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Record W7001020279

Improving aerosolization and potency of a thermally stable spray dried vaccine platform for inhalation delivery

2023· dissertation· en· W7001020279 on OpenAlexfundno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMcMaster UniversityOntario Centres of Excellence
KeywordsAerosolizationInhalationPotencyImmunizationAerosolSpray dryingMicrosphere
DOInot available

Abstract

fetched live from OpenAlex

Pulmonary immunization via inhalation of aerosolized vaccines offers enhanced immunity against respiratory diseases, in addition to the ability to mitigate cold-chain requirements. Achieving ideal particle size and consistent aerosolization of vaccine powders to achieve efficient lung deposition is challenging. In contrast to respiratory delivery of non-biological pharmaceuticals, there is no well characterized formulation platform readily adaptable to incorporating sensitive biologics like viruses and viral vectors. This work focuses on improving the aerosolization and bioactivity of spray dried vaccines to achieve the end goal of effective immunization via inhalation. The biologics studied in this work were human serotype 5 adenovirus (AdHu5) and Influenza H1N1 PR8 (influenza). First, eight amino acids, known to improve the dispersibility, were screened for improving aerosolization of adenovirus containing mannitol/dextran (control) powder. However, no amino acid significantly improved the aerosolization performance compared to the control, but the presence of amino acids was found to decrease the viral activity in dry powders. To understand the effect of amino acid concentration on aerosolization and underlying negative effects on adenovirus activity, L-leucine was chosen for further investigation due to its comparable aerosol performance to the control. While a high concentration of L-leucine (>50% (w/w)) improved the aerosolization significantly, it was found to cause aggregation of adenoviruses in liquid formulation prior to spray drying. Further, the factors underlying the activity losses of adenovirus in spray dried powders were explored. It was found that the presence of internal solid air interface within the microstructure of the dry particles are important indicators of activity losses in spray dried formulations. This work provides a deeper understanding of the previously unexplored causes of viral activity losses in liquid formulation (negative effects of amino acids on viruses) and dry particles (inactivation of viruses at solid air interfaces). Finally, a thermally stable dry powder vaccine platform with high aerosolization was developed for influenza vaccine and was tested for immunogenicity in mice. The work outlined the challenges associated with dry powder administration in murine models for preclinical studies and used a custom made dosator device. The final dry powder vaccine platform developed for influenza virus showed high aerosolization (>40% FPF) and elicited efficient immune response in mice. The dry powder formulation platform developed in this work can be explored for other vaccines. The knowledge gained from this work offers a major step forward in the development of marketable dry powder vaccines for inhalation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.222
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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