inhibition of microglia activation as a phenotypic assay in early drug discovery

Complex biological processes such as inflammation, cell death, migration, proliferation, and the release of biologically active molecules can be used as outcomes in phenotypic assays during early stages of drug discovery. Although target-based approaches have been widely used over the past decades, a disproportionate number of first-in-class drugs have been identified using phenotypic screening. This review details phenotypic assays based on inhibition of microglial activation and their utility in primary and secondary screening, target validation, and pathway elucidation. The role of microglia, both in normal as well as in pathological conditions such as chronic neurodegenerative diseases, is reviewed. Methodologies to assess microglia activation in vitro are discussed in detail, and classes of therapeutic drugs known to decrease the proinflammatory and cytotoxic responses of activated microglia are appraised, including inhibitors of glutaminase, cystine/glutamate antiporter, nuclear factor κB, and mitogen-activated protein kinases.

Functional interrogation of breast cancer: from models to drugs

Functional genomics allows for the activity of the whole genome to be surveyed at once. Using this technology for the identification of novel targets and their validation in disease-specific contexts has profound implications for the future of drug discovery. Now researchers have the technological means to gather comprehensive data on basic biological phenomena and disease mechanisms, while monitoring the effect of drug candidates on a molecular level. Pathway analysis can facilitate the genetic profiling of patients and, in turn, predict individual responses to treatment regimes. Functional interrogation of a disease-specific phenotype at a whole genome level (through, for example, the use of whole genome RNAi libraries) allows for the identification of critical regulators in complex biological systems, and the detection of putative targets for future therapeutic intervention. The authors describe the applications of functional genomics in models of breast cancer and the integration of these disparate technologies, specifically in the context of the search for novel therapeutic targets.

Drug target central

BACKGROUND

One of the primary pillars of drug discovery is the drug target, its relationship to both the drugs designed against it and the biological processes in which it is involved. Here we review the informatics approaches required to build a complete catalogue of known drug targets.

OBJECTIVE

Using Pfizer's internal target database as a narrative, we review the steps involved in the construction of an integrated, enterprise target-informatics system. We consider how compiling the drug target universe requires integration across several resources such as competitor intelligence and pharmacological activity databases, as well as input from techniques such as text-mining. In particular, we address data standards and the complexities of representing targets in a structured ontology as well as opportunities for future development.

CONCLUSION

Drug target-orientated databases address important areas of drug discovery such as chemogenomics, drug/candidate repurposing and business intelligence. As research in industry and academia drives continued expansion of the druggable genome, it is crucial that such systems be maintained to provide an accurate picture of the landscape. This power of this information stretches beyond drug discovery and into the wider scientific community where small molecule tool compounds can enable the dissection of complex cellular pathways.

Target mRNA inhibition by oligonucleotide drugs in man

Oligonucleotide delivery in vivo is commonly seen as the principal hurdle to the successful development of oligonucleotide drugs. In an analysis of 26 oligonucleotide drugs recently evaluated in late-stage clinical trials we found that to date at least half have demonstrated suppression of the target mRNA and/or protein levels in the relevant cell types in man, including those present in liver, muscle, bone marrow, lung, blood and solid tumors. Overall, this strongly implies that the drugs are being delivered to the appropriate disease tissues. Strikingly we also found that the majority of the drug targets of the oligonucleotides lie outside of the drugable genome and represent new mechanisms of action not previously investigated in a clinical setting. Despite the high risk of failure of novel mechanisms of action in the clinic, a subset of the targets has been validated by the drugs. While not wishing to downplay the technical challenges of oligonucleotide delivery in vivo, here we demonstrate that target selection and validation are of equal importance for the success of this field.

Target Identification and Validation of (+)-2-(1-Hydroxyl-4-Oxocyclohexyl) Ethyl Caffeate, an Anti-Inflammatory Natural Product

(+)-2-(1-hydroxyl-4-oxocyclohexyl) ethyl caffeate (HOEC) was isolated from Incarvillea mairei var. granditlora (Wehrhahn) Grierson. The plants of the Incarvillea genus have long been used as folk medicines for the treatment of inflammation-related diseases in China. 5-Lipoxygenase (5-LOX), a key enzyme in the arachidonic acid (AA) cascade, was identified as a potential target of HOEC by a pulldown assay, and then extensively validated by biosensor-based affinity detection, enzyme-based activity assays, cell-based AA metabolite analysis and computer-aided AA network simulation. Further in vivo studies of AA-induced ear oedema, ovalbumin (OVA)-induced lung inflammation and collagen-induced arthritis demonstrated the anti-inflammatory potency and validated the therapeutic target of HOEC. This work revealed that HOEC acted as an anti-inflammatory agent targeting 5-LOX, which not only confirmed the key role of 5-LOX in inflammation but also provided a paradigm for the exploration of natural product mechanisms of action.

Drug repurposing: far beyond new targets for old drugs

Repurposing drugs requires finding novel therapeutic indications compared to the ones for which they were already approved. This is an increasingly utilized strategy for finding novel medicines, one that capitalizes on previous investments while derisking clinical activities. This approach is of interest primarily because we continue to face significant gaps in the drug-target interactions matrix and to accumulate safety and efficacy data during clinical studies. Collecting and making publicly available as much data as possible on the target profile of drugs offer opportunities for drug repurposing, but may limit the commercial applications by patent applications. Certain clinical applications may be more feasible for repurposing than others because of marked differences in side effect tolerance. Other factors that ought to be considered when assessing drug repurposing opportunities include relevance to the disease in question and the intellectual property landscape. These activities go far beyond the identification of new targets for old drugs.