Development and <i>in vitro</i> characterization of chitosan-coated polymeric nanoparticles for oral delivery and sustained release of the immunosuppressant drug mycophenolate mofetil
Bibliographic record
Abstract
OBJECTIVE: To develop an oral sustained release formulation of mycophenolate mofetil (MMF) for once-daily dosing, using chitosan-coated polylactic acid (PLA) or poly(lactic-co-glycolic) acid (PLGA) nanoparticles. The role of polymer molecular weight (MW) and drug to polymer ratio in encapsulation efficiency (EE) and release from the nanoparticles was explored in vitro. METHODS: Nanoparticles were prepared by a single emulsion solvent evaporation method where MMF was encapsulated with PLGA or PLA at various polymer MW and drug: polymer ratios. Subsequently, chitosan was added to create coated cationic particles, also at several chitosan MW grades and drug: polymer ratios. All the formulations were evaluated for mean diameter and polydispersity, EE as well as in vitro drug release. Differential scanning calorimetry (DSC), surface morphology, and in vitro mucin binding of the nanoparticles were performed for further characterization. RESULTS: Two lead formulations comprise MMF: high MW, PLA: medium MW chitosan 1:7:7 (w/w/w), and MMF: high MW, PLGA: high MW chitosan 1:7:7 (w/w/w), which had high EE (94.34% and 75.44%, respectively) and sustained drug release over 12 h with a minimal burst phase. DSC experiments revealed an amorphous form of MMF in the nanoparticle formulations. The surface morphology of the MMF NP showed spherical nanoparticles with minimal visible porosity. The potential for mucoadhesiveness was assessed by changes in zeta potential after incubation of the nanoparticles in mucin. CONCLUSION: Two chitosan-coated nanoparticles formulations of MMF had high EE and a desirable sustained drug release profile in the effort to design a once-daily dosage form for MMF.
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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".