'RE:fine drugs': an interactive dashboard to access drug repurposing opportunities

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA Collapse

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Grants

• R01 HL143885 NHLBI NIH HHS
• R01 HL163262 NHLBI NIH HHS
• R01 HL142302 NHLBI NIH HHS
• R01 HL151152 NHLBI NIH HHS
• N01HC25195 NHLBI NIH HHS
• R01 HG010297 NHGRI NIH HHS
• R01 HL092577 NHLBI NIH HHS
• P20 MD000170 NIMHD NIH HHS
• 75N92019D00031 NHLBI NIH HHS
• R01 HD057194 NICHD NIH HHS
• UL1 TR000371 NCATS NIH HHS
• R01 DK122503 NIDDK NIH HHS

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Affiliation(s)

Hanfei Xu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Shreyash Gupta Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ian Dinsmore Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Abbey Kollu Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC, 27599, USA
Anne Marie Cawley Marsico Lung Institute, University of North Carolina, 125 Mason Farm Rd, Chapel Hill, NC, 27599, USA
Mohammad Y. Anwar Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Hung-Hsin Chen Institute of Biomedical Sciences, Academia Sinica, No. 128, Section 2, Academia Rd., Taipei, Nangang District, 115201, Taiwan Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Lauren E. Petty Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Sudha Seshadri Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio, 8300 Floyd Curl Drive, San Antonio, TX, 78229, USA
Misa Graff Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Piper Below Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Jennifer A. Brody Department of Medicine, Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Seattle, WA, 98101, USA
Geetha Chittoor Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Susan P. Fisher-Hoch Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Nancy L. Heard-Costa Framingham Heart Study, 73 Mt Wayte Ave, Framingham, MA, 01702, USA Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA
Daniel Levy Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, 6701 Rockledge Drive, Bethesda, MD, 20892, USA
Honghuang Lin Department of Medicine, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, USA
Ruth JF. Loos Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3A, 2200, Copenhagen, Denmark
Joseph B. Mccormick Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Jerome I. Rotter Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA
Tooraj Mirshahi Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Christopher D. Still Center for Obesity and Metabolic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Anita Destefano Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA
L. Adrienne Cupples Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Karen L Mohlke Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA
Kari E. North Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Anne E. Justice Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ching-Ti Liu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA

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The landscape of the methodology in drug repurposing using human genomic data: a systematic review

The process of drug development is expensive and time-consuming. In contrast, drug repurposing can be introduced to clinical practice more quickly and at a reduced cost. Over the last decade, there has been a significant expansion of large biobanks that link genomic data to electronic health record data, public availability of various databases containing biological and clinical information and rapid development of novel methodologies and algorithms in integrating different sources of data. This review aims to provide a thorough summary of different strategies that utilize genomic data to seek drug-repositioning opportunities. We searched MEDLINE and EMBASE databases to identify eligible studies up until 1 May 2023, with a total of 102 studies finally included after two-step parallel screening. We summarized commonly used strategies for drug repurposing, including Mendelian randomization, multi-omic-based and network-based studies and illustrated each strategy with examples, as well as the data sources implemented. By leveraging existing knowledge and infrastructure to expedite the drug discovery process and reduce costs, drug repurposing potentially identifies new therapeutic uses for approved drugs in a more efficient and targeted manner. However, technical challenges when integrating different types of data and biased or incomplete understanding of drug interactions are important hindrances that cannot be disregarded in the pursuit of identifying novel therapeutic applications. This review offers an overview of drug repurposing methodologies, providing valuable insights and guiding future directions for advancing drug repurposing studies.

Identification of Drug Compounds for Capsular Contracture Based on Text Mining and Deep Learning

BACKGROUND

Capsular contracture is a common and unpredictable complication after breast implant placement. Currently, the pathogenesis of capsular contracture is unclear, and the effectiveness of nonsurgical treatment is still doubtful. The authors' study aimed to investigate new drug therapies for capsular contracture by using computational methods.

METHODS

Genes related to capsular contracture were identified by text mining and GeneCodis. Then, the candidate key genes were selected through protein-protein interaction analysis in Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape. Drugs targeting the candidate genes with relation to capsular contracture were screened out in Pharmaprojects. Based on the drug-target interaction analysis by DeepPurpose, candidate drugs with highest predicted binding affinity were obtained eventually.

RESULTS

The authors' study identified 55 genes related to capsular contracture. Gene set enrichment analysis and protein-protein interaction analysis generated eight candidate genes. One hundred drugs targeting the candidate genes were selected. The seven candidate drugs with the highest predicted binding affinity were determined by DeepPurpose, including tumor necrosis factor alpha antagonist, estrogen receptor agonist, insulin-like growth factor 1 receptor, tyrosine kinase inhibitor, and matrix metallopeptidase 1 inhibitor.

CONCLUSION

Text mining and DeepPurpose can be used as a promising tool for drug discovery in exploring nonsurgical treatment to capsular contracture.

CLINICAL QUESTION/LEVEL OF EVIDENCE

Therapeutic, V.

StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit

Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.

The druggable schizophrenia genome: from repurposing opportunities to unexplored drug targets

There have been no new drugs for the treatment of schizophrenia in several decades and treatment resistance represents a major unmet clinical need. The drugs that exist are based on serendipitous clinical observations rather than an evidence-based understanding of disease pathophysiology. In the present review, we address these bottlenecks by integrating common, rare, and expression-related schizophrenia risk genes with knowledge of the druggability of the human genome as a whole. We highlight novel drug repurposing opportunities, clinical trial candidates which are supported by genetic evidence, and unexplored therapeutic opportunities in the lesser-known regions of the schizophrenia genome. By identifying translational gaps and opportunities across the schizophrenia disease space, we discuss a framework for translating increasingly well-powered genetic association studies into personalized treatments for schizophrenia and initiating the vital task of characterizing clinically relevant drug targets in underexplored regions of the human genome.

Text Mining-Based Drug Discovery for Connective Tissue Disease–Associated Pulmonary Arterial Hypertension

Background: The current medical treatments for connective tissue disease–associated pulmonary arterial hypertension (CTD-PAH) do not show favorable efficiency for all patients, and identification of novel drugs is desired.

Methods: Text mining was performed to obtain CTD- and PAH-related gene sets, and the intersection of the two gene sets was analyzed for functional enrichment through DAVID. The protein–protein interaction network of the overlapping genes and the significant gene modules were determined using STRING. The enriched candidate genes were further analyzed by Drug Gene Interaction database to identify drugs with potential therapeutic effects on CTD-PAH.

Results: Based on text mining analysis, 179 genes related to CTD and PAH were identified. Through enrichment analysis of the genes, 20 genes representing six pathways were obtained. To further narrow the scope of potential existing drugs, we selected targeted drugs with a Query Score ≥5 and Interaction Score ≥1. Finally, 13 drugs targeting the six genes were selected as candidate drugs, which were divided into four drug–gene interaction types, and 12 of them had initial drug indications approved by the FDA. The potential gene targets of the drugs on this list are IL-6 (one drug) and IL-1β (two drugs), MMP9 (one drug), VEGFA (three drugs), TGFB1 (one drug), and EGFR (five drugs). These drugs might be used to treat CTD-PAH.

Conclusion: We identified 13 drugs targeting six genes that may have potential therapeutic effects on CTD-PAH.

Computational Methods for Drug Repurposing

The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.

Past, Present, and Future of Therapies for Pituitary Neuroendocrine Tumors: Need for Omics and Drug Repositioning Guidance

Innovation roadmaps are important, because they encourage the actors in an innovation ecosystem to creatively imagine multiple possible science future(s), while anticipating the prospects and challenges on the innovation trajectory. In this overarching context, this expert review highlights the present unmet need for therapeutic innovations for pituitary neuroendocrine tumors (PitNETs), also known as pituitary adenomas. Although there are many drugs used in practice to treat PitNETs, many of these drugs can have negative side effects and show highly variable outcomes in terms of overall recovery. Building innovation roadmaps for PitNETs' treatments can allow incorporation of systems biology approaches to bring about insights at multiple levels of cell biology, from genes to proteins to metabolites. Using the systems biology techniques, it will then be possible to offer potential therapeutic strategies for the convergence of preventive approaches and patient-centered disease treatment. Here, we first provide a comprehensive overview of the molecular subtypes of PitNETs and therapeutics for these tumors from the past to the present. We then discuss examples of clinical trials and drug repositioning studies and how multi-omics studies can help in discovery and rational development of new therapeutics for PitNETs. Finally, this expert review offers new public health and personalized medicine approaches on cases that are refractory to conventional treatment or recur despite currently used surgical and/or drug therapy.

Effects of Apremilast on Induced Hypertrophic Scar of Rabbits

The present study aimed to assess the effect of Apremilast on experimentally induced hypertrophic scars in rabbits. A total of 40 healthy male New Zealand White rabbits between 6 and 12 months of age were assigned to four groups (n=10). Group I consists of apparently healthy control rabbits, in group II, the rabbits with an induced hypertrophic scar received no treatment, except for base gel. In group III, the rabbits with induced hypertrophic scar were treated with triamcinolone acetonide (TAC) 0.1% as standard medication. In group IV, rabbits with induced hypertrophic scars were treated with Apremilast 5%. On the first day, four surgical incisions were made using an 8-mm biopsy punch on the ventral surface of the rabbit ear down to cartilage. The TAC and Apremilast were topically administered to the developed scars on day 31. The results included an examination of skin histopathology, the level of transforming growth factor beta-1 (TGF-β1), and collagen III in skin tissue. In the treatments, the inflammatory score, scar index, as well as immunological scores of TGFβ1 and collagen III, significantly decreased, compared to the hypertrophic induced scar group (P≤ 0.001). Moreover, there was a significant reduction in fibroblast count, compared to the group of induced hypertrophic scars (P<0.05). Apremilast was efficacious in the treatment of hypertrophic scars due to its ability to reduce inflammations and fibroblast counts and scar index. Nonetheless, the reduction of immunological scores was almost comparable to that of topical TAC.

Fenfluramine as antiseizure medication for epilepsy

Fenfluramine hydrochloride has classically been described as acting pharmacologically through a serotonergic mechanism. Therefore, it was initially used as an anorectic drug, given that impaired serotonin homeostasis may be associated with increased food intake. Although positive results were documented, cardiovascular concerns resulted in its temporary withdrawal. Nevertheless, a novel role in patients with epilepsy was later suggested by isolated clinical observations. The wide application of genetic testing allowed the classification (predominantly as Dravet syndrome) of patients in whom benefit was seen, while with the development of zebrafish models, its antiepileptic properties were confirmed at a molecular level. Data from randomized clinical trials have shown a beneficial effect of fenfluramine, as an adjunct therapy, on seizure control for children with Dravet syndrome, though there is still uncertainty about the impact on neurodevelopment in these patients. No signs of heart valve disease have been documented to date. Long-term and appropriately designed clinical studies will verify whether fenfluramine is a therapeutic agent of high importance, living up to the promise shown so far. What this paper adds Fenfluramine is a very promising repurposed therapy specifically for seizures in Dravet syndrome. The long-term effect of fenfluramine on neurodevelopmental prognosis requires further investigation.

Text Mining-Based Drug Discovery in Osteoarthritis

Background

Osteoarthritis (OA) is a chronic and degenerative joint disease, which causes stiffness, pain, and decreased function. At the early stage of OA, nonsteroidal anti-inflammatory drugs (NSAIDs) are considered the first-line treatment. However, the efficacy and utility of available drug therapies are limited. We aim to use bioinformatics to identify potential genes and drugs associated with OA.

Methods

The genes related to OA and NSAIDs therapy were determined by text mining. Then, the common genes were performed for GO, KEGG pathway analysis, and protein-protein interaction (PPI) network analysis. Using the MCODE plugin-obtained hub genes, the expression levels of hub genes were verified using quantitative real-time polymerase chain reaction (qRT-PCR). The confirmed genes were queried in the Drug Gene Interaction Database to determine potential genes and drugs.

Results

The qRT-PCR result showed that the expression level of 15 genes was significantly increased in OA samples. Finally, eight potential genes were targetable to a total of 53 drugs, twenty-one of which have been employed to treat OA and 32 drugs have not yet been used in OA.

Conclusions

The 15 genes (including PTGS2, NLRP3, MMP9, IL1RN, CCL2, TNF, IL10, CD40, IL6, NGF, TP53, RELA, BCL2L1, VEGFA, and NOTCH1) and 32 drugs, which have not been used in OA but approved by the FDA for other diseases, could be potential genes and drugs, respectively, to improve OA treatment. Additionally, those methods provided tremendous opportunities to facilitate drug repositioning efforts and study novel target pharmacology in the pharmaceutical industry.

Web-based Tools for Drug Repurposing: Successful Examples of Collaborative Research

Computational approaches have been proven to be complementary tools of interest in identifying potential candidates for drug repurposing. However, although the methods developed so far offer interesting opportunities and could contribute to solving issues faced by the pharmaceutical sector, they also come with their constraints. Indeed, specific challenges ranging from data access, standardization and integration to the implementation of reliable and coherent validation methods must be addressed to allow systematic use at a larger scale. In this mini-review, we cover computational tools recently developed for addressing some of these challenges. This includes specific databases providing accessibility to a large set of curated data with standardized annotations, web-based tools integrating flexible user interfaces to perform fast computational repurposing experiments and standardized datasets specifically annotated and balanced for validating new computational drug repurposing methods. Interestingly, these new databases combined with the increasing number of information about the outcomes of drug repurposing studies can be used to perform a meta-analysis to identify key properties associated with successful drug repurposing cases. This information could further be used to design estimation methods to compute a priori assessment of the repurposing possibilities.

Identification of drug compounds for keloids and hypertrophic scars: drug discovery based on text mining and DeepPurpose

Background

Keloids (KL) and hypertrophic scars (HS) are forms of abnormal cutaneous scarring characterized by excessive deposition of extracellular matrix and fibroblast proliferation. Currently, the efficacy of drug therapies for KL and HS is limited. The present study aimed to investigate new drug therapies for KL and HS by using computational methods.

Methods

Text mining and GeneCodis were used to mine genes closely related to KL and HS. Protein-protein interaction analysis was performed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. The selection of drugs targeting the genes closely related to KL and HS was carried out using Pharmaprojects. Drug-target interaction prediction was performed using DeepPurpose, through which candidate drugs with the highest predicted binding affinity were finally obtained.

Results

Our analysis using text mining identified 69 KL- and HS-related genes. Gene enrichment analysis generated 25 genes, representing 7 pathways and 130 targeting drugs. DeepPurpose recommended 14 drugs as the final drug list, including 2 phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) inhibitors, 10 prostaglandin-endoperoxide synthase 2 (PTGS2) inhibitors and 2 vascular endothelial growth factor A (VEGFA) antagonists.

Conclusions

Drug discovery using in silico text mining and DeepPurpose may be a powerful and effective way to identify drugs targeting the genes related to KL and HS.

Identification of key genes and pathways in scleral extracellular matrix remodeling in glaucoma: Potential therapeutic agents discovered using bioinformatics analysis

Background: Glaucoma is a leading cause of irreversible blindness. Remodeling of the scleral extracellular matrix (ECM) plays an important role in the development of glaucoma. The aim of this study was to identify the key genes and pathways for the ECM remodeling of sclera in glaucoma by bioinformatics analysis and to explore potential therapeutic agents for glaucoma management. Methods: Genes associated with glaucoma, sclera and ECM remodeling were detected using the text mining tool pubmed2ensembl, and assigned Gene Ontology (GO) biological process terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using the GeneCodis program. A protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape, module analysis was performed using the Molecular Complex Detection (MCODE) plugin, and GO and KEGG analyses of the gene modules were performed using the Database of Annotation, Visualization and Integrated Discovery (DAVID) platform. The genes that clustered in the significant module were selected as core genes, and functions and pathways of the core genes were visualized using ClueGO and CluePedia. Lastly, the drug-gene interaction database was used to explore drug-gene interactions of the core genes to find drug candidates for glaucoma. Results: We identified 125 genes common to "Glaucoma", "Sclera", and "ECM remodeling" by text mining. Gene functional enrichment analysis yielded 30 enriched GO terms and 20 associated KEGG pathways. A PPI network that included 60 nodes with 249 edges was constructed, and three gene modules were obtained using the MCODE. We selected 13 genes that clustered in module 1 as core candidate genes that were associated mainly with ECM degradation and cell proliferation and division. The HIF-1 signaling pathway, FOXO signaling pathway, PI3K-Akt signaling pathway and TGFB signaling pathway were found to be enriched. We found that 11 of the 13 selected genes could be targeted by 26 existing drugs. Conclusions: The results showed that VEGFA, TGFB1, TGFB2, TGFB3, IGF2, IGF1, EGF, FN1, KNG1, TIMP1, SERPINE1, THBS1, and VWF were potential key genes involved to scleral ECM remodeling. Furthermore, 26 drugs were identified as potential therapeutic agents for glaucoma treatment and management.

OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features

As the field of precision medicine progresses, treatments for patients with cancer are starting to be tailored to their molecular as well as their clinical features. The emerging cancer subtypes defined by these molecular features require that dedicated resources be used to assist the discovery of drug candidates for preclinical evaluation. Voluminous gene expression profiles of patients with cancer have been accumulated in public databases, enabling the creation of cancer-specific expression signatures. Meanwhile, large-scale gene expression profiles of cellular responses to chemical compounds have also recently became available. By matching the cancer-specific expression signature to compound-induced gene expression profiles from large drug libraries, researchers can prioritize small molecules that present high potency to reverse expression of signature genes for further experimental testing of their efficacy. This approach has proven to be an efficient and cost-effective way to identify efficacious drug candidates. However, the success of this approach requires multiscale procedures, imposing considerable challenges to many labs. To address this, we developed Open Cancer TherApeutic Discovery (OCTAD; http://octad.org ): an open workspace for virtually screening compounds targeting precise groups of patients with cancer using gene expression features. Its database includes 19,127 patient tissue samples covering more than 50 cancer types and expression profiles for 12,442 distinct compounds. The program is used to perform deep-learning-based reference tissue selection, disease gene expression signature creation, drug reversal potency scoring and in silico validation. OCTAD is available as a web portal and a standalone R package to allow experimental and computational scientists to easily navigate the tool.

Drug Repurposing: Considerations to Surpass While Re-directing Old Compounds for New Treatments

Drug repurposing has increased in recent years as an attractive option for treating a number of diseases. Compared to those brought forward via traditional chemical development, drugs intended for repurposing can enter the market faster and with lower investment from pharmaceutical companies. However, a common trend is to focus on diseases that yield higher returns to the industry, such as cancer and common metabolic and inflammatory conditions, resulting in orphan illnesses and neglected tropical diseases having fewer repurposing options for affected patients. In addition, certain legal concerns, including limited patent coverage for the repurposed drugs and pharmacological challenges in performing clinical trials, reduce the likelihood of success. In this review, we discuss the most important concerns that affect the pathway of drug repurposing, with special emphasis on the economic revenues, government-industry associations, and legal considerations that together impact the pharmaceutical industry's decision-making on which compounds may be eligible for repurposing.

Repurposing approved drugs on the pathway to novel therapies

The time and cost of developing new drugs have led many groups to limit their search for therapeutics to compounds that have previously been approved for human use. Many "repurposed" drugs, such as derivatives of thalidomide, antibiotics, and antivirals have had clinical success in treatment areas well beyond their original approved use. These include applications in treating antibiotic-resistant organisms, viruses, cancers and to prevent burn scarring. The major theoretical justification for reusing approved drugs is that they have known modes of action and controllable side effects. Coadministering antibiotics with inhibitors of bacterial toxins or enzymes that mediate multidrug resistance can greatly enhance their activity. Drugs that control host cell pathways, including inflammation, tumor necrosis factor, interferons, and autophagy, can reduce the "cytokine storm" response to injury, control infection, and aid in cancer therapy. An active compound, even if previously approved for human use, will be a poor clinical candidate if it lacks specificity for the new target, has poor solubility or can cause serious side effects. Synergistic combinations can reduce the dosages of the individual components to lower reactivity. Preclinical analysis should take into account that severely ill patients with comorbidities will be more sensitive to side effects than healthy trial subjects. Once an active, approved drug has been identified, collaboration with medicinal chemists can aid in finding derivatives with better physicochemical properties, specificity, and efficacy, to provide novel therapies for cancers, emerging and rare diseases.

Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

Identification of key candidate genes and molecular pathways in white fat browning: an anti-obesity drug discovery based on computational biology

Background

Obesity—with its increased risk of obesity-associated metabolic diseases—has become one of the greatest public health epidemics of the twenty-first century in affluent countries. To date, there are no ideal drugs for treating obesity. Studies have shown that activation of brown adipose tissue (BAT) can promote energy consumption and inhibit obesity, which makes browning of white adipose tissue (WAT) a potential therapeutic target for obesity. Our objective was to identify genes and molecular pathways associated with WAT and the activation of BAT to WAT browning, by using publicly available data and computational tools; this knowledge might help in targeting relevant signaling pathways for treating obesity and other related metabolic diseases.

Results

In this study, we used text mining to find out genes related to brown fat and white fat browning. Combined with biological process and pathway analysis in GeneCodis and protein-protein interaction analysis by using STRING and Cytoscape, a list of high priority target genes was developed. The Human Protein Atlas was used to analyze protein expression. Candidate drugs were derived on the basis of the drug-gene interaction analysis of the final genes. Our study identified 18 genes representing 6 different pathways, targetable by a total of 33 drugs as possible drug treatments. The final list included 18 peroxisome proliferator-activated receptor gamma (PPAR-γ) agonists, 4 beta 3 adrenoceptor (β3-AR) agonists, 1 insulin sensitizer, 3 insulins, 6 lipase clearing factor stimulants and other drugs.

Conclusions

Drug discovery using in silico text mining, pathway, and protein-protein interaction analysis tools may be a method of exploring drugs targeting the activation of brown fat or white fat browning, which provides a basis for the development of novel targeted therapies as potential treatments for obesity and related metabolic diseases.

Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy

INTRODUCTION

Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy.

OBJECTIVES

Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways.

METHODS

OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM.

RESULTS

Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success.

CONCLUSIONS

Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

Enhancing the Promise of Drug Repositioning through Genetics

The development of new drugs has become challenging as the necessary investments in time and money have increased while drug approval rates have decreased. A potential solution to this problem is drug repositioning which aims to use existing drugs to treat conditions for which they were not originally intended. One approach that may enhance the likelihood of success is to reposition drugs against a target that has a genetic basis. The multitude of genome-wide association studies (GWASs) conducted in recent years represents a large potential pool of novel targets for drug repositioning. Although trait-associated variants identified from GWAS still need to be causally linked to a target gene, recently developed functional genomic techniques, databases, and workflows are helping to remove this bottleneck. The pre-clinical validation of repositioning against these targets also needs to be carefully performed to ensure that findings are not confounded by off-target effects or limitations of the techniques used. Nevertheless, the approaches described in this review have the potential to provide a faster, cheaper and more certain route to clinical approval.

Integrative network and transcriptomics-based approach predicts genotype- specific drug combinations for melanoma

Computational methods for drug combination predictions are needed to identify effective therapies that improve durability and prevent drug resistance in an efficient manner. In this paper, we present SynGeNet, a computational method that integrates transcriptomics data characterizing disease and drug z-score profiles with network mining algorithms in order to predict synergistic drug combinations. We compare SynGeNet to other available transcriptomics-based tools to predict drug combinations validated across melanoma cell lines in three genotype groups: BRAF-mutant, NRAS-mutant and combined. We showed that SynGeNet outperforms other available tools in predicting validated drug combinations and single agents tested as part of additional drug pairs. Interestingly, we observed that the performance of SynGeNet decreased when the network construction step was removed and improved when the proportion of matched-genotype validation cell lines increased. These results suggest that delineating functional information from transcriptomics data via network mining and genomic features can improve drug combination predictions.

Drug repurposing from the perspective of pharmaceutical companies

Drug repurposing holds the potential to bring medications with known safety profiles to new patient populations. Numerous examples exist for the identification of new indications for existing molecules, most stemming from serendipitous findings or focused recent efforts specifically limited to the mode of action of a specific drug. In recent years, the need for new approaches to drug research and development, combined with the advent of big data repositories and associated analytical methods, has generated interest in developing systematic approaches to drug repurposing. A variety of innovative computational methods to enable systematic repurposing screens, experimental as well as through in silico approaches, have emerged. An efficient drug repurposing pipeline requires the combination of access to molecular data, appropriate analytical expertise to enable robust insights, expertise and experimental set-up for validation and clinical development know-how. In this review, we describe some of the main approaches to systematic repurposing and discuss the various players in this field and the need for strategic collaborations to increase the likelihood of success in bringing existing molecules to new indications, as well as the current advantages, considerations and challenges in repurposing as a drug development strategy pursued by pharmaceutical companies.

LINKED ARTICLES

This article is part of a themed section on Inventing New Therapies Without Reinventing the Wheel: The Power of Drug Repurposing. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.2/issuetoc.

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

The promise of drug repurposing is that existing drugs may be used for new disease indications in order to curb the high costs and time for approval. The goal of computational methods for drug repurposing is to enable solutions for safer, cheaper and faster drug discovery. Towards this end, we developed a novel method that integrates genetic and clinical phenotype data from large-scale GWAS and PheWAS studies with detailed drug information on the concept of transitive Drug-Gene-Disease triads. We created "RE:fine Drugs," a freely available, interactive dashboard that automates gene, disease and drug-based searches to identify drug repurposing candidates. This web-based tool supports a user-friendly interface that includes an array of advanced search and export options. Results can be prioritized in a variety of ways, including but not limited to, biomedical literature support, strength and statistical significance of GWAS and/or PheWAS associations, disease indications and molecular drug targets. Here we provide a protocol that illustrates the functionalities available in the "RE:fine Drugs" system and explores the different advanced options through a case study.

Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study

Background

Drug repurposing (defined as discovering new indications for existing drugs) could play a significant role in drug development, especially considering the declining success rates of developing novel drugs. Typically, new indications for existing medications are identified by accident. However, new technologies and a large number of available resources enable the development of systematic approaches to identify and validate drug-repurposing candidates. Patients today report their experiences with medications on social media and reveal side effects as well as beneficial effects of those medications.

Objective

Our aim was to assess the feasibility of using patient reviews from social media to identify potential candidates for drug repurposing.

Methods

We retrieved patient reviews of 180 medications from an online forum, WebMD. Using dictionary-based and machine learning approaches, we identified disease names in the reviews. Several publicly available resources were used to exclude comments containing known indications and adverse drug effects. After manually reviewing some of the remaining comments, we implemented a rule-based system to identify beneficial effects.

Results

The dictionary-based system and machine learning system identified 2178 and 6171 disease names respectively in 64,616 patient comments. We provided a list of 10 common patterns that patients used to report any beneficial effects or uses of medication. After manually reviewing the comments tagged by our rule-based system, we identified five potential drug repurposing candidates.

Conclusions

To our knowledge, this is the first study to consider using social media data to identify drug-repurposing candidates. We found that even a rule-based system, with a limited number of rules, could identify beneficial effect mentions in patient comments. Our preliminary study shows that social media has the potential to be used in drug repurposing.