Testing a vaccine candidate against Hepatitis C virus designed by combinatorial optimization
Autor:
Malaina, Iker
; Martinez, Luis
; Salcines-Cuevas, David
; Teran-Navarro, Héctor
; Ocejo-Vinyals, J. Gonzalo
; Gonzalez-Lopez, Elena
; Soriano, Vicente
; Úbeda Cantera, María
; Pérez Pinilla, Martin-Blas
; Martínez de la Fuente, Ildefonso
; Álvarez-Domínguez, Carmen
Fecha:
2023Revista / editorial:
Scientific ReportsCitación:
Malaina, I., Martinez, L., Salcines-Cuevas, D. et al. Testing a vaccine candidate against Hepatitis C virus designed by combinatorial optimization. Sci Rep 13, 21746 (2023). https://doi.org/10.1038/s41598-023-48458-xTipo de Ítem:
Articulo Revista IndexadaDirección web:
https://www.nature.com/articles/s41598-023-48458-xResumen:
This paper presents a new procedure for vaccine design against highly variable viruses such as Hepatitis C. The procedure uses an optimization algorithm to design vaccines that maximize the coverage of epitopes across different virus variants. Weighted epitopes based on the success ratio of immunological assays are used to prioritize the selection of epitopes for vaccine design. The procedure was successfully applied to design DC vaccines loaded with two HCV peptides, STG and DYP, which were shown to be safe, immunogenic, and able to induce significant levels of anti-viral cytokines, peptide-specific cellular immune responses and IgG antibodies. The procedure could potentially be applied to other highly variable viruses that currently lack effective vaccines.
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Nombre: Testing_a_vaccine_candidate_against_Hepatitis_C_virus.pdf
Tamaño: 1.249Mb
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