Improving predictive capacity of models for universal influenza vaccine development

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The need for improved and more universally protective influenza vaccines is well recognized. Central to efforts towards improvements is the development of animal models more predictive of the human response to immunization and/or infection. Indeed, this need has been highlighted by the NIAID Strategic Plan for a Universal Influenza Vaccine. While animal models may never be able to fully predict the human response, understanding their full strengths and weaknesses and identifying the optimal models for different purposes is a significant public health need and is the scientific premise behind our proposed objectives. These objectives, which are built upon our extensive use of influenza animal models, are to optimize animal modeling of immunologic imprinting, to improve vaccine efficacy testing, and to identify immune correlates of protection and boosting immune responses. Our overall goal is to provide superior preclinical models to support universal influenza vaccine development. We will achieve this goal through three complementary and interrelated specific aims, 1) optimal modeling of human serologic responses to repeat influenza antigen exposure in animal models; 2) improving the quantitative nature of the ferret influenza challenge model; and 3) defining serologic correlates of influenza virus induced clinical symptoms. Our ability to conduct these aims is supported through our participation in, and collaboration with, a recently NIAID-funded human infant cohort, the DIVINCI study. We will mirror the influenza antigen exposures of a selection of these infants in three animal models and compare immunologic data sets to identify which most accurately reflects the human response (Aim 1). This marriage of human and animal data sets and samples offers an innovative way forward and will provide a unique set of differentially primed animals with which to determine immune correlates of novel physiologic parameters of infection and immune responses (Aim 2) using original machine learning algorithms (Aim 3).