Influenza virus (flu) ranks highest in disease burden of all infectious diseases as measured in disability-adjusted life years. Seasonal epidemics cause 200,000-500,000 worldwide deaths annually. The total economic burden of seasonal flu is estimated to range from approximately $26B to $87B each year in the US in terms of direct medical expenses and lost work and productivity. Additionally, at least six known flu pandemics have become global human catastrophes, most notably the Spanish Flu pandemic of 1918, which killed 3-5% of the worlds population. Any reduction in the infection rate, transmission, and severity of flu infection would greatly reduce our healthcare expenditures and improve the quality of life for millions of people every year. The current vaccines are formulated annually based on predictions of which circulating flu strains may be prevalent in a given season. The effectiveness of these vaccines varies from year to year based on the circulation of unexpected antigenic variants and other factors. Vaccine design is complicated the by the multiplicity of flu strains, each with rapidly-evolving dominant antigen epitopes (decoy epitopes) that largely stimulate strain- restricted immunity. One strategy for rational antigen design, termed Immune Refocusing Technology (IRT), involves introducing mutations that reduce the immunogenicity of these decoy epitopes thus shifting the immune response to target more widely-conserved subdominant epitopes. BMI has previously applied this IRT approach with some notable successes to other viral antigens (e.g. HRV and the RSV F protein), and we now focus on the major flu surface antigen glycoprotein HA using H1, H3, and B vaccine strains as parental antigens. The anticipated effort to design a suitably modified antigen would ordinarily involve a protracted process of trial-and-error testing of many potential candidates. However, we have recently developed the ANATOPE automated B cell epitope prediction software package with algorithm parameters tuned using methods in artificial intelligence. Our algorithm identifies epitopes with a significantly higher success rate than previously available prediction programs. This breakthrough allows us to assign immunogenicity strength scores to particular antigen surface patches and will further guide and accelerate the design of mutant antigens that refocus the immune response to cross-strain conserved epitopes. In this application, we propose to engineer and test the immunogenicity of rationally-designed HA antigens containing mutations that both 1) dampen the immunogenicity of dominant strain-restricted decoy epitopes and 2) enhance the immunogenicity of conserved subdominant epitopes associated with broadly neutralizing antibodies. Follow- up studies will assess the rationally-designed antigens in a ferret challenge study and prepare the approach for translation into humans as a universal vaccine that does not require annual reformulation.
Public Health Relevance Statement: NARRATIVE Influenza is among the most important pathogens in terms of negative impact upon human health and healthcare expense. The current seasonal vaccines have a mixed record in terms of preventing illness and death. Development of improved vaccines is complicated by the rapid antigenic evolution of circulating viruses and the strain-restricted protections developed by our immune systems. In this proposal, we combine two novel technologies to develop universal influenza vaccines. The first, the Immune Refocusing Technology, is used to alter antibody binding sites, epitopes, such that the immune system can produce a broadened, cross-strain protective response. The second, a computational B cell epitope analysis program called ANATOPE, is used to guide the rational design of antigenic mutants bearing amino acid substitutions that stimulate improved immune responses. This project will focus on improving the breadth of protection stimulated by the three major components of the seasonal vaccine to reduce the need for annual reformulations. If successful, follow-up studies will include additional analysis in alternative animal models, a more comprehensive analysis of T cell immune responses, and preparation for advancement into IND-enabling studies.
Project Terms: Algorithms; Amino Acid Substitution; Animal Model; Animal Testing Alternatives; Antibodies; Antibody Binding Sites; Antibody Formation; Antibody Response; Antigens; Antiviral Agents; antiviral immunity; Artificial Intelligence; B-Lymphocyte Epitopes; Baculoviruses; base; Binding Sites; Biological Assay; Blood Circulation; burden of illness; California; Cells; Cellular Immunity; Cessation of life; Communicable Diseases; Computational algorithm; Computer Analysis; Computer software; cross reactivity; Cryoelectron Microscopy; design; Development; disability-adjusted life years; Distant; Economic Burden; Engineering; Epidemic; Epitopes; Evolution; Ferrets; flu; Follow-Up Studies; Future; Glycoproteins; Goals; Ha antigen; Health; Health Expenditures; Healthcare; Hemagglutination; Hemagglutinin; Hong Kong; Human; Immune; Immune response; Immune system; Immunity; Immunization; immunogenicity; improved; in silico; indexing; infection rate; Influenza; Influenza A Virus, H1N1 Subtype; influenza virus vaccine; influenzavirus; Insecta; Manuals; Measures; Medical; Methods; Modeling; Modification; Mus; mutant; Mutation; neutralizing antibody; new technology; novel; pandemic influenza; pathogen; Pattern Recognition; Population; Preparation; prevent; Process; Productivity; programs; Proteins; Quality of life; Recombinants; response; seasonal influenza; Seasons; Sequence Analysis; Series; Serological; Severities; Singapore; Site; Spanish flu; Statistical Data Interpretation; Structure; success; Surface; Surface Antigens; Switzerland; T-Lymphocyte; Technology; Testing; Texas; Translations; transmission process; universal influenza vaccine; universal vaccine; Vaccine Design; vaccine effectiveness; Vaccines; Validation; Variant; Viral Antibodies; Viral Antigens; Viral Physiology; Virus; Work
