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Record W4413127918 · doi:10.3390/vaccines13080853

Freeze-Drying of mRNA-LNPs Vaccines: A Review

2025· review· en· W4413127918 on OpenAlexafffund
MD Faizul Hussain Khan, Ayyappasamy Sudalaiyadum Perumal, Amine Kamen

Bibliographic record

VenueVaccines · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Bee Research Fund
KeywordsMessenger RNAScalabilityBiochemical engineeringProtein stabilityFreeze-dryingNanotechnologyProcess (computing)Computer scienceChemistryMaterials scienceEngineeringChromatographyBiochemistry

Abstract

fetched live from OpenAlex

The instability of mRNA vaccines presents significant challenges for their storage, transportation, and large-scale distribution, particularly in resource-limited countries. Recently, freeze-drying (lyophilization) has been considered as a promising approach for preserving mRNA vaccine efficacy. This formulation technique enhances the long-term stability of mRNA vaccines by converting them into a stable dry powder. The purpose of this review is to provide an overview of the current knowledge on the progress of freeze-drying techniques for mRNA vaccines, with emphasis on the associated challenges. This review highlights the factors influencing the stability of freeze-dried mRNA vaccines and provides a comprehensive overview of the formulation components, including excipients, buffers, and surfactants, as well as the process parameters and storage conditions that aim to improve stability and shelf-life. By providing these insights, this review supports the advancement of more robust, scalable, and efficient lyophilization protocols, ultimately addressing the stability limitations of mRNA vaccines and enhancing their global accessibility.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.840
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.332
Teacher spread0.306 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2025
Admission routes2
Has abstractyes

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