Continuous Processing of Liposomes to Control and Predict Physical Properties
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Liposomes are specialized drug delivery systems that deliver drugs efficiently and may be used in targeted and/or extended-release applications.Currently, the processing and manufacturing of these drug products is by batch processing in the pharmaceutical industry.Batch processing has disadvantages such as scalability, irreproducibility, down-time between batches and other issues leading to reduced product availability, product waste and increased monetary costs.As a way to circumvent traditional problems associated with batch processing, the U.S. FDA has published guidance focusing on the continuous manufacturing of drug products, quality by design and the incorporation of process analytical technology.In the current work, a continuous process for the formation of liposomes was developed.This process was based on the ethanol-injection process, which includes injecting ethanol with dissolved lipid into an aqueous phase.The process included additional downstream processes such as in-line dilution, in-line concentrating, and at-line particle size analysis.National Instruments (NI) LabVIEW was used to develop the entire process into an automatic, continuous process.All control and measurement devices were controlled by a single computer program.The computer program contained algorithms that enabled prediction measurement of liposomal characteristics (e.g.particle size, particle size distribution and lipid concentration).Moreover, a quality-by-design (QbD) approach was followed from the onset of the project.Following QbD minimized the overall risk in developing the system and established an extensive understanding of liposomes.With the use of multiple design of experiment studies, algorithms and prediction equations were included in the custom-built computer program and established accurate control over the liposome formation process.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it