Despite advances in drug discovery, viral infections remain a major challenge for scientists across the globe. The recent pandemic of COVID-19 (coronavirus disease 2019), caused by a viral infection with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), has created a disastrous situation all over the world. As no drugs are available to treat this life-threatening disease and the mortality rate due to COVID-19 is high, there is an utmost need to attempt to treat the infection using drug repurposing. Some countries are against the use of these drugs because of adverse effects associated with drug repurposing and lack of statistically significant clinical data, but they have been found to be effective in some countries to treat COVID-19 patients (off-label/investigational). This article emphasises possible drug candidates in the treatment of COVID-19. Most of these drugs were found to be effective in in vitro studies. There is a need to re-assess in vitro data and to carry out randomised clinical trials. Further investigations of these drugs are recommended on a priority basis.
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http://dx.doi.org/10.1016/j.ijantimicag.2020.105984 | 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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