Grand Challenges is a family of initiatives fostering innovation to solve key global health and development problems. Each initiative is an experiment in the use of challenges to focus innovation on making an impact. Individual challenges address some of the same problems, but from differing perspectives.
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.
Peter Kwong of the National Institute of Allergy and Infectious Diseases in the U.S. will use an informatics-based approach to identify influenza virus epitopes that are especially suited to induce a strong and broad immune response, being conserved, accessible, and with a specific structural flexibility, and develop them as vaccine targets. Influenza is highly contagious and can cause severe illness, particularly among the young, elderly, and those with pre-existing conditions. Current vaccines are only partially effective, protect against single strains, and are generally ineffective against pandemic strains. Starting with three exposed influenza viral proteins, hemagglutinin (HA), neuraminidase (NA), and matrix protein 2 ectodomain (M2e), they will identify and rank candidate epitopes by determining their structural flexibility using all-atom molecular dynamic simulations, and by evaluating their conservation and surface accessibility, to promote recognition by the immune system and the generation of antibodies that target different viral strains. Candidate epitopes will be tested for antigenicity in vitro, and the best used to immunize mice to characterize the antibody response. After optimizing the vaccine regimen, they will ultimately evaluate the ability of the candidate vaccines to provide protection against diverse influenza strains in multiple 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.
Jonah Sacha of the Oregon Health & Science University in the U.S. will explore a new approach to vaccine design by using a cytomegalovirus (CMV) vector expressing conserved influenza antigens to induce an effector memory T cell response that persists in the lungs and can provide lifelong immunity against influenza. The development of a universal influenza vaccine is a top global health priority: four pandemic outbreaks in the last 100 years killed tens of millions of people, and current antibody-mediated vaccines target highly variable antigenic proteins that are extremely strain-specific and unable to protect against future threats. Work on HIV and tuberculosis vaccines has shown that CMV-vectored vaccines can elicit and maintain effector memory T cells (TEM) at high frequency in the lung. While TEM don’t protect against infection, they restrict pathogen replication in the lung to the point that it is effectively eliminated. They will extend this work to influenza vaccine development, determining whether a CMV vaccine can protect cynomolgus macaque monkeys from the highly pathogenic 1918 influenza strain. After establishing the minimum lethal dose of the virus, they will vaccinate monkeys with a CMV vector expressing three highly conserved influenza viral proteins. They will characterize the subsequent cellular immune response, and then challenge the vaccinated animals with a low dose of 1918 influenza to evaluate the ability of the vaccine to protect them from infection.
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.
Jonathan Heeney of the University of Cambridge in the United Kingdom will combine computational design with high-throughput synthetic biology to deliver an effective, universal influenza vaccine candidate for clinical trials in 24 months. Influenza infection impacts public health and the global economy, yet the high mutation rate of the virus has thwarted traditional approaches to develop a broadly effective vaccine. To address this, they developed a Digital Immune Optimized and selected Synthetic Vaccine (DioSynVax) technology platform, which has been successfully used for hemorrhagic fever vaccine development. They will now apply it to influenza vaccine development, by first analyzing structural and antibody-binding data to identify highly conserved antigens from three essential viral proteins: M2, neuraminidase (NA), and hemagglutinin (HA), which will ultimately result in broader and longer-lasting immune protection. These data will then be used to generate libraries of synthetic antigens for high-throughput screening to identify those that bind the most strongly to panels of broadly reactive monoclonal antibodies, and a subset will be tested for their ability to induce an antibody response in mice. The most promising candidate for each of the three viral proteins will then be combined and tested as a vaccine for protection against multiple influenza strains in ferrets.
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.
Jacob Glanville of Distributed Bio in the U.S. will complete the pre-clinical development of Centivax Flu, a universal vaccine to protect humans and livestock against all forms of seasonal and pandemic influenza, in order to begin first-in-human studies in 2021. Pandemic and seasonal influenza have killed millions of people – the 1918 pandemic alone caused more deaths than World War One – and has led to widespread culling of infected swine and poultry populations. Current vaccines target highly variable regions of seasonal influenza and must be redesigned annually. In addition, population growth, expanded travel networks, and industrial farming practices have increased the risk of pandemic outbreaks. Centivax consists of low doses of 30 computationally-selected, distant evolutionary variants of influenza hemagluttinin (HA) protein epitopes. When the vaccine is administered, only B cells that recognize conserved epitopes respond, and the result is a broad and highly effective immune response that destroys the virus. In this project, they will optimize the formulation with four new adjuvant types, and confirm the conformational stability of the proteins in each. They will also optimize in vivo delivery of the vaccine in domestic pig, and evaluate its safety and efficacy in pigs and ferrets. The veterinary vaccine is expected to be on the market, and the human vaccine ready for human testing, in 2021.