Modifying the Release Properties of Liposomes Toward Personalized Medicine
Bibliographic record
Abstract
Surfactant-liposome interactions have historically been investigated as a simplified model of solubilization and breakdown of biological membranes by surfactants. In contrast, our goal was to utilize surfactants to modify the encapsulation and release properties of liposomes. The ability to manufacture one liposomal formulation, which could be modified by the addition of a surfactant to support a wide range of release profiles, would provide greater flexibility than manufacturing multiple batches of liposomes, each differing in composition and with its own specific release profile. A liposomal ciprofloxacin formulation was modified by the addition of various surfactants. These formulations were characterized in terms of liposome structure by cryo-TEM imaging, vesicle size by dynamic light scattering, drug encapsulation by centrifugation-filtration, and in vitro release (IVR) performance. The addition of polysorbate 20 or polysorbate 80 to liposomal ciprofloxacin, in a hypotonic environment, resulted in a concentration-dependent loss of encapsulated drug, and above 0.4% polysorbate 20, or 0.2% polysorbate 80, a modified IVR profile as well. This study demonstrates that the encapsulation and release properties of a liposomal formulation can be modified postmanufacture by the addition of judiciously chosen surfactants in combination with osmotic swelling of the liposomes and may support a personalized approach to treating patients.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".