Spray freeze drying for protein encapsulation: Impact of the formulation to morphology and stability
Bibliographic record
Abstract
Proteins, the building blocks of life, are increasingly being used as therapeutics for treating several diseases. Yet, there are challenges in the delivery of highly labile materials like proteins, which is often circumvented with the help of encapsulation for targeted delivery and enhanced stability. Spray drying technology has recently been employed for encapsulation due to its’ low cost and scale-up capabilities, yet the high temperatures of drying air makes the technology unsuitable for proteins. More recently, spray freeze drying has evolved as an emerging technology that combines spray drying with freeze drying by using low temperatures, and is thus suitable for maintaining the stability of proteins. This study investigates the correlation between formulation parameters and the properties of protein encapsulated microparticles prepared by spray freeze drying. Morphology was investigated using microscopic methods, and protein stability was examined using infrared and mass spectrometry. By using bovine serum albumin, we verify that increasing the total weight to 15 mg/ml results in microencapsulates with a projected area equivalent diameter of 100 µm larger. We demonstrate that some types of amino acids are essential for shell formation; however, glutamine generates an increase in dimer areas in mass spectra of 5.5. D-Mannitol is the suggested carrier for high encapsulation efficiency (above 90 %). The formulation containing polyvinylpyrrolidone, mannitol, and leucine (at 6, 9, and 2 mg/ml, respectively) produced the lowest reduction in the stability of a few types of proteins; deconvoluted infrared peaks show a difference of less than 2% compared to the free protein. Understanding the spray freeze drying phenomenon for protein encapsulation would allow the control over morphological and chemical properties of microparticles containing active proteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
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".