Conv. Plasma
Nigella Sativa

Home 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

Tipranavir for COVID-19

Tipranavir has been reported as potentially beneficial for treatment of COVID-19. We have not reviewed these studies. See all other treatments.
Ridha Al Fiqri et al., Basic Structure of the Pharmacophore Virtual Screening Protein 7kg7 for Candidate Therapeutic Options COVID-19, Indonesian Journal of Medical Chemistry and Bioinformatics, doi:10.7454/ijmcb.v2i2.1004
Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome, namely coronaviruses (SARS-CoV-2) has been a pandemic to date and is contagious with relatively high mortality rates. Various efforts have been made to control the pandemic to finding the best solution to reduce the spread such as rapid detection based on molecular and serological, as well as efforts to find the best medicine for COVID-19 patients continue to be carried out. We analyzed and concluded that the presatovir compound is capable of being a substitute for the native protein ligand 7KG7, this is proved with a ΔG value of -13.22 kcal/mol and a constant inhibition value of 202.12 pM smaller than the native ligand. Other compounds such as tipranavir and montelukast with ΔG values of -10.99 and -10.81 kcal/mol as well as constant inhibition values at 11.95 and 8.77 nM also indicate that the three test ligands are better than the native ligands. Another supporting factor of this finding was the fact that test ligands were discovered to possess hydrogen bonds that were either greater than or equivalent to those of the initial ligand. The third test ligand exhibited a promising affinity as a possible substitute for native ligand, however, it is imperative to carefully evaluate and take into account the ADMETOX (Absorption, Distribution, Metabolism, Excretion, and Toxicology) elements before to proceeding with the in-vitro or in-vivo phase.
Farag et al., Identification of FDA Approved Drugs Targeting COVID-19 Virus by Structure-Based Drug Repositioning, American Chemical Society (ACS), doi:10.26434/chemrxiv.12003930.v1
The new strain of Coronaviruses (SARS-CoV-2), and the resulting Covid-19 disease has spread swiftly across the globe after its initial detection in late December 2019 in Wuhan, China, resulting in a pandemic status declaration by WHO within 3 months. Given the heavy toll of this pandemic, researchers are actively testing various strategies including new and repurposed drugs as well as vaccines. In the current brief report, we adopted a repositioning approach using insilico molecular modeling screening using FDA approved drugs with established safety profiles for potential inhibitory effects on Covid-19 virus. We started with structure based drug design by screening more than 2000 FDA approved drugsagainst Covid-19 virus main protease enzyme (Mpro) substrate-binding pocket to identify potential hits based on their binding energies, binding modes, interacting amino acids, and therapeutic indications. In addition, we elucidate preliminary pharmacophore features for candidates bound to Covid-19 virus Mpro substratebinding pocket. The top hits include anti-viral drugs such as Darunavir, Nelfinavirand Saquinavir, some of which are already being tested in Covid-19 patients. Interestingly, one of the most promising hits in our screen is the hypercholesterolemia drug Rosuvastatin. These results certainly do not confirm or indicate antiviral activity, but can rather be used as a starting point for further in vitro and in vivo testing, either individually or in combination.
Kumar et al., In Silico Identification and Docking-Based Drug Repurposing Against the Main Protease of SARS-CoV-2, Causative Agent of COVID-19, American Chemical Society (ACS), doi:10.26434/chemrxiv.12049590.v1
The rapidly enlarging COVID-19 pandemic caused by novel SARS-coronavirus 2 is a globalpublic health emergency of unprecedented level. Therefore the need of a drug or vaccine thatcounter SARS-CoV-2 is an utmost requirement at this time. Upon infection the ssRNA genomeof SARS-CoV-2 is translated into large polyprotein which further processed into differentnonstructural proteins to form viral replication complex by virtue of virus specific proteases:main protease (3-CL protease) and papain protease. This indispensable function of main proteasein virus replication makes this enzyme a promising target for the development of inhibitors andpotential treatment therapy for novel coronavirus infection. The recently concluded α-ketoamideligand bound X-ray crystal structure of SARS-CoV-2 Mpro (PDB ID: 6Y2F) from Zhang et al.has revealed the potential inhibitor binding mechanism and the determinants responsible forinvolved molecular interactions. Here, we have carried out a virtual screening and moleculardocking study of FDA approved drugs primarily targeted for other viral infections, to investigatetheir binding affinity in Mpro active site. Virtual screening has identified a number of antiviraldrugs, top ten of which on the basis of their bending energy score are further examined through molecular docking with Mpro. Docking studies revealed that drug Lopinavir-Ritonavir, Tipranavirand Raltegravir among others binds in the active site of the protease with similar or higheraffinity than the crystal bound inhibitor α-ketoamide. However, the in-vitro efficacies of the drugmolecules tested in this study, further needs to be corroborated by carrying out biochemical andstructural investigation. Moreover, this study advances the potential use of existing drugs to beinvestigated and used to contain the rapidly expanding SARS-CoV-2 infection.
Mohapatra et al., Repurposing Therapeutics for COVID-19: Rapid Prediction of Commercially available drugs through Machine Learning and Docking, medRxiv, doi:10.1101/2020.04.05.20054254
ABSTRACTBackgroundThe outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2.Methods and findingsWe perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naïve Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we suggest that the antiretroviral drug Atazanavir (DrugBank ID – DB01072) would probably be one of the most effective drugs based on the selected criterions.ConclusionsOur study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.Author summaryWhy was this study done?The recent outbreak of novel coronavirus disease (COVID-19) is now considered to be a pandemic threat to the global population. The new coronavirus, 2019-nCoV has now affected more than 200 countries with over 17,83,941 cases confirmed and 1,09,312 deaths reported all over the world [as on 12 April 2020].There is an urgent need for the development of drugs or vaccine which can save people worldwide. However, the vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease. Recently, Artificial Intelligence (AI) technology have been utilized to revolutionize the drug development process. Can we use AI based repurposing of existing drugs for accelerated clinical trial in the treatment of COVID-19?What did the researchers do and find?Here, we report the Machine Learning (ML) model based on the Naïve Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the..
Guo et al., Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy, Briefings in Bioinformatics, doi:10.1093/bib/bbac628
Abstract Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-learning methods. To overcome this limitation, in a novel end-to-end architecture (named FeatNN), we develop a coevolutionary strategy to jointly represent the structure and sequence features of proteins and ultimately optimize the mathematical models for predicting CPA. Furthermore, from the perspective of data-driven approach, we proposed a rational method that can utilize both high- and low-quality databases to optimize the accuracy and generalization ability of FeatNN in CPA prediction tasks. Notably, we visually interpret the feature interaction process between sequence and structure in the rationally designed architecture. As a result, FeatNN considerably outperforms the state-of-the-art (SOTA) baseline in virtual drug evaluation tasks, indicating the feasibility of this approach for practical use. FeatNN provides an outstanding method for higher CPA prediction accuracy and better generalization ability by efficiently representing multimodal information of proteins via a coevolutionary strategy.
Mody et al., Identification of 3-chymotrypsin like protease (3CLPro) inhibitors as potential anti-SARS-CoV-2 agents, Communications Biology, doi:10.1038/s42003-020-01577-x
AbstractEmerging outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is a major threat to public health. The morbidity is increasing due to lack of SARS-CoV-2 specific drugs. Herein, we have identified potential drugs that target the 3-chymotrypsin like protease (3CLpro), the main protease that is pivotal for the replication of SARS-CoV-2. Computational molecular modeling was used to screen 3987 FDA approved drugs, and 47 drugs were selected to study their inhibitory effects on SARS-CoV-2 specific 3CLpro enzyme in vitro. Our results indicate that boceprevir, ombitasvir, paritaprevir, tipranavir, ivermectin, and micafungin exhibited inhibitory effect towards 3CLpro enzymatic activity. The 100 ns molecular dynamics simulation studies showed that ivermectin may require homodimeric form of 3CLpro enzyme for its inhibitory activity. In summary, these molecules could be useful to develop highly specific therapeutically viable drugs to inhibit the SARS-CoV-2 replication either alone or in combination with drugs specific for other SARS-CoV-2 viral targets.
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.