Comparing in vitro Permeability of a Nanocarrier-Hydrogel Hybrid System with an Alcoholic Hydrogel for Sustained Transdermal Drug Delivery
Bibliographic record
Abstract
Nanocarriers are commonly used as various types of drug delivery systems.However, the topical application of aqueous dispersions of nanocarriers is limited due to high fluidity of the formulations.In contrast, the topical application of various types of hydrogels are common for the good consistency and spreadability properties.For example, most marketed topical preparations of oestradiol (E2), which are used as hormone replacement therapy in menopausal women, are alcoholic hydrogels.Though, such formulations must be applied daily and chronic usage of alcohol can be damaging to the skin.In this paper, a lipid nanocapsule (LNC)hydrogel hybrid system containing E2 was developed without the use of any alcohol or other organic solvents with an aim to achieve sustained transdermal drug delivery.In the LNCs, more than 95% of the drug was encapsulated and the dispersion was used to prepare a hydrogel using carboxypolymethylene.Moreover, an alcoholic hydrogel containing E2 was formulated to compare the in vitro transdermal E2 permeability using synthetic model STRAT-M®.Additionally, an apparatus for the permeability study was developed by modifying USP dissolution apparatus-I and its suitability was realized by comparing its results with the permeability in Franz diffusion cell apparatus.The LNC-hydrogel hybrid system showed more stable and sustained delivery of E2 compared to the alcoholic hydrogels.Therefore, the E2-LNC-hydrogel hybrid system can be a promising formulation for hormone replacement therapy in menopausal women with the potential to increase dosing intervals, reduce hormone label fluctuations by its more stable flux, and eliminate the damaging effects of chronic alcohol application on 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) | 0.000 | 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".