What are some historical examples of successful drug repurposing?
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Drug repurposing has been successful in the past with examples such as thalidomide and sildenafil being repurposed for multiple disease conditions . Another example is clemastine fumarate, which was identified as a promising drug for neurodegenerative diseases through repurposing efforts . In the field of psychiatry, drug repurposing has been actively pursued, leading to the development of new drugs for various purposes such as reducing side effects and improving symptom control . These examples demonstrate the potential of drug repurposing to provide new treatment options for different diseases.
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| The paper does not provide specific historical examples of successful drug repurposing. | |
1 Citations | The paper does not provide any historical examples of successful drug repurposing. The paper is about the potential use of clemastine fumarate for neurodegenerative diseases. |
09 Feb 2022 4 Citations | The paper mentions thalidomide and sildenafil as examples of previously failed drugs that were successfully repurposed for multiple disease conditions. |
17 Aug 2022 | The paper mentions thalidomide, sildenafil, and baricitinib as historical examples of successful drug repurposing. |
| The paper does not provide any historical examples of successful drug repurposing. The paper discusses the use of drug repurposing in skeletal muscle ion channelopathies. |
Related Questions
What are the different approaches for drug repurposing based on a knowledge graph?4 answersDrug repurposing based on a knowledge graph can be approached in several ways. One approach is to use a "semantic multi-layer guilt-by-association" method that leverages the principle of guilt-by-association at the drug-gene-disease level. This approach uses semantic information-guided random walk to generate drug and disease-populated node sequences, allowing for effective mapping of both drugs and diseases in a unified embedding space. Another approach involves combining the reasoning paths and predictions provided by probabilistic case-based reasoning with those of a knowledge graph embedding method to identify putative drug repositioning indications. Additionally, a model called DREG (Drug Repurposing Entity Knowledge graph) uses a large-scale knowledge graph to extract association information between known drug and disease entities and predict unknown drug-disease relationships. An ensemble learning model can also be used, where the full knowledge graph is sampled into smaller subgraphs and knowledge graph embedding models generate embeddings for each subgraph, which are then aggregated to generate link predictions. Finally, a knowledge graph embedding approach can be applied to represent drugs and diseases, addressing challenges such as data representation and imbalanced datasets.
What are limitations of drug repositioning?5 answersDrug repositioning, or repurposing, is a strategy to identify new therapeutic uses for existing drugs. However, there are several limitations associated with this approach. Firstly, many existing models suffer from the massive number of unvalidated drug-disease associations and the use of negative sampling techniques, which are invalid in real-world settings. Secondly, some drugs may not be able to effectively cross the blood-brain barrier, limiting their potential therapeutic success. Additionally, the repositioning process often requires drugs to go through phase II and III clinical trials for their new purpose, which can be time-consuming and costly. Furthermore, the business model of large pharmaceutical companies and issues related to patents can pose obstacles to drug repositioning efforts. Finally, link prediction methods in knowledge graphs may lack sufficient evidence to prove drug-disease associations, especially for rare diseases.
What are the potential benefits of drug repurposing?5 answersDrug repurposing, also known as drug repositioning, offers several potential benefits. It is a time-saving and cost-effective strategy that maximizes the therapeutic value of existing drugs. By identifying new therapeutic uses for existing drugs, drug repurposing increases the likelihood of success and reduces the risk of failure compared to traditional drug discovery methods. It combines experimental and computational approaches to rationally develop or identify new uses for drug molecules. Drug repurposing leverages the existing safety data of drugs tested in humans, making it a valuable alternative for addressing rare, difficult-to-treat diseases and neglected diseases. It also presents an emerging strategy for optimizing the therapeutic potential of existing medicines, rapidly identifying effective treatments, and repurposing approved drugs for new indications. Additionally, drug repurposing has the potential to combat life-threatening diseases and provide a quick response to unpredictable situations like pandemics.
What are some examples of computational and machine learning approaches in drug repurposing?5 answersComputational and machine learning approaches have been used in drug repurposing. For example, Ouzounis et al. developed a machine learning framework to predict cytochrome P450 (CYP450) inhibition using interaction fingerprints derived from molecular docking simulations. Aqeel and Majid used machine learning regression approaches to model binding affinities and identify potential COVID-19 treatments through drug repurposing. They employed decision tree regression (DTR) model and demonstrated its improved statistical measures. Additionally, Feng et al. used machine learning predictors to screen DrugBank compounds for repurposing in opioid use disorder (OUD) treatment. They analyzed binding affinities of DrugBank compounds on opioid receptors and provided guidance for repurposing based on machine learning predictions. These studies highlight the potential of computational and machine learning approaches in accelerating drug repurposing by predicting inhibition, identifying potential treatments, and screening compounds for specific indications.
What are some unsolved challenges in drug repurposing research?5 answersDrug repurposing research faces several unsolved challenges. One challenge is the need to identify suitable candidates that can effectively compete with the numerous drugs commonly used in clinical practice for specific diseases. Another challenge is the lack of clear regulatory guidance for using real-world data in drug repurposing studies. Additionally, there is a lack of sex-aware methods in drug repurposing research, which hinders the identification of drugs that have sex-specific responses. Furthermore, obtaining research funds for generic drug repurposing is challenging, and commercial parties often lack interest in pursuing marketing authorization for repurposed drugs. These challenges highlight the need for increased public funding for generic drug repurposing research and a reduction in the regulatory burden for repurposed generic drugs. Overall, addressing these challenges will contribute to the advancement of drug repurposing research and its potential benefits in terms of cost and time savings, as well as improved healthcare outcomes.
What is the current state of drug repurposing research?5 answersDrug repurposing research is currently in an advanced state, with increasing publications and global collaboration. Major research areas include emerging viruses, cancer, methodological approaches, preclinical research, and infectious diseases. The United States is the most active country in publishing drug repurposing research, followed by China, India, the United Kingdom, and Italy. Pharmaceutical companies are embracing drug repurposing as an efficient, cost-effective, and time-saving strategy. This approach combines experimental and computational methods to identify new therapeutic uses for existing drugs, leveraging existing safety data and maximizing the therapeutic value of approved drugs. Drug repurposing holds great promise in addressing rare, difficult-to-treat diseases and neglected diseases. It offers a pathway to rapidly identify effective treatments and repurpose approved drugs for new indications, benefiting patients and healthcare systems. The strategy of repurposing drugs is particularly relevant in the treatment of malignant glioma, where pre-existing physiological pathways can be targeted. Novel techniques, such as experimental screening and computational approaches, are being developed to enhance the drug repurposing process.