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Folic acid for COVID-19

Folic acid has been reported as potentially beneficial for treatment of COVID-19. We have not reviewed these studies. See all other treatments.
Trischitta et al., Pseudovirus-Based Systems for Screening Natural Antiviral Agents: A Comprehensive Review, International Journal of Molecular Sciences, doi:10.3390/ijms25105188
Since the outbreak of COVID-19, researchers have been working tirelessly to discover effective ways to combat coronavirus infection. The use of computational drug repurposing methods and molecular docking has been instrumental in identifying compounds that have the potential to disrupt the binding between the spike glycoprotein of SARS-CoV-2 and human ACE2 (hACE2). Moreover, the pseudovirus approach has emerged as a robust technique for investigating the mechanism of virus attachment to cellular receptors and for screening targeted small molecule drugs. Pseudoviruses are viral particles containing envelope proteins, which mediate the virus’s entry with the same efficiency as that of live viruses but lacking pathogenic genes. Therefore, they represent a safe alternative to screen potential drugs inhibiting viral entry, especially for highly pathogenic enveloped viruses. In this review, we have compiled a list of antiviral plant extracts and natural products that have been extensively studied against enveloped emerging and re-emerging viruses by pseudovirus technology. The review is organized into three parts: (1) construction of pseudoviruses based on different packaging systems and applications; (2) knowledge of emerging and re-emerging viruses; (3) natural products active against pseudovirus-mediated entry. One of the most crucial stages in the life cycle of a virus is its penetration into host cells. Therefore, the discovery of viral entry inhibitors represents a promising therapeutic option in fighting against emerging viruses.
Masoudi-Sobhanzadeh et al., Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries, Briefings in Bioinformatics, doi:10.1093/bib/bbab113
AbstractTo attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.
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.
Pennisi et al., An Integrated In Silico and In Vitro Approach for the Identification of Natural Products Active against SARS-CoV-2, Biomolecules, doi:10.3390/biom14010043
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has provoked a global health crisis due to the absence of a specific therapeutic agent. 3CLpro (also known as the main protease or Mpro) and PLpro are chymotrypsin-like proteases encoded by the SARS-CoV-2 genome, and play essential roles during the virus lifecycle. Therefore, they are recognized as a prospective therapeutic target in drug discovery against SARS-CoV-2 infection. Thus, this work aims to collectively present potential natural 3CLpro and PLpro inhibitors by in silico simulations and in vitro entry pseudotype-entry models. We screened luteolin-7-O-glucuronide (L7OG), cynarin (CY), folic acid (FA), and rosmarinic acid (RA) molecules against PLpro and 3CLpro through a luminogenic substrate assay. We only reported moderate inhibitory activity on the recombinant 3CLpro and PLpro by L7OG and FA. Afterward, the entry inhibitory activity of L7OG and FA was tested in cell lines transduced with the two different SARS-CoV-2 pseudotypes harboring alpha (α) and omicron (o) spike (S) protein. The results showed that both compounds have a consistent inhibitory activity on the entry for both variants. However, L7OG showed a greater degree of entry inhibition against α-SARS-CoV-2. Molecular modeling studies were used to determine the inhibitory mechanism of the candidate molecules by focusing on their interactions with residues recognized by the protease active site and receptor-binding domain (RBD) of spike SARS-CoV-2. This work allowed us to identify the binding sites of FA and L7OG within the RBD domain in the alpha and omicron variants, demonstrating how FA is active in both variants. We have confidence that future in vivo studies testing the safety and effectiveness of these natural compounds are warranted, given that they are effective against a variant of concerns.
Huang et al., DeepCoVDR: deep transfer learning with graph transformer and cross-attention for predicting COVID-19 drug response, Bioinformatics, doi:10.1093/bioinformatics/btad244
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
Srivastava et al., A Brief Review on Medicinal Plants-At-Arms against COVID-19, Interdisciplinary Perspectives on Infectious Diseases, doi:10.1155/2023/7598307
COVID-19 pandemic caused by the novel SARS-CoV-2 has impacted human livelihood globally. Strenuous efforts have been employed for its control and prevention; however, with recent reports on mutated strains with much higher infectivity, transmissibility, and ability to evade immunity developed from previous SARS-CoV-2 infections, prevention alternatives must be prepared beforehand in case. We have perused over 128 recent works (found on Google Scholar, PubMed, and ScienceDirect as of February 2023) on medicinal plants and their compounds for anti-SARS-CoV-2 activity and eventually reviewed 102 of them. The clinical application and the curative effect were reported high in China and in India. Accordingly, this review highlights the unprecedented opportunities offered by medicinal plants and their compounds, candidates as the therapeutic agent, against COVID-19 by acting as viral protein inhibitors and immunomodulator in (32 clinical trials and hundreds of in silico experiments) conjecture with modern science. Moreover, the associated foreseeable challenges for their viral outbreak management were discussed in comparison to synthetic drugs.
Hosseini et al., Computational molecular docking and virtual screening revealed promising SARS-CoV-2 drugs, Precision Clinical Medicine, doi:10.1093/pcmedi/pbab001
AbstractThe pandemic of novel coronavirus disease 2019 (COVID-19) has rampaged the world, with more than 58.4 million confirmed cases and over 1.38 million deaths across the world by 23 November 2020. There is an urgent need to identify effective drugs and vaccines to fight against the virus. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) belongs to the family of coronaviruses consisting of four structural and 16 non-structural proteins (NSP). Three non-structural proteins, main protease (Mpro), papain-like protease (PLpro), and RNA-dependent RNA polymerase (RdRp), are believed to have a crucial role in replication of the virus. We applied computational ligand-receptor binding modeling and performed comprehensive virtual screening on FDA-approved drugs against these three SARS-CoV-2 proteins using AutoDock Vina, Glide, and rDock. Our computational studies identified six novel ligands as potential inhibitors against SARS-CoV-2, including antiemetics rolapitant and ondansetron for Mpro; labetalol and levomefolic acid for PLpro; and leucal and antifungal natamycin for RdRp. Molecular dynamics simulation confirmed the stability of the ligand-protein complexes. The results of our analysis with some other suggested drugs indicated that chloroquine and hydroxychloroquine had high binding energy (low inhibitory effect) with all three proteins—Mpro, PLpro, and RdRp. In summary, our computational molecular docking approach and virtual screening identified some promising candidate SARS-CoV-2 inhibitors that may be considered for further clinical studies.
Bello et al., Innovative, rapid, high-throughput method for drug repurposing in a pandemic—A case study of SARS-CoV-2 and COVID-19, Frontiers in Pharmacology, doi:10.3389/fphar.2023.1130828
Several efforts to repurpose drugs for COVID-19 treatment have largely either failed to identify a suitable agent or agents identified did not translate to clinical use. Reasons that have been suggested to explain the failures include use of inappropriate doses, that are not clinically achievable, in the screening experiments, and the use of inappropriate pre-clinical laboratory surrogates to predict efficacy. In this study, we used an innovative algorithm, that incorporates dissemination and implementation considerations, to identify potential drugs for COVID-19 using iterative computational and wet laboratory methods. The drugs were screened at doses that are known to be achievable in humans. Furthermore, inhibition of viral induced cytopathic effect (CPE) was used as the laboratory surrogate to predict efficacy. Erythromycin, pyridoxine, folic acid and retapamulin were found to inhibit SARS-CoV-2 induced CPE in Vero cells at concentrations that are clinically achievable. Additional studies may be required to further characterize the inhibitions of CPE and the possible mechanisms.
Bello et al., Erythromycin, Retapamulin, Pyridoxine, Folic acid and Ivermectin dose dependently inhibit cytopathic effect, Papain-like Protease and MPROof SARS-CoV-2, bioRxiv, doi:10.1101/2022.12.28.522082
AbstractWe previously showed that Erythromycin, Retapamulin, Pyridoxine, Folic acid and Ivermectin inhibit SARS-COV-2 induced cytopathic effect (CPE) in Vero cells. In this study and using validated quantitative neutral red assay, we show that the inhibition of CPE is concentration dependent with Inhibitory Concentration-50(IC50) of 3.27 μM, 4.23 μM, 9.29 μM, 3.19 μM and 84.31 μM respectively. Furthermore, Erythromycin, Retapamulin, Pyridoxine, Folic acid and Ivermectin dose dependently inhibit SARS-CoV-2 Papain-like Protease with IC50of 0.94 μM, 0.88 μM, 1.14 μM, 1.07 μM, 1.51 μM respectively and the main protease(MPRO) with IC50of 1.35 μM, 1.25 μM, 7.36 μM, 1.15 μM and 2.44 μM respectively. The IC50for all the drugs, except ivermectin, are at the clinically achievable plasma concentration in human, which supports a possible role for the drugs in the management of COVID-19. The lack of inhibition of CPE by Ivermectin at clinical concentrations could be part of the explanation for its lack of effectiveness in clinical trials.
Sperry et al., Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients, PLOS Computational Biology, doi:10.1371/journal.pcbi.1011050 (Table 2)
Drug repurposing requires distinguishing established drug class targets from novel molecule-specific mechanisms and rapidly derisking their therapeutic potential in a time-critical manner, particularly in a pandemic scenario. In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. However, it is unknown if different statins exhibit consistent function or may have varying therapeutic benefit. A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2. Top drug predictions included statins, which were then assessed using electronic medical records containing over 4,000 COVID-19 patients on statins to determine mortality risk in patients prescribed specific statins versus untreated matched controls. The same drugs were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins, including atorvastatin, were predicted to be active in > 50% of analyses. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin. In vitro testing of SARS-CoV-2 infected cells revealed simvastatin to be a potent direct inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared drug target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and clinically evaluate non-obvious mechanisms and derisk and accelerate drug repurposing opportunities.
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.
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