Supercritical Fluid Technologies to Fabricate Proliposomes
Bibliographic record
Abstract
Proliposomes are stable drug carrier systems designed to form liposomes upon addition of an aqueous phase. In this review, current trends in the use of supercritical fluid (SCF) technologies to prepare proliposomes are discussed. SCF methods are used in pharmaceutical research and industry to address limitations associated with conventional methods of pro/liposome fabrication. The SCF solvent methods of proliposome preparation are eco-friendly (known as green technology) and, along with the SCF anti-solvent methods, could be advantageous over conventional methods; enabling better design of particle morphology (size and shape). The major hurdles of SCF methods include poor scalability to industrial manufacturing which may result in variable particle characteristics. In the case of SCF anti-solvent methods, another hurdle is the reliance on organic solvents. However, the amount of solvent required is typically less than that used by the conventional methods. Another hurdle is that most of the SCF methods used have complicated manufacturing processes, although once the setup has been completed, SCF technologies offer a single-step process in the preparation of proliposomes compared to the multiple steps required by many other methods. Furthermore, there is limited research into how proliposomes will be converted into liposomes for the end-user, and how such a product can be prepared reproducibly in terms of vesicle size and drug loading. These hurdles must be overcome and with more research, SCF methods, especially where the SCF acts as a solvent, have the potential to offer a strong alternative to the conventional methods to prepare proliposomes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".