This analysis presents a systematic evaluation of the extent of therapeutic opportunities that can be obtained from drug repurposing by connecting drug targets with disease genes. When using FDA-approved indications as a reference level we found that drug repurposing can offer an average of an 11-fold increase in disease coverage, with the maximum number of diseases covered per drug being increased from 134 to 167 after extending the drug targets with their high confidence first neighbors. Additionally, by network analysis to connect drugs to disease modules we found that drugs on average target 4 disease modules, yet the similarity between disease modules targeted by the same drug is generally low and the maximum number of disease modules targeted per drug increases from 158 to 229 when drug targets are neighbor-extended. Moreover, our results highlight that drug repurposing is more dependent on target proteins being shared between diseases than on polypharmacological properties of drugs. We apply our drug repurposing and network module analysis to COVID-19 and show that Fostamatinib is the drug with the highest module coverage.
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http://dx.doi.org/10.1038/s41598-021-99721-y | DOI Listing |
Front Pharmacol
December 2024
Syreon Research Institute, Budapest, Hungary.
Background: Non-adherence to medication remains a persistent and significant challenge, with profound implications for patient outcomes and the long-term sustainability of healthcare systems. Two decades ago, the World Health Organization (WHO) dedicated its seminal report to adherence to long-term therapies, catalysing notable changes that advanced both research and practice in medication adherence. The aim of this paper was to identify the most important progress made over the last 2 decades in medication adherence management and to initiate a discussion on future objectives, suggesting priority targets for the next 20 years.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
Division of Software, Yonsei University, Mirae Campus, Yeonsedae-gil 1, Wonju-si, 26493 Gangwon-do Korea.
Purpose: Drug repositioning, a strategy that repurposes already-approved drugs for novel therapeutic applications, provides a faster and more cost-effective alternative to traditional drug discovery. Network-based models have been adopted by many computational methodologies, especially those that use graph neural networks to predict drug-disease associations. However, these techniques frequently overlook the quality of the input network, which is a critical factor for achieving accurate predictions.
View Article and Find Full Text PDFBenchmarking is an important step in the improvement, assessment, and comparison of the performance of drug discovery platforms and technologies. We revised the existing benchmarking protocols in our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery platform to improve utility and performance. We optimized multiple parameters used in drug candidate prediction and assessment with these updated benchmarking protocols.
View Article and Find Full Text PDFFront Immunol
December 2024
Aix-Marseille Univ, CNRS, INP, Inst Neurophysiopathol, GlioME Team, Marseille, France.
In recent decades, immunometabolism in cancers has emerged as an interesting target for treatment development. Indeed, the tumor microenvironment (TME) unique characteristics such as hypoxia and limitation of nutrients availability lead to a switch in metabolic pathways in both tumor and TME cells in order to support their adaptation and grow. Glioblastoma (GBM), the most frequent and aggressive primary brain tumor in adults, has been extensively studied in multiple aspects regarding its immune population, but research focused on immunometabolism remains limited.
View Article and Find Full Text PDFBioinform Biol Insights
January 2025
Department of Pathology & Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
While deep learning (DL) is used in patients' outcome predictions, the insufficiency of patient samples limits the accuracy. In this study, we investigated how transfer learning (TL) alleviates the small sample size problem. A 2-step TL framework was constructed for a difficult task: predicting the response of the drug temozolomide (TMZ) in glioblastoma (GBM) cell cultures.
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