Strategies to Overcome Undesired Physicochemical Changes in Particle Engineering for Inhalation
Bibliographic record
Abstract
Particle engineering broadly refers to the controlled production of drug particles optimized for size, morphology, and structure. It encompasses both destructive (top-down) and constructive (bottom-up) particle formation processes, of which the most used for commercial dry powder inhaler products are milling and spray drying. In both cases, undesirable physicochemical changes may occur because of thermal and mechanical stresses and through interactions with solvents, and can be further potentiated through storage and interaction with atmospheric water. The occurrence and extent of these phenomena are dependent upon the process parameters and the starting material, which necessitates a thorough understanding of these factors to create a stable product with the necessary characteristics for lung deposition. This review covers commonly arising issues in particle engineering and mechanisms of prevention. Topics to be discussed relating to physical changes include (1) the unintended generation of crystalline disorder and amorphous regions in particles; (2) polymorphic transformations; (3) unintended crystallization when amorphization is desired; and (3) triboelectric charging. Topics to be discussed relating to chemical changes include (1) thermal and mechanically activated chemical reactions; and (2) crystalline disorder and chemical reactivity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".