Pluronic® F-127 and Pluronic Lecithin Organogel (PLO): Main Features and their Applications in Topical and Transdermal Administration of Drugs
Bibliographic record
Abstract
Topical drug treatment aims at providing high concentrations of drugs at the site of application so as to avoid adverse systemic effects associated with oral administration. Smart polymers, or stimuli-responsive polymers, are able to respond to a stimulus by showing physical or chemical changes in their behaviour as, for example, the delivery of the drug carried by them. The thermo-responsive nature of Pluronic® F-127 (Basf, Ludwigshafen, Germany) makes it an excellent candidate for the delivery of drugs at various application sites. In recent years, PF-127, and later, Pluronic lecithin organogels (PLO), have attracted particular interest in the design of dermal and transdermal delivery systems with a view to promoting, improving or retarding drug permeation through the skin, bearing in mind that for topical delivery systems, accumulation in the skin with minimal permeation is desired, while for systemic delivery, the opposite behaviour is preferred. In this review, we discuss the properties and characteristics of PF-127 and Pluronic lecithin organogels (PLO), and present many examples and advantages of the application of these polymeric systems in topical and transdermal administration of drugs. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".