Continuous Manufacturing of Complex Parenterals such as mRNA Vaccines and Liposomes
Bibliographic record
Abstract
This presentation will focus on the development of a continuous manufacturing platform for complex parenterals.Currently there are 15 US FDA approved products produced via continuous manufacturing.However, all of these products are in the solid oral space.Our laboratory has been developing continuous processing for complex parenterals.Following the Covid 19 pandemic, it has become even more apparent that such an approach is necessary for injectable products.The benefits associated with continuous manufacturing can; reduce cost, increase quality through online process analytical technology (PAT), and increase throughput, to achieve rapid production of high-quality products.Liposomes as well as polymeric micelles, and lipid nanoparticles (LNPs) will be discussed.Key aspects in the development of these novel therapeutics will be addressed together with insights into critical issues in the manufacturing process.Our laboratory has developed a novel continuous manufacturing platform for complex parenteral dosage forms which allows precise control over particle size and can also ensure monodisperse particles.This platform is fully equipped with PAT to ensure all aspects of product quality.An overview of this manufacturing platform will be presented.The platform is based on co-flow technology and employs the formation of a turbulent jet at the site where the two flows mix, promoting vesicle formation.Case studies on different therapeutics prepared using this technology will be discussed as well as the utilization of this technology for quality standards preparation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".