Mechanistic Formulation Design of Spray-Dried Powders
Bibliographic record
Abstract
Spray drying is gaining traction in the pharmaceutical industry as one of the processing methods of choice for the manufacture of solid dosage forms intended for pulmonary, oral, and parenteral delivery. This process is particularly advantageous because of its ability to produce engineered particles with improved efficacy and stability by combining active pharmaceutical ingredients or biologics with appropriate excipients. Moreover, due to its high throughput, continuous operation, and ability to produce thermostable solid powders, spray drying can be a manufacturing method of choice in the production of drugs and other formulations, including vaccines, for global distribution. Formulation design based on a mechanistic understanding of the different phenomena that occur during the spray drying of powders is complicated and can therefore make the use of available particle formation models difficult for the practitioner. This review aims to provide step-by-step guidance accompanied by critical background information for the successful formulation design of spray-dried microparticles. These include discussion of the tools needed to estimate the surface concentration of each solute during droplet drying, their times and modes of solidification, and the amount of glass stabilizers and shell formers required to produce stable and dispersible powders.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".