Diltiazem for COVID-19

Abstract Background Viruses such as Ebola, Marburg, influenza, mpox, MERS-CoV, SARS-CoV, and SARS-CoV-2 pose a significant risk for future pandemics. Developing novel antiviral medicines can be time-consuming and resource intensive. Repurposing existing medicines with antiviral activity offers a faster, cost-effective strategy to expand treatment options during public health emergencies. This scan aimed to identify and synthesise recent evidence on repurposed antiviral medicines under investigation for these viruses. Method A horizon scanning approach was employed, starting with a targeted search in Embase, followed by a systematic search of ClinicalTrials.gov to capture the developmental stages of the technologies. Eligible technologies included UK- or EU-licensed medicines repurposed as antiviral therapies for the viruses of interest. Vaccines, unlicensed medicines, and already approved treatments for the targeted viruses were excluded. Results A total of 196 repurposed technologies targeting the viruses were identified from published literature, and the expanded search on the clinical trials registry yielded 58 technologies in active clinical development. Interventional clinical trial activity was limited to influenza and COVID-19, with 29 technologies for COVID-19 and two for influenza advancing to phase III evaluation. For other viruses, proposed antiviral candidates were identified in the literature but had not progressed into clinical development. Commonly investigated pharmacological classes included direct-acting antivirals, tyrosine kinase inhibitors, immunomodulators, and anti-inflammatory agents. Conclusion Repurposing antiviral medicines represents a pragmatic strategy for rapid therapeutic deployment against emerging viral threats. Collaboration among researchers, policymakers, research funders, and regulatory bodies will be essential to improve pandemic preparedness and support repurposing efforts in emergency situations.

Abstract Motivation The coronavirus disease 2019 (COVID-19) remains a global public health emergency. Although people, especially those with underlying health conditions, could benefit from several approved COVID-19 therapeutics, the development of effective antiviral COVID-19 drugs is still a very urgent problem. Accurate and robust drug response prediction to a new chemical compound is critical for discovering safe and effective COVID-19 therapeutics. Results In this study, we propose DeepCoVDR, a novel COVID-19 drug response prediction method based on deep transfer learning with graph transformer and cross-attention. First, we adopt a graph transformer and feed-forward neural network to mine the drug and cell line information. Then, we use a cross-attention module that calculates the interaction between the drug and cell line. After that, DeepCoVDR combines drug and cell line representation and their interaction features to predict drug response. To solve the problem of SARS-CoV-2 data scarcity, we apply transfer learning and use the SARS-CoV-2 dataset to fine-tune the model pretrained on the cancer dataset. The experiments of regression and classification show that DeepCoVDR outperforms baseline methods. We also evaluate DeepCoVDR on the cancer dataset, and the results indicate that our approach has high performance compared with other state-of-the-art methods. Moreover, we use DeepCoVDR to predict COVID-19 drugs from FDA-approved drugs and demonstrate the effectiveness of DeepCoVDR in identifying novel COVID-19 drugs. Availability and implementation https://github.com/Hhhzj-7/DeepCoVDR.