A survey of current trends in computational drug repositioning.
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paper_title: Old drugs, new tricks.

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A meta-analysis last year linking aspirin to reduced death rates from certain types of can cer signalled the latest reinvention of this well established drug.1 Developed as an analgesic, greater understanding of its properties has led to it being used to treat stroke and reduce the risk of cardiovascular disease, and it has also been suggested to help prevent pre-eclampsia and dementia. Aspirin is just one of many drugs that have found new uses, some before they even get to market (see table). Raloxifene, for osteoporo sis, was a failed oral contraceptive found to increase bone density. The antidepressant fluoxetine flunked hypertension trials but affected mood. The erectile dysfunction treat ment sildenafil was ineffective for angina. All three medicines were repurposed to great effect. Discovering new uses for old drugs offers great promise for quickly bringing new thera peutic options to patient care; the lag between drug discovery and availability, often 10 years or longer, can be shortened by dusting off drugs already marketed and repurposing them. But the discoveries are not simply down to serendip ity. New uses are increasingly being discovered not by pharmacologists but by computer scien tists in university departments of computational drug discovery or biomedical informatics.
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paper_title: Predicting new molecular targets for known drugs

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Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug–target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug–target associations were confirmed, five of which were potent (<100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs. Most drugs are intended to be selective for a single protein target, but will commonly bind to several other targets too. Some 'off-target' events induce side effects of varying degrees of severity, though some may be essential for a drug's efficacy. A new strategy to identify potential off-target effects for known drugs is reported in this issue. The structures of 3,665 FDA-approved and investigational drugs were computationally screened against hundreds of protein targets as defined by the ligands that bind to them. Chemical similarities between the drugs and various sets of ligands predicted thousands of off-target associations, some of which were confirmed in pharmacological assays. This approach may help predict and explain the side effects of known drugs and drug candidates, and may also lead to the identification of new clinical applications for drugs that have been previously approved for use in humans. Drugs that are chemically quite similar often bind to biologically diverse protein targets, and it is unclear how selective many of these compounds are. Because many drug–target combinations exist, it would be useful to explore possible interactions computationally. Here, 3,665 drugs are tested against hundreds of targets; chemical similarities between drugs and ligand sets are found to predict thousands of unanticipated associations.
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paper_title: Exploiting drug–disease relationships for computational drug repositioning

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Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.
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paper_title: PROMISCUOUS: a database for network-based drug-repositioning

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The procedure of drug approval is time-consuming, costly and risky. Accidental findings regarding multi-specificity of approved drugs led to block-busters in new indication areas. Therefore, the interest in systematically elucidating new areas of application for known drugs is rising. Furthermore, the knowledge, understanding and prediction of so-called off-target effects allow a rational approach to the understanding of side-effects. With PROMISCUOUS we provide an exhaustive set of drugs (25 000), including withdrawn or experimental drugs, annotated with drug–protein and protein–protein relationships (21 500/104 000) compiled from public resources via text and data mining including manual curation. Measures of structural similarity for drugs as well as known side-effects can be easily connected to protein–protein interactions to establish and analyse networks responsible for multi-pharmacology. This network-based approach can provide a starting point for drug-repositioning. PROMISCUOUS is publicly available at http://bioinformatics.charite.de/promiscuous.
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paper_title: How to improve R&D productivity: the pharmaceutical industry's grand challenge

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The pharmaceutical industry is under growing pressure from a range of environmental issues, including major losses of revenue owing to patent expirations, increasingly cost-constrained healthcare systems and more demanding regulatory requirements. In our view, the key to tackling the challenges such issues pose to both the future viability of the pharmaceutical industry and advances in healthcare is to substantially increase the number and quality of innovative, cost-effective new medicines, without incurring unsustainable R&D costs. However, it is widely acknowledged that trends in industry R&D productivity have been moving in the opposite direction for a number of years. Here, we present a detailed analysis based on comprehensive, recent, industry-wide data to identify the relative contributions of each of the steps in the drug discovery and development process to overall R&D productivity. We then propose specific strategies that could have the most substantial impact in improving R&D productivity.
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paper_title: Discovery of drug mode of action and drug repositioning from transcriptional responses.

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A bottleneck in drug discovery is the identification of the molecular targets of a compound (mode of action, MoA) and of its off-target effects. Previous approaches to elucidate drug MoA include analysis of chemical structures, transcriptional responses following treatment, and text mining. Methods based on transcriptional responses require the least amount of information and can be quickly applied to new compounds. Available methods are inefficient and are not able to support network pharmacology. We developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA. We constructed a "drug network" of 1,302 nodes (drugs) and 41,047 edges (indicating similarities between pair of drugs). We applied network theory, partitioning drugs into groups of densely interconnected nodes (i.e., communities). These communities are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compound-targeted biological pathways. New compounds can be integrated into the network to predict their therapeutic and off-target effects. Using this network, we correctly predicted the MoA for nine anticancer compounds, and we were able to discover an unreported effect for a well-known drug. We verified an unexpected similarity between cyclin-dependent kinase 2 inhibitors and Topoisomerase inhibitors. We discovered that Fasudil (a Rho-kinase inhibitor) might be "repositioned" as an enhancer of cellular autophagy, potentially applicable to several neurodegenerative disorders. Our approach was implemented in a tool (Mode of Action by NeTwoRk Analysis, MANTRA, http://mantra.tigem.it).
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paper_title: Repositioning of an existing drug for the neglected tropical disease Onchocerciasis

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Onchocerciasis, or river blindness, is a neglected tropical disease caused by the filarial nematode Onchocerca volvulus that affects more than 37 million people, mainly in third world countries. Currently, the only approved drug available for mass treatment is ivermectin, however, drug resistance is beginning to emerge, thus, new therapeutic targets and agents are desperately needed to treat and cure this devastating disease. Chitin metabolism plays a central role in invertebrate biology due to the critical structural function of chitin for the organism. Taken together with its absence in mammals, targeting chitin is an appealing therapeutic avenue. Importantly, the chitinase OvCHT1 from O. volvulus was recently discovered, however, its exact role in the worm’s metabolism remains unknown. A screening effort against OvCHT1 was conducted using the Johns Hopkins Clinical Compound Library that contains over 1,500 existing drugs. Closantel, a veterinary anthelmintic with known proton ionophore activities, was identified as a potent and specific inhibitor of filarial chitinases, an activity not previously reported for this compound. Notably, closantel was found also to completely inhibit molting of O. volvulus infective L3 stage larvae. Closantel appears to target two important biochemical processes essential to filarial parasites. To begin to unravel closantel’s effects, a retro-fragment-based study was used to define structural elements critical for closantel’s chitinase inhibitor function. As resources towards the development of new agents that target neglected tropical diseases are scant, the finding of an existing drug with impact against O. volvulus provides promise in the hunt for new therapies against river blindness.
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paper_title: Genomics-enabled drug repositioning and repurposing: insights from an IOM Roundtable activity.

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Drug repositioning or drug repurposing (terms often used interchangeably) is the process whereby alternative uses or indications are identified for a drug. Repurposing can encompass the pursuit of novel use of a drug during its normal development cycle or after a drug has been approved and marketed. More recently the term drug repositioning has been specifically applied to the use of compounds previously discontinued from development for novel indications. The increasing costs of drug development and reduced output in the pharmaceutical industry (fewer new molecules entering drug development and higher failure rates in phase 3 trials) have led to renewed interest in drug repurposing as a potentially viable strategy to speed the delivery of new medicines to patients with unaddressed needs.1
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paper_title: The Year's New Drugs & Biologics, 2013: Part I

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This article provides a comprehensive overview of the 56 new drugs and biologics introduced for the first time in 2013, the largest number in at least a decade. This includes 20 new orphan drugs and 10 first-in-class agents, as well as the first three products bearing the FDA's new Breakthrough Therapy Designation. The review also covers 30 important new line extensions, encompassing new indications, new formulations and new combinations of previously marketed agents. In addition to this bumper crop of new launches, another 19 products were approved for the first time during the year but not yet launched by the close of this article; these new products are also discussed.
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paper_title: IDMap : facilitating the detection of potential leads with therapeutic targets

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UNLABELLED: Pharmaceutical industry has been striving to reduce the costs of drug development and increase productivity. Among the many different attempts, drug repositioning (retargeting existing drugs) comes into the spotlight because of its financial efficiency. We introduce IDMap which predicts novel relationships between targets and chemicals and thus is capable of repositioning the marketed drugs by using text mining and chemical structure information. Also capable of mapping commercial chemicals to possible drug targets and vice versa, IDMap creates convenient environments for identifying the potential lead and its targets, especially in the field of drug repositioning. AVAILABILITY: IDMap executable and its user manual including color images are freely available to non-commercial users at http://www.equispharm.com/idmap
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paper_title: Short-term safety and efficacy of intravitreal bevacizumab (Avastin) for neovascular age-related macular degeneration.

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PURPOSE ::: To evaluate the safety and efficacy of intravitreal bevacizumab (Avastin, Genentech Inc.) for the treatment of neovascular age-related macular degeneration (ARMD). ::: ::: ::: METHODS ::: A retrospective review was performed on consented patients with neovascular ARMD receiving intravitreal bevacizumab therapy. All patients received intravitreal bevacizumab at baseline with additional monthly injections given at the discretion of the treating physician. At each visit, a routine Snellen visual acuity assessment was performed followed by an ophthalmic examination and optical coherence tomography (OCT) imaging. ::: ::: ::: RESULTS ::: Fifty-three eyes of 50 patients received an intravitreal bevacizumab injection between May and August 2005. Including the month 3 visit, the average number of injections was 2.3 out of a maximum of 4 injections. No serious drug-related ocular or systemic adverse events were identified. Improvements in visual acuity and central retinal thickness measurements were evident by week 1 and continued through month 3. At month 3, the mean visual acuity improved from 20/160 to 20/125 (P < 0.001) and the mean central retinal thickness decreased by 99.6 microm (P < 0.001). ::: ::: ::: CONCLUSION ::: Off-label intravitreal bevacizumab therapy for neovascular ARMD was well tolerated over 3 months with improvements in visual acuity and OCT central retinal thickness measurements. While the long-term safety and efficacy of intravitreal bevacizumab remain unknown, these short-term results suggest that intravitreal bevacizumab may be the most cost effective therapy for the treatment of neovascular ARMD.
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paper_title: Drug repositioning for treatment of movement disorders: from serendipity to rational discovery strategies.

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Movement disorders are a heterogeneous group of both common and rare neurological conditions characterized by abnormalities of motor functions and movement patterns. This work overviews recent successes and ongoing studies of repositioning relating to this disease group, which underscores the challenge of integrating the voluminous and heterogeneous findings required for making suitable drug repositioning decisions. In silico drug repositioning methods hold the promise of automated fusion of heterogeneous information sources, but the controllable, flexible and transparent incorporation of the expertise of medicinal chemists throughout the repositioning process remains an open challenge. In support of a more systematic approach toward repositioning, we summarize the application of a computational repurposing method based on statistically rooted knowledge fusion. To foster the spread of this technique, we provide a step-by-step guide to the complete workflow, together with a case study in Parkinson's disease.
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paper_title: A Drug Repositioning Approach Identifies Tricyclic Antidepressants as Inhibitors of Small Cell Lung Cancer and Other Neuroendocrine Tumors

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UNLABELLED ::: Small cell lung cancer (SCLC) is an aggressive neuroendocrine subtype of lung cancer with high mortality. We used a systematic drug repositioning bioinformatics approach querying a large compendium of gene expression profiles to identify candidate U.S. Food and Drug Administration (FDA)-approved drugs to treat SCLC. We found that tricyclic antidepressants and related molecules potently induce apoptosis in both chemonaïve and chemoresistant SCLC cells in culture, in mouse and human SCLC tumors transplanted into immunocompromised mice, and in endogenous tumors from a mouse model for human SCLC. The candidate drugs activate stress pathways and induce cell death in SCLC cells, at least in part by disrupting autocrine survival signals involving neurotransmitters and their G protein-coupled receptors. The candidate drugs inhibit the growth of other neuroendocrine tumors, including pancreatic neuroendocrine tumors and Merkel cell carcinoma. These experiments identify novel targeted strategies that can be rapidly evaluated in patients with neuroendocrine tumors through the repurposing of approved drugs. ::: ::: ::: SIGNIFICANCE ::: Our work shows the power of bioinformatics-based drug approaches to rapidly repurpose FDA-approved drugs and identifies a novel class of molecules to treat patients with SCLC, a cancer for which no effective novel systemic treatments have been identified in several decades. In addition, our experiments highlight the importance of novel autocrine mechanisms in promoting the growth of neuroendocrine tumor cells.
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paper_title: Identification of links between small molecules and miRNAs in human cancers based on transcriptional responses

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The use of small molecules to target miRNAs is a new type of therapy for human diseases, particularly cancers. We proposed a novel high-throughput approach to identify the biological links between small molecules and miRNAs in 23 different cancers and constructed the Small Molecule-MiRNA Network (SMirN) for each cancer to systematically analyze the properties of their associations. In each SMirN, we partitioned small molecules (miRNAs) into modules, in which small molecules (miRNAs) were connected with one miRNA (small molecule). Almost all of the miRNA modules comprised miRNAs that had similar target genes and functions or were members of the same miRNA family. Most of the small molecule modules involved compounds with similar chemical structures, modes of action, or drug interactions. These modules can be used to identify drug candidates and new indications for existing drugs. Therefore, our approach is valuable to drug discovery and cancer therapy.
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paper_title: DAVID: Database for Annotation, Visualization, and Integrated Discovery

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The distributed nature of biological knowledge poses a major challenge to the interpretation of genome-scale datasets, including those derived from microarray and proteomic studies. This report describes DAVID, a web-accessible program that integrates functional genomic annotations with intuitive graphical summaries. Lists of gene or protein identifiers are rapidly annotated and summarized according to shared categorical data for Gene Ontology, protein domain, and biochemical pathway membership. DAVID assists in the interpretation of genome-scale datasets by facilitating the transition from data collection to biological meaning.
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paper_title: MicroRNAs as predictive biomarkers and therapeutic targets in prostate cancer.

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Prostatectomy or irradiation is the most common traditional treatments for localized prostate cancer. In the event of recurrence and/or metastasis, androgen ablation therapy has been the mainstay treatment for many years. Although initially effective, the cancer inevitably recurs as androgen-independent PCa, a disease with limited effective treatments. Enhanced predictive biomarkers are needed at the time of diagnosis to better tailor therapies for patients. MicroRNAs are short nucleotide sequences which can complementary bind to and control gene expression at the post-transcriptional level. Recent studies have demonstrated that many miRNAs are variably expressed in cancers vs. normal tissues, including PCa. In this review, we summarize PCa-specific miRNAs that show potential for their utilization as identifiers of aggressive disease and predictors for risk of recurrence. Additionally, we discuss their potential clinical applications as biomarkers and therapeutic targets.
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paper_title: MicroRNAs: regulators of cancer metastasis and epithelial-mesenchymal transition (EMT)

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Tumor metastasis is the main cause of death in patients with solid tumors. The epithelial-mesenchymal transition (EMT) process, in which epithelial cells are converted into mesenchymal cells, is frequently activated during cancer invasion and metastasis. MicroRNAs (miRNAs) are small, non-coding RNAs that provide widespread expressional control by repressing mRNA translation and inducing mRNA degradation. The fundamental roles of miRNAs in tumor growth and metastasis have been increasingly well recognized. A growing number of miRNAs are reported to regulate tumor invasion/metastasis through EMT-related and/or non-EMT–related mechanisms. In this review, we discuss the functional role and molecular mechanism of miRNAs in regulating cancer metastasis and EMT.
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paper_title: MicroRNAs and anticancer drugs

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MicroRNAs (miRNAs) are a class of small, non-coding, and endogenous RNA molecules, which are evolutionarily conserved but play a significant role in regulation of protein-coding gene expression at posttranscriptional and translational levels. Strikingly, a single miRNA is able to trigger hundreds of putative target genes by incomplete or complete complementary binding to their 3' untranslated regions. Given their appearance in almost all types of tissues, miRNAs have been demonstrated to be intensively involved in normal and pathological processes of human cells. Aside from the role as invaluable biomarkers in indication of tumorigenesis and tumor progression, numerous studies have revealed the potential of miRNAs as novel targets of anticancer drugs in cancer therapy. In this review article, we focus on the summary of the latest publications on the topic of miRNA and anticancer drugs, and expect to shed light on understanding the molecular mechanisms of chemoresistance involving miRNA regulation. These pieces of evidence will eventually provide insight into the development of novel and more efficacious anticancer drugs in the future.
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paper_title: NCBI GEO: mining millions of expression profiles—database and tools

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The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecularabundancedata,primarilygeneexpression data.Thedatabasehasaflexibleandopendesignthat allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as nonarray-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recentdatabasedevelopmentsthatfacilitateeffective mining and visualization of these data. Features are provided to examine data from both experimentand gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi. nlm.nih.gov/geo.
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paper_title: Computational Repositioning of the Anticonvulsant Topiramate for Inflammatory Bowel Disease

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Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract for which there are few safe and effective therapeutic options for long-term treatment and disease maintenance. Here, we applied a computational approach to discover new drug therapies for IBD in silico, using publicly available molecular data reporting gene expression in IBD samples and 164 small-molecule drug compounds. Among the top compounds predicted to be therapeutic for IBD by our approach were prednisolone, a corticosteroid used to treat IBD, and topiramate, an anticonvulsant drug not previously described to have efficacy for IBD or any related disorders of inflammation or the gastrointestinal tract. Using a trinitrobenzenesulfonic acid (TNBS)-induced rodent model of IBD, we experimentally validated our topiramate prediction in vivo. Oral administration of topiramate significantly reduced gross pathological signs and microscopic damage in primary affected colon tissue in the TNBS-induced rodent model of IBD. These findings suggest that topiramate might serve as a therapeutic option for IBD in humans and support the use of public molecular data and computational approaches to discover new therapeutic options for disease.
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paper_title: DvD: An R/Cytoscape pipeline for drug repurposing using public repositories of gene expression data

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Summary: Drug versus Disease (DvD) provides a pipeline, available through R or Cytoscape, for the comparison of drug and disease gene expression profiles from public microarray repositories. Negatively correlated profiles can be used to generate hypotheses of drug-repurposing, whereas positively correlated profiles may be used to infer side effects of drugs. DvD allows users to compare drug and disease signatures with dynamic access to databases Array Express, Gene Expression Omnibus and data from the Connectivity Map. ::: ::: Availability and implementation: R package (submitted to Bioconductor) under GPL 3 and Cytoscape plug-in freely available for download at www.ebi.ac.uk/saezrodriguez/DVD/. ::: ::: Contact: ku.ca.ibe@zeugirdorzeas ::: ::: Supplementary information: Supplementary data are available at Bioinformatics online.
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paper_title: Systematic identification of genomic markers of drug sensitivity in cancer cells

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Human cancer cell lines are screened with drugs, undergoing clinical or preclinical investigation, to determine specific genomic alterations associated with response to therapeutic agents.
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paper_title: Pharmaco-miR: linking microRNAs and drug effects

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MicroRNAs (miRNAs) are short regulatory RNAs that down-regulate gene expression. They are essential for cell homeostasis and active in many disease states. A major discovery is the ability of miRNAs to determine the efficacy of drugs, which has given rise to the field of ‘miRNA pharmacogenomics’ through ‘Pharmaco-miRs’. miRNAs play a significant role in pharmacogenomics by down-regulating genes that are important for drug function. These interactions can be described as triplet sets consisting of a miRNA, a target gene and a drug associated with the gene. We have developed a web server which links miRNA expression and drug function by combining data on miRNA targeting and protein–drug interactions. miRNA targeting information derive from both experimental data and computational predictions, and protein–drug interactions are annotated by the Pharmacogenomics Knowledge base (PharmGKB). Pharmaco-miR’s input consists of miRNAs, genes and/or drug names and the output consists of miRNA pharmacogenomic sets or a list of unique associated miRNAs, genes and drugs. We have furthermore built a database, named Pharmaco-miR Verified Sets (VerSe), which contains miRNA pharmacogenomic data manually curated from the literature, can be searched and downloaded via Pharmaco-miR and informs on trends and generalities published in the field. Overall, we present examples of how Pharmaco-miR provides possible explanations for previously published observations, including how the cisplatin and 5-fluorouracil resistance induced by miR-148a may be caused by miR-148a targeting of the gene KIT. The information is available at www.Pharmaco-miR.org.
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paper_title: Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis

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BackgroundCystic fibrosis (CF) is a fatal genetic disorder caused by mutations in the CF transmembrane conductance regulator (CFTR) gene that primarily affects the lungs and the digestive system, and the current drug treatment is mainly able to alleviate symptoms. To improve disease management for CF, we considered the repurposing of approved drugs and hypothesized that specific microRNA (miRNA) transcription factors (TF) gene networks can be used to generate feed-forward loops (FFLs), thus providing treatment opportunities on the basis of disease specific FFLs.MethodsComprehensive database searches revealed significantly enriched TFs and miRNAs in CF and CFTR gene networks. The target genes were validated using ChIPBase and by employing a consensus approach of diverse algorithms to predict miRNA gene targets. STRING analysis confirmed protein-protein interactions (PPIs) among network partners and motif searches defined composite FFLs. Using information extracted from SM2miR and Pharmaco-miR, an in silico drug repurposing pipeline was established based on the regulation of miRNA/TFs in CF/CFTR networks.ResultsIn human airway epithelium, a total of 15 composite FFLs were constructed based on CFTR specific miRNA/TF gene networks. Importantly, nine of them were confirmed in patient samples and CF epithelial cells lines, and STRING PPI analysis provided evidence that the targets interacted with each other. Functional analysis revealed that ubiquitin-mediated proteolysis and protein processing in the endoplasmic reticulum dominate the composite FFLs, whose major functions are folding, sorting, and degradation. Given that the mutated CFTR gene disrupts the function of the chloride channel, the constructed FFLs address mechanistic aspects of the disease and, among 48 repurposing drug candidates, 26 were confirmed with literature reports and/or existing clinical trials relevant to the treatment of CF patients.ConclusionThe construction of FFLs identified promising drug repurposing candidates for CF and the developed strategy may be applied to other diseases as well.
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paper_title: SM2miR: a database of the experimentally validated small molecules' effects on microRNA expression

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UNLABELLED ::: The inappropriate expression of microRNAs (miRNAs) is closely related with disease diagnosis, prognosis and therapy response. Recently, many studies have demonstrated that bioactive small molecules (or drugs) can regulate miRNA expression, which indicates that targeting miRNAs with small molecules is a new therapy for human diseases. In this study, we established the SM2miR database, which recorded 2925 relationships between 151 small molecules and 747 miRNAs in 17 species after manual curation from nearly 2000 articles. Each entry contains the detailed information about small molecules, miRNAs and evidences of their relationships, such as species, miRBase Accession number, DrugBank Accession number, PubChem Compound Identifier (CID), expression pattern of miRNA, experimental method, tissues or conditions for detection. SM2miR database has a user-friendly interface to retrieve by miRNA or small molecule. In addition, we offered a submission page. Thus, SM2miR provides a fairly comprehensive repository about the influences of small molecules on miRNA expression, which will promote the development of miRNA therapeutics. ::: ::: ::: AVAILABILITY ::: SM2miR is freely available at http://bioinfo.hrbmu.edu.cn/SM2miR/.
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paper_title: The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

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To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. We demonstrate that this ‘‘Connectivity Map’’ resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs. These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.
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paper_title: Transcriptional data: a new gateway to drug repositioning?

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Recent advances in computational biology suggest that any perturbation to the transcriptional programme of the cell can be summarised by a proper ‘signature’: a set of genes combined with a pattern of expression. Therefore, it should be possible to generate proxies of clinicopathological phenotypes and drug effects through signatures acquired via DNA microarray technology. ::: ::: Gene expression signatures have recently been assembled and compared through genome-wide metrics, unveiling unexpected drug–disease and drug–drug ‘connections’ by matching corresponding signatures. Consequently, novel applications for existing drugs have been predicted and experimentally validated. ::: ::: Here, we describe related methods, case studies and resources while discussing challenges and benefits of exploiting existing repositories of microarray data that could serve as a search space for systematic drug repositioning.
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paper_title: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

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Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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paper_title: Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action

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The Library of Integrated Network-based Cellular Signatures (LINCS) project is a large-scale coordinated effort to build a comprehensive systems biology reference resource. The goals of the program include the generation of a very large multidimensional data matrix and informatics and computational tools to integrate, analyze, and make the data readily accessible. LINCS data include genome-wide transcriptional signatures, biochemical protein binding profiles, cellular phenotypic response profiles and various other datasets for a wide range of cell model systems and molecular and genetic perturbations. Here we present a partial survey of this data facilitated by data standards and in particular a robust compound standardization workflow; we integrated several types of LINCS signatures and analyzed the results with a focus on mechanism of action (MoA) and chemical compounds. We illustrate how kinase targets can be related to disease models and relevant drugs. We identified some fundamental trends that appear to link Kinome binding profiles and transcriptional signatures to chemical information and biochemical binding profiles to transcriptional responses independent of chemical similarity. To fill gaps in the datasets we developed and applied predictive models. The results can be interpreted at the systems level as demonstrated based on a large number of signaling pathways. We can identify clear global relationships, suggesting robustness of cellular responses to chemical perturbation. Overall, the results suggest that chemical similarity is a useful measure at the systems level, which would support phenotypic drug optimization efforts. With this study we demonstrate the potential of such integrated analysis approaches and suggest prioritizing further experiments to fill the gaps in the current data.
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paper_title: Genetics of rheumatoid arthritis contributes to biology and drug discovery

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A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ∼10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2, 3, 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses—as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes—to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.
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paper_title: A phenome-guided drug repositioning through a latent variable model

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BackgroundThe phenome represents a distinct set of information in the human population. It has been explored particularly in its relationship with the genome to identify correlations for diseases. The phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. For a comprehensive analysis of the phenome, we assumed that all phenotypes (indications and side effects) were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. Correspondingly, we employed Latent Dirichlet Allocation (LDA), which introduces latent variables (topics) to govern the phenome distribution.ResultsWe developed our model on the phenome information in Side Effect Resource (SIDER). We first developed a LDA model optimized based on its recovery potential through perturbing the drug-phenotype matrix for each of the drug-indication pairs where each drug-indication relationship was switched to “unknown” one at the time and then recovered based on the remaining drug-phenotype pairs. Of the probabilistically significant pairs, 70% was successfully recovered. Next, we applied the model on the whole phenome to narrow down repositioning candidates and suggest alternative indications. We were able to retrieve approved indications of 6 drugs whose indications were not listed in SIDER. For 908 drugs that were present with their indication information, our model suggested alternative treatment options for further investigations. Several of the suggested new uses can be supported with information from the scientific literature.ConclusionsThe results demonstrated that the phenome can be further analyzed by a generative model, which can discover probabilistic associations between drugs and therapeutic uses. In this regard, LDA serves as an enrichment tool to explore new uses of existing drugs by narrowing down the search space.
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paper_title: Construction of Drug Network Based on Side Effects and Its Application for Drug Repositioning

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Drugs with similar side-effect profiles may share similar therapeutic properties through related mechanisms of action. In this study, a drug-drug network was constructed based on the similarities between their clinical side effects. The indications of a drug may be inferred by the enriched FDA-approved functions of its neighbouring drugs in the network. We systematically screened new indications for 1234 drugs with more than 2 network neighbours, 36.87% of the drugs achieved a performance score of Normalized Discounted Cumulative Gain in the top 5 positions (NDCG@5) ≥ 0.7, which means most of the known FDA-approved indications were well predicted at the top 5 positions. In particular, drugs for diabetes, obesity, laxatives and antimycobacterials had extremely high performance with more than 80% of them achieving NDCG@5 ≥ 0.7. Additionally, by manually checking the predicted 1858 drug-indication pairs with Expression Analysis Systematic Explorer (EASE) score ≤ 10(-5) (EASE score is a rigorously modified Fisher exact test p value), we found that 80.73% of such pairs could be verified by preclinical/clinical studies or scientific literature. Furthermore, our method could be extended to predict drugs not covered in the network. We took 98 external drugs not covered in the network as the test sample set. Based on our similarity criteria using side effects, we identified 41 drugs with significant similarities to other drugs in the network. Among them, 36.59% of the drugs achieved NDCG@5 ≥ 0.7. In all of the 106 drug-indication pairs with an EASE score ≤ 0.05, 50.94% of them are supported by FDA approval or preclinical/clinical studies. In summary, our method which is based on the indications enriched by network neighbors may provide new clues for drug repositioning using side effects.
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paper_title: Systematic Drug Repositioning Based on Clinical Side-Effects

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Drug repositioning helps fully explore indications for marketed drugs and clinical candidates. Here we show that the clinical side-effects (SEs) provide a human phenotypic profile for the drug, and this profile can suggest additional disease indications. We extracted 3,175 SE-disease relationships by combining the SE-drug relationships from drug labels and the drug-disease relationships from PharmGKB. Many relationships provide explicit repositioning hypotheses, such as drugs causing hypoglycemia are potential candidates for diabetes. We built Naive Bayes models to predict indications for 145 diseases using the SEs as features. The AUC was above 0.8 in 92% of these models. The method was extended to predict indications for clinical compounds, 36% of the models achieved AUC above 0.7. This suggests that closer attention should be paid to the SEs observed in trials not just to evaluate the harmful effects, but also to rationally explore the repositioning potential based on this “clinical phenotypic assay”.
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paper_title: A side effect resource to capture phenotypic effects of drugs

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The molecular understanding of phenotypes caused by drugs in humans is essential for elucidating mechanisms of action and for developing personalized medicines. Side effects of drugs (also known as adverse drug reactions) are an important source of human phenotypic information, but so far research on this topic has been hampered by insufficient accessibility of data. Consequently, we have developed a public, computer-readable side effect resource (SIDER) that connects 888 drugs to 1450 side effect terms. It contains information on frequency in patients for one-third of the drug–side effect pairs. For 199 drugs, the side effect frequency of placebo administration could also be extracted. We illustrate the potential of SIDER with a number of analyses. The resource is freely available for academic research at http://sideeffects.embl.de.
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paper_title: Linking PharmGKB to Phenotype Studies and Animal Models of Disease for Drug Repurposing

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The investigation of phenotypes in model organisms has the potential to reveal the molecular mechanisms underlying disease. The large-scale comparative analysis of phenotypes across species can reveal novel associations between genotypes and diseases. We use the PhenomeNET network of phenotypic similarity to suggest genotype-disease association, combine them with drug-gene associations available from the PharmGKB database, and infer novel associations between drugs and diseases. We evaluate and quantify our results based on our method's capability to reproduce known drug-disease associations. We find and discuss evidence that levonorgestrel, tretinoin and estradiol are associated with cystic fibrosis (p < 2.65 · 10(-6), p < 0.002 and p < 0.031, Wilcoxon signed-rank test, Bonferroni correction) and that ibuprofen may be active in chronic lymphocytic leukemia (p < 2.63 · 10(-23), Wilcoxon signed-rank test, Bonferroni correction). To enable access to our results, we implement a web server and make our raw data freely available. Our results are the first steps in implementing an integrated system for the analysis and prediction of drug-disease associations for rare and orphan diseases for which the molecular basis is not known.
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paper_title: Improved survival for BRCA2‐associated serous ovarian cancer compared with both BRCA‐negative and BRCA1‐associated serous ovarian cancer

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BACKGROUND: ::: ::: Multiple observational studies have suggested that breast cancer gene (BRCA)-associated ovarian cancers have improved survival compared with BRCA-negative ovarian cancers. However, most of those studies combined BRCA1 and BRCA2 patients or evaluated only BRCA1 patients. The objective of the current study was to examine whether BRCA1-associated and BRCA2-associated ovarian cancers were associated with different outcomes. ::: ::: ::: ::: METHODS: ::: ::: This was a single-institution, retrospective analysis of patients who had a new diagnosis of histologically confirmed stage III or IV serous ovarian, fallopian tube, or primary peritoneal cancer between January 1, 1996 and February 1, 2011 and who underwent BRCA mutation testing on 1 of 2 institutional review board-approved follow-up studies. Patients who had been tested for BRCA mutations beyond 24 months of diagnosis were excluded from analysis to minimize selection bias from including patients who were referred for genetic testing because of long survival. ::: ::: ::: ::: RESULTS: ::: ::: Data from 190 patients (143 BRCA-negative patients, 30 BRCA1-positive patients, and 17 BRCA2-positive patients) were analyzed. During the study period, 73 deaths were observed (60 BRCA-negative patients, 10 BRCA1-positive patients, 3 BRCA2-positive patients). The median follow-up for the remaining 117 survivors was 2.5 years. At 3 years, 69.4%, 90.7%, and 100% of BRCA-negative patients, BRCA1-positive patients, and BRCA2-positive patients were alive, respectively. On univariate analysis, age, BRCA2 mutations, debulking status, and type of first-line therapy (intravenous or intraperitoneal) were significant predictors of overall survival. On multivariate analysis, BRCA2 mutations (hazard ratio, 0.20; 95% confidence interval, 0.06-0.65; P = .007), but not BRCA1 mutations (hazard ratio, 0.70; 95% confidence interval, 0.36-1.38; P = .31), predicted for improved overall survival compared with BRCA-negative patients. When carriers of BRCA2 mutations were directly compared with carriers of BRCA1 mutations, BRCA2 mutations appeared to confer improved overall survival (hazard ratio, 0.29; 95% confidence interval, 0.08-1.05; P = .060), although this finding did not reach significance. ::: ::: ::: ::: CONCLUSIONS: ::: ::: The current data suggests that BRCA2 mutations confer an overall survival advantage compared with either being BRCA-negative or having a BRCA1 mutation in high-grade serous ovarian cancer. This finding may have important implications for clinical trial design. Cancer 2012. © 2011 American Cancer Society.
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paper_title: Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data

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Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with P < 4.6 × 10⁻⁶ (false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort (n = 7,406). These findings validate PheWAS as a tool to allow unbiased interrogation across multiple phenotypes in EMR-based cohorts and to enhance analysis of the genomic basis of human disease.
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paper_title: The challenges, advantages and future of phenome-wide association studies

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Over the last decade, significant technological breakthroughs have revolutionized human genomic research in the form of genome-wide association studies (GWASs). GWASs have identified thousands of statistically significant genetic variants associated with hundreds of human conditions including many with immunological aetiologies (e.g. multiple sclerosis, ankylosing spondylitis and rheumatoid arthritis). Unfortunately, most GWASs fail to identify clinically significant associations. Identifying biologically significant variants by GWAS also presents a challenge. The GWAS is a phenotype-to-genotype approach. As a complementary/alternative approach to the GWAS, investigators have begun to exploit extensive electronic medical record systems to conduct a genotype-to-phenotype approach when studying human disease - specifically, the phenome-wide association study (PheWAS). Although the PheWAS approach is in its infancy, this method has already demonstrated its capacity to rediscover important genetic associations related to immunological diseases/conditions. Furthermore, PheWAS has the advantage of identifying genetic variants with pleiotropic properties. This is particularly relevant for HLA variants. For example, PheWAS results have demonstrated that the HLA-DRB1 variant associated with multiple sclerosis may also be associated with erythematous conditions including rosacea. Likewise, PheWAS has demonstrated that the HLA-B genotype is not only associated with spondylopathies, uveitis, and variability in platelet count, but may also play an important role in other conditions, such as mastoiditis. This review will discuss and compare general PheWAS methodologies, describe both the challenges and advantages of the PheWAS, and provide insight into the potential directions in which PheWAS may lead.
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paper_title: Mining small-molecule screens to repurpose drugs

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Repurposing and repositioning drugscdiscovering new uses for existing and experimental medicinescis an attractive strategy for rescuing stalled pharmaceutical projects, finding treatments for neglected diseases, and reducing the time, cost and risk of drug development. As this strategy emerged, academic researchers began performing high-throughput screens (HTS) of small moleculescthe type of experiments once exclusively conducted in industrycand making the data from these screens available to all. Several methods can mine this data to inform repurposing and repositioning efforts. Despite these methods’ limitations, it is hopeful that they will accelerate the discovery of new uses for known drugs, but this hope has not yet been realized.
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paper_title: Drug repositioning by applying 'expression profiles' generated by integrating chemical structure similarity and gene semantic similarity.

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Drug repositioning, also known as drug repurposing or reprofiling, is the process of finding new indications for established drugs. Because drug repositioning can reduce costs and enhance the efficiency of drug development, it is of paramount importance in medical research. Here, we present a systematic computational method to identify potential novel indications for a given drug. This method utilizes some prior knowledge such as 3D drug chemical structure information, drug-target interactions and gene semantic similarity information. Its prediction is based on another form of 'expression profile', which contains scores ranging from -1 to 1, reflecting the consensus response scores (CRSs) between each drug of 965 and 1560 proteins. The CRS integrates chemical structure similarity and gene semantic similarity information. We define the degree of similarity between two drugs as the absolute value of their correlation coefficients. Finally, we establish a drug similarity network (DSN) and obtain 33 modules of drugs with similar modes of action, determining their common indications. Using these modules, we predict new indications for 143 drugs and identify previously unknown indications for 42 drugs without ATC codes. This method overcomes the instability of gene expression profiling derived from experiments due to experimental conditions, and predicts indications for a new compound feasibly, requiring only the 3D structure of the compound. In addition, the high literature validation rate of 71.8% also suggests that our method has the potential to discover novel drug indications for existing drugs.
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paper_title: The Protein Data Bank

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The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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paper_title: Anti-Infectious Drug Repurposing Using an Integrated Chemical Genomics and Structural Systems Biology Approach

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The emergence of multi-drug and extensive drug resistance of microbes to antibiotics poses a great threat to human health. Although drug repurposing is a promising solution for accelerating the drug development process, its application to anti-infectious drug discovery is limited by the scope of existing phenotype-, ligand-, or target-based methods. In this paper we introduce a new computational strategy to determine the genome-wide molecular targets of bioactive compounds in both human and bacterial genomes. Our method is based on the use of a novel algorithm, ligand Enrichment of Network Topological Similarity (ligENTS), to map the chemical universe to its global pharmacological space. ligENTS outperforms the state-of-the-art algorithms in identifying novel drug-target relationships. Furthermore, we integrate ligENTS with our structural systems biology platform to identify drug repurposing opportunities via target similarity profiling. Using this integrated strategy, we have identified novel P. falciparum targets of drug-like active compounds from the Malaria Box, and suggest that a number of approved drugs may be active against malaria. This study demonstrates the potential of an integrative chemical genomics and structural systems biology approach to drug repurposing.
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paper_title: Blockade of oncogenic I B kinase activity in diffuse large B-cell lymphoma by bromodomain and extraterminal domain protein inhibitors

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In the activated B-cell-like (ABC) subtype of diffuse large B-cell lymphoma (DLBCL), NF-κB activity is essential for viability of the malignant cells and is sustained by constitutive activity of IκB kinase (IKK) in the cytoplasm. Here, we report an unexpected role for the bromodomain and extraterminal domain (BET) proteins BRD2 and BRD4 in maintaining oncogenic IKK activity in ABC DLBCL. IKK activity was reduced by small molecules targeting BET proteins as well as by genetic knockdown of BRD2 and BRD4 expression, thereby inhibiting downstream NF-κB-driven transcriptional programs and killing ABC DLBCL cells. Using a high-throughput platform to screen for drug-drug synergy, we observed that the BET inhibitor JQ1 combined favorably with multiple drugs targeting B-cell receptor signaling, one pathway that activates IKK in ABC DLBCL. The BTK kinase inhibitor ibrutinib, which is in clinical development for the treatment of ABC DLBCL, synergized strongly with BET inhibitors in killing ABC DLBCL cells in vitro and in a xenograft mouse model. These findings provide a mechanistic basis for the clinical development of BET protein inhibitors in ABC DLBCL, particularly in combination with other modulators of oncogenic IKK signaling.
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paper_title: Identification of Drug Combinations Containing Imatinib for Treatment of BCR-ABL+ Leukemias

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The BCR-ABL translocation is found in chronic myeloid leukemia (CML) and in Ph+ acute lymphoblastic leukemia (ALL) patients. Although imatinib and its analogues have been used as front-line therapy to target this mutation and control the disease for over a decade, resistance to the therapy is still observed and most patients are not cured but need to continue the therapy indefinitely. It is therefore of great importance to find new therapies, possibly as drug combinations, which can overcome drug resistance. In this study, we identified eleven candidate anti-leukemic drugs that might be combined with imatinib, using three approaches: a kinase inhibitor library screen, a gene expression correlation analysis, and literature analysis. We then used an experimental search algorithm to efficiently explore the large space of possible drug and dose combinations and identified drug combinations that selectively kill a BCR-ABL+ leukemic cell line (K562) over a normal fibroblast cell line (IMR-90). Only six iterations of the algorithm were needed to identify very selective drug combinations. The efficacy of the top forty-nine combinations was further confirmed using Ph+ and Ph- ALL patient cells, including imatinib-resistant cells. Collectively, the drug combinations and methods we describe might be a first step towards more effective interventions for leukemia patients, especially those with the BCR-ABL translocation.
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paper_title: A Hadoop-Based Method to Predict Potential Effective Drug Combination

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Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naive Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.
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paper_title: Systematic prediction of drug combinations based on clinical side-effects

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Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures.
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paper_title: DCDB: Drug combination database

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Summary: Rapid advances in pharmaceutical sciences have brought ever-increasing interests in combined therapies for better clinical efficacy and safety, especially in cases of complicated and refractory diseases. Innovative experimental technologies and theoretical frameworks are being actively developed for multicomponent drug research. In this work, we present the Drug Combination Database, with aims to facilitate analyses of known drug combinations, to summarize patterns of beneficial drug interactions, and to provide a basis for theoretical modeling and simulation of such drug interactions. Its current version (1.0) collected 499 approved or investigational drug combinations, including 40 unsuccessful drug combinations, involving 485 individual drugs, from >6000 references.
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paper_title: Characteristics of Drug Combination Therapy in Oncology by Analyzing Clinical Trial Data on Clinicaltrials.Gov

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Within the past few decades, drug combination therapy has been intensively studied in oncology and other complex disease areas, especially during the early drug discovery stage, as drug combinations have the potential to improve treatment response, minimize development of resistance or minimize adverse events. In the present, designing combination trials relies mainly on clinical and empirical experience. While empirical experience has indeed crafted efficacious combination therapy clinical trials (combination trials), however, garnering experience with patients can take a lifetime. The preliminary step to eliminating this barrier of time, then, is to understand the current state of combination trials. Thus, we present the first large-scale study of clinical trials (2008-2013) from ClinicalTrials.gov to compare combination trials to non-combination trials, with a focus on oncology. In this work, we developed a classifier to identify combination trials and oncology trials through natural language processing techniques. After clustering trials, we categorized them based on selected characteristics and observed trends present. Among the characteristics studied were primary purpose, funding source, endpoint measurement, allocation, and trial phase. We observe a higher prevalence of combination therapy in oncology (25.6% use combination trials) in comparison to other disease trials (6.9%). However, surprisingly the prevalence of combinations does not increase over the years. In addition, the trials supported by the NIH are significantly more likely to use combinations of drugs than those supported by industry. Our preliminary study of current combination trials may facilitate future trial design and move more preclinical combination studies to the clinical trial stage.
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paper_title: Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity.

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In response to the high cost and high risk associated with traditional de novo drug discovery, investigation of potential additional uses for existing drugs, also known as drug repositioning, has attracted increasing attention from both the pharmaceutical industry and the research community. In this paper, we propose a unified computational framework, called DDR, to predict novel drug-disease associations. DDR formulates the task of hypothesis generation for drug repositioning as a constrained nonlinear optimization problem. It utilizes multiple drug similarity networks, multiple disease similarity networks, and known drug-disease associations to explore potential new associations among drugs and diseases with no known links. A large-scale study was conducted using 799 drugs against 719 diseases. Experimental results demonstrated the effectiveness of the approach. In addition, DDR ranked drug and disease information sources based on their contributions to the prediction, thus paving the way for prioritizing multiple data sources and building more reliable drug repositioning models. Particularly, some of our novel predictions of drug-disease associations were supported by clinical trials databases, showing that DDR could serve as a useful tool in drug discovery to efficiently identify potential novel uses for existing drugs.
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paper_title: PREDICT: a method for inferring novel drug indications with application to personalized medicine

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Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large-scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross-validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue-specific expression information on the drug targets. We further show that disease-specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease-specific signatures.
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paper_title: Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties

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Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC₅₀ values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC₅₀ values in a 8-fold cross-validation and an independent blind test with coefficient of determination R² of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R² of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC₅₀ values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.
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paper_title: Drug repositioning: a machine-learning approach through data integration

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Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.
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paper_title: A Novel Method of Transcriptional Response Analysis to Facilitate Drug Repositioning for Cancer Therapy

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Little research has been done to address the huge opportunities that may exist to reposition existing approved or generic drugs for alternate uses in cancer therapy. Additionally, there has been little work on strategies to reposition experimental cancer agents for testing in alternate settings that could shorten their clinical development time. Progress in each area has lagged in part due to the lack of systematic methods to define drug off-target effects (OTEs) that might affect important cancer cell signaling pathways. In this study, we addressed this critical gap by developing an OTE-based method to repurpose drugs for cancer therapeutics, based on transcriptional responses made in cells before and after drug treatment. Specifically, we defined a new network component called cancer-signaling bridges (CSBs) and integrated it with Bayesian Factor Regression Model (BFRM) to form a new hybrid method termed CSB-BFRM. Proof of concept studies were performed in breast and prostate cancer cells and in promyelocytic leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses to >90% of FDA-approved drugs and >75% of experimental clinical drugs that were tested. Mechanistic investigation of OTEs for several high-ranking drug-dose pairs suggested repositioning opportunities for cancer therapy, based on the ability to enforce Rb-dependent repression of important E2F-dependent cell cycle genes. Together, our findings establish new methods to identify opportunities for drug repositioning or to elucidate the mechanisms of action of repositioned drugs.
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paper_title: Molecular networks as sensors and drivers of common human diseases

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The molecular biology revolution led to an intense focus on the study of interactions between DNA, RNA and protein biosynthesis in order to develop a more comprehensive understanding of the cell. One consequence of this focus was a reduced attention to whole-system physiology, making it difficult to link molecular biology to clinical medicine. Equipped with the tools emerging from the genomics revolution, we are now in a position to link molecular states to physiological ones through the reverse engineering of molecular networks that sense DNA and environmental perturbations and, as a result, drive variations in physiological states associated with disease.
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paper_title: Computational drug repositioning through heterogeneous network clustering

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BackgroundGiven the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational drug-repositioning candidate discovery platforms.ResultsUsing known disease-gene and drug-target relationships from the KEGG database, we built a weighted disease and drug heterogeneous network. The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible drug-disease pairs (putative drug repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with drug indications that were either reported in published literature or investigated in clinical trials.ConclusionsPrevious computational approaches for drug repositioning focused either on drug-drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering drug-disease relationships also. Further, we considered not only gene but also other features to build the disease drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with drug indications that are under investigation, we believe our approach could complement the current computational approaches for drug repositioning candidate discovery.
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paper_title: Network based elucidation of drug response: from modulators to targets

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Network-based drug discovery aims at harnessing the power of networks to investigate the mechanism of action of existing drugs, or new molecules, in order to identify innovative therapeutic treatments. In this review, we describe some of the most recent advances in the field of network pharmacology, starting with approaches relying on computational models of transcriptional networks, then moving to protein and signaling network models and concluding with “drug networks”. These networks are derived from different sources of experimental data, or literature-based analysis, and provide a complementary view of drug mode of action. Molecular and drug networks are powerful integrated computational and experimental approaches that will likely speed up and improve the drug discovery process, once fully integrated into the academic and industrial drug discovery pipeline.
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paper_title: A new method for computational drug repositioning using drug pairwise similarity

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The traditional de novo drug discovery is known as a high cost and high risk process. In response, recently there is an increasing interest in discovering new indications for known drugs — a process known as drug repositioning — using computational methods. In this study, we present a new systematic approach for identifying potential new indications of an existing drug through its relation to similar drugs. Different from the previous similarity-based methods, we adapted a novel bipartite-graph based method when considering common drug targets and their interaction information. Furthermore, we added drug structure information into the calculation of drug pairwise similarity. In cross-validation experiments, our method achieved a sensitivity of 0.77 and specificity of 0.92 (AUC = 0.888) and compared favorably to the state of the art. When compared with a control group of drug uses, our drug repositioning results were found to be significantly enriched in both the biomedicai literature and clinical trials. Our results indicate that combining chemical structure and drug target information results in better prediction performance and that the proposed approach successfully captures the implicit information between drug targets.
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paper_title: Pathway-based drug repositioning using causal inference

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BackgroundRecent in vivo studies showed new hopes of drug repositioning through causality inference from drugs to disease. Inspired by their success, here we present an in silico method for building a causal network (CauseNet) between drugs and diseases, in an attempt to systematically identify new therapeutic uses of existing drugs.MethodsUnlike the traditional 'one drug-one target-one disease' causal model, we simultaneously consider all possible causal chains connecting drugs to diseases via target- and gene-involved pathways based on rich information in several expert-curated knowledge-bases. With statistical learning, our method estimates transition likelihood of each causal chain in the network based on known drug-disease treatment associations (e.g. bexarotene treats skin cancer).ResultsTo demonstrate its validity, our method showed high performance (AUC = 0.859) in cross validation. Moreover, our top scored prediction results are highly enriched in literature and clinical trials. As a showcase of its utility, we show several drugs for potential re-use in Crohn's Disease.ConclusionsWe successfully developed a computational method for discovering new uses of existing drugs based on casual inference in a layered drug-target-pathway-gene- disease network. The results showed that our proposed method enables hypothesis generation from public accessible biological data for drug repositioning.
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paper_title: Drug—target network

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The global set of relationships between protein targets of all drugs and all disease-gene products in the human protein-protein interaction or 'interactome' network remains uncharacterized. We built a bipartite graph composed of US Food and Drug Administration-approved drugs and proteins linked by drug-target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types according to Anatomical Therapeutic Chemical classification. Topological analyses of this network quantitatively showed an overabundance of 'follow-on' drugs, that is, drugs that target already targeted proteins. By including drugs currently under investigation, we identified a trend toward more functionally diverse targets improving polypharmacology. To analyze the relationships between drug targets and disease-gene products, we measured the shortest distance between both sets of proteins in current models of the human interactome network. Significant differences in distance were found between etiological and palliative drugs. A recent trend toward more rational drug design was observed.
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paper_title: Systematic identification of pharmacogenomics information from clinical trials

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Graphical abstractDisplay Omitted Highlights? Clinical outcome is gaining more attention in pharmacogenomics (PGx) studies. ? ClinicalTrials.gov is rich in clinical outcome information for PGx studies. ? ClinicalTrials.gov provides more timely PGx data than the biomedical literature. ? We developed a method for identifying PGx information from ClinicalTrials.gov. ? This research has potentials in enriching the existing knowledge on PGx relations. Recent progress in high-throughput genomic technologies has shifted pharmacogenomic research from candidate gene pharmacogenetics to clinical pharmacogenomics (PGx). Many clinical related questions may be asked such as 'what drug should be prescribed for a patient with mutant alleles?' Typically, answers to such questions can be found in publications mentioning the relationships of the gene-drug-disease of interest. In this work, we hypothesize that ClinicalTrials.gov is a comparable source rich in PGx related information. In this regard, we developed a systematic approach to automatically identify PGx relationships between genes, drugs and diseases from trial records in ClinicalTrials.gov. In our evaluation, we found that our extracted relationships overlap significantly with the curated factual knowledge through the literature in a PGx database and that most relationships appear on average 5years earlier in clinical trials than in their corresponding publications, suggesting that clinical trials may be valuable for both validating known and capturing new PGx related information in a more timely manner. Furthermore, two human reviewers judged a portion of computer-generated relationships and found an overall accuracy of 74% for our text-mining approach. This work has practical implications in enriching our existing knowledge on PGx gene-drug-disease relationships as well as suggesting crosslinks between ClinicalTrials.gov and other PGx knowledge bases.
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paper_title: Understanding PubMed® user search behavior through log analysis

paper_content:

This article reports on a detailed investigation of PubMed users’ needs and behavior as a step toward improving biomedical information retrieval. PubMed is providing free service to researchers with access to more than 19 million citations for biomedical articles from MEDLINE and life science journals. It is accessed by millions of users each day. Efficient search tools are crucial for biomedical researchers to keep abreast of the biomedical literature relating to their own research. This study provides insight into PubMed users’ needs and their behavior. This investigation was conducted through the analysis of one month of log data, consisting of more than 23 million user sessions and more than 58 million user queries. Multiple aspects of users’ interactions with PubMed are characterized in detail with evidence from these logs. Despite having many features in common with general Web searches, biomedical information searches have unique characteristics that are made evident in this study. PubMed users are more persistent in seeking information and they reformulate queries often. The three most frequent types of search are search by author name, search by gene/protein, and search by disease. Use of abbreviation in queries is very frequent. Factors such as result set size influence users’ decisions. Analysis of characteristics such as these plays a critical role in identifying users’ information needs and their search habits. In turn, such an analysis also provides useful insight for improving biomedical information retrieval. ::: ::: Database URL: http://www.ncbi.nlm.nih.gov/PubMed
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paper_title: DNorm: disease name normalization with pairwise learning to rank

paper_content:

Motivation: Despite the central role of diseases in biomedical research, there have been much fewer attempts to automatically determine which diseases are mentioned in a text—the task of disease name normalization (DNorm)—compared with other normalization tasks in biomedical text mining research. ::: ::: Methods: In this article we introduce the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM. Our method is a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data. The technique is based on pairwise learning to rank, which has not previously been applied to the normalization task but has proven successful in large optimization problems for information retrieval. ::: ::: Results: We compare our method with several techniques based on lexical normalization and matching, MetaMap and Lucene. Our algorithm achieves 0.782 micro-averaged F-measure and 0.809 macro-averaged F-measure, an increase over the highest performing baseline method of 0.121 and 0.098, respectively. ::: ::: Availability: The source code for DNorm is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/DNorm, along with a web-based demonstration and links to the NCBI disease corpus. Results on PubMed abstracts are available in PubTator: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator ::: ::: Contact: vog.hin@ul.gnoyihz
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paper_title: Assessing Drug Target Association Using Semantic Linked Data

paper_content:

The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.
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paper_title: PubTator: a web-based text mining tool for assisting biocuration

paper_content:

Manually curating knowledge from biomedical literature into structured databases is highly expensive and time-consuming, making it difficult to keep pace with the rapid growth of the literature. There is therefore a pressing need to assist biocuration with automated text mining tools. Here, we describe PubTator, a web-based system for assisting biocuration. PubTator is different from the few existing tools by featuring a PubMed-like interface, which many biocurators find familiar, and being equipped with multiple challenge-winning text mining algorithms to ensure the quality of its automatic results. Through a formal evaluation with two external user groups, PubTator was shown to be capable of improving both the efficiency and accuracy of manual curation. PubTator is publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/.
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paper_title: PubMed and beyond: a survey of web tools for searching biomedical literature

paper_content:

The past decade has witnessed the modern advances of high-throughput technology and rapid growth of research capacity in producing large-scale biological data, both of which were concomitant with an exponential growth of biomedical literature. This wealth of scholarly knowledge is of significant importance for researchers in making scientific discoveries and healthcare professionals in managing health-related matters. However, the acquisition of such information is becoming increasingly difficult due to its large volume and rapid growth. In response, the National Center for Biotechnology Information (NCBI) is continuously making changes to its PubMed Web service for improvement. Meanwhile, different entities have devoted themselves to developing Web tools for helping users quickly and efficiently search and retrieve relevant publications. These practices, together with maturity in the field of text mining, have led to an increase in the number and quality of various Web tools that provide comparable literature search service to PubMed. In this study, we review 28 such tools, highlight their respective innovations, compare them to the PubMed system and one another, and discuss directions for future development. Furthermore, we have built a website dedicated to tracking existing systems and future advances in the field of biomedical literature search. Taken together, our work serves information seekers in choosing tools for their needs and service providers and developers in keeping current in the field. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/search
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paper_title: Literature mining, ontologies and information visualization for drug repurposing.

paper_content:

The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.
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paper_title: Systematic drug repurposing through text mining.

paper_content:

Drug development remains a time-consuming and highly expensive process with high attrition rates at each stage. Given the safety hurdles drugs must pass due to increased regulatory scrutiny, it is essential for pharmaceutical companies to maximize their return on investment by effectively extending drug life cycles. There have been many effective techniques, such as phenotypic screening and compound profiling, which identify new indications for existing drugs, often referred to as drug repurposing or drug repositioning. This chapter explores the use of text mining leveraging several publicly available knowledge resources and mechanism of action representations to link existing drugs to new diseases from biomedical abstracts in an attempt to generate biologically meaningful alternative drug indications.
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paper_title: Exploring the Pharmacogenomics Knowledge Base (PharmGKB) for Repositioning Breast Cancer Drugs by Leveraging Web Ontology Language (OWL) and Cheminformatics Approaches

paper_content:

Computational drug repositioning leverages computational technology and high volume of biomedical data to identify new indications for existing drugs. Since it does not require costly experiments that have a high risk of failure, it has attracted increasing interest from diverse fields such as biomedical, pharmaceutical, and informatics areas. In this study, we used pharmacogenomics data generated from pharmacogenomics studies, applied informatics and Semantic Web technologies to address the drug repositioning problem. Specifically, we explored PharmGKB to identify pharmacogenomics related associations as pharmacogenomics profiles for US Food and Drug Administration (FDA) approved breast cancer drugs. We then converted and represented these profiles in Semantic Web notations, which support automated semantic inference. We successfully evaluated the performance and efficacy of the breast cancer drug pharmacogenomics profiles by case studies. Our results demonstrate that combination of pharmacogenomics data and Semantic Web technology/Cheminformatics approaches yields better performance of new indication and possible adverse effects prediction for breast cancer drugs.
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paper_title: Identification of Drug Combinations Containing Imatinib for Treatment of BCR-ABL+ Leukemias

paper_content:

The BCR-ABL translocation is found in chronic myeloid leukemia (CML) and in Ph+ acute lymphoblastic leukemia (ALL) patients. Although imatinib and its analogues have been used as front-line therapy to target this mutation and control the disease for over a decade, resistance to the therapy is still observed and most patients are not cured but need to continue the therapy indefinitely. It is therefore of great importance to find new therapies, possibly as drug combinations, which can overcome drug resistance. In this study, we identified eleven candidate anti-leukemic drugs that might be combined with imatinib, using three approaches: a kinase inhibitor library screen, a gene expression correlation analysis, and literature analysis. We then used an experimental search algorithm to efficiently explore the large space of possible drug and dose combinations and identified drug combinations that selectively kill a BCR-ABL+ leukemic cell line (K562) over a normal fibroblast cell line (IMR-90). Only six iterations of the algorithm were needed to identify very selective drug combinations. The efficacy of the top forty-nine combinations was further confirmed using Ph+ and Ph- ALL patient cells, including imatinib-resistant cells. Collectively, the drug combinations and methods we describe might be a first step towards more effective interventions for leukemia patients, especially those with the BCR-ABL translocation.
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paper_title: A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation

paper_content:

Using meta-analysis of eight independent transplant datasets (236 graft biopsy samples) from four organs, we identified a common rejection module (CRM) consisting of 11 genes that were significantly overexpressed in acute rejection (AR) across all transplanted organs. The CRM genes could diagnose AR with high specificity and sensitivity in three additional independent cohorts (794 samples). In another two independent cohorts (151 renal transplant biopsies), the CRM genes correlated with the extent of graft injury and predicted future injury to a graft using protocol biopsies. Inferred drug mechanisms from the literature suggested that two FDA-approved drugs (atorvastatin and dasatinib), approved for nontransplant indications, could regulate specific CRM genes and reduce the number of graft-infiltrating cells during AR. We treated mice with HLA-mismatched mouse cardiac transplant with atorvastatin and dasatinib and showed reduction of the CRM genes, significant reduction of graft-infiltrating cells, and extended graft survival. We further validated the beneficial effect of atorvastatin on graft survival by retrospective analysis of electronic medical records of a single-center cohort of 2,515 renal transplant patients followed for up to 22 yr. In conclusion, we identified a CRM in transplantation that provides new opportunities for diagnosis, drug repositioning, and rational drug design.
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paper_title: Drug Repurposing for Gastrointestinal Stromal Tumor

paper_content:

Despite significant treatment advances over the past decade, metastatic gastrointestinal stromal tumor (GIST) remains largely incurable. Rare diseases, such as GIST, individually affect small groups of patients but collectively are estimated to affect 25 to 30 million people in the United States alone. Given the costs associated with the discovery, development, and registration of new drugs, orphan diseases such as GIST are often not pursued by mainstream pharmaceutical companies. As a result, "drug repurposing" or "repositioning," has emerged as an alternative to the traditional drug development process. In this study, we screened 796 U.S. Food and Drug Administration (FDA)-approved drugs and found that two of these compounds, auranofin (Ridaura) and fludarabine phosphate, effectively and selectively inhibited the proliferation of GISTs, including imatinib-resistant cells. One of the most notable drug hits, auranofin, an oral, gold-containing agent approved by the FDA in 1985 for the treatment of rheumatoid arthritis, was found to inhibit thioredoxin reductase activity and induce reactive oxygen species (ROS) production, leading to dramatic inhibition of GIST cell growth and viability. Importantly, the anticancer activity associated with auranofin was independent of imatinib-resistant status, but was closely related to the endogenous and inducible levels of ROS. Coupled with the fact that auranofin has an established safety profile in patients, these findings suggest for the first time that auranofin may have clinical benefit for patients with GIST, particularly in those suffering from imatinib-resistant and recurrent forms of this disease.
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paper_title: Biomarker-Guided Repurposing of Chemotherapeutic Drugs for Cancer Therapy: A Novel Strategy in Drug Development

paper_content:

Cancer is a leading cause of mortality worldwide and matters are only set to worsen as its incidence continues to rise. Traditional approaches to combat cancer include improved prevention, early diagnosis, optimized surgery, development of novel drugs and honing regimens of existing anti-cancer drugs. Although discovery and development of novel and effective anti-cancer drugs is a major research area, it is well known that oncology drug development is a lengthy process, extremely costly and with high attrition rates. Furthermore, those drugs that do make it through the drug development mill are often quite expensive, laden with severe side-effects and, unfortunately, to date, have only demonstrated minimal increases in overall survival. Therefore, a strong interest has emerged to identify approved non-cancer drugs that possess anti-cancer activity, thus shortcutting the development process. This research strategy is commonly known as drug repurposing or drug repositioning and provides a faster path to the clinics. We have developed and implemented a modification of the standard drug repurposing strategy that we review here; rather than investigating target-promiscuous non-cancer drugs for possible anti-cancer activity, we focus on the discovery of novel cancer indications for already approved chemotherapeutic anti-cancer drugs. Clinical implementation of this strategy is normally commenced at clinical phase II trials and includes pre-treated patients. As the response rates to any non-standard chemotherapeutic drug will be relatively low in such a patient cohort it is a pre-requisite that such testing is based on predictive biomarkers. This review describes our strategy of biomarker-guided repurposing of chemotherapeutic drugs for cancer therapy, taking the repurposing of topoisomerase I inhibitors and topoisomerase I as a potential predictive biomarker as case in point.
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paper_title: Experimental confirmation of new drug-target interactions predicted by Drug Profile Matching.

paper_content:

We recently introduced Drug Profile Matching (DPM), a novel affinity fingerprinting-based in silico drug repositioning approach. DPM is able to quantitatively predict the complete effect profiles of compounds via probability scores. In the present work, in order to investigate the predictive power of DPM, three effect categories, namely, angiotensin-converting enzyme inhibitor, cyclooxygenase inhibitor, and dopamine agent, were selected and predictions were verified by literature analysis as well as experimentally. A total of 72% of the newly predicted and tested dopaminergic compounds were confirmed by tests on D1 and D2 expressing cell cultures. 33% and 23% of the ACE and COX inhibitory predictions were confirmed by in vitro tests, respectively. Dose-dependent inhibition curves were measured for seven drugs, and their inhibitory constants (Ki) were determined. Our study overall demonstrates that DPM is an effective approach to reveal novel drug-target pairs that may result in repositioning these drugs.
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paper_title: Repositioning metformin for cancer prevention and treatment

paper_content:

Metformin is the most commonly prescribed drug for type 2 diabetes (T2DM). Retrospective studies show that metformin is associated with decreased cancer risk. This historical correlation has driven vigorous research campaigns to determine the anticancer mechanisms of metformin. Consolidating the preclinical data is a challenge because unanswered questions remain concerning relevant mechanisms, bioavailability, and genetic factors that confer metformin sensitivity. Perhaps the most important unanswered question is whether metformin has activity against cancer in non-diabetics. In this review we highlight the proposed mechanisms of metformin action in cancer and discuss ongoing clinical trials with metformin in cancer. Improved understanding of these issues will increase the chances for successful application of metformin as an inexpensive, well-tolerated, and effective anticancer agent.
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paper_title: Repositioning of the anthelmintic drug mebendazole for the treatment for colon cancer

paper_content:

AbstractPurpose ::: In the present study, we screened a compound library containing 1,600 clinically used compounds with the aim to identify compounds, which potentially could be repositioned for colon cancer therapy.Methods Two established colon cancer cell lines were tested using the fluorometric microculture cytotoxicity assay (FMCA). For compound comparison connectivity map (CMAP) analysis, NCI 60 data mining and protein kinase binding measurements were performed.ResultsSixty-eight compounds were defined as hits with activity in both of these cell lines (<40 % cell survival compared with control) at 10 μM drug concentration. Analysis of chemical similarity of the hit compounds revealed several distinct clusters, among them the antiparasitic benzimidazole group. Two of these compounds, mebendazole (MBZ) and albendazole (ABZ) are registered for human use. Data from the NCI 60 cell line panel revealed only modest correlation between MBZ and ABZ, indicating differences in mechanism of action. This was further supported when gene expression signatures were compared in the CMAP database; ABZ ranked very low when MBZ was used as the query signature. Furthermore, MBZ, but not ABZ, was found to significantly interact with several protein kinases including BCR–ABL and BRAF. Analysis of the diagnosis-specific activity of MBZ showed activity in 80 % of the colon cancer cell lines in the NCI 60 panel. Three additional colon cancer cell lines and three cell models with non-malignant phenotypes were subsequently tested, confirming selective colon cancer activity of MBZ.ConclusionMBZ seemingly has repositioning potential for colorectal cancer therapy.
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paper_title: Trifluoperazine, an antipsychotic agent, inhibits cancer stem cell growth and overcomes drug resistance of lung cancer.

paper_content:

RATIONALE ::: Cancer stem cell (CSC) theory has drawn much attention, with evidence supporting the contribution of stem cells to tumor initiation, relapse, and therapy resistance. ::: ::: ::: OBJECTIVES ::: To screen drugs that target CSCs to improve the current treatment outcome and overcome drug resistance in patients with lung cancer. ::: ::: ::: METHODS ::: We used publicly available embryonic stem cell and CSC-associated gene signatures to query the Connectivity Map for potential drugs that can, at least in part, reverse the gene expression profile of CSCs. High scores were noted for several phenothiazine-like antipsychotic drugs, including trifluoperazine. We then treated lung CSCs with different EGFR mutation status with trifluoperazine to examine its anti-CSC properties. Lung CSCs resistant to epidermal growth factor receptor-tyrosine kinase inhibitor or cisplatin were treated with trifluoperazine plus gefitinib or trifluoperazine plus cisplatin. Animal models were used for in vivo validation of the anti-CSC effect and synergistic effect of trifluoperazine with gefitinib. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: We demonstrated that trifluoperazine inhibited CSC tumor spheroid formation and down-regulated the expression of CSC markers (CD44/CD133). Trifluoperazine inhibited Wnt/β-catenin signaling in gefitinib-resistant lung cancer spheroids. The combination of trifluoperazine with either gefitinib or cisplatin overcame drug resistance in lung CSCs. Trifluoperazine inhibited the tumor growth and enhanced the inhibitory activity of gefitinib in lung cancer metastatic and orthotopic CSC animal models. ::: ::: ::: CONCLUSIONS ::: Using in silico drug screening by Connectivity Map followed by empirical validations, we repurposed an existing phenothiazine-like antipsychotic drug, trifluoperazine, as a potential anti-CSC agent that could overcome epidermal growth factor receptor-tyrosine kinase inhibitor and chemotherapy resistance.
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paper_title: Individualized Systems Medicine Strategy to Tailor Treatments for Patients with Chemorefractory Acute Myeloid Leukemia

paper_content:

UNLABELLED ::: We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one patient at a time. ISM is based on (i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, (ii) clinical implementation of therapies predicted to be effective, and (iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug-response subtypes based on DSRT profiles, some with distinct genomic features (e.g., MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution and novel genomic changes potentially explaining resistance, whereas ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs. ::: ::: ::: SIGNIFICANCE ::: Here, we demonstrate an ISM strategy to optimize safe and effective personalized cancer therapies for individual patients as well as to understand and predict disease evolution and the next line of therapy. This approach could facilitate systematic drug repositioning of approved targeted drugs as well as help to prioritize and de-risk emerging drugs for clinical testing.
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paper_title: Genomic responses in mouse models greatly mimic human inflammatory diseases

paper_content:

The use of mice as animal models has long been considered essential in modern biomedical research, but the role of mouse models in research was challenged by a recent report that genomic responses in mouse models poorly mimic human inflammatory diseases. Here we reevaluated the same gene expression datasets used in the previous study by focusing on genes whose expression levels were significantly changed in both humans and mice. Contrary to the previous findings, the gene expression levels in the mouse models showed extraordinarily significant correlations with those of the human conditions (Spearman's rank correlation coefficient: 0.43-0.68; genes changed in the same direction: 77-93%; P = 6.5 × 10(-11) to 1.2 × 10(-35)). Moreover, meta-analysis of those datasets revealed a number of pathways/biogroups commonly regulated by multiple conditions in humans and mice. These findings demonstrate that gene expression patterns in mouse models closely recapitulate those in human inflammatory conditions and strongly argue for the utility of mice as animal models of human disorders.
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paper_title: Evaluating cell lines as tumour models by comparison of genomic profiles

paper_content:

Cell lines are widely used in cancer research to study tumour biology. Here Domcke et al. compare genomic data from ovarian cancer cell lines with those from clinical ovarian tumour samples and identify cell lines that most closely resemble the genomic features of high-grade serous ovarian cancer.
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paper_title: PREDICT: a method for inferring novel drug indications with application to personalized medicine

paper_content:

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large-scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross-validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue-specific expression information on the drug targets. We further show that disease-specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease-specific signatures.
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paper_title: Genomic responses in mouse models poorly mimic human inflammatory diseases

paper_content:

A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R(2) between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.
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paper_title: Scaling drug indication curation through crowdsourcing

paper_content:

Motivated by the high cost of human curation of biological databases, there is an increasing interest in using computational approaches to assist human curators and accelerate the manual curation process. Towards the goal of cataloging drug indications from FDA drug labels, we recently developed LabeledIn, a human-curated drug indication resource for 250 clinical drugs. Its development required over 40 h of human effort across 20 weeks, despite using well-defined annotation guidelines. In this study, we aim to investigate the feasibility of scaling drug indication annotation through a crowdsourcing technique where an unknown network of workers can be recruited through the technical environment of Amazon Mechanical Turk (MTurk). To translate the expert-curation task of cataloging indications into human intelligence tasks (HITs) suitable for the average workers on MTurk, we first simplify the complex task such that each HIT only involves a worker making a binary judgment of whether a highlighted disease, in context of a given drug label, is an indication. In addition, this study is novel in the crowdsourcing interface design where the annotation guidelines are encoded into user options. For evaluation, we assess the ability of our proposed method to achieve high-quality annotations in a time-efficient and cost-effective manner. We posted over 3000 HITs drawn from 706 drug labels on MTurk. Within 8 h of posting, we collected 18 775 judgments from 74 workers, and achieved an aggregated accuracy of 96% on 450 control HITs (where gold-standard answers are known), at a cost of $1.75 per drug label. On the basis of these results, we conclude that our crowdsourcing approach not only results in significant cost and time saving, but also leads to accuracy comparable to that of domain experts. ::: ::: Database URL: ftp://ftp.ncbi.nlm.nih.gov/pub/lu/LabeledIn/Crowdsourcing/.
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paper_title: Drug-Induced Regulation of Target Expression

paper_content:

Drug perturbations of human cells lead to complex responses upon target binding. One of the known mechanisms is a (positive or negative) feedback loop that adjusts the expression level of the respective target protein. To quantify this mechanism systems-wide in an unbiased way, drug-induced differential expression of drug target mRNA was examined in three cell lines using the Connectivity Map. To overcome various biases in this valuable resource, we have developed a computational normalization and scoring procedure that is applicable to gene expression recording upon heterogeneous drug treatments. In 1290 drug-target relations, corresponding to 466 drugs acting on 167 drug targets studied, 8% of the targets are subject to regulation at the mRNA level. We confirmed systematically that in particular G-protein coupled receptors, when serving as known targets, are regulated upon drug treatment. We further newly identified drug-induced differential regulation of Lanosterol 14-alpha demethylase, Endoplasmin, DNA topoisomerase 2-alpha and Calmodulin 1. The feedback regulation in these and other targets is likely to be relevant for the success or failure of the molecular intervention.
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paper_title: Recent Advances in Drug Repositioning for the Discovery of New Anticancer Drugs

paper_content:

Drug repositioning (also referred to as drug repurposing), the process of finding new uses of existing drugs, has been gaining popularity in recent years. The availability of several established clinical drug libraries and rapid advances in disease biology, genomics and bioinformatics has accelerated the pace of both activity-based and in silico drug repositioning. Drug repositioning has attracted particular attention from the communities engaged in anticancer drug discovery due to the combination of great demand for new anticancer drugs and the availability of a wide variety of cell- and target-based screening assays. With the successful clinical introduction of a number of non-cancer drugs for cancer treatment, drug repositioning now became a powerful alternative strategy to discover and develop novel anticancer drug candidates from the existing drug space. In this review, recent successful examples of drug repositioning for anticancer drug discovery from non-cancer drugs will be discussed.
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paper_title: LabeledIn: Cataloging labeled indications for human drugs

paper_content:

Abstract Drug–disease treatment relationships, i.e., which drug(s) are indicated to treat which disease(s) , are among the most frequently sought information in PubMed®. Such information is useful for feeding the Google Knowledge Graph, designing computational methods to predict novel drug indications, and validating clinical information in EMRs. Given the importance and utility of this information, there have been several efforts to create repositories of drugs and their indications. However, existing resources are incomplete. Furthermore, they neither label indications in a structured way nor differentiate them by drug-specific properties such as dosage form, and thus do not support computer processing or semantic interoperability. More recently, several studies have proposed automatic methods to extract structured indications from drug descriptions; however, their performance is limited by natural language challenges in disease named entity recognition and indication selection. In response, we report LabeledIn: a human-reviewed, machine-readable and source-linked catalog of labeled indications for human drugs. More specifically, we describe our semi-automatic approach to derive LabeledIn from drug descriptions through human annotations with aids from automatic methods. As the data source, we use the drug labels (or package inserts) submitted to the FDA by drug manufacturers and made available in DailyMed. Our machine-assisted human annotation workflow comprises: (i) a grouping method to remove redundancy and identify representative drug labels to be used for human annotation, (ii) an automatic method to recognize and normalize mentions of diseases in drug labels as candidate indications, and (iii) a two-round annotation workflow for human experts to judge the pre-computed candidates and deliver the final gold standard. In this study, we focused on 250 highly accessed drugs in PubMed Health, a newly developed public web resource for consumers and clinicians on prevention and treatment of diseases. These 250 drugs corresponded to more than 8000 drug labels (500 unique) in DailyMed in which 2950 candidate indications were pre-tagged by an automatic tool. After being reviewed independently by two experts, 1618 indications were selected, and additional 97 (missed by computer) were manually added, with an inter-annotator agreement of 88.35% as measured by the Kappa coefficient. Our final annotation results in LabeledIn consist of 7805 drug–disease treatment relationships where drugs are represented as a triplet of ingredient, dose form, and strength. A systematic comparison of LabeledIn with an existing computer-derived resource revealed significant discrepancies, confirming the need to involve humans in the creation of such a resource. In addition, LabeledIn is unique in that it contains detailed textual context of the selected indications in drug labels, making it suitable for the development of advanced computational methods for the automatic extraction of indications from free text. Finally, motivated by the studies on drug nomenclature and medication errors in EMRs, we adopted a fine-grained drug representation scheme, which enables the automatic identification of drugs with indications specific to certain dose forms or strengths. Future work includes expanding our coverage to more drugs and integration with other resources. The LabeledIn dataset and the annotation guidelines are available at http://ftp.ncbi.nlm.nih.gov/pub/lu/LabeledIn/ .
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paper_title: PREDICT: a method for inferring novel drug indications with application to personalized medicine

paper_content:

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large-scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross-validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue-specific expression information on the drug targets. We further show that disease-specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease-specific signatures.
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Title: A Survey of Current Trends in Computational Drug Repositioning

Section 1: Introduction
Description 1: Discuss the motivation behind drug repositioning, including its benefits over traditional de novo drug discovery methods, historical context, and recent support for drug repositioning.

Section 2: Genome
Description 2: Highlight the role of genomic and transcriptomic data in drug repositioning, key data sources like Connectivity Map, and methods for utilizing these data, such as 'signature reversion' and 'guilt-by-association'.

Section 3: Phenome
Description 3: Explain how phenotypic information, including side effects and PheWAS approaches, can be used for drug repositioning. Mention integration of phenome data with other types of data.

Section 4: Drug Chemical Structures
Description 4: Summarize methods that leverage chemical structure similarities for drug repositioning, including integrating chemical structure data with other data types.

Section 5: Drug Combinations
Description 5: Discuss the importance of drug combinations in treating complex diseases, computational methods for predicting effective combinations, and examples of successful combinations.

Section 6: Available Data Sources
Description 6: Provide a brief overview of various data sources available for supporting computational drug repositioning.

Section 7: Machine Learning
Description 7: Describe the use of machine learning models in predicting new drug indications based on various features and data types, including drug-related and disease-related features.

Section 8: Network Analysis
Description 8: Explore network-based strategies for computational drug repositioning, such as drug-target networks, drug-disease networks, and protein interaction networks.

Section 9: Text Mining and Semantic Inference
Description 9: Explain how text mining and semantic technologies can help identify new drug indications by extracting and integrating information from literature and databases.

Section 10: Validation for Computational Drug Repositioning
Description 10: Discuss the importance of experimental validation for computational predictions, including in vitro, in vivo models, and electronic health records for validation.

Section 11: Current and Future Target Areas: Cancers, Infectious and Orphan Diseases, and Personalized Medicine
Description 11: Highlight high-potential areas for drug repositioning, such as cancer, infectious diseases, orphan diseases, and personalized medicine, with examples.

Section 12: Discussion
Description 12: Examine the advantages and limitations of various computational repositioning strategies and propose potential integrative approaches for better results.

Section 13: Conclusions
Description 13: Summarize the significance of computational drug repositioning, its potential to accelerate drug discovery, and future directions.