In Vivo and Ex Vivo Evaluation of a Novel Method for Topical Delivery of Macromolecules Through the Stratum Corneum for Cosmetic Applications
Bibliographic record
Abstract
BACKGROUND: Effective topical delivery of large/charged molecules into skin has always been challenging. Chemical penetration enhancers, organic substances that increase permeability of skin, have been in use for decades with variable success. One application of enhancers involves multilamellar vesicles composed of submicron emulsion droplets and micelles surrounded by concentric phospholipid bilayers. OBJECTIVE: This report introduces the next generation of multilamellar vesicles, termed Tiered-Release Vesicles (TRVs), as a new platform for topical delivery of macromolecules such as peptides and hyaluronic acid (HA). METHODS: Fluorescently labeled peptides and HA, diffusion cells, and confocal microscopy were employed to assess the penetration efficiency of macromolecules in TRV formulations using an ex vivo human skin model. Two in vivo studies utilized punch biopsies followed by histochemical staining and analysis. RESULTS: Based on fluorescent intensity, TRV formulations delivered a large peptide more completely (2-5 fold) into ex vivo human skin than optimized liposomes. The penetration of 2 HA species in TRV formulations was 3- to 13-fold higher than with a simple gel vehicle. In the case studies, reduction of solar elastosis was observed from a topical TRV formulation. CONCLUSION: Topical delivery of large peptides and HA into human skin using TRV technology has been demonstrated.
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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".