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Enregistrement W7019843825

Incorporation of Lipid Nanoparticles into Pullulan Based Oral Thin Films for the Delivery of Vaccines

2022· dissertation· en· W7019843825 sur OpenAlexaboutno aff

Notice bibliographique

RevueMacSphere (McMaster University) · 2022
Typedissertation
Langueen
DomainePharmacology, Toxicology and Pharmaceutics
ThématiqueAdvanced Drug Delivery Systems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBuccal administrationActive ingredientSyringeDosage formHypodermic needleOral administrationDrugVaccinationImmune system
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Infectious diseases are most effectively controlled by vaccines that can elicit an immune response and create antibodies. As it stands, most vaccines are administered using subcutaneous or intramuscular injections. Injections via needle and syringe often invoke anxiety for individuals, which can result in low vaccination rates. The cost associated with administering vaccines is also high due to the requirement of trained health care professionals for administration. Additionally, administration via injection produces significant amounts of biohazardous waste. Administration of a vaccine by the swallowing of a pill or capsule also presents challenges. The active ingredient must be able to withstand passage through the gastrointestinal tract where enzymatic or acid related degradation is possible. Two promising sites for vaccine delivery are the sublingual region which is located on the floor of the mouth and the underside of the tongue, and the buccal region located on the gums, cheeks, and inner lip. These regions, present in the oral cavity, allow for an active ingredient to be rapidly absorbed into the bloodstream without having to undergo first pass metabolism. One drug delivery method that has recently gained interest is the oral thin film. Upon placing an oral thin film on the buccal or sublingual region of the mouth, the film will rapidly dissolve and release the active ingredient to be absorbed into the bloodstream. Rapid Dose Therapeutics, a company in Burlington, Ontario, has developed the QuickStrip, an oral thin film that can deliver caffeine, vitamin B12, melatonin, and tetrahydrocannabinol (THC) through the buccal and sublingual route. Rapid Dose Therapeutics is interested in expanding the applications of the QuickStrip into vaccine delivery. Incorporating vaccines into oral thin films would allow for a pain-free, self-administrable inoculation process. This would result in a decrease in costs associated with vaccine distribution, making vaccines more accessible, while also eliminating associated biohazardous waste. Vaccines are traditionally classified as live and non-live. Live vaccines contain a live, weakened strain of the pathogenic organism whereas non-live vaccines contain an inactivated whole pathogenic organism or a subunit of the pathogen. Over the course of the COVID-19 pandemic, a new vaccine type, messenger RNA (mRNA) -based vaccines, emerged. The mRNA within these vaccines encodes for the target antigen and employs the ribosomes within the host cell to transcribe the mRNA into the target antigen. The mRNA is encapsulated within lipid nanoparticles (LNP) which assist in delivering and protecting the RNA. mRNA based vaccines are beneficial over traditional vaccine types as they are safe and faster to manufacture. This thesis was performed in conjunction with Rapid Dose Therapeutics and worked toward being able to use the QuickStrip as a vaccine delivery method. In this work, lipid polymer hybrid nanoparticles were prepared using a probe sonication technique. The effect of sonication amplitude and temperature on particle size was studied. Next, ibuprofen loaded lipid polymer hybrid nanoparticles were synthesized using a nanoprecipitation technique. A fluorescent dye, fluorescein isothiocyanate, was then loaded into the hybrid nanoparticles. This allowed the acquisition of super resolution optical microscopy images of the particles, both prior to and after being incorporated into an oral thin film. Commercially produced lipid nanoparticles were then acquired from Acuitas Therapeutics, the company that produces the lipid nanoparticles for the Pfizer-BioNTech vaccine. These lipid nanoparticles contain a model mRNA strand as well as the fluorescent dye 1,1’-dioctadecyl-3,3,3’,3’-tetramethylindocarbocyanine. Super resolution optical microscopy was employed to visualize the Acuitas Therapeutics lipid nanoparticles on a glass coverslip and in oral thin films. The images acquired suggested that the lipid nanoparticles were unharmed during the film casting process. The integrity of the mRNA within the lipid nanoparticles was then assessed and confirmed using gel electrophoresis. To ensure the mRNA was remaining encapsulated within the particles, the Quant-iT RiboGreen RNA assay was used. Initial studies indicated that the RNA was becoming unencapsulated from the nanoparticles once the film mixture had been cast. This resulted in several modifications to the preparation and formulation of the oral thin films. Ultimately, a film formulation containing a lipid-PEG molecule was used to stabilize the lipid nanoparticles within the oral thin film.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,053
Tête enseignante GPT0,331
Écart entre enseignants0,277 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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