In vitro dose comparison of Respimat<sup>&reg;</sup> inhaler with dry powder inhalers for COPD maintenance therapy
Bibliographic record
Abstract
BACKGROUND: Combining in vitro mouth-throat deposition measurements, cascade impactor data and computational fluid dynamics (CFD) simulations, four different inhalers were compared which are indicated for chronic obstructive pulmonary disease (COPD) treatment. METHODS: The Respimat inhaler, the Breezhaler, the Genuair, and the Ellipta were coupled to the idealized Alberta throat model. The modeled dose to the lung (mDTL) was collected downstream of the Alberta throat model using either a filter or a next generation impactor (NGI). Idealized breathing patterns from COPD patient groups - moderate and very severe COPD - were applied. Theoretical lung deposition patterns were assessed by an individual path model. RESULTS AND CONCLUSION: For the Respimat the mDTL was found to be 59% (SD 5%) for the moderate COPD breathing pattern and 67% (SD 5%) for very severe COPD breathing pattern. The percentages refer to nominal dose (ND) in vitro. This is in the range of 44%-63% in vivo in COPD patients who display large individual variability. Breezhaler showed a mDTL of 43% (SD 2%) for moderate disease simulation and 51% (SD 2%) for very severe simulation. The corresponding results for Genuair are mDTL of 32% (SD 2%) for moderate and 42% (SD 1%) for very severe disease. Ellipta vilanterol particles showed a mDTL of 49% (SD 3%) for moderate and 55% (SD 2%) for very severe disease simulation, and Ellipta fluticasone particles showed a mDTL of 33% (SD 3%) and 41% (SD 2%), respectively for the two breathing patterns. Based on the throat output and average flows of the different inhalers, CFD simulations were performed. Laminar and turbulent steady flow calculations indicated that deposition occurs mainly in the small airways. In summary, Respimat showed the lowest amount of particles depositing in the mouth-throat model and the highest amount reaching all regions of the simulation lung model.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".