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Khan, Saba, Jaya Agnihotri, Sunanda Patil, and Nikhat Khan. "Drug repurposing: A futuristic approach in drug discovery." Journal of Pharmaceutical and Biological Sciences 11, no. 1 (July 15, 2023): 66–69. http://dx.doi.org/10.18231/j.jpbs.2023.011.
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Drug repurposing (DR), also known as drug repositioning, is a strategy aimed at identifying new therapeutic uses for existing drugs. It offers an effective approach to discovering or developing drug molecules with novel pharmacological or therapeutic indications. In recent years, pharmaceutical companies have increasingly embraced the drug repurposing strategy in their drug discovery and development programs, leading to the identification of new biological targets. This strategy is highly efficient, time-saving, cost-effective, and carries a lower risk of failure compared to traditional drug discovery methods. By maximizing the therapeutic value of existing drugs, drug repurposing increases the likelihood of success. It serves as a valuable alternative to the lengthy, expensive, and resource-intensive process of finding new molecular entities (NMEs) through traditional or de novo drug discovery approaches. Drug repurposing combines activity-based or experimental methods with in silico-based or computational approaches to rationally develop or identify new uses for drug molecules. It leverages the existing safety data of drugs tested in humans and redirects their application based on valid target molecules. This approach holds great promise, particularly in addressing rare, difficult-to-treat diseases, and neglected diseases. By utilizing the wealth of knowledge and resources available, drug repurposing presents an emerging strategy for optimizing the therapeutic potential of existing medicines. It offers a pathway to rapidly identify effective treatments and repurpose approved drugs for new indications, benefiting patients and healthcare systems alike.
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Zhu, Yongjun, Chao Che, Bo Jin, Ningrui Zhang, Chang Su, and Fei Wang. "Knowledge-driven drug repurposing using a comprehensive drug knowledge graph." Health Informatics Journal 26, no. 4 (July 17, 2020): 2737–50. http://dx.doi.org/10.1177/1460458220937101.
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Due to the huge costs associated with new drug discovery and development, drug repurposing has become an important complement to the traditional de novo approach. With the increasing number of public databases and the rapid development of analytical methodologies, computational approaches have gained great momentum in the field of drug repurposing. In this study, we introduce an approach to knowledge-driven drug repurposing based on a comprehensive drug knowledge graph. We design and develop a drug knowledge graph by systematically integrating multiple drug knowledge bases. We describe path- and embedding-based data representation methods of transforming information in the drug knowledge graph into valuable inputs to allow machine learning models to predict drug repurposing candidates. The evaluation demonstrates that the knowledge-driven approach can produce high predictive results for known diabetes mellitus treatments by only using treatment information on other diseases. In addition, this approach supports exploratory investigation through the review of meta paths that connect drugs with diseases. This knowledge-driven approach is an effective drug repurposing strategy supporting large-scale prediction and the investigation of case studies.
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Islam, Md Mohaiminul, Yang Wang, and Pingzhao Hu. "A Maximum Flow-Based Approach to Prioritize Drugs for Drug Repurposing of Chronic Diseases." Life 11, no. 11 (October 20, 2021): 1115. http://dx.doi.org/10.3390/life11111115.
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The discovery of new drugs is required in the time of global aging and increasing populations. Traditional drug development strategies are expensive, time-consuming, and have high risks. Thus, drug repurposing, which treats new/other diseases using existing drugs, has become a very admired tactic. It can also be referred to as the re-investigation of the existing drugs that failed to indicate the usefulness for the new diseases. Previously published literature used maximum flow approaches to identify new drug targets for drug-resistant infectious diseases but not for drug repurposing. Therefore, we are proposing a maximum flow-based protein–protein interactions (PPIs) network analysis approach to identify new drug targets (proteins) from the targets of the FDA (Food and Drug Administration) drugs and their associated drugs for chronic diseases (such as breast cancer, inflammatory bowel disease (IBD), and chronic obstructive pulmonary disease (COPD)) treatment. Experimental results showed that we have successfully turned the drug repurposing into a maximum flow problem. Our top candidates of drug repurposing, Guanidine, Dasatinib, and Phenethyl Isothiocyanate for breast cancer, IBD, and COPD were experimentally validated by other independent research as the potential candidate drugs for these diseases, respectively. This shows the usefulness of the proposed maximum flow approach for drug repurposing.
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Trivedi, Jay, Mahesh Mohan, and Siddappa N. Byrareddy. "Drug Repurposing Approaches to Combating Viral Infections." Journal of Clinical Medicine 9, no. 11 (November 23, 2020): 3777. http://dx.doi.org/10.3390/jcm9113777.
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Development of novel antiviral molecules from the beginning costs an average of $350 million to $2 billion per drug, and the journey from the laboratory to the clinic takes about 10–15 years. Utilization of drug repurposing approaches has generated substantial interest in order to overcome these drawbacks. A drastic reduction in the failure rate, which otherwise is ~92%, is achieved with the drug repurposing approach. The recent exploration of the drug repurposing approach to combat the COVID-19 pandemic has further validated the fact that it is more beneficial to reinvestigate the in-practice drugs for a new application instead of designing novel drugs. The first successful example of drug repurposing is zidovudine (AZT), which was developed as an anti-cancer agent in the 1960s and was later approved by the US FDA as an anti-HIV therapeutic drug in the late 1980s after fast track clinical trials. Since that time, the drug repurposing approach has been successfully utilized to develop effective therapeutic strategies against a plethora of diseases. Hence, an extensive application of the drug repurposing approach will not only help to fight the current pandemics more efficiently but also predict and prepare for newly emerging viral infections. In this review, we discuss in detail the drug repurposing approach and its advancements related to viral infections such as Human Immunodeficiency Virus (HIV) and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2).
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Udrescu, Lucreţia, Paul Bogdan, Aimée Chiş, Ioan Ovidiu Sîrbu, Alexandru Topîrceanu, Renata-Maria Văruţ, and Mihai Udrescu. "Uncovering New Drug Properties in Target-Based Drug–Drug Similarity Networks." Pharmaceutics 12, no. 9 (September 16, 2020): 879. http://dx.doi.org/10.3390/pharmaceutics12090879.
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Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach—based on knowledge about the chemical structures—can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug–target interactions to infer drug properties. To this end, we define drug similarity based on drug–target interactions and build a weighted Drug–Drug Similarity Network according to the drug–drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate’s repurposing.
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Meera, Muthu, Sindhu Sekar, and Rajkishore Mahatao. "A novel approach for drug discovery-drug repurposing." National Journal of Physiology, Pharmacy and Pharmacology 12, no. 5 (2022): 1. http://dx.doi.org/10.5455/njppp.2022.12.03127202230032022.
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Khataniar, Ankita, Upasana Pathak, Sanchaita Rajkhowa, and Anupam Nath Jha. "A Comprehensive Review of Drug Repurposing Strategies against Known Drug Targets of COVID-19." COVID 2, no. 2 (January 26, 2022): 148–67. http://dx.doi.org/10.3390/covid2020011.
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Drug repurposing is a more inexpensive and shorter approach than the traditional drug discovery and development process. The concept of identifying a potent molecule from a library of pre-existing molecules or an already approved drug has become a go-to tactic to accelerate the identification of drugs that can prevent COVID-19. This seemingly uncontrollable disease is caused by SARS-CoV-2. It is a novel virus of the Betacoronavirus genus, exhibiting similarities to the previously reported SAR-CoV genome structure and viral pathogenesis. The emergence of SARS-CoV-2 and the rapid outbreak of COVID-19 have resulted in a global pandemic. Researchers are hard-pressed to develop new drugs for total containment of the disease, thus making the cost-effective drug repurposing a much more feasible approach. Therefore, the current review attempts to collate both the experimental and computational drug repurposing strategies that have been utilized against significant drug targets of SARS-CoV-2. Along with the strategies, the available druggable targets shall also be discussed. However, the occurrence of frequent recombination of the viral genome and time-bound primary analysis, resulting in insignificant data, are two major challenges that drug repurposing still faces.
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Lee, Hyeong-Min, and Yuna Kim. "Drug Repurposing Is a New Opportunity for Developing Drugs against Neuropsychiatric Disorders." Schizophrenia Research and Treatment 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/6378137.
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Better the drugs you know than the drugs you do not know. Drug repurposing is a promising, fast, and cost effective method that can overcome traditional de novo drug discovery and development challenges of targeting neuropsychiatric and other disorders. Drug discovery and development targeting neuropsychiatric disorders are complicated because of the limitations in understanding pathophysiological phenomena. In addition, traditional de novo drug discovery and development are risky, expensive, and time-consuming processes. One alternative approach, drug repurposing, has emerged taking advantage of off-target effects of the existing drugs. In order to identify new opportunities for the existing drugs, it is essential for us to understand the mechanisms of action of drugs, both biologically and pharmacologically. By doing this, drug repurposing would be a more effective method to develop drugs against neuropsychiatric and other disorders. Here, we review the difficulties in drug discovery and development in neuropsychiatric disorders and the extent and perspectives of drug repurposing.
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Zhu, Yongjun, Woojin Jung, Fei Wang, and Chao Che. "Drug repurposing against Parkinson's disease by text mining the scientific literature." Library Hi Tech 38, no. 4 (April 24, 2020): 741–50. http://dx.doi.org/10.1108/lht-08-2019-0170.
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PurposeDrug repurposing involves the identification of new applications for existing drugs. Owing to the enormous rise in the costs of pharmaceutical R&D, several pharmaceutical companies are leveraging repurposing strategies. Parkinson's disease is the second most common neurodegenerative disorder worldwide, affecting approximately 1–2 percent of the human population older than 65 years. This study proposes a literature-based drug repurposing strategy in Parkinson's disease.Design/methodology/approachThe literature-based drug repurposing strategy proposed herein combined natural language processing, network science and machine learning methods for analyzing unstructured text data and producing actional knowledge for drug repurposing. The approach comprised multiple computational components, including the extraction of biomedical entities and their relationships, knowledge graph construction, knowledge representation learning and machine learning-based prediction.FindingsThe proposed strategy was used to mine information pertaining to the mechanisms of disease treatment from known treatment relationships and predict drugs for repurposing against Parkinson's disease. The F1 score of the best-performing method was 0.97, indicating the effectiveness of the proposed approach. The study also presents experimental results obtained by combining the different components of the strategy.Originality/valueThe drug repurposing strategy proposed herein for Parkinson's disease is distinct from those existing in the literature in that the drug repurposing pipeline includes components of natural language processing, knowledge representation and machine learning for analyzing the scientific literature. The results of the study provide important and valuable information to researchers studying different aspects of Parkinson's disease.
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Karaman, Berin, and Wolfgang Sippl. "Computational Drug Repurposing: Current Trends." Current Medicinal Chemistry 26, no. 28 (October 25, 2019): 5389–409. http://dx.doi.org/10.2174/0929867325666180530100332.
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: Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of ‘big data’ approaches and the discovery of new indications for pre-existing drugs has been accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or incorporating molecular structure information of drugs and protein targets with other kinds of data derived from systems biology provide great potential to accelerate drug discovery and improve the success of drug repurposing attempts. In this review, we highlight commonly used computational drug repurposing strategies, including bioinformatics and cheminformatics tools, to integrate large-scale data emerging from the systems biology, and consider both the challenges and opportunities of using this approach. Moreover, we provide successful examples and case studies that combined various in silico drug-repurposing strategies to predict potential novel uses for known therapeutics.
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Singh, Thakur Uttam, Subhashree Parida, Madhu Cholenahalli Lingaraju, Manickam Kesavan, Dinesh Kumar, and Raj Kumar Singh. "Drug repurposing approach to fight COVID-19." Pharmacological Reports 72, no. 6 (September 5, 2020): 1479–508. http://dx.doi.org/10.1007/s43440-020-00155-6.
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Dandu, Kamakshi, Prathap R. Kallamadi, Suman S. Thakur, and Ch Mohan Rao. "Drug Repurposing for Retinoblastoma: Recent Advances." Current Topics in Medicinal Chemistry 19, no. 17 (September 19, 2019): 1535–44. http://dx.doi.org/10.2174/1568026619666190119152706.
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Retinoblastoma is the intraocular malignancy that occurs during early childhood. The current standard of care includes chemotherapy followed by focal consolidative therapies, and enucleation. Unfortunately, these are associated with many side and late effects. New drugs and/or drug combinations need to be developed for safe and effective treatment. This compelling need stimulated efforts to explore drug repurposing for retinoblastoma. While conventional drug development is a lengthy and expensive process, drug repurposing is a faster, alternate approach, where an existing drug, not meant for treating cancer, can be repurposed to treat retinoblastoma. The present article reviews various attempts to test drugs approved for different purposes such as calcium channels blockers, non-steroidal antiinflammatory drugs, cardenolides, antidiabetic, antibiotics and antimalarial for treating retinoblastoma. It also discusses other promising candidates that could be explored for repurposing for retinoblastoma.
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Anand, Jigisha, Tanmay Ghildiyal, Aakanksha Madhwal, Rishabh Bhatt, Devvret Verma, and Nishant Rai. "Computational guided approach for drug repurposing against SARS-CoV-2." Future Virology 16, no. 3 (March 2021): 211–43. http://dx.doi.org/10.2217/fvl-2020-0403.
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Background: In the current SARS-CoV-2 outbreak, drug repositioning emerges as a promising approach to develop efficient therapeutics in comparison to de novo drug development. The present investigation screened 130 US FDA-approved drugs including hypertension, cardiovascular diseases, respiratory tract infections (RTI), antibiotics and antiviral drugs for their inhibitory potential against SARS-CoV-2. Materials & methods: The molecular drug targets against SARS-CoV-2 proteins were determined by the iGEMDOCK computational docking tool. The protein homology models were generated through SWISS Model workspace. The pharmacokinetics of all the ligands was determined by ADMET analysis. Results: The study identified 15 potent drugs exhibiting significant inhibitory potential against SARS-CoV-2. Conclusion: Our investigation has identified possible repurposed drug candidates to improve the current modus operandi of the treatment given to COVID-19 patients.
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Wu, Yonghui, Jeremy L. Warner, Liwei Wang, Min Jiang, Jun Xu, Qingxia Chen, Hui Nian, et al. "Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–9. http://dx.doi.org/10.1200/cci.19.00001.
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PURPOSEDrug development is becoming increasingly expensive and time consuming. Drug repurposing is one potential solution to accelerate drug discovery. However, limited research exists on the use of electronic health record (EHR) data for drug repurposing, and most published studies have been conducted in a hypothesis-driven manner that requires a predefined hypothesis about drugs and new indications. Whether EHRs can be used to detect drug repurposing signals is not clear. We want to demonstrate the feasibility of mining large, longitudinal EHRs for drug repurposing by detecting candidate noncancer drugs that can potentially be used for the treatment of cancer.PATIENTS AND METHODSBy linking cancer registry data to EHRs, we identified 43,310 patients with cancer treated at Vanderbilt University Medical Center (VUMC) and 98,366 treated at the Mayo Clinic. We assessed the effect of 146 noncancer drugs on cancer survival using VUMC EHR data and sought to replicate significant associations (false discovery rate < .1) using the identical approach with Mayo Clinic EHR data. To evaluate replicated signals further, we reviewed the biomedical literature and clinical trials on cancers for corroborating evidence.RESULTSWe identified 22 drugs from six drug classes (statins, proton pump inhibitors, angiotensin-converting enzyme inhibitors, β-blockers, nonsteroidal anti-inflammatory drugs, and α-1 blockers) associated with improved overall cancer survival (false discovery rate < .1) from VUMC; nine of the 22 drug associations were replicated at the Mayo Clinic. Literature and cancer clinical trial evaluations also showed very strong evidence to support the repurposing signals from EHRs.CONCLUSIONMining of EHRs for drug exposure–mediated survival signals is feasible and identifies potential candidates for antineoplastic repurposing. This study sets up a new model of mining EHRs for drug repurposing signals.
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Saberian, Nafiseh, Azam Peyvandipour, Michele Donato, Sahar Ansari, and Sorin Draghici. "A new computational drug repurposing method using established disease–drug pair knowledge." Bioinformatics 35, no. 19 (March 6, 2019): 3672–78. http://dx.doi.org/10.1093/bioinformatics/btz156.
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Abstract Motivation Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs. Results We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing. Availability and implementation The R scripts are available on demand from the authors. Supplementary information Supplementary data are available at Bioinformatics online.
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Sukhai, Mahadeo A., Paul A. Spagnuolo, Scott Weir, James Kasper, Lavonne Patton, and Aaron D. Schimmer. "New sources of drugs for hematologic malignancies." Blood 117, no. 25 (June 23, 2011): 6747–55. http://dx.doi.org/10.1182/blood-2011-02-315283.
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Abstract Advancing novel therapeutic agents for the treatment of malignancy into the marketplace is an increasingly costly and lengthy process. As such, new strategies for drug discovery are needed. Drug repurposing represents an opportunity to rapidly advance new therapeutic strategies into clinical trials at a relatively low cost. Known on-patent or off-patent drugs with unrecognized anticancer activity can be rapidly advanced into clinical testing for this new indication by leveraging their known pharmacology, pharmacokinetics, and toxicology. Using this approach, academic groups can participate in the drug discovery field and smaller biotechnology companies can “de-risk” early-stage drug discovery projects. Here, several scientific approaches used to identify drug repurposing opportunities are highlighted, with a focus on hematologic malignancies. In addition, a discussion of the regulatory issues that are unique to drug repurposing and how they impact developing old drugs for new indications is included. Finally, the mechanisms to enhance drug repurposing through increased collaborations between academia, industry, and nonprofit charitable organizations are discussed.
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Kumari, Priyanka, Bikram Pradhan, Maria Koromina, George P. Patrinos, and Kristel Van Steen. "Discovery of new drug indications for COVID-19: A drug repurposing approach." PLOS ONE 17, no. 5 (May 24, 2022): e0267095. http://dx.doi.org/10.1371/journal.pone.0267095.
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Motivation The outbreak of coronavirus health issues caused by COVID-19(SARS-CoV-2) creates a global threat to public health. Therefore, there is a need for effective remedial measures using existing and approved therapies with proven safety measures has several advantages. Dexamethasone (Pubchem ID: CID0000005743), baricitinib(Pubchem ID: CID44205240), remdesivir (PubchemID: CID121304016) are three generic drugs that have demonstrated in-vitro high antiviral activity against SARS-CoV-2. The present study aims to widen the search and explore the anti-SARS-CoV-2 properties of these potential drugs while looking for new drug indications with optimised benefits via in-silico research. Method Here, we designed a unique drug-similarity model to repurpose existing drugs against SARS-CoV-2, using the anti-Covid properties of dexamethasone, baricitinib, and remdesivir as references. Known chemical-chemical interactions of reference drugs help extract interactive compounds withimprovedanti-SARS-CoV-2 properties. Here, we calculated the likelihood of these drug compounds treating SARS-CoV-2 related symptoms using chemical-protein interactions between the interactive compounds of the reference drugs and SARS-CoV-2 target genes. In particular, we adopted a two-tier clustering approach to generate a drug similarity model for the final selection of potential anti-SARS-CoV-2 drug molecules. Tier-1 clustering was based on t-Distributed Stochastic Neighbor Embedding (t-SNE) and aimed to filter and discard outlier drugs. The tier-2 analysis incorporated two cluster analyses performed in parallel using Ordering Points To Identify the Clustering Structure (OPTICS) and Hierarchical Agglomerative Clustering (HAC). As a result, itidentified clusters of drugs with similar actions. In addition, we carried out a docking study for in-silico validation of top candidate drugs. Result Our drug similarity model highlighted ten drugs, including reference drugs that can act as potential therapeutics against SARS-CoV-2. The docking results suggested that doxorubicin showed the least binding energy compared to reference drugs. Their practical utility as anti-SARS-CoV-2 drugs, either individually or in combination, warrants further investigation.
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Zanello, Galliano, Diego Ardigò, Florence Guillot, Anneliene H. Jonker, Oxana Iliach, Hervé Nabarette, Daniel O’Connor, and Virginie Hivert. "Sustainable approaches for drug repurposing in rare diseases: recommendations from the IRDiRC Task Force." Rare Disease and Orphan Drugs Journal 2, no. 2 (2023): 9. http://dx.doi.org/10.20517/rdodj.2023.04.
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Drug repurposing represents a real opportunity to address unmet needs and improve the lives of rare disease patients. It is often presented as a faster, safer and cheaper path for bringing drugs into new indications. However, several economic, regulatory and scientific barriers can impede the successful repurposing of drugs for rare diseases. The International Rare Diseases Research Consortium (IRDiRC) set up the Task Force on Sustainable Models in Drug Repurposing with the objective of identifying key factors for achieving sustainable repurposing approaches in rare diseases. In order to help inform expert opinion, the Task Force investigated six cases of medicinal products repurposed into new rare indications and four cases of ongoing development programs. A questionnaire addressing the major steps of the repurposing approach was developed by the Task Force and sent to contact points of the organizations. In addition, interviews were conducted with the relevant organization representatives to conduct a deeper dive into the sustainability of the repurposing approach for each of the selected cases. Based on the collective experience of the members of the Task Force and the output from the questionnaires/interviews, we have identified ten key factors that should be considered by those embarking on repurposing projects. These factors include the identification of unmet patient needs and partnership with patients, collection of evidence concerning disease prevalence, patient numbers, drug pharmacology and disease etiology, drug industrial property status, off-label or compounding use, data from past clinical studies and needs for extended non-clinical and clinical studies. The development of a collaborative funding framework and early discussion with regulators and payers are additional factors to implement early in the development of sustainable drug repurposing projects.
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Hudson, Matthew L., and Ram Samudrala. "Multiscale Virtual Screening Optimization for Shotgun Drug Repurposing Using the CANDO Platform." Molecules 26, no. 9 (April 28, 2021): 2581. http://dx.doi.org/10.3390/molecules26092581.
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Drug repurposing, the practice of utilizing existing drugs for novel clinical indications, has tremendous potential for improving human health outcomes and increasing therapeutic development efficiency. The goal of multi-disease multitarget drug repurposing, also known as shotgun drug repurposing, is to develop platforms that assess the therapeutic potential of each existing drug for every clinical indication. Our Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget repurposing implements several pipelines for the large-scale modeling and simulation of interactions between comprehensive libraries of drugs/compounds and protein structures. In these pipelines, each drug is described by an interaction signature that is compared to all other signatures that are subsequently sorted and ranked based on similarity. Pipelines within the platform are benchmarked based on their ability to recover known drugs for all indications in our library, and predictions are generated based on the hypothesis that (novel) drugs with similar signatures may be repurposed for the same indication(s). The drug-protein interactions used to create the drug-proteome signatures may be determined by any screening or docking method, but the primary approach used thus far has been BANDOCK, our in-house bioanalytical or similarity docking protocol. In this study, we calculated drug-proteome interaction signatures using the publicly available molecular docking method Autodock Vina and created hybrid decision tree pipelines that combined our original bio- and chem-informatic approach with the goal of assessing and benchmarking their drug repurposing capabilities and performance. The hybrid decision tree pipeline outperformed the two docking-based pipelines from which it was synthesized, yielding an average indication accuracy of 13.3% at the top10 cutoff (the most stringent), relative to 10.9% and 7.1% for its constituent pipelines, and a random control accuracy of 2.2%. We demonstrate that docking-based virtual screening pipelines have unique performance characteristics and that the CANDO shotgun repurposing paradigm is not dependent on a specific docking method. Our results also provide further evidence that multiple CANDO pipelines can be synthesized to enhance drug repurposing predictive capability relative to their constituent pipelines. Overall, this study indicates that pipelines consisting of varied docking-based signature generation methods can capture unique and useful signals for accurate comparison of drug-proteome interaction signatures, leading to improvements in the benchmarking and predictive performance of the CANDO shotgun drug repurposing platform.
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Law, G. Lynn, Jennifer Tisoncik-Go, Marcus J. Korth, and Michael G. Katze. "Drug repurposing: a better approach for infectious disease drug discovery?" Current Opinion in Immunology 25, no. 5 (October 2013): 588–92. http://dx.doi.org/10.1016/j.coi.2013.08.004.
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Balasundaram, Preethi, Shanthi Veerappapillai, Suthindhiran Krishnamurthy, and Ramanathan Karuppasamy. "Drug repurposing: An approach to tackle drug resistance inS. typhimurium." Journal of Cellular Biochemistry 119, no. 3 (November 20, 2017): 2818–31. http://dx.doi.org/10.1002/jcb.26457.
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Kifle, Zemene Demelash, Akeberegn Gorems Ayele, and Engidaw Fentahun Enyew. "Drug Repurposing Approach, Potential Drugs, and Novel Drug Targets for COVID-19 Treatment." Journal of Environmental and Public Health 2021 (April 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/6631721.
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Novel coronavirus first appeared in Wuhan, China, in December 2019, and it speedily expanded globally. Some medications which are used to treat other diseases seem to be effective in treating COVID-19 even without explicit support. The existing drugs that are summarized in this review primarily focused on therapeutic agents that possessed activity against other RNA viruses such as MERS-CoV and SARS-CoV. Drug repurposing or repositioning is a promising field in drug discovery that identifies new therapeutic opportunities for existing drugs such as corticosteroids, RNA-dependent RNA polymerase inhibitors, interferons, protease inhibitors, ivermectin, melatonin, teicoplanin, and some others. A search for new drug/drug targets is underway. Thus, blocking coronavirus structural protein, targeting viral enzyme, dipeptidyl peptidase 4, and membrane fusion blocker (angiotensin-converting enzyme 2 and CD147 inhibitor) are major sites based on molecular targets for the management of COVID-19 infection. The possible impact of biologics for the management of COVID19 is promising and includes a wide variety of options such as cytokines, nucleic acid-based therapies targeting virus gene expression, bioengineered and vectored antibodies, and various types of vaccines. This review demonstrates that the available data are not sufficient to suggest any treatment for the eradication of COVID-19 to be used at the clinical level. This article aims to review the roles of existing drugs and drug targets for COVID-19 treatment.
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Liang, Siqi, and Haiyuan Yu. "Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach." Bioinformatics 36, no. 16 (May 12, 2020): 4490–97. http://dx.doi.org/10.1093/bioinformatics/btaa495.
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Abstract Motivation In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases. Results Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug–gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By providing newly predicted drug–target associations, we uncover novel opportunities of drug repurposing that may benefit cancer treatment through action on either known drug targets or currently undrugged genes. Supplementary information Supplementary data are available at Bioinformatics online.
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Chen, Xin, Giuseppe Gumina, and Kristopher G. Virga. "Recent Advances in Drug Repurposing for Parkinson’s Disease." Current Medicinal Chemistry 26, no. 28 (October 25, 2019): 5340–62. http://dx.doi.org/10.2174/0929867325666180719144850.
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:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.
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Parrales, Alejandro, Peter McDonald, Megan Ottomeyer, Anuradha Roy, Frank J. Shoenen, Melinda Broward, Tyce Bruns, et al. "Comparative oncology approach to drug repurposing in osteosarcoma." PLOS ONE 13, no. 3 (March 26, 2018): e0194224. http://dx.doi.org/10.1371/journal.pone.0194224.
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Venkatraman, Simran, Brinda Balasubramanian, Pisut Pongchaikul, Rutaiwan Tohtong, and Somchai Chutipongtanate. "Molecularly Guided Drug Repurposing for Cholangiocarcinoma: An Integrative Bioinformatic Approach." Genes 13, no. 2 (January 29, 2022): 271. http://dx.doi.org/10.3390/genes13020271.
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Background: Cholangiocarcinoma (CCA) has a complex immune microenvironment architecture, thus possessing challenges in its characterization and treatment. This study aimed to repurpose FDA-approved drugs for cholangiocarcinoma by transcriptomic-driven bioinformatic approach. Methods: Cox-proportional univariate regression was applied to 3017 immune-related genes known a priori to identify a list of mortality-associated genes, so-called immune-oncogenic gene signature, in CCA tumor-derived RNA-seq profiles of two independent cohorts. Unsupervised clustering stratified CCA tumors into two groups according to the immune-oncogenic gene signature expression, which then confirmed its clinical relevance by Kaplan–Meier curve. Molecularly guided drug repurposing was performed by an integrative connectivity map-prioritized drug-gene network analysis. Results: The immune-oncogenic gene signature consists of 26 mortality-associated immune-related genes. Patients with high-expression signature had a poorer overall survival (log-rank p < 0.001), while gene enrichment analysis revealed cell-cycle checkpoint regulation and inflammatory-immune response signaling pathways affected this high-risk group. The integrative drug-gene network identified eight FDA-approved drugs as promising candidates, including Dasatinib a multi-kinase inhibitor currently investigated for advanced CCA with isocitrate-dehydrogenase mutations. Conclusion: This study proposes the use of the immune-oncogenic gene signature to identify high-risk CCA patients. Future preclinical and clinical studies are required to elucidate the therapeutic efficacy of the molecularly guided drugs as the adjunct therapy, aiming to improve the survival outcome.
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Martínez-Parada Gonzalo, Galeana-Ascencio Ricardo, Anaya-Ruiz Maricruz, and Carrasco-Carballo Alan. "In silico screening of drug Bank data base to PDE10: A drug repurposing approach." GSC Biological and Pharmaceutical Sciences 24, no. 3 (September 30, 2023): 010–21. http://dx.doi.org/10.30574/gscbps.2023.24.3.0350.
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Drug repurposing has emerged as a promising strategy for expediting drug development by identifying new therapeutic applications for existing drugs. In this study employed in silico screening approach to explore the DrugBank database for potential phosphodiesterase 10 (PDE10) inhibitors with applications in neurological, psychiatric disorders and cancer treatment. PDE10 plays a crucial role in regulating cyclic nucleotide levels in the brain and has been implicated in various diseases, including schizophrenia, Parkinson’s, Huntington’s diseases, and certain types of cancer. Through molecular docking, we evaluated the interactions and energetics of 28 candidate inhibitors with PDE10. Notably, 17 candidates met all selection criteria, presenting excellent potential for further investigation. The theoretical inhibitors demonstrated favorable ADMETx properties, and their adverse effects were comparable or lower than controls. These findings indicate the viability of repurposing existing drugs, such as Nebivolol, Fluvastatin, Pioglitazone and others, for PDE10 inhibition in diverse pathologies. Validation of these candidates in preclinical studies may open new avenues for drug development and clinical applications, addressing unmet medical needs in various disorders and cancer treatment.
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Puhl, Ana C., Patricia A. Vignaux, Eni Minerali, Jennifer J. Klein, Tammy M. Havener, Edward Anderson, Anthony J. Hickey, and Sean Ekins. "Repurposing drugs for CLN1 Batten disease: An integrative drug discovery approach." Molecular Genetics and Metabolism 132, no. 2 (February 2021): S88—S89. http://dx.doi.org/10.1016/j.ymgme.2020.12.212.
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Nishimura, Kaneyasu, and Kazuyuki Takata. "Combination of Drugs and Cell Transplantation: More Beneficial Stem Cell-Based Regenerative Therapies Targeting Neurological Disorders." International Journal of Molecular Sciences 22, no. 16 (August 22, 2021): 9047. http://dx.doi.org/10.3390/ijms22169047.
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Cell transplantation therapy using pluripotent/multipotent stem cells has gained attention as a novel therapeutic strategy for treating neurodegenerative diseases, including Parkinson’s disease, Alzheimer’s disease, Huntington’s disease, ischemic stroke, and spinal cord injury. To fully realize the potential of cell transplantation therapy, new therapeutic options that increase cell engraftments must be developed, either through modifications to the grafted cells themselves or through changes in the microenvironment surrounding the grafted region. Together these developments could potentially restore lost neuronal function by better supporting grafted cells. In addition, drug administration can improve the outcome of cell transplantation therapy through better accessibility and delivery to the target region following cell transplantation. Here we introduce examples of drug repurposing approaches for more successful transplantation therapies based on preclinical experiments with clinically approved drugs. Drug repurposing is an advantageous drug development strategy because drugs that have already been clinically approved can be repurposed to treat other diseases faster and at lower cost. Therefore, drug repurposing is a reasonable approach to enhance the outcomes of cell transplantation therapies for neurological diseases. Ideal repurposing candidates would result in more efficient cell transplantation therapies and provide a new and beneficial therapeutic combination.
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Nakagawa, Chihiro, Satoshi Yokoyama, Kouichi Hosomi, and Mitsutaka Takada. "Repurposing haloperidol for the treatment of rheumatoid arthritis: an integrative approach using data mining techniques." Therapeutic Advances in Musculoskeletal Disease 13 (January 2021): 1759720X2110470. http://dx.doi.org/10.1177/1759720x211047057.
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Introduction: Treatment of rheumatoid arthritis (RA) has advanced with the introduction of biological disease-modifying antirheumatic drugs. However, more than 20% of patients with RA still have moderate or severe disease activity. Hence, novel antirheumatic drugs are required. Recently, drug repurposing, a process of identifying new indications for existing drugs, has received great attention. Furthermore, a few reports have shown that antipsychotics are capable of affecting several cytokines that are also modulated by existing antirheumatic drugs. Therefore, we investigated the association between antipsychotics and RA by data mining using real-world data and bioinformatics databases. Methods: Disproportionality and sequence symmetry analyses were employed to identify the associations between the investigational drugs and RA using the US Food and Drug Administration Adverse Event Reporting System (2004–2016) and JMDC administrative claims database (January 2005–April 2017; JMDC Inc., Tokyo, Japan), respectively. The reporting odds ratio (ROR) and information component (IC) were used in the disproportionality analysis to indicate a signal. The adjusted sequence ratio (SR) was used in the sequence symmetry analysis to indicate a signal. The bioinformatics analysis suite, BaseSpace Correlation Engine (Illumina, CA, USA) was employed to explore the molecular mechanisms associated with the potential candidates identified by the drug-repurposing approach. Results: A potential inverse association between the antipsychotic haloperidol and RA, which exhibited significant inverse signals with ROR, IC, and adjusted SR, was found. Furthermore, the results suggested that haloperidol may exert antirheumatic effects by modulating various signaling pathways, including cytokine and chemokine signaling, major histocompatibility complex class-II antigen presentation, and Toll-like receptor cascade pathways. Conclusion: Our drug-repurposing approach using data mining techniques identified haloperidol as a potential antirheumatic drug candidate.
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Torricelli, Federica, Elisabetta Sauta, Veronica Manicardi, Vincenzo Dario Mandato, Andrea Palicelli, Alessia Ciarrocchi, and Gloria Manzotti. "An Innovative Drug Repurposing Approach to Restrain Endometrial Cancer Metastatization." Cells 12, no. 5 (March 3, 2023): 794. http://dx.doi.org/10.3390/cells12050794.
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Background: Endometrial cancer (EC) is the most common gynecologic tumor and the world’s fourth most common cancer in women. Most patients respond to first-line treatments and have a low risk of recurrence, but refractory patients, and those with metastatic cancer at diagnosis, remain with no treatment options. Drug repurposing aims to discover new clinical indications for existing drugs with known safety profiles. It provides ready-to-use new therapeutic options for highly aggressive tumors for which standard protocols are ineffective, such as high-risk EC. Methods: Here, we aimed at defining new therapeutic opportunities for high-risk EC using an innovative and integrated computational drug repurposing approach. Results: We compared gene-expression profiles, from publicly available databases, of metastatic and non-metastatic EC patients being metastatization the most severe feature of EC aggressiveness. A comprehensive analysis of transcriptomic data through a two-arm approach was applied to obtain a robust prediction of drug candidates. Conclusions: Some of the identified therapeutic agents are already successfully used in clinical practice to treat other types of tumors. This highlights the potential to repurpose them for EC and, therefore, the reliability of the proposed approach.
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Ribeiro, Eduarda, Bárbara Costa, Francisco Vasques-Nóvoa, and Nuno Vale. "In Vitro Drug Repurposing: Focus on Vasodilators." Cells 12, no. 4 (February 20, 2023): 671. http://dx.doi.org/10.3390/cells12040671.
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Drug repurposing aims to identify new therapeutic uses for drugs that have already been approved for other conditions. This approach can save time and resources compared to traditional drug development, as the safety and efficacy of the repurposed drug have already been established. In the context of cancer, drug repurposing can lead to the discovery of new treatments that can target specific cancer cell lines and improve patient outcomes. Vasodilators are a class of drugs that have been shown to have the potential to influence various types of cancer. These medications work by relaxing the smooth muscle of blood vessels, increasing blood flow to tumors, and improving the delivery of chemotherapy drugs. Additionally, vasodilators have been found to have antiproliferative and proapoptotic effects on cancer cells, making them a promising target for drug repurposing. Research on vasodilators for cancer treatment has already shown promising results in preclinical and clinical studies. However, additionally research is needed to fully understand the mechanisms of action of vasodilators in cancer and determine the optimal dosing and combination therapy for patients. In this review, we aim to explore the molecular mechanisms of action of vasodilators in cancer cell lines and the current state of research on their repurposing as a treatment option. With the goal of minimizing the effort and resources required for traditional drug development, we hope to shed light on the potential of vasodilators as a viable therapeutic strategy for cancer patients.
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Zeng, Xiangxiang, Siyi Zhu, Xiangrong Liu, Yadi Zhou, Ruth Nussinov, and Feixiong Cheng. "deepDR: a network-based deep learning approach to in silico drug repositioning." Bioinformatics 35, no. 24 (May 22, 2019): 5191–98. http://dx.doi.org/10.1093/bioinformatics/btz418.
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Abstract Motivation Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging. Results In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug–disease, one drug-side-effect, one drug–target and seven drug–drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multi-modal deep autoencoder. Then the learned low-dimensional representation of drugs together with clinically reported drug–disease pairs are encoded and decoded collectively via a variational autoencoder to infer candidates for approved drugs for which they were not originally approved. We found that deepDR revealed high performance [the area under receiver operating characteristic curve (AUROC) = 0.908], outperforming conventional network-based or machine learning-based approaches. Importantly, deepDR-predicted drug–disease associations were validated by the ClinicalTrials.gov database (AUROC = 0.826) and we showcased several novel deepDR-predicted approved drugs for Alzheimer’s disease (e.g. risperidone and aripiprazole) and Parkinson’s disease (e.g. methylphenidate and pergolide). Availability and implementation Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR Supplementary information Supplementary data are available online at Bioinformatics.
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Mavridou, Dimitra, Konstantina Psatha, and Michalis Aivaliotis. "Proteomics and Drug Repurposing in CLL towards Precision Medicine." Cancers 13, no. 14 (July 6, 2021): 3391. http://dx.doi.org/10.3390/cancers13143391.
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CLL is a hematological malignancy considered as the most frequent lymphoproliferative disease in the western world. It is characterized by high molecular heterogeneity and despite the available therapeutic options, there are many patient subgroups showing the insufficient effectiveness of disease treatment. The challenge is to investigate the individual molecular characteristics and heterogeneity of these patients. Proteomics analysis is a powerful approach that monitors the constant state of flux operators of genetic information and can unravel the proteome heterogeneity and rewiring into protein pathways in CLL patients. This review essences all the available proteomics studies in CLL and suggests the way these studies can be exploited to find effective therapeutic options combined with drug repurposing approaches. Drug repurposing utilizes all the existing knowledge of the safety and efficacy of FDA-approved or investigational drugs and anticipates drug alignment to crucial CLL therapeutic targets, leading to a better disease outcome. The drug repurposing studies in CLL are also discussed in this review. The next goal involves the integration of proteomics-based drug repurposing in precision medicine, as well as the application of this procedure into clinical practice to predict the most appropriate drugs combination that could ensure therapy and the long-term survival of each CLL patient.
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Giunta, Salvatore, Agata Grazia D’Amico, Grazia Maugeri, Claudio Bucolo, Giovanni Luca Romano, Settimio Rossi, Chiara M. Eandi, Elisabetta Pricoco, and Velia D’Agata. "Drug-Repurposing Strategy for Dimethyl Fumarate." Pharmaceuticals 16, no. 7 (July 7, 2023): 974. http://dx.doi.org/10.3390/ph16070974.
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In the area of drug discovery, repurposing strategies represent an approach to discover new uses of approved drugs besides their original indications. We used this approach to investigate the effects of dimethyl fumarate (DMF), a drug approved for relapsing–remitting multiple sclerosis and psoriasis treatment, on early injury associated with diabetic retinopathy (DR). We used an in vivo streptozotocin (STZ)-induced diabetic rat model. Diabetes was induced by a single injection of STZ in rats, and after 1 week, a group of animals was treated with a daily intraperitoneal injection of DMF or a vehicle. Three weeks after diabetes induction, the retinal expression levels of key enzymes involved in DR were evaluated. In particular, the biomarkers COX-2, iNOS, and HO-1 were assessed via Western blot and immunohistochemistry analysis. Diabetic rats showed a significant retinal upregulation of COX-2 and iNOS compared to the retina of normal rats (non-diabetic), and an increase in HO-1 was also observed in the STZ group. This latter result was due to a mechanism of protection elicited by the pathological condition. DMF treatment significantly induced the retinal expression of HO-1 in STZ-induced diabetic animals with a reduction in iNOS and COX-2 retinal levels. Taken together, these results suggested that DMF might be useful to counteract the inflammatory process and the oxidative response in DR. In conclusion, we believe that DMF represents a potential candidate to treat diabetic retinopathy and warrants further in vivo and clinical evaluation.
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Loganathan, Tamizhini, Srimathy Ramachandran, Prakash Shankaran, Devipriya Nagarajan, and Suma Mohan S. "Host transcriptome-guided drug repurposing for COVID-19 treatment: a meta-analysis based approach." PeerJ 8 (June 10, 2020): e9357. http://dx.doi.org/10.7717/peerj.9357.
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Background Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been declared a pandemic by the World Health Organization, and the identification of effective therapeutic strategy is a need of the hour to combat SARS-CoV-2 infection. In this scenario, the drug repurposing approach is widely used for the rapid identification of potential drugs against SARS-CoV-2, considering viral and host factors. Methods We adopted a host transcriptome-based drug repurposing strategy utilizing the publicly available high throughput gene expression data on SARS-CoV-2 and other respiratory infection viruses. Based on the consistency in expression status of host factors in different cell types and previous evidence reported in the literature, pro-viral factors of SARS-CoV-2 identified and subject to drug repurposing analysis based on DrugBank and Connectivity Map (CMap) using the web tool, CLUE. Results The upregulated pro-viral factors such as TYMP, PTGS2, C1S, CFB, IFI44, XAF1, CXCL2, and CXCL3 were identified in early infection models of SARS-CoV-2. By further analysis of the drug-perturbed expression profiles in the connectivity map, 27 drugs that can reverse the expression of pro-viral factors were identified, and importantly, twelve of them reported to have anti-viral activity. The direct inhibition of the PTGS2 gene product can be considered as another therapeutic strategy for SARS-CoV-2 infection and could suggest six approved PTGS2 inhibitor drugs for the treatment of COVID-19. The computational study could propose candidate repurposable drugs against COVID-19, and further experimental studies are required for validation.
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Siddiqui, Arif Jamal, Mohammad Faheem Khan, Walid Sabri Hamadou, Manish Goyal, Sadaf Jahan, Arshad Jamal, Syed Amir Ashraf, et al. "Molecular Docking and Dynamics Simulation Revealed Ivermectin as Potential Drug against Schistosoma-Associated Bladder Cancer Targeting Protein Signaling: Computational Drug Repositioning Approach." Medicina 57, no. 10 (October 3, 2021): 1058. http://dx.doi.org/10.3390/medicina57101058.
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Urogenital schistosomiasis is caused by Schistosoma haematobium (S. haematobium) infection, which has been linked to the development of bladder cancer. In this study, three repurposing drugs, ivermectin, arteether and praziquantel, were screened to find the potent drug-repurposing candidate against the Schistosoma-associated bladder cancer (SABC) in humans by using computational methods. The biology of most glutathione S-transferases (GSTs) proteins and vascular endothelial growth factor (VEGF) is complex and multifaceted, according to recent evidence, and these proteins actively participate in many tumorigenic processes such as cell proliferation, cell survival and drug resistance. The VEGF and GSTs are now widely acknowledged as an important target for antitumor therapy. Thus, in this present study, ivermectin displayed promising inhibition of bladder cancer cells via targeting VEGF and GSTs signaling. Moreover, molecular docking and molecular dynamics (MD) simulation analysis revealed that ivermectin efficiently targeted the binding pockets of VEGF receptor proteins and possessed stable dynamics behavior at binding sites. Therefore, we proposed here that these compounds must be tested experimentally against VEGF and GST signaling in order to control SABC. Our study lies within the idea of discovering repurposing drugs as inhibitors against the different types of human cancers by targeting essential pathways in order to accelerate the drug development cycle.
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Entonu, Moses Edache, Mbateudi Danjuma IKA, Ekpa Emmanuel, Clifford Liki Barnabas, Daniel Danladi Gaiya, and Stella Kuyet UDU. "Drug repurposing: Recent advancements, challenges, and future therapeutics for cancer treatment." Journal of Bacteriology & Mycology: Open Access 10, no. 2 (2022): 26–30. http://dx.doi.org/10.15406/jbmoa.2022.10.00322.
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Cancer is a prime public health burden that accounts for approximately 9.9 million deaths worldwide. Despite recent advances in treatment regimen and huge capital investment in the pharmaceutical sector, there has been little success in improving the chances of survival of cancer patients. Drug repurposing sometimes termed drug repositioning is a strategy of discovery and redeveloping existing drugs for new therapeutic purposes. This novel approach is highly efficient, considerably cuts research and development costs, reduces the drug development timeline, maximizes therapeutic value and consequently increases success rate with minimum risk of failure. In this review, prioritizing drug repurposing to activate immune and inflammatory responses to target tumor cells through immune surveillance mechanism is a promising strategy for cancer immunotherapy. Cancer immunotherapy cover myriad of therapeutic approaches as cytokine therapy, immune checkpoint blockade therapy, cancer vaccines, natural killer cells, adoptive T cell therapies, monoclonal antibodies, oncolytic viruses, computational approach and host of others. In the current pipeline, drug repurposing is devoid of adequate funding and the necessary legal support for research and development by stakeholders. At the moment, immunotherapy strategies combine with computational biology could be considered the new milestone in drug re-profiling for cancer treatment.
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Tejera, Eduardo, Yunierkis Pérez-Castillo, Andrea Chamorro, Alejandro Cabrera-Andrade, and Maria Eugenia Sanchez. "A Multi-Objective Approach for Drug Repurposing in Preeclampsia." Molecules 26, no. 4 (February 3, 2021): 777. http://dx.doi.org/10.3390/molecules26040777.
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Preeclampsia is a hypertensive disorder that occurs during pregnancy. It is a complex disease with unknown pathogenesis and the leading cause of fetal and maternal mortality during pregnancy. Using all drugs currently under clinical trial for preeclampsia, we extracted all their possible targets from the DrugBank and ChEMBL databases and labeled them as “targets”. The proteins labeled as “off-targets” were extracted in the same way but while taking all antihypertensive drugs which are inhibitors of ACE and/or angiotensin receptor antagonist as query molecules. Classification models were obtained for each of the 55 total proteins (45 targets and 10 off-targets) using the TPOT pipeline optimization tool. The average accuracy of the models in predicting the external dataset for targets and off-targets was 0.830 and 0.850, respectively. The combinations of models maximizing their virtual screening performance were explored by combining the desirability function and genetic algorithms. The virtual screening performance metrics for the best model were: the Boltzmann-Enhanced Discrimination of ROC (BEDROC)α=160.9 = 0.258, the Enrichment Factor (EF)1% = 31.55 and the Area Under the Accumulation Curve (AUAC) = 0.831. The most relevant targets for preeclampsia were: AR, VDR, SLC6A2, NOS3 and CHRM4, while ABCG2, ERBB2, CES1 and REN led to the most relevant off-targets. A virtual screening of the DrugBank database identified estradiol, estriol, vitamins E and D, lynestrenol, mifrepristone, simvastatin, ambroxol, and some antibiotics and antiparasitics as drugs with potential application in the treatment of preeclampsia.
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Cavalcante, Bruno Raphael Ribeiro, Raíza Dias Freitas, Leonardo de Oliveira Siquara da Rocha, Gisele Vieira Rocha, Túlio Cosme de Carvalho Pachêco, Pablo Ivan Pereira Ramos, and Clarissa Araújo Gurgel Rocha. "In silico approaches for drug repurposing in oncology: Protocol for a scoping review of existing evidence." PLOS ONE 17, no. 7 (July 7, 2022): e0271002. http://dx.doi.org/10.1371/journal.pone.0271002.
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Drug repurposing has been applied in the biomedical field to optimize the use of existing drugs, leading to a more efficient allocation of research resources. In oncology, this approach is particularly interesting, considering the high cost related to the discovery of new drugs with therapeutic potential. Computational methods have been applied to predict associations between drugs and their targets. However, drug repurposing has not always been promising and its efficiency has yet to be proven. Therefore, the present scoping review protocol was developed to screen the literature on how in silico strategies can be implemented in drug repurposing in oncology. The scoping review will be conducted according to the Arksey and O’Malley framework (2005) and the Joanna Briggs Institute recommendations. We will search the PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We will include peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between January 1, 2003, and December 31, 2021. Data will be charted and findings described according to review questions. We will report the scoping review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review guidelines (PRISMA-ScR).
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Qin, Shimei, Wan Li, Hongzheng Yu, Manyi Xu, Chao Li, Lei Fu, Shibin Sun, et al. "Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks." International Journal of Molecular Sciences 24, no. 3 (January 23, 2023): 2244. http://dx.doi.org/10.3390/ijms24032244.
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Drug repositioning aims to discover novel clinical benefits of existing drugs, is an effective way to develop drugs for complex diseases such as cancer and may facilitate the process of traditional drug development. Meanwhile, network-based computational biology approaches, which allow the integration of information from different aspects to understand the relationships between biomolecules, has been successfully applied to drug repurposing. In this work, we developed a new strategy for network-based drug repositioning against cancer. Combining the mechanism of action and clinical efficacy of the drugs, a cancer-related drug similarity network was constructed, and the correlation score of each drug with a specific cancer was quantified. The top 5% of scoring drugs were reviewed for stability and druggable potential to identify potential repositionable drugs. Of the 11 potentially repurposable drugs for non-small cell lung cancer (NSCLC), 10 were confirmed by clinical trial articles and databases. The targets of these drugs were significantly enriched in cancer-related pathways and significantly associated with the prognosis of NSCLC. In light of the successful application of our approach to colorectal cancer as well, it provides an effective clue and valuable perspective for drug repurposing in cancer.
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Shao, Mengting, Leiming Jiang, Zhigang Meng, and Jianzhen Xu. "Computational Drug Repurposing Based on a Recommendation System and Drug–Drug Functional Pathway Similarity." Molecules 27, no. 4 (February 18, 2022): 1404. http://dx.doi.org/10.3390/molecules27041404.
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Drug repurposing identifies new clinical indications for existing drugs. It can be used to overcome common problems associated with cancers, such as heterogeneity and resistance to established therapies, by rapidly adapting known drugs for new treatment. In this study, we utilized a recommendation system learning model to prioritize candidate cancer drugs. We designed a drug–drug pathway functional similarity by integrating multiple genetic and epigenetic alterations such as gene expression, copy number variation (CNV), and DNA methylation. When compared with other similarities, such as SMILES chemical structures and drug targets based on the protein–protein interaction network, our approach provided better interpretable models capturing drug response mechanisms. Furthermore, our approach can achieve comparable accuracy when evaluated with other learning models based on large public datasets (CCLE and GDSC). A case study about the Erlotinib and OSI-906 (Linsitinib) indicated that they have a synergistic effect to reduce the growth rate of tumors, which is an alternative targeted therapy option for patients. Taken together, our computational method characterized drug response from the viewpoint of a multi-omics pathway and systematically predicted candidate cancer drugs with similar therapeutic effects.
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Advani, Dia, Rohan Gupta, Rahul Tripathi, Sudhanshu Sharma, Rashmi K. Ambasta, and Pravir Kumar. "Protective role of anticancer drugs in neurodegenerative disorders: A drug repurposing approach." Neurochemistry International 140 (November 2020): 104841. http://dx.doi.org/10.1016/j.neuint.2020.104841.
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Lesmana, Mohammad Hendra Setia, Nguyen Quoc Khanh Le, Wei-Che Chiu, Kuo-Hsuan Chung, Chih-Yang Wang, Lalu Muhammad Irham, and Min-Huey Chung. "Genomic-Analysis-Oriented Drug Repurposing in the Search for Novel Antidepressants." Biomedicines 10, no. 8 (August 11, 2022): 1947. http://dx.doi.org/10.3390/biomedicines10081947.
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From inadequate prior antidepressants that targeted monoamine neurotransmitter systems emerged the discovery of alternative drugs for depression. For instance, drugs targeted interleukin 6 receptor (IL6R) in inflammatory system. Genomic analysis-based drug repurposing using single nucleotide polymorphism (SNP) inclined a promising method for several diseases. However, none of the diseases was depression. Thus, we aimed to identify drug repurposing candidates for depression treatment by adopting a genomic-analysis-based approach. The 5885 SNPs obtained from the machine learning approach were annotated using HaploReg v4.1. Five sets of functional annotations were applied to determine the depression risk genes. The STRING database was used to expand the target genes and identify drug candidates from the DrugBank database. We validated the findings using the ClinicalTrial.gov and PubMed databases. Seven genes were observed to be strongly associated with depression (functional annotation score = 4). Interestingly, IL6R was auspicious as a target gene according to the validation outcome. We identified 20 drugs that were undergoing preclinical studies or clinical trials for depression. In addition, we identified sarilumab and satralizumab as drugs that exhibit strong potential for use in the treatment of depression. Our findings indicate that a genomic-analysis-based approach can facilitate the discovery of drugs that can be repurposed for treating depression.
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Anaya-Ruiz, Maricruz, and Martin Perez-Santos. "Drug repurposing of adapalene for melanoma treatment." Pharmaceutical Patent Analyst 11, no. 1 (January 2022): 9–14. http://dx.doi.org/10.4155/ppa-2021-0021.
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Cancer drug repurposing is an attractive approach that leads to savings in time and investment. Adapalene, the first medical application of which was for the treatment of acne, has been described as a repurposing drug for the treatment of various types of cancer. Patent application CN111329851 describes the use of adapalene for the treatment of melanoma, by assays carried out on melanoma cell lines. Adapalene demonstrated antiproliferative activity in melanoma cell lines via S-phase arrest-dependent apoptosis mediated by DNA damage through an increase in the expression of p-ATM and p-chk2 and a decrease in the expression of p-BRCA1 and Rad51. Even though no evidence on efficacy and efficiency is shown in preclinical and clinical studies, CN111329851 patent shows that adapalene may be a repurposing drug for the treatment of melanoma.
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Kubick, Norwin, Marta Pajares, Ioana Enache, Gina Manda, and Michel-Edwar Mickael. "Repurposing Zileuton as a Depression Drug Using an AI and In Vitro Approach." Molecules 25, no. 9 (May 5, 2020): 2155. http://dx.doi.org/10.3390/molecules25092155.
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Repurposing drugs to target M1 macrophages inflammatory response in depression constitutes a bright alternative for commonly used antidepressants. Depression is a significant type of mood disorder, where patients suffer from pathological disturbances associated with a proinflammatory M1 macrophage phenotype. Presently, the most commonly used antidepressants such as Zoloft and Citalopram can reduce inflammation, but suffer from dangerous side effects without offering specificity toward macrophages. We employed a new strategy for drug repurposing based on the integration of RNA-seq analysis and text mining using deep neural networks. Our system employs a Google semantic AI universal encoder to compute sentences embedding. Sentences similarity is calculated using a sorting function to identify drug compounds. Then sentence relevance is computed using a custom-built convolution differential network. Our system highlighted the NRF2 pathway as a critical drug target to reprogram M1 macrophage response toward an anti-inflammatory profile (M2). Using our approach, we were also able to predict that lipoxygenase inhibitor drug zileuton could modulate NRF2 pathway in vitro. Taken together, our results indicate that reorienting zileuton usage to modulate M1 macrophages could be a novel and safer therapeutic option for treating depression.
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Kumar, Sahil, and Vandana Roy. "Repurposing Drugs: An Empowering Approach to Drug Discovery and Development." Drug Research, July 21, 2023. http://dx.doi.org/10.1055/a-2095-0826.
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AbstractDrug discovery and development is a time-consuming and costly procedure that necessitates a substantial effort. Drug repurposing has been suggested as a method for developing medicines that takes less time than developing brand new medications and will be less expensive. Also known as drug repositioning or re-profiling, this strategy has been in use from the time of serendipitous drug discoveries to the modern computer aided drug designing and use of computational chemistry. In the light of the COVID-19 pandemic too, drug repurposing emerged as a ray of hope in the dearth of available medicines. Data availability by electronic recording, libraries, and improvements in computational techniques offer a vital substrate for systemic evaluation of repurposing candidates. In the not-too-distant future, it could be possible to create a global research archive for us to access, thus accelerating the process of drug development and repurposing. This review aims to present the evolution, benefits and drawbacks including current approaches, key players and the legal and regulatory hurdles in the field of drug repurposing. The vast quantities of available data secured in multiple drug databases, assisting in drug repurposing is also discussed.
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Wu, Patrick, QiPing Feng, Vern Eric Kerchberger, Scott D. Nelson, Qingxia Chen, Bingshan Li, Todd L. Edwards, et al. "Integrating gene expression and clinical data to identify drug repurposing candidates for hyperlipidemia and hypertension." Nature Communications 13, no. 1 (January 10, 2022). http://dx.doi.org/10.1038/s41467-021-27751-1.
Full textAbstract:
AbstractDiscovering novel uses for existing drugs, through drug repurposing, can reduce the time, costs, and risk of failure associated with new drug development. However, prioritizing drug repurposing candidates for downstream studies remains challenging. Here, we present a high-throughput approach to identify and validate drug repurposing candidates. This approach integrates human gene expression, drug perturbation, and clinical data from publicly available resources. We apply this approach to find drug repurposing candidates for two diseases, hyperlipidemia and hypertension. We screen >21,000 compounds and replicate ten approved drugs. We also identify 25 (seven for hyperlipidemia, eighteen for hypertension) drugs approved for other indications with therapeutic effects on clinically relevant biomarkers. For five of these drugs, the therapeutic effects are replicated in the All of Us Research Program database. We anticipate our approach will enable researchers to integrate multiple publicly available datasets to identify high priority drug repurposing opportunities for human diseases.
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Bhagat, Rani Teksinh, and Santosh Ramarao Butle. "Drug Repurposing: A Review." Journal of Pharmaceutical Research International, June 14, 2021, 161–69. http://dx.doi.org/10.9734/jpri/2021/v33i31b31704.
Full textAbstract:
The drug development is a very time consuming and complex process. Drug development Process is Expensive. Success rate for the new drug development is very small. In recent years, decreases the new drugs development. The powerful tools are developed to support the research and development (R&D) process is essential. The Drug repurposing are helpful for research and development process. The drug re-purposing as an approach finds new therapeutic uses for current candidates or existing candidates or approved drugs, different from its original application. The main aimed of Drug repurposing is to reduce costs and research time investments in Research & Development. It is used for the diagnosis and treatment of various diseases. Repositioning is important over traditional approaches and need for effective therapies. Drug re-purposing identifies new application for already banned or existing drugs from market. In drug design, drug repurposing plays important role, because it helps to preclinical development. It reducing time efforts, expenses and failures in drug discovery process. It is also called as drug repositioning, drug redirecting, drug reprofiling.
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Das, Shyam Sundar, Pritish Ranjan, and Ibrahim Roshan Kunnakkattu. "Literature-based drug–drug similarity for drug repurposing: impact of Medical Subject Headings term refinement and hierarchical clustering." Future Medicinal Chemistry, August 26, 2022. http://dx.doi.org/10.4155/fmc-2022-0074.
Full textAbstract:
Background: We describe herein, an improved procedure for drug repurposing based on refined Medical Subject Headings (MeSH) terms and hierarchical clustering method. Materials & methods: In the present study, we have employed MeSH data from MEDLINE (2019), 1669 US FDA approved drugs from Open FDA and a refined set of MeSH terms. Refinement of MeSH terms was performed to include terms related to mechanistic information of drugs and diseases. Results and Conclusions: In-depth analysis of the results obtained, demonstrated greater efficiency of the proposed approach, based on refined MeSH terms and hierarchical clustering, in terms of number of selected drug candidates for repurposing. Further, analysis of misclustering and size of noise clusters suggest that the proposed approach is reliable and can be employed in drug repurposing.