Analgesics
Antiandrogens
Azvudine
Bromhexine
Budesonide
Colchicine
Conv. Plasma
Curcumin
Famotidine
Favipiravir
Fluvoxamine
Hydroxychlor..
Ivermectin
Lifestyle
Melatonin
Metformin
Minerals
Molnupiravir
Monoclonals
Naso/orophar..
Nigella Sativa
Nitazoxanide
Paxlovid
Quercetin
Remdesivir
Thermotherapy
Vitamins
More

Other
Feedback
Home
Top
 
Feedback
Home
c19early.org COVID-19 treatment researchSelect treatment..Select..
Melatonin Meta
Metformin Meta
Azvudine Meta
Bromhexine Meta Molnupiravir Meta
Budesonide Meta
Colchicine Meta
Conv. Plasma Meta Nigella Sativa Meta
Curcumin Meta Nitazoxanide Meta
Famotidine Meta Paxlovid Meta
Favipiravir Meta Quercetin Meta
Fluvoxamine Meta Remdesivir Meta
Hydroxychlor.. Meta Thermotherapy Meta
Ivermectin Meta

Thalidomide for COVID-19

Thalidomide has been reported as potentially beneficial for treatment of COVID-19. We have not reviewed these studies. See all other treatments.
Gysi et al., Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19, arXiv, doi:10.48550/arXiv.2004.07229
The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
Kumar, S., COVID-19: A Drug Repurposing and Biomarker Identification by Using Comprehensive Gene-Disease Associations through Protein-Protein Interaction Network Analysis, MDPI AG, doi:10.20944/preprints202003.0440.v1
COVID-19 (2019-nCoV) is a pandemic disease with an estimated mortality rate of 3.4% (estimated by the WHO as of March 3, 2020). Until now there is no antiviral drug and vaccine for COVID-19. The current overwhelming situation by COVID-19 patients in hospitals is likely to increase in the next few months. About 15 percent of patients with serious disease in COVID-19 require immediate health services. Rather than waiting for new anti-viral drugs or vaccines that take a few months to years to develop and test, several researchers and public health agencies are attempting to repurpose medicines that are already approved for another similar disease and have proved to be fairly effective. This study aims to identify FDA approved drugs that can be used for drug repurposing and identify biomarkers among high- risk and asymptomatic groups. In this study gene-disease association related to COVID-19 reported mild, severe symptoms and clinical outcomes were determined. The high-risk group was studied related to SARS-CoV-2 viral entry and life cycle by using Disgenet and compared with curated COVID-19 gene data sets from the CTD database. The overlapped gene sets were enriched and the selected genes were constructed for protein-protein interaction networks. Through interactome, key genes were identified for COVID-19 and also for high risk and asymptomatic groups. The key hub genes involved in COVID-19 were VEGFA, TNF, IL-6, CXCL8, IL10, CCL2, IL1B, TLR4, ICAM1, MMP9. The identified key genes were used for drug-gene interaction for drug repurposing. The chloroquine, lenalidomide, pentoxifylline, thalidome, sorafenib, pacitaxel, rapamycin, cortisol, statins were proposed to be probable drug repurposing candidates for the treatment of COVID-19. However, these predicted drug candidates need to be validated through randomized clinical trials. Also, a key gene involved in high risk and the asymptomatic group were identified, which can be used as probable biomarkers for early identification.
Islam et al., Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding, Scientific Reports, doi:10.1038/s41598-023-30095-z
AbstractThe search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns “ensemble embeddings” of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.
Please send us corrections, updates, or comments. c19early involves the extraction of 100,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Treatments and other interventions are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
  or use drag and drop   
Thanks for your feedback! Please search before submitting papers and note that studies are listed under the date they were first available, which may be the date of an earlier preprint.
Submit