Building a Predictive Framework for Adjuvant Combinatorics in Vaccine Development

建立疫苗开发中佐剂组合的预测框架

• 批准号: 9562727
• 负责人: Nicolas Chevrier
• 金额: $ 243万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9562727
• 关键词: Address; Adjuvant; Biological Assay; Biological Process; Cancer Model; Cancer Vaccines; Coculture Techniques; Combinatorics; Complex; Development; Disease; Drug Combinations; Drug usage; Genomics; HIV-1; Image; Immune; Immune response; Immunologics; Immunomodulators; In Vitro; Malignant Neoplasms; Measures; Methods; Pathway interactions; Pharmaceutical Preparations; Property; Proteomics; Quadruplet Multiple Birth; Stimulus; Testing; Triplet Multiple Birth; Vaccine Design; Work; bacterial resistance; base; design; experimental study; in vitro Assay; in vivo; in vivo evaluation; innovation; mouse model; novel; novel strategies; novel vaccines; paired stimuli; pathogen; predictive modeling; vaccine development

Project Summary It is becoming clear that one solution to the development of new treatments for complex diseases such as HIV-1, cancer, or resistant bacterial strains is to use efficient drug combinations whose properties cannot be achieved by one drug alone. However, studying all possible combinations of drugs is impractical – if not infeasible – and thus, there is a critical need for new approaches to tackle this challenge. In an effort to illuminate this currently intractable question, we will develop a computational and experimental framework to predict the effects of multiple stimuli on the induction of immune responses, and evaluate the utility of this principle for the development of new vaccines. In this project, we hypothesize that the effects of higher-order combinations of stimuli (i.e., triplets or quadruplets) can be accurately predicted by using information about the effects of single and pairs of stimuli only. Specifically, we will test this idea by combining novel high-throughput in vitro assays followed by in vivo testing in mouse models of cancer, in an effort to develop innovative anti-tumor vaccines. We will (1) study the effects of millions of immune modulating agents through computations and experiments in vitro by developing high-throughput co-culture assays using live imaging; (2) measure the effects of thousands of immune stimuli combinations on DCs in vitro by developing genomics and proteomics methods; and (3) design and test new vaccine formulations based on immune stimuli combinations in vivo using mouse models of cancer. This innovative and interdisciplinary work marks a departure from current approaches to investigate immunological pathways and their interactions, and is poised to make a significant impact on vaccine design efforts, and more broadly, on how to rationally select combinations of drugs to enable desired effects on biological processes.

项目摘要 越来越清楚的是，一种解决方案，以发展新的治疗复杂的 疾病，如艾滋病毒-1，癌症，或耐药菌株是使用有效的药物 这些组合的性质不能由单独的一种药物实现。然而，研究所有 可能的药物组合是不切实际的-如果不是不可行的-因此，有一个关键的 需要采取新的办法来应对这一挑战。为了阐明这一点， 这个棘手的问题，我们将开发一个计算和实验框架来预测 多种刺激对诱导免疫应答的影响，并评估其效用。 新疫苗的研制原则。在这个项目中，我们假设， 更高阶的刺激组合（即，三胞胎或四胞胎）可以准确预测， 仅使用关于单个和成对刺激的影响的信息。具体来说，我们将测试 这一想法是通过将新的高通量体外测定与小鼠体内试验相结合， 癌症模型，努力开发创新的抗肿瘤疫苗。我们将（1）研究 通过计算和体外实验研究数百万种免疫调节剂的作用 通过开发使用活体成像的高通量共培养试验;（2）测量 通过开发基因组学，在体外对DC进行数千种免疫刺激组合， 蛋白质组学方法;（3）设计和测试基于免疫的新疫苗制剂 使用小鼠癌症模型的体内刺激组合。这一创新和跨学科的 这项工作标志着偏离了目前研究免疫途径的方法， 它们之间的相互作用，并准备对疫苗设计工作产生重大影响，等等。 广泛地说，关于如何合理地选择药物组合，以实现对生物学的期望效果， 流程.