Antibody IgG response from vaccine composition

Background: Pathogens such as influenza, coronavirus, and HIV, cause significant mortality and morbidity, primarily because their rapid rate of mutation presents serious challenges to vaccine design.  For example, seasonal influenza vaccine effectiveness often falls below 20%, because the time lag between the start of vaccine production and the flu season allows the virus to mutate.  To this day, there exists no effective HIV vaccine.  Many anti-viral vaccines are currently in development, but their success depends on being effective against future strains.  For example, nanoparticle-based vaccines are designed to present panels of different antigens to the host immune system simultaneously, so as to direct the immune response to conserved epitopes and thus increase the breadth of the immune response. Often, viral adaptation conceals such conserved epitopes from the immune system, instead guiding the host immunity towards highly variable, “distracting'' epitopes.   Success in developing vaccines against rapidly mutating viruses thus depends critically on being able to predict their efficacy against, essentially, arbitrary drifted viral strains.

Results: We developed a simple computational model of vaccination titers against viral variants, parametrized using a small sample of experimental antibody binding data for influenza or SARS-CoV-2 nanoparticle vaccines.  The model is able to recapitulate the experimental data to within experimental uncertainty, is relatively insensitive to the choice of the parametrization set, and provides qualitative predictions about the antigenic epitopes exploited by the vaccine, which are broadly consistent with experimentally known epitopes. Overall, our study suggests that simple models of vaccine efficacy could be incorporated into the vaccine development process to improve the effectiveness of designed vaccines.

Reference:

V. Ovchinnikov and M. Karplus. Phenomenological Modeling of Antibody Response from Vaccine Strain Composition. Antibodies, 14(6):1–24, doi=doi.org/10.3390/antib14010006, 2025.