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Relevant bibliographies by topics / DRUG REPURPOSING APPROACH

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Academic literature on the topic 'DRUG REPURPOSING APPROACH'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "DRUG REPURPOSING APPROACH"

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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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Dissertations / Theses on the topic "DRUG REPURPOSING APPROACH"

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Regan-Fendt, Kelly E. "Integrative Network and Transcriptomics Approach Enables Computational Drug Repurposing and Drug Combination Discovery in Melanoma." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1521209048981327.

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Jary, Calvin. "Pre-Clinical Assessment of the Proteasomal Inhibitor Bortezomib as a Generalized Therapeutic Approach for Recessively Inherited Disorders." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36066.

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The number of known monogenic rare diseases (~7000) exceeds the number of effective treatments (~500) by more than an order of magnitude underlining the pressing need for generalizable therapeutic approaches for this class of conditions. In this regard, the majority of recessive and x-linked recessive disorders are caused by missense mutations encoding proteins that frequently have residual function but are rapidly degraded by the 26S proteasome. Bortezomib is a small molecule that inhibits the 26S proteasome and has been approved for use in patients for an unrelated condition; multiple myeloma. Previous work has shown that, for a small number of disorders, bortezomib can inhibit the degradation of the mutant protein, thereby increasing the protein level and activity, holding out the promise of a beneficial therapeutic effect by the repurposing of this agent. We present here a high level western blot based survey of nine recessive disorders to characterize the general effectiveness of such an approach. Thirteen patient fibroblast cell lines comprising 9 different diseases with 19 known mutations were selected on the basis of missense mutations protein expression data when available. The cell lines were incubated with bortezomib (10 nM and 50 nM; 24 hrs) and levels of the mutated protein were quantified by western blot. Unfortunately, no consistent, appreciable increase was observed for any of the conditions tested. The general therapeutic value of re-purposing bortezomib for recessive and x-linked diseases appears limited at best. The few reported cases of bortezomib successfully working in increasing mutated protein levels appear to be the exceptions and not the norm. Moreover successes are more often limited to cell lines carrying a transgene expressing the mutated protein rather than endogenous mutated protein in patient cell lines.

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Wolf, Stefan. "Novel Approaches in the Treatment of Virus- Induced Inflammatory Disease." Thesis, Griffith University, 2016. http://hdl.handle.net/10072/366853.

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This PhD thesis combines four chapters on different fields of basic research and sets the focus on two circulating viruses of global concern, the orthomyxovirus influenza A virus (IAV) and the alphavirus Ross River virus (RRV). The first three chapters include swine influenza A virus (sIAV) surveillance for the detection and characterisation of IAV subtypes, an in vitro high throughput screening (HTS) on host micro RNAs (miRNAs) for the discovery of novel anti-IAV (H7N9) targets and their underlying mechanisms, and an approach to reduce disease pathogenesis in mice infected with H7N9 by targeting the pro-inflammatory factor CCL2. In a fourth chapter, drug repurposing with the interleukin-1 (IL-1) inhibitor anakinra was investigated to treat RRV-induced bone loss in mice. By combining these four chapters, a broad range of drug discovery is covered in this PhD thesis; Surveillance, HTS target discovery and the application of drug repurposing in animal models of viral diseases.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Glycomics
Science, Environment, Engineering and Technology
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Hänzelmann, Sonja 1981. "Pathway-centric approaches to the analysis of high-throughput genomics data." Doctoral thesis, Universitat Pompeu Fabra, 2012. http://hdl.handle.net/10803/108337.

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In the last decade, molecular biology has expanded from a reductionist view to a systems-wide view that tries to unravel the complex interactions of cellular components. Owing to the emergence of high-throughput technology it is now possible to interrogate entire genomes at an unprecedented resolution. The dimension and unstructured nature of these data made it evident that new methodologies and tools are needed to turn data into biological knowledge. To contribute to this challenge we exploited the wealth of publicly available high-throughput genomics data and developed bioinformatics methodologies focused on extracting information at the pathway rather than the single gene level. First, we developed Gene Set Variation Analysis (GSVA), a method that facilitates the organization and condensation of gene expression proﬁles into gene sets. GSVA enables pathway-centric downstream analyses of microarray and RNA-seq gene expression data. The method estimates sample-wise pathway variation over a population and allows for the integration of heterogeneous biological data sources with pathway-level expression measurements. To illustrate the features of GSVA, we applied it to several use-cases employing diﬀerent data types and addressing biological questions. GSVA is made available as an R package within the Bioconductor project. Secondly, we developed a pathway-centric genome-based strategy to reposition drugs in type 2 diabetes (T2D). This strategy consists of two steps, ﬁrst a regulatory network is constructed that is used to identify disease driving modules and then these modules are searched for compounds that might target them. Our strategy is motivated by the observation that disease genes tend to group together in the same neighborhood forming disease modules and that multiple genes might have to be targeted simultaneously to attain an eﬀect on the pathophenotype. To ﬁnd potential compounds, we used compound exposed genomics data deposited in public databases. We collected about 20,000 samples that have been exposed to about 1,800 compounds. Gene expression can be seen as an intermediate phenotype reﬂecting underlying dysregulatory pathways in a disease. Hence, genes contained in the disease modules that elicit similar transcriptional responses upon compound exposure are assumed to have a potential therapeutic eﬀect. We applied the strategy to gene expression data of human islets from diabetic and healthy individuals and identiﬁed four potential compounds, methimazole, pantoprazole, bitter orange extract and torcetrapib that might have a positive eﬀect on insulin secretion. This is the ﬁrst time a regulatory network of human islets has been used to reposition compounds for T2D. In conclusion, this thesis contributes with two pathway-centric approaches to important bioinformatic problems, such as the assessment of biological function and in silico drug repositioning. These contributions demonstrate the central role of pathway-based analyses in interpreting high-throughput genomics data.
En l'última dècada, la biologia molecular ha evolucionat des d'una perspectiva reduccionista cap a una perspectiva a nivell de sistemes que intenta desxifrar les complexes interaccions entre els components cel•lulars. Amb l'aparició de les tecnologies d'alt rendiment actualment és possible interrogar genomes sencers amb una resolució sense precedents. La dimensió i la naturalesa desestructurada d'aquestes dades ha posat de manifest la necessitat de desenvolupar noves eines i metodologies per a convertir aquestes dades en coneixement biològic. Per contribuir a aquest repte hem explotat l'abundància de dades genòmiques procedents d'instruments d'alt rendiment i disponibles públicament, i hem desenvolupat mètodes bioinformàtics focalitzats en l'extracció d'informació a nivell de via molecular en comptes de fer-ho al nivell individual de cada gen. En primer lloc, hem desenvolupat GSVA (Gene Set Variation Analysis), un mètode que facilita l'organització i la condensació de perfils d'expressió dels gens en conjunts. GSVA possibilita anàlisis posteriors en termes de vies moleculars amb dades d'expressió gènica provinents de microarrays i RNA-seq. Aquest mètode estima la variació de les vies moleculars a través d'una població de mostres i permet la integració de fonts heterogènies de dades biològiques amb mesures d'expressió a nivell de via molecular. Per il•lustrar les característiques de GSVA, l'hem aplicat a diversos casos usant diferents tipus de dades i adreçant qüestions biològiques. GSVA està disponible com a paquet de programari lliure per R dins el projecte Bioconductor. En segon lloc, hem desenvolupat una estratègia centrada en vies moleculars basada en el genoma per reposicionar fàrmacs per la diabetis tipus 2 (T2D). Aquesta estratègia consisteix en dues fases: primer es construeix una xarxa reguladora que s'utilitza per identificar mòduls de regulació gènica que condueixen a la malaltia; després, a partir d'aquests mòduls es busquen compostos que els podrien afectar. La nostra estratègia ve motivada per l'observació que els gens que provoquen una malaltia tendeixen a agrupar-se, formant mòduls patogènics, i pel fet que podria caldre una actuació simultània sobre múltiples gens per assolir un efecte en el fenotipus de la malaltia. Per trobar compostos potencials, hem usat dades genòmiques exposades a compostos dipositades en bases de dades públiques. Hem recollit unes 20.000 mostres que han estat exposades a uns 1.800 compostos. L'expressió gènica es pot interpretar com un fenotip intermedi que reflecteix les vies moleculars desregulades subjacents a una malaltia. Per tant, considerem que els gens d'un mòdul patològic que responen, a nivell transcripcional, d'una manera similar a l'exposició del medicament tenen potencialment un efecte terapèutic. Hem aplicat aquesta estratègia a dades d'expressió gènica en illots pancreàtics humans corresponents a individus sans i diabètics, i hem identificat quatre compostos potencials (methimazole, pantoprazole, extracte de taronja amarga i torcetrapib) que podrien tenir un efecte positiu sobre la secreció de la insulina. Aquest és el primer cop que una xarxa reguladora d'illots pancreàtics humans s'ha utilitzat per reposicionar compostos per a T2D. En conclusió, aquesta tesi aporta dos enfocaments diferents en termes de vies moleculars a problemes bioinformàtics importants, com ho son el contrast de la funció biològica i el reposicionament de fàrmacs "in silico". Aquestes contribucions demostren el paper central de les anàlisis basades en vies moleculars a l'hora d'interpretar dades genòmiques procedents d'instruments d'alt rendiment.

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Lee, Kun-Pu, and 李坤樸. "Literature-based Discovery for Drug Repurposing: A Path-importance-based Approach." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/14698997967264068188.

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碩士
國立臺灣大學
資訊管理學研究所
105
Drug development is costly and time-consuming. According to United States Food and Drug Administration (FDA), drug development consists of five stages, including drug discovery, clinical test, FDA review, etc. However, once one of the stages fails, the investment on candidate drug seldom returns. As a result, to overcome the challenges of drug development, researchers start to explore alternative methods for drug development. Drug repurposing discovery, finding new indications for existing drugs, has been proposed to help reduce cost and time needed for drug development. Swanson (1986) originally proposed a drug repurposing approach that analyzes biomedical literatures to uncover implicit relationships. Previous studies following Swanson’s ABC model encountered several limitations. Therefore, in this research, we propose a path-importance-based approach, which constructs a concept network based on semantic predication, trains a classification model to determine the importance of paths that connecting a focal drug and a candidate disease, and finally ranks candidate diseases according to the importance of paths identified by the path importance classification model. In our systematic evaluation experiments, we prove that our path importance classification model achieves a satisfactory effectiveness, and that adopting the concept of path importance into the ranking of candidate drugs for drug repurposing outperforms the traditional method.

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BHARDWAJ, SHANU. "A DRUG REPURPOSING APPROACH THROUGH PHARMACOPHORE MODELING AND MOLECULAR DOCKING TO MANAGE ALZHEIMER’S DISEASE VIA GSK-3β MODULATION." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19814.

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Alzheimer’s Disease is progressive disorder whose pathophysiology and therapeutic status still stands unclear. As of now, all the therapies are confined to symptomatic relief, disease modifying therapies are therefore the need of the hour. Modulation in Wnt cascade has already been linked to varied disorders which include AD and type 2 diabetes mellitus too. The interlink between insulin signaling pathway and Wnt cascade has been well acknowledged through a number of preclinical and clinical studies. This in silico-based study is focused upon investigating the link between curative effects of FDA approved anti- diabetic drug and Wnt signaling cascade. We prepared a library consisting of 143 FDA approved antidiabetic medicines with an aim of repurposing them as GSK 3 beta inhibitor, which is present in increased amounts in AD brain. Pharmacophore modelling was performed for these drugs and the lead hits were then subjected to ligand- protein based molecular docking followed by molecular dynamics simulation. ZINC04803471 emerged as a clear winner that can inhibit GSK 3 beta, which leads to beta catenin degeneration and thereby downregulates the canonical Wnt signaling cascade in the AD brain. Although this is just a tip of an iceberg, further in vitro investigation is necessary to validate the effectiveness of the compound.

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Philips, Santosh. "Computational biology approaches in drug repurposing and gene essentiality screening." Diss., 2016. http://hdl.handle.net/1805/10978.

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Indiana University-Purdue University Indianapolis (IUPUI)
The rapid innovations in biotechnology have led to an exponential growth of data and electronically accessible scientific literature. In this enormous scientific data, knowledge can be exploited, and novel discoveries can be made. In my dissertation, I have focused on the novel molecular mechanism and therapeutic discoveries from big data for complex diseases. It is very evident today that complex diseases have many factors including genetics and environmental effects. The discovery of these factors is challenging and critical in personalized medicine. The increasing cost and time to develop new drugs poses a new challenge in effectively treating complex diseases. In this dissertation, we want to demonstrate that the use of existing data and literature as a potential resource for discovering novel therapies and in repositioning existing drugs. The key to identifying novel knowledge is in integrating information from decades of research across the different scientific disciplines to uncover interactions that are not explicitly stated. This puts critical information at the fingertips of researchers and clinicians who can take advantage of this newly acquired knowledge to make informed decisions. This dissertation utilizes computational biology methods to identify and integrate existing scientific data and literature resources in the discovery of novel molecular targets and drugs that can be repurposed. In chapters 1 of my dissertation, I extensively sifted through scientific literature and identified a novel interaction between Vitamin A and CYP19A1 that could lead to a potential increase in the production of estrogens. Further in chapter 2 by exploring a microarray dataset from an estradiol gene sensitivity study I was able to identify a potential novel anti-estrogenic indication for the commonly used urinary analgesic, phenazopyridine. Both discoveries were experimentally validated in the laboratory. In chapter 3 of my dissertation, through the use of a manually curated corpus and machine learning algorithms, I identified and extracted genes that are essential for cell survival. These results brighten the reality that novel knowledge with potential clinical applications can be discovered from existing data and literature by integrating information across various scientific disciplines.

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Hsieh, Hao-Wen, and 謝皓雯. "Guiding drug repurposing for precision medicine via novel big data approaches." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/88272910321654470910.

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Chou, Ting, and 周庭. "Repurposing small-molecular drugs to block the interaction between PD-1 and PD-L1 using bioinformatic approaches." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/17257607979584380986.

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Books on the topic "DRUG REPURPOSING APPROACH"

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Kollur, Shiva Prasad. Drug Repurposing Approach for Non-Small Cell Lung Cancer Targeting MAPK Signaling Pathway. Eliva Press, 2021.

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Drug Repurposing in Cancer Therapy: Approaches and Applications. Elsevier Science & Technology, 2020.

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Cho, William C. S., and Kenneth K. W. To. Drug Repurposing in Cancer Therapy: Approaches and Applications. Elsevier Science & Technology Books, 2020.

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Book chapters on the topic "DRUG REPURPOSING APPROACH"

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Contini, Simone, and Simona E. Rombo. "A Collaborative Filtering Approach for Drug Repurposing." In New Trends in Database and Information Systems, 381–87. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15743-1_35.

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Khare, Ruchi, Sandeep Kumar Jhade, Manoj Kumar Tripathi, and Rahul Shrivastava. "Drug Repurposing: An Approach for Reducing Multidrug Resistance." In Non-traditional Approaches to Combat Antimicrobial Drug Resistance, 179–90. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9167-7_7.

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Zhao, Kai, and Hon-Cheong So. "Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing." In Methods in Molecular Biology, 219–37. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8955-3_13.

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Aggarwal, Geeta, Pankaj Musyuni, Bharti Mangla, and Ramesh K. Goyal. "Reverse Translational Approach in Repurposing of Drugs for Anticancer Therapy." In Drug Repurposing for Emerging Infectious Diseases and Cancer, 299–328. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5399-6_14.

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Yadav, Monu, Pratibha Dhakla, Rahul Rawat, Mini Dahiya, and Anil Kumar. "Therapeutic Repurposing Approach: New Opportunity for Developing Drugs Against COVID-19." In Drug Repurposing for Emerging Infectious Diseases and Cancer, 543–68. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5399-6_24.

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Kuang, Zhaobin, Yujia Bao, James Thomson, Michael Caldwell, Peggy Peissig, Ron Stewart, Rebecca Willett, and David Page. "A Machine-Learning-Based Drug Repurposing Approach Using Baseline Regularization." In Methods in Molecular Biology, 255–67. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8955-3_15.

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Lakshmi Prasanna Marise, V., Surekha Surekha, G. N. S. Hema Sree, and G. R. Saraswathy. "An In Silico Target Specific Drug Repurposing Approach for Multiple Sclerosis." In Special Publications, 79–83. Cambridge: Royal Society of Chemistry, 2019. http://dx.doi.org/10.1039/9781839160783-00079.

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Sankhe, R., A. Kumar, E. Rathi, and A. Kishore. "Development of New Neprilysin Inhibitor as a Modulator of Chronic Kidney and Heart Disease Using In Silico Drug Repurposing Approach." In Special Publications, 45–49. Cambridge: Royal Society of Chemistry, 2019. http://dx.doi.org/10.1039/9781839160783-00045.

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Sharma, Tripti, Ipsa Padhy, and Chita Ranjan Sahoo. "Approaches, Strategies, and Advances in Computational Drug Discovery and Drug Repurposing." In Drug Repurposing and Computational Drug Discovery, 27–58. New York: Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003347705-2.

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Costa, Giosuè, Anna Artese, Francesco Ortuso, and Stefano Alcaro. "From Homology Modeling to the Hit Identification and Drug Repurposing: A Structure-Based Approach in the Discovery of Novel Potential Anti-Obesity Compounds." In Methods in Molecular Biology, 263–77. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1209-5_15.

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Conference papers on the topic "DRUG REPURPOSING APPROACH"

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Amatya, Pratuat, Paola Stolfi, Flavio Lombardi, and Paolo Tieri. "DruSiLa: an integrated, in-silico disease similarity-based approach for drug repurposing." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995073.

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PHATAK, SHARANGDHAR S., and SHUXING ZHANG. "A NOVEL MULTI-MODAL DRUG REPURPOSING APPROACH FOR IDENTIFICATION OF POTENT ACK1 INHIBITORS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2012. http://dx.doi.org/10.1142/9789814447973_0004.

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Ibrahim, Sara. "Abstract 2906: Towards breast cancer drug repurposing based on a pathway modeling approach." In Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1538-7445.am2013-2906.

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Gupta, Nancy Sanjay, and Pravir Kumar. "TDP-43 Inhibitors in Amyotrophic Lateral Sclerosis: An Application of Drug Repurposing Approach Using FDA-Approved Drugs." In 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES). IEEE, 2023. http://dx.doi.org/10.1109/cises58720.2023.10183592.

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NG, CLARA, RUTH HAUPTMAN, YINLIANG ZHANG, PHILIP E. BOURNE, and LEI XIE. "ANTI-INFECTIOUS DRUG REPURPOSING USING AN INTEGRATED CHEMICAL GENOMICS AND STRUCTURAL SYSTEMS BIOLOGY APPROACH." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2013. http://dx.doi.org/10.1142/9789814583220_0014.

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Barrero, C. A., and F. Wang. "Identified Drug Repurposing Targets for Chronic Obstructive Pulmonary Disease Using a Systems Biology Approach." In American Thoracic Society 2022 International Conference, May 13-18, 2022 - San Francisco, CA. American Thoracic Society, 2022. http://dx.doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a4664.

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Tripathi, Animan, and Pravir Kumar. "Identification of Putative LRRK2 Inhibitors in the Pathogensis of Parkinson's Disease: A Drug-Repurposing Approach." In 2021 5th International Conference on Information Systems and Computer Networks (ISCON). IEEE, 2021. http://dx.doi.org/10.1109/iscon52037.2021.9702406.

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Bastikar, Alpana, Virupaksha Bastikar, Santosh Chhajed, and PramodKumar Gupta. "Targeting SARS-CoV2 Main Protease using HTVS and simulation analysis: A drug repurposing approach against COVID-19." In 6th International Electronic Conference on Medicinal Chemistry. Basel, Switzerland: MDPI, 2020. http://dx.doi.org/10.3390/ecmc2020-07803.

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Adnan, Md, Md Nazim Uddin Chy, Md Riad Chowdhury, and A. T. M. Mostafa Kamal. "<em>In silico</em> virtual screening of known drugs against SARS-CoV-2 3CL protease: A drug repurposing approach for COVID-19." In 6th International Electronic Conference on Medicinal Chemistry. Basel, Switzerland: MDPI, 2020. http://dx.doi.org/10.3390/ecmc2020-07363.

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Chowdhury, Md Riad, and Sadia Akter. "<em>In silico </em>screening of therapeutic agents for COVID-19: A drug repurposing approach." In 7th International Electronic Conference on Medicinal Chemistry. Basel, Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/ecmc2021-11359.

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Reports on the topic "DRUG REPURPOSING APPROACH"

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Dawson, Stephanie. D11.6 REPO4EU Open Science Strategy. REPO4EU, April 2023. http://dx.doi.org/10.58647/repo4eu.202300d11.6.

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Abstract:

To ensure the widest possible dissemination of the results to the research community, pharmaceutical industry, patients and to the broader public, the REPO4EU project, in line with goals of the European Commission, is committed to an Open Science approach. Because Open Science can be interpreted widely this document lays out the strategy of the project with regard to Open Access publishing, alternative metrics, Intellectual Property and FAIR data. The Open Science Strategy forms the theoretical framework for the REPO4EU Open Science publishing portal that will develop into an open hub of research results and communication for the entire drug repurposing community.

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