Drug repositioning for orphan diseases

Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit

Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep learning-based method, CGARDP, for predicting drug-related candidate disease indications. CGARDP establishes a feature matrix by exploiting a variety of biological premises related to drugs and diseases. A novel model based on convolutional neural network (CNN) and gated recurrent unit (GRU) is constructed to learn the local and path representations for a drug-disease pair. The CNN-based framework on the left of the model learns the local representation of the drug-disease pair from their feature matrix. As the different paths have discriminative contributions to the drug-disease association prediction, we construct an attention mechanism at the path level to learn the informative paths. In the right part, a GRU-based framework learns the path representation based on path information between the drug and the disease. Cross-validation results indicate that CGARDP performs better than several state-of-the-art methods. Further, CGARDP retrieves more real drug-disease associations in the top part of the prediction result that are of concern to biologists. Case studies on five drugs demonstrate that CGARDP can discover potential drug-related disease indications.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Quantitative analysis of a rare disease network's international contact database and E-repository provides insights into biobanking in the electronic consent era

BACKGROUND

Castleman disease (CD) describes a group of rare and poorly understood lymphoproliferative disorders that include unicentric CD (UCD), Human Herpes Virus-8 (HHV8)-associated multicentric CD (HHV8 + MCD), and HHV8-negative/idiopathic MCD (iMCD). Efforts to advance research and drug discovery for CD have been slowed by challenges shared by other rare diseases, such as collecting and centralizing data and biospecimens for research. To collect disease characteristic data and identify individuals interested in contributing biospecimens for research, a global research organization - the Castleman Disease Collaborative Network (CDCN) - established an international Contact Database and electronic repository (E-repository). Herein, we performed analyses of these datasets to further characterize CD and gain insights into research biospecimen acquisition.

RESULTS

Descriptive statistical analyses were performed on 891 participants from the Contact Database and 166 patients in the E-repository. The median age of patients at the time of enrollment in the Contact Database and E-repository was 42 ± 15.7 and 35 ± 14.8, respectively. The E-repository had increased representation from patients with MCD and the iMCD subtype compared to other sub-groups. Though the majority of participants were from the USA, a total of 49 countries on 6 continents were represented. Several patient characteristics in the Contact Database were associated with subsequent enrollment in the E-repository. There were significantly more MCD patients (p < 0.0001) and females (p = 0.002) enrolled in the E-repository compared to the Contact Database. Patient's year of birth, date of registration, preferred method of communication, and relationship to the patient were also significantly associated with enrollment in the e-Repository.

CONCLUSIONS

This study of the largest- dataset of CD patients worldwide provides insights into disease phenotypes, characteristics of patients interested in contributing data and biospecimens for research, and methods for successfully acquiring data and biospecimens. Generally, the factors associated with enrollment in the E-repository represented severity of disease subtype, proximity to the research, and patient motivation. We hope that these findings and the sample documentation (e.g., electronic consent, recruitment materials) provided with this article will assist future rare disease efforts with overcoming hurdles.

Large-scale data-driven integrative framework for extracting essential targets and processes from disease-associated gene data sets

Populations worldwide currently face several public health challenges, including growing prevalence of infections and the emergence of new pathogenic organisms. The cost and risk associated with drug development make the development of new drugs for several diseases, especially orphan or rare diseases, unappealing to the pharmaceutical industry. Proof of drug safety and efficacy is required before market approval, and rigorous testing makes the drug development process slow, expensive and frequently result in failure. This failure is often because of the use of irrelevant targets identified in the early steps of the drug discovery process, suggesting that target identification and validation are cornerstones for the success of drug discovery and development. Here, we present a large-scale data-driven integrative computational framework to extract essential targets and processes from an existing disease-associated data set and enhance target selection by leveraging drug-target-disease association at the systems level. We applied this framework to tuberculosis and Ebola virus diseases combining heterogeneous data from multiple sources, including protein-protein functional interaction, functional annotation and pharmaceutical data sets. Results obtained demonstrate the effectiveness of the pipeline, leading to the extraction of essential drug targets and to the rational use of existing approved drugs. This provides an opportunity to move toward optimal target-based strategies for screening available drugs and for drug discovery. There is potential for this model to bridge the gap in the production of orphan disease therapies, offering a systematic approach to predict new uses for existing drugs, thereby harnessing their full therapeutic potential.

A review of computational drug repurposing

Although sciences and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In view of these circumstances, ‘drug repurposing’ (or ‘drug repositioning’) has appeared as an alternative tool to accelerate drug development process by seeking new indications for already approved drugs rather than discovering de novo drug compounds, nowadays accounting for 30% of newly marked drugs in the U.S. In the meantime, the explosive and large-scale growth of molecular, genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called computational drug repurposing. This review provides an overview of recent progress in the area of computational drug repurposing. First, it summarizes available repositioning strategies, followed by computational methods commonly used. Then, it describes validation techniques for repurposing studies. Finally, it concludes by discussing the remaining challenges in computational repurposing.

Use of big data in drug development for precision medicine: an update

INTRODUCTION

Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios.

AREAS COVERED

Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery.

EXPERT OPINION

In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.

Integrative microRNA and gene expression analysis identifies new drug repurposing candidates for fetal hemoglobin induction in β-hemoglobinopathies

Therapeutic induction of fetal hemoglobin (HbF) is one of the most promising approaches to ameliorate the severity of hemoglobinopathies like β-thalassemia and sickle cell anemia. Although several pharmacological agents have been investigated for HbF induction in adults, the majority of these are associated with significant side-effects. While drug repurposing is known to open new doors for the use of approved drugs in unexplored clinical conditions, the primary challenge lies in identifying such candidates. In this study, we aimed to identify repurposing candidates for HbF induction using a novel in silico approach utilizing microRNA-pathway-drug relationships. A computational drug repurposing strategy identified several unique candidates for HbF induction; among which Curcumin, Ginsenoside, Valproate, and Vorinostat were found to be most suitable for future trials. This study identified new drug repurposing candidates for HbF induction and demonstrates an easily adaptable methodology that can be used for other pathophysiological conditions.

A systems-level analysis of drug-target-disease associations for drug repositioning

Drug repositioning is the process of finding new therapeutic uses for existing, approved drugs-a process thathas value when considering the exorbitant costs of novel drug development. Several computational strategies exist as a way to predict these alternative applications. In this study, we used datasets on: (1) human biological drug targets and (2) disease-associated genes and, based on a direct functional interaction between them, searched for potential opportunities for drug repositioning. From the set of 1125 unique drug targets and their 88 490 interactions with disease-associated genes, 30 drug targets were analyzed and (3) discussed in detail for the purpose of this article. The current indications of the drugs thattarget them were validated through the interactions, and new opportunities for repositioning were predicted. Among the set of drugs for potential repositioning werebenzodiazepines for the treatment of autism spectrum disorders; nortriptyline for the treatment of melanoma, glioma and other cancers; and vitamin B6 in prevention of spontaneous abortions and cleft palate birth defects. Special emphasis was also placed on those new potential indications that pertained to orphan diseases-these are diseases whose rarity means that development of novel treatment is not financially viable. This computational drug repositioning approach uses existing information on drugs and drug targets, and insights into the genetic basis of disease, as a means to systematically generate the most probable new uses for the drugs on offer, and in this way harness their true therapeutic power.

Drug repositioning screening identifies etravirine as a potential therapeutic for friedreich's ataxia

BACKGROUND

Friedreich's ataxia is an autosomal-recessive cerebellar ataxia caused by mutation of the frataxin gene, resulting in decreased frataxin expression, mitochondrial dysfunction, and oxidative stress. Currently, no treatment is available for Friedreich's ataxia patients. Given that levels of residual frataxin critically affect disease severity, the main goal of a specific therapy for Friedreich's ataxia is to increase frataxin levels.

OBJECTIVES

With the aim to accelerate the development of a new therapy for Friedreich's ataxia, we took a drug repositioning approach to identify market-available drugs able to increase frataxin levels.

METHODS

Using a cell-based reporter assay to monitor variation in frataxin amount, we performed a high-throughput screening of a library containing 853 U.S. Food and Drug Administration-approved drugs.

RESULTS

Among the potentially interesting candidates isolated from the screening, we focused our attention on etravirine, an antiviral drug currently in use as an anti-human immunodeficiency virus therapy. Here, we show that etravirine can promote a significant increase in frataxin levels in cells derived from Friedreich's ataxia patients, by enhancing frataxin messenger RNA translation. Importantly, frataxin accumulation in treated patient cell lines is comparable to frataxin levels in unaffected carrier cells, suggesting that etravirine could be therapeutically relevant. Indeed, etravirine treatment restores the activity of the iron-sulphur cluster containing enzyme aconitase and confers resistance to oxidative stress in cells derived from Friedreich's ataxia patients.

CONCLUSIONS

Considering its excellent safety profile along with its ability to increase frataxin levels and correct some of the disease-related defects, etravirine represents a promising candidate as a therapeutic for Friedreich's ataxia. © 2019 International Parkinson and Movement Disorder Society.

Drug Repositioning by Integrating Known Disease-Gene and Drug-Target Associations in a Semi-supervised Learning Model

Computational drug repositioning has been proven as a promising and efficient strategy for discovering new uses from existing drugs. To achieve this goal, a number of computational methods have been proposed, which are based on different data sources of drugs and diseases. These methods approach the problem using either machine learning- or network-based models with an assumption that similar drugs can be used for similar diseases to identify new indications of drugs. Therefore, similarities between drugs and between diseases are usually used as inputs. In addition, known drug-disease associations are also needed for the methods as prior information. It should be noted that those associations are still not well established due to the fact that many of marketed drugs have been withdrawn and this could affect the outcome of the methods. In this study, we propose a novel method named RLSDR (Regularized Least Square for Drug Repositioning) to find new uses of drugs. More specifically, it relies on a semi-supervised learning model, Regularized Least Square, thus it does not require definition of non-drug-disease associations as previously proposed machine learning-based methods. In addition, the similarity between drugs measured by chemical structures of drug compounds and the similarity between diseases which share phenotypes can be represented in a form of either similarity network or similarity matrix as inputs of the method. Moreover, instead of using a gold-standard set of known drug-disease associations, we construct an artificial set of the associations based on known disease-gene and drug-target associations. Experiment results demonstrate that RLSDR achieves better prediction performance on the artificial set of drug-disease associations than that on the gold-standard ones in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC). In addition, it outperforms two representative network-based methods irrespective of the prior information of drug-disease associations. Novel indications for a number of drugs are also identified and validated by evidences from a different data resource.

Repurposing of Approved Cardiovascular Drugs against Ischemic Cerebrovascular Disease by Disease-Disease Associated Network-Assisted Prediction

Stroke is one of the leading causes of death and disability globally, while intravenous thrombolysis with recombinant tissue plasminogen activator remains the only Food and Drug Administration (FDA)-approved therapy for ischemic stroke. The attempts to develop new treatments for acute ischemic stroke meet costly and spectacularly disappointing results, which requires both long time and high costs, whereas repurposing of safe existing drugs to new indications provides a cost-effective and not time-consuming alternative. Vascular protection is a promising strategy for improving stroke outcome, as vascular function is critical to both cardiovascular diseases (CVD) and ischemic cerebrovascular disease (ICD). Vascular function related biological processes and pathways maybe the critical associations between CVD and ICD. In this study, a multi-database, in silico target identification, gene function enrichment, and network pharmacology analysis integration approach was proposed and applied to investigate the FDA-approved CVD drugs repurposing for ICD. A list of 119 candidate drugs can be obtained for further investigation of their potential in ICD treatment. As a pleiotropic drug with multi-target, carvedilol was set an example to investigate its promising potential for ICD therapy. Our results indicated that the mode of action of carvedilol for ICD treatment may tightly associated with vascular function regulation and the mechanism is multi-target and multi-signaling pathway related. The disease-disease association network-assisted prediction needs further investigations. In summary, the proposed methods herein may provide a promising alternative to inferring novel disease indications for known drugs.

CNS repurposing - Potential new uses for old drugs: Examples of screens for Alzheimer's disease, Parkinson's disease and spasticity

Drug repurposing is recently gaining increasing attention, not just from pharmaceutical companies but also from government agencies in an attempt to generate new medications to address increasing unmet medical needs in a cost effective and expedite manner. There are several approaches to identify novel indications for known drugs. Many are based on rational selection e.g. the known or a new mechanism of action of a drug. This review will focus rather on phenotypic or high content screening of compounds in models that are believed to be predictive of effectiveness of compounds irrespective of their mechanism of action. Three short cases studies of screens for Alzheimer's disease, Parkinson's disease and spasticity will be given as examples. This article is part of the Special Issue entitled 'Drug Repurposing: old molecules, new ways to fast track drug discovery and development for CNS disorders'.

Review of Drug Repositioning Approaches and Resources

Drug discovery is a time-consuming, high-investment, and high-risk process in traditional drug development. Drug repositioning has become a popular strategy in recent years. Different from traditional drug development strategies, the strategy is efficient, economical and riskless. There are usually three kinds of approaches: computational approaches, biological experimental approaches, and mixed approaches, all of which are widely used in drug repositioning. In this paper, we reviewed computational approaches and highlighted their characteristics to provide references for researchers to develop more powerful approaches. At the same time, the important findings obtained using these approaches are listed. Furthermore, we summarized 76 important resources about drug repositioning. Finally, challenges and opportunities in drug repositioning are discussed from multiple perspectives, including technology, commercial models, patents and investment.

Insights into the pharmaceuticals and mechanisms of neurological orphan diseases: Current Status and future expectations

Several rare or orphan diseases have been characterized that singly affect low numbers of people, but cumulatively reach ∼6%-10% of the population in Europe and in the United States. Human genetics has shown to be broadly effective when evaluating subjacent genetic defects such as orphan genetic diseases, but on the other hand, a modest progress has been achieved toward comprehending the molecular pathologies and designing new therapies. Chemical genetics, placed at the interface of chemistry and genetics, could be employed to understand the molecular mechanisms of subjacent illnesses and for the discovery of new remediation processes. This review debates current progress in chemical genetics, and how a variety of compounds and reaction mechanisms can be used to study and ultimately treat rare genetic diseases. We focus here on a study involving Amyotrophic lateral sclerosis (ALS), Duchenne Muscular Dystrophy (DMD), Spinal muscular atrophy (SMA) and Familial Amyloid Polyneuropathy (FAP), approaching different treatment methods and the reaction mechanisms of several compounds, trying to elucidate new routes capable of assisting in the treatment profile.

Changing Trends in Computational Drug Repositioning

Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocated-"The most fruitful basis for the discovery of a new drug is to start with an old drug". However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of "serendipity", discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodules-pharmacologically relevant sub-networks of biomolecules and/or pathways-from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning.

Comparative assessment of strategies to identify similar ligand-binding pockets in proteins

Background

Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges.

Results

We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock.

Conclusions

Thorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5–10%. All data reported in this paper are freely available at https://osf.io/6ngbs/.

Evolutionary and genetic features of drug targets

In the modern drug discovery pipeline, identification of novel drug targets is a critical step. Despite rapid progress in developing biomedical techniques, it is still a great challenge to find promising new targets from the ample space of human genes. This fact is partially responsible for the situation of "more investments, fewer drugs" in the pharmaceutical industry. A series of recent researches revealed that successfully targeted genes share some common evolutionary and genetic features, which means that the knowledge accumulated in modern evolutionary biology and genetics is very helpful to identify potential drug targets and to find new drugs as well. In this article, we comprehensively summarize the links between human drug targets and genetic diseases and their evolutionary origins, with an attempt to introduce these novel concepts and their medical implications to the biomedical community.

A bibliometric review of drug repurposing

We have conducted a bibliometric review of drug repurposing by scanning >25 million papers in PubMed and using text-mining methods to gather, count and analyze chemical-disease therapeutic relationships. We find that >60% of the ∼35,000 drugs or drug candidates identified in our study have been tried in more than one disease, including 189 drugs that have been tried in >300 diseases each. Whereas in the majority of cases these drugs were applied in therapeutic areas close to their original use, there have been striking, and perhaps instructive, successful attempts of drug repurposing for unexpected, novel therapeutic areas.

eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs

About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/.

Rare Diseases: Drug Discovery and Informatics Resource

A rare disease refers to any disease with very low prevalence individually. Although the impacted population is small for a single disease, more than 6000 rare diseases affect millions of people across the world. Due to the small market size, high cost and possibly low return on investment, only in recent years, the research and development of rare disease drugs have gradually risen globally, in several domains including gene therapy, enzyme replacement therapy, and drug repositioning. Due to the complex etiology and heterogeneous symptoms, there is a large gap between basic research and patient unmet needs for rare disease drug discovery. As computational biology increasingly arises researchers' awareness, the informatics database on rare disease have grown rapidly in the recent years, including drug targets, genetic variant and mutation, phenotype and ontology and patient registries. Along with the advances of informatics database and networks, new computational models will help accelerate the target identification and lead optimization process for rare disease pre-clinical drug development.

Computational drug repositioning for rare diseases in the era of precision medicine

There are tremendous unmet needs in drug development for rare diseases. Computational drug repositioning is a promising approach and has been successfully applied to the development of treatments for diseases. However, how to utilize this knowledge and effectively conduct and implement computational drug repositioning approaches for rare disease therapies is still an open issue. Here, we focus on the means of utilizing accumulated genomic data for accelerating and facilitating drug repositioning for rare diseases. First, we summarize the current genome landscape of rare diseases. Second, we propose several promising bioinformatics approaches and pipelines for computational drug repositioning for rare diseases. Finally, we discuss recent regulatory incentives and other enablers in rare disease drug development and outline the remaining challenges.

Drug repurposing in idiopathic pulmonary fibrosis filtered by a bioinformatics-derived composite score

Idiopathic Pulmonary Fibrosis (IPF) is a rare disease of the respiratory system in which the lungs stiffen and get scarred, resulting in breathing weakness and eventually leading to death. Drug repurposing is a process that provides evidence for existing drugs that may also be effective in different diseases. In this study, we present a computational pipeline having as input a number of gene expression datasets from early and advanced stages of IPF and as output lists of repurposed drugs ranked with a novel composite score. We have devised and used a scoring formula in order to rank the repurposed drugs, consolidating the standard repurposing score with structural, functional and side effects' scores for each drug per stage of IPF. The whole pipeline involves the selection of proper gene expression datasets, data preprocessing and statistical analysis, selection of the most important genes related to the disease, analysis of biological pathways, investigation of related molecular mechanisms, identification of fibrosis-related microRNAs, drug repurposing, structural and literature-based analysis of the repurposed drugs.

A signalome screening approach in the autoinflammatory disease TNF receptor associated periodic syndrome (TRAPS) highlights the anti-inflammatory properties of drugs for repurposing

TNF receptor associated periodic syndrome (TRAPS) is an autoinflammatory disease caused by mutations in TNF Receptor 1 (TNFR1). Current therapies for TRAPS are limited and do not target the pro-inflammatory signalling pathways that are central to the disease mechanism. Our aim was to identify drugs for repurposing as anti-inflammatories based on their ability to down-regulate molecules associated with inflammatory signalling pathways that are activated in TRAPS. This was achieved using rigorously optimized, high through-put cell culture and reverse phase protein microarray systems to screen compounds for their effects on the TRAPS-associated inflammatory signalome. 1360 approved, publically available, pharmacologically active substances were investigated for their effects on 40 signalling molecules associated with pro-inflammatory signalling pathways that are constitutively upregulated in TRAPS. The drugs were screened at four 10-fold concentrations on cell lines expressing both wild-type (WT) TNFR1 and TRAPS-associated C33Y mutant TNFR1, or WT TNFR1 alone; signalling molecule levels were then determined in cell lysates by the reverse-phase protein microarray. A novel mathematical methodology was developed to rank the compounds for their ability to reduce the expression of signalling molecules in the C33Y-TNFR1 transfectants towards the level seen in the WT-TNFR1 transfectants. Seven high-ranking drugs were selected and tested by RPPA for effects on the same 40 signalling molecules in lysates of peripheral blood mononuclear cells (PBMCs) from C33Y-TRAPS patients compared to PBMCs from normal controls. The fluoroquinolone antibiotic lomefloxacin, as well as others from this class of compounds, showed the most significant effects on multiple pro-inflammatory signalling pathways that are constitutively activated in TRAPS; lomefloxacin dose-dependently significantly reduced expression of 7/40 signalling molecules across the Jak/Stat, MAPK, NF-κB and PI3K/AKT pathways. This study demonstrates the power of signalome screening for identifying candidates for drug repurposing.

Clinical dosage of meclozine promotes longitudinal bone growth, bone volume, and trabecular bone quality in transgenic mice with achondroplasia

Achondroplasia (ACH) is the most common short-limbed skeletal dysplasia caused by gain-of-function mutations in the fibroblast growth factor receptor 3 (FGFR3). No effective FGFR3-targeted therapies for ACH are currently available. By drug repositioning strategies, we identified that meclozine, which has been used as an anti-motion-sickness, suppressed FGFR3 signaling in chondrocytes and rescued short-limbed phenotype in ACH mouse model. Here, we conducted various pharmacological tests for future clinical application in ACH. Pharmacokinetic analyses demonstrated that peak drug concentration (C_max) and area under the concentration-time curve (AUC) of 2 mg/kg of meclozine to mice was lower than that of 25 mg/body to human, which is a clinical usage for anti-motion-sickness. Pharmacokinetic simulation studies showed that repeated dose of 2 mg/kg of meclozine showed no accumulation effects. Short stature phenotype in the transgenic mice was significantly rescued by twice-daily oral administration of 2 mg/kg/day of meclozine. In addition to stimulation of longitudinal bone growth, bone volume and metaphyseal trabecular bone quality were improved by meclozine treatment. We confirmed a preclinical proof of concept for applying meclozine for the treatment of short stature in ACH, although toxicity and adverse events associated with long-term administration of this drug should be examined.

Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification

Drug repositioning strategies have improved substantially in recent years. At present, two advances are poised to facilitate new strategies. First, the LINCS project can provide rich transcriptome data that reflect the responses of cells upon exposure to various drugs. Second, machine learning algorithms have been applied successfully in biomedical research. In this paper, we developed a systematic method to discover novel indications for existing drugs by approaching drug repositioning as a multi-label classification task and used a Softmax regression model to predict previously unrecognized therapeutic properties of drugs based on LINCS transcriptome data. This approach to complete the said task has not been achieved in previous studies. By performing in silico comparison, we demonstrated that the proposed Softmax method showed markedly superior performance over those of other methods. Once fully trained, the method showed a training accuracy exceeding 80% and a validation accuracy of approximately 70%. We generated a highly credible set of 98 drugs with high potential to be repositioned for novel therapeutic purposes. Our case studies included zonisamide and brinzolamide, which were originally developed to treat indications of the nervous system and sensory organs, respectively. Both drugs were repurposed to the cardiovascular category.

High-content drug screening for rare diseases

Per definition, rare diseases affect only a small number of subjects within a given population. Taken together however, they represent a considerable medical burden, which remains poorly addressed in terms of treatment. Compared to other diseases, obstacles to the development of therapies for rare diseases include less extensive physiopathology knowledge, limited number of patients to test treatments, and poor commercial interest from the industry. Recently, advances in high-throughput and high-content screening (HTS and HCS) have been fostered by the development of specific routines that use robot- and computer-assisted technologies to automatize tasks, allowing screening of a large number of compounds in a short period of time, using experimental model of diseases. These approaches are particularly relevant for drug repositioning in rare disease, which restricts the search to compounds that have already been tested in humans, thereby reducing the need for extensive preclinical tests. In the future, these same tools, combined with computational modeling and artificial neural network analyses, may also be used to predict individual clinical responses to drugs in a personalized medicine approach.

DrugSig: A resource for computational drug repositioning utilizing gene expression signatures

Computational drug repositioning has been proved as an effective approach to develop new drug uses. However, currently existing strategies strongly rely on drug response gene signatures which scattered in separated or individual experimental data, and resulted in low efficient outputs. So, a fully drug response gene signatures database will be very helpful to these methods. We collected drug response microarray data and annotated related drug and targets information from public databases and scientific literature. By selecting top 500 up-regulated and down-regulated genes as drug signatures, we manually established the DrugSig database. Currently DrugSig contains more than 1300 drugs, 7000 microarray and 800 targets. Moreover, we developed the signature based and target based functions to aid drug repositioning. The constructed database can serve as a resource to quicken computational drug repositioning. Database URL: http://biotechlab.fudan.edu.cn/database/drugsig/.

Proteasome inhibitors to alleviate aberrant IKBKAP mRNA splicing and low IKAP/hELP1 synthesis in familial dysautonomia

FD is a rare neurodegenerative disorder caused by a mutation of the IKBKAP gene, which induces low expression levels of the Elongator subunit IKAP/hELP1 protein. A rational strategy for FD treatment could be to identify drugs increasing IKAP/hELP1 expression levels by blocking protein degradation pathways such as the 26S proteasome. Proteasome inhibitors are promising molecules emerging in cancer treatment and could thus constitute an enticing pharmaceutical strategy for FD treatment. Therefore, we tested three proteasome inhibitors on FD human olfactory ecto-mesenchymal stem cells (hOE-MSCs): two approved by the Food and Drug Administration (FDA) and European Medicines Agency (EMA), bortezomib and carfilzomib, as well as epoxomicin. Although all 3 inhibitors demonstrated activity in correcting IKBKAP mRNA aberrant splicing, carfilzomib was superior in enhancing IKAP/hELP1 quantity. Moreover, we observed a synergistic effect of suboptimal doses of carfilzomib on kinetin in improving IKBKAP isoforms ratio and IKAP/hELP1 expression levels allowing to counterbalance carfilzomib toxicity. Finally, we identified several dysregulated miRNAs after carfilzomib treatment that target proteasome-associated mRNAs and determined that IKAP/hELP1 deficiency in FD pathology is correlated to an overactivity of the 26S proteasome. Altogether, these results reinforce the rationale for using chemical compounds inhibiting the 26S proteasome as an innovative option for FD and a promising therapeutic pathway for many other neurodegenerative diseases.

Repurposing of approved cardiovascular drugs

Research and development of new drugs requires both long time and high costs, whereas safety and tolerability profiles make the success rate of approval very low. Drug repurposing, applying known drugs and compounds to new indications, has been noted recently as a cost-effective and time-unconsuming way in developing new drugs, because they have already been proven safe in humans. In this review, we discuss drug repurposing of approved cardiovascular drugs, such as aspirin, beta-blockers, angiotensin converting enzyme inhibitors, angiotensin II receptor blockers, cardiac glycosides and statins. Regarding anti-tumor activities of these agents, a number of experimental studies have demonstrated promising pleiotropic properties, whereas all clinical trials have not shown expected results. In pathological conditions other than cancer, repurposing of cardiovascular drugs is also expanding. Numerous experimental studies have reported possibilities of drug repurposing in this field and some of them have been tried for new indications ('bench to bedside'), while unexpected results of clinical studies have given hints for drug repurposing and some unknown mechanisms of action have been demonstrated by experimental studies ('bedside to bench'). The future perspective of experimental and clinical studies using cardiovascular drugs are also discussed.

Anti-sarcopenic effects of diamino-diphenyl sulfone observed in elderly female leprosy survivors: a cross-sectional study

BACKGROUND

It has been reported that 4,4'-diamino-diphenyl sulfone (DDS), the longtime treatment of choice for leprosy, prolongs the lifespan and increases mobility in animal models by reducing the levels of reactive oxygen species and inhibiting muscle pyruvate kinase activity. This study aimed to investigate whether sarcopenic status in leprosy survivors was influenced by recent history of DDS medication.

METHODS

Forty-one elderly female leprosy survivors were recruited. The DDS group was defined as survivors who had been taking the drug for the past year or more. Body composition measured by dual energy X-ray absorptiometry, limb muscle strength, short physical performance battery, and International Physical Activity Questionnaire in Korean were compared.

RESULTS

The DDS group tended to have higher skeletal muscle mass index (24.4 ± 2.7 vs. 22.6 ± 2.2%, P = 0.066) and regional skeletal muscle mass index in non-dominant leg (8.9 ± 1.0 vs. 7.9 ± 0.9%, P = 0.018) than those of the control group although they had significantly worse leprosy disability than the control group (P = 0.027). The DDS group had greater strength than the control group in non-dominant shoulder abductor, elbow flexor, hip flexor, and knee extensor (P = 0.005, P = 0.029, P = 0.021, and P = 0.002, respectively). Weekly walking amount was significantly longer (P = 0.020) in the DDS group than the control group. The total lifetime DDS exposure significantly correlated with skeletal muscle mass of the lower extremity in non-dominant leg (r = 0.379, P = 0.015).

CONCLUSIONS

DDS-taking leprosy survivors had larger skeletal muscle mass and greater muscle strength over non-taking survivors. There was a dose-response relationship between total lifetime DDS exposure and skeletal muscle mass of lower extremity. These findings might suggest potential anti-sarcopenic effects of DDS.

In silico methods for drug repurposing and pharmacology

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.

Resveratrol and Myopathy

Resveratrol is a natural polyphenolic compound produced by plants under various stress conditions. Resveratrol has been reported to exhibit antioxidant, anti-inflammatory, and anti-proliferative properties in mammalian cells and animal models, and might therefore exert pleiotropic beneficial effects in different pathophysiological states. More recently, resveratrol has also been shown to potentially target many mitochondrial metabolic pathways, including fatty acid β-oxidation or oxidative phosphorylation, leading to the up-regulation of the energy metabolism via signaling pathways involving PGC-1α, SIRT1, and/or AMP-kinase, which are not yet fully delineated. Some of resveratrol beneficial effects likely arise from its cellular effects in the skeletal muscle, which, surprisingly, has been given relatively little attention, compared to other target tissues. Here, we review the potential for resveratrol to ameliorate or correct mitochondrial metabolic deficiencies responsible for myopathies, due to inherited fatty acid β-oxidation or to respiratory chain defects, for which no treatment exists to date. We also review recent data supporting therapeutic effects of resveratrol in the Duchenne Muscular Dystrophy, a fatal genetic disease affecting the production of muscle dystrophin, associated to a variety of mitochondrial dysfunctions, which likely contribute to disease pathogenesis.

High-throughput screening for modulators of ACVR1 transcription: discovery of potential therapeutics for fibrodysplasia ossificans progressiva

The ACVR1 gene encodes a type I receptor of bone morphogenetic proteins (BMPs). Activating mutations in ACVR1 are responsible for fibrodysplasia ossificans progressiva (FOP), a rare disease characterized by congenital toe malformation and progressive heterotopic endochondral ossification leading to severe and cumulative disability. Until now, no therapy has been available to prevent soft-tissue swelling (flare-ups) that trigger the ossification process. With the aim of finding a new therapeutic strategy for FOP, we developed a high-throughput screening (HTS) assay to identify inhibitors of ACVR1 gene expression among drugs already approved for the therapy of other diseases. The screening, based on an ACVR1 promoter assay, was followed by an in vitro and in vivo test to validate and characterize candidate molecules. Among compounds that modulate the ACVR1 promoter activity, we selected the one showing the highest inhibitory effect, dipyridamole, a drug that is currently used as a platelet anti-aggregant. The inhibitory effect was detectable on ACVR1 gene expression, on the whole Smad-dependent BMP signaling pathway, and on chondrogenic and osteogenic differentiation processes by in vitro cellular assays. Moreover, dipyridamole reduced the process of heterotopic bone formation in vivo. Our drug repositioning strategy has led to the identification of dipyridamole as a possible therapeutic tool for the treatment of FOP. Furthermore, our study has also defined a pipeline of assays that will be useful for the evaluation of other pharmacological inhibitors of heterotopic ossification.

Summary: We describe the identification of dipyridamole as a potential therapeutic tool for FOP, through a series of in vitro and in vivo assays to screen and validate FDA-approved compounds.

KCa 3.1-a microglial target ready for drug repurposing?

Over the past decade, glial cells have attracted attention for harboring unexploited targets for drug discovery. Several glial targets have attracted de novo drug discovery programs, as highlighted in this GLIA Special Issue. Drug repurposing, which has the objective of utilizing existing drugs as well as abandoned, failed, or not yet pursued clinical development candidates for new indications, might provide a faster opportunity to bring drugs for glial targets to patients with unmet needs. Here, we review the potential of the intermediate-conductance calcium-activated potassium channels KCa 3.1 as the target for such a repurposing effort. We discuss the data on KCa 3.1 expression on microglia in vitro and in vivo and review the relevant literature on the two KCa 3.1 inhibitors TRAM-34 and Senicapoc. Finally, we provide an outlook of what it might take to harness the potential of KCa 3.1 as a bona fide microglial drug target. GLIA 2016;64:1733-1741.

Protein localization vector propagation: a method for improving the accuracy of drug repositioning

Identifying alternative indications for known drugs is important for the pharmaceutical industry. Many computational methods have been proposed for predicting unknown associations between drugs and target proteins associated with diseases. To produce better prediction, researchers should not only develop accurate algorithms but identify good features that reflect intracellular systems. In this paper, we proposed a novel method for exploiting protein localization. We generated localization vectors (LVs) from protein localization and propagated LVs through a protein interaction network to increase the coverage of the localization information. The LVs showed distinct patterns among targets of known drugs as well as independent characteristics compared to existing features. Based on the experimental results, we determined that including LVs improves cross-validation accuracy and, produces better novel predictions with real and independent clinical trial data. Moreover, the propagation of LVs showed a positive result that it can help in increasing the coverage of the prediction results.

Matched analysis on orphan drug designations and approvals: cross regional analysis in the United States, the European Union, and Japan

Orphan drugs have become a key area of focus in drug development for resolving unmet medical needs. The Orphan Drug Act in the USA and similar legislation in Japan, the European Union (EU), and several other countries has been enacted since 1983. This study provides a quantitative review of all orphan drug designations and approvals since the implementation of orphan drug legislation in key three regions. This study also identified and reviewed 'commonly designated' drugs across regions. Out of approximately 5000 designations, approximately 800 designations were common among the USA, EU, and/or Japan. Regional similarities, differences, and trends were identified. It is important to understand these aspects and the crucial role of orphan drug designation in global drug development.

A Practical Guide for Exploring Opportunities of Repurposing Drugs for CNS Diseases in Systems Biology

Systems biology has shown its potential in facilitating pathway-focused therapy development for central nervous system (CNS) diseases. An integrated network can be utilized to explore the multiple disease mechanisms and to discover repositioning opportunities. This review covers current therapeutic gaps for CNS diseases and the role of systems biology in pharmaceutical industry. We conclude with a Multiple Level Network Modeling (MLNM) example to illustrate the great potential of systems biology for CNS diseases. The system focuses on the benefit and practical applications in pathway centric therapy and drug repositioning.

Repurposing of rutin for the inhibition of norovirus replication

Drug repurposing is a strategy employed to circumvent some of the bottlenecks involved in drug development, such as the cost and time needed for developing new molecular entities. Noroviruses cause recurrent epidemics and sporadic outbreaks of gastroenteritis associated with significant mortality and economic costs, but no treatment has been approved to date. Herein, a library of molecules previously used in humans was screened to find compounds with anti-noroviral activity. Antiviral testing for four selected compounds against murine norovirus infection revealed that rutin has anti-murine norovirus activity in cell-based assays.

DrugNet: network-based drug-disease prioritization by integrating heterogeneous data

OBJECTIVE

Computational drug repositioning can lead to a considerable reduction in cost and time in any drug development process. Recent approaches have addressed the network-based nature of biological information for performing complex prioritization tasks. In this work, we propose a new methodology based on heterogeneous network prioritization that can aid researchers in the drug repositioning process.

METHODS

We have developed DrugNet, a new methodology for drug-disease and disease-drug prioritization. Our approach is based on a network-based prioritization method called ProphNet which has recently been developed by the authors. ProphNet is able to integrate data from complex networks involving a wide range of types of elements and interactions. In this work, we built a network of interconnected drugs, proteins and diseases and applied DrugNet to different types of tests for drug repositioning.

RESULTS

We tested the performance of our approach on different validation tests, including cross validation and tests based on real clinical trials. DrugNet achieved a mean AUC value of 0.9552±0.0015 in 5-fold cross validation tests, and a mean AUC value of 0.8364 for tests based on recent clinical trials (phases 0-4) not present in our data. These results suggest that DrugNet could be very useful for discovering new drug uses. We also studied specific cases of particular interest, proving the benefits of heterogeneous data integration in this problem.

CONCLUSIONS

Our methodology suggests that new drugs can be repositioned by generating ranked lists of drugs based on a given disease query or vice versa. Our study shows that the simultaneous integration of information about diseases, drugs and targets can lead to a significant improvement in drug repositioning tasks. DrugNet is available as a web tool from http://genome2.ugr.es/drugnet/ (accessed 23.09.14). Matlab source code is also available on the website.

In Vitro Screening for Drug Repositioning

Drug repositioning or repurposing has received much coverage in the scientific literature in recent years and has been responsible for the generation of both new intellectual property and investigational new drug submissions. The literature indicates a significant trend toward the use of computational- or informatics-based methods for generating initial repositioning hypotheses, followed by focused assessment of biological activity in phenotypic assays. Another viable method for drug repositioning is in vitro screening of known drugs or drug-like molecules, initially in disease-relevant phenotypic assays, to identify and validate candidates for repositioning. This approach can use large compound libraries or can focus on subsets of known drugs or drug-like molecules. In this short review, we focus on ways to generate and validate repositioning candidates in disease-related in vitro and phenotypic assays, and we discuss specific examples of this approach as applied to a variety of disease areas. We propose that in vitro screens offer several advantages over biochemical or in vivo methods as a starting point for drug repositioning.

Strategy for identifying repurposed drugs for the treatment of cerebral cavernous malformation

BACKGROUND

Cerebral cavernous malformation (CCM) is a hemorrhagic stroke disease affecting up to 0.5% of North Americans that has no approved nonsurgical treatment. A subset of patients have a hereditary form of the disease due primarily to loss-of-function mutations in KRIT1, CCM2, or PDCD10. We sought to identify known drugs that could be repurposed to treat CCM.

METHODS AND RESULTS

We developed an unbiased screening platform based on both cellular and animal models of loss of function of CCM2. Our discovery strategy consisted of 4 steps: an automated immunofluorescence and machine-learning-based primary screen of structural phenotypes in human endothelial cells deficient in CCM2, a secondary screen of functional changes in endothelial stability in these same cells, a rapid in vivo tertiary screen of dermal microvascular leak in mice lacking endothelial Ccm2, and finally a quaternary screen of CCM lesion burden in these same mice. We screened 2100 known drugs and bioactive compounds and identified 2 candidates, cholecalciferol (vitamin D3) and tempol (a scavenger of superoxide), for further study. Each drug decreased lesion burden in a mouse model of CCM vascular disease by ≈50%.

CONCLUSIONS

By identifying known drugs as potential therapeutics for CCM, we have decreased the time, cost, and risk of bringing treatments to patients. Each drug also prompts additional exploration of biomarkers of CCM disease. We further suggest that the structure-function screening platform presented here may be adapted and scaled to facilitate drug discovery for diverse loss-of-function genetic vascular disease.