Hepatitis C Virus Saint Petersburg Variant Detection With Machine Learning Methods

Abstract

Hepatitis C virus infection is a significant global health concern, affecting millions worldwide. Although direct-acting antivirals achieve over 90% success rate, treatment failures still occur, particularly when pan-genotypic DAAs are unavailable, and drugs need to be chosen based on the present HCV genotype. Genotyping tests can be misleading, especially in cases involving the 2k/1b recombinant variant. The 2k/1b variant was first discovered in Saint Petersburg in 2002 and is most commonly observed in Eastern European countries, including Russia, Georgia, and Ukraine. Due to migration, the 2k/1b variant has spread to Western Europe and other regions, potentially increasing HCV transmission and changing the virus's epidemiological landscape. The situation highlights the importance of molecular epidemiology in monitoring the spread of the 2k/1b variant. Accurate detection and characterization of the 2k/1b variant are crucial for an effective treatment if no pan-genotypic DAAs are available. To address this need, machine learning models were developed to predict the 2k/1b variant based on 1b and 2k/1b sequence data from nonstructural proteins. They were integrated into the geno2pheno_HCV tool, providing physicians and researchers with an open-access resource for determining HCV genotypes, including the 2k/1b variant.