Development, QbD-based optimisation, <i>in-vivo</i> pharmacokinetics, and <i>ex-vivo</i> evaluation of Eudragit <sup>®</sup> RS 100 loaded flurbiprofen nanoparticles for oral drug delivery
Bibliographic record
Abstract
This study aims to develop and evaluate flurbiprofen-loaded polymeric nanoparticles to achieve sustained drug release, enhancing therapeutic efficacy and minimising dosing frequency for improved patient outcomes. Flurbiprofen-loaded polymeric nanoparticles were prepared using a tubular microreactor and spray drying, optimised via Box-Behnken Design. Characterisation included particle size, encapsulation efficiency, in vitro and in vivo drug release, and techniques like FTIR, DSC, XRD, and SEM. Statistical analysis ensured robust formulation optimisation and evaluation of performance. The optimised batch of flurbiprofen-loaded polymeric nanoparticles was characterised for mean diameter, PDI, zeta potential, drug release, and EE% were found to be 306.1 ± 6.00 nm, 0.184 ± 0.02 Mw, -23.6 ± 1.51 mV, 85.46 ± 0.53% and 92.31 ± 0.84 (% w/w) respectively. Pharmacokinetic analysis further confirmed the sustained release, extending up to 12 hours and enhancing permeation compared to the pure flurbiprofen. Sustained release of flurbiprofen-loaded polymeric nanoparticles significantly enhances therapeutic effectiveness for inflammatory conditions.
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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 | 0.000 |
| 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 | 0.001 |
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".