Process development of tangential flow filtration and sterile filtration for manufacturing of mRNA-lipid nanoparticles: A study on membrane performance and filtration modeling
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) are the most studied delivery systems for mRNA therapeutics and have gained importance in both industry and academia following the approval of multiple mRNA-LNP-based vaccines since 2021. The COVID-19 pandemic proved the remarkable efficacy and rapid deployment of mRNA-LNP vaccines, reinforcing their potential for broader therapeutic and vaccine applications. Currently, multiple mRNA-LNPs are in various stages of preclinical and clinical development. LNPs are sensitive to the manufacturing process, and to mitigate the risks associated with bringing an mRNA-LNP from benchtop to industrial scale, it is recommended to have a systematic process development approach, including mathematical modeling and statistical analysis. Among the unit operations required for mRNA-LNP manufacturing, concentration and buffer exchange by tangential flow filtration (TFF), as well as sterile filtration, are challenging and must be optimized to guarantee process scalability and product quality, while avoiding issues such as membrane fouling and incorrect filter capacity. This study investigates the optimization of TFF and sterile filtration parameters to manufacture higher concentration mRNA-LNPs necessary for cancer vaccines, particularly personalized cancer vaccines. Various flat-sheet TFF cartridges were tested under different process parameters, including flow rate and transmembrane pressure (TMP), to identify the most effective process conditions. Furthermore, the sterile filtration of mRNA-LNPs was analyzed using the gradual plugging model, providing insights into improving filter capacity, optimizing filtration pressures, and defining the design space for large-scale manufacturing. These findings contribute to the development of a robust and scalable mRNA-LNP manufacturing process ensuring product quality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".