Novel Vehicles For Drug Delivery In Atopic Dermatitis: A Narrative Review
Bibliographic record
Abstract
INTRODUCTION: Atopic dermatitis (AD) causes dry and itchy skin and inflammation that severely impairs the quality of life of affected children and adults. While topical glucocorticosteroid application is typically the first-line treatment of choice, steroid treatment is associated with side effects and, increasingly, patient concerns about prolonged use. Novel drugs and drug delivery vehicles are required for patients with AD. OBJECTIVES: To summarize the current literature on novel topical agents for atopic dermatitis and novel delivery vehicles. METHODS: A literature search was conducted, and a narrative review was compiled to summarize recent evidence. RESULTS: Novel topical drugs approved or in late-phase clinical trials for the treatment of AD include the Janus kinase inhibitor ruxolitinib, the phosphodiesterase-4 inhibitors crisaborole, and roflumilast, and the aryl hydrocarbon receptor activator tapinarof. While current topical drugs for AD are delivered via creams, ointments, gels, and related vehicles, novel delivery approaches such as electrospun patches, sprays, liposomes, nanoparticles, and lasers are being developed to enhance transdermal delivery, reduce side effects, and increase treatment adherence. CONCLUSIONS: Topical application of creams or ointments is currently the predominant vehicle for the delivery of atopic dermatitis drugs. In vitro studies on novel vehicles show promising results to overcome the issues associated with topical delivery. Still, these findings have to be corroborated by controlled studies with human patients in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".