Amprenavir for COVID-19

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.

Abstract Using as a template the crystal structure of the SARS-CoV-2 main protease, we developed a pharmacophore model of functional centers of the protease inhibitor-binding pocket. With this model, we conducted data mining of the conformational database of FDA-approved drugs. This search brought 64 compounds that can be potential inhibitors of the SARS-CoV-2 protease. The conformations of these compounds undergone 3D fingerprint similarity clusterization. Then we conducted docking of possible conformers of these drugs to the binding pocket of the protease. We also conducted the same docking of random compounds. Free energies of the docking interaction for the selected compounds were clearly lower than random compounds. Three of the selected compounds were carfilzomib, cyclosporine A, and azithromycin—the drugs that already are tested for COVID-19 treatment. Among the selected compounds are two HIV protease inhibitors and two hepatitis C protease inhibitors. We recommend testing of the selected compounds for treatment of COVID-19.

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..

IntroductionWith the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease. Computational models have the ability to quickly predict potential reusable drug candidates to treat diseases.MethodsIn this study, two matrix decomposition-based methods, i.e., Matrix Decomposition with Heterogeneous Graph Inference (MDHGI) and Bounded Nuclear Norm Regularization (BNNR), were integrated to predict anti-viral drugs. Moreover, global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation were implemented to evaluate the performance of the proposed model based on datasets of DrugVirus that consist of 933 known associations between 175 drugs and 95 viruses.ResultsThe results showed that the area under the receiver operating characteristics curve (AUC) of global LOOCV and local LOOCV are 0.9035 and 0.8786, respectively. The average AUC and the standard deviation of the 5-fold cross-validation for DrugVirus datasets are 0.8856 ± 0.0032. We further implemented cross-validation based on MDAD and aBiofilm, respectively, to evaluate the performance of the model. In particle, MDAD (aBiofilm) dataset contains 2,470 (2,884) known associations between 1,373 (1,470) drugs and 173 (140) microbes. In addition, two types of case studies were carried out further to verify the effectiveness of the model based on the DrugVirus and MDAD datasets. The results of the case studies supported the effectiveness of MHBVDA in identifying potential virus-drug associations as well as predicting potential drugs for new microbes.

Context: The whole universe is facing a coronavirus catastrophe, and prompt treatment for the health crisis is primarily significant. The primary way to improve health conditions in this battle is to boost our immunity and alter our diet patterns. A common bulb veggie used to flavor cuisine is garlic. Compounds in the plant that are physiologically active are present, contributing to its pharmacological characteristics. Among several food items with nutritional value and immunity improvement, garlic stood predominant and more resourceful natural antibiotic with a broad spectrum of antiviral potency against diverse viruses. However, earlier reports have depicted its efficacy in the treatment of a variety of viral illnesses. Nonetheless, there is no information on its antiviral activities and underlying molecular mechanisms. Objectives: The bioactive compounds in garlic include organosulfur (allicin and alliin) and flavonoid (quercetin) compounds. These compounds have shown immunomodulatory effects and inhibited attachment of coronavirus to the angiotensin-converting enzyme 2 (ACE2) receptor and the Mpro of SARS-CoV-2. Further, we have discussed the contradictory impacts of garlic used as a preventive measure against the novel coronavirus. Method: The GC/MS analysis revealed 18 active chemicals, including 17 organosulfur compounds in garlic. Using the molecular docking technique, we report for the first time the inhibitory effect of the under-consideration compounds on the host receptor ACE2 protein in the human body, providing a crucial foundation for understanding individual compound coronavirus resistance on the main protease protein of SARS-CoV-2. Allyl disulfide and allyl trisulfide, which make up the majority of the compounds in garlic, exhibit the most potent activity. Results: Conventional medicine has proven its efficiency from ancient times. Currently, our article's prime spotlight was on the activity of Allium sativum on the relegation of viral load and further highlighted artificial intelligence technology to study the attachment of the allicin compound to the SARS-CoV-2 receptor to reveal its efficacy. Conclusions: The COVID-19 pandemic has triggered interest among researchers to conduct future research on molecular docking with clinical trials before releasing salutary remedies against the deadly malady.