Martin Karplus of Harvard University in the U.S. will integrate informatics and artificial intelligence approaches to design stable, synthetic antigens based on the viral hemagglutinin (HA) protein to be used as a universal influenza vaccine. Seasonal influenza causes substantial morbidity worldwide, and the development of a universal vaccine is a global health priority. The current HA-based vaccines do not provide broad coverage against multiple strains, and must be administered annually because of the high mutability of the virus. To address this, they will use a new approach to develop vaccines that elicit broadly-neutralizing antibodies effective against many different influenza strains, including avian, swine, and human varieties. First, they will assemble the publicly-available DNA sequences of the HA gene of avian, swine, and human influenza A strains, and use homology modeling with existing protein structures as templates to create a library of antigens that together are predicted to both maintain the immune system’s memory of influenza while enabling a rapid immune response to future seasonal or pandemic strains. The antigen library will be optimized by performing affinity maturation simulations initiated from known germline precursors of broadly-neutralizing antibodies, and combining the results with machine learning, to predict which antigen cocktails will produce antibodies with the greatest breadth of reactivity. The most promising of these immunization cocktails will be efficacy-tested in small animal models.

Patrick Wilson of the University of Chicago in the U.S. will generate a universal influenza vaccine based on multiple conserved and protective viral protein epitopes selected for their ability to produce a broad and lasting immune response. Influenza causes tens of thousands of deaths worldwide each year, and current vaccines designed around individual epitopes are only 20% to 60% effective. To develop a more effective vaccine, they will use a large panel of human antibodies that bind the influenza virus to screen an assortment of hemagglutinin (HA) and neuraminidase (NA) viral proteins from different strains to identify conserved fragments that are the most immunogenic. These data will be combined with computational models to incorporate additional features such as epitope stability, and used to assemble a mosaic of HA and NA antigens from different influenza strains, which should elicit a more effective immune response, including long-term protection against multiple strains. The mosaic antigens will be iteratively tested during development, both in vitro and in vivo, in anticipation of pre-clinical testing and transition to clinical trials.

Dr. Yoshihiro Kawaoka of the University of Tokyo in Japan will develop broadly effective influenza vaccines by mixing together epitopes of conserved fragments of the viral hemagglutinin (HA) protein, which only elicit a weak immune response, together with millions of different, non-naturally occurring fragments that elicit a strong immune response, to induce broadly cross-reacting antibodies. Influenza is of world-wide concern severely impacting public health and the global economy. Tens of millions of reported cases result in tens of thousands of deaths annually in the U.S. alone, and the rapid spread of the virus between countries causes epidemic or pandemic outbreaks. Traditional vaccines are directed towards selected epitopes in the head region of the viral HA protein because they elicit a strong immune response. However, these regions are frequently mutated, rendering the vaccines useless. Vaccines directed towards more conserved epitopes only elicit a weak immune response, but this can be strengthened using HA proteins previously unseen by the immune system. They will produce a library of millions of HA epitopes that contain artificial mutations in the immune-dominant regions while preserving the conserved regions. This should focus the production of highly reactive antibodies against the conserved HA epitopes, which will eliminate a wider range of influenza strains. They will test this using single and repeat immunizations of different mixes in ferrets. Once optimized, the vaccination strategy will be tested in ferrets pre-exposed to influenza virus to mimic the human situation. The result will be a single vaccination that protects against a wider range of influenza strains than traditional vaccines.

Alice McHardy of the Helmholtz Centre for Infection Research in Germany will use a computational approach to engineer a more stable, neuraminidase (NA)-like antigen for use in influenza vaccines to increase both the duration and the breadth of protection against multiple influenza strains. Seasonal influenza viruses are constantly mutating, and current vaccines designed to target the variable, but strongly immunogenic, surface antigen, hemagglutinin (HA), must be frequently replaced. Vaccines designed to target another immunogenic antigen, neuraminidase (NA), fail to elicit a strong immune response likely due to the instability of the NA protein during the manufacturing process. They will combine genetic, structural, and evolutionary data to design an NA-like antigen with epitopes conserved across at least two influenza subtypes to provide protection against a broader range of influenza strains. The protein will be optimized for stability and immunogenicity, and then tested in mouse and ferret influenza infection models. The antigen will be co-formulated with c-di-AMP (known to improve responsiveness to vaccines in vulnerable populations including infants and the elderly) as the adjuvant. The expected result of this project is an antigen that provides increased duration and breadth of immune protection against multiple strains of influenza.