Identifying mutations conferring resistance to Sofosbuvir in DAA naive Genotype 3a isolates from HCV-infected patients in Kolkata, West Bengal

Background and Aim: Hepatitis C Virus (HCV) is a common transmission transmitted infection (TTI) and a leading cause of liver cirrhosis and hepatocellular carcinoma. Direct Acting Antivirals (DAAs) are the gold standard for HCV treatment. Resistance-associated substitutions (RASs) pre-exist at baseline and are often responsible for therapy failure. This study aimed at understanding the mutations in HCV Genotype 3a isolates from DAAs naive patients and used in-silico approaches to predict the effect of these mutations on drug binding. Methods: The Full-length NS5B gene (1773bp) was amplified from 18 Gen-3a plasma samples. Sanger sequencing was performed and reads were assembled using BioEdit. HCV-GLUE online analysis tool was used for identifying RASs. Phylogenetic analysis was performed using MEGA-X, followed by mutational analysis using BioAider v1.423. Homology models carrying mutations in the NS5B protein were generated and refined using online servers. Template selection was done based on high sequence identity. Docking studies were carried out using AutoDock Vina software. Ligand and protein preparation was done using MGL Tools. Sofosbuvir (SOF) and other polymerase inhibitors were docked with wild-type (WT) and mutant NS5B and their molecular interactions were analyzed based on active site binding. Results: Mutational analysis reveals the presence of baseline Resistance Associated Substitutions (RASs) such as A150V and K206E which reduce SOF susceptibility in 30.79% and 38.46% of the samples. Other than these, atypical substitutions like I293V (23.07%) and C316F (7.69%) were also found in this study. In-silico prediction of drug binding studies showed that mutations in and around the active site cleft altered drug binding, leading to interactions in non-specific sites. Conclusion: This study aims to predict the treatment outcome of HCV patients by subjecting identified mutations to in-silico drug binding. This approach will help us understand the impact of NS5B mutations on SOF binding and asses its efficacy.