Double-Controlled Release of Poorly Water-Soluble Paliperidone Palmitate from Self-Assembled Albumin-Oleic Acid Nanoparticles in PLGA in situ Forming Implant
Bibliographic record
Abstract
PURPOSE: To investigate the effects of solvents on the formation of self-assembled nanonization of albumin-oleic acid conjugates (AOCs) using a solvent exchange mechanism for the construction of in situ forming implants (ISFI). METHODS: A poorly water-soluble drug, paliperidone palmitate (PPP), was chosen as the model drug. AOC was synthesized with the 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) reaction. Dichloromethane, tetrahydrofuran, ethanol, N-methyl-2-pyrrolidone, dimethyl sulfoxide, and deionized water were selected to investigate the formation of self-assembled AOC nanoparticles (AONs). The volume ratios of organic solvents against water could determine the miscibility, injectability, and in situ nanonizing capability without aggregation. RESULTS: As the polarity of the organic solvents increased, the AONs exhibited a spherical shape, and the larger the volume of the solvent, the smaller the size of the AONs. To use AOC in ISFI for controlled release of PPP, poly(d,l-lactide-co-glycolide) (PLGA) was combined with the AOC in 2 mL of N-methyl-2-pyrrolidone and water solution (1.8/0.2 ratio). The release rates of all formulations exhibited similar curve patterns overall but were more controlled in decreasing order as follows: AOC, PLGA, and AOC/PLGA for 14 days. CONCLUSION: A combined formulation of AOC and PLGA was found to effectively control the initial burst release of the drug.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".