Enabling mRNA Therapeutics: Current Landscape and Challenges in Manufacturing
Bibliographic record
Abstract
Recent advances and discoveries in the structure and role of mRNA as well as novel lipid-based delivery modalities have enabled the advancement of mRNA therapeutics into the clinical trial space. The manufacturing of these products is relatively simple and eliminates many of the challenges associated with cell culture production of viral delivery systems for gene and cell therapy applications, allowing rapid production of mRNA for personalized treatments, cancer therapies, protein replacement and gene editing. The success of mRNA vaccines during the COVID-19 pandemic highlighted the immense potential of this technology as a vaccination platform, but there are still particular challenges to establish mRNA as a widespread therapeutic tool. Immunostimulatory byproducts can pose a barrier for chronic treatments and different production scales may need to be considered for these applications. Moreover, long-term storage of mRNA products is notoriously difficult. This review provides a detailed overview of the manufacturing steps for mRNA therapeutics, including sequence design, DNA template preparation, mRNA production and formulation, while identifying the challenges remaining in the dose requirements, long-term storage and immunotolerance of the product.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".