Optimizing a Universal Influenza Subunit Nano/Microparticulate Vaccine
Optimizing a Universal Influenza Subunit Nano/Microparticulate Vaccine
批准号:
10540741
负责人:
Kristy M Ainslie
金额:
$56.04万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-01-15 至 2024-12-31
关键词:
AcetalsAcidsAddressAdjuvantAffectAgonistAlgorithmsAnimal ModelAnimalsAnthrax diseaseAntibody ResponseAntigen PresentationAntigensAntiviral AgentsAvian InfluenzaB-Cell ActivationBiopolymersBirdsCD4 Positive T LymphocytesCellsCessation of lifeConsensusCytosolDataDependenceDextransDoseDrug Delivery SystemsEmergency SituationEmulsionsEncapsulatedEndotoxinsEvaluationFDA approvedFerretsFormulationGenetic DriftGlycolatesGoalsHemagglutininHourHumanImmuneImmune responseIndividualInfectionInfection preventionInfluenzaInfluenza A Virus, H1N1 SubtypeInfluenza A virusIntramuscularMethodologyModelingMusMutationNeuraminidaseNoseParticulatePathogenesisPathogenicityPathologyPeriodicityPhagocytesPhagosomesPlaguePolyestersPolymersPopulationProtein DenaturationProtein SubunitsRouteScheduleSeasonsShapesStimulator of Interferon GenesSubunit VaccinesSurfaceSystemTestingToxic effectVaccinatedVaccinationVaccinesViral Load resultVirus Diseasesaluminum sulfatecomputer generatedcontrolled releasecost effectivedesignfluhead-to-head comparisonimmunological synapseimmunoregulationimprovedinfluenza infectioninfluenza virus vaccineinfluenzavirusinnovationmouse modelnanonanoparticlenew pandemicnovelpandemic diseasepandemic influenzaparticlepreventprocess optimizationprotective efficacyreceptorresponsevaccine deliveryvaccine efficacyvaccine evaluationvaccine formulation
中文摘要
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英文摘要
ABSTRACT
The WHO estimates there are approximately 5 million cases of influenza infections annually, with approximately
500,000 deaths occurring globally. The most cost-effective protection against influenza is vaccination.
Unfortunately, due to yearly antigenic shifts and drifts, current seasonal vaccines are ineffective. There is a need
for a better flu vaccine. In order to design a better flu vaccine, we plan on optimizing the immune synapse using
nano/microparticles (MPs) fabricated from the polymer acetalated dextran (Ac-DEX). Our previous data has
shown a dependence of particle degradation and optimal immune response against an influenza antigen. Not
only does the release of the antigen effect the immune response, the release of the adjuvant is also important.
The optimized degradation of both adjuvant and antigen has a drastic change in survival compared to non-
optimized formulations. Our particle system is unique because it relies on the highly tunable polymer Ac-DEX.
Ac-DEX is ideal for delivery of agents to phagocytic cells because it is acid-sensitive and has significantly
increased degradation in the low acid (~pH 5) of the phagosome. In addition to this it has tunable degradation
rates that can range from hours to months, which is a unique range from commonly used polyesters (e.g.
poly(lactic-co-glycolic acid) (PLGA)) that have degradation on the order of months. Moreover, Ac-DEX is unique
from polyesters because its degradation products are pH neutral, and do not have the potential to shift the local
pH or damage sensitive payloads. We have three specific aims exploring various optimizations of our particle
system. Aim 1 is focused on formulation of the polymer and particles. The release rate of the adjuvant will be
explored. Ac-DEX polymer with various cyclic acetal coverages will be fabricated to degrade over a broad range
of times. In Aim 2 we will evaluate the effect of loading of a novel influenza antigen either on the surface or
encapsulated into the MPs. We will explore degradation rates on antigen release as well as delivery routes in
determining the optimal delivery of influenza antigens that provide a broad range of protection. In Aim 3 we will
explore our optimized system in protecting ferrets. Ferrets are the ideal large animal model for influenza infection.
Using this model, we will evaluate the vaccine efficacy of our formulation, in comparison to a commercially
available flu vaccine.
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