Improving predictive capacity of models for universal influenza vaccine development

人们充分认识到需要改进和更普遍的保护性流感疫苗。努力改进的核心是开发更能预测人类对免疫和/或感染反应的动物模型。事实上，NIAID的通用流感疫苗战略计划强调了这一需要。虽然动物模型可能永远无法完全预测人类的反应，但了解它们的全部优点和缺点并确定用于不同目的的最佳模型是一项重要的公共卫生需求，也是我们提出目标的科学前提。这些目标建立在我们广泛使用流感动物模型的基础上，旨在优化免疫印记的动物模型，改善疫苗效力测试，并确定保护和增强免疫应答的免疫相关性。我们的总体目标是提供上级临床前模型，以支持通用流感疫苗的开发。我们将通过三个互补且相互关联的具体目标来实现这一目标，1）在动物模型中重复流感抗原暴露的人血清学应答的最佳建模; 2）改善雪貂流感攻击模型的定量性质;以及3）定义流感病毒诱导的临床症状的血清学相关性。我们能够实现这些目标，是通过我们参与和合作，最近NIAID资助的人类婴儿队列，DIVINCI研究。我们将在三种动物模型中反映这些婴儿的流感抗原暴露情况，并比较免疫学数据集，以确定哪种最准确地反映了人类反应（目标1）。人类和动物数据集和样本的这种结合提供了一种创新的前进方式，并将提供一组独特的差异致敏动物，使用原始机器学习算法（Aim 3）确定感染和免疫反应（Aim 2）的新生理参数的免疫相关性。

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).