Mitoxantrone for COVID-19

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.

Background: COVID-19 is a critical pandemic that has affected human communities worldwide. Although it is urgent to rapidly develop effective drugs, large number of candidate drug compounds may be useful for treating COVID-19, and evaluation of these drugs is time-consuming and costly. Thus, screening to identify potentially effective drugs prior to experimental validation is necessary. Method: In this study, we applied the recently proposed method tensor decomposition (TD)-based unsupervised feature extraction (FE) to gene expression profiles of multiple lung cancer cell lines infected with severe acute respiratory syndrome coronavirus 2. We identified drug candidate compounds that significantly altered the expression of the 163 genes selected by TD-based unsupervised FE. Results: Numerous drugs were successfully screened, including many known antiviral drug compounds. Conclusions: The drugs screened using our strategy may be effective candidates for treating patients with COVID-19.

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.

Abstract Background For a deeper comprehension of the condition and the development of more potent therapies, it is essential to understand COVID-19 pathogenesis. Transmembrane serine protease 2 (TMPRSS2) and disintegrin and metalloproteinase 17 (ADAM17) are two of the most significant proteases in the pathogenesis of COVID-19. An intrinsic tissue protector with antiviral and anti-inflammatory effects is called alpha-1-antitrypsin (A1AT), and it inhibits the protein TMPRSS2, which is crucial for SARS-CoV-2-S protein priming and viral infection. It also prevents the activity of pro-inflammatory chemicals like neutrophil elastase, TNF-, and IL-8.Objective According to current findings, repurposing available medications will result in more effective functioning than using newly designed medications. Based on this, we used FDA-approved drugs and did a computational study to find out what role A1AT plays in SARS-CoV-2 infections and how it stops Covid-19 from spreading.Method This computational study comprises the screening of FDA approved drugs by using molecular networking studies via cytoscape version 3.9.1 to identify any drugs binding interactions with SERPINA1, a gene that provides instructions for making a protein called A1AT, which is a type of serine protease inhibitor, followed by the generation of a pharmacophore model, virtual screening, and docking studies.Result The 22 compounds that were selected from this molecular-networking model were subjected to pharmacophore modelling followed by virtual screening. Through this screening, we have selected 22 molecules based on the Lipinski rule and low RMSD value, i.e., below 0.069235 Ao. From the ZINC database, the top six molecules discovered were found to have a higher affinity for A1AT when compared to the co-crystal ligand (-12.8236). The highest scores obtained by alpha-1-antitrypsin (PDB ID: 7NPK) are − 22.0254 and − 21.676 for ZINC00896543 and ZINC05316843, respectively.Conclusion Consequently, the molecules found by using different software programmes may be employed to control and treat COVID 19. By increasing the levels of A1AT, we may thus infer that these molecules have excellent action in the reversal of COVID-19.