Poly n-butylcyanoacrylate (PNBCA) nanocapsules as a carrier for NSAIDs: in vitro release and in vivo skin penetration.
Bibliographic record
Abstract
PURPOSE: The aim of this work was to prepare poly n-butylcyanoacrylate (PNBCA) nanocapsules loaded with indomethacin and to evaluate the ability of this carrier system to deliver the drug systemically after its topical application. METHODS: Poly n-butylcyanoacrylate (PNBCA) nanocapsules of indomethacin were prepared by interfacial polymerization. The physicochemical characterization of the PNBCA nanocapsules was performed by measuring the drug content by HPLC and analyzing the particle size using scanning electron microscopy. The in vitro permeation of indomethacin through excised rat skin and an artificial membrane was determined for PNBCA nanocapsules in pH 7.4 phosphate buffer (I), and in PLF-127 gel (II) and were compared against indomethacin incorporated into 25%w/w PLF-127 gel alone (III). The in vivo percutaneous absorption of indomethacin following the application of the PNBCA nanocapsules and a 25%w/w Pluronic F-127 (PLF-127) gel (III) was monitored by the determination of drug plasma levels in rats. RESULTS: The drug loading results indicated that approximately 76.6% of indomethacin was loaded onto the PNBCA nanocapsules; the average particle size was 188 nm. The in vitro results indicated a rank order for the three formulations (I, II and III) in both the flux at steady state and the cumulative amounts permeated at 8 hrs. The higher drug plasma levels over 6 hrs of indomethacin PNBCA nanocapsules are in agreement with the determined in vitro permeation results. CONCLUSION: The presented data show that indomethacin loaded PNBCA nanocapsules can improve the transdermal delivery of indomethacin compared to a conventional gel formulation using Pluronic F-127. This might be due to their ultra fine particle size and their hydrophilic and hydrophobic surface characteristics.
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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) | 0.000 | 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".