Analgesics
Antiandrogens
Antihistamines
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
Antihistamines Meta
Azvudine Meta Molnupiravir Meta
Bromhexine Meta
Budesonide Meta
Colchicine Meta Nigella Sativa Meta
Conv. Plasma Meta Nitazoxanide Meta
Curcumin Meta Paxlovid Meta
Famotidine Meta Quercetin Meta
Favipiravir Meta Remdesivir Meta
Fluvoxamine Meta Thermotherapy Meta
Hydroxychlor.. Meta
Ivermectin Meta

Moxifloxacin for COVID-19

Moxifloxacin has been reported as potentially beneficial for treatment of COVID-19. We have not reviewed these studies. See all other treatments.
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.
Mostafa et al., FDA-Approved Drugs with Potent In Vitro Antiviral Activity against Severe Acute Respiratory Syndrome Coronavirus 2, Pharmaceuticals, doi:10.3390/ph13120443
(1) Background: Drug repositioning is an unconventional drug discovery approach to explore new therapeutic benefits of existing drugs. Currently, it emerges as a rapid avenue to alleviate the COVID-19 pandemic disease. (2) Methods: Herein, we tested the antiviral activity of anti-microbial and anti-inflammatory Food and Drug Administration (FDA)-approved drugs, commonly prescribed to relieve respiratory symptoms, against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the viral causative agent of the COVID-19 pandemic. (3) Results: Of these FDA-approved antimicrobial drugs, Azithromycin, Niclosamide, and Nitazoxanide showed a promising ability to hinder the replication of a SARS-CoV-2 isolate, with IC50 of 0.32, 0.16, and 1.29 µM, respectively. We provided evidence that several antihistamine and anti-inflammatory drugs could partially reduce SARS-CoV-2 replication in vitro. Furthermore, this study showed that Azithromycin can selectively impair SARS-CoV-2 replication, but not the Middle East Respiratory Syndrome Coronavirus (MERS-CoV). A virtual screening study illustrated that Azithromycin, Niclosamide, and Nitazoxanide bind to the main protease of SARS-CoV-2 (Protein data bank (PDB) ID: 6lu7) in binding mode similar to the reported co-crystalized ligand. Also, Niclosamide displayed hydrogen bond (HB) interaction with the key peptide moiety GLN: 493A of the spike glycoprotein active site. (4) Conclusions: The results suggest that Piroxicam should be prescribed in combination with Azithromycin for COVID-19 patients.
Nandi et al., Repurposing of Drugs and HTS to Combat SARS-CoV-2 Main Protease Utilizing Structure-Based Molecular Docking, Letters in Drug Design & Discovery, doi:10.2174/1570180818666211007111105
Background: COVID-19, first reported in China, from the new strain of severe acute respiratory syndrome coronaviruses (SARS-CoV-2), poses a great threat to the world by claiming uncountable lives. SARS-CoV-2 is a highly infectious virus that has been spreading rapidly throughout the world. In the absence of any specific medicine to cure COVID-19, there is an urgent need to develop novel therapeutics, including drug repositioning along with diagnostics and vaccines to combat the COVID-19. Many antivirals, antimalarials, antiparasitic, antibacterials, immunosuppressive anti-inflammatory, and immunoregulatory agents are being clinically investigated for the treatment of COVID-19. Objectives: The earlier developed one parameter regression model correlating the dock scores with in vitro anti-SARS-CoV-2 main protease activity well predicted the six drugs viz remdesivir, chloroquine, favipiravir, ribavirin, penciclovir, and nitazoxanide as potential anti-COVID agents. To further validate our earlier model, the biological activity of nine more recently published SARS-CoV-2 main protease inhibitors has been predicted using our previously reported model. Methods: In the present study, this regression model has been used to screen the existing antiviral, antiparasitic, antitubercular, and anti pneumonia chemotherapeutics utilizing dock score analyses to explore the potential including mechanism of action of these compounds in combating SARS-CoV-2 main protease. Results: The high correlation (R=0.91) explaining 82.3% variance between the experimental versus predicted activities for the nine compounds is observed. It proves the robustness of our developed model. Therefore, this robust model has been further improved, taking a total number of 15 compounds to formulate another model with an R-value of 0.887 and the explained variance of 78.6%. These models have been used for high throughput screening (HTS) of the 21 diverse compounds belonging to antiviral, antiparasitic, antitubercular, and anti pneumonia chemotherapeutics as potential repurpose agents to combat SARS-CoV-2 main protease. The models screened that the drugs bedaquiline and lefamulin have higher binding affinities (dock scores of -8.989 and -9.153 Kcal/mol respectively) than the reference compound N-[2-(5-fluoranyl-1~H-indol-3-yl)ethyl]ethanamide (dock score of -7.998 Kcal/Mol), as well as higher predicted activities with pEC50 of 0.783 and 0.937 μM and the 0.611 and 0.724 μM respectively. The clinically used repurposed drugs dexamethasone and cefixime have been predicted with pEC50 values of -0.463 and -0.622 μM and -0.311 and -0.428 μM respectively for optimal inhibition. The drugs such as doxycycline, cefpodoxime, ciprofloxacin, sparfloxacin, moxifloxacin, and TBAJ-876 showed moderate binding affinity corresponding to the moderate predicted activity (-1.540 to -1.109 μM). Conclusion: In the present study, validation of our previously developed dock score-based one..
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