Cefpiramide 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.

In this virtual drug repurposing study, we used 7922 FDA approved drugs and compounds in clinical investigation from NPC database. Both apo and holo forms of SARS-CoV-2 Main Protease as well as Spike Protein/ACE2 were used for virtual screening. Initially, docking was performed for these compounds at target binding sites. The compounds were then sorted according to their docking scores which represent binding energies. The first 100 compounds from each docking simulations were initially subjected to short (10 ns) MD simulations (in total 300 ligand-bound complexes), and average binding energies during MD simulations were calculated using the MM/GBSA method. Then, the selected promising hit compounds based on average MM/GBSA scores were used in long (100-ns and 500-ns) MD simulations. In total around 15 µs MD simulations were performed in this study. Both docking and MD simulations binding free energy calculations showed that holo form of the target protein is more appropriate choice for virtual drug screening studies. These numerical calculations have shown that the following 8 compounds can be considered as SARS-CoV-2 Main Protease inhibitors: Pimelautide, Rotigaptide, Telinavir, Ritonavir, Pinokalant, Terlakiren, Cefotiam and Cefpiramide. In addition, following 5 compounds were identified as potential SARS-CoV-2 ACE-2/Spike protein domain inhibitors: Denopamine, Bometolol, Naminterol, Rotigaptide and Benzquercin. These compounds can be clinically tested and if the simulation results validated, they may be considered to be used as treatment for COVID-19.

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.

SARS-CoV-2, the causative agent of COVID-19, has caused more than 60 million cases worldwide with almost 1.5 million deaths as of November 2020. Repurposing existing drugs is the most rapid path to clinical intervention for emerging diseases.

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.