Enhancement of Skin Penetration of Hydrophilic and Lipophilic Compounds by pH-sensitive Liposomes
Bibliographic record
Abstract
PURPOSE: Enhance skin penetration of hydrophilic and lipophilic compounds using liposomes that are responsible to the pH of the skin surface. METHODS: pH-sensitive liposomes were prepared by a thin layer and freeze-thaw method with dioleoyl phosphatidyl ethanolamine and cholesteryl hemisuccinate. Liposomal fusion with stratum corneum lipid liposomes was measured using fluorescence resonance energy transfer. Particle diameter and zeta potential of the liposomes after fusion were measured by dynamic light scattering and electrophoresis. RESULTS: Under neutral pH conditions, the diameter of the pH-sensitive liposomes was 130 nm and their zeta potential was -70 mV. In weakly acidic conditions, the diameter was larger than 3,000 nm and the zeta potential was -50 mV. In contrast, the particle diameter and the zeta potential of the non-pH-sensitive liposomes remained constant under various pH conditions. A skin penetration study was performed on hairless mice skin using vertical diffusion cells, showing that the fusion ability of pH-sensitive liposomes was higher than that of non-pH-sensitive liposomes. In the skin penetration study was carried out using hydrophilic (calcein) and lipophilic (N-(7-nitrobenz- 2-oxa-1,3-diazol-4yl)-PE) (NBD-PE) model compounds which were applied to the skin with pH-sensitive liposomes as carrier. The fluorescent compounds contained within the pH-sensitive liposomes permeated the skin more effectively than those within non-pH-sensitive liposomes, and this ability was further enhanced with the lipophilic compound. CONCLUSION: These studies suggest that pH-sensitive liposomes have potential as an important tool for delivery of compounds into the skin.
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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) | 0.001 | 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".