Ultra-low dose Influenza vaccines

超低剂量流感疫苗

基本信息

  • 批准号:
    8853806
  • 负责人:
  • 金额:
    $ 19.99万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-06-01 至 2017-05-31
  • 项目状态:
    已结题

项目摘要

7. Project Summary/Abstract Current seasonal Influenza vaccines are proving to be ineffective, especially in at risk populations. For example, this year's inactivated flu vaccine had only a 9% efficacy rate against H3N2 infections in the elderly and the current live-attenuated Influenza vaccine (LAIV) is aged restricted for those above 49 years of age. Therefore there is glaring unmet need - seasonal Influenza vaccines that are effective. A translational, vaccine platform technology developed at Stony Brook University entitled SAVE (Synthetic Attenuated Virus Engineering) has shown initial success in yielding an anti-Influenza A vaccine in the laboratory strain A/Puerto Rico/8/34 that is effective in animals at very low doses. This SHIFT award seeks to transform this academic discovery into the beginnings of commercial product, by applying the SAVE technology to seasonally relevant human strains and then compare efficacy against the current LAIV. Demonstration of superiority will increase the commercial viability of the technology as well as fulfill a current unmet medical need - flu vaccines that have high efficacy in all populations. The drawbacks of current flu vaccines are two-fold- 1) both the inactivated injectable vaccine or the current LAIV require a large quantity of viral particles per dose >107, and 2) both have low efficacy in the aged population. The SAVE technology could provide a solution to both of these limitations. The SAVE platform relies on synthetic biology and the "re-designing" of a target virus's entire genome to yield a vaccine strain. This customization process uses software- based algorithms to 're-code' the genome of a target virus. Genomic 're-coding' results in a virus that is antigenically identical (i.e. looks exactly like the wild-type, virulent strain) but possesses a genome with hundreds of mutations rendering it attenuated in the host. Since proteins of the SAVE-designed vaccine strain are one hundred percent identical to the virulent strain, animals vaccinated with SAVE-designed vaccines develop a robust and protective immune response. SAVE is a platform technology that has had preliminary success constructing vaccine candidates for multiple, unrelated target viruses including poliovirus and Influenza a virus (Science 2008, Nature Biotech 2010). In Phase I of this proposal we will apply the SAVE technology to construct vaccine candidates for seasonal influenza strains that are clinically relevant and subsequently we will compare these strains to the current commercial live-attenuated influenza vaccine to demonstrate commercial viability. In Phase II we will build upon our success and test our SAVE-designed seasonal influenza vaccine candidates in a ferret model.
7.项目总结/摘要 目前的季节性流感疫苗被证明是无效的,特别是在风险 人口。例如,今年的灭活流感疫苗只有9%的有效率 预防老年人感染H3 N2病毒和目前的减毒活流感疫苗 (LAIV)年龄限制为49岁以上。因此, 有效的季节性流感疫苗。 斯托尼布鲁克大学开发的转化疫苗平台技术 名为SAVE(合成减毒病毒工程)的研究已经取得了初步成功, 在实验室菌株A/波多黎各/8/34中产生抗甲型流感疫苗, 在动物中以非常低的剂量有效。这个奖项旨在改变这种 通过应用SAVE,将学术发现带入商业产品的开端 技术,以季节性相关的人类菌株,然后比较功效, 当前LAIV。优势的展示将增加商业可行性, 技术以及满足目前未满足医疗需求-流感疫苗, 在所有人群中的疗效。当前流感疫苗的缺点有两个方面-- 1)两者都有 灭活的可注射疫苗或目前的LAIV需要大量的病毒 颗粒/剂量>107,和2)在老年人群中均具有低功效。的 SAVE技术可以为这两种限制提供解决方案。 SAVE平台依赖于合成生物学和对目标病毒的“重新设计”, 整个基因组以产生疫苗株。这个定制过程使用软件- 基于算法来“重新编码”目标病毒的基因组。基因组“重新编码”结果 在抗原性相同的病毒中(即,看起来完全像野生型,毒性 菌株),但拥有数百个突变的基因组,使其在 主持人由于SAVE设计的疫苗株的蛋白质为100 与强毒株相同的百分比,用SAVE设计的 疫苗会产生强有力的保护性免疫应答。SAVE是一个平台 这项技术已经初步成功地构建了用于 多种不相关的靶病毒,包括脊髓灰质炎病毒和甲型流感病毒(Science 2008,Nature Biotech 2010)。 在本提案的第一阶段,我们将应用SAVE技术构建疫苗 具有临床相关性的季节性流感病毒株候选者, 我们将把这些毒株与目前商业化的减毒活流感病毒进行比较, 证明疫苗的商业可行性。在第二阶段,我们将在成功的基础上再接再厉, 并在雪貂模型中测试我们SAVE设计的季节性流感候选疫苗。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Live-attenuated H1N1 influenza vaccine candidate displays potent efficacy in mice and ferrets.
候选 H1N1 流感减毒活疫苗在小鼠和雪貂中显示出强大的功效。
  • DOI:
    10.1371/journal.pone.0223784
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Stauft,CharlesB;Yang,Chen;Coleman,JRobert;Boltz,David;Chin,Chiahsuan;Kushnir,Anna;Mueller,Steffen
  • 通讯作者:
    Mueller,Steffen
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Steffen Mueller其他文献

Steffen Mueller的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Steffen Mueller', 18)}}的其他基金

Final Preclinical Testing and Formulation of a Scalable, Live-attenuated SARS-CoV-2 Vaccine
可扩展的 SARS-CoV-2 减毒活疫苗的最终临床前测试和配制
  • 批准号:
    10255845
  • 财政年份:
    2021
  • 资助金额:
    $ 19.99万
  • 项目类别:
Rapid generation and testing of live-attenuated vaccines against SARS-CoV-2
快速生成和测试 SARS-CoV-2 减毒活疫苗
  • 批准号:
    10184147
  • 财政年份:
    2020
  • 资助金额:
    $ 19.99万
  • 项目类别:
A rationally-designed, live-attenuated RSV vaccine for the elderly
设计合理的老年人RSV减毒活疫苗
  • 批准号:
    10449335
  • 财政年份:
    2017
  • 资助金额:
    $ 19.99万
  • 项目类别:
A rationally-designed, live-attenuated RSV vaccine for the elderly
设计合理的老年人RSV减毒活疫苗
  • 批准号:
    10208694
  • 财政年份:
    2017
  • 资助金额:
    $ 19.99万
  • 项目类别:
A rationally-designed, live-attenuated RSV vaccine for the elderly
设计合理的老年人RSV减毒活疫苗
  • 批准号:
    10080661
  • 财政年份:
    2017
  • 资助金额:
    $ 19.99万
  • 项目类别:
Ultra-low dose Influenza vaccines
超低剂量流感疫苗
  • 批准号:
    8648282
  • 财政年份:
    2014
  • 资助金额:
    $ 19.99万
  • 项目类别:

相似海外基金

CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 19.99万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 19.99万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 19.99万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 19.99万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 19.99万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 19.99万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 19.99万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 19.99万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 19.99万
  • 项目类别:
    Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 19.99万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了