Revealing the potential of nano spray drying for effective delivery of pharmaceuticals and biologicals
Bibliographic record
Abstract
The emergence of nano spray drying has revolutionized conventional spray drying by offering a simple and streamlined approach to obtaining ultrafine powders in the submicron and nanoscale range. Unlike traditional approaches, this innovative technology enables the direct conversion of solutions into dried nanoparticles, with high yields of up to 90%. The resulting particles exhibit a narrow size distribution, ranging from 300 nm to 5 μm, rendering them highly suitable for diverse drug delivery applications. Using characteristic features such as its piezoelectric atomizing technology and electrostatic particle collector, it encompasses the size spectrum of discrete particles down to the nano-scale with minimal product loss. The resultant nano spray dried powders can be administered via various routes, including oral, topical, ocular, nasal, and inhalation, offering improved drug delivery and enhanced therapeutic efficacy. This review explores the potential and applications of nano spray drying in pharmaceutical formulations, Highlighting its transformative impact on healthcare and its role in improving patient outcomes. However, several challenges need to be overcome before nano spray drying technology can be applied widely in the industry.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".