Comparison of post-emulsification freeze drying or spray drying processes for the microencapsulation of plasmid DNA
Bibliographic record
Abstract
In this work, methods used to microencapsulate plasmid DNA in a biodegradable polymer were compared for their effects on the physicochemical characteristics of DNA-loaded microparticles and on the release and integrity of encapsulated DNA. Microparticles were formulated by either w/o/w emulsification and freeze-drying (EFD) or by w/o/w emulsification and spray-drying (ESD). The influence of both manufacturing processes on particle morphology, charge, release characteristics and biological activity of encapsulated DNA was evaluated. Particles produced by emulsification/spray-drying exhibited more diversity in shape and size than those produced by emulsification/freeze-drying. These particles also exhibited higher plasmid DNA encapsulation efficiency than particles produced by emulsification/freeze-drying. The fractional DNA release rates were similar over the first 25 days for both formulations, release rate declining more rapidly at later times for the ESD product. Mammalian cell transfection assays confirmed the biological activity of encapsulated DNA extracted from both types of particles, with significantly higher transfection levels being observed for ESD particles. Application of a double emulsion (w/o/w) before spray drying resulted in higher encapsulation levels (> 90%) relative to previous literature values, which used single (w/o) emulsions before spray drying. The emulsification/spray-drying technique described here appears to be a rapid and efficient method for the preparation of PLGA microparticles loaded with plasmid DNA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".