A critical review on developments in drying technologies for enhanced stability and bioavailability of pharmaceuticals
Bibliographic record
Abstract
Drying is a widely adopted unit operation that enhances the stability of pharmaceuticals. Conventionally, freeze-drying has been chosen, as evidenced by the numerous freeze-dried products available on the market. However, there are drawbacks related to freeze drying, extended drying times, freezing and processing-related stresses, and it operates as a batch process, making integration into continuous manufacturing schemes challenging. Similarly, spray drying has garnered significant consideration in the chemical and pharmaceutical industries, but limited production yields hinder its adoption. These shortcomings prompted a quest for next-generation drying technologies tailored for therapeutic products. The emerging drying technologies include nano-spray drying, spray freeze drying, thin film freeze drying, supercritical fluid drying, foam drying, and other miscellaneous techniques to cater to stability issues and enhanced bioavailability. While certain drying technologies have been successfully implemented in the processing of therapeutics, others are currently undergoing early-stage feasibility assessments. This review aims to comprehensively understand novel drying technologies and their potential to tailor pharmaceuticals and biologicals for optimal therapeutic outcomes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".