Abstract
Current treatments for chronic hepatitis B (CHB) are lifelong, often accompanied by side effects and the risk of drug resistance, highlighting the urgent need for alternative therapies such as therapeutic vaccines. However, challenges such as selecting appropriate antigens and addressing multiple hepatitis B virus (HBV) genotypes hinder the development of these vaccines. One approach to overcoming these challenges is reverse vaccinology (RV) combined with immunoinformatics. RV uses computational methods to identify antigens from pathogen genetic information, including genomic and proteomic data. These methods have helped researchers identify conserved epitopes across bacterial strains or viral species, including multiple HBV genotypes. Computational tools, such as epitope mapping algorithms, molecular docking analysis, molecular dynamics simulations, and immune response simulations, enable key epitope identification, predict vaccine candidates' binding potential to immune cell receptors, and forecast the immune response. Together, these approaches streamline therapeutic vaccine design for CHB, making it faster, more cost-effective, and accurate. This review aims to explore the potential role of RV and immunoinformatics in advancing therapeutic vaccine design for CHB.