Zaleplon nanospanlastics loaded transdermal patches: formulation, optimization, ex-vivo permeation, and in-vivo studies
Bibliographic record
Abstract
Zaleplon (ZLP) is a commonly used sedative-hypnotic drug that has low oral bioavailability because of its poor water solubility and extensive hepatic metabolism. This study aimed to encapsulate ZLP into spanlastic nanovesicles to enhance its bioavailability via transdermal delivery. Using Span 60 and Tween 80, ZLP-loaded spanlastics were fabricated using thin film hydration technique according to 3 2 full factorial design. In the applied design, the influence of formulation variables on vesicle size, entrapment efficiency, and cumulative drug amount released over 24 h was investigated, leading to the identification of the optimal formulation. The optimized spanlastics were nanosized, spherical vesicles measuring 297.2 ± 8.17 nm, with an encapsulation efficiency of 65.75 ± 3.28% and a 24-hour drug release rate of 76.44 ± 5.66%. FT-IR studies revealed no significant chemical interactions between ZLP and the excipients used. HPMC K100M transdermal patches loaded with the optimized ZLP-spanlastics were formulated utilizing solvent casting technique. The patches were smooth and elastic with uniform drug content, ranging from 92.65 ± 2.54 to 96.12 ± 1.57%. The steady-state flux (Jss) of the spanlastic transdermal patch (ZLP-SP1) across rabbit skin was over 3.62 times higher than that of the control transdermal patch, indicating a significant enhancement in drug permeation. The investigated transdermal patches were stable under accelerated conditions with non-irritating properties. The in-vivo studies have shown that the pharmacokinetic parameters of ZLP oral suspension and ZLP-SP1 are significantly different, with the relative bioavailability of ZLP-SP1 being 2.681%. Therefore, these fabricated transdermal patches could be effectively used to treat insomnia.
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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".