Drug repurposing has become increasingly important, particularly in light of the COVID-19 pandemic. This process involves identifying new therapeutic uses for existing drugs, which can significantly reduce the cost, risk, and time associated with developing new drugs, de novo development. A previous conducted study proved that Deep Learning can be used to streamline this process by identifying drug repurposing hypotheses. The study presented a model called REDIRECTION, which utilized the rich biomedical information available in graph form and combined it with Geometric Deep Learning to find new indications for existing drugs. The reported metrics for this model were 0.87 for AUROC and 0.83 for AUPRC. In this current study, the importance of node features in GNNs is explored. Specifically, the study used GNNs to embed two-dimensional drug molecular structures and obtain corresponding features. These features were incorporated into the drug repurposing graph, along with some other enhancements, resulting in an improved model called DMSR. Performance score for the reported metrics values raised by 0.0448 in AUROC and 0.0919 in AUPRC. Based on these findings, we believe that the method used for embedding drug molecular structures is interesting and captures valuable information about drugs. Its incorporation in the graph for drug repurposing can significantly benefit the process, leading to improved performance evaluation metrics.

Rare diseases are a collection of unusual pathologies that afflict millions of individuals globally. However, the creation of treatments for these conditions is frequently limited due to the high expenses and lack of profitability associated with drug development. Orphan drugs, which are medications specifically designed for rare diseases, have played a pivotal role in treating these diseases over the past several years. Nevertheless, their creation remains challenging, and many rare diseases lack approved therapies. Therefore, drug repurposing has emerged as a viable strategy for identifying potential new treatments for these pathologies. A technique that consists in using existing drugs to treat a new disease different from the one that they were developed. This approach can significantly reduce the time and cost of drug development while increasing the likelihood of success. In this paper, we examined the temporal progression of orphan drugs since their introduction and assess the impact of drug repositioning on treatments for rare diseases. Additionally, we aim to identify biological patterns that may be unique to rare diseases treated with repurposed orphan drugs. To this end, we analyzed various biological components associated with these diseases, categorized linked diseases, and obtained the type of orphan drug associated with them. Lastly, we evaluated the phenotypic similarity between diseases treated with an orphan drug through repurposing. Through these findings, we have gained insight into the evolution of orphan drug development in recent years and identified specific patterns that characterize rare diseases associated with them.

Drug repurposing, the process of finding new uses for existing drugs, has gained considerable attention due to its potential to reduce the time and costs associated with drug development. Personalized drug repurposing, in which drugs are selected based on the characteristics of individual patients, is an emerging approach that holds promise for improving clinical outcomes. In this context, exploring disease-drug pairs in already conducted clinical trials can provide valuable insights to identify promising patient populations for further study that may lead to personalized drug repositioning. Our analysis aims to shed a light into clinical outcomes by selecting the most appropriate repurposed drug based on clinical trials patient groups' characteristics, such as age and gender. It also gives information about the state of the clinical trials studying these disease-drug pairs, gathering information about the study type, phase and statistical method used to calculate the p-value of the chosen outcome measurement, among others. Overall, this study highlights the importance of using existing knowledge as an initial framework to facilitate further research, particularly in providing patient-specific information. Furthermore, it underlines the importance of building on previous research to facilitate a comprehensive understanding of the research topic, which can eventually improve patient outcomes.

In recent years and due to COVID-19 pandemic, drug repurposing or repositioning has been placed in the spotlight. Giving new therapeutic uses to already existing drugs, this discipline allows to streamline the drug discovery process, reducing the costs and risks inherent to de novo development. Computational approaches have gained momentum, and emerging techniques from the machine learning domain have proved themselves as highly exploitable means for repurposing prediction. Against this backdrop, one can find that biomedical data can be represented in terms of graphs, which allow depicting in a very expressive manner the underlying structure of the information. Combining these graph data structures with deep learning models enhances the prediction of new links, such as potential disease-drug connections. In this paper, we present a new model named REDIRECTION, which aims to predict new disease-drug links in the context of drug repurposing. It has been trained with a part of the DISNET biomedical graph, formed by diseases, symptoms, drugs, and their relationships. The reserved testing graph for the evaluation has yielded to an AUROC of 0.93 and an AUPRC of 0.90. We have performed a secondary validation of REDIRECTION using RepoDB data as the testing set, which has led to an AUROC of 0.87 and a AUPRC of 0.83. In the light of these results, we believe that REDIRECTION can be a meaningful and promising tool to generate drug repurposing hypotheses.