Adjuvanted influenza vaccine dynamics

Abstract

Adjuvanted influenza vaccines constitute a key element towards inducing neutralizing antibody responses in populations with reduced responsiveness, such as infants and elderly subjects, as well as in devising antigen-sparing strategies. In particular, squalene-containing adjuvants have been observed to induce enhanced antibody responses, as well as having an influence on cross-reactive immunity. To explore the effects of adjuvanted vaccine formulations on antibody response and their relation to protein-specific immunity, we propose different mathematical models of antibody production dynamics in response to influenza vaccination. Data from ferrets immunized with commercial H1N1pdm09 vaccine antigen alone or formulated with different adjuvants was instrumental to adjust model parameters. While the affinity maturation process complexity is abridged, the proposed model is able to recapitulate the essential features of the observed dynamics. Our numerical results suggest that there exists a qualitative shift in protein-specific antibody response, with enhanced production of antibodies targeting the NA protein in adjuvanted versus non-adjuvanted formulations, in conjunction with a protein-independent boost that is over one order of magnitude larger for squalene-containing adjuvants. Furthermore, simulations predict that vaccines formulated with squalene-containing adjuvants are able to induce sustained antibody titers in a robust way, with little impact of the time interval between immunizations.

Tracking influenza a virus infection in the lung from hematological data with machine learning

“…Mathematical modeling of host immune responses has largely contributed to improving our understanding of the overall course of influenza infection [28][29][30][31][32][33][34][35][36][37] as well as the personalization of therapies and vaccines [38][39][40] . Mathematical models consist of systems of ordinary differential equations describing the viral dynamics within the host.…”

Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning

“…Mathematical modeling of host immune responses has largely contributed to improving our understanding of the overall course of influenza virus infection ( 24 , 26 – 34 ) as well as the personalization of therapies and vaccines ( 35 – 37 ). Mathematical models consist of systems of ordinary differential equations describing the viral dynamics within the host.…”

A Computationally Optimized Broadly Reactive Hemagglutinin and Neuraminidase Vaccine Boosts Antibody-Secreting Cells and Induces a Robust Serological Response, Preventing Lung Damage in a Pre-Immune Model

“…Most influenza vaccine platforms use hemagglutinin (HA) and/or neuraminidase (NA) as vaccine antigens to elicit immune responses that will interfere with the HA and NA activity during viral infection [15][16][17][18]. The formulation of a recombinant protein with a potent adjuvant is a promising approach to prevent the severe disease caused by infection [19][20][21][22][23]. Our laboratory has developed a computationally optimized broadly reactive antigen (COBRA) methodology for the design of seasonal and pre-pandemic influenza vaccines.…”