In-silico based multi epitope vaccine construction against glioblastoma (GBM): A comparative study

Abstract

The most aggressive and fatal malignant primary tumor of the central nervous system with rapid proliferation, extensive invasion, and frequent recurrence even following standard treatment is glioblastoma (GBM).The existing management approaches such as maximum surgical resection, radiotherapy, and temozolomide chemotherapy only offer limited survival benefits due to heterogeneity in tumors, resistance to treatments, evasion of immunity, and lack of ability of drug to penetrate all parts of the body via the blood brain barrier. Hence, there is an urgent need to develop effective immunotherapy plans like therapeutic vaccination to enhance the outcomes and prevent recurrence. The proposed work provides an in silico multi-epitope vaccine design plan based on immunoinformatics against glioblastoma. The comparative analyses of calculations were carried out using seven chosen glioblastoma-related proteins. The screening and prioritization of the antigenic proteins were followed by prediction of cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and B-cell epitopes. Toxicity, allergenicity, and the antigenicity of the predicted epitopes were filtered to ensure the predicted epitopes are safe and effective antigens. Appropriate linkers and a component of adjuvants were used to reconstitute epitopes of interest into a multi-epitope vaccine construct to enhance the immunogenicity. The vaccine candidate that was designed was tested using physicochemical property analysis, structural modeling and validation, receptor-binding testing of the vaccine candidate using molecular docking, and immune simulation to estimate the potential of the immune response to the vaccine. In general, this work offers a cost-efficient computational vaccine design framework and also provides a promising GBM vaccine candidate that can be subjected to experimental validation in the future.