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 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".