The coronavirus pandemic could be the most threatening outbreak in the twenty-first century. According to the latest records of world health organization, more than 130 millions have been infected by COVID-19, with more than 2.9 million reported deaths. Yet, there is no magic cure for treatment of COVID-19. The concept of drug repurposing has been introduced as a fast, life-saving approach for drug discovery. Drug repurposing infers investigating already approved drugs for new indications, using the available information about pathophysiology of diseases and pharmacodynamics of drugs. In a recent work, more than 3000 FDA approved drugs were tested using virtual screening as potential antiviral agents for COVID-19. In this work, the top ranked five hits from the previous docking results together with drugs of similar chemical feature and/or mechanistic destinations were further tested using AutoDock Vina. The results showed that anti-HCV combinations could be potential therapeutic regimens for COVID-19 infections.

HCV NS3 protease domain has been one of the most attractive targets for developing new drugs for HCV infection and many drugs were successfully developed, but all of them were designed for targeting HCV genotype 1 infection. HCV genotype 4a dominant in Egypt has paid less attention. Here, we describe our protocol of virtual screening in identification of novel potential potent inhibitors for HCV NS3 of genotype 4a using homology modeling, PLIF (protein-ligand interaction fingerprint), docking, pharmacophore, and dynamic simulation. A high-quality 3D model of HCV NS3 protease of genotype 4a was constructed using crystal structure of HCV NS3 protease of genotype 1b (PDB ID: 4u01) as a template. PLIF was generated using five crystal structures of HCV NS3 (PDB ID: 4u01, 3kee, 4ktc, 4i33, and 5epn) which revealed the most important residues and their interactions with the co-crystalized ligands. A 3D pharmacophore model consisting of six features was developed from the generated PLIF data and then used as a screening filter for 11,244 compounds. Only 423 compounds passed the pharmacophore filter and entered the docking-based virtual screening stage. The highest ranked five hits from docking result (compound (C1-C5)) were selected for further analysis. They exhibited stronger interaction and higher binding affinity than HCV NS3 protease ligands. Dynamic simulation of the protein-best lead complex was performed to validate and augment the virtual screening results and it showed that these compounds have a strong binding affinity and could be very effective in treating HCV genotype 4a infections.

HCV NS3 protease domain has been attractive site for inhibition by several direct-acting antiviral drugs. A great success was achieved in treatment of HCV genotype 1 but HCV genotype 4a dominant in Egypt is resistant for many of these drugs while sensitive to some and the causes of these observations have not been deeply investigated. So we constructed a 3D model of HCV NS3 of genotype 4a using HCV NS3 genotype 1b as a template PDB (1DY9) and after close inspection of differences between the model and the template that would alter the drug susceptibility in HCV genotype 4a we performed a comparative computational docking study of Simeprevir, Vaniprevir, and Paritaprevir in NS3 protease domain of both the genotypes. The result of our study successfully explains the difference in response to treatment by HCV NS3 protease inhibitors drugs for both genotypes. It shows that Simeprevir retains its activity in both genotypes but Paritaprevir loses a significant part of its activity that cannot be used alone in HCV genotype 4a, while Vaniprevir remarkably loses its activity that cannot be used in HCV genotype 4a at all. Dynamic simulation of the 3D model of HCV NS3 of genotype 4a was done to augment the docking result. Then a series of some modified inhibitors were virtually screened against our model and this would open a new era to use structure based drug design in developing new drugs that act preferentially in treatment of HCV genotype 4a or other genotypes dominant in developing countries.