Optimization of a computationally designed antiviral for influenza

计算设计的流感抗病毒药物的优化

基本信息

批准号：
9046012
负责人：
Deborah H. Fuller
金额：
$ 20.94万
依托单位：
VIRVIO, INC.
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-02-15 至 2018-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9046012
关键词：
Adverse effects Affinity Amino Acids Antiviral Agents Binding Binding Proteins Biological Availability Cause of Death Cessation of life Computer Simulation Computing Methodologies Data Development Disease Dose Drug resistance Elderly Future General Population Hemagglutinin Hospitalization Human Immune Immune response In Vitro Infection Inflammatory Response Influenza Influenza A Virus, H1N1 Subtype Influenza A Virus, H5N1 Subtype Lead Longevity Marketing Mediating Medical Methods Morbidity - disease rate Mus Pathogenesis Peptides Population Protein Engineering Proteins Public Health Recovery Resistance Therapeutic Time Vaccination Variant Viral Virus Work cost design flu in vivo influenza virus vaccine influenzavirus innovation mucosal site novel pandemic disease pandemic influenza pre-clinical prevent prophylactic public health relevance receptor research study resistant strain seasonal influenza stem

项目摘要

DESCRIPTION (provided by applicant): Influenza is a leading cause of death and morbidity in the US, resulting in up to 40,000 deaths, ~200,000 hospitalizations and > $10.4 billion in expenses per year. Current antivirals incur high resistance rates and have low efficacy so there is a crucial need to develop better approaches to protect from seasonal influenza and future pandemics. We aim to develop computationally designed peptides as a pre-exposure prophylactic and post-exposure therapeutic against influenza. We have shown that a small computationally designed protein (HB36.6), interferes with influenza infection by binding the highly conserved hemagglutinin stem region and neutralizes a wide range of influenza variants including pandemic strains and strains resistant to current antivirals. When delivered intranasally, this influenza binder confers complete prophylactic and therapeutic protection against different influenza strains in mice, a result that demonstrates for the first time, transformation of a theoretical target designed by this computational method into a new antiviral with strong biopotency in vivo. In this application, we propose to build on our promising findings with HB36.6 and broaden efficacy by computationally designing de novo hyperstable mini-protein binders (30-40 amino acids) that will be effective against influenza providing pan-specific protection against a broader range of influenza strains. These new hyperstable mini-binders, will be smaller, more stable, have increased bioavailability, and will have a lower cost of manufacturing than our current lead Group 1 binder HB36.6. We hypothesize that the optimized computationally designed mini-protein binders will afford superior broad, potent prophylactic and therapeutic protection in mice against Group 1 viruses. We will determine expression and in vitro neutralization of computationally designed mini-proteins and identify a single mini-protein binder that affords the best prophylactic and therapeutic protection in mice challenged with a lethal dose of Group 1 influenza virus. If successful, these experiments will establish a new class of flu antivirals that could overcome the limitations of currently marketed flu antivirals an build a strong preclinical data package for future development.

描述（由申请人提供）：流感是美国死亡和发病的主要原因，每年导致多达 40,000 人死亡、约 200,000 人住院治疗以及超过 104 亿美元的费用，目前的抗病毒药物耐药率高且疗效低，因此。迫切需要开发更好的方法来预防季节性流感和未来的流行病，我们的目标是开发计算设计的肽作为预暴露。我们已经证明，一种经过计算设计的小蛋白 (HB36.6) 通过结合高度保守的血凝素干区来干扰流感感染，并中和多种流感病毒变异体，包括大流行毒株和耐药毒株。当鼻内给药时，这种流感结合剂可以对小鼠的不同流感病毒株提供完全的预防和治疗保护，这一结果首次证明了通过这种计算设计的理论目标的转变。在此应用中，我们建议以 HB36.6 为基础，通过计算设计从头设计超稳定微型蛋白结合剂（30-40 个氨基酸）来扩大疗效。有效对抗流感，提供泛特异性与我们目前领先的 1 类粘合剂 HB36.6 相比，这些新型超稳定迷你粘合剂更小、更稳定，具有更高的生物利用度，并且制造成本更低。优化的计算设计的微型蛋白结合剂将为小鼠提供针对第 1 组病毒的更广泛、有效的预防和治疗保护。我们将确定计算设计的微型蛋白的表达和体外中和作用，并鉴定单个微型蛋白结合剂。为受到致死剂量的 1 类流感病毒攻击的小鼠提供最佳的预防和治疗保护。如果成功，这些实验将建立一类新型流感抗病毒药物，可以克服目前市售流感抗病毒药物的局限性，并建立强大的临床前数据。包以供未来发展。